Kilian M Pohl

All Publications

• Spectral Graph Sample Weighting for Interpretable Sub-cohort Analysis in Predictive Models for Neuroimaging. PRedictive Intelligence in MEdicine. PRIME (Workshop) Paschali, M., Jiang, Y. H., Siegel, S., Gonzalez, C., Pohl, K. M., Chaudhari, A., Zhao, Q. 2025; 15155: 24-34

  Abstract

  Recent advancements in medicine have confirmed that brain disorders often comprise multiple subtypes of mechanisms, developmental trajectories, or severity levels. Such heterogeneity is often associated with demographic aspects (e.g., sex) or disease-related contributors (e.g., genetics). Thus, the predictive power of machine learning models used for symptom prediction varies across subjects based on such factors. To model this heterogeneity, one can assign each training sample a factor-dependent weight, which modulates the subject's contribution to the overall objective loss function. To this end, we propose to model the subject weights as a linear combination of the eigenbases of a spectral population graph that captures the similarity of factors across subjects. In doing so, the learned weights smoothly vary across the graph, highlighting sub-cohorts with high and low predictability. Our proposed sample weighting scheme is evaluated on two tasks. First, we predict initiation of heavy alcohol drinking in young adulthood from imaging and neuropsychological measures from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA). Next, we detect Dementia vs. Mild Cognitive Impairment (MCI) using imaging and demographic measurements in subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Compared to existing sample weighting schemes, our sample weights improve interpretability and highlight sub-cohorts with distinct characteristics and varying model accuracy.

  View details for DOI 10.1007/978-3-031-74561-4_3

  View details for PubMedID 39525051

  View details for PubMedCentralID PMC11549025

• The Transition From Homogeneous to Heterogeneous Machine Learning in Neuropsychiatric Research BIOLOGICAL PSYCHIATRY: GLOBAL OPEN SCIENCE Zhao, Q., Nooner, K. B., Tapert, S. F., Adeli, E., Pohl, K. M., Kuceyeski, A., Sabuncu, M. R. 2025; 5 (1)

  View details for DOI 10.1016/j.bpsgos.2024.100397

  View details for Web of Science ID 001344771600001

• The Transition From Homogeneous to Heterogeneous Machine Learning in Neuropsychiatric Research. Biological psychiatry global open science Zhao, Q., Nooner, K. B., Tapert, S. F., Adeli, E., Pohl, K. M., Kuceyeski, A., Sabuncu, M. R. 2025; 5 (1): 100397

  Abstract

  Despite the advantage of neuroimaging-based machine learning (ML) models as pivotal tools for investigating brain-behavior relationships in neuropsychiatric studies, these data-driven predictive approaches have yet to yield substantial, clinically actionable insights for mental health care. A notable impediment lies in the inadequate accommodation of most ML research to the natural heterogeneity within large samples. Although commonly thought of as individual-level analyses, many ML algorithms are unimodal and homogeneous and thus incapable of capturing the potentially heterogeneous relationships between biology and psychopathology. We review the current landscape of computational research targeting population heterogeneity and argue that there is a need to expand from brain subtyping and behavioral phenotyping to analyses that focus on heterogeneity at the relational level. To this end, we review and suggest several existing ML models with the capacity to discern how external environmental and sociodemographic factors moderate the brain-behavior mapping function in a data-driven fashion. These heterogeneous ML models hold promise for enhancing the discovery of individualized brain-behavior associations and advancing precision psychiatry.

  View details for DOI 10.1016/j.bpsgos.2024.100397

  View details for PubMedID 39526023

  View details for PubMedCentralID PMC11546160

• Multi-domain predictors of grip strength differentiate individuals with and without alcohol use disorder. Addiction biology Paschali, M., Zhao, Q., Sassoon, S. A., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M. 2024; 29 (11): e70007

  Abstract

  Grip strength is considered one of the simplest and reliable indices of general health. Although motor ability and strength are commonly affected in people with alcohol use disorder (AUD), factors predictive of grip strength decline in AUD have not been investigated. Here, we employed a data-driven analysis predicting grip strength from measurements in 53 controls and 110 AUD participants, 53 of whom were comorbid with HIV infection. Controls and AUD were matched on sex, age, and body mass index. Measurements included commonly available metrics of brain structure, neuropsychological functioning, behavioural status, haematological and health status, and demographics. Based on 5-fold stratified cross-validation, a machine learning approach predicted grip strength separately for each cohort. The strongest (top 10%) predictors of grip were then tested against grip strength with correlational analysis. Leading grip strength predictors for both cohorts were cerebellar volume and mean corpuscular haemoglobin concentration. Predictors specific to controls were Backwards Digit Span, precentral gyrus volume, diastolic blood pressure, and mean platelet volume, which together significantly predicted grip strength (R2 = 0.255, p = 0.001). Unique predictors for AUD were comorbidity for HIV infection, social functioning, insular volume, and platelet count, which together significantly predicted grip strength (R2 = 0.162, p = 0.002). These cohort-specific predictors were doubly dissociated. Salient predictors of grip strength differed by diagnosis with only modest overlap. The constellation of cohort-specific predictive measurements of compromised grip strength provides insight into brain, behavioural, and physiological factors that may signal subtly affected yet treatable processes of physical decline and frailty.

  View details for DOI 10.1111/adb.70007

  View details for PubMedID 39532141

• Aging, HIV infection, and alcohol exert synergist effects on regional thalamic volumes resulting in functional impairment. NeuroImage. Clinical Pfefferbaum, A., Zahr, N. M., Sassoon, S. A., Fama, R., Saranathan, M., Pohl, K. M., Sullivan, E. V. 2024; 44: 103684

  Abstract

  OBJECTIVE: Pharmacologically-treated people living with HIV infection have near-normal life spans with more than 50% living into at-risk age for dementia and a disproportionate number relative to uninfected people engaging in unhealthy drinking. Accelerated aging in HIV occurs in some brain structures including the multinucleated thalamus. Unknown is whether aging with HIV affects thalamic nuclei and associated functions differentially and whether the common comorbidity of alcohol use disorder (AUD)+HIV accelerates aging.METHODS: This mixed cross-sectional/longitudinal design examined 216 control, 69 HIV, and 74 HIV+AUD participants, age 25-75years old at initial visit, examined 1-8 times. MRI thalamic volumetry, parcellated using THalamus Optimized Multi-Atlas Segmentation (THOMAS), identified 10 nuclei grouped into 4 functional regions for correlation with age and measures of neuropsychological, clinical, and hematological status.RESULTS: Aging in the control group was best modeled with quadratic functions in the Anterior and Ventral regions and with linear functions in the Medial and Posterior regions. Relative to controls, age-related decline was even steeper in the Anterior and Ventral regions of the HIV group and in the Anterior region of the comorbid group. Anterior volumes of each HIV group declined significantly faster after age 50 (HIV=-2.4%/year; HIV+AUD=-2.8%/year) than that of controls (-1.8%/year). Anterior and Ventral volumes were significantly smaller in the HIV+AUD than HIV-only group when controlling for infection factors. Although compared with controls HIV+AUD declined faster than HIV alone, the two HIV groups did not differ significantly from each other in aging rates. Declining Attention/Working Memory and Motor Skills performance correlated with Anterior and Posterior volume declines in the HIV+AUD group.CONCLUSIONS: Regional thalamic volumetry detected normal aging declines, differential and accelerated volume losses in HIV, relations between age-related nuclear and performance declines, and exacerbation of volume declines in comorbid AUD contributing to functional deficits.

  View details for DOI 10.1016/j.nicl.2024.103684

  View details for PubMedID 39423567

• Anterior and Posterior Thalamic Volumes Differentially Correlate with Memory, Attention, and Motor Processes in HIV Infection and Alcohol Use Disorder Comorbidity. Brain research bulletin Fama, R., Sassoon, S. A., Müller-Oehring, E. M., Saranathan, M., Pohl, K. M., Zahr, N. M., Pfefferbaum, A., Sullivan, E. V. 2024: 111085

  Abstract

  The thalamus, with its reciprocal connections to and from cortical, subcortical, and cerebellar regions, is a central active participant in multiple functional brain networks. Structural MRI studies measuring the entire thalamus without respect to its regional or nuclear divisions report volume shrinkage in diseases including HIV infection, alcohol use disorder (AUD), and their comorbidity (HIV+AUD). Here, we examined relations between thalamic subregions (anterior, ventral, medial, and posterior) and neuropsychological functions (attention/working memory, executive functioning, episodic memory, and motor skills). Volumes of thalamic subregions were derived from automatic segmentations of standard T1 weighted MRIs of 65 individuals with HIV, 189 with AUD, 80 with HIV+AUD comorbidity, and 141 healthy controls (CTRL). Total thalamic volume was smaller and cognitive and motor composite scores were lower in the three diagnostic groups relative to the CTRL group. The AUD and HIV+AUD groups had significantly smaller thalamic subregional volumes than the CTRL group. The HIV+AUD group had smaller anterior thalamic volume than the HIV-only group and smaller ventral thalamic volume than the AUD-only group. In the HIV+AUD group, memory scores correlated with anterior thalamic volumes, attention/working memory scores correlated with posterior and medial thalamic volumes, and motor skill scores correlated with posterior thalamic volumes. Exploratory analyses focused on the HIV+AUD group indicated that within the posterior thalamic region, the pulvinar and medial geniculate nuclei were related to attention/working memory scores, and the pulvinar was related to motor skills scores. This study is novel in locating volume deficits in specific thalamic subregions, in addition to the thalamus as a whole, in HIV, AUD, and their comorbidity and in identifying functional ramifications of these deficits. Taken together, this study highlights the relevance of thalamic subregional volume deficits to dissociable cognitive and motor processes.

  View details for DOI 10.1016/j.brainresbull.2024.111085

  View details for PubMedID 39343322

• Sex-specific differences in brain activity dynamics of youth with a family history of substance use disorder. bioRxiv : the preprint server for biology Schilling, L., Singleton, S. P., Tozlu, C., Hédo, M., Zhao, Q., Pohl, K. M., Jamison, K., Kuceyeski, A. 2024

  Abstract

  An individual's risk of substance use disorder (SUD) is shaped by a complex interplay of potent biosocial factors. Current neurodevelopmental models posit vulnerability to SUD in youth is due to an overreactive reward system and reduced inhibitory control. Having a family history of SUD is a particularly strong risk factor, yet few studies have explored its impact on brain function and structure prior to substance exposure. Herein, we utilized a network control theory approach to quantify sex-specific differences in brain activity dynamics in youth with and without a family history of SUD, drawn from a large cohort of substance-naïve youth from the Adolescent Brain Cognitive Development Study. We summarize brain dynamics by calculating transition energy, which probes the ease with which a whole brain, region or network drives the brain towards a specific spatial pattern of activation (i.e., brain state). Our findings reveal that a family history of SUD is associated with alterations in the brain's dynamics wherein: i) independent of sex, certain regions' transition energies are higher in those with a family history of SUD and ii) there exist sex-specific differences in SUD family history groups at multiple levels of transition energy (global, network, and regional). Family history-by-sex effects reveal that energetic demand is increased in females with a family history of SUD and decreased in males with a family history of SUD, compared to their same-sex counterparts with no SUD family history. Specifically, we localize these effects to higher energetic demands of the default mode network in females with a family history of SUD and lower energetic demands of attention networks in males with a family history of SUD. These results suggest a family history of SUD may increase reward saliency in males and decrease efficiency of top-down inhibitory control in females. This work could be used to inform personalized intervention strategies that may target differing cognitive mechanisms that predispose individuals to the development of SUD.

  View details for DOI 10.1101/2024.09.03.610959

  View details for PubMedID 39282344

  View details for PubMedCentralID PMC11398379

• Metadata-conditioned generative models to synthesize anatomically-plausible 3D brain MRIs. Medical image analysis Peng, W., Bosschieter, T., Ouyang, J., Paul, R., Sullivan, E. V., Pfefferbaum, A., Adeli, E., Zhao, Q., Pohl, K. M. 2024; 98: 103325

  Abstract

  Recent advances in generative models have paved the way for enhanced generation of natural and medical images, including synthetic brain MRIs. However, the mainstay of current AI research focuses on optimizing synthetic MRIs with respect to visual quality (such as signal-to-noise ratio) while lacking insights into their relevance to neuroscience. To generate high-quality T1-weighted MRIs relevant for neuroscience discovery, we present a two-stage Diffusion Probabilistic Model (called BrainSynth) to synthesize high-resolution MRIs conditionally-dependent on metadata (such as age and sex). We then propose a novel procedure to assess the quality of BrainSynth according to how well its synthetic MRIs capture macrostructural properties of brain regions and how accurately they encode the effects of age and sex. Results indicate that more than half of the brain regions in our synthetic MRIs are anatomically plausible, i.e., the effect size between real and synthetic MRIs is small relative to biological factors such as age and sex. Moreover, the anatomical plausibility varies across cortical regions according to their geometric complexity. As is, the MRIs generated by BrainSynth significantly improve the training of a predictive model to identify accelerated aging effects in an independent study. These results indicate that our model accurately capture the brain's anatomical information and thus could enrich the data of underrepresented samples in a study. The code of BrainSynth will be released as part of the MONAI project at https://github.com/Project-MONAI/GenerativeModels.

  View details for DOI 10.1016/j.media.2024.103325

  View details for PubMedID 39208560

• Data-driven discovery of movement-linked heterogeneity in neurodegenerative diseases NATURE MACHINE INTELLIGENCE Endo, M., Nerrise, F., Zhao, Q., Sullivan, E. V., Fei-Fei, L., Henderson, V. W., Pohl, K. M., Poston, K. L., Adeli, E. 2024

  View details for DOI 10.1038/s42256-024-00882-y

  View details for Web of Science ID 001287474600001

• Multi-dimensional predictors of first drinking initiation and regular drinking onset in adolescence: A prospective longitudinal study. Developmental cognitive neuroscience Nguyen-Louie, T. T., Thompson, W. K., Sullivan, E. V., Pfefferbaum, A., Gonzalez, C., Eberson-Shumate, S. C., Wade, N. E., Clark, D. B., Nagel, B. J., Baker, F. C., Luna, B., Nooner, K. B., de Zambotti, M., Goldston, D. B., Knutson, B., Pohl, K. M., Tapert, S. F. 2024; 69: 101424

  Abstract

  Early adolescent drinking onset is linked to myriad negative consequences. Using the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) baseline to year 8 data, this study (1) leveraged best subsets selection and Cox Proportional Hazards regressions to identify the most robust predictors of adolescent first and regular drinking onset, and (2) examined the clinical utility of drinking onset in forecasting later binge drinking and withdrawal effects. Baseline predictors included youth psychodevelopmental characteristics, cognition, brain structure, family, peer, and neighborhood domains. Participants (N=538) were alcohol-naïve at baseline. The strongest predictors of first and regular drinking onset were positive alcohol expectancies (Hazard Ratios [HRs]=1.67-1.87), easy home alcohol access (HRs=1.62-1.67), more parental solicitation (e.g., inquiring about activities; HRs=1.72-1.76), and less parental control and knowledge (HRs=.72-.73). Robust linear regressions showed earlier first and regular drinking onset predicted earlier transition into binge and regular binge drinking (βs=0.57-0.95). Zero-inflated Poisson regressions revealed that delayed first and regular drinking increased the likelihood (Incidence Rate Ratios [IRR]=1.62 and IRR=1.29, respectively) of never experiencing withdrawal. Findings identified behavioral and environmental factors predicting temporal paths to youthful drinking, dissociated first from regular drinking initiation, and revealed adverse sequelae of younger drinking initiation, supporting efforts to delay drinking onset.

  View details for DOI 10.1016/j.dcn.2024.101424

  View details for PubMedID 39089172

• Identifying high school risk factors that forecast heavy drinking onset in understudied young adults. Developmental cognitive neuroscience Zhao, Q., Paschali, M., Dehoney, J., Baker, F. C., de Zambotti, M., De Bellis, M. D., Goldston, D. B., Nooner, K. B., Clark, D. B., Luna, B., Nagel, B. J., Brown, S. A., Tapert, S. F., Eberson, S., Thompson, W. K., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M. 2024; 68: 101413

  Abstract

  Heavy alcohol drinking is a major, preventable problem that adversely impacts the physical and mental health of US young adults. Studies seeking drinking risk factors typically focus on young adults who enrolled in 4-year residential college programs (4YCP) even though most high school graduates join the workforce, military, or community colleges. We examined 106 of these understudied young adults (USYA) and 453 4YCPs from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) by longitudinally following their drinking patterns for 8 years from adolescence to young adulthood. All participants were no-to-low drinkers during high school. Whereas 4YCP individuals were more likely to initiate heavy drinking during college years, USYA participants did so later. Using mental health metrics recorded during high school, machine learning forecasted individual-level risk for initiating heavy drinking after leaving high school. The risk factors differed between demographically matched USYA and 4YCP individuals and between sexes. Predictors for USYA drinkers were sexual abuse, physical abuse for girls, and extraversion for boys, whereas 4YCP drinkers were predicted by the ability to recognize facial emotion and, for boys, greater openness. Thus, alcohol prevention programs need to give special consideration to those joining the workforce, military, or community colleges, who make up the majority of this age group.

  View details for DOI 10.1016/j.dcn.2024.101413

  View details for PubMedID 38943839

• Multi-level prediction of substance use: Interaction of white matter integrity, resting-state connectivity and inhibitory control measured repeatedly in every-day life. Addiction biology Chirokoff, V., Pohl, K. M., Berthoz, S., Fatseas, M., Misdrahi, D., Serre, F., Auriacombe, M., Pfefferbaum, A., Sullivan, E. V., Chanraud, S. 2024; 29 (5): e13400

  Abstract

  Substance use disorders are characterized by inhibition deficits related to disrupted connectivity in white matter pathways, leading via interaction to difficulties in resisting substance use. By combining neuroimaging with smartphone-based ecological momentary assessment (EMA), we questioned how biomarkers moderate inhibition deficits to predict use. Thus, we aimed to assess white matter integrity interaction with everyday inhibition deficits and related resting-state network connectivity to identify multi-dimensional predictors of substance use. Thirty-eight patients treated for alcohol, cannabis or tobacco use disorder completed 1 week of EMA to report substance use five times and complete Stroop inhibition testing twice daily. Before EMA tracking, participants underwent resting state functional MRI and diffusion tensor imaging (DTI) scanning. Regression analyses were conducted between mean Stroop performances and whole-brain fractional anisotropy (FA) in white matter. Moderation testing was conducted between mean FA within significant clusters as moderator and the link between momentary Stroop performance and use as outcome. Predictions between FA and resting-state connectivity strength in known inhibition-related networks were assessed using mixed modelling. Higher FA values in the anterior corpus callosum and bilateral anterior corona radiata predicted higher mean Stroop performance during the EMA week and stronger functional connectivity in occipital-frontal-cerebellar regions. Integrity in these regions moderated the link between inhibitory control and substance use, whereby stronger inhibition was predictive of the lowest probability of use for the highest FA values. In conclusion, compromised white matter structural integrity in anterior brain systems appears to underlie impairment in inhibitory control functional networks and compromised ability to refrain from substance use.

  View details for DOI 10.1111/adb.13400

  View details for PubMedID 38706091

  View details for PubMedCentralID PMC11070496

• Contributions of Cerebral White Matter Hyperintensities to Postural Instability in Aging with and without Alcohol Use Disorder. Biological psychiatry. Cognitive neuroscience and neuroimaging Sullivan, E. V., Zahr, N. M., Zhao, Q., Pohl, K. M., Sassoon, S. A., Pfefferbaum, A. 2024

  Abstract

  Postural instability and brain white matter hyperintensities (WMH) are both noted markers of normal aging and alcohol use disorder (AUD). Here, we questioned what variables contribute to sway path/WMH relations in individuals with AUD and healthy control participants.The data comprised 404 balance platform sessions, yielding sway path length and MRI acquired cross-sectionally or longitudinally, in 102 control and 158 AUD participants, ages 25-80 years. Balance sessions were typically conducted on the same day as MRI FLAIR acquisitions, permitting WMH volume quantification. Factors considered in multiple regression analyses as potential contributors to relations between WMH volumes and postural instability were age, sex, socioeconomic status, education, pedal 2-point discrimination, systolic and diastolic blood pressure, body mass index, depressive symptoms, total alcohol consumed in the past year, and race.Initial analysis identified diagnosis, age, sex, and race as significant contributors to observed sway path/WMH relations. Inclusion of these factors as predictors in multiple regression analysis substantially attenuated the sway/WMH relations in both AUD and healthy control groups. Women, irrespective of diagnosis or race, had shorter sway paths than men. Black participants, irrespective of diagnosis or sex, had shorter sway paths than non-Black participants despite having modestly larger WMH volumes than non-Black participants, possibly a reflection of the younger age of the Black sample.Longer sway paths were related to larger WMH volumes in healthy men and women, with and without AUD. Critically, however, age nearly fully accounted for these relations.

  View details for DOI 10.1016/j.bpsc.2024.03.005

  View details for PubMedID 38569932

• Contributions of cerebral white matter hyperintensities, age, and pedal perception to postural sway in people living with HIV. AIDS (London, England) Sullivan, E. V., Zahr, N. M., Zhao, Q., Pohl, K. M., Sassoon, S. A., Pfefferbaum, A. 2024

  Abstract

  With aging, people living with HIV (PLWH) have diminishing postural stability that increases liability for falls. Factors and neuromechanisms contributing to instability are incompletely known. Brain white matter abnormalities seen as hyperintense (WMH) signals have been considered to underlie instability in normal aging and PLWH. We questioned whether sway-WMH relations endured after accounting for potentially relevant demographic, physiological, and HIV-related variables.Mixed cross-sectional/longitudinal data acquired over 15 years in 141 PLWH and 102 age-range matched controls, 25-80 years old.Multimodal structural MRI data were quantified for 7 total and regional WMH volumes. Static posturography acquired with a force platform measured sway path length separately with eyes closed and eyes open. Statistical analyses used multiple regression with mixed modeling to test contributions from non-MRI and non-path data on sway path-WMH relations.In simple correlations, longer sway paths were associated with larger WMH volumes in PWLH and controls. When demographic, physiological, and HIV-related variables were entered into multiple regressions, the sway-WMH relations under both vision conditions in the controls were attenuated when accounting for age and 2-point pedal discrimination. Although the sway-WMH relations in PLWH were influenced by age, 2-point pedal discrimination, and years with HIV infection, the sway-WMH relations endured for 5 of the 7 regions in the eyes-open condition.The constellation of age-related increasing instability while standing, degradation of brain white matter integrity, and peripheral pedal neuropathy is indicative of advancing fraility and liability for falls as people age with HIV infection.

  View details for DOI 10.1097/QAD.0000000000003894

  View details for PubMedID 38537080

• Federated learning with knowledge distillation for multi-organ segmentation with partially labeled datasets. Medical image analysis Kim, S., Park, H., Kang, M., Jin, K. H., Adeli, E., Pohl, K. M., Park, S. H. 2024; 95: 103156

  Abstract

  The state-of-the-art multi-organ CT segmentation relies on deep learning models, which only generalize when trained on large samples of carefully curated data. However, it is challenging to train a single model that can segment all organs and types of tumors since most large datasets are partially labeled or are acquired across multiple institutes that may differ in their acquisitions. A possible solution is Federated learning, which is often used to train models on multi-institutional datasets where the data is not shared across sites. However, predictions of federated learning can be unreliable after the model is locally updated at sites due to 'catastrophic forgetting'. Here, we address this issue by using knowledge distillation (KD) so that the local training is regularized with the knowledge of a global model and pre-trained organ-specific segmentation models. We implement the models in a multi-head U-Net architecture that learns a shared embedding space for different organ segmentation, thereby obtaining multi-organ predictions without repeated processes. We evaluate the proposed method using 8 publicly available abdominal CT datasets of 7 different organs. Of those datasets, 889 CTs were used for training, 233 for internal testing, and 30 volumes for external testing. Experimental results verified that our proposed method substantially outperforms other state-of-the-art methods in terms of accuracy, inference time, and the number of parameters.

  View details for DOI 10.1016/j.media.2024.103156

  View details for PubMedID 38603844

• Frontal cortical volume deficits as enduring evidence of childhood abuse in community adults with AUD and HIV infection comorbidity. Neurobiology of stress Sassoon, S. A., Fama, R., Pohl, K. M., Pfefferbaum, A., Sullivan, E. V. 2024; 29: 100608

  Abstract

  Background: Childhood abuse is an underappreciated source of stress, associated with adverse mental and physical health consequences. Childhood abuse has been directly associated with risky behavior thereby increasing the likelihood of alcohol misuse and risk of HIV infection, conditions associated with brain structural and functional deficits. Here, we examined the neural and behavioral correlates of childhood trauma history in alcohol use disorder (AUD), HIV infection (HIV), and their comorbidity (AUD+HIV).Methods: Occurrence of childhood trauma was evaluated by retrospective interview. Cortical (frontal, temporal, parietal, and occipital), subcortical (hippocampus, amygdala), and regional frontal volumes were derived from structural MRI, adjusted for intracranial volume and age. Test scores of executive functioning, attention/working memory, verbal/visual learning, verbal/visual memory, and motor speed functional domains were standardized on age and education of a laboratory control group.Results: History of childhood abuse was associated with smaller frontal lobe volumes regardless of diagnosis. For frontal subregional volumes, history of childhood abuse was selectively associated with smaller orbitofrontal and supplementary motor volumes. In participants with a child abuse history, poorer verbal/visual memory performance was associated with smaller orbitofrontal and frontal middle volumes, whereas in those without childhood abuse, poorer verbal/visual memory performance was associated with smaller orbitofrontal, frontal superior, and supplemental motor volumes.Conclusions: Taken together, these results comport with and extend the findings that childhood abuse is associated with brain and behavioral sequelae in AUD, HIV, and AUD+HIV comorbidity. Further, these findings suggest that sequelae of abuse in childhood may be best conceptualized as a spectrum disorder as significant deficits may be present in those who may not meet criteria for a formal trauma-related diagnosis yet may be suffering enduring stress effects on brain structural and functional health.

  View details for DOI 10.1016/j.ynstr.2024.100608

  View details for PubMedID 38323165

• FedNN: Federated learning on concept drift data using weight and adaptive group normalizations PATTERN RECOGNITION Kang, M., Kim, S., Jin, K., Adeli, E., Pohl, K. M., Park, S. 2024; 149

  View details for DOI 10.1016/j.patcog.2023.110230

  View details for Web of Science ID 001154871600001

• Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection Kim, S., An, S., Chikontwe, P., Kang, M., Adeli, E., Pohl, K. M., Park, S., Wooldridge, M., Dy, J., Natarajan, S. ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2024: 8591-8599

  View details for Web of Science ID 001239938200076

• Brain Volume in Fetal Alcohol Spectrum Disorders Over a 20-Year Span. JAMA network open Pfefferbaum, A., Sullivan, E. V., Pohl, K. M., Bischoff-Grethe, A., Stoner, S. A., Moore, E. M., Riley, E. P. 2023; 6 (11): e2343618

  Abstract

  Anomalous brain development and mental health problems are prevalent in fetal alcohol spectrum disorders (FASD), but there is a paucity of longitudinal brain imaging research into adulthood. This study presents long-term follow-up of brain volumetrics in a cohort of participants with FASD.To test whether brain tissue declines faster with aging in individuals with FASD compared with control participants.This cohort study used magnetic resonance imaging (MRI) data collected from individuals with FASD and control individuals (age 13-37 years at first magnetic resonance imaging [MRI1] acquired 1997-2000) compared with data collected 20 years later (MRI2; 2018-2021). Participants were recruited for MRI1 through the University of Washington Fetal Alcohol Syndrome (FAS) Follow-Up Study. For MRI2, former participants were recruited by the University of Washington Fetal Alcohol and Drug Unit. Data were analyzed from October 2022 to August 2023.Intracranial volume (ICV) and regional cortical and cerebellar gray matter, white matter, and cerebrospinal fluid volumes were quantified automatically and analyzed, with group and sex as between-participant factors and age as a within-participant variable.Of 174 individuals with MRI1 data, 48 refused participation, 36 were unavailable, and 24 could not be located. The remaining 66 individuals (37.9%) were rescanned for MRI2, including 26 controls, 18 individuals with nondysmorphic heavily exposed fetal alcohol effects (FAE; diagnosed prior to MRI1), and 22 individuals with FAS. Mean (SD) age was 22.9 (5.6) years at MRI1 and 44.7 (6.5) years at MRI2, and 35 participants (53%) were male. The FAE and FAS groups exhibited enduring stepped volume deficits at MRI1 and MRI2; volumes among control participants were greater than among participants with FAE, which were greater than volumes among participants with FAS (eg, mean [SD] ICV: control, 1462.3 [119.3] cc at MRI1 and 1465.4 [129.4] cc at MRI2; FAE, 1375.6 [134.1] cc at MRI1 and 1371.7 [120.3] cc at MRI2; FAS, 1297.3 [163.0] cc at MRI1 and 1292.7 [172.1] cc at MRI2), without diagnosis-by-age interactions. Despite these persistent volume deficits, the FAE participants and FAS participants showed patterns of neurodevelopment within reference ranges: increase in white matter and decrease in gray matter of the cortex and decrease in white matter and increase in gray matter of the cerebellum.The findings of this cohort study support a nonaccelerating enduring, brain structural dysmorphic spectrum following prenatal alcohol exposure and a diagnostic distinction based on the degree of dysmorphia. FASD was not a progressive brain structural disorder by middle age, but whether accelerated decline occurs in later years remains to be determined.

  View details for DOI 10.1001/jamanetworkopen.2023.43618

  View details for PubMedID 37976065

• One-shot Federated Learning on Medical Data using Knowledge Distillation with Image Synthesis and Client Model Adaptation. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Kang, M., Chikontwe, P., Kim, S., Jin, K. H., Adeli, E., Pohl, K. M., Park, S. H. 2023; 14221: 521-531

  Abstract

  One-shot federated learning (FL) has emerged as a promising solution in scenarios where multiple communication rounds are not practical. Notably, as feature distributions in medical data are less discriminative than those of natural images, robust global model training with FL is non-trivial and can lead to overfitting. To address this issue, we propose a novel one-shot FL framework leveraging Image Synthesis and Client model Adaptation (FedISCA) with knowledge distillation (KD). To prevent overfitting, we generate diverse synthetic images ranging from random noise to realistic images. This approach (i) alleviates data privacy concerns and (ii) facilitates robust global model training using KD with decentralized client models. To mitigate domain disparity in the early stages of synthesis, we design noise-adapted client models where batch normalization statistics on random noise (synthetic images) are updated to enhance KD. Lastly, the global model is trained with both the original and noise-adapted client models via KD and synthetic images. This process is repeated till global model convergence. Extensive evaluation of this design on five small- and three large-scale medical image classification datasets reveals superior accuracy over prior methods. Code is available at https://github.com/myeongkyunkang/FedISCA.

