Mary Ellen grew up in Annapolis, Maryland. She attended college at Duke University, where she majored in Biomedical Engineering with a minor in Chemistry. After graduation, Mary Ellen moved to Nashville, Tennessee to join the Medical Scientist Training Program (MSTP) and earned her combined MD/PhD at Vanderbilt University. She completed her PhD in Human Genetics in the laboratory of Dr. Tricia Thornton-Wells, where she studied the radiogenomics of Alzheimer’s Disease. She developed a pipeline of tools to analyze and quantify images from MR and PET images, which she then used as quantitative traits in genetic analyses to tease apart the etiology of Alzheimer Disease. She is continuing her quantitative MR and PET imaging research with various mentors across Stanford.
Outside of radiology, Mary Ellen is excited about curriculum development, wellness, mentorship, and leadership. She developed and co-leads the RadCombinator curriculum for protected research time for radiology residents, co-leads the radiology wellness committee, for which she secured funding for a resident wellness program, is a member of the GME Women in Medicine Leadership Council, and is co-chair of the GME Women in Medicine Community Development committee. She is also an active mentor through the Women in Medicine Mentorship Program and the Stanford Women Association of Physician Scientists (SWAPS).
Outside of work, Mary Ellen enjoys swimming with the Stanford Masters' team, shopping farmer's markets, mountain biking, and hiking the amazing trails around Northern California, all with her husband Cody. They also love playing with their dog, Bowser.
2017 – Present
Stanford University – Palo Alto, CA
Department of Radiology
2016 – 2017
University of Tennessee – Nashville, TN
Department of Internal Medicine
2009 – 2016
Vanderbilt University – Nashville, TN
M.D. Medical Scientist Training Program
2011 – 2014
Vanderbilt University – Nashville, TN
Ph.D. Human Genetics
2005 – 2009
Duke University – Durham, NC
B.S.E., Graduation with Distinction
AWARDS AND HONORS:
Etta Kalin Moskowitz Fund Research Award
Awarded by Stanford University Radiology Residency Program, 2017
Awarded by Vanderbilt University School of Medicine to 1st in class, 2016
Glasgow-Rubin Citation for Academic Achievement
Awarded by Vanderbilt University School of Medicine to 1st female in class, 2016
Awarded by Vanderbilt University School of Medicine Department of Radiology, 2016
Fellow and Resident Annual Meeting Scholarship
Awarded by the World Congress of Interventional Oncology for the annual WCIO Conference, 2016
Alpha Omega Alpha Honor Medical Society
Vanderbilt University, 2015
Dr. Constantin Cope Medical Student Research Award
Awarded by the Society of Interventional Radiology Foundation, 2015
Society of Interventional Radiology Medical Student Travel Scholarship
Awarded by the Society of Interventional Radiology for the annual SIR Conference, 2014
Flexner Dean’s Lecture Student Lecturer
Awarded by Vanderbilt University, 2013
NIGMS Travel Fellowship
Awarded by the National Institute of General Medical Sciences for the Short Course on Statistical Genetics and Genomics, 2011
Medical Scientist Training Program
Funded by NIH T32 GM07347, full tuition scholarship and stipend for both medical and graduate school, 2009 – 2016
Baldwin Scholars Unsung Heroine Award
Awarded by Duke University, 2009
Research in Practice Program Grant
Awarded by Duke University, 2008
Pratt Undergraduate Research Fellowship
Awarded by Duke University, 2007-2009
True ultra-low-dose amyloid PET/MRI enhanced with deep learning for clinical interpretation.
European journal of nuclear medicine and molecular imaging
While sampled or short-frame realizations have shown the potential power of deep learning to reduce radiation dose for PET images, evidence in true injected ultra-low-dose cases is lacking. Therefore, we evaluated deep learning enhancement using a significantly reduced injected radiotracer protocol for amyloid PET/MRI.Eighteen participants underwent two separate 18F-florbetaben PET/MRI studies in which an ultra-low-dose (6.64 ± 3.57 MBq, 2.2 ± 1.3% of standard) or a standard-dose (300 ± 14 MBq) was injected. The PET counts from the standard-dose list-mode data were also undersampled to approximate an ultra-low-dose session. A pre-trained convolutional neural network was fine-tuned using MR images and either the injected or sampled ultra-low-dose PET as inputs. Image quality of the enhanced images was evaluated using three metrics (peak signal-to-noise ratio, structural similarity, and root mean square error), as well as the coefficient of variation (CV) for regional standard uptake value ratios (SUVRs). Mean cerebral uptake was correlated across image types to assess the validity of the sampled realizations. To judge clinical performance, four trained readers scored image quality on a five-point scale (using 15% non-inferiority limits for proportion of studies rated 3 or better) and classified cases into amyloid-positive and negative studies.The deep learning-enhanced PET images showed marked improvement on all quality metrics compared with the low-dose images as well as having generally similar regional CVs as the standard-dose. All enhanced images were non-inferior to their standard-dose counterparts. Accuracy for amyloid status was high (97.2% and 91.7% for images enhanced from injected and sampled ultra-low-dose data, respectively) which was similar to intra-reader reproducibility of standard-dose images (98.6%).Deep learning methods can synthesize diagnostic-quality PET images from ultra-low injected dose simultaneous PET/MRI data, demonstrating the general validity of sampled realizations and the potential to reduce dose significantly for amyloid imaging.