  View details for DOI 10.1007/978-3-031-43895-0_49

  View details for PubMedID 38204983

  View details for PubMedCentralID PMC10781197

• An Explainable Geometric-Weighted Graph Attention Network for Identifying Functional Networks Associated with Gait Impairment. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Nerrise, F., Zhao, Q., Poston, K. L., Pohl, K. M., Adeli, E. 2023; 14221: 723-733

  Abstract

  One of the hallmark symptoms of Parkinson's Disease (PD) is the progressive loss of postural reflexes, which eventually leads to gait difficulties and balance problems. Identifying disruptions in brain function associated with gait impairment could be crucial in better understanding PD motor progression, thus advancing the development of more effective and personalized therapeutics. In this work, we present an explainable, geometric, weighted-graph attention neural network (xGW-GAT) to identify functional networks predictive of the progression of gait difficulties in individuals with PD. xGW-GAT predicts the multi-class gait impairment on the MDS-Unified PD Rating Scale (MDS-UPDRS). Our computational- and data-efficient model represents functional connectomes as symmetric positive definite (SPD) matrices on a Riemannian manifold to explicitly encode pairwise interactions of entire connectomes, based on which we learn an attention mask yielding individual- and group-level explainability. Applied to our resting-state functional MRI (rs-fMRI) dataset of individuals with PD, xGW-GAT identifies functional connectivity patterns associated with gait impairment in PD and offers interpretable explanations of functional subnetworks associated with motor impairment. Our model successfully outperforms several existing methods while simultaneously revealing clinically-relevant connectivity patterns. The source code is available at https://github.com/favour-nerrise/xGW-GAT.

  View details for DOI 10.1007/978-3-031-43895-0_68

  View details for PubMedID 37982132

  View details for PubMedCentralID PMC10657737

• Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Peng, W., Adeli, E., Bosschieter, T., Hyun Park, S., Zhao, Q., Pohl, K. M. 2023; 14227: 14-24

  Abstract

  As acquiring MRIs is expensive, neuroscience studies struggle to attain a sufficient number of them for properly training deep learning models. This challenge could be reduced by MRI synthesis, for which Generative Adversarial Networks (GANs) are popular. GANs, however, are commonly unstable and struggle with creating diverse and high-quality data. A more stable alternative is Diffusion Probabilistic Models (DPMs) with a fine-grained training strategy. To overcome their need for extensive computational resources, we propose a conditional DPM (cDPM) with a memory-efficient process that generates realistic-looking brain MRIs. To this end, we train a 2D cDPM to generate an MRI subvolume conditioned on another subset of slices from the same MRI. By generating slices using arbitrary combinations between condition and target slices, the model only requires limited computational resources to learn interdependencies between slices even if they are spatially far apart. After having learned these dependencies via an attention network, a new anatomy-consistent 3D brain MRI is generated by repeatedly applying the cDPM. Our experiments demonstrate that our method can generate high-quality 3D MRIs that share a similar distribution to real MRIs while still diversifying the training set. The code is available at https://github.com/xiaoiker/mask3DMRI_diffusion and also will be released as part of MONAI, at https://github.com/Project-MONAI/GenerativeModels.

  View details for DOI 10.1007/978-3-031-43993-3_2

  View details for PubMedID 38169668

  View details for PubMedCentralID PMC10758344

• LSOR: Longitudinally-Consistent Self-Organized Representation Learning. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Ouyang, J., Zhao, Q., Adeli, E., Peng, W., Zaharchuk, G., Pohl, K. M. 2023; 14220: 279-289

  Abstract

  Interpretability is a key issue when applying deep learning models to longitudinal brain MRIs. One way to address this issue is by visualizing the high-dimensional latent spaces generated by deep learning via self-organizing maps (SOM). SOM separates the latent space into clusters and then maps the cluster centers to a discrete (typically 2D) grid preserving the high-dimensional relationship between clusters. However, learning SOM in a high-dimensional latent space tends to be unstable, especially in a self-supervision setting. Furthermore, the learned SOM grid does not necessarily capture clinically interesting information, such as brain age. To resolve these issues, we propose the first self-supervised SOM approach that derives a high-dimensional, interpretable representation stratified by brain age solely based on longitudinal brain MRIs (i.e., without demographic or cognitive information). Called Longitudinally-consistent Self-Organized Representation learning (LSOR), the method is stable during training as it relies on soft clustering (vs. the hard cluster assignments used by existing SOM). Furthermore, our approach generates a latent space stratified according to brain age by aligning trajectories inferred from longitudinal MRIs to the reference vector associated with the corresponding SOM cluster. When applied to longitudinal MRIs of the Alzheimer's Disease Neuroimaging Initiative (ADNI, N=632), LSOR generates an interpretable latent space and achieves comparable or higher accuracy than the state-of-the-art representations with respect to the downstream tasks of classification (static vs. progressive mild cognitive impairment) and regression (determining ADAS-Cog score of all subjects). The code is available at https://github.com/ouyangjiahong/longitudinal-som-single-modality.

  View details for DOI 10.1007/978-3-031-43907-0_27

  View details for PubMedID 37961067

• Brain structural covariance network features are robust markers of early heavy alcohol use. Addiction (Abingdon, England) Ottino-Gonzalez, J., Cupertino, R. B., Cao, Z., Hahn, S., Pancholi, D., Albaugh, M. D., Brumback, T., Baker, F. C., Brown, S. A., Clark, D. B., de Zambotti, M., Goldston, D. B., Luna, B., Nagel, B. J., Nooner, K. B., Pohl, K. M., Tapert, S. F., Thompson, W. K., Jernigan, T. L., Conrod, P., Mackey, S., Garavan, H. 2023

  Abstract

  BACKGROUND AND AIMS: Recently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies.DESIGN AND SETTING: Cross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n=400, age range=14-22years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n=272, age range=17-22years) and the Human Connectome Project (HCP) (n=375, age range=22-37years).CASES: Cases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected.MEASUREMENTS: Graph theory metrics of segregation and integration were used to summarize SCN.FINDINGS: Mirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC)=-0.029, P=0.002], lower modularity (AUC=-0.14, P=0.004), lower average shortest path length (AUC=-0.078, P=0.017) and higher global efficiency (AUC=0.007, P=0.010). Local efficiency differences were marginal (AUC=-0.017, P=0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar.CONCLUSION: Structural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology.

  View details for DOI 10.1111/add.16330

  View details for PubMedID 37724052

• White matter microstructural integrity continues to develop from adolescence to young adulthood in mice and humans: Same phenotype, different mechanism. Neuroimage. Reports Piekarski, D. J., Zahr, N. M., Zhao, Q., Ferizi, U., Pohl, K. M., Sullivan, E. V., Pfefferbaum, A. 2023; 3 (3)

  Abstract

  As direct evaluation of a mouse model of human neurodevelopment, adolescent and young adult mice and humans underwent MR diffusion tensor imaging to quantify age-related differences in microstructural integrity of brain white matter fibers. Fractional anisotropy (FA) was greater in older than younger mice and humans. Despite the cross-species commonality, the underlying developmental mechanism differed: whereas evidence for greater axonal extension contributed to higher FA in older mice, evidence for continuing myelination contributed to higher FA in human adolescent development. These differences occurred in the context of species distinctions in overall brain growth: whereas the continued growth of the brain and skull in the murine model can accommodate volume expansion into adulthood, human white matter volume and myelination continue growth into adulthood within a fixed intracranial volume. Appreciation of the similarities and differences in developmental mechanism can enhance the utility of animal models of brain white matter structure, function, and response to exogenous manipulation.

  View details for DOI 10.1016/j.ynirp.2023.100179

  View details for PubMedID 37916059

• Age-Accelerated Increase of White Matter Hyperintensity Volumes is Exacerbated by Heavy Alcohol Use in People Living with HIV. Biological psychiatry Pfefferbaum, A., Zhao, Q., Pohl, K. M., Sassoon, S. A., Zahr, N. M., Sullivan, E. V. 2023

  Abstract

  Antiretroviral treatment has enabled people living with HIV infection to have a near-normal lifespan. With longevity come opportunities for engaging in risky behavior, including initiation of excessive drinking. Given that both HIV infection and alcohol use disorder (AUD) can disrupt brain white matter integrity, we questioned whether HIV infection, even if successfully treated, or AUD alone results in signs of accelerated white matter aging and whether HIV+AUD comorbidity further accelerates brain aging.Longitudinal MRI-FLAIR data were acquired over 15 years in 179 controls, 204 AUD participants, 70 with HIV, and 75 comorbid for HIV+AUD. White matter hyperintensity (WMH) volumes were quantified and localized and their functional relevance was examined with cognitive and motor testing.The three diagnostic groups each had larger WMH volumes than controls. Although all four groups exhibited accelerating volume increases with aging, only the HIV groups showed faster WMH enlargement than controls; the comorbid group showed faster acceleration than the HIV-only group. Sex and HIV infection length, but not viral suppression status, moderated acceleration. Correlations emerged between WMH volumes and Attention/Working Memory and Executive Function scores of the AUD and HIV groups, and between WMH volumes and Motor Skills in the three diagnostic groups.Even treated HIV can show accelerated aging, possibly from treatment sequelae or legacy effects, and notably from AUD comorbidity. WMH volumes may be especially relevant for tracking HIV and AUD brain health because each condition is associated with liability for hypertensive processes, for which WMHs are considered a marker.

  View details for DOI 10.1016/j.biopsych.2023.07.023

  View details for PubMedID 37597798

• An Explainable Geometric-Weighted Graph Attention Network for Identifying Functional Networks Associated with Gait Impairment. ArXiv Nerrise, F., Zhao, Q., Poston, K. L., Pohl, K. M., Adeli, E. 2023

  Abstract

  One of the hallmark symptoms of Parkinson's Disease (PD) is the progressive loss of postural reflexes, which eventually leads to gait difficulties and balance problems. Identifying disruptions in brain function associated with gait impairment could be crucial in better understanding PD motor progression, thus advancing the development of more effective and personalized therapeutics. In this work, we present an explainable, geometric, weighted-graph attention neural network (xGW-GAT) to identify functional networks predictive of the progression of gait difficulties in individuals with PD. xGW-GAT predicts the multi-class gait impairment on the MDS-Unified PD Rating Scale (MDS-UPDRS). Our computational- and data-efficient model represents functional connectomes as symmetric positive definite (SPD) matrices on a Riemannian manifold to explicitly encode pairwise interactions of entire connectomes, based on which we learn an attention mask yielding individual- and group-level explain-ability. Applied to our resting-state functional MRI (rs-fMRI) dataset of individuals with PD, xGW-GAT identifies functional connectivity patterns associated with gait impairment in PD and offers interpretable explanations of functional subnetworks associated with motor impairment. Our model successfully outperforms several existing methods while simultaneously revealing clinically-relevant connectivity patterns. The source code is available at https://github.com/favour-nerrise/xGW-GAT.

  View details for PubMedID 37547656

  View details for PubMedCentralID PMC10402187

• Episodic memory deficit in HIV infection: common phenotype with Parkinson's disease, different neural substrates. Brain structure & function Fama, R., Müller-Oehring, E. M., Levine, T. F., Sullivan, E. V., Sassoon, S. A., Asok, P., Brontë-Stewart, H. M., Poston, K. L., Pohl, K. M., Pfefferbaum, A., Schulte, T. 2023

  Abstract

  Episodic memory deficits occur in people living with HIV (PLWH) and individuals with Parkinson's disease (PD). Given known effects of HIV and PD on frontolimbic systems, episodic memory deficits are often attributed to executive dysfunction. Although executive dysfunction, evidenced as retrieval deficits, is relevant to mnemonic deficits, learning deficits may also contribute. Here, the California Verbal Learning Test-II, administered to 42 PLWH, 41 PD participants, and 37 controls, assessed learning and retrieval using measures of free recall, cued recall, and recognition. Executive function was assessed with a composite score comprising Stroop Color-Word Reading and Backward Digit Spans. Neurostructural correlates were examined with MRI of frontal (precentral, superior, orbital, middle, inferior, supplemental motor, medial) and limbic (hippocampus, thalamus) volumes. HIV and PD groups were impaired relative to controls on learning and free and cued recall trials but did not differ on recognition or retention of learned material. In no case did executive functioning solely account for the observed mnemonic deficits or brain-performance relations. Critically, the shared learning and retrieval deficits in HIV and PD were related to different substrates of frontolimbic mnemonic neurocircuitry. Specifically, diminished learning and poorer free and cued recall were related to smaller orbitofrontal volume in PLWH but not PD, whereas diminished learning in PD but not PLWH was related to smaller frontal superior volume. In PD, poorer recognition correlated with smaller thalamic volume and poorer retention to hippocampal volume. Although memory deficits were similar, the neural correlates in HIV and PD suggest different pathogenic mechanisms.

  View details for DOI 10.1007/s00429-023-02626-x

  View details for PubMedID 37069296

  View details for PubMedCentralID 9804536

• Machine Learning Approaches to Understand Cognitive Phenotypes in People With HIV. The Journal of infectious diseases Mukerji, S. S., Petersen, K. J., Pohl, K. M., Dastgheyb, R. M., Fox, H. S., Bilder, R. M., Brouillette, M. J., Gross, A. L., Scott-Sheldon, L. A., Paul, R. H., Gabuzda, D. 2023; 227 (Supplement_1): S48-S57

  Abstract

  Cognitive disorders are prevalent in people with HIV (PWH) despite antiretroviral therapy. Given the heterogeneity of cognitive disorders in PWH in the current era and evidence that these disorders have different etiologies and risk factors, scientific rationale is growing for using data-driven models to identify biologically defined subtypes (biotypes) of these disorders. Here, we discuss the state of science using machine learning to understand cognitive phenotypes in PWH and their associated comorbidities, biological mechanisms, and risk factors. We also discuss methods, example applications, challenges, and what will be required from the field to successfully incorporate machine learning in research on cognitive disorders in PWH. These topics were discussed at the National Institute of Mental Health meeting on "Biotypes of CNS Complications in People Living with HIV" held in October 2021. These ongoing research initiatives seek to explain the heterogeneity of cognitive phenotypes in PWH and their associated biological mechanisms to facilitate clinical management and tailored interventions.

  View details for DOI 10.1093/infdis/jiac293

  View details for PubMedID 36930638

• Postural instability in HIV infection: relation to central and peripheral nervous system markers. AIDS (London, England) Sullivan, E. V., Zahr, N. M., Sassoon, S. A., Pohl, K. M., Pfefferbaum, A. 2023

  Abstract

  Determine the independent contributions of central nervous system (CNS) and peripheral nervous system (PNS) metrics to balance instability in people with HIV (PWH) compared with people without HIV (PWoH).Volumetric MRI (CNS) and two-point pedal discrimination (PNS) were tested as substrates of stance instability measured with balance platform posturography.125 PWH and 88 PWoH underwent balance testing and brain MRI.The PWH exhibited stability deficits that were disproportionately greater with eyes closed than eyes open compared with PWoH. Further analyses revealed that greater postural imbalance measured as longer sway paths correlated with smaller cortical and cerebellar lobular brain volumes known to serve sensory integration; identified brain/sway path relations endured after accounting for contributions from physiological and disease factors as potential moderators; and multiple regression identified PNS and CNS metrics as independent predictors of postural instability in PWH that differed with the use of visual information to stabilize balance. With eyes closed, temporal volumes and two-point pedal discrimination were significant independent predictors of sway; with eyes open, occipital volume was an additional predictor of sway. These relations were selective to PWH and were not detected in PWoH.CNS and PNS factors were independent contributors to postural instability in PWH. Recognizing that myriad inputs must be detected by peripheral systems and brain networks to integrate sensory and musculoskeletal information for maintenance of postural stability, age- or disease-related degradation of either or both nervous systems may contribute to imbalance and liability for falls.

  View details for DOI 10.1097/QAD.0000000000003531

  View details for PubMedID 36927610

• Brain volumetrics differ by fiebig stage in acute HIV infection. AIDS (London, England) Bolzenius, J., Sacdalan, C., Ndhlovu, L. C., Sailasuta, N., Trautmann, L., Tipsuk, S., Crowell, T. A., Suttichom, D., Colby, D. J., Phanuphak, N., Chan, P., Premeaux, T., Kroon, E., Vasan, S., Hsu, D. C., Valcour, V., Ananworanich, J., Robb, M. L., Ake, J. A., Pohl, K. M., Sriplienchan, S., Spudich, S., Paul, R. 2023

  Abstract

  People with chronic HIV exhibit lower regional brain volumes compared to people without HIV (PWOH). Whether imaging alterations observed in chronic infection occur in acute HIV infection (AHI) remains unknown.Cross-sectional study of Thai participants with AHI.112 Thai males with AHI (age 20-46) and 18 male Thai PWOH (age 18-40) were included. Individuals with AHI were stratified into early (Fiebig I-II; n = 32) and late (Fiebig III-V; n = 80) stages of acute infection using validated assays. T1-weighted scans were acquired using a 3T MRI performed within five days of antiretroviral therapy (ART) initiation. Volumes for the amygdala, caudate nucleus, hippocampus, nucleus accumbens, pallidum, putamen, and thalamus were compared across groups.Participants in late Fiebig stages exhibited larger volumes in the nucleus accumbens (8% larger; p = .049) and putamen (19%; p < .001) when compared to participants in the early Fiebig. Compared to PWOH, participants in late Fiebig exhibited larger volumes of the amygdala (9% larger; p = .002), caudate nucleus (11%; p = .005), nucleus accumbens (15%; p = .004), pallidum (19%; p = .001), and putamen (31%; p < .001). Brain volumes in the nucleus accumbens, pallidum, and putamen correlated modestly with stimulant use over the past four months among late Fiebig individuals (ps < .05).Findings indicate that brain volume alterations occur in acute infection, with the most prominent differences evident in the later stages of AHI. Additional studies are needed to evaluate mechanisms for possible brain disruption following ART, including viral factors and markers of neuroinflammation.

  View details for DOI 10.1097/QAD.0000000000003496

  View details for PubMedID 36723491

• The distortions of the free water model for diffusion MRI data when assuming single compartment relaxometry and proton density. Physics in medicine and biology Ferizi, U., Müller-Oehring, E. M., Peterson, E. T., Pohl, K. M. 2023

  Abstract

  To document the bias of the simplified free water model of diffusion MRI (dMRI) signal vis-a-vis a specific model which, in addition to diffusion, incorporates compartment-specific proton density (PD), T1 recovery during repetition time (TR), and T2 decay during echo time (TE).Both models assume that volume fraction f of the total signal in any voxel arises from the free water compartment (fw) such as cerebrospinal fluid (CSF) or edema, and the remainder (1-f) from hindered water (hw) which is constrained by cellular structures such as white matter (WM). The specific and simplified models are compared on a synthetic dataset, using a range of PD, T1 and T2 values. We then fit the models to an in vivo healthy brain dMRI dataset. For both synthetic and in vivo data we use experimentally feasible TR, TE, signal-to-noise ratio (SNR) and physiologically plausible diffusion profiles.From the simulations we see that the difference between the estimated simplified f and specific f is largest for mid-range ground-truth f, and it increases as SNR increases. The estimation of volume fraction f is sensitive to the choice of model, simplified or specific, but the estimated diffusion parameters are robust. Specific f is more accurate and precise than simplified f. In the white matter (WM) regions of the in vivo images, specific f is lower than simplified f.In dMRI models for free water, accounting for compartment specific PD, T1 and T2, in addition to diffusion, improves the estimation of model parameters. This extra model specification attenuates the estimation bias of compartmental volume fraction without affecting the estimation of other diffusion parameters.

  View details for DOI 10.1088/1361-6560/acb30b

  View details for PubMedID 36638532

• One-Shot Federated Learning on Medical Data Using Knowledge Distillation with Image Synthesis and Client Model Adaptation Kang, M., Chikontwe, P., Kim, S., Jin, K., Adeli, E., Pohl, K. M., Park, S., Greenspan, H., Madabhushi, A., Mousavi, P., Salcudean, S., Duncan, J., Syeda-Mahmood, T., Taylor, R. SPRINGER INTERNATIONAL PUBLISHING AG. 2023: 521-531

  View details for DOI 10.1007/978-3-031-43895-0_49

  View details for Web of Science ID 001109624900049

• Imputing Brain Measurements Across Data Sets via Graph Neural Networks. PRedictive Intelligence in MEdicine. PRIME (Workshop) Wang, Y., Peng, W., Tapert, S. F., Zhao, Q., Pohl, K. M. 2023; 14277: 172-183

  Abstract

  Publicly available data sets of structural MRIs might not contain specific measurements of brain Regions of Interests (ROIs) that are important for training machine learning models. For example, the curvature scores computed by Freesurfer are not released by the Adolescent Brain Cognitive Development (ABCD) Study. One can address this issue by simply reapplying Freesurfer to the data set. However, this approach is generally computationally and labor intensive (e.g., requiring quality control). An alternative is to impute the missing measurements via a deep learning approach. However, the state-of-the-art is designed to estimate randomly missing values rather than entire measurements. We therefore propose to re-frame the imputation problem as a prediction task on another (public) data set that contains the missing measurements and shares some ROI measurements with the data sets of interest. A deep learning model is then trained to predict the missing measurements from the shared ones and afterwards is applied to the other data sets. Our proposed algorithm models the dependencies between ROI measurements via a graph neural network (GNN) and accounts for demographic differences in brain measurements (e.g. sex) by feeding the graph encoding into a parallel architecture. The architecture simultaneously optimizes a graph decoder to impute values and a classifier in predicting demographic factors. We test the approach, called Demographic Aware Graph-based Imputation (DAGI), on imputing those missing Freesurfer measurements of ABCD (N=3760; minimum age 12 years) by training the predictor on those publicly released by the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA, N=540). 5-fold cross-validation on NCANDA reveals that the imputed scores are more accurate than those generated by linear regressors and deep learning models. Adding them also to a classifier trained in identifying sex results in higher accuracy than only using those Freesurfer scores provided by ABCD.

  View details for DOI 10.1007/978-3-031-46005-0_15

  View details for PubMedID 37946742

• Disentangling Normal Aging From Severity of Disease via Weak Supervision on Longitudinal MRI IEEE TRANSACTIONS ON MEDICAL IMAGING Ouyang, J., Zhao, Q., Adeli, E., Zaharchuk, G., Pohl, K. M. 2022; 41 (10): 2558-2569

  Abstract

  The continuous progression of neurological diseases are often categorized into conditions according to their severity. To relate the severity to changes in brain morphometry, there is a growing interest in replacing these categories with a continuous severity scale that longitudinal MRIs are mapped onto via deep learning algorithms. However, existing methods based on supervised learning require large numbers of samples and those that do not, such as self-supervised models, fail to clearly separate the disease effect from normal aging. Here, we propose to explicitly disentangle those two factors via weak-supervision. In other words, training is based on longitudinal MRIs being labelled either normal or diseased so that the training data can be augmented with samples from disease categories that are not of primary interest to the analysis. We do so by encouraging trajectories of controls to be fully encoded by the direction associated with brain aging. Furthermore, an orthogonal direction linked to disease severity captures the residual component from normal aging in the diseased cohort. Hence, the proposed method quantifies disease severity and its progression speed in individuals without knowing their condition. We apply the proposed method on data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, N =632 ). We then show that the model properly disentangled normal aging from the severity of cognitive impairment by plotting the resulting disentangled factors of each subject and generating simulated MRIs for a given chronological age and condition. Moreover, our representation obtains higher balanced accuracy when used for two downstream classification tasks compared to other pre-training approaches. The code for our weak-supervised approach is available at https://github.com/ouyangjiahong/longitudinal-direction-disentangle.

  View details for DOI 10.1109/TMI.2022.3166131

  View details for Web of Science ID 000862400100002

  View details for PubMedID 35404811

  View details for PubMedCentralID PMC9578549

• Multi-atlas thalamic nuclei segmentation on standard T1-weighed MRI with application to normal aging. Human brain mapping Pfefferbaum, A., Sullivan, E. V., Zahr, N. M., Pohl, K. M., Saranathan, M. 2022

  Abstract

  Specific thalamic nuclei are implicated in healthy aging and age-related neurodegenerative diseases. However, few methods are available for robust automated segmentation of thalamic nuclei. The threefold aims of this study were to validate the use of a modified thalamic nuclei segmentation method on standard T1 MRI data, to apply this method to quantify age-related volume declines, and to test functional meaningfulness by predicting performance on motor testing. A modified version of THalamus Optimized Multi-Atlas Segmentation (THOMAS) generated 22 unilateral thalamic nuclei. For validation, we compared nuclear volumes obtained from THOMAS parcellation of white-matter-nulled (WMn) MRI data to T1 MRI data in 45 participants. To examine the effects of age/sex on thalamic nuclear volumes, T1 MRI available from a second data set of 121 men and 117 women, ages 20-86years, were segmented using THOMAS. To test for functional ramifications, composite regions and constituent nuclei were correlated with Grooved Pegboard test scores. THOMAS on standard T1 data showed significant quantitative agreement with THOMAS from WMn data, especially for larger nuclei. Sex differences revealing larger volumes in men than women were accounted for by adjustment with supratentorial intracranial volume (sICV). Significant sICV-adjusted correlations between age and thalamic nuclear volumes were detected in 20 of the 22 unilateral nuclei and whole thalamus. Composite Posterior and Ventral regions and Ventral Anterior/Pulvinar nuclei correlated selectively with higher scores from the eye-hand coordination task. These results support the use of THOMAS for standard T1-weighted data as adequately robust for thalamic nuclear parcellation.

  View details for DOI 10.1002/hbm.26088

  View details for PubMedID 36181510

• Multiple Instance Neuroimage Transformer. PRedictive Intelligence in MEdicine. PRIME (Workshop) Singla, A., Zhao, Q., Do, D. K., Zhou, Y., Pohl, K. M., Adeli, E. 2022; 13564: 36-48

  Abstract

  For the first time, we propose using a multiple instance learning based convolution-free transformer model, called Multiple Instance Neuroimage Transformer (MINiT), for the classification of T1-weighted (T1w) MRIs. We first present several variants of transformer models adopted for neuroimages. These models extract non-overlapping 3D blocks from the input volume and perform multi-headed self-attention on a sequence of their linear projections. MINiT, on the other hand, treats each of the non-overlapping 3D blocks of the input MRI as its own instance, splitting it further into non-overlapping 3D patches, on which multi-headed self-attention is computed. As a proof-of-concept, we evaluate the efficacy of our model by training it to identify sex from T1w-MRIs of two public datasets: Adolescent Brain Cognitive Development (ABCD) and the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA). The learned attention maps highlight voxels contributing to identifying sex differences in brain morphometry. The code is available at https://github.com/singlaayush/MINIT.

  View details for DOI 10.1007/978-3-031-16919-9_4

  View details for PubMedID 36331280

  View details for PubMedCentralID PMC9629332

• Bridging the Gap between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing. PRedictive Intelligence in MEdicine. PRIME (Workshop) Paschali, M., Zhao, Q., Adeli, E., Pohl, K. M. 2022; 13564: 13-23

  Abstract

  A fundamental approach in neuroscience research is to test hypotheses based on neuropsychological and behavioral measures, i.e., whether certain factors (e.g., related to life events) are associated with an outcome (e.g., depression). In recent years, deep learning has become a potential alternative approach for conducting such analyses by predicting an outcome from a collection of factors and identifying the most "informative" ones driving the prediction. However, this approach has had limited impact as its findings are not linked to statistical significance of factors supporting hypotheses. In this article, we proposed a flexible and scalable approach based on the concept of permutation testing that integrates hypothesis testing into the data-driven deep learning analysis. We apply our approach to the yearly self-reported assessments of 621 adolescent participants of the National Consortium of Alcohol and Neurodevelopment in Adolescence (NCANDA) to predict negative valence, a symptom of major depressive disorder according to the NIMH Research Domain Criteria (RDoC). Our method successfully identifies categories of risk factors that further explain the symptom.

  View details for DOI 10.1007/978-3-031-16919-9_2

  View details for PubMedID 36342897

  View details for PubMedCentralID PMC9632755

• GaitForeMer: Self-Supervised Pre-Training of Transformers via Human Motion Forecasting for Few-Shot Gait Impairment Severity Estimation. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Endo, M., Poston, K. L., Sullivan, E. V., Fei-Fei, L., Pohl, K. M., Adeli, E. 2022; 13438: 130-139

  Abstract

  Parkinson's disease (PD) is a neurological disorder that has a variety of observable motor-related symptoms such as slow movement, tremor, muscular rigidity, and impaired posture. PD is typically diagnosed by evaluating the severity of motor impairments according to scoring systems such as the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Automated severity prediction using video recordings of individuals provides a promising route for non-intrusive monitoring of motor impairments. However, the limited size of PD gait data hinders model ability and clinical potential. Because of this clinical data scarcity and inspired by the recent advances in self-supervised large-scale language models like GPT-3, we use human motion forecasting as an effective self-supervised pre-training task for the estimation of motor impairment severity. We introduce GaitForeMer, Gait Forecasting and impairment estimation transforMer, which is first pre-trained on public datasets to forecast gait movements and then applied to clinical data to predict MDS-UPDRS gait impairment severity. Our method outperforms previous approaches that rely solely on clinical data by a large margin, achieving an F1 score of 0.76, precision of 0.79, and recall of 0.75. Using GaitForeMer, we show how public human movement data repositories can assist clinical use cases through learning universal motion representations. The code is available at https://github.com/markendo/GaitForeMer.

  View details for DOI 10.1007/978-3-031-16452-1_13

  View details for PubMedID 36342887

  View details for PubMedCentralID PMC9635991

• A Penalty Approach for Normalizing Feature Distributions to Build Confounder-Free Models. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Vento, A., Zhao, Q., Paul, R., Pohl, K., Adeli, E. 2022; 13433: 387-397

  Abstract

  Translating the use of modern machine learning algorithms into clinical applications requires settling challenges related to explain-ability and management of nuanced confounding factors. To suitably interpret the results, removing or explaining the effect of confounding variables (or metadata) is essential. Confounding variables affect the relationship between input training data and target outputs. Accordingly, when we train a model on such data, confounding variables will bias the distribution of the learned features. A recent promising solution, Meta-Data Normalization (MDN), estimates the linear relationship between the metadata and each feature based on a non-trainable closed-form solution. However, this estimation is confined by the sample size of a mini-batch and thereby may result in an oscillating performance. In this paper, we extend the MDN method by applying a Penalty approach (referred to as PDMN). We cast the problem into a bi-level nested optimization problem. We then approximate that objective using a penalty method so that the linear parameters within the MDN layer are trainable and learned on all samples. This enables PMDN to be plugged into any architectures, even those unfit to run batch-level operations such as transformers and recurrent models. We show improvement in model accuracy and independence from the confounders using PMDN over MDN in a synthetic experiment and a multi-label, multi-site classification of magnetic resonance images.

  View details for DOI 10.1007/978-3-031-16437-8_37

  View details for PubMedID 36331278

  View details for PubMedCentralID PMC9629333

• Self-supervised learning of neighborhood embedding for longitudinal MRI. Medical image analysis Ouyang, J., Zhao, Q., Adeli, E., Zaharchuk, G., Pohl, K. M. 2022; 82: 102571

  Abstract

  In recent years, several deep learning models recommend first to represent Magnetic Resonance Imaging (MRI) as latent features before performing a downstream task of interest (such as classification or regression). The performance of the downstream task generally improves when these latent representations are explicitly associated with factors of interest. For example, we derived such a representation for capturing brain aging by applying self-supervised learning to longitudinal MRIs and then used the resulting encoding to automatically identify diseases accelerating the aging of the brain. We now propose a refinement of this representation by replacing the linear modeling of brain aging with one that is consistent in local neighborhoods in the latent space. Called Longitudinal Neighborhood Embedding (LNE), we derive an encoding so that neighborhoods are age-consistent (i.e., brain MRIs of different subjects with similar brain ages are in close proximity of each other) and progression-consistent, i.e., the latent space is defined by a smooth trajectory field where each trajectory captures changes in brain ages between a pair of MRIs extracted from a longitudinal sequence. To make the problem computationally tractable, we further propose a strategy for mini-batch sampling so that the resulting local neighborhoods accurately approximate the ones that would be defined based on the whole cohort. We evaluate LNE on three different downstream tasks: (1) to predict chronological age from T1-w MRI of 274 healthy subjects participating in a study at SRI International; (2) to distinguish Normal Control (NC) from Alzheimer's Disease (AD) and stable Mild Cognitive Impairment (sMCI) from progressive Mild Cognitive Impairment (pMCI) based on T1-w MRI of 632 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI); and (3) to distinguish no-to-low from moderate-to-heavy alcohol drinkers based on fractional anisotropy derived from diffusion tensor MRIs of 764 adolescents recruited by the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA). Across the three data sets, the visualization of the smooth trajectory vector fields and superior accuracy on downstream tasks demonstrate the strength of the proposed method over existing self-supervised methods in extracting information related to brain aging, which could help study the impact of substance use and neurodegenerative disorders. The code is available at https://github.com/ouyangjiahong/longitudinal-neighbourhood-embedding.

  View details for DOI 10.1016/j.media.2022.102571

  View details for PubMedID 36115098

• Earlier Bedtime and Effective Coping Skills Predict a Return to Low-Risk of Depression in Young Adults during the COVID-19 Pandemic. International journal of environmental research and public health Zhao, Q., Wang, K., Kiss, O., Yuksel, D., de Zambotti, M., Clark, D. B., Goldston, D. B., Nooner, K. B., Brown, S. A., Tapert, S. F., Thompson, W. K., Nagel, B. J., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M., Baker, F. C. 2022; 19 (16)

  Abstract

  To determine the persistent effects of the pandemic on mental health in young adults, we categorized depressive symptom trajectories and sought factors that promoted a reduction in depressive symptoms in high-risk individuals. Specifically, longitudinal analysis investigated changes in the risk for depression before and during the pandemic until December 2021 in 399 young adults (57% female; age range: 22.8 ± 2.6 years) in the United States (U.S.) participating in the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study. The Center for Epidemiologic Studies Depression Scale (CES-D-10) was administered multiple times before and during the pandemic. A score ≥10 identified individuals at high-risk for depression. Self-reported sleep behavior, substance use, and coping skills at the start of the pandemic were assessed as predictors for returning to low-risk levels while controlling for demographic factors. The analysis identified four trajectory groups regarding depression risk, with 38% being at low-risk pre-pandemic through 2021, 14% showing persistent high-risk pre-pandemic through 2021, and the remainder converting to high-risk either in June 2020 (30%) or later (18%). Of those who became high-risk in June 2020, 51% were no longer at high-risk in 2021. Logistic regression revealed that earlier bedtime and, for the older participants (mid to late twenties), better coping skills were associated with this declining risk. Results indicate divergence in trajectories of depressive symptoms, with a considerable number of young adults developing persistent depressive symptoms. Healthy sleep behavior and specific coping skills have the potential to promote remittance from depressive symptoms in the context of the pandemic.

  View details for DOI 10.3390/ijerph191610300

  View details for PubMedID 36011934

• Prior test experience confounds longitudinal tracking of adolescent cognitive and motor development. BMC medical research methodology Sullivan, E. V., Thompson, W. K., Brumback, T., Prouty, D., Tapert, S. F., Brown, S. A., De Bellis, M. D., Nooner, K. B., Baker, F. C., Colrain, I. M., Clark, D. B., Nagel, B. J., Pohl, K. M., Pfefferbaum, A. 2022; 22 (1): 177

  Abstract

  BACKGROUND: Accurate measurement of trajectories in longitudinal studies, considered the gold standard method for tracking functional growth during adolescence, decline in aging, and change after head injury, is subject to confounding by testing experience.METHODS: We measured change in cognitive and motor abilities over four test sessions (baseline and three annual assessments) in 154 male and 165 female participants (baseline age 12-21years) from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study. At each of the four test sessions, these participants were given a test battery using computerized administration and traditional pencil and paper tests that yielded accuracy and speed measures for multiple component cognitive (Abstraction, Attention, Emotion, Episodic memory, Working memory, and General Ability) and motor (Ataxia and Speed) functions. The analysis aim was to dissociate neurodevelopment from testing experience by using an adaptation of the twice-minus-once tested method, which calculated the difference between longitudinal change (comprising developmental plus practice effects) and practice-free initial cross-sectional performance for each consecutive pairs of test sessions. Accordingly, the first set of analyses quantified the effects of learning (i.e., prior test experience) on accuracy and after speed domain scores. Then developmental effects were determined for each domain for accuracy and speed having removed the measured learning effects.RESULTS: The greatest gains in performance occurred between the first and second sessions, especially in younger participants, regardless of sex, but practice gains continued to accrue thereafter for several functions. For all 8 accuracy composite scores, the developmental effect after accounting for learning was significant across age and was adequately described by linear fits. The learning-adjusted developmental effects for speed were adequately described by linear fits for Abstraction, Emotion, Episodic Memory, General Ability, and Motor scores, although a nonlinear fit was better for Attention, Working Memory, and Average Speed scores.CONCLUSION: Thus, what appeared as accelerated cognitive and motor development was, in most cases, attributable to learning. Recognition of the substantial influence of prior testing experience is critical for accurate characterization of normal development and for developing norms for clinical neuropsychological investigations of conditions affecting the brain.

  View details for DOI 10.1186/s12874-022-01606-9

  View details for PubMedID 35751025

• Detecting negative valence symptoms in adolescents based on longitudinal self-reports and behavioral assessments. Journal of affective disorders Paschali, M., Kiss, O., Zhao, Q., Adeli, E., Podhajsky, S., Muller-Oehring, E. M., Gotlib, I. H., Pohl, K. M., Baker, F. C. 2022

  Abstract

  BACKGROUND: Given the high prevalence of depressive symptoms reported by adolescents and associated risk of experiencing psychiatric disorders as adults, differentiating the trajectories of the symptoms related to negative valence at an individual level could be crucial in gaining a better understanding of their effects later in life.METHODS: A longitudinal deep learning framework is presented, identifying self-reported and behavioral measurements that detect the depressive symptoms associated with the Negative Valence System domain of the NIMH Research Domain Criteria (RDoC).RESULTS: Applied to the annual records of 621 participants (age range: 12 to 17 years) of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA), the deep learning framework identifies predictors of negative valence symptoms, which include lower extraversion, poorer sleep quality, impaired executive control function and factors related to substance use.LIMITATIONS: The results rely mainly on self-reported measures and do not provide information about the underlying neural correlates. Also, a larger sample is required to understand the role of sex and other demographics related to the risk of experiencing symptoms of negative valence.CONCLUSIONS: These results provide new information about predictors of negative valence symptoms in individuals during adolescence that could be critical in understanding the development of depression and identifying targets for intervention. Importantly, findings can inform preventive and treatment approaches for depression in adolescents, focusing on a unique predictor set of modifiable modulators to include factors such as sleep hygiene training, cognitive-emotional therapy enhancing coping and controllability experience and/or substance use interventions.

  View details for DOI 10.1016/j.jad.2022.06.002

  View details for PubMedID 35688394

• Did the acute impact of the COVID-19 pandemic on drinking or nicotine use persist? Evidence from a cohort of emerging adults followed for up to nine years. Addictive behaviors Pelham, W. E., Yuksel, D., Tapert, S. F., Baker, F. C., Pohl, K. M., Thompson, W. K., Podhajsky, S., Reuter, C., Zhao, Q., Eberson-Shumate, S. C., Clark, D. B., Goldston, D. B., Nooner, K. B., Brown, S. A. 2022; 131: 107313

  Abstract

  This study examined the impact of the COVID-19 pandemic on drinking and nicotine use through June of 2021 in a community-based sample of young adults.Data were from 348 individuals (49% female) enrolled in a long-term longitudinal study with an accelerated longitudinal design: the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) Study. Individuals completed pre-pandemic assessments biannually from 2016 to early 2020, then completed up to three web-based, during-pandemic surveys in June 2020, December 2020, and June 2021. Assessments when individuals were 18.8-22.4 years old (N = 1,458) were used to compare drinking and nicotine use pre-pandemic vs. at each of the three during-pandemic timepoints, adjusting for the age-related increases expected over time.Compared to pre-pandemic, participants were less likely to report past-month drinking in June or December 2020, but there was an increase in drinking days among drinkers in June 2020. By June 2021, both the prevalence of past-month drinking and number of drinking days among drinks were similar to pre-pandemic levels. On average, there were no statistically significant differences between pre-pandemic and during-pandemic time points for binge drinking, typical drinking quantity, or nicotine use. Young adults who reported an adverse financial impact of the pandemic showed increased nicotine use while their peers showed stable or decreased nicotine use.Initial effects of the pandemic on alcohol use faded by June 2021, and on average there was little effect of the pandemic on nicotine use.

  View details for DOI 10.1016/j.addbeh.2022.107313

  View details for PubMedID 35413486

• The Pandemic's Toll on Young Adolescents: Prevention and Intervention Targets to Preserve Their Mental Health. The Journal of adolescent health : official publication of the Society for Adolescent Medicine Kiss, O., Alzueta, E., Yuksel, D., Pohl, K. M., de Zambotti, M., Muller-Oehring, E. M., Prouty, D., Durley, I., Pelham, W. E., McCabe, C. J., Gonzalez, M. R., Brown, S. A., Wade, N. E., Marshall, A. T., Sowell, E. R., Breslin, F. J., Lisdahl, K. M., Dick, A. S., Sheth, C. S., McCandliss, B. D., Guillaume, M. J., Van Rinsveld, A. M., Dowling, G. J., Tapert, S. F., Baker, F. C. 1800

  Abstract

  PURPOSE: Adolescence is characterized by dramatic physical, social, and emotional changes, making teens particularly vulnerable to the mental health effects of the COVID-19 pandemic. This longitudinal study identifies young adolescents who are most vulnerable to the psychological toll of the pandemic and provides insights to inform strategies to help adolescents cope better in times of crisis.METHODS: A data-driven approach was applied to a longitudinal, demographically diverse cohort of more than 3,000 young adolescents (10-14years) participating in the ongoing Adolescent Brain Cognitive Development Study in the United States, including multiple prepandemic visits and three assessments during the COVID-19 pandemic (May-August 2020). We fitted machine learning models and provided a comprehensive list of predictors of psychological distress in individuals.RESULTS: Positive affect, stress, anxiety, and depressive symptoms were accurately detected with our classifiers. Female sex and prepandemic internalizing symptoms and sleep problems were strong predictors of psychological distress. Parent- and youth-reported pandemic-related psychosocial factors, including poorer quality and functioning of family relationships, more screen time, and witnessing discrimination in relation to the pandemic further predicted youth distress. However, better social support, regular physical activities, coping strategies, and healthy behaviors predicted better emotional well-being.CONCLUSIONS: Findings highlight the importance of social connectedness and healthy behaviors, such as sleep and physical activity, as buffering factors against the deleterious effects of the pandemic on adolescents' mental health. They also point to the need for greater attention toward coping strategies that help the most vulnerable adolescents, particularly girls and those with prepandemic psychological problems.

  View details for DOI 10.1016/j.jadohealth.2021.11.023

  View details for PubMedID 35090817

• GaitForeMer: Self-supervised Pre-training of Transformers via Human Motion Forecasting for Few-Shot Gait Impairment Severity Estimation Endo, M., Poston, K. L., Sullivan, E. V., Fei-Fei, L., Pohl, K. M., Adeli, E., Wang, L., Dou, Q., Fletcher, P. T., Speidel, S., Li, S. SPRINGER INTERNATIONAL PUBLISHING AG. 2022: 130-139

  View details for DOI 10.1007/978-3-031-16452-1_13

  View details for Web of Science ID 000867418200013

• Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome Li, Y., Wei, Q., Adeli, E., Pohl, K. M., Zhao, Q., Wang, L., Dou, Q., Fletcher, P. T., Speidel, S., Li, S. SPRINGER INTERNATIONAL PUBLISHING AG. 2022: 231-240

  Abstract

  The white-matter (micro-)structural architecture of the brain promotes synchrony among neuronal populations, giving rise to richly patterned functional connections. A fundamental problem for systems neuroscience is determining the best way to relate structural and functional networks quantified by diffusion tensor imaging and resting-state functional MRI. As one of the state-of-the-art approaches for network analysis, graph convolutional networks (GCN) have been separately used to analyze functional and structural networks, but have not been applied to explore inter-network relationships. In this work, we propose to couple the two networks of an individual by adding inter-network edges between corresponding brain regions, so that the joint structure-function graph can be directly analyzed by a single GCN. The weights of inter-network edges are learnable, reflecting non-uniform structure-function coupling strength across the brain. We apply our Joint-GCN to predict age and sex of 662 participants from the public dataset of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) based on their functional and micro-structural white-matter networks. Our results support that the proposed Joint-GCN outperforms existing multi-modal graph learning approaches for analyzing structural and functional networks.

  View details for DOI 10.1007/978-3-031-16431-6_22

  View details for Web of Science ID 000867524300022

  View details for PubMedID 36321855

  View details for PubMedCentralID PMC9620868

• Bridging the Gap Between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing Paschali, M., Zhao, Q., Adeli, E., Pohl, K. M., Rekik, Adeli, E., Park, S. H., Cintas, C. SPRINGER INTERNATIONAL PUBLISHING AG. 2022: 13-23

  View details for DOI 10.1007/978-3-031-16919-9_2

  View details for Web of Science ID 000867616800002

• Multiple Instance Neuroimage Transformer Singla, A., Zhao, Q., Do, D. K., Zhou, Y., Pohl, K. M., Adeli, E., Rekik, Adeli, E., Park, S. H., Cintas, C. SPRINGER INTERNATIONAL PUBLISHING AG. 2022: 36-48

  View details for DOI 10.1007/978-3-031-16919-9_4

  View details for Web of Science ID 000867616800004

• A Penalty Approach for Normalizing Feature Distributions to Build Confounder-Free Models Vento, A., Zhao, Q., Paul, R., Pohl, K. M., Adeli, E., Wang, L., Dou, Q., Fletcher, P. T., Speidel, S., Li, S. SPRINGER INTERNATIONAL PUBLISHING AG. 2022: 387-397

  View details for DOI 10.1007/978-3-031-16437-8_37

  View details for Web of Science ID 000867397400037

• Growth Trajectories of Cognitive and Motor Control in Adolescence: How Much Is Development and How Much Is Practice? NEUROPSYCHOLOGY Lannoy, S., Pfefferbaum, A., Le Berre, A., Thompson, W. K., Brumback, T., Schulte, T., Pohl, K. M., De Bellis, M. D., Nooner, K. B., Baker, F. C., Prouty, D., Colrain, I. M., Nagel, B. J., Brown, S. A., Clark, D. B., Tapert, S. F., Sullivan, E., Mueller-Oehring, E. M. 2021

  View details for DOI 10.1037/neu0000771

  View details for Web of Science ID 000735242200001

• Risk for depression tripled during the COVID-19 pandemic in emerging adults followed for the last 8 years. Psychological medicine Alzueta, E., Podhajsky, S., Zhao, Q., Tapert, S. F., Thompson, W. K., de Zambotti, M., Yuksel, D., Kiss, O., Wang, R., Volpe, L., Prouty, D., Colrain, I. M., Clark, D. B., Goldston, D. B., Nooner, K. B., De Bellis, M. D., Brown, S. A., Nagel, B. J., Pfefferbaum, A., Sullivan, E. V., Baker, F. C., Pohl, K. M. 2021: 1-8

  Abstract

  BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has significantly increased depression rates, particularly in emerging adults. The aim of this study was to examine longitudinal changes in depression risk before and during COVID-19 in a cohort of emerging adults in the U.S. and to determine whether prior drinking or sleep habits could predict the severity of depressive symptoms during the pandemic.METHODS: Participants were 525 emerging adults from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA), a five-site community sample including moderate-to-heavy drinkers. Poisson mixed-effect models evaluated changes in the Center for Epidemiological Studies Depression Scale (CES-D-10) from before to during COVID-19, also testing for sex and age interactions. Additional analyses examined whether alcohol use frequency or sleep duration measured in the last pre-COVID assessment predicted pandemic-related increase in depressive symptoms.RESULTS: The prevalence of risk for clinical depression tripled due to a substantial and sustained increase in depressive symptoms during COVID-19 relative to pre-COVID years. Effects were strongest for younger women. Frequent alcohol use and short sleep duration during the closest pre-COVID visit predicted a greater increase in COVID-19 depressive symptoms.CONCLUSIONS: The sharp increase in depression risk among emerging adults heralds a public health crisis with alarming implications for their social and emotional functioning as this generation matures. In addition to the heightened risk for younger women, the role of alcohol use and sleep behavior should be tracked through preventive care aiming to mitigate this looming mental health crisis.

  View details for DOI 10.1017/S0033291721004062

  View details for PubMedID 34726149

• Multi-label, multi-domain learning identifies compounding effects of HIV and cognitive impairment. Medical image analysis Zhang, J., Zhao, Q., Adeli, E., Pfefferbaum, A., Sullivan, E. V., Paul, R., Valcour, V., Pohl, K. M. 2021; 75: 102246

  Abstract

  Older individuals infected by Human Immunodeficiency Virus (HIV) are at risk for developing HIV-Associated Neurocognitive Disorder (HAND), i.e., from reduced cognitive functioning similar to HIV-negative individuals with Mild Cognitive Impairment (MCI) or to Alzheimer's Disease (AD) if more severely affected. Incompletely understood is how brain structure can serve to differentiate cognitive impairment (CI) in the HIV-positive (i.e., HAND) from the HIV-negative cohort (i.e., MCI and AD). To that end, we designed a multi-label classifier that labels the structural magnetic resonance images (MRI) of individuals by their HIV and CI status via two binary variables. Proper training of such an approach traditionally requires well-curated datasets containing large number of samples for each of the corresponding four cohorts (healthy controls, CI HIV-negative adults a.k.a. CI-only, HIV-positive patients without CI a.k.a. HIV-only, and HAND). Because of the rarity of such datasets, we proposed to improve training of the multi-label classifier via a multi-domain learning scheme that also incorporates domain-specific classifiers on auxiliary single-label datasets specific to either binary label. Specifically, we complement the training dataset of MRIs of the four cohorts (Control: 156, CI-only: 335, HIV-only: 37, HAND: 145) acquired by the Memory and Aging Center at the University of California - San Francisco with a CI-specific dataset only containing MRIs of HIV-negative subjects (Controls: 229, CI-only: 397) from the Alzheimer's Disease Neuroimaging Initiative and an HIV-specific dataset (Controls: 75, HIV-only: 75) provided by SRI International. Based on cross-validation on the UCSF dataset, the multi-domain and multi-label learning strategy leads to superior classification accuracy compared with one-domain or multi-class learning approaches, specifically for the undersampled HIV-only cohort. The 'prediction logits' of CI computed by the multi-label formulation also successfully stratify motor performance among the HIV-positive subjects (including HAND). Finally, brain patterns driving the subject-level predictions across all four cohorts characterize the independent and compounding effects of HIV and CI in the HAND cohort.

  View details for DOI 10.1016/j.media.2021.102246

  View details for PubMedID 34706304

• Adversarial Bayesian Optimization for Quantifying Motion Artifact Within MRI. PRedictive Intelligence in MEdicine. PRIME (Workshop) Butskova, A., Juhl, R., Zukic, D., Chaudhary, A., Pohl, K. M., Zhao, Q. 2021; 12928: 83-92

  Abstract

  Subject motion during an MRI sequence can cause ghosting effects or diffuse image noise in the phase-encoding direction and hence is likely to bias findings in neuroimaging studies. Detecting motion artifacts often relies on experts visually inspecting MRIs, which is subjective and expensive. To improve this detection, we develop a framework to automatically quantify the severity of motion artifact within a brain MRI. We formulate this task as a regression problem and train the regressor from a data set of MRIs with various amounts of motion artifacts. To resolve the issue of missing fine-grained ground-truth labels (level of artifacts), we propose Adversarial Bayesian Optimization (ABO) to infer the distribution of motion parameters (i.e., rotation and translation) underlying the acquired MRI data and then inject synthetic motion artifacts sampled from that estimated distribution into motion-free MRIs. After training the regressor on the synthetic data, we applied the model to quantify the motion level in 990 MRIs collected by the National Consortium on Alcohol and Neurodevelopment in Adolescence. Results show that the motion level derived by our approach is more reliable than the traditional metric based on Entropy Focus Criterion and manually defined binary labels.

  View details for DOI 10.1007/978-3-030-87602-9_8

  View details for PubMedID 35749100

• Longitudinal Correlation Analysis for Decoding Multi-modal Brain Development. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Zhao, Q., Adeli, E., Pohl, K. M. 2021; 12907: 400-409

  Abstract

  Starting from childhood, the human brain restructures and rewires throughout life. Characterizing such complex brain development requires effective analysis of longitudinal and multi-modal neuroimaging data. Here, we propose such an analysis approach named Longitudinal Correlation Analysis (LCA). LCA couples the data of two modalities by first reducing the input from each modality to a latent representation based on autoencoders. A self-supervised strategy then relates the two latent spaces by jointly disentangling two directions, one in each space, such that the longitudinal changes in latent representations along those directions are maximally correlated between modalities. We applied LCA to analyze the longitudinal T1-weighted and diffusion-weighted MRIs of 679 youths from the National Consortium on Alcohol and Neurodevelopment in Adolescence. Unlike existing approaches that focus on either cross-sectional or single-modal modeling, LCA successfully unraveled coupled macrostructural and microstructural brain development from morphological and diffusivity features extracted from the data. A retesting of LCA on raw 3D image volumes of those subjects successfully replicated the findings from the feature-based analysis. Lastly, the developmental effects revealed by LCA were inline with the current understanding of maturational patterns of the adolescent brain.

  View details for DOI 10.1007/978-3-030-87234-2_38

  View details for PubMedID 35253021

  View details for PubMedCentralID PMC8896397

• Self-Supervised Longitudinal Neighbourhood Embedding. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Ouyang, J., Zhao, Q., Adeli, E., Sullivan, E. V., Pfefferbaum, A., Zaharchuk, G., Pohl, K. M. 2021; 12902: 80-89

  Abstract

  Longitudinal MRIs are often used to capture the gradual deterioration of brain structure and function caused by aging or neurological diseases. Analyzing this data via machine learning generally requires a large number of ground-truth labels, which are often missing or expensive to obtain. Reducing the need for labels, we propose a self-supervised strategy for representation learning named Longitudinal Neighborhood Embedding (LNE). Motivated by concepts in contrastive learning, LNE explicitly models the similarity between trajectory vectors across different subjects. We do so by building a graph in each training iteration defining neighborhoods in the latent space so that the progression direction of a subject follows the direction of its neighbors. This results in a smooth trajectory field that captures the global morphological change of the brain while maintaining the local continuity. We apply LNE to longitudinal T1w MRIs of two neuroimaging studies: a dataset composed of 274 healthy subjects, and Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 632). The visualization of the smooth trajectory vector field and superior performance on downstream tasks demonstrate the strength of the proposed method over existing self-supervised methods in extracting information associated with normal aging and in revealing the impact of neurodegenerative disorders. The code is available at https://github.com/ouyangjiahong/longitudinal-neighbourhood-embedding.

  View details for DOI 10.1007/978-3-030-87196-3_8

  View details for PubMedID 35727732

  View details for PubMedCentralID PMC9204645

• Memory impairment in alcohol use disorder is associated with regional frontal brain volumes. Drug and alcohol dependence Fama, R., Le Berre, A., Sassoon, S. A., Zahr, N. M., Pohl, K. M., Pfefferbaum, A., Sullivan, E. V. 2021; 228: 109058

  Abstract

  BACKGROUND: Episodic memory deficits occur in alcohol use disorder (AUD), but their anatomical substrates remain in question. Although persistent memory impairment is classically associated with limbic circuitry disruption, learning and retrieval of new information also relies on frontal systems. Despite AUD vulnerability of frontal lobe integrity, relations between frontal regions and memory processes have been under-appreciated.METHODS: Participants included 91 AUD (49 with a drug diagnosis history) and 36 controls. Verbal and visual episodic memory scores were age- and education-corrected. Structural magnetic resonance imaging (MRI) data yielded regional frontal lobe (precentral, superior, orbital, middle, inferior, supplemental motor, and medial) and total hippocampal volumes.RESULTS: AUD were impaired on all memory scores and had smaller precentral frontal and hippocampal volumes than controls. Orbital, superior, and inferior frontal volumes and lifetime alcohol consumption were independent predictors of episodic memory in AUD. Selectivity was established with a double dissociation, where orbital frontal volume predicted verbal but not visual memory, whereas inferior frontal volumes predicted visual but not verbal memory. Further, superior frontal volumes predicted verbal memory in AUD alone, whereas orbital frontal volumes predicted verbal memory in AUD+drug abuse history.CONCLUSIONS: Selective relations among frontal subregions and episodic memory processes highlight the relevance of extra-limbic regions in mnemonic processes in AUD. Memory deficits resulting from frontal dysfunction, unlike the episodic memory impairment associated with limbic dysfunction, may be more amenable to recovery with cessation or reduction of alcohol misuse and may partially explain the heterogeneity in episodic memory abilities in AUD.

  View details for DOI 10.1016/j.drugalcdep.2021.109058

  View details for PubMedID 34610518

• Representation Disentanglement for Multi-modal Brain MRI Analysis. Information processing in medical imaging : proceedings of the ... conference Ouyang, J., Adeli, E., Pohl, K. M., Zhao, Q., Zaharchuk, G. 2021; 12729: 321-333

  Abstract

  Multi-modal MRIs are widely used in neuroimaging applications since different MR sequences provide complementary information about brain structures. Recent works have suggested that multi-modal deep learning analysis can benefit from explicitly disentangling anatomical (shape) and modality (appearance) information into separate image presentations. In this work, we challenge mainstream strategies by showing that they do not naturally lead to representation disentanglement both in theory and in practice. To address this issue, we propose a margin loss that regularizes the similarity in relationships of the representations across subjects and modalities. To enable robust training, we further use a conditional convolution to design a single model for encoding images of all modalities. Lastly, we propose a fusion function to combine the disentangled anatomical representations as a set of modality-invariant features for downstream tasks. We evaluate the proposed method on three multi-modal neuroimaging datasets. Experiments show that our proposed method can achieve superior disentangled representations compared to existing disentanglement strategies. Results also indicate that the fused anatomical representation has potential in the downstream task of zero-dose PET reconstruction and brain tumor segmentation.

  View details for DOI 10.1007/978-3-030-78191-0_25

  View details for PubMedID 35173402

• Longitudinal Pooling & Consistency Regularization to Model Disease Progression From MRIs. IEEE journal of biomedical and health informatics Ouyang, J., Zhao, Q., Sullivan, E. V., Pfefferbaum, A., Tapert, S. F., Adeli, E., Pohl, K. M. 2021; 25 (6): 2082-2092

  Abstract

  Many neurological diseases are characterized by gradual deterioration of brain structure andfunction. Large longitudinal MRI datasets have revealed such deterioration, in part, by applying machine and deep learning to predict diagnosis. A popular approach is to apply Convolutional Neural Networks (CNN) to extract informative features from each visit of the longitudinal MRI and then use those features to classify each visit via Recurrent Neural Networks (RNNs). Such modeling neglects the progressive nature of the disease, which may result in clinically implausible classifications across visits. To avoid this issue, we propose to combine features across visits by coupling feature extraction with a novel longitudinal pooling layer and enforce consistency of the classification across visits in line with disease progression. We evaluate the proposed method on the longitudinal structural MRIs from three neuroimaging datasets: Alzheimer's Disease Neuroimaging Initiative (ADNI, N=404), a dataset composed of 274 normal controls and 329 patients with Alcohol Use Disorder (AUD), and 255 youths from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA). In allthree experiments our method is superior to other widely used approaches for longitudinal classification thus making a unique contribution towards more accurate tracking of the impact of conditions on the brain. The code is available at https://github.com/ouyangjiahong/longitudinal-pooling.