View details for DOI 10.1007/s00259-020-05151-9
View details for PubMedID 33416955
Application of Deep Learning to Predict Standardized Uptake Value Ratio and Amyloid Status on 18F-Florbetapir PET Using ADNI Data.
AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Cortical amyloid quantification on PET by using the standardized uptake value ratio is valuable for research studies and clinical trials in Alzheimer disease. However, it is resource intensive, requiring co-registered MR imaging data and specialized segmentation software. We investigated the use of deep learning to automatically quantify standardized uptake value ratio and used this for classification.MATERIALS AND METHODS: Using the Alzheimer's Disease Neuroimaging Initiative dataset, we identified 2582 18F-florbetapir PET scans, which were separated into positive and negative cases by using a standardized uptake value ratio threshold of 1.1. We trained convolutional neural networks (ResNet-50 and ResNet-152) to predict standardized uptake value ratio and classify amyloid status. We assessed performance based on network depth, number of PET input slices, and use of ImageNet pretraining. We also assessed human performance with 3 readers in a subset of 100 randomly selected cases.RESULTS: We have found that 48% of cases were amyloid positive. The best performance was seen for ResNet-50 by using regression before classification, 3 input PET slices, and pretraining, with a standardized uptake value ratio root-mean-square error of 0.054, corresponding to 95.1% correct amyloid status prediction. Using more than 3 slices did not improve performance, but ImageNet initialization did. The best trained network was more accurate than humans (96% versus a mean of 88%, respectively).CONCLUSIONS: Deep learning algorithms can estimate standardized uptake value ratio and use this to classify 18F-florbetapir PET scans. Such methods have promise to automate this laborious calculation, enabling quantitative measurements rapidly and in settings without extensive image processing manpower and expertise.
View details for DOI 10.3174/ajnr.A6573
View details for PubMedID 32499247
DualNet: a Deep Neural Network to Predict Individual Tau and Amyloid PET Images from a Combined Dose Image using the Disambiguation of Dual Dose Amyloid-Tau PET Scans Using The ADNI Dataset
SOC NUCLEAR MEDICINE INC. 2020
View details for Web of Science ID 000568290501578
Visual Read Protocols for Clinicians Analyzing 18F-PI-2620 tau PET/MRI Images
SOC NUCLEAR MEDICINE INC. 2020
View details for Web of Science ID 000568290501466
- Devising Productivity Benchmarks for IR: Findings from a National Survey of IR Practices. Journal of vascular and interventional radiology : JVIR 2020
Tau PET imaging with 18F-PI-2620 in aging and neurodegenerative diseases.
European journal of nuclear medicine and molecular imaging
In vivo measurement of the spatial distribution of neurofibrillary tangle pathology is critical for early diagnosis and disease monitoring of Alzheimer's disease (AD).Forty-nine participants were scanned with 18F-PI-2620 PET to examine the distribution of this novel PET ligand throughout the course of AD: 36 older healthy controls (HC) (age range 61 to 86), 11 beta-amyloid+ (Aβ+) participants with cognitive impairment (CI; clinical diagnosis of either mild cognitive impairment or AD dementia, age range 57 to 86), and 2 participants with semantic variant primary progressive aphasia (svPPA, age 66 and 78). Group differences in brain regions relevant in AD (medial temporal lobe, posterior cingulate cortex, and lateral parietal cortex) were examined using standardized uptake value ratios (SUVRs) normalized to the inferior gray matter of the cerebellum.SUVRs in target regions were relatively stable 60 to 90 min post-injection, with the exception of very high binders who continued to show increases over time. Robust elevations in 18F-PI-2620 were observed between HC and Aβ+ CI across all AD regions. Within the HC group, older age was associated with subtle elevations in target regions. Mildly elevated focal uptake was observed in the anterior temporal pole in one svPPA patient.Preliminary results suggest strong differences in the medial temporal lobe and cortical regions known to be impacted in AD using 18F-PI-2620 in patients along the AD trajectory. This work confirms that 18F-PI-2620 holds promise as a tool to visualize tau aggregations in AD.
View details for DOI 10.1007/s00259-020-04923-7
View details for PubMedID 32572562
Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning.