  View details for DOI 10.1109/JBHI.2020.3042447

  View details for PubMedID 33270567

• GROWTH TRAJECTORIES OF COGNITIVE AND MOTOR CONTROL IN ADOLESCENCE: HOW MUCH IS DEVELOPMENT AND HOW MUCH IS PRACTICE? Lannoy, S. WILEY. 2021: 188A

  Abstract

  Executive control continues to develop throughout adolescence and is vulnerable to alcohol use. Although longitudinal assessment is ideal for tracking executive function development and onset of alcohol use, prior testing experience must be distinguished from developmental trajectories.We used the Stroop Match-to-Sample task to examine the improvement of processing speed and specific cognitive and motor control over 4 years in 445 adolescents. The twice-minus-once-tested method was used and expanded to four test sessions to delineate prior experience (i.e., learning) from development. A General Additive Model evaluated the predictive value of age and sex on executive function development and potential influences of alcohol use on development.Results revealed strong learning between the first two assessments. Adolescents significantly improved their speed processing over 4 years. Compared with boys, girls enhanced ability to control cognitive interference and motor reactions. Finally, the influence of alcohol use initiation was tested over 4 years for development in 110 no/low, 110 moderate/heavy age- and sex-matched drinkers; alcohol effects were not detected in the matched groups.Estimation of learning effects is crucial for examining developmental changes longitudinally. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

  View details for Web of Science ID 000661449201259

  View details for PubMedID 34807641

• Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models. Information processing in medical imaging : proceedings of the ... conference Liu, Z., Adeli, E., Pohl, K. M., Zhao, Q. 2021; 12729: 71-82

  Abstract

  Interpretability is a critical factor in applying complex deep learning models to advance the understanding of brain disorders in neuroimaging studies. To interpret the decision process of a trained classifier, existing techniques typically rely on saliency maps to quantify the voxel-wise or feature-level importance for classification through partial derivatives. Despite providing some level of localization, these maps are not human-understandable from the neuroscience perspective as they often do not inform the specific type of morphological changes linked to the brain disorder. Inspired by the image-to-image translation scheme, we propose to train simulator networks to inject (or remove) patterns of the disease into a given MRI based on a warping operation, such that the classifier increases (or decreases) its confidence in labeling the simulated MRI as diseased. To increase the robustness of training, we propose to couple the two simulators into a unified model based on conditional convolution. We applied our approach to interpreting classifiers trained on a synthetic dataset and two neuroimaging datasets to visualize the effect of Alzheimer's disease and alcohol dependence. Compared to the saliency maps generated by baseline approaches, our simulations and visualizations based on the Jacobian determinants of the warping field reveal meaningful and understandable patterns related to the diseases.

  View details for DOI 10.1007/978-3-030-78191-0_6

  View details for PubMedID 34548772

• Preliminary Evidence for a Relationship between Elevated Plasma TNFα and Smaller Subcortical White Matter Volume in HCV Infection Irrespective of HIV or AUD Comorbidity. International journal of molecular sciences Zahr, N. M., Pohl, K. M., Kwong, A. J., Sullivan, E. V., Pfefferbaum, A. 2021; 22 (9)

  Abstract

  Classical inflammation in response to bacterial, parasitic, or viral infections such as HIV includes local recruitment of neutrophils and macrophages and the production of proinflammatory cytokines and chemokines. Proposed biomarkers of organ integrity in Alcohol Use Disorders (AUD) include elevations in peripheral plasma levels of proinflammatory proteins. In testing this proposal, previous work included a group of human immunodeficiency virus (HIV)-infected individuals as positive controls and identified elevations in the soluble proteins TNFα and IP10; these cytokines were only elevated in AUD individuals seropositive for hepatitis C infection (HCV). The current observational, cross-sectional study evaluated whether higher levels of these proinflammatory cytokines would be associated with compromised brain integrity. Soluble protein levels were quantified in 86 healthy controls, 132 individuals with AUD, 54 individuals seropositive for HIV, and 49 individuals with AUD and HIV. Among the patient groups, HCV was present in 24 of the individuals with AUD, 13 individuals with HIV, and 20 of the individuals in the comorbid AUD and HIV group. Soluble protein levels were correlated to regional brain volumes as quantified with structural magnetic resonance imaging (MRI). In addition to higher levels of TNFα and IP10 in the 2 HIV groups and the HCV-seropositive AUD group, this study identified lower levels of IL1β in the 3 patient groups relative to the control group. Only TNFα, however, showed a relationship with brain integrity: in HCV or HIV infection, higher peripheral levels of TNFα correlated with smaller subcortical white matter volume. These preliminary results highlight the privileged status of TNFα on brain integrity in the context of infection.

  View details for DOI 10.3390/ijms22094953

  View details for PubMedID 34067023

• Preliminary Evidence for a Relationship between Elevated Plasma TNF alpha and Smaller Subcortical White Matter Volume in HCV Infection Irrespective of HIV or AUD Comorbidity INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES Zahr, N. M., Pohl, K. M., Kwong, A. J., Sullivan, E. V., Pfefferbaum, A. 2021; 22 (9)

  View details for DOI 10.3390/ijms22094953

  View details for Web of Science ID 000650336900001

• POOR SLEEP AS A PREDICTOR OF COVID-19 RELATED STRESS, FEAR AND SADNESS IN YOUNG ADOLESCENTS: A LONGITUDINAL STUDY Kiss, O., Alzueta, E., Yuksel, D., Durley, I., Volpe, L., Dulai, T., Arra, N., Obilor, T., Pohl, K., de Zambotti, M., Baker, F. OXFORD UNIV PRESS INC. 2021: A90-A91

  View details for Web of Science ID 000698984300226

• Longitudinal self-supervised learning. Medical image analysis Zhao, Q., Liu, Z., Adeli, E., Pohl, K. M. 2021; 71: 102051

  Abstract

  Machine learning analysis of longitudinal neuroimaging data is typically based on supervised learning, which requires large number of ground-truth labels to be informative. As ground-truth labels are often missing or expensive to obtain in neuroscience, we avoid them in our analysis by combing factor disentanglement with self-supervised learning to identify changes and consistencies across the multiple MRIs acquired of each individual over time. Specifically, we propose a new definition of disentanglement by formulating a multivariate mapping between factors (e.g., brain age) associated with an MRI and a latent image representation. Then, factors that evolve across acquisitions of longitudinal sequences are disentangled from that mapping by self-supervised learning in such a way that changes in a single factor induce change along one direction in the representation space. We implement this model, named Longitudinal Self-Supervised Learning (LSSL), via a standard autoencoding structure with a cosine loss to disentangle brain age from the image representation. We apply LSSL to two longitudinal neuroimaging studies to highlight its strength in extracting the brain-age information from MRI and revealing informative characteristics associated with neurodegenerative and neuropsychological disorders. Moreover, the representations learned by LSSL facilitate supervised classification by recording faster convergence and higher (or similar) prediction accuracy compared to several other representation learning techniques.

  View details for DOI 10.1016/j.media.2021.102051

  View details for PubMedID 33882336

• Performance ramifications of abnormal functional connectivity of ventral posterior lateral thalamus with cerebellum in abstinent individuals with Alcohol Use Disorder. Drug and alcohol dependence Honnorat, N. n., Saranathan, M. n., Sullivan, E. V., Pfefferbaum, A. n., Pohl, K. M., Zahr, N. M. 2021; 220: 108509

  Abstract

  The extant literature supports the involvement of the thalamus in the cognitive and motor impairment associated with chronic alcohol consumption, but clear structure/function relationships remain elusive. Alcohol effects on specific nuclei rather than the entire thalamus may provide the basis for differential cognitive and motor decline in Alcohol Use Disorder (AUD). This functional MRI (fMRI) study was conducted in 23 abstinent individuals with AUD and 27 healthy controls to test the hypothesis that functional connectivity between anterior thalamus and hippocampus would be compromised in those with an AUD diagnosis and related to mnemonic deficits. Functional connectivity between 7 thalamic structures [5 thalamic nuclei: anterior ventral (AV), mediodorsal (MD), pulvinar (Pul), ventral lateral posterior (VLP), and ventral posterior lateral (VPL); ventral thalamus; the entire thalamus] and 14 "functional regions" was evaluated. Relative to controls, the AUD group exhibited different VPL-based functional connectivity: an anticorrelation between VPL and a bilateral middle temporal lobe region observed in controls became a positive correlation in the AUD group; an anticorrelation between the VPL and the cerebellum was stronger in the AUD than control group. AUD-associated altered connectivity between anterior thalamus and hippocampus as a substrate of memory compromise was not supported; instead, connectivity differences from controls selective to VPL and cerebellum demonstrated a relationship with impaired balance. These preliminary findings support substructure-level evaluation in future studies focused on discerning the role of the thalamus in AUD-associated cognitive and motor deficits.

  View details for DOI 10.1016/j.drugalcdep.2021.108509

  View details for PubMedID 33453503

• Longitudinal Correlation Analysis for Decoding Multi-modal Brain Development Zhao, Q., Adeli, E., Pohl, K. M., DeBruijne, M., Cattin, P. C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. SPRINGER INTERNATIONAL PUBLISHING AG. 2021: 400-409

  View details for DOI 10.1007/978-3-030-87234-2_38

  View details for Web of Science ID 000712024400038

• Metadata Normalization. Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Lu, M., Zhao, Q., Zhang, J., Pohl, K. M., Fei-Fei, L., Niebles, J. C., Adeli, E. 2021; 2021: 10912-10922

  Abstract

  Batch Normalization (BN) and its variants have delivered tremendous success in combating the covariate shift induced by the training step of deep learning methods. While these techniques normalize feature distributions by standardizing with batch statistics, they do not correct the influence on features from extraneous variables or multiple distributions. Such extra variables, referred to as metadata here, may create bias or confounding effects (e.g., race when classifying gender from face images). We introduce the Metadata Normalization (MDN) layer, a new batch-level operation which can be used end-to-end within the training framework, to correct the influence of metadata on feature distributions. MDN adopts a regression analysis technique traditionally used for preprocessing to remove (regress out) the metadata effects on model features during training. We utilize a metric based on distance correlation to quantify the distribution bias from the metadata and demonstrate that our method successfully removes metadata effects on four diverse settings: one synthetic, one 2D image, one video, and one 3D medical image dataset.

  View details for DOI 10.1109/cvpr46437.2021.01077

  View details for PubMedID 34776724

  View details for PubMedCentralID PMC8589298

• Self-supervised Longitudinal Neighbourhood Embedding Ouyang, J., Zhao, Q., Adeli, E., Sullivan, E., Pfefferbaum, A., Zaharchuk, G., Pohl, K. M., DeBruijne, M., Cattin, P. C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. SPRINGER INTERNATIONAL PUBLISHING AG. 2021: 80-89

  View details for DOI 10.1007/978-3-030-87196-3_8

  View details for Web of Science ID 000712020700008

• Representation Learning with Statistical Independence to Mitigate Bias. IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Adeli, E., Zhao, Q., Pfefferbaum, A., Sullivan, E. V., Fei-Fei, L., Niebles, J. C., Pohl, K. M. 2021; 2021: 2512-2522

  Abstract

  Presence of bias (in datasets or tasks) is inarguably one of the most critical challenges in machine learning applications that has alluded to pivotal debates in recent years. Such challenges range from spurious associations between variables in medical studies to the bias of race in gender or face recognition systems. Controlling for all types of biases in the dataset curation stage is cumbersome and sometimes impossible. The alternative is to use the available data and build models incorporating fair representation learning. In this paper, we propose such a model based on adversarial training with two competing objectives to learn features that have (1) maximum discriminative power with respect to the task and (2) minimal statistical mean dependence with the protected (bias) variable(s). Our approach does so by incorporating a new adversarial loss function that encourages a vanished correlation between the bias and the learned features. We apply our method to synthetic data, medical images (containing task bias), and a dataset for gender classification (containing dataset bias). Our results show that the learned features by our method not only result in superior prediction performance but also are unbiased.

  View details for DOI 10.1109/wacv48630.2021.00256

  View details for PubMedID 34522832

• Quantifying Parkinson's disease motor severity under uncertainty using MDS-UPDRS videos. Medical image analysis Lu, M., Zhao, Q., Poston, K. L., Sullivan, E. V., Pfefferbaum, A., Shahid, M., Katz, M., Kouhsari, L. M., Schulman, K., Milstein, A., Niebles, J. C., Henderson, V. W., Fei-Fei, L., Pohl, K. M., Adeli, E. 2021; 73: 102179

  Abstract

  Parkinson's disease (PD) is a brain disorder that primarily affects motor function, leading to slow movement, tremor, and stiffness, as well as postural instability and difficulty with walking/balance. The severity of PD motor impairments is clinically assessed by part III of the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), a universally-accepted rating scale. However, experts often disagree on the exact scoring of individuals. In the presence of label noise, training a machine learning model using only scores from a single rater may introduce bias, while training models with multiple noisy ratings is a challenging task due to the inter-rater variabilities. In this paper, we introduce an ordinal focal neural network to estimate the MDS-UPDRS scores from input videos, to leverage the ordinal nature of MDS-UPDRS scores and combat class imbalance. To handle multiple noisy labels per exam, the training of the network is regularized via rater confusion estimation (RCE), which encodes the rating habits and skills of raters via a confusion matrix. We apply our pipeline to estimate MDS-UPDRS test scores from their video recordings including gait (with multiple Raters, R=3) and finger tapping scores (single rater). On a sizable clinical dataset for the gait test (N=55), we obtained a classification accuracy of 72% with majority vote as ground-truth, and an accuracy of ∼84% of our model predicting at least one of the raters' scores. Our work demonstrates how computer-assisted technologies can be used to track patients and their motor impairments, even when there is uncertainty in the clinical ratings. The latest version of the code will be available at https://github.com/mlu355/PD-Motor-Severity-Estimation.

  View details for DOI 10.1016/j.media.2021.102179

  View details for PubMedID 34340101

• Attenuated cerebral blood flow in frontolimbic and insular cortices in Alcohol Use Disorder: Relation to working memory. Journal of psychiatric research Sullivan, E. V., Zhao, Q. n., Pohl, K. M., Zahr, N. M., Pfefferbaum, A. n. 2021; 136: 140–48

  Abstract

  Chronic, excessive alcohol consumption is associated with cerebrovascular hypoperfusion, which has the potential to interfere with cognitive processes. Magnetic resonance pulsed continuous arterial spin labeling (PCASL) provides a noninvasive approach for measuring regional cerebral blood flow (CBF) and was used to study 24 men and women with Alcohol Use Disorder (AUD) and 20 age- and sex-matched controls. Two analysis approaches tested group differences: a data-driven, regionally-free method to test for group differences on a voxel-by-voxel basis and a region of interest (ROI) approach, which focused quantification on atlas-determined brain structures. Whole-brain, voxel-wise quantification identified low AUD-related cerebral perfusion in large volumes of medial frontal and cingulate cortices. The ROI analysis also identified lower CBF in the AUD group relative to the control group in medial frontal, anterior/middle cingulate, insular, and hippocampal/amygdala ROIs. Further, years of AUD diagnosis negatively correlated with temporal cortical CBF, and scores on an alcohol withdrawal scale negatively correlated with posterior cingulate and occipital gray matter CBF. Regional volume deficits did not account for AUD CBF deficits. Functional relevance of attenuated regional CBF in the AUD group emerged with positive correlations between episodic working memory test scores and anterior/middle cingulum, insula, and thalamus CBF. The frontolimbic and insular cortical neuroconstellation with dampened perfusion suggests a mechanism of dysfunction associated with these brain regions in AUD.

  View details for DOI 10.1016/j.jpsychires.2021.01.053

  View details for PubMedID 33592385

• Association of Heavy Drinking With Deviant Fiber Tract Development in Frontal Brain Systems in Adolescents. JAMA psychiatry Zhao, Q., Sullivan, E. V., Honnorat, N., Adeli, E., Podhajsky, S., De Bellis, M. D., Voyvodic, J., Nooner, K. B., Baker, F. C., Colrain, I. M., Tapert, S. F., Brown, S. A., Thompson, W. K., Nagel, B. J., Clark, D. B., Pfefferbaum, A., Pohl, K. M. 2020

  Abstract

  Importance: Maturation of white matter fiber systems subserves cognitive, behavioral, emotional, and motor development during adolescence. Hazardous drinking during this active neurodevelopmental period may alter the trajectory of white matter microstructural development, potentially increasing risk for developing alcohol-related dysfunction and alcohol use disorder in adulthood.Objective: To identify disrupted adolescent microstructural brain development linked to drinking onset and to assess whether the disruption is more pronounced in younger rather than older adolescents.Design, Setting, and Participants: This case-control study, conducted from January 13, 2013, to January 15, 2019, consisted of an analysis of 451 participants from the National Consortium on Alcohol and Neurodevelopment in Adolescence cohort. Participants were aged 12 to 21 years at baseline and had at least 2 usable magnetic resonance diffusion tensor imaging (DTI) scans and up to 5 examination visits spanning 4 years. Participants with a youth-adjusted Cahalan score of 0 were labeled as no-to-low drinkers; those with a score of greater than 1 for at least 2 consecutive visits were labeled as heavy drinkers. Exploratory analysis was conducted between no-to-low and heavy drinkers. A between-group analysis was conducted between age- and sex-matched youths, and a within-participant analysis was performed before and after drinking.Exposures: Self-reported alcohol consumption in the past year summarized by categorical drinking levels.Main Outcomes and Measures: Diffusion tensor imaging measurement of fractional anisotropy (FA) in the whole brain and fiber systems quantifying the developmental change of each participant as a slope.Results: Analysis of whole-brain FA of 451 adolescents included 291 (64.5%) no-to-low drinkers and 160 (35.5%) heavy drinkers who indicated the potential for a deleterious association of alcohol with microstructural development. Among the no-to-low drinkers, 142 (48.4%) were boys with mean (SD) age of 16.5 (2.2) years and 149 (51.2%) were girls with mean (SD) age of 16.5 (2.1) years and 192 (66.0%) were White participants. Among the heavy drinkers, 86 (53.8%) were boys with mean (SD) age of 20.1 (1.5) years and 74 (46.3%) were girls with mean (SD) age of 20.5 (2.0) years and 142 (88.8%) were White participants. A group analysis revealed FA reduction in heavy-drinking youth compared with age- and sex-matched controls (t154=-2.7, P=.008). The slope of this reduction correlated with log of days of drinking since the baseline visit (r156=-0.21, 2-tailed P=.008). A within-participant analysis contrasting developmental trajectories of youths before and after they initiated heavy drinking supported the prediction that drinking onset was associated with and potentially preceded disrupted white matter integrity. Age-alcohol interactions (t152=3.0, P=.004) observed for the FA slopes indicated that the alcohol-associated disruption was greater in younger than older adolescents and was most pronounced in the genu and body of the corpus callosum, regions known to continue developing throughout adolescence.Conclusions and Relevance: This case-control study of adolescents found a deleterious association of alcohol use with white matter microstructural integrity. These findings support the concept of heightened vulnerability to environmental agents, including alcohol, associated with attenuated development of major white matter tracts in early adolescence.

  View details for DOI 10.1001/jamapsychiatry.2020.4064

  View details for PubMedID 33377940

• Alcohol Use Disorder and Its Comorbidity With HIV Infection Disrupts Anterior Cingulate Cortex Functional Connectivity. Biological psychiatry. Cognitive neuroscience and neuroimaging Honnorat, N., Fama, R., Muller-Oehring, E. M., Zahr, N. M., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M. 2020

  Abstract

  BACKGROUND: Individuals with alcohol use disorder (AUD) have a heightened risk of contracting HIV infection. The effects of these two diseases and their comorbidity on brain structure have been well described, but their effects on brain function have never been investigated at the scale of whole-brain connectomes.METHODS: In contrast with prior studies that restricted analyses to specific brain networks or examined relatively small groups of participants, our analyses are based on whole-brain functional connectomes of 292 participants.RESULTS: Relative to participants without AUD, the functional connectivity between the anterior cingulate cortex and orbitofrontal cortex was lower for participants with AUD. Compared with participants without AUD+HIV comorbidity, the functional connectivity between the anterior cingulate cortex and hippocampus was lower for the AUD+HIV participants. Compromised connectivity between these pairs was significantly correlated with greater total lifetime alcohol consumption; the effects of total lifetime alcohol consumption on executive functioning were significantly mediated by the functional connectivity between the pairs.CONCLUSIONS: Taken together, our results suggest that the functional connectivity of the anterior cingulate cortex is disrupted in individuals with AUD alone and AUD with HIV infection comorbidity. Moreover, the affected connections are associated with deficits in executive functioning, including heightened impulsiveness.

  View details for DOI 10.1016/j.bpsc.2020.11.012

  View details for PubMedID 33558196

• Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Gadgil, S., Zhao, Q., Pfefferbaum, A., Sullivan, E. V., Adeli, E., Pohl, K. M. 2020; 12267: 528–38

  Abstract

  The Blood-Oxygen-Level-Dependent (BOLD) signal of resting-state fMRI (rs-fMRI) records the temporal dynamics of intrinsic functional networks in the brain. However, existing deep learning methods applied to rs-fMRI either neglect the functional dependency between different brain regions in a network or discard the information in the temporal dynamics of brain activity. To overcome those shortcomings, we propose to formulate functional connectivity networks within the context of spatio-temporal graphs. We train a spatio-temporal graph convolutional network (ST-GCN) on short sub-sequences of the BOLD time series to model the non-stationary nature of functional connectivity. Simultaneously, the model learns the importance of graph edges within ST-GCN to gain insight into the functional connectivities contributing to the prediction. In analyzing the rs-fMRI of the Human Connectome Project (HCP, N = 1,091) and the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA, N = 773), ST-GCN is significantly more accurate than common approaches in predicting gender and age based on BOLD signals. Furthermore, the brain regions and functional connections significantly contributing to the predictions of our model are important markers according to the neuroscience literature.

  View details for DOI 10.1007/978-3-030-59728-3_52

  View details for PubMedID 33257918

• Inpainting Cropped Diffusion MRI using Deep Generative Models. PRedictive Intelligence in MEdicine. PRIME (Workshop) Ayub, R., Zhao, Q., Meloy, M. J., Sullivan, E. V., Pfefferbaum, A., Adeli, E., Pohl, K. M. 2020; 12329: 91-100

  Abstract

  Minor artifacts introduced during image acquisition are often negligible to the human eye, such as a confined field of view resulting in MRI missing the top of the head. This cropping artifact, however, can cause suboptimal processing of the MRI resulting in data omission or decreasing the power of subsequent analyses. We propose to avoid data or quality loss by restoring these missing regions of the head via variational autoencoders (VAE), a deep generative model that has been previously applied to high resolution image reconstruction. Based on diffusion weighted images (DWI) acquired by the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA), we evaluate the accuracy of inpainting the top of the head by common autoencoder models (U-Net, VQVAE, and VAE-GAN) and a custom model proposed herein called U-VQVAE. Our results show that U-VQVAE not only achieved the highest accuracy, but also resulted in MRI processing producing lower fractional anisotropy (FA) in the supplementary motor area than FA derived from the original MRIs. Lower FA implies that inpainting reduces noise in processing DWI and thus increase the quality of the generated results. The code is available at https://github.com/RdoubleA/DWIinpainting.

  View details for DOI 10.1007/978-3-030-59354-4_9

  View details for PubMedID 33997866

• Vision-based Estimation of MDS-UPDRS Gait Scores for Assessing Parkinson's Disease Motor Severity. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Lu, M., Poston, K., Pfefferbaum, A., Sullivan, E. V., Fei-Fei, L., Pohl, K. M., Niebles, J. C., Adeli, E. 2020; 12263: 637–47

  Abstract

  Parkinson's disease (PD) is a progressive neurological disorder primarily affecting motor function resulting in tremor at rest, rigidity, bradykinesia, and postural instability. The physical severity of PD impairments can be quantified through the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), a widely used clinical rating scale. Accurate and quantitative assessment of disease progression is critical to developing a treatment that slows or stops further advancement of the disease. Prior work has mainly focused on dopamine transport neuroimaging for diagnosis or costly and intrusive wearables evaluating motor impairments. For the first time, we propose a computer vision-based model that observes non-intrusive video recordings of individuals, extracts their 3D body skeletons, tracks them through time, and classifies the movements according to the MDS-UPDRS gait scores. Experimental results show that our proposed method performs significantly better than chance and competing methods with an F 1-score of 0.83 and a balanced accuracy of 81%. This is the first benchmark for classifying PD patients based on MDS-UPDRS gait severity and could be an objective biomarker for disease severity. Our work demonstrates how computer-assisted technologies can be used to non-intrusively monitor patients and their motor impairments. The code is available at https://github.com/mlu355/PD-Motor-Severity-Estimation.

  View details for DOI 10.1007/978-3-030-59716-0_61

  View details for PubMedID 33103164

• Deep Parametric Mixtures for Modeling the Functional Connectome. PRedictive Intelligence in MEdicine. PRIME (Workshop) Honnorat, N., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M. 2020; 12329: 133–43

  Abstract

  Functional connectivity between brain regions is often estimated by correlating brain activity measured by resting-state fMRI in those regions. The impact of factors (e.g, disorder or substance use) are then modeled by their effects on these correlation matrices in individuals. A crucial step in better understanding their effects on brain function could lie in estimating connectomes, which encode the correlation matrices across subjects. Connectomes are mostly estimated by creating a single average for a specific cohort, which works well for binary factors (such as sex) but is unsuited for continuous ones, such as alcohol consumption. Alternative approaches based on regression methods usually model each pair of regions separately, which generally produces incoherent connectomes as correlations across multiple regions contradict each other. In this work, we address these issues by introducing a deep learning model that predicts connectomes based on factor values. The predictions are defined on a simplex spanned across correlation matrices, whose convex combination guarantees that the deep learning model generates well-formed connectomes. We present an efficient method for creating these simplexes and improve the accuracy of the entire analysis by defining loss functions based on robust norms. We show that our deep learning approach is able to produce accurate models on challenging synthetic data. Furthermore, we apply the approach to the resting-state fMRI scans of 281 subjects to study the effect of sex, alcohol, and HIV on brain function.

  View details for DOI 10.1007/978-3-030-59354-4_13

  View details for PubMedID 33163995

• Deep Learning Identifies Morphological Determinants of Sex Differences in the Pre-Adolescent Brain. NeuroImage Adeli, E., Zhao, Q., Zahr, N. M., Goldstone, A., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M. 2020: 117293

  Abstract

  The application of data-driven deep learning to identify sex differences in developing brain structures of pre-adolescents has heretofore not been accomplished. Here, the approach identifies sex differences by analyzing the minimally processed MRIs of the first 8,144 participants (age 9 and 10 years) recruited by the Adolescent Brain Cognitive Development (ABCD) study. The identified pattern accounted for confounding factors (i.e., head size, age, puberty development, socioeconomic status) and comprised cerebellar (corpus medullare, lobules III, IV/V, and VI) and subcortical (pallidum, amygdala, hippocampus, parahippocampus, insula, putamen) structures. While these have been individually linked to expressing sex differences, a novel discovery was that their grouping accurately predicted the sex in individual pre-adolescents. Another novelty was relating differences specific to the cerebellum to pubertal development. Finally, we found that reducing the pattern to a single score not only accurately predicted sex but also correlated with cognitive behavior linked to working memory. The predictive power of this score and the constellation of identified brain structures provide evidence for sex differences in pre-adolescent neurodevelopment and may augment understanding of sex-specific vulnerability or resilience to psychiatric disorders and presage sex-linked learning disabilities.

  View details for DOI 10.1016/j.neuroimage.2020.117293

  View details for PubMedID 32841716

• Accelerated aging and motor control deficits are related to regional deformation of central cerebellar white matter in alcohol use disorder ADDICTION BIOLOGY Zhao, Q., Pfefferbaum, A., Podhajsky, S., Pohl, K. M., Sullivan, E. V. 2020; 25 (3)

  View details for DOI 10.1111/adb.12746

  View details for Web of Science ID 000528674100024

• Corrigendum: Graded Cerebellar Lobular Volume Deficits in Adolescents and Young Adults with Fetal Alcohol Spectrum Disorders (FASD). Cerebral cortex (New York, N.Y. : 1991) Sullivan, E. V., Moore, E. M., Lane, B., Pohl, K. M., Riley, E. P., Pfefferbaum, A. 2020

  View details for DOI 10.1093/cercor/bhaa091

  View details for PubMedID 32249891

• Graded Cerebellar Lobular Volume Deficits in Adolescents and Young Adults with Fetal Alcohol Spectrum Disorders (FASD). Cerebral cortex (New York, N.Y. : 1991) Sullivan, E. V., Moore, E. M., Lane, B., Pohl, K. M., Riley, E. P., Pfefferbaum, A. 2020

  Abstract

  The extensive prenatal developmental growth period of the cerebellum renders it vulnerable to unhealthy environmental agents, especially alcohol. Fetal alcohol spectrum disorders (FASD) is marked by neurodysmorphology including cerebral and cerebellar volume deficits, but the cerebellar lobular deficit profile has not been delineated. Legacy MRI data of 114 affected and 60 unaffected adolescents and young adults were analyzed for lobular gray matter volume and revealed graded deficits supporting a spectrum of severity. Graded deficits were salient in intracranial volume (ICV), where the fetal alcohol syndrome (FAS) group was smaller than the fetal alcohol effects (FAE) group, which was smaller than the controls. Adjusting for ICV, volume deficits were present in VIIB and VIIIA of the FAE group and were more widespread in FAS and included lobules I, II, IV, V, VI, Crus II, VIIB, and VIIA. Graded deficits (FAS

  View details for DOI 10.1093/cercor/bhaa020

  View details for PubMedID 32133485

• Regional growth trajectories of cortical myelination in adolescents and young adults: longitudinal validation and functional correlates BRAIN IMAGING AND BEHAVIOR Kwon, D., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M. 2020; 14 (1): 242–66

  View details for DOI 10.1007/s11682-018-9980-3

  View details for Web of Science ID 000511797100023

• Training confounder-free deep learning models for medical applications. Nature communications Zhao, Q. n., Adeli, E. n., Pohl, K. M. 2020; 11 (1): 6010

  Abstract

  The presence of confounding effects (or biases) is one of the most critical challenges in using deep learning to advance discovery in medical imaging studies. Confounders affect the relationship between input data (e.g., brain MRIs) and output variables (e.g., diagnosis). Improper modeling of those relationships often results in spurious and biased associations. Traditional machine learning and statistical models minimize the impact of confounders by, for example, matching data sets, stratifying data, or residualizing imaging measurements. Alternative strategies are needed for state-of-the-art deep learning models that use end-to-end training to automatically extract informative features from large set of images. In this article, we introduce an end-to-end approach for deriving features invariant to confounding factors while accounting for intrinsic correlations between the confounder(s) and prediction outcome. The method does so by exploiting concepts from traditional statistical methods and recent fair machine learning schemes. We evaluate the method on predicting the diagnosis of HIV solely from Magnetic Resonance Images (MRIs), identifying morphological sex differences in adolescence from those of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA), and determining the bone age from X-ray images of children. The results show that our method can accurately predict while reducing biases associated with confounders. The code is available at https://github.com/qingyuzhao/br-net .

  View details for DOI 10.1038/s41467-020-19784-9

  View details for PubMedID 33243992

• Adolescent alcohol use disrupts functional neurodevelopment in sensation seeking girls. Addiction biology Zhao, Q. n., Sullivan, E. V., Műller-Oehring, E. M., Honnorat, N. n., Adeli, E. n., Podhajsky, S. n., Baker, F. C., Colrain, I. M., Prouty, D. n., Tapert, S. F., Brown, S. A., Meloy, M. J., Brumback, T. n., Nagel, B. J., Morales, A. M., Clark, D. B., Luna, B. n., De Bellis, M. D., Voyvodic, J. T., Nooner, K. B., Pfefferbaum, A. n., Pohl, K. M. 2020: e12914

  Abstract

  Exogenous causes, such as alcohol use, and endogenous factors, such as temperament and sex, can modulate developmental trajectories of adolescent neurofunctional maturation. We examined how these factors affect sexual dimorphism in brain functional networks in youth drinking below diagnostic threshold for alcohol use disorder (AUD). Based on the 3-year, annually acquired, longitudinal resting-state functional magnetic resonance imaging (MRI) data of 526 adolescents (12-21 years at baseline) from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) cohort, developmental trajectories of 23 intrinsic functional networks (IFNs) were analyzed for (1) sexual dimorphism in 259 participants who were no-to-low drinkers throughout this period; (2) sex-alcohol interactions in two age- and sex-matched NCANDA subgroups (N = 76 each), half no-to-low, and half moderate-to-heavy drinkers; and (3) moderating effects of gender-specific alcohol dose effects and a multifactorial impulsivity measure on IFN connectivity in all NCANDA participants. Results showed that sex differences in no-to-low drinkers diminished with age in the inferior-occipital network, yet girls had weaker within-network connectivity than boys in six other networks. Effects of adolescent alcohol use were more pronounced in girls than boys in three IFNs. In particular, girls showed greater within-network connectivity in two motor networks with more alcohol consumption, and these effects were mediated by sensation-seeking only in girls. Our results implied that drinking might attenuate the naturally diminishing sexual differences by disrupting the maturation of network efficiency more severely in girls. The sex-alcohol-dose effect might explain why women are at higher risk of alcohol-related health and psychosocial consequences than men.

  View details for DOI 10.1111/adb.12914

  View details for PubMedID 32428984

• Multi-modal imaging reveals differential brain volumetric, biochemical, and white matter fiber responsivity to repeated intermittent ethanol vapor exposure in male and female rats. Neuropharmacology Zahr, N. M., Lenart, A. M., Karpf, J. A., Casey, K. M., Pohl, K. M., Sullivan, E. V., Pfefferbaum, A. n. 2020: 108066

  Abstract

  A generally accepted framework derived predominately from animal models asserts that repeated cycles of chronic intermittent ethanol (EtOH; CIE) exposure cause progressive brain adaptations associated with anxiety and stress that promote voluntary drinking, alcohol dependence, and further brain changes that contribute to the pathogenesis of alcoholism. The current study used CIE exposure via vapor chambers to test the hypothesis that repeated episodes of withdrawals from chronic EtOH would be associated with accrual of brain damage as quantified using in vivo magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), and MR spectroscopy (MRS). The initial study group included 16 male (∼325g) and 16 female (∼215g) wild-type Wistar rats exposed to 3 cycles of 1-month in vapor chambers + 1 week of abstinence. Half of each group (n = 8) was given vaporized EtOH to blood alcohol levels approaching 250 mg/dL. Blood and behavior markers were also quantified. There was no evidence for dependence (i.e., increased voluntary EtOH consumption), increased anxiety, or an accumulation of pathology. Neuroimaging brain responses to exposure included increased cerebrospinal fluid (CSF) and decreased gray matter volumes, increased Choline/Creatine, and reduced fimbria-fornix fractional anisotropy (FA) with recovery seen after one or more cycles and effects in female more prominent than in male rats. These results show transient brain integrity changes in response to CIE sufficient to induce acute withdrawal but without evidence for cumulative or escalating damage. Together, the current study suggests that nutrition, age, and sex should be considered when modeling human alcoholism.

  View details for DOI 10.1016/j.neuropharm.2020.108066

  View details for PubMedID 32240669

• Age differences in brain structural and metabolic responses to binge ethanol exposure in fisher 344 rats. Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology Zahr, N. M., Sullivan, E. V., Pohl, K. M., Pfefferbaum, A. n. 2020

  Abstract

  An overarching goal of our research has been to develop a valid animal model of alcoholism with similar imaging phenotypes as those observed in humans with the ultimate objective of assessing the effectiveness of pharmacological agents. In contrast to our findings in humans with alcohol use disorders (AUD), our animal models have not demonstrated enduring brain pathology despite chronic, high ethanol (EtOH) exposure protocols. Relative to healthy controls, older individuals with AUD demonstrate accelerating brain tissue loss with advanced age. Thus, this longitudinally controlled study was conducted in 4-month old (equivalent to ~16-year-old humans) and 17-month old (equivalent to ~45-year-old humans) male and female Fisher 344 rats to test the hypothesis that following equivalent alcohol exposure protocols, older relative to younger would exhibit more brain changes as evaluated using in vivo structural magnetic resonance imaging (MRI) and MR spectroscopy (MRS). At baseline, total brain volume as well as the volumes of each of the three constituent tissue types (i.e., cerebral spinal fluid (CSF), gray matter, white matter) were greater in old relative to young rats. Baseline metabolite levels (except for GSH) were higher in older than younger animals. Effects of binge ethanol (EtOH) exposure on brain volumes and neurometabolites replicated our previous findings in Wistar rats and included ventricular enlargement and reduced MRS-derived creatine levels. Brain changes in response to binge EtOH treatment were more pronounced in young relative to older animals, negating our hypothesis. Additional metabolite changes including low inositol levels in response to high blood alcohol levels suggest a mechanism of reversible osmolarity disturbances due to temporarily altered brain energy metabolism. Higher baseline GSH levels in female than male rats suggest that female rats are perhaps protected against the more pronounced changes in CSF and gray matter volumes observed in male rats due to superior metabolic homeostasis mechanisms.

  View details for DOI 10.1038/s41386-020-0744-6

  View details for PubMedID 32580206

• Jacobian Mapping Reveals Converging Substrates of Disruption and Repair in Response to Ethanol Exposure and Abstinence in Two Strains of Rats. Alcoholism, clinical and experimental research Zhao, Q. n., Pohl, K. M., Sullivan, E. V., Pfefferbaum, A. n., Zahr, N. M. 2020

  Abstract

  In a previous study using Jacobian mapping to evaluate the morphological effects on the brain of binge (4-day) intragastric ethanol (EtOH) on wild-type Wistar rats, we reported reversible thalamic shrinkage and lateral ventricular enlargement, but persistent superior and inferior colliculi shrinkage in response to binge EtOH treatment.Herein, we used similar voxel-based comparisons of Magnetic Resonance Images collected in EtOH-exposed relative to control animals to test the hypothesis that regardless of the intoxication protocol or the rat strain, the hippocampi, thalami, and colliculi would be affected.Two experiments [binge (4-day) intragastric EtOH in Fisher 344 rats and chronic (1-month) vaporized EtOH in Wistar rats] showed similarly affected brain regions including retrosplenial and cingulate cortices, dorsal hippocampi, central and ventroposterior thalami, superior and inferior colliculi, periaqueductal gray, and corpus callosum. While most of these regions showed significant recovery, volumes of the colliculi and periaqueductal gray continued to show response to each proximal alcohol exposure but at diminished levels with repeated exposures.Given the high metabolic rate of these enduringly affected regions, the current findings suggest that EtOH per se may affect cellular respiration leading to brain volume deficits. Further, responsivity greatly diminished likely reflecting neuroadaptation to repeated alcohol exposure. In summary, this unbiased, in vivo based approach demonstrating convergent brain systems responsive to two EtOH exposure protocols in two rat strains highlights regions that warrant further investigation in both animal models of alcoholism and in humans with Alcohol Use Disorder.

  View details for DOI 10.1111/acer.14496

  View details for PubMedID 33119896

• Pattern of Cerebellar Lobular Volume Deficits in Adolescents and Adults With Fetal Alcohol Effects (FAE) and Fetal Alcohol Syndrome (FAS) Sullivan, E., Moore, E., Pohl, K., Riley, E., Pfefferbaum, A. NATURE PUBLISHING GROUP. 2019: 281–82

  View details for Web of Science ID 000509665600522

• Confounder-Aware Visualization of ConvNets. Machine learning in medical imaging. MLMI (Workshop) Zhao, Q., Adeli, E., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M. 2019; 11861: 328–36

  Abstract

  With recent advances in deep learning, neuroimaging studies increasingly rely on convolutional networks (ConvNets) to predict diagnosis based on MR images. To gain a better understanding of how a disease impacts the brain, the studies visualize the salience maps of the ConvNet highlighting voxels within the brain majorly contributing to the prediction. However, these salience maps are generally confounded, i.e., some salient regions are more predictive of confounding variables (such as age) than the diagnosis. To avoid such misinterpretation, we propose in this paper an approach that aims to visualize confounder-free saliency maps that only highlight voxels predictive of the diagnosis. The approach incorporates univariate statistical tests to identify confounding effects within the intermediate features learned by ConvNet. The influence from the subset of confounded features is then removed by a novel partial back-propagation procedure. We use this two-step approach to visualize confounder-free saliency maps extracted from synthetic and two real datasets. These experiments reveal the potential of our visualization in producing unbiased model-interpretation.

  View details for DOI 10.1007/978-3-030-32692-0_38

  View details for PubMedID 32549051

• Covariance Shrinkage for Dynamic Functional Connectivity. Connectomics in neuroImaging : third International Workshop, CNI 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings. CNI (Workshop) (3rd : 2019 : Shenzhen Shi, China) Honnorat, N., Adeli, E., Zhao, Q., Pfefferbaum, A., Sullivan, E. V., Pohl, K. 2019; 11848: 32–41

  Abstract

  The tracking of dynamic functional connectivity (dFC) states in resting-state fMRI scans aims to reveal how the brain sequentially processes stimuli and thoughts. Despite the recent advances in statistical methods, estimating the high dimensional dFC states from a small number of available time points remains a challenge. This paper shows that the challenge is reduced by linear covariance shrinkage, a statistical method used for the estimation of large covariance matrices from small number of samples. We present a computationally efficient formulation of our approach that scales dFC analysis up to full resolution resting-state fMRI scans. Experiments on synthetic data demonstrate that our approach produces dFC estimates that are closer to the ground-truth than state-of-the-art estimation approaches. When comparing methods on the rs-fMRI scans of 162 subjects, we found that our approach is better at extracting functional networks and capturing differences in rs-fMRI acquisition and diagnosis.

  View details for DOI 10.1007/978-3-030-32391-2_4

  View details for PubMedID 32924030

• Data Augmentation Based on Substituting Regional MRIs Volume Scores. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention : International Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, held in c... Leng, T., Zhao, Q., Yang, C., Lu, Z., Adeli, E., Pohl, K. M. 2019; 11851: 32–41

  Abstract

  Due to difficulties in collecting sufficient training data, recent advances in neural-network-based methods have not been fully explored in the analysis of brain Magnetic Resonance Imaging (MRI). A possible solution to the limited-data issue is to augment the training set with synthetically generated data. In this paper, we propose a data augmentation strategy based on regional feature substitution. We demonstrate the advantages of this strategy with respect to training a simple neural-network-based classifier in predicting when individual youth transition from no-to-low to medium-to-heavy alcohol drinkers solely based on their volumetric MRI measurements. Based on 20-fold cross-validation, we generate more than one million synthetic samples from less than 500 subjects for each training run. The classifier achieves an accuracy of 74.1% in correctly distinguishing non-drinkers from drinkers at baseline and a 43.2% weighted accuracy in predicting the transition over a three year period (5-group classification task). Both accuracy scores are significantly better than training the classifier on the original dataset.

  View details for DOI 10.1007/978-3-030-33642-4_4

  View details for PubMedID 32924031

• Disturbed Cerebellar Growth Trajectories in Adolescents Who Initiate Alcohol Drinking. Biological psychiatry Sullivan, E. V., Brumback, T., Tapert, S. F., Brown, S. A., Baker, F. C., Colrain, I. M., Prouty, D., De Bellis, M. D., Clark, D. B., Nagel, B. J., Pohl, K. M., Pfefferbaum, A. 2019

  Abstract

  BACKGROUND: The cerebellum is a target of alcoholism-related brain damage in adults, yet no study has prospectively tracked deviations from normal cerebellar growth trajectories in adolescents before and after initiating drinking.METHODS: Magnetic resonance imaging tracked developmental volume trajectories of 10 cerebellar lobule and vermis tissue constituents in 548 no/low drinking youths age 12 to 21 years at induction into this 5-site, NCANDA (National Consortium on Alcohol and NeuroDevelopment in Adolescence) study. Over the 3- to 4-year longitudinal examination yielding 2043 magnetic resonance imaging scans, 328 youths remained no/low drinkers, whereas 220 initiated substantial drinking after initial neuroimaging.RESULTS: Normal growth trajectories derived from no/low drinkers indicated that gray matter volumes of lobules V and VI, crus II, lobule VIIB, and lobule X declined faster with age in male youths than in female youths, whereas white matter volumes in crus I and crus II and lobules VIIIA and VIIIB expanded faster in female youths than in male youths; cerebrospinal fluid volume expanded faster in most cerebellar regions of male youths than female youths. Drinkers exhibited accelerated gray matter decline in anterior lobules and vermis, accelerated vermian white matter expansion, and accelerated cerebrospinal fluid volumes expansion of anterior lobules relative to youths who remained no/low drinkers. Analyses including both alcohol and marijuana did not support an independent role for marijuana in alcohol effects on cerebellar gray matter trajectories.CONCLUSIONS: Alcohol use-related cerebellar growth trajectory differences from normal involved anterior lobules and vermis of youths who initiated substantial drinking. These regions are commonly affected in alcohol-dependent adults, raising the possibility that cerebellar structures affected by youthful drinking may be vulnerable to age-alcohol interactions in later adulthood.

  View details for DOI 10.1016/j.biopsych.2019.08.026

  View details for PubMedID 31653477

• Dissociable Contributions of Precuneus and Cerebellum to Subjective and Objective Neuropathy in HIV JOURNAL OF NEUROIMMUNE PHARMACOLOGY Zahr, N. M., Pohl, K. M., Pfefferbaum, A., Sullivan, E. 2019; 14 (3): 436–47

  View details for DOI 10.1007/s11481-019-09837-2

  View details for Web of Science ID 000484524100009

• CNS Correlates of "Objective" Neuropathy in Alcohol Use Disorder. Alcoholism, clinical and experimental research Zahr, N. M., Pohl, K. M., Pfefferbaum, A., Sullivan, E. V. 2019

  Abstract

  BACKGROUND: Among the neurological consequences of alcoholism is peripheral neuropathy. Relative to HIV or diabetes-related neuropathies, neuropathy associated with Alcohol Use Disorders (AUD) is understudied. In both the diabetes and HIV literature, emerging evidence supports a CNS component to peripheral neuropathy.METHODS: In seeking a central substrate for AUD-related neuropathy, the current study was conducted in 154 individuals with AUD (43 women, ages 21-74) and 99 healthy controls (41 women, ages 21-77) and explored subjective symptoms (self-report) and objective signs (perception of vibration, deep tendon ankle reflex, position sense, 2-point discrimination) of neuropathy separately. In addition to regional brain volumes, risk factors for AUD-related neuropathy, including age, sex, total lifetime ethanol consumed, nutritional indices (i.e., thiamine, folate), and measures of liver integrity (i.e., gamma-glutamyl-transferase) were evaluated.RESULTS: The AUD group described more subjective symptoms of neuropathy and were more frequently impaired on bilateral perception of vibration. From 5 correlates, the number of AUD-related seizures was most significantly associated with subjective symptoms of neuropathy. There were 15 correlates of impaired perception of vibration among the AUD participants: of these, age and volume of frontal precentral cortex were the most robust predictors.CONCLUSIONS: This study supports CNS involvement in objective signs of neuropathy in AUD. This article is protected by copyright. All rights reserved.

  View details for DOI 10.1111/acer.14162

  View details for PubMedID 31386216

• Variational Autoencoder with Truncated Mixture of Gaussians for Functional Connectivity Analysis. Information processing in medical imaging : proceedings of the ... conference Zhao, Q., Honnorat, N., Adeli, E., Pohl, K. M. 2019; 11492: 867-879

  Abstract

  Resting-state functional connectivity states are often identified as clusters of dynamic connectivity patterns. However, existing clustering approaches do not distinguish major states from rarely occurring minor states and hence are sensitive to noise. To address this issue, we propose to model major states using a non-linear generative process guided by a Gaussian-mixture distribution in a low-dimensional latent space, while separately modeling the connectivity patterns of minor states by a non-informative uniform distribution. We embed this truncated Gaussian-Mixture model in a Variational Autoencoder framework to obtain a general joint clustering and outlier detection approach, tGM-VAE. When applied to synthetic data with known ground-truth, tGM-VAE is more accurate in clustering connectivity patterns than existing approaches. On the rs-fMRI of 593 healthy adolescents, tGM-VAE identifies meaningful major connectivity states. The dwell time of these states significantly correlates with age.

  View details for DOI 10.1007/978-3-030-20351-1_68

  View details for PubMedID 32699491

  View details for PubMedCentralID PMC7375028

• Accelerated aging and motor control deficits are related to regional deformation of central cerebellar white matter in alcohol use disorder. Addiction biology Zhao, Q., Pfefferbaum, A., Podhajsky, S., Pohl, K. M., Sullivan, E. V. 2019

  Abstract

  The World Health Organization estimates a 12-month prevalence rate of 8+% for an alcohol use disorder (AUD) diagnosis in people age 15years and older in the United States and Europe, presenting significant health risks that have the potential of accelerating age-related functional decline. According to neuropathological studies, white matter systems of the cerebellum are vulnerable to chronic alcohol dependence. To pursue the effect of AUD on white matter structure and functions in vivo, this study used T1-weighted, magnetic resonance imaging (MRI) to quantify the total corpus medullare of the cerebellum and a finely grained analysis of its surface in 135 men and women with AUD (mean duration of abstinence, 248d) and 128 age- and sex-matched control participants; subsets of these participants completed motor testing. We identified an AUD-related volume deficit and accelerated aging in the total corpus medullare. Novel deformation-based surface morphometry revealed regional shrinkage of surfaces adjacent to lobules I-V, lobule IX, and vermian lobule X. In addition, accelerated aging was detected in the regional surface areas adjacent to lobules I-V, lobule VI, lobule VIIB, and lobules VIII, IX, and X. Sex differences were not identified for any measure. For both volume-based and surface-based analyses, poorer performance in gait and balance, manual dexterity, and grip strength were linked to greater regional white matter structural deficits. Our results suggest that local deformation of the corpus medullare has the potential of identifying structurally and functionally segregated networks affected in AUD.

  View details for PubMedID 30932270

• Hippocampal subfield CA2+3 exhibits accelerated aging in Alcohol Use Disorder: A preliminary study. NeuroImage. Clinical Zahr, N. M., Pohl, K. M., Saranathan, M., Sullivan, E. V., Pfefferbaum, A. 2019; 22: 101764

  Abstract

  The profile of brain structural dysmorphology of individuals with Alcohol Use Disorders (AUD) involves disruption of the limbic system. In vivo imaging studies report hippocampal volume loss in AUD relative to controls, but only recently has it been possible to articulate different regions of this complex structure. Volumetric analysis of hippocampal regions rather than total hippocampal volume may augment differentiation of disease processes. For example, damage to hippocampal subfield cornu ammonis 1 (CA1) is often reported in Alzheimer's disease (AD), whereas deficits in CA4/dentate gyrus are described in response to stress and trauma. Two previous studies explored the effects of chronic alcohol use on hippocampal subfields: one reported smaller volume of the CA2+3 in alcohol-dependent subjects relative to controls, associated with years of alcohol consumption; the other, smaller volumes of presubiculum, subiculum, and fimbria in alcohol-dependent relative to control men. The current study, conducted in 24 adults with DSM5-diagnosed AUD (7 women, 53.7 ± 8.8) and 20 controls (7 women, 54.1 ± 9.3), is the first to use FreeSurfer 6.0, which provides state-of-the art hippocampal parcellation, to explore the sensitivity of hippocampal sufields to alcoholism. T1- and T2- images were collected on a GE MR750 system with a 32-channel Nova head coil. FreeSurfer 6.0 hippocampal subfield analysis produced 12 subfields: parasubiculum; presubiculum; subiculum; CA1; CA2+3; CA4; GC-ML-DG (Granule Cell (GC) and Molecular Layer (ML) of the Dentate Gyrus (DG)); molecular layer; hippocampus-amygdala-transition-area (HATA); fimbria; hippocampal tail; hippocampal fissure; and whole volume for left and right hippocampi. A comprehensive battery of neuropsychological tests comprising attention, memory and learning, visuospatial abilities, and executive functions was administered. Multiple regression analyses of raw volumetric data for each subfields by group, age, sex, hemisphere, and supratentorial volume (svol) showed significant effects of svol (p < .04) on nearly all structures (excluding tail and fissure). Volumes corrected for svol showed effects of age (fimbria, fissure) and group (subiculum, CA1, CA4, GC-ML-DG, HATA, fimbria); CA2+3 showed a diagnosis-by-age interaction indicating older AUD individuals had a smaller volume than would be expected for their age. There were no selective relations between hippocampal subfields and performance on neuropsychological tests, likely due to lack of statistical power. The current results concur with the previous study identifying CA2+3 as sensitive to alcoholism, extend them by identifying an alcoholism-age interaction, and suggest an imaging phenotype distinguishing AUD from AD and stress/trauma.

  View details for PubMedID 30904825

• Novel Machine Learning Identifies Brain Patterns Distinguishing Diagnostic Membership of Human Immunodeficiency Virus, Alcoholism, and Their Comorbidity of Individuals. Biological psychiatry. Cognitive neuroscience and neuroimaging Adeli, E., Zahr, N. M., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M. 2019

  Abstract

  The incidence of alcohol use disorder (AUD) in human immunodeficiency virus (HIV) infection is twice that of the rest of the population. This study documents complex radiologically identified, neuroanatomical effects of AUD+HIV comorbidity by identifying structural brain systems that predicted diagnosis on an individual basis. Applying novel machine learning analysis to 549 participants (199 control subjects, 222 with AUD, 68 with HIV, 60 with AUD+HIV), 298 magnetic resonance imaging brain measurements were automatically reduced to small subsets per group. Significance of each diagnostic pattern was inferred from its accuracy in predicting diagnosis and performance on six cognitive measures. While all three diagnostic patterns predicted the learning and memory score, the AUD+HIV pattern was the largest and had the highest predication accuracy (78.1%). Providing a roadmap for analyzing large, multimodal datasets, the machine learning analysis revealed imaging phenotypes that predicted diagnostic membership of magnetic resonance imaging scans of individuals with AUD, HIV, and their comorbidity.

  View details for DOI 10.1016/j.bpsc.2019.02.003

  View details for PubMedID 30982583

• Longitudinally consistent estimates of intrinsic functional networks. Human brain mapping Zhao, Q., Kwon, D., Müller-Oehring, E. M., Le Berre, A. P., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M. 2019

  Abstract

  Increasing numbers of neuroimaging studies are acquiring data to examine changes in brain architecture by investigating intrinsic functional networks (IFN) from longitudinal resting-state functional MRI (rs-fMRI). At the subject level, these IFNs are determined by cross-sectional procedures, which neglect intra-subject dependencies and result in suboptimal estimates of the networks. Here, a novel longitudinal approach simultaneously extracts subject-specific IFNs across multiple visits by explicitly modeling functional brain development as an essential context for seeking change. On data generated by an innovative simulation based on real rs-fMRI, the method was more accurate in estimating subject-specific IFNs than cross-sectional approaches. Furthermore, only group-analysis based on longitudinally consistent estimates identified significant developmental effects within IFNs of 246 adolescents from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study. The findings were confirmed by the cross-sectional estimates when the corresponding group analysis was confined to the developmental effects. Those effects also converged with current concepts of neurodevelopment.

  View details for DOI 10.1002/hbm.24541

  View details for PubMedID 30806009

• Dissociable Contributions of Precuneus and Cerebellum to Subjective and Objective Neuropathy in HIV. Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology Zahr, N. M., Pohl, K. M., Pfefferbaum, A., Sullivan, E. V. 2019

  Abstract

  Neuropathy, typically diagnosed by the presence of either symptoms or signs of peripheral nerve dysfunction, remains a frequently reported complication in the antiretroviral (ART)-treated HIV population. This study was conducted in 109 healthy controls and 57 HIV-infected individuals to investigate CNS regions associated with neuropathy. An index of objective neuropathy was computed based on 4 measures: deep tendon ankle reflex, vibration sense (great toes), position sense (great toes), and 2-point discrimination (feet). Subjective neuropathy (self-report of pain, aching, or burning; pins and needles; or numbness in legs or feet) was also evaluated. Structural MRI data were available for 126/166 cases. The HIV relative to the healthy control group was impaired on all 4 signs of neuropathy. Within the HIV group, an objective neuropathy index of 1 (bilateral impairment on 1 measure) or 2 (bilateral impairment on at least 2/4 measures) was associated with older age and a smaller volume of the cerebellar vermis. Moderate to severe symptoms of neuropathy were associated with more depressive symptoms, reduced quality of life, and a smaller volume of the parietal precuneus. This study is consistent with the recent contention that ART-treated HIV-related neuropathy has a CNS component. Distinguishing subjective symptoms from objective signs of neuropathy allowed for a dissociation between the precuneus, a brain region involved in conscious information processing and the vermis, involved in fine tuning of limb movements. Graphical Abstract In HIV patients, objective signs of neuropathy correlated with smaller cerebellar vermis (red) volumes whereas subjective symptoms of neuropathy were associated with smaller precuneus (blue) volumes.

  View details for PubMedID 30741374

• Logistic Regression Confined by Cardinality-Constrained Sample and Feature Selection. IEEE transactions on pattern analysis and machine intelligence Adeli, E. n., Li, X. n., Kwon, D. n., Zhang, Y. n., Pohl, K. n. 2019

  Abstract

  Many vision-based applications rely on logistic regression for embedding classification within a probabilistic context, such as recognition in images and videos or identifying disease-specific image phenotypes from neuroimages. Logistic regression, however, often performs poorly when trained on data that is noisy, has irrelevant features, or when the samples are distributed across the classes in an imbalanced setting; a common occurrence in visual recognition tasks. To deal with those issues, researchers generally rely on ad-hoc regularization techniques or model a subset of these issues. We instead propose a mathematically sound logistic regression model that selects a subset of (relevant) features and (informative and balanced) set of samples during the training process. The model does so by applying cardinality constraints (via l0 -'norm' sparsity) on the features and samples. l0 defines sparsity in mathematical settings but in practice has mostly been approximated (e.g., via l1 or its variations) for computational simplicity. We prove that a local minimum to the non-convex optimization problems induced by cardinality constraints can be computed by combining block coordinate descent with penalty decomposition. On synthetic, image recognition, and neuroimaging datasets, we furthermore show that the accuracy of the method is higher than alternative methods and classifiers commonly used in the literature.

  View details for DOI 10.1109/TPAMI.2019.2901688

  View details for PubMedID 30835210

• Variational Autoencoder with Truncated Mixture of Gaussians for Functional Connectivity Analysis Zhao, Q., Honnorat, N., Adeli, E., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M., Chung, A. C., Gee, J. C., Yushkevich, P. A., Bao, S. SPRINGER INTERNATIONAL PUBLISHING AG. 2019: 867–79

  View details for DOI 10.1007/978-3-030-20351-1_68

  View details for Web of Science ID 000493380900068

• Convergence of three parcellation approaches demonstrating cerebellar lobule volume deficits in Alcohol Use Disorder. NeuroImage. Clinical Sullivan, E. V., Zahr, N. M., Saranathan, M. n., Pohl, K. M., Pfefferbaum, A. n. 2019; 24: 101974

  Abstract

  Recent advances in robust and reliable methods of MRI-derived cerebellar lobule parcellation volumetry present the opportunity to assess effects of Alcohol Use Disorder (AUD) on selective cerebellar lobules and relations with indices of nutrition and motor functions. In pursuit of this opportunity, we analyzed high-resolution MRI data acquired in 24 individuals with AUD and 20 age- and sex-matched controls with a 32-channel head coil using three different atlases: the online automated analysis pipeline volBrain Ceres, SUIT, and the Johns Hopkins atlas. Participants had also completed gait and balance examination and hematological analysis of nutritional and liver status, enabling testing of functional meaningfulness of each cerebellar parcellation scheme. Compared with controls, each quantification approach yielded similar patterns of group differences in regional volumes: All three approaches identified AUD-related deficits in total tissue and total gray matter, but only Ceres identified a total white matter volume deficit. Convergent volume differences occurred in lobules I-V, Crus I, VIIIB, and IX. Coefficients of variation (CVs) were <20% for 46 of 56 regions measured and in general were graded: Ceres

  View details for DOI 10.1016/j.nicl.2019.101974

  View details for PubMedID 31419768

• Variational AutoEncoder for Regression: Application to Brain Aging Analysis Zhao, Q., Adeli, E., Honnorat, N., Leng, T., Pohl, K. M., Shen, D., Liu, T., Peters, T. M., Staib, L. H., Essert, C., Zhou, S., Yap, P. T., Khan, A. SPRINGER INTERNATIONAL PUBLISHING AG. 2019: 823–31

  Abstract

  While unsupervised variational autoencoders (VAE) have become a powerful tool in neuroimage analysis, their application to supervised learning is under-explored. We aim to close this gap by proposing a unified probabilistic model for learning the latent space of imaging data and performing supervised regression. Based on recent advances in learning disentangled representations, the novel generative process explicitly models the conditional distribution of latent representations with respect to the regression target variable. Performing a variational inference procedure on this model leads to joint regularization between the VAE and a neural-network regressor. In predicting the age of 245 subjects from their structural Magnetic Resonance (MR) images, our model is more accurate than state-of-the-art methods when applied to either region-of-interest (ROI) measurements or raw 3D volume images. More importantly, unlike simple feed-forward neural-networks, disentanglement of age in latent representations allows for intuitive interpretation of the structural developmental patterns of the human brain.