European journal of nuclear medicine and molecular imaging
We aimed to evaluate the performance of deep learning-based generalization of ultra-low-count amyloid PET/MRI enhancement when applied to studies acquired with different scanning hardware and protocols.Eighty simultaneous [18F]florbetaben PET/MRI studies were acquired, split equally between two sites (site 1: Signa PET/MRI, GE Healthcare, 39 participants, 67 ± 8 years, 23 females; site 2: mMR, Siemens Healthineers, 64 ± 11 years, 23 females) with different MRI protocols. Twenty minutes of list-mode PET data (90-110 min post-injection) were reconstructed as ground-truth. Ultra-low-count data obtained from undersampling by a factor of 100 (site 1) or the first minute of PET acquisition (site 2) were reconstructed for ultra-low-dose/ultra-short-time (1% dose and 5% time, respectively) PET images. A deep convolution neural network was pre-trained with site 1 data and either (A) directly applied or (B) trained further on site 2 data using transfer learning. Networks were also trained from scratch based on (C) site 2 data or (D) all data. Certified physicians determined amyloid uptake (+/-) status for accuracy and scored the image quality. The peak signal-to-noise ratio, structural similarity, and root-mean-squared error were calculated between images and their ground-truth counterparts. Mean regional standardized uptake value ratios (SUVR, reference region: cerebellar cortex) from 37 successful site 2 FreeSurfer segmentations were analyzed.All network-synthesized images had reduced noise than their ultra-low-count reconstructions. Quantitatively, image metrics improved the most using method B, where SUVRs had the least variability from the ground-truth and the highest effect size to differentiate between positive and negative images. Method A images had lower accuracy and image quality than other methods; images synthesized from methods B-D scored similarly or better than the ground-truth images.Deep learning can successfully produce diagnostic amyloid PET images from short frame reconstructions. Data bias should be considered when applying pre-trained deep ultra-low-count amyloid PET/MRI networks for generalization.
View details for DOI 10.1007/s00259-020-04897-6
View details for PubMedID 32535655
Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus.
Journal of neurosurgery. Pediatrics
Imaging evaluation of the cerebral ventricles is important for clinical decision-making in pediatric hydrocephalus. Although quantitative measurements of ventricular size, over time, can facilitate objective comparison, automated tools for calculating ventricular volume are not structured for clinical use. The authors aimed to develop a fully automated deep learning (DL) model for pediatric cerebral ventricle segmentation and volume calculation for widespread clinical implementation across multiple hospitals.The study cohort consisted of 200 children with obstructive hydrocephalus from four pediatric hospitals, along with 199 controls. Manual ventricle segmentation and volume calculation values served as "ground truth" data. An encoder-decoder convolutional neural network architecture, in which T2-weighted MR images were used as input, automatically delineated the ventricles and output volumetric measurements. On a held-out test set, segmentation accuracy was assessed using the Dice similarity coefficient (0 to 1) and volume calculation was assessed using linear regression. Model generalizability was evaluated on an external MRI data set from a fifth hospital. The DL model performance was compared against FreeSurfer research segmentation software.Model segmentation performed with an overall Dice score of 0.901 (0.946 in hydrocephalus, 0.856 in controls). The model generalized to external MR images from a fifth pediatric hospital with a Dice score of 0.926. The model was more accurate than FreeSurfer, with faster operating times (1.48 seconds per scan).The authors present a DL model for automatic ventricle segmentation and volume calculation that is more accurate and rapid than currently available methods. With near-immediate volumetric output and reliable performance across institutional scanner types, this model can be adapted to the real-time clinical evaluation of hydrocephalus and improve clinician workflow.
View details for DOI 10.3171/2020.6.PEDS20251
View details for PubMedID 33260138
APOE epsilon4-specific associations of VEGF gene family expression with cognitive aging and Alzheimer's disease.
Neurobiology of aging
Literature suggests vascular endothelial growth factor A (VEGFA) is protective among those at highest risk for Alzheimer's disease (AD). Apolipoprotein E (APOE) epsilon4 allele carriers represent a highly susceptible population for cognitive decline, and VEGF may confer distinct protection among APOE-epsilon4 carriers. We evaluated interactions between cortical expression of 10 VEGF gene family members and APOE-epsilon4 genotype to clarify which VEGF genes modify the association between APOE-epsilon4 and cognitive decline. Data were obtained from the Religious Orders Study and Rush Memory and Aging Project (N= 531). Linear regression assessed interactions on global cognition. VEGF genes NRP1 and VEGFA interacted with APOE-epsilon4 on cognitive performance (p.fdr < 0.05). Higher NRP1 expression correlated with worse outcomes among epsilon4 carriers but better outcomes among epsilon4 noncarriers, suggesting NRP1 modifies the risk for poor cognitive scores based on APOE-epsilon4 status. NRP1 regulates angiogenesis, and literature suggests vessels in APOE-epsilon4 brains are more prone to leaking, perhaps placing young vessels at risk for ischemia. Results suggest that future therapeutics targeting brain angiogenesis should also consider epsilon4 allele status.
View details for DOI 10.1016/j.neurobiolaging.2019.10.021
View details for PubMedID 31791659
Brain expression of the vascular endothelial growth factor gene family in cognitive aging and alzheimer's disease.
Vascular endothelial growth factor (VEGF) is associated with the clinical manifestation of Alzheimer's disease (AD). However, the role of the VEGF gene family in neuroprotection is complex due to the number of biological pathways they regulate. This study explored associations between brain expression of VEGF genes with cognitive performance and AD pathology