  View details for DOI 10.1007/978-3-030-32245-8_91

  View details for Web of Science ID 000548438900091

  View details for PubMedID 32705091

  View details for PubMedCentralID PMC7377006

• Longitudinally consistent estimates of intrinsic functional networks Human Brain Mapping Zhao, Q., Kwon, D., Müller-Oehring, E. M., Le Berre, A., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M. 2019

  View details for DOI 10.1002/hbm.24541

• Hippocampal subfield CA2+3 exhibits accelerated aging in Alcohol Use Disorder: A preliminary study NEUROIMAGE-CLINICAL Zahr, N. M., Pohl, K. M., Saranathan, M., Sullivan, E., Pfefferbaum, A. 2019; 22

  View details for DOI 10.1016/j.nicl.2019.101764

  View details for Web of Science ID 000470123000082

• Relations between cognitive and motor deficits and regional brain volumes in individuals with alcoholism. Brain structure & function Fama, R. n., Le Berre, A. P., Sassoon, S. A., Zahr, N. M., Pohl, K. M., Pfefferbaum, A. n., Sullivan, E. V. 2019

  Abstract

  Despite the common co-occurrence of cognitive impairment and brain structural deficits in alcoholism, demonstration of relations between regional gray matter volumes and cognitive and motor processes have been relatively elusive. In pursuit of identifying brain structural substrates of impairment in alcoholism, we assessed executive functions (EF), episodic memory (MEM), and static postural balance (BAL) and measured regional brain gray matter volumes of cortical, subcortical, and cerebellar structures commonly affected in individuals with alcohol dependence (ALC) compared with healthy controls (CTRL). ALC scored lower than CTRL on all composite scores (EF, MEM, and BAL) and had smaller frontal, cingulate, insular, parietal, and hippocampal volumes. Within the ALC group, poorer EF scores correlated with smaller frontal and temporal volumes; MEM scores correlated with frontal volume; and BAL scores correlated with frontal, caudate, and pontine volumes. Exploratory analyses investigating relations between subregional frontal volumes and composite scores in ALC yielded different patterns of associations, suggesting that different neural substrates underlie these functional deficits. Of note, orbitofrontal volume was a significant predictor of memory scores, accounting for almost 15% of the variance; however, this relation was evident only in ALC with a history of a non-alcohol substance diagnosis and not in ALC without a non-alcohol substance diagnosis. The brain-behavior relations observed provide evidence that the cognitive and motor deficits in alcoholism are likely a result of different neural systems and support the hypothesis that a number of identifiable neural systems rather than a common or diffuse neural pathway underlies cognitive and motor deficits observed in chronic alcoholism.

  View details for DOI 10.1007/s00429-019-01894-w

  View details for PubMedID 31161472

• Sensitivity of ventrolateral posterior thalamic nucleus to back pain in alcoholism and CD4 nadir in HIV. Human brain mapping Zahr, N. M., Sullivan, E. V., Pohl, K. M., Pfefferbaum, A. n., Saranathan, M. n. 2019

  Abstract

  Volumes of thalamic nuclei are differentially affected by disease-related processes including alcoholism and human immunodeficiency virus (HIV) infection. This MRI study included 41 individuals diagnosed with alcohol use disorders (AUD, 12 women), 17 individuals infected with HIV (eight women), and 49 healthy controls (24 women) aged 39 to 75 years. A specialized, high-resolution acquisition protocol enabled parcellation of five thalamic nuclei: anterior [anterior ventral (AV)], posterior [pulvinar (Pul)], medial [mediodorsal (MD)], and ventral [including ventral lateral posterior (VLp) and ventral posterior lateral (VPl)]. An omnibus mixed-model approach solving for volume considered the "fixed effects" of nuclei, diagnosis, and their interaction while covarying for hemisphere, sex, age, and supratentorial volume (svol). The volume by diagnosis interaction term was significant; the effects of hemisphere and sex were negligible. Follow-up mixed-model tests thus evaluated the combined (left + right) volume of each nucleus separately for effects of diagnosis while controlling for age and svol. Only the VLp showed diagnoses effects and was smaller in the AUD (p = .04) and HIV (p = .0003) groups relative to the control group. In the AUD group, chronic back pain (p = .008) and impaired deep tendon ankle reflex (p = .0005) were associated with smaller VLp volume. In the HIV group, lower CD4 nadir (p = .008) was associated with smaller VLp volume. These results suggest that the VLp is differentially sensitive to disease processes associated with AUD and HIV.

  View details for DOI 10.1002/hbm.24880

  View details for PubMedID 31785046

• Jacobian Maps Reveal Under-reported Brain Regions Sensitive to Extreme Binge Ethanol Intoxication in the Rat. Frontiers in neuroanatomy Zhao, Q., Fritz, M., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M., Zahr, N. M. 2018; 12: 108

  Abstract

  Individuals aged 12-20 years drink 11% of all alcohol consumed in the United States with more than 90% consumed in the form of binge drinking. Early onset alcohol use is a strong predictor of future alcohol dependence. The study of the effects of excessive alcohol use on the human brain is hampered by limited information regarding the quantity and frequency of exposure to alcohol. Animal models can control for age at alcohol exposure onset and enable isolation of neural substrates of exposure to different patterns and quantities of ethanol (EtOH). As with humans, a frequently used binge exposure model is thought to produce dependence and affect predominantly corticolimbic brain regions. in vivo neuroimaging enables animals models to be examined longitudinally, allowing for each animal to serve as its own control. Accordingly, we conducted 3 magnetic resonance imaging (MRI) sessions (baseline, binge, recovery) to track structure throughout the brains of wild type Wistar rats to test the hypothesis that binge EtOH exposure affects specific brain regions in addition to corticolimbic circuitry. Voxel-based comparisons of 13 EtOH- vs. 12 water- exposed animals identified significant thalamic shrinkage and lateral ventricular enlargement as occurring with EtOH exposure, but recovering with a week of abstinence. By contrast, pretectal nuclei and superior and inferior colliculi shrank in response to binge EtOH treatment but did not recover with abstinence. These results identify brainstem structures that have been relatively underreported but are relevant for localizing neurocircuitry relevant to the dynamic course of alcoholism.

  View details for DOI 10.3389/fnana.2018.00108

  View details for PubMedID 30618652

  View details for PubMedCentralID PMC6297262

• Jacobian Maps Reveal Under-reported Brain Regions Sensitive to Extreme Binge Ethanol Intoxication in the Rat FRONTIERS IN NEUROANATOMY Zhao, Q., Fritz, M., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M., Zahr, N. M. 2018; 12

  View details for DOI 10.3389/fnana.2018.00108

  View details for Web of Science ID 000452849300001

• Chained regularization for identifying brain patterns specific to HIV infection NEUROIMAGE Adeli, E., Kwon, D., Zhao, Q., Pfefferbaum, A., Zahr, N. M., Sullivan, E. V., Pohl, K. M. 2018; 183: 425–37

  View details for DOI 10.1016/j.neuroimage.2018.08.022

  View details for Web of Science ID 000447750200038

• Distribution of brain iron accrual in adolescence: Evidence from cross-sectional and longitudinal analysis. Human brain mapping Peterson, E. T., Kwon, D., Luna, B., Larsen, B., Prouty, D., De Bellis, M. D., Voyvodic, J., Liu, C., Li, W., Pohl, K. M., Sullivan, E. V., Pfefferbaum, A. 2018

  Abstract

  To track iron accumulation and location in the brain across adolescence, we repurposed diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data acquired in 513 adolescents and validated iron estimates with quantitative susceptibility mapping (QSM) in 104 of these subjects. DTI and fMRI data were acquired longitudinally over 1year in 245 male and 268 female, no-to-low alcohol-consuming adolescents (12-21 years at baseline) from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study. Brain region average signal values were calculated for susceptibility to nonheme iron deposition: pallidum, putamen, dentate nucleus, red nucleus, and substantia nigra. To estimate nonheme iron, the corpus callosum signal (robust to iron effects) was divided by regional signals to generate estimated R2 (edwR2 for DTI) and R2 * (eR2 * for fMRI). Longitudinal iron deposition was measured using the normalized signal change across time for each subject. Validation using baseline QSM, derived from susceptibility-weighted imaging, was performed on 46 male and 58 female participants. Normalized iron deposition estimates from DTI and fMRI correlated with age in most regions; both estimates indicated less iron in boys than girls. QSM results correlated highly with DTI and fMRI results (adjusted R2 = 0.643 for DTI, 0.578 for fMRI). Cross-sectional and longitudinal analyses indicated an initial rapid increase in iron, notably in the putamen and red nucleus, that slowed with age. DTI and fMRI data can be repurposed for identifying regional brain iron deposition in developing adolescents as validated with high correspondence with QSM.

  View details for PubMedID 30496644

• Regional growth trajectories of cortical myelination in adolescents and young adults: longitudinal validation and functional correlates. Brain imaging and behavior Kwon, D., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M. 2018

  Abstract

  Adolescence is a time of continued cognitive and emotional evolution occurring with continuing brain development involving synaptic pruning and cortical myelination. The hypothesis of this study is that heavy myelination occurs in cortical regions with relatively direct, predetermined circuitry supporting unimodal sensory or motor functions and shows a steep developmental slope during adolescence (12-21years) until young adulthood (22-35years) when further myelination decelerates. By contrast, light myelination occurs in regions with highly plastic circuitry supporting complex functions and follows a delayed developmental trajectory. In support of this hypothesis, cortical myelin content was estimated and harmonized across publicly available datasets provided by the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) and the Human Connectome Project (HCP). The cross-sectional analysis of 226 no-to-low alcohol drinking NCANDA adolescents revealed relatively steeper age-dependent trajectories of myelin growth in unimodal primary motor cortex and flatter age-dependent trajectories in multimodal mid/posterior cingulate cortices. This pattern of continued myelination showed smaller gains when the same analyses were performed on 686 young adults of the HCP cohort free of neuropsychiatric diagnoses. Critically, a predicted correlation between a motor task and myelin content in motor or cingulate cortices was found in the NCANDA adolescents, supporting the functional relevance of this imaging neurometric. Furthermore, the regional trajectory slopes were confirmed by performing longitudinally consistent analysis of cortical myelin. In conclusion, coordination of myelin content and circuit complexity continues to develop throughout adolescence, contributes to performance maturation, and may represent active cortical development climaxing in young adulthood.

  View details for PubMedID 30406353

• Multi-Label Transduction for Identifying Disease Comorbidity Patterns. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Adeli, E., Kwon, D., Pohl, K. M. 2018; 11072: 575-583

  Abstract

  Study of the untoward effects associated with the comorbidity of multiple diseases on brain morphology requires identifying differences across multiple diagnostic groupings. To identify such effects and differentiate between groups of patients and normal subjects, conventional methods often compare each patient group with healthy subjects using binary or multi-class classifiers. However, testing inferences across multiple diagnostic groupings of complex disorders commonly yield inconclusive or conflicting findings when the classifier is confined to modeling two cohorts at a time or considers class labels mutually-exclusive (as in multi-class classifiers). These shortcomings are potentially caused by the difficulties associated with modeling compounding factors of diseases with these approaches. Multi-label classifiers, on the other hand, can appropriately model disease comorbidity, as each subject can be assigned to two or more labels. In this paper, we propose a multi-label transductive (MLT) method based on low-rank matrix completion that is able not only to classify the data into multiple labels but also to identify patterns from MRI data unique to each cohort. To evaluate the method, we use a dataset containing individuals with Alcohol Use Disorder (AUD) and human immunodeficiency virus (HIV) infection (specifically 244 healthy controls, 227 AUD, 70 HIV, and 61 AUD+HIV). On this dataset, our proposed method is more accurate in correctly labeling subjects than common approaches. Furthermore, our method identifies patterns specific to each disease and AUD+HIV comorbidity that shows that the comorbidity is characterized by a compounding effect of AUD and HIV infection.

  View details for DOI 10.1007/978-3-030-00931-1_66

  View details for PubMedID 33688637

  View details for PubMedCentralID PMC7938692

• End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification. Machine learning in medical imaging. MLMI (Workshop) Esmaeilzadeh, S., Belivanis, D. I., Pohl, K. M., Adeli, E. 2018; 11046: 337-345

  Abstract

  As shown in computer vision, the power of deep learning lies in automatically learning relevant and powerful features for any perdition task, which is made possible through end-to-end architectures. However, deep learning approaches applied for classifying medical images do not adhere to this architecture as they rely on several pre- and post-processing steps. This shortcoming can be explained by the relatively small number of available labeled subjects, the high dimensionality of neuroimaging data, and difficulties in interpreting the results of deep learning methods. In this paper, we propose a simple 3D Convolutional Neural Networks and exploit its model parameters to tailor the end-to-end architecture for the diagnosis of Alzheimer's disease (AD). Our model can diagnose AD with an accuracy of 94.1% on the popular ADNI dataset using only MRI data, which outperforms the previous state-of-the-art. Based on the learned model, we identify the disease biomarkers, the results of which were in accordance with the literature. We further transfer the learned model to diagnose mild cognitive impairment (MCI), the prodromal stage of AD, which yield better results compared to other methods.

  View details for DOI 10.1007/978-3-030-00919-9_39

  View details for PubMedID 32832936

  View details for PubMedCentralID PMC7440044

• A Riemannian Framework for Longitudinal Analysis of Resting-State Functional Connectivity. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Zhao, Q., Kwon, D., Pohl, K. M. 2018; 11072: 145-153

  Abstract

  Even though the number of longitudinal resting-state-fMRI studies is increasing, accurately characterizing the changes in functional connectivity across visits is a largely unexplored topic. To improve characterization, we design a Riemannian framework that represents the functional connectivity pattern of a subject at a visit as a point on a Riemannian manifold. Geodesic regression across the 'sample' points of a subject on that manifold then defines the longitudinal trajectory of their connectivity pattern. To identify group differences specific to regions of interest (ROI), we map the resulting trajectories of all subjects to a common tangent space via the Lie group action. We account for the uncertainty in choosing the common tangent space by proposing a test procedure based on the theory of latent p-values. Unlike existing methods, our proposed approach identifies sex differences across 246 subjects, each of them being characterized by three rs-fMRI scans.

  View details for DOI 10.1007/978-3-030-00931-1_17

  View details for PubMedID 33005907

  View details for PubMedCentralID PMC7526985

• Chained regularization for identifying brain patterns specific to HIV infection. NeuroImage Adeli, E., Kwon, D., Zhao, Q., Pfefferbaum, A., Zahr, N. M., Sullivan, E. V., Pohl, K. M. 2018

  Abstract

  Human Immunodeficiency Virus (HIV) infection continues to have major adverse public health and clinical consequences despite the effectiveness of combination Antiretroviral Therapy (cART) in reducing HIV viral load and improving immune function. As successfully treated individuals with HIV infection age, their cognition declines faster than reported for normal aging. This phenomenon underlines the importance of improving long-term care, which requires better understanding of the impact of HIV on the brain. In this paper, automated identification of patients and brain regions affected by HIV infection are modeled as a classification problem, whose solution is determined in two steps within our proposed Chained-Regularization framework. The first step focuses on selecting the HIV pattern (i.e., the most informative constellation of brain region measurements for distinguishing HIV infected subjects from healthy controls) by constraining the search for the optimal parameter setting of the classifier via group sparsity (ℓ2,1-norm). The second step improves classification accuracy by constraining the parameterization with respect to the selected measurements and the Euclidean regularization (ℓ2-norm). When applied to the cortical and subcortical structural Magnetic Resonance Images (MRI) measurements of 65 controls and 65 HIV infected individuals, this approach is more accurate in distinguishing the two cohorts than more common models. Finally, the brain regions of the identified HIV pattern concur with the HIV literature that uses traditional group analysis models.

  View details for PubMedID 30138676

• Accelerated and Premature Aging Characterizing Regional Cortical Volume Loss in Human Immunodeficiency Virus Infection: Contributions From Alcohol, Substance Use, and Hepatitis C Coinfection. Biological psychiatry. Cognitive neuroscience and neuroimaging Pfefferbaum, A., Zahr, N. M., Sassoon, S. A., Kwon, D., Pohl, K. M., Sullivan, E. V. 2018

  Abstract

  BACKGROUND: Life expectancy of successfully treated human immunodeficiency virus (HIV)-infected individuals is approaching normal longevity. The growing HIV population ≥50 years of age is now at risk of developing HIV-associated neurocognitive disorder, acquiring coinfection with the hepatitis C virus (HCV), and engaging in hazardous drinking or drug consumption that can adversely affect trajectories of the healthy aging of brain structures.METHODS: This cross-sectional/longitudinal study quantified regional brain volumes from 1101 magnetic resonance imaging scans collected over 14 years in 549 participants (25 to 75 years of age): 68 HIV-infected individuals without alcohol dependence, 60 HIV-infected individuals with alcohol dependence, 222 alcohol-dependent individuals, and 199 control subjects. We tested 1) whether localized brain regions in HIV-infected individuals exhibited accelerated aging, or alternatively, nonaccelerated premature aging deficits; and 2) the extent to which alcohol or substance dependence or HCV coinfection altered brain aging trajectories.RESULTS: The HIV-infected cohort exhibited steeper declining volume trajectories than control subjects, consistently in the frontal cortex. Nonaccelerated volume deficits occurred in the temporal, parietal, insular, and cingulate regions of all three diagnostic groups. Alcohol and drug dependence comorbidities and HCV coinfection exacerbated HIV-related volume deficits. Accelerated age interactions in frontal and posterior parietal volumes endured in HIV-infected individuals free of alcohol or substance dependence and HCV infection comorbidities. Functionally, poorer HIV-associated neurocognitive disorder scores and Veterans Aging Cohort Study indices correlated with smaller regional brain volumes in the HIV-infected individuals without alcohol dependence and alcohol-dependent groups.CONCLUSIONS: HIV infection itself may confer a heightened risk of accelerated brain aging, potentially exacerbated by HCV coinfection and substance dependency. Confirmation would require a prospective study with a preinfection baseline.

  View details for PubMedID 30093343

• Alcohol use effects on adolescent brain development revealed by simultaneously removing confounding factors, identifying morphometric patterns, and classifying individuals SCIENTIFIC REPORTS Park, S., Zhang, Y., Kwon, D., Zhao, Q., Zahr, N. M., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M. 2018; 8: 8297

  Abstract

  Group analysis of brain magnetic resonance imaging (MRI) metrics frequently employs generalized additive models (GAM) to remove contributions of confounding factors before identifying cohort specific characteristics. For example, the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) used such an approach to identify effects of alcohol misuse on the developing brain. Here, we hypothesized that considering confounding factors before group analysis removes information relevant for distinguishing adolescents with drinking history from those without. To test this hypothesis, we introduce a machine-learning model that identifies cohort-specific, neuromorphometric patterns by simultaneously training a GAM and generic classifier on macrostructural MRI and microstructural diffusion tensor imaging (DTI) metrics and compare it to more traditional group analysis and machine-learning approaches. Using a baseline NCANDA MR dataset (N = 705), the proposed machine learning approach identified a pattern of eight brain regions unique to adolescents who misuse alcohol. Classifying high-drinking adolescents was more accurate with that pattern than using regions identified with alternative approaches. The findings of the joint model approach thus were (1) impartial to confounding factors; (2) relevant to drinking behaviors; and (3) in concurrence with the alcohol literature.

  View details for PubMedID 29844507

• The Role of Aging, Drug Dependence, and Hepatitis C Comorbidity in Alcoholism Cortical Compromise JAMA PSYCHIATRY Sullivan, E. V., Zahr, N. M., Sassoon, S. A., Thompson, W. K., Kwon, D., Pohl, K. M., Pfefferbaum, A. 2018; 75 (5): 474–83

  Abstract

  The prevalence of alcohol misuse increased substantially over a decade in adults, particularly in those aged 65 years or older. Ramifications for brain structural integrity are significant, especially in older adults.To combine cross-sectional, longitudinal data to test age-alcoholism interactions and examine the association between prevalent comorbidities (drug dependence and hepatitis C virus [HCV] infection) and cortical volume deficits in alcohol dependence.During 14 years, 826 structural magnetic resonance images were acquired in 222 individuals with alcohol dependence and 199 age-matched control participants (aged 25-75 years at initial study), parcellated with a common atlas, and adjusted for brain volume. Longitudinal data were available on 116 participants with alcoholism and 96 control participants. DSM-IV criteria determined alcohol and drug diagnoses; serology testing determined HCV status. The study was conducted at SRI International and Stanford University School of Medicine from April 11, 2003, to March 3, 2017.Magnetic resonance imaging-derived regional cortical volumes corrected for supratentorial volume and sex.Of the 222 participants with alcoholism, 156 (70.3%) were men; mean (SD) age was 48.0 (10.0) years; the mean age for the 199 control participants was 47.6 (14.0) years. Participants with alcohol dependence had volume deficits in frontal (t = -5.732, P < .001), temporal (t = -3.151, P = .002), parietal (t = -5.063, P < .001), cingulate (t = -3.170, P = .002), and insular (t = -4.920, P < .001) cortices; deficits were prominent in frontal subregions and were not sex dependent. Accelerated aging occurred in frontal cortex (t = -3.019, P < .02) and precentral (t = -2.691, P < .05) and superior gyri (t = -2.763, P < .05) and could not be attributed to the amount of alcohol consumed, which was greater in younger-onset than older-onset participants with alcoholism (t = 6.1191, P < .001). Given the high drug-dependence incidence (54.5%) in the alcoholism group, analysis examined drug subgroups (cocaine, cannabis, amphetamines, opiates) compared with drug-dependence-free alcoholism and control groups. Although the alcohol plus cocaine (t = -2.310, P = .04) and alcohol plus opiate (t = -2.424, P = .04) groups had smaller frontal volumes than the drug-dependence-free alcoholism group, deficits in precentral (t = -2.575, P = .01), supplementary motor (t = -2.532, P = .01), and medial (t = -2.800, P = .01) volumes endured in drug-dependence-free participants with alcoholism compared with control participants. Those with HCV infection had greater deficits than those without HCV infection in frontal (t = 3.468, P = .01), precentral (t = 2.513, P = .03), superior (t = 2.533, P = .03), and orbital (t = 2.506, P = .03) volumes, yet total frontal (t = 2.660, P = .02), insular (t = 3.526, P = .003), parietal (t = 2.414, P = .03), temporal (t = 3.221, P = .005), and precentral (t = 3.180, P = .01) volume deficits persisted in the uninfected participants with alcoholism compared with control participants with known HCV status.Drug dependence and HCV infection compounded deleterious effects of alcohol dependence on frontal cortical volumes but could not account for the frontally distributed volume deficits in the drug-free participants with alcoholism. We speculate that age-alcohol interactions notable in frontal cortex put older adults at heightened risk for age-associated neurocompromise even if alcohol misuse is initiated later in life.

  View details for PubMedID 29541774

  View details for PubMedCentralID PMC5875381

• eCurves: A Temporal Shape Encoding IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING Bernardis, E., Zhang, Y., Konukoglu, E., Ou, Y., Javitz, H. S., Axel, L., Metaxas, D., Desjardins, B., Pohl, K. M. 2018; 65 (4): 733–44

  Abstract

  This paper presents a framework for temporal shape analysis to capture the shape and changes of anatomical structures from three-dimensional+t(ime) medical scans.We first encode the shape of a structure at each time point with the spectral signature, i.e., the eigenvalues and eigenfunctions of the Laplace operator. We then expand it to capture morphing shapes by tracking the eigenmodes across time according to the similarity of their eigenfunctions. The similarity metric is motivated by the fact that small-shaped deformations lead to minor changes in the eigenfunctions. Following each eigenmode from the beginning to end results in a set of eigenmode curves representing the shape and its changes over time.We apply our encoding to a cardiac dataset consisting of series of segmentations outlining the right and left ventricles over time. We measure the accuracy of our encoding by training classifiers on discriminating healthy adults from patients that received reconstructive surgery for Tetralogy of Fallot (TOF). The classifiers based on our encoding significantly surpass deformation-based encodings of the right ventricle, the structure most impacted by TOF.The strength of our framework lies in its simplicity: It only assumes pose invariance within a time series but does not assume point-to-point correspondence across time series or a (statistical or physical) model. In addition, it is easy to implement and only depends on a single parameter, i.e., the number of curves.

  View details for DOI 10.1109/TBME.2017.2716365

  View details for Web of Science ID 000428526000003

  View details for PubMedID 28641243

  View details for PubMedCentralID PMC5732904

• CNS CORRELATES OF HIV-ASSOCIATED PERIPHERAL NEUROPATHY AND POSTURAL INSTABILITY Zahr, N., Kwon, D., Pohl, K., Sullivan, E., Pfefferbaum, A. SPRINGER. 2018: S94

  View details for Web of Science ID 000434755400325

• CNS CORRELATES OF HIV-ASSOCIATED PERIPHERAL NEUROPATHY AND POSTURAL INSTABILITY Zahr, N., Kwon, D., Pohl, K., Sullivan, E., Pfefferbaum, A. SPRINGER. 2018: S94

  View details for Web of Science ID 000429149100325

• Altered Brain Developmental Trajectories in Adolescents After Initiating Drinking. The American journal of psychiatry Pfefferbaum, A., Kwon, D., Brumback, T., Thompson, W. K., Cummins, K., Tapert, S. F., Brown, S. A., Colrain, I. M., Baker, F. C., Prouty, D., De Bellis, M. D., Clark, D. B., Nagel, B. J., Chu, W., Park, S. H., Pohl, K. M., Sullivan, E. V. 2018; 175 (4): 370–80

  Abstract

  OBJECTIVE: The authors sought evidence for altered adolescent brain growth trajectory associated with moderate and heavy alcohol use in a large national, multisite, prospective study of adolescents before and after initiation of appreciable alcohol use.METHOD: This study examined 483 adolescents (ages 12-21) before initiation of drinking and 1 and 2 years later. At the 2-year assessment, 356 participants continued to meet the study's no/low alcohol consumption entry criteria, 65 had initiated moderate drinking, and 62 had initiated heavy drinking. MRI was used to quantify regional cortical and white matter volumes. Percent change per year (slopes) in adolescents who continued to meet no/low criteria served as developmental control trajectories against which to compare those who initiated moderate or heavy drinking.RESULTS: In no/low drinkers, gray matter volume declined throughout adolescence and slowed in many regions in later adolescence. Complementing gray matter declines, white matter regions grew at faster rates at younger ages and slowed toward young adulthood. Youths who initiated heavy drinking exhibited an accelerated frontal cortical gray matter trajectory, divergent from the norm. Although significant effects on trajectories were not observed in moderate drinkers, their intermediate position between no/low and heavy drinkers suggests a dose effect. Neither marijuana co-use nor baseline volumes contributed significantly to the alcohol effect.CONCLUSIONS: Initiation of drinking during adolescence, with or without marijuana co-use, disordered normal brain growth trajectories. Factors possibly contributing to abnormal cortical volume trajectories include peak consumption in the past year and family history of alcoholism.

  View details for PubMedID 29084454

• The mediating role of cortical thickness and gray matter volume on sleep slow-wave activity during adolescence BRAIN STRUCTURE & FUNCTION Goldstone, A., Willoughby, A. R., de Zambotti, M., Franzen, P. L., Kwon, D., Pohl, K. M., Pfefferbaum, A., Sullivan, E. V., Muller-Oehring, E. M., Prouty, D. E., Hasler, B. P., Clark, D. B., Colrain, I. M., Baker, F. C. 2018; 223 (2): 669–85

  Abstract

  During the course of adolescence, reductions occur in cortical thickness and gray matter (GM) volume, along with a 65% reduction in slow-wave (delta) activity during sleep (SWA) but empirical data linking these structural brain and functional sleep differences, is lacking. Here, we investigated specifically whether age-related differences in cortical thickness and GM volume and cortical thickness accounted for the typical age-related difference in slow-wave (delta) activity (SWA) during sleep. 132 healthy participants (age 12-21 years) from the National Consortium on Alcohol and NeuroDevelopment in Adolescence study were included in this cross-sectional analysis of baseline polysomnographic, electroencephalographic, and magnetic resonance imaging data. By applying mediation models, we identified a large, direct effect of age on SWA in adolescents, which explained 45% of the variance in ultra-SWA (0.3-1 Hz) and 52% of the variance in delta-SWA (1 to <4 Hz), where SWA was lower in older adolescents, as has been reported previously. In addition, we provide evidence that the structure of several, predominantly frontal, and parietal brain regions, partially mediated this direct age effect, models including measures of brain structure explained an additional 3-9% of the variance in ultra-SWA and 4-5% of the variance in delta-SWA, with no differences between sexes. Replacing age with pubertal status in models produced similar results. As reductions in GM volume and cortical thickness likely indicate synaptic pruning and myelination, these results suggest that diminished SWA in older, more mature adolescents may largely be driven by such processes within a number of frontal and parietal brain regions.

  View details for PubMedID 28913599

  View details for PubMedCentralID PMC5828920

• End-To-End Alzheimer’s Disease Diagnosis and Biomarker Identification Stanford University / SRI International Esmaeilzadeh, S. 2018
• Multi-label Transduction for Identifying Disease Comorbidity Patterns Adeli, E., Kwon, D., Pohl, K. M., Frangi, A. F., Schnabel, J. A., Davatzikos, C., AlberolaLopez, C., Fichtinger, G. SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 575–83

  View details for DOI 10.1007/978-3-030-00931-1_66

  View details for Web of Science ID 000477769700066

• A Riemannian Framework for Longitudinal Analysis of Resting-State Functional Connectivity Zhao, Q., Kwon, D., Pohl, K. M., Frangi, A. F., Schnabel, J. A., Davatzikos, C., AlberolaLopez, C., Fichtinger, G. SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 145–53

  View details for DOI 10.1007/978-3-030-00931-1_17

  View details for Web of Science ID 000477769700017

• End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification Esmaeilzadeh, S., Belivanis, D., Pohl, K. M., Adeli, E., Shi, Y., Suk, H. I., Liu, M. SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 337–45

  View details for DOI 10.1007/978-3-030-00919-9_39

  View details for Web of Science ID 000477767800039

• Adolescent Executive Dysfunction in Daily Life: Relationships to Risks, Brain Structure and Substance Use FRONTIERS IN BEHAVIORAL NEUROSCIENCE Clark, D. B., Chung, T., Martin, C. S., Hasler, B. P., Fitzgerald, D. H., Luna, B., Brown, S. A., Tapert, S. F., Brumback, T., Cummins, K., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M., Colrain, I. M., Baker, F. C., De Bellis, M. D., Nooner, K. B., Nagel, B. J. 2017; 11

  View details for DOI 10.3389/fnbeh.2017.00223

  View details for Web of Science ID 000414932200001

• Adolescent Executive Dysfunction in Daily Life: Relationships to Risks, Brain Structure and Substance Use. Frontiers in behavioral neuroscience Clark, D. B., Chung, T., Martin, C. S., Hasler, B. P., Fitzgerald, D. H., Luna, B., Brown, S. A., Tapert, S. F., Brumback, T., Cummins, K., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M., Colrain, I. M., Baker, F. C., De Bellis, M. D., Nooner, K. B., Nagel, B. J. 2017; 11: 223

  Abstract

  During adolescence, problems reflecting cognitive, behavioral and affective dysregulation, such as inattention and emotional dyscontrol, have been observed to be associated with substance use disorder (SUD) risks and outcomes. Prior studies have typically been with small samples, and have typically not included comprehensive measurement of executive dysfunction domains. The relationships of executive dysfunction in daily life with performance based testing of cognitive skills and structural brain characteristics, thought to be the basis for executive functioning, have not been definitively determined. The aims of this study were to determine the relationships between executive dysfunction in daily life, measured by the Behavior Rating Inventory of Executive Function (BRIEF), cognitive skills and structural brain characteristics, and SUD risks, including a global SUD risk indicator, sleep quality, and risky alcohol and cannabis use. In addition to bivariate relationships, multivariate models were tested. The subjects (n = 817; ages 12 through 21) were participants in the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study. The results indicated that executive dysfunction was significantly related to SUD risks, poor sleep quality, risky alcohol use and cannabis use, and was not significantly related to cognitive skills or structural brain characteristics. In multivariate models, the relationship between poor sleep quality and risky substance use was mediated by executive dysfunction. While these cross-sectional relationships need to be further examined in longitudinal analyses, the results suggest that poor sleep quality and executive dysfunction may be viable preventive intervention targets to reduce adolescent substance use.

  View details for DOI 10.3389/fnbeh.2017.00223

  View details for PubMedID 29180956

  View details for PubMedCentralID PMC5694208

• Peripheral TNF alpha Levels Correlate With Hippocampal Volume in Alcoholism but not in HIV Infection Zahr, N., Kwon, D., Pohl, K., Sullivan, E., Pfefferbaum, A. NATURE PUBLISHING GROUP. 2017: S277–S278

  View details for Web of Science ID 000416846301257

• 3D Motion Modeling and Reconstruction of Left Ventricle Wall in Cardiac MRI. Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH Yang, D., Wu, P., Tan, C., Pohl, K. M., Axel, L., Metaxas, D. 2017; 10263: 481-492

  Abstract

  The analysis of left ventricle (LV) wall motion is a critical step for understanding cardiac functioning mechanisms and clinical diagnosis of ventricular diseases. We present a novel approach for 3D motion modeling and analysis of LV wall in cardiac magnetic resonance imaging (MRI). First, a fully convolutional network (FCN) is deployed to initialize myocardium contours in 2D MR slices. Then, we propose an image registration algorithm to align MR slices in space and minimize the undesirable motion artifacts from inconsistent respiration. Finally, a 3D deformable model is applied to recover the shape and motion of myocardium wall. Utilizing the proposed approach, we can visually analyze 3D LV wall motion, evaluate cardiac global function, and diagnose ventricular diseases.

  View details for DOI 10.1007/978-3-319-59448-4_46

  View details for PubMedID 28664198

  View details for PubMedCentralID PMC5484578

• Eveningness and Later Sleep Timing Are Associated with Greater Risk for Alcohol and Marijuana Use in Adolescence: Initial Findings from the National Consortium on Alcohol and Neurodevelopment in Adolescence Study. Alcoholism, clinical and experimental research Hasler, B. P., Franzen, P. L., de Zambotti, M., Prouty, D., Brown, S. A., Tapert, S. F., Pfefferbaum, A., Pohl, K. M., Sullivan, E. V., De Bellis, M. D., Nagel, B. J., Baker, F. C., Colrain, I. M., Clark, D. B. 2017; 41 (6): 1154-1165

  Abstract

  Abundant cross-sectional evidence links eveningness (a preference for later sleep-wake timing) and increased alcohol and drug use among adolescents and young adults. However, longitudinal studies are needed to examine whether eveningness is a risk factor for subsequent alcohol and drug use, particularly during adolescence, which is marked by parallel peaks in eveningness and risk for the onset of alcohol use disorders. This study examined whether eveningness and other sleep characteristics were associated with concurrent or subsequent substance involvement in a longitudinal study of adolescents.Participants were 729 adolescents (368 females; age 12 to 21 years) in the National Consortium on Alcohol and Neurodevelopment in Adolescence study. Associations between the sleep variables (circadian preference, sleep quality, daytime sleepiness, sleep timing, and sleep duration) and 3 categorical substance variables (at-risk alcohol use, alcohol bingeing, and past-year marijuana use [y/n]) were examined using ordinal and logistic regression with baseline age, sex, race, ethnicity, socioeconomic status, and psychiatric problems as covariates.At baseline, greater eveningness was associated with greater at-risk alcohol use, greater bingeing, and past-year use of marijuana. Later weekday and weekend bedtimes, but not weekday or weekend sleep duration, showed similar associations across the 3 substance outcomes at baseline. Greater baseline eveningness was also prospectively associated with greater bingeing and past-year use of marijuana at the 1-year follow-up, after covarying for baseline bingeing and marijuana use. Later baseline weekday and weekend bedtimes, and shorter baseline weekday sleep duration, were similarly associated with greater bingeing and past-year use of marijuana at the 1-year follow-up after covarying for baseline values.Findings suggest that eveningness and sleep timing may be under recognized risk factors and future areas of intervention for adolescent involvement in alcohol and marijuana that should be considered along with other previously identified sleep factors such as insomnia and insufficient sleep.

  View details for DOI 10.1111/acer.13401

  View details for PubMedID 28421617

• Influences of Age, Sex, and Moderate Alcohol Drinking on the Intrinsic Functional Architecture of Adolescent Brains. Cerebral cortex Müller-Oehring, E. M., Kwon, D., Nagel, B. J., Sullivan, E. V., Chu, W., Rohlfing, T., Prouty, D., Nichols, B. N., Poline, J., Tapert, S. F., Brown, S. A., Cummins, K., Brumback, T., Colrain, I. M., Baker, F. C., De Bellis, M. D., Voyvodic, J. T., Clark, D. B., Pfefferbaum, A., Pohl, K. M. 2017: 1-15

  View details for DOI 10.1093/cercor/bhx014

  View details for PubMedID 28168274

• Effects of prior testing lasting a full year in NCANDA adolescents: Contributions from age, sex, socioeconomic status, ethnicity, site, family history of alcohol or drug abuse, and baseline performance. Developmental cognitive neuroscience Sullivan, E. V., Brumback, T., Tapert, S. F., Prouty, D., Fama, R., Thompson, W. K., Brown, S. A., Cummins, K., Colrain, I. M., Baker, F. C., Clark, D. B., Chung, T., De Bellis, M. D., Hooper, S. R., Nagel, B. J., Nichols, B. N., Chu, W., Kwon, D., Pohl, K. M., Pfefferbaum, A. 2017; 24: 72-83

  Abstract

  Longitudinal study provides a robust method for tracking developmental trajectories. Yet inherent problems of retesting pose challenges in distinguishing biological developmental change from prior testing experience. We examined factors potentially influencing change scores on 16 neuropsychological test composites over 1year in 568 adolescents in the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) project. The twice-minus-once-tested method revealed that performance gain was mainly attributable to testing experience (practice) with little contribution from predicted developmental effects. Group mean practice slopes for 13 composites indicated that 60% to ∼100% variance was attributable to test experience; General Ability accuracy showed the least practice effect (29%). Lower baseline performance, especially in younger participants, was a strong predictor of greater gain. Contributions from age, sex, ethnicity, examination site, socioeconomic status, or family history of alcohol/substance abuse were nil to small, even where statistically significant. Recognizing that a substantial proportion of change in longitudinal testing, even over 1-year, is attributable to testing experience indicates caution against assuming that performance gain observed during periods of maturation necessarily reflects development. Estimates of testing experience, a form of learning, may be a relevant metric for detecting interim influences, such as alcohol use or traumatic episodes, on behavior.

  View details for DOI 10.1016/j.dcn.2017.01.003

  View details for PubMedID 28214667

• Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models SIAM JOURNAL ON IMAGING SCIENCES Niethammer, M., Pohl, K. M., Janoos, F., Wells, W. M. 2017; 10 (3): 1069–1103

  Abstract

  Segmentation is a fundamental task for extracting semantically meaningful regions from an image. The goal of segmentation algorithms is to accurately assign object labels to each image location. However, image-noise, shortcomings of algorithms, and image ambiguities cause uncertainty in label assignment. Estimating the uncertainty in label assignment is important in multiple application domains, such as segmenting tumors from medical images for radiation treatment planning. One way to estimate these uncertainties is through the computation of posteriors of Bayesian models, which is computationally prohibitive for many practical applications. On the other hand, most computationally efficient methods fail to estimate label uncertainty. We therefore propose in this paper the Active Mean Fields (AMF) approach, a technique based on Bayesian modeling that uses a mean-field approximation to efficiently compute a segmentation and its corresponding uncertainty. Based on a variational formulation, the resulting convex model combines any label-likelihood measure with a prior on the length of the segmentation boundary. A specific implementation of that model is the Chan-Vese segmentation model (CV), in which the binary segmentation task is defined by a Gaussian likelihood and a prior regularizing the length of the segmentation boundary. Furthermore, the Euler-Lagrange equations derived from the AMF model are equivalent to those of the popular Rudin-Osher-Fatemi (ROF) model for image denoising. Solutions to the AMF model can thus be implemented by directly utilizing highly-efficient ROF solvers on log-likelihood ratio fields. We qualitatively assess the approach on synthetic data as well as on real natural and medical images. For a quantitative evaluation, we apply our approach to the icgbench dataset.

  View details for DOI 10.1137/16M1058601

  View details for Web of Science ID 000412157400004

  View details for PubMedID 29051796

  View details for PubMedCentralID PMC5642306

• Computing group cardinality constraint solutions for logistic regression problems MEDICAL IMAGE ANALYSIS Zhang, Y., Kwon, D., Pohl, K. M. 2017; 35: 58–69

  Abstract

  We derive an algorithm to directly solve logistic regression based on cardinality constraint, group sparsity and use it to classify intra-subject MRI sequences (e.g. cine MRIs) of healthy from diseased subjects. Group cardinality constraint models are often applied to medical images in order to avoid overfitting of the classifier to the training data. Solutions within these models are generally determined by relaxing the cardinality constraint to a weighted feature selection scheme. However, these solutions relate to the original sparse problem only under specific assumptions, which generally do not hold for medical image applications. In addition, inferring clinical meaning from features weighted by a classifier is an ongoing topic of discussion. Avoiding weighing features, we propose to directly solve the group cardinality constraint logistic regression problem by generalizing the Penalty Decomposition method. To do so, we assume that an intra-subject series of images represents repeated samples of the same disease patterns. We model this assumption by combining series of measurements created by a feature across time into a single group. Our algorithm then derives a solution within that model by decoupling the minimization of the logistic regression function from enforcing the group sparsity constraint. The minimum to the smooth and convex logistic regression problem is determined via gradient descent while we derive a closed form solution for finding a sparse approximation of that minimum. We apply our method to cine MRI of 38 healthy controls and 44 adult patients that received reconstructive surgery of Tetralogy of Fallot (TOF) during infancy. Our method correctly identifies regions impacted by TOF and generally obtains statistically significant higher classification accuracy than alternative solutions to this model, i.e., ones relaxing group cardinality constraints.

  View details for PubMedID 27318592

  View details for PubMedCentralID PMC5099121

• Structural brain anomalies in healthy adolescents in the NCANDA cohort: relation to neuropsychological test performance, sex, and ethnicity. Brain imaging and behavior Sullivan, E. V., Lane, B., Kwon, D., Meloy, M. J., Tapert, S. F., Brown, S. A., Colrain, I. M., Baker, F. C., De Bellis, M. D., Clark, D. B., Nagel, B. J., Pohl, K. M., Pfefferbaum, A. 2016: -?

  Abstract

  Structural MRI of volunteers deemed "normal" following clinical interview provides a window into normal brain developmental morphology but also reveals unexpected dysmorphology, commonly known as "incidental findings." Although unanticipated, these anatomical findings raise questions regarding possible treatment that could even ultimately require neurosurgical intervention, which itself carries significant risk but may not be indicated if the anomaly is nonprogressive or of no functional consequence. Neuroradiological readings of 833 structural MRI from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) cohort found an 11.8 % incidence of brain structural anomalies, represented proportionately across the five collection sites and ethnic groups. Anomalies included 26 mega cisterna magna, 15 subarachnoid cysts, 12 pineal cysts, 12 white matter dysmorphologies, 5 tonsillar ectopias, 5 prominent perivascular spaces, 5 gray matter heterotopias, 4 pituitary masses, 4 excessively large or asymmetrical ventricles, 4 cavum septum pellucidum, 3 developmental venous anomalies, 1 exceptionally large midsagittal vein, and single cases requiring clinical followup: cranio-cervical junction stenosis, parietal cortical mass, and Chiari I malformation. A case of possible demyelinating disorder (e.g., neuromyelitis optica or multiple sclerosis) newly emerged at the 1-year NCANDA followup, requiring clinical referral. Comparing test performance of the 98 anomalous cases with 619 anomaly-free no-to-low alcohol consuming adolescents revealed significantly lower scores on speed measures of attention and motor functions; these differences were not attributed to any one anomaly subgroup. Further, we devised an automated approach for quantifying posterior fossa CSF volumes for detection of mega cisterna magna, which represented 26.5 % of clinically identified anomalies. Automated quantification fit a Gaussian distribution with a rightward skew. Using a 3SD cut-off, quantification identified 22 of the 26 clinically-identified cases, indicating that cases with percent of CSF in the posterior-inferior-middle aspect of the posterior fossa ≥3SD merit further review, and support complementing clinical readings with objective quantitative analysis. Discovery of asymptomatic brain structural anomalies, even when no clinical action is indicated, can be disconcerting to the individual and responsible family members, raising a disclosure dilemma: refrain from relating the incidental findings to avoid unnecessary alarm or anxiety; or alternatively, relate the neuroradiological findings as "normal variants" to the study volunteers and family, thereby equipping them with knowledge for the future should they have the occasion for a brain scan following an illness or accident that the incidental findings predated the later event.

  View details for PubMedID 27722828

• Adolescent Development of Cortical and White Matter Structure in the NCANDA Sample: Role of Sex, Ethnicity, Puberty, and Alcohol Drinking. Cerebral cortex Pfefferbaum, A., Rohlfing, T., Pohl, K. M., Lane, B., Chu, W., Kwon, D., Nolan Nichols, B., Brown, S. A., Tapert, S. F., Cummins, K., Thompson, W. K., Brumback, T., Meloy, M. J., Jernigan, T. L., Dale, A., Colrain, I. M., Baker, F. C., Prouty, D., De Bellis, M. D., Voyvodic, J. T., Clark, D. B., Luna, B., Chung, T., Nagel, B. J., Sullivan, E. V. 2016; 26 (10): 4101-4121

  Abstract

  Brain structural development continues throughout adolescence, when experimentation with alcohol is often initiated. To parse contributions from biological and environmental factors on neurodevelopment, this study used baseline National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) magnetic resonance imaging (MRI) data, acquired in 674 adolescents meeting no/low alcohol or drug use criteria and 134 adolescents exceeding criteria. Spatial integrity of images across the 5 recruitment sites was assured by morphological scaling using Alzheimer's disease neuroimaging initiative phantom-derived volume scalar metrics. Clinical MRI readings identified structural anomalies in 11.4%. Cortical volume and thickness were smaller and white matter volumes were larger in older than in younger adolescents. Effects of sex (male > female) and ethnicity (majority > minority) were significant for volume and surface but minimal for cortical thickness. Adjusting volume and area for supratentorial volume attenuated or removed sex and ethnicity effects. That cortical thickness showed age-related decline and was unrelated to supratentorial volume is consistent with the radial unit hypothesis, suggesting a universal neural development characteristic robust to sex and ethnicity. Comparison of NCANDA with PING data revealed similar but flatter, age-related declines in cortical volumes and thickness. Smaller, thinner frontal, and temporal cortices in the exceeds-criteria than no/low-drinking group suggested untoward effects of excessive alcohol consumption on brain structural development.

  View details for DOI 10.1093/cercor/bhv205

  View details for PubMedID 26408800

• "Cognitive, emotion control, and motor performance of adolescents in the NCANDA study: Contributions from alcohol consumption, age, sex, ethnicity, and family history of addiction": Correction to Sullivan et al. (2016). Neuropsychology Sullivan, E. V., Tapert, S. F., Prouty, D., Colrain, I. M., Baker, F. C., Hooper, S. R., Nagel, B. J., Rohlfing, T., Chu, W., Brumback, T., Fama, R., Brown, S. A., Cummins, K., Thompson, W. K., De Bellis, M. D., Clark, D. B., Chung, T., Nichols, B. N., Pohl, K. M., Pfefferbaum, A. 2016; 30 (7): 829

  Abstract

  Reports an error in "Cognitive, emotion control, and motor performance of adolescents in the NCANDA study: Contributions from alcohol consumption, age, sex, ethnicity, and family history of addiction" by Edith V. Sullivan, Ty Brumback, Susan F. Tapert, Rosemary Fama, Devin Prouty, Sandra A. Brown, Kevin Cummins, Wesley K. Thompson, Ian M. Colrain, Fiona C. Baker, Michael D. De Bellis, Stephen R. Hooper, Duncan B. Clark, Tammy Chung, Bonnie J. Nagel, B. Nolan Nichols, Torsten Rohlfing, Weiwei Chu, Kilian M. Pohl and Adolf Pfefferbaum (Neuropsychology, 2016[May], Vol 30[4], 449-473). A problem with a computation to invert speed scores is noted and explained in this correction. All statements indicating group differences in speed scores, as well as Table 5 and Figure 8A, have been corrected in the online version of this article. (The following abstract of the original article appeared in record 2016-00613-001.)To investigate development of cognitive and motor functions in healthy adolescents and to explore whether hazardous drinking affects the normal developmental course of those functions.Participants were 831 adolescents recruited across 5 United States sites of the National Consortium on Alcohol and NeuroDevelopment in Adolescence 692 met criteria for no/low alcohol exposure, and 139 exceeded drinking thresholds. Cross-sectional, baseline data were collected with computerized and traditional neuropsychological tests assessing 8 functional domains expressed as composite scores. General additive modeling evaluated factors potentially modulating performance (age, sex, ethnicity, socioeconomic status, and pubertal developmental stage).Older no/low-drinking participants achieved better scores than younger ones on 5 accuracy composites (general ability, abstraction, attention, emotion, and balance). Speeded responses for attention, motor speed, and general ability were sensitive to age and pubertal development. The exceeds-threshold group (accounting for age, sex, and other demographic factors) performed significantly below the no/low-drinking group on balance accuracy and on general ability, attention, episodic memory, emotion, and motor speed scores and showed evidence for faster speed at the expense of accuracy. Delay Discounting performance was consistent with poor impulse control in the younger no/low drinkers and in exceeds-threshold drinkers regardless of age.Higher achievement with older age and pubertal stage in general ability, abstraction, attention, emotion, and balance suggests continued functional development through adolescence, possibly supported by concurrently maturing frontal, limbic, and cerebellar brain systems. Determination of whether low scores by the exceeds-threshold group resulted from drinking or from other preexisting factors requires longitudinal study.

  View details for DOI 10.1037/neu0000306

  View details for PubMedID 27504610

  View details for PubMedCentralID PMC7405886

• Joint Data Harmonization and Group Cardinality Constrained Classification. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Zhang, Y., Park, S. H., Pohl, K. M. 2016; 9900: 282–90

  Abstract

  To boost the power of classifiers, studies often increase the size of existing samples through the addition of independently collected data sets. Doing so requires harmonizing the data for demographic and acquisition differences based on a control cohort before performing disease specific classification. The initial harmonization often mitigates group differences negatively impacting classification accuracy. To preserve cohort separation, we propose the first model unifying linear regression for data harmonization with a logistic regression for disease classification. Learning to harmonize data is now an adaptive process taking both disease and control data into account. Solutions within that model are confined by group cardinality to reduce the risk of overfitting (via sparsity), to explicitly account for the impact of disease on the inter-dependency of regions (by grouping them), and to identify disease specific patterns (by enforcing sparsity via the l0-'norm'). We test those solutions in distinguishing HIV-Associated Neurocognitive Disorder from Mild Cognitive Impairment of two independently collected, neuroimage data sets; each contains controls and samples from one disease. Our classifier is impartial to acquisition difference between the data sets while being more accurate in diseases seperation than sequential learning of harmonization and classification parameters, and non-sparsity based logistic regressors.

  View details for PubMedID 28758167

• Extracting patterns of morphometry distinguishing HIV associated neurodegeneration from mild cognitive impairment via group cardinality constrained classification. Human brain mapping Zhang, Y., Kwon, D., Esmaeili-Firidouni, P., Pfefferbaum, A., Sullivan, E. V., Javitz, H., Valcour, V., Pohl, K. M. 2016

  Abstract

  HIV-Associated Neurocognitive Disorder (HAND) is the most common constellation of cognitive dysfunctions in chronic HIV infected patients age 60 or older in the U.S. Only few published methods assist in distinguishing HAND from other forms of age-associated cognitive decline, such as Mild Cognitive Impairment (MCI). In this report, a data-driven, nonparameteric model to identify morphometric patterns separating HAND from MCI due to non-HIV conditions in this older age group was proposed. This model enhanced the potential for group separation by combining a smaller, longitudinal data set containing HAND samples with a larger, public data set including MCI cases. Using cross-validation, a linear model on healthy controls to harmonize the volumetric scores extracted from MRIs for demographic and acquisition differences between the two independent, disease-specific data sets was trained. Next, patterns distinguishing HAND from MCI via a group sparsity constrained logistic classifier were identified. Unlike existing approaches, our classifier directly solved the underlying minimization problem by decoupling the minimization of the logistic regression function from enforcing the group sparsity constraint. The extracted patterns consisted of eight regions that distinguished HAND from MCI on a significant level while being indifferent to differences in demographics and acquisition between the two sets. Individually selecting regions through conventional morphometric group analysis resulted in a larger number of regions that were less accurate. In conclusion, simultaneously analyzing all brain regions and time points for disease specific patterns contributed to distinguishing with high accuracy HAND-related impairment from cognitive impairment found in the HIV uninfected, MCI cohort. Hum Brain Mapp 37:4523-4538, 2016. © 2016 Wiley Periodicals, Inc.

  View details for DOI 10.1002/hbm.23326

  View details for PubMedID 27489003

• Cognitive, Emotion Control, and Motor Performance of Adolescents in the NCANDA Study: Contributions From Alcohol Consumption, Age, Sex, Ethnicity, and Family History of Addiction NEUROPSYCHOLOGY Sullivan, E. V., Brumback, T., Tapert, S. F., Fama, R., Prouty, D., Brown, S. A., Cummins, K., Thompson, W. K., Colrain, I. M., Baker, F. C., De Bellis, M. D., Hooper, S. R., Clark, D. B., Chung, T., Nagel, B. J., Nichols, B. N., Rohlfing, T., Chu, W., Pohl, K. M., Pfefferbaum, A. 2016; 30 (4): 449-473

  Abstract

  To investigate development of cognitive and motor functions in healthy adolescents and to explore whether hazardous drinking affects the normal developmental course of those functions.Participants were 831 adolescents recruited across 5 United States sites of the National Consortium on Alcohol and NeuroDevelopment in Adolescence 692 met criteria for no/low alcohol exposure, and 139 exceeded drinking thresholds. Cross-sectional, baseline data were collected with computerized and traditional neuropsychological tests assessing 8 functional domains expressed as composite scores. General additive modeling evaluated factors potentially modulating performance (age, sex, ethnicity, socioeconomic status, and pubertal developmental stage).Older no/low-drinking participants achieved better scores than younger ones on 5 accuracy composites (general ability, abstraction, attention, emotion, and balance). Speeded responses for attention, motor speed, and general ability were sensitive to age and pubertal development. The exceeds-threshold group (accounting for age, sex, and other demographic factors) performed significantly below the no/low-drinking group on balance accuracy and on general ability, attention, episodic memory, emotion, and motor speed scores and showed evidence for faster speed at the expense of accuracy. Delay Discounting performance was consistent with poor impulse control in the younger no/low drinkers and in exceeds-threshold drinkers regardless of age.Higher achievement with older age and pubertal stage in general ability, abstraction, attention, emotion, and balance suggests continued functional development through adolescence, possibly supported by concurrently maturing frontal, limbic, and cerebellar brain systems. Determination of whether low scores by the exceeds-threshold group resulted from drinking or from other preexisting factors requires longitudinal study. (PsycINFO Database Record

  View details for DOI 10.1037/neu0000259

  View details for PubMedID 26752122

• Harmonizing DTI measurements across scanners to examine the development of white matter microstructure in 803 adolescents of the NCANDA study NEUROIMAGE Pohl, K. M., Sullivan, E. V., Rohlfing, T., Chu, W., Kwon, D., Nichols, B. N., Zhang, Y., Brown, S. A., Tapert, S. F., Cummins, K., Thompson, W. K., Brumback, T., Colrain, I. M., Baker, F. C., Prouty, D., De Bellis, M. D., Voyvodic, J. T., Clark, D. B., Schirda, C., Nagel, B. J., Pfefferbaum, A. 2016; 130: 194-213

  Abstract

  Neurodevelopment continues through adolescence, with notable maturation of white matter tracts comprising regional fiber systems progressing at different rates. To identify factors that could contribute to regional differences in white matter microstructure development, large samples of youth spanning adolescence to young adulthood are essential to parse these factors. Recruitment of adequate samples generally relies on multi-site consortia but comes with the challenge of merging data acquired on different platforms. In the current study, diffusion tensor imaging (DTI) data were acquired on GE and Siemens systems through the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA), a multi-site study designed to track the trajectories of regional brain development during a time of high risk for initiating alcohol consumption. This cross-sectional analysis reports baseline Tract-Based Spatial Statistic (TBSS) of regional fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (L1), and radial diffusivity (LT) from the five consortium sites on 671 adolescents who met no/low alcohol or drug consumption criteria and 132 adolescents with a history of exceeding consumption criteria. Harmonization of DTI metrics across manufacturers entailed the use of human-phantom data, acquired multiple times on each of three non-NCANDA participants at each site's MR system, to determine a manufacturer-specific correction factor. Application of the correction factor derived from human phantom data measured on MR systems from different manufacturers reduced the standard deviation of the DTI metrics for FA by almost a half, enabling harmonization of data that would have otherwise carried systematic error. Permutation testing supported the hypothesis of higher FA and lower diffusivity measures in older adolescents and indicated that, overall, the FA, MD, and L1 of the boys were higher than those of the girls, suggesting continued microstructural development notable in the boys. The contribution of demographic and clinical differences to DTI metrics was assessed with General Additive Models (GAM) testing for age, sex, and ethnicity differences in regional skeleton mean values. The results supported the primary study hypothesis that FA skeleton mean values in the no/low-drinking group were highest at different ages. When differences in intracranial volume were covaried, FA skeleton mean reached a maximum at younger ages in girls than boys and varied in magnitude with ethnicity. Our results, however, did not support the hypothesis that youth who exceeded exposure criteria would have lower FA or higher diffusivity measures than the no/low-drinking group; detecting the effects of excessive alcohol consumption during adolescence on DTI metrics may require longitudinal study.

  View details for DOI 10.1016/j.neuroimage.2016.01.061

  View details for Web of Science ID 000372745600018

  View details for PubMedCentralID PMC4808415

• Harmonizing DTI measurements across scanners to examine the development of white matter microstructure in 803 adolescents of the NCANDA study. NeuroImage Pohl, K. M., Sullivan, E. V., Rohlfing, T., Chu, W., Kwon, D., Nichols, B. N., Zhang, Y., Brown, S. A., Tapert, S. F., Cummins, K., Thompson, W. K., Brumback, T., Colrain, I. M., Baker, F. C., Prouty, D., De Bellis, M. D., Voyvodic, J. T., Clark, D. B., Schirda, C., Nagel, B. J., Pfefferbaum, A. 2016; 130: 194-213

  Abstract

  Neurodevelopment continues through adolescence, with notable maturation of white matter tracts comprising regional fiber systems progressing at different rates. To identify factors that could contribute to regional differences in white matter microstructure development, large samples of youth spanning adolescence to young adulthood are essential to parse these factors. Recruitment of adequate samples generally relies on multi-site consortia but comes with the challenge of merging data acquired on different platforms. In the current study, diffusion tensor imaging (DTI) data were acquired on GE and Siemens systems through the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA), a multi-site study designed to track the trajectories of regional brain development during a time of high risk for initiating alcohol consumption. This cross-sectional analysis reports baseline Tract-Based Spatial Statistic (TBSS) of regional fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (L1), and radial diffusivity (LT) from the five consortium sites on 671 adolescents who met no/low alcohol or drug consumption criteria and 132 adolescents with a history of exceeding consumption criteria. Harmonization of DTI metrics across manufacturers entailed the use of human-phantom data, acquired multiple times on each of three non-NCANDA participants at each site's MR system, to determine a manufacturer-specific correction factor. Application of the correction factor derived from human phantom data measured on MR systems from different manufacturers reduced the standard deviation of the DTI metrics for FA by almost a half, enabling harmonization of data that would have otherwise carried systematic error. Permutation testing supported the hypothesis of higher FA and lower diffusivity measures in older adolescents and indicated that, overall, the FA, MD, and L1 of the boys were higher than those of the girls, suggesting continued microstructural development notable in the boys. The contribution of demographic and clinical differences to DTI metrics was assessed with General Additive Models (GAM) testing for age, sex, and ethnicity differences in regional skeleton mean values. The results supported the primary study hypothesis that FA skeleton mean values in the no/low-drinking group were highest at different ages. When differences in intracranial volume were covaried, FA skeleton mean reached a maximum at younger ages in girls than boys and varied in magnitude with ethnicity. Our results, however, did not support the hypothesis that youth who exceeded exposure criteria would have lower FA or higher diffusivity measures than the no/low-drinking group; detecting the effects of excessive alcohol consumption during adolescence on DTI metrics may require longitudinal study.

  View details for DOI 10.1016/j.neuroimage.2016.01.061

  View details for PubMedID 26872408

• The National Consortium on Alcohol and Neuro-Development in Adolescence (NCANDA): A Multisite Study of Adolescent Development and Substance Use JOURNAL OF STUDIES ON ALCOHOL AND DRUGS Brown, S. A., Brumback, T. Y., Tomlinson, K., Cummins, K., Thompson, W. K., Nagel, B. J., De Bellis, M. D., Hooper, S. R., Clark, D. B., Chung, T., Hasler, B. P., Colrain, I. M., Baker, F. C., Prouty, D., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M., Rohlfing, T., Nichols, B. N., Chu, W., Tapert, S. F. 2015; 76 (6): 895-908

  Abstract

  During adolescence, neurobiological maturation occurs concurrently with social and interpersonal changes, including the initiation of alcohol and other substance use. The National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) is designed to disentangle the complex relationships between onset, escalation, and desistance of alcohol use and changes in neurocognitive functioning and neuromaturation.A sample of 831 youth, ages 12-21 years, was recruited at five sites across the United States, oversampling those at risk for alcohol use problems. Most (83%) had limited or no history of alcohol or other drug use, and a smaller portion (17%) exceeded drinking thresholds. A comprehensive assessment of biological development, family background, psychiatric symptomatology, and neuropsychological functioning-in addition to anatomical, diffusion, and functional brain magnetic resonance imaging-was completed at baseline.The NCANDA sample of youth is nationally representative of sex and racial/ethnic groups. More than 50% have at least one risk characteristic for subsequent heavy drinking (e.g., family history, internalizing or externalizing symptoms). As expected, those who exceeded drinking thresholds (n = 139) differ from those who did not (n = 692) on identified factors associated with early alcohol use and problems.NCANDA successfully recruited a large sample of adolescents and comprehensively assessed psychosocial functioning across multiple domains. Based on the sample's risk profile, NCANDA is well positioned to capture the transition into drinking and alcohol problems in a large portion of the cohort, as well as to help disentangle the associations between alcohol use, neurobiological maturation, and neurocognitive development and functioning.

  View details for PubMedID 26562597

• Neuroinformatics Software Applications Supporting Electronic Data Capture, Management, and Sharing for the Neuroimaging Community NEUROPSYCHOLOGY REVIEW Nichols, B. N., Pohl, K. M. 2015; 25 (3): 356-368

  Abstract

  Accelerating insight into the relation between brain and behavior entails conducting small and large-scale research endeavors that lead to reproducible results. Consensus is emerging between funding agencies, publishers, and the research community that data sharing is a fundamental requirement to ensure all such endeavors foster data reuse and fuel reproducible discoveries. Funding agency and publisher mandates to share data are bolstered by a growing number of data sharing efforts that demonstrate how information technologies can enable meaningful data reuse. Neuroinformatics evaluates scientific needs and develops solutions to facilitate the use of data across the cognitive and neurosciences. For example, electronic data capture and management tools designed to facilitate human neurocognitive research can decrease the setup time of studies, improve quality control, and streamline the process of harmonizing, curating, and sharing data across data repositories. In this article we outline the advantages and disadvantages of adopting software applications that support these features by reviewing the tools available and then presenting two contrasting neuroimaging study scenarios in the context of conducting a cross-sectional and a multisite longitudinal study.

  View details for DOI 10.1007/s11065-015-9293-x

  View details for Web of Science ID 000360912800010

  View details for PubMedID 26267019

• Solving Logistic Regression with Group Cardinality Constraints for Time Series Analysis Zhang, Y., Pohl, K. M., Navab, N., Hornegger, J., Wells, W. M., Frangi, A. F. SPRINGER INT PUBLISHING AG. 2015: 459–66

  Abstract

  We propose an algorithm to distinguish 3D+t images of healthy from diseased subjects by solving logistic regression based on cardinality constrained, group sparsity. This method reduces the risk of overfitting by providing an elegant solution to identifying anatomical regions most impacted by disease. It also ensures that consistent identification across the time series by grouping each image feature across time and counting the number of non-zero groupings. While popular in medical imaging, group cardinality constrained problems are generally solved by relaxing counting with summing over the groupings. We instead solve the original problem by generalizing a penalty decomposition algorithm, which alternates between minimizing a logistic regression function with a regularizer based on the Frobenius norm and enforcing sparsity. Applied to 86 cine MRIs of healthy cases and subjects with Tetralogy of Fallot (TOF), our method correctly identifies regions impacted by TOF and obtains a statistically significant higher classification accuracy than logistic regression without and relaxed grouped sparsity constraint.

  View details for PubMedID 27610425

• White matter microstructural recovery with abstinence and decline with relapse in alcohol dependence interacts with normal ageing: a controlled longitudinal DTI study. The lancet. Psychiatry Pfefferbaum, A., Rosenbloom, M. J., Chu, W., Sassoon, S. A., Rohlfing, T., Pohl, K. M., Zahr, N. M., Sullivan, E. V. 2014; 1 (3): 202-212

  Abstract

  Alcohol dependence exacts a toll on brain white matter microstructure, which has the potential of repair with prolonged sobriety. Diffusion tensor imaging (DTI) enables in-vivo quantification of tissue constituents and localisation of tracts potentially affected in alcohol dependence and its recovery. We did an extended longitudinal study of alcoholism's trajectory of effect on selective fibre bundles with sustained sobriety or decline with relapse.Participants were drawn from a longitudinal, 1·5T DTI database of 841 scans of individuals with various medical or neuropsychiatric conditions and normal ageing. Participants diagnosed with alcohol dependence had to meet the criteria from DSM-IV for alcohol dependence. Controls were screened and free of any DSM-IV axis I diagnosis, including being without history of alcohol or drug abuse or dependence. Tract-based spatial statistics (TBSS) quantified white matter integrity throughout the brain in 47 alcohol-dependent individuals and 56 controls examined 2-5 times over 1-8 year intervals. We identified regions showing group differences with a white matter atlas. For macrostructural comparison, we measured corpus callosum and centrum semiovale volumes on MRI.This study took place in the USA, between June 23, 2000, and Sept 6, 2011. TBSS identified a large cluster (threshold p<0·001), where controls showed significant fractional anisotropy (FA) decrease with ageing and alcohol-dependent individuals had significantly lower FA than controls regardless of age. Over the examination interval, 27 (57%) alcohol-dependent individuals abstained, ten (21%) relapsed into light drinking, and ten (21%) relapsed into heavy drinking (>5 kg of alcohol/year). Despite abnormally low FA, age trajectories of the abstainers were positive and progressing toward normality, whereas those of the relapsers and controls were negative. Axial diffusivity (lower values indexing myelin integrity) was abnormally high in the total alcohol-dependent group; however, the abstainers' slopes paralleled those of controls, whereas the heavy-drinking relapsers' slopes showed accelerated ageing. Callosal genu and body microstructure but not macrostructure showed untoward alcohol-related effects. Affected projection and association tracts had an anterior and superior neuroanatomical distribution.Return to heavy drinking resulted in accelerating microstructural white matter damage. Despite evidence for damage, alcohol-dependent individuals maintaining sobriety over extended periods showed improvement in brain fibre tract integrity reflective of fibre reorganisation and myelin restoration, indicative of a neural mechanism explaining recovery.US National Institute on Alcohol Abuse and Alcoholism (AA012388, AA017168, AA005965, AA013521-INIA).

  View details for DOI 10.1016/S2215-0366(14)70301-3

  View details for PubMedID 26360732

• White matter microstructural recovery with abstinence and decline with relapse in alcohol dependence interacts with normal ageing: a controlled longitudinal DTI study LANCET PSYCHIATRY Pfefferbaum, A., Rosenbloom, M. J., Chu, W., Sassoon, S. A., Rohlfing, T., Pohl, K. M., Zahr, N. M., Sullivan, E. V. 2014; 1 (3): 202-212

  Abstract

  Alcohol dependence exacts a toll on brain white matter microstructure, which has the potential of repair with prolonged sobriety. Diffusion tensor imaging (DTI) enables in-vivo quantification of tissue constituents and localisation of tracts potentially affected in alcohol dependence and its recovery. We did an extended longitudinal study of alcoholism's trajectory of effect on selective fibre bundles with sustained sobriety or decline with relapse.Participants were drawn from a longitudinal, 1·5T DTI database of 841 scans of individuals with various medical or neuropsychiatric conditions and normal ageing. Participants diagnosed with alcohol dependence had to meet the criteria from DSM-IV for alcohol dependence. Controls were screened and free of any DSM-IV axis I diagnosis, including being without history of alcohol or drug abuse or dependence. Tract-based spatial statistics (TBSS) quantified white matter integrity throughout the brain in 47 alcohol-dependent individuals and 56 controls examined 2-5 times over 1-8 year intervals. We identified regions showing group differences with a white matter atlas. For macrostructural comparison, we measured corpus callosum and centrum semiovale volumes on MRI.This study took place in the USA, between June 23, 2000, and Sept 6, 2011. TBSS identified a large cluster (threshold p<0·001), where controls showed significant fractional anisotropy (FA) decrease with ageing and alcohol-dependent individuals had significantly lower FA than controls regardless of age. Over the examination interval, 27 (57%) alcohol-dependent individuals abstained, ten (21%) relapsed into light drinking, and ten (21%) relapsed into heavy drinking (>5 kg of alcohol/year). Despite abnormally low FA, age trajectories of the abstainers were positive and progressing toward normality, whereas those of the relapsers and controls were negative. Axial diffusivity (lower values indexing myelin integrity) was abnormally high in the total alcohol-dependent group; however, the abstainers' slopes paralleled those of controls, whereas the heavy-drinking relapsers' slopes showed accelerated ageing. Callosal genu and body microstructure but not macrostructure showed untoward alcohol-related effects. Affected projection and association tracts had an anterior and superior neuroanatomical distribution.Return to heavy drinking resulted in accelerating microstructural white matter damage. Despite evidence for damage, alcohol-dependent individuals maintaining sobriety over extended periods showed improvement in brain fibre tract integrity reflective of fibre reorganisation and myelin restoration, indicative of a neural mechanism explaining recovery.US National Institute on Alcohol Abuse and Alcoholism (AA012388, AA017168, AA005965, AA013521-INIA).

  View details for Web of Science ID 000343703500022

• Regional Manifold Learning for Disease Classification IEEE TRANSACTIONS ON MEDICAL IMAGING Ye, D. H., Desjardins, B., Hamm, J., Litt, H., Pohl, K. M. 2014; 33 (6): 1236-1247

  Abstract

  While manifold learning from images itself has become widely used in medical image analysis, the accuracy of existing implementations suffers from viewing each image as a single data point. To address this issue, we parcellate images into regions and then separately learn the manifold for each region. We use the regional manifolds as low-dimensional descriptors of high-dimensional morphological image features, which are then fed into a classifier to identify regions affected by disease. We produce a single ensemble decision for each scan by the weighted combination of these regional classification results. Each weight is determined by the regional accuracy of detecting the disease. When applied to cardiac magnetic resonance imaging of 50 normal controls and 50 patients with reconstructive surgery of Tetralogy of Fallot, our method achieves significantly better classification accuracy than approaches learning a single manifold across the entire image domain.

  View details for DOI 10.1109/TMI.2014.2305751

  View details for Web of Science ID 000337125400003

  View details for PubMedCentralID PMC5450500

• PORTR: Pre-Operative and Post-Recurrence Brain Tumor Registration IEEE TRANSACTIONS ON MEDICAL IMAGING Kwon, D., Niethammer, M., Akbari, H., Bilello, M., Davatzikos, C., Pohl, K. M. 2014; 33 (3): 651-667

  Abstract

  We propose a new method for deformable registration of pre-operative and post-recurrence brain MR scans of glioma patients. Performing this type of intra-subject registration is challenging as tumor, resection, recurrence, and edema cause large deformations, missing correspondences, and inconsistent intensity profiles between the scans. To address this challenging task, our method, called PORTR, explicitly accounts for pathological information. It segments tumor, resection cavity, and recurrence based on models specific to each scan. PORTR then uses the resulting maps to exclude pathological regions from the image-based correspondence term while simultaneously measuring the overlap between the aligned tumor and resection cavity. Embedded into a symmetric registration framework, we determine the optimal solution by taking advantage of both discrete and continuous search methods. We apply our method to scans of 24 glioma patients. Both quantitative and qualitative analysis of the results clearly show that our method is superior to other state-of-the-art approaches.

  View details for DOI 10.1109/TMI.2013.2293478

  View details for Web of Science ID 000332599500005

  View details for PubMedID 24595340

• AUTO-ENCODING OF DISCRIMINATING MORPHOMETRY FROM CARDIAC MRI Ye, D., Desjardins, B., Ferrari, V., Metaxas, D., Pohl, K. M., IEEE IEEE. 2014: 217–21

  Abstract

  We propose a fully-automatic morphometric encoding targeted towards differentiating diseased from healthy cardiac MRI. Existing encodings rely on accurate segmentations of each scan. Segmentation generally includes labour-intensive editing and increases the risk associated with intra- and inter-rater variability. Our morphometric framework only requires the segmentation of a template scan. This template is non-rigidly registered to the other scans. We then confine the resulting deformation maps to the regions outlined by the segmentations. We learn a manifold for each region and identify the most informative coordinates with respect to distinguishing diseased from healthy scans. Compared with volumetric measurements and a deformation-based score, this encoding is much more accurate in capturing morphometric patterns distinguishing healthy subjects from those with Tetralogy of Fallot, diastolic dysfunction, and hypertrophic cardiomyopathy.

  View details for PubMedID 28593032

  View details for PubMedCentralID PMC5459374

• WESD-Weighted Spectral Distance for Measuring Shape Dissimilarity IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Konukoglu, E., Glocker, B., Criminisi, A., Pohl, K. M. 2013; 35 (9): 2284–97

  Abstract

  This paper presents a new distance for measuring shape dissimilarity between objects. Recent publications introduced the use of eigenvalues of the Laplace operator as compact shape descriptors. Here, we revisit the eigenvalues to define a proper distance, called Weighted Spectral Distance (WESD), for quantifying shape dissimilarity. The definition of WESD is derived through analyzing the heat trace. This analysis provides the proposed distance with an intuitive meaning and mathematically links it to the intrinsic geometry of objects. We analyze the resulting distance definition, present and prove its important theoretical properties. Some of these properties include: 1) WESD is defined over the entire sequence of eigenvalues yet it is guaranteed to converge, 2) it is a pseudometric, 3) it is accurately approximated with a finite number of eigenvalues, and 4) it can be mapped to the [0,1) interval. Last, experiments conducted on synthetic and real objects are presented. These experiments highlight the practical benefits of WESD for applications in vision and medical image analysis.

  View details for DOI 10.1109/TPAMI.2012.275

  View details for Web of Science ID 000322029000017

  View details for PubMedID 23868785

  View details for PubMedCentralID PMC5513679

• FLOOR: Fusing Locally Optimal Registrations Ye, D., Hamm, J., Desjardins, B., Pohl, K. M., Mori, K., Sakuma, Sato, Y., Barillot, C., Navab, N. SPRINGER-VERLAG BERLIN. 2013: 195–202

  Abstract

  Most registration algorithms, such as Demons, align two scans by iteratively finding the deformation minimizing the image dissimilarity at each location and smoothing this minimum across the image domain. These methods generally get stuck in local minima, are negatively impacted by missing correspondences between the images, and require careful tuning of the smoothing parameters to achieve optimal results. In this paper, we propose to improve on those issues by choosing the minimum from a set of candidates. Our method generates such candidates by running the registration algorithm multiple times varying the setting of the smoothing and the image domain. We iteratively refine those candidates by fusing them with the outcome of alternative approaches and locally adapting the smoothing parameters. We implement our algorithm based on Demons and find alternative minima via manifold learning. Compared to those two methods, our 600 pairwise registrations of cardiac MRIs significantly better handle the large shape variations of the heart and the different field of views captured by scans.

  View details for Web of Science ID 000333633500025

  View details for PubMedID 24505761

• Collaborative Multi Organ Segmentation by Integrating Deformable and Graphical Models Uzunbas, M., Chen, C., Zhang, S., Pohl, K. M., Li, K., Metaxas, D., Sakuma, Barillot, C., Navab, N. SPRINGER-VERLAG BERLIN. 2013: 157–64

  Abstract

  Organ segmentation is a challenging problem on which significant progress has been made. Deformable models (DM) and graphical models (GM) are two important categories of optimization based image segmentation methods. Efforts have been made on integrating two types of models into one framework. However, previous methods are not designed for segmenting multiple organs simultaneously and accurately. In this paper, we propose a hybrid multi organ segmentation approach by integrating DM and GM in a coupled optimization framework. Specifically, we show that region-based deformable models can be integrated with Markov Random Fields (MRF), such that multiple models' evolutions are driven by a maximum a posteriori (MAP) inference. It brings global and local deformation constraints into a unified framework for simultaneous segmentation of multiple objects in an image. We validate this proposed method on two challenging problems of multi organ segmentation, and the results are promising.

  View details for Web of Science ID 000342835100020

  View details for PubMedID 24579136

  View details for PubMedCentralID PMC5809157

• Extracting evolving pathologies via spectral clustering. Information processing in medical imaging : proceedings of the ... conference Bernardis, E., Pohl, K. M., Davatzikos, C. 2013; 23: 680-91

  Abstract

  A bottleneck in the analysis of longitudinal MR scans with white matter brain lesions is the temporally consistent segmentation of the pathology. We identify pathologies in 3D+t(ime) within a spectral graph clustering framework. Our clustering approach simultaneously segments and tracks the evolving lesions by identifying characteristic image patterns at each time-point and voxel correspondences across time-points. For each 3D image, our method constructs a graph where weights between nodes capture the likeliness of two voxels belonging to the same region. Based on these weights, we then establish rough correspondences between graph nodes at different time-points along estimated pathology evolution directions. We combine the graphs by aligning the weights to a reference time-point, thus integrating temporal information across the 3D images, and formulate the 3D+t segmentation problem as a binary partitioning of this graph. The resulting segmentation is very robust to local intensity fluctuations and yields better results than segmentations generated for each time-point.

  View details for PubMedID 24684009

• Multinomial probabilistic fiber representation for connectivity driven clustering. Information processing in medical imaging : proceedings of the ... conference Tunç, B., Smith, A. R., Wasserman, D., Pennec, X., Wells, W. M., Verma, R., Pohl, K. M. 2013; 23: 730-41

  Abstract

  The clustering of fibers into bundles is an important task in studying the structure and function of white matter. Existing technology mostly relies on geometrical features, such as the shape of fibers, and thus only provides very limited information about the neuroanatomical function of the brain. We advance this issue by proposing a multinomial representation of fibers decoding their connectivity to gray matter regions. We then simplify the clustering task by first deriving a compact encoding of our representation via the logit transformation. Furthermore, we define a distance between fibers that is in theory invariant to parcellation biases and is equivalent to a family of Riemannian metrics on the simplex of multinomial probabilities. We apply our method to longitudinal scans of two healthy subjects showing high reproducibility of the resulting fiber bundles without needing to register the corresponding scans to a common coordinate system. We confirm these qualitative findings via a simple statistical analyse of the fiber bundles.

  View details for PubMedID 24684013

  View details for PubMedCentralID PMC3974202

• Discriminative Segmentation-Based Evaluation Through Shape Dissimilarity IEEE TRANSACTIONS ON MEDICAL IMAGING Konukoglu, E., Glocker, B., Ye, D., Criminisi, A., Pohl, K. M. 2012; 31 (12): 2278–89

  Abstract

  Segmentation-based scores play an important role in the evaluation of computational tools in medical image analysis. These scores evaluate the quality of various tasks, such as image registration and segmentation, by measuring the similarity between two binary label maps. Commonly these measurements blend two aspects of the similarity: pose misalignments and shape discrepancies. Not being able to distinguish between these two aspects, these scores often yield similar results to a widely varying range of different segmentation pairs. Consequently, the comparisons and analysis achieved by interpreting these scores become questionable. In this paper, we address this problem by exploring a new segmentation-based score, called normalized Weighted Spectral Distance (nWSD), that measures only shape discrepancies using the spectrum of the Laplace operator. Through experiments on synthetic and real data we demonstrate that nWSD provides additional information for evaluating differences between segmentations, which is not captured by other commonly used scores. Our results demonstrate that when jointly used with other scores, such as Dice's similarity coefficient, the additional information provided by nWSD allows richer, more discriminative evaluations. We show for the task of registration that through this addition we can distinguish different types of registration errors. This allows us to identify the source of errors and discriminate registration results which so far had to be treated as being of similar quality in previous evaluation studies.

  View details for DOI 10.1109/TMI.2012.2216281

  View details for Web of Science ID 000313690600010

  View details for PubMedID 22955890

  View details for PubMedCentralID PMC5507673

• GLISTR: Glioma Image Segmentation and Registration IEEE TRANSACTIONS ON MEDICAL IMAGING Gooya, A., Pohl, K. M., Bilello, M., Cirillo, L., Biros, G., Melhem, E. R., Davatzikos, C. 2012; 31 (10): 1941–54

  Abstract

  We present a generative approach for simultaneously registering a probabilistic atlas of a healthy population to brain magnetic resonance (MR) scans showing glioma and segmenting the scans into tumor as well as healthy tissue labels. The proposed method is based on the expectation maximization (EM) algorithm that incorporates a glioma growth model for atlas seeding, a process which modifies the original atlas into one with tumor and edema adapted to best match a given set of patient's images. The modified atlas is registered into the patient space and utilized for estimating the posterior probabilities of various tissue labels. EM iteratively refines the estimates of the posterior probabilities of tissue labels, the deformation field and the tumor growth model parameters. Hence, in addition to segmentation, the proposed method results in atlas registration and a low-dimensional description of the patient scans through estimation of tumor model parameters. We validate the method by automatically segmenting 10 MR scans and comparing the results to those produced by clinical experts and two state-of-the-art methods. The resulting segmentations of tumor and edema outperform the results of the reference methods, and achieve a similar accuracy from a second human rater. We additionally apply the method to 122 patients scans and report the estimated tumor model parameters and their relations with segmentation and registration results. Based on the results from this patient population, we construct a statistical atlas of the glioma by inverting the estimated deformation fields to warp the tumor segmentations of patients scans into a common space.

  View details for DOI 10.1109/TMI.2012.2210558

  View details for Web of Science ID 000310149700010

  View details for PubMedID 22907965

  View details for PubMedCentralID PMC4371551

• Validation of DRAMMS among 12 Popular Methods in Cross-Subject Cardiac MRI Registration. Biomedical image registration, ... proceedings. WBIR (Workshop : 2006- ) Ou, Y., Ye, D. H., Pohl, K. M., Davatzikos, C. 2012; 7359: 209-219

  Abstract

  Cross-subject image registration is the building block for many cardiac studies. In the literature, it is often handled by voxel-wise registration methods. However, studies are lacking to show which methods are more accurate and stable in this context. Aiming at answering this question, this paper evaluates 12 popular registration methods and validates a recently developed method DRAMMS [16] in the context of cross-subject cardiac registration. Our dataset consists of short-axis end-diastole cardiac MR images from 24 subjects, in which non-cardiac structures are removed. Each registration method was applied to all 552 image pairs. Registration accuracy is approximated by Jaccard overlap between deformed expert annotation of source image and the corresponding expert annotation of target image. This accuracy surrogate is further correlated with deformation aggressiveness, which is reflected by minimum, maximum and range of Jacobian determinants. Our study shows that DRAMMS [16] scores high in accuracy and well balances accuracy and aggressiveness in this dataset, followed by ANTs [13], MI-FFD [14], Demons [15], and ART [12]. Our findings in cross-subject cardiac registrations echo those findings in brain image registrations [7].

  View details for DOI 10.1007/978-3-642-31340-0_22

  View details for PubMedID 28603787

  View details for PubMedCentralID PMC5462118

• Temporal Shape Analysis via the Spectral Signature Bernardis, E., Konukoglu, E., Ou, Y., Metaxas, D. N., Desjardins, B., Pohl, K. M., Ayache, N., Delingette, H., Golland, P., Mori, K. SPRINGER-VERLAG BERLIN. 2012: 49–56

  Abstract

  In this paper, we adapt spectral signatures for capturing morphological changes over time. Advanced techniques for capturing temporal shape changes frequently rely on first registering the sequence of shapes and then analyzing the corresponding set of high dimensional deformation maps. Instead, we propose a simple encoding motivated by the observation that small shape deformations lead to minor refinements in the spectral signature composed of the eigenvalues of the Laplace operator. The proposed encoding does not require registration, since spectral signatures are invariant to pose changes. We apply our representation to the shapes of the ventricles extracted from 22 cine MR scans of healthy controls and Tetralogy of Fallot patients. We then measure the accuracy score of our encoding by training a linear classifier, which outperforms the same classifier based on volumetric measurements.

  View details for Web of Science ID 000371316700007

  View details for PubMedID 23286031

• Regional Manifold Learning for Deformable Registration of Brain MR Images Ye, D., Hamm, J., Kwon, D., Davatzikos, C., Pohl, K. M., Ayache, N., Delingette, H., Golland, P., Mori, K. SPRINGER-VERLAG BERLIN. 2012: 131–38

  Abstract

  We propose a method for deformable registration based on learning the manifolds of individual brain regions. Recent publications on registration of medical images advocate the use of manifold learning in order to confine the search space to anatomically plausible deformations. Existing methods construct manifolds based on a single metric over the entire image domain thus frequently miss regional brain variations. We address this issue by first learning manifolds for specific regions and then computing region-specific deformations from these manifolds. We then determine deformations for the entire image domain by learning the global manifold in such a way that it preserves the region-specific deformations. We evaluate the accuracy of our method by applying it to the LPBA40 dataset and measuring the overlap of the deformed segmentations. The result shows significant improvement in registration accuracy on cortex regions compared to other state of the art methods.

  View details for Web of Science ID 000371317400017

  View details for PubMedID 23286123

  View details for PubMedCentralID PMC5459478

• COMBINING REGIONAL METRICS FOR DISEASE-RELATED BRAIN POPULATION ANALYSIS Ye, D., Hamm, J., Pohl, K. M., IEEE IEEE. 2012: 1515–18

  Abstract

  In this paper, we present a new metric combining regional measurements to improve image based population studies that use manifold learning techniques. These studies currently rely on a single score over the whole brain image domain. Thus, they require large amount of training data to uncover spatially complex variation in the whole brain impacted by diseases. We reduce the impact of this issue by first computing pairwise measurements in local regions separately and then combining regional measurements into a single pairwise metric. We apply the new metric to learn the manifold of ADNI data and evaluate the resulting morphological representation by fitting multiple linear regression models to the mini-mental state examination (MMSE) score. The regression models show that the morphological representations from the proposed metric achieves higher estimation accuracy of MMSE score compared to those from the conventional global scores.

  View details for Web of Science ID 000312384100392

  View details for PubMedID 28593031

  View details for PubMedCentralID PMC5459375

• Automatic Segmentation of Breast Carcinomas from DCE-MRI using a Statistical Learning Algorithm Jayender, J., Vosburgh, K. G., Gombos, E., Ashraf, A., Kontos, D., Gavenonis, S. C., Jolesz, F. A., Pohl, K., IEEE IEEE. 2012: 122–25

  Abstract

  Segmenting regions of high angiogenic activity corresponding to malignant tumors from DCE-MRI is a time-consuming task requiring processing of data in 4 dimensions. Quantitative analyses developed thus far are highly sensitive to external factors and are valid only under certain operating assumptions, which need not be valid for breast carcinomas. In this paper, we have developed a novel Statistical Learning Algorithm for Tumor Segmentation (SLATS) for automatically segmenting cancer from a region selected by the user on DCE-MRI. In this preliminary study, SLATS appears to demonstrate high accuracy (78%) and sensitivity (100%) in segmenting cancers from DCE-MRI when compared to segmentations performed by an expert radiologist. This may be a useful tool for delineating tumors for image-guided interventions.

  View details for Web of Science ID 000312384100031

  View details for PubMedID 28603582

  View details for PubMedCentralID PMC5464330

• SEGMENTATION OF MYOCARDIUM USING DEFORMABLE REGIONS AND GRAPH CUTS Uzunbas, M., Zhang, S., Pohl, K. M., Metaxas, D., Axel, L., IEEE IEEE. 2012: 254–57

  Abstract

  Deformable models and graph cuts are two standard image segmentation techniques. Combining some of their benefits, we introduce a new segmentation system for (semi-) automatic delineation of epicardium and endocardium of Left Ventricle of the heart in Magnetic Resonance Images (MRI). Specifically, a temporal information among consecutive phases is exploited via a coupling between deformable models and graph cuts which provides automated accurate cues for graph cuts and also good initialization scheme for deformable model that ultimately leads to more accurate and smooth segmentation results with lower interaction costs than using only graph cut segmentation. In addition, we define deformable model as a region defined by two nested contours and segment epicardium and endocardium in an unified way by optimizing single energy functional. This approach provides inherent coherency among the two contours thus leads to more accurate results than deforming separate contours for each target. We show promising results on the challenging problems of left ventricle segmentation.

  View details for Web of Science ID 000312384100064

  View details for PubMedID 28603583

  View details for PubMedCentralID PMC5463182

• NONRIGID VOLUME REGISTRATION USING SECOND-ORDER MRF MODEL Kwon, D., Yun, I., Pohl, K. M., Davatzikos, C., Lee, S., IEEE IEEE. 2012: 708–11

  Abstract

  In this paper, we introduce a nonrigid registration method using a Markov Random Field (MRF) energy model with second-order smoothness priors. The registration determines an optimal labeling of the MRF energy model where the label corresponds to a 3D displacement vector. In the MRF energy model, spatial relationships are constructed between nodes using second-order smoothness priors. This model improves limitations of first-order spatial priors which cannot fully incorporate the natural smoothness of deformations. Specifically, the second-order smoothness priors can generate desired smoother displacement vector fields and do not suffer from fronto-parallel effects commonly occurred in first-order priors. The usage of second-order priors in the energy model enables this method to produce more accurate registration results. In the experiments, we will show comparative results using uni- and multi-modal Brain MRI volumes.

  View details for Web of Science ID 000312384100178

  View details for PubMedID 28626513

  View details for PubMedCentralID PMC5470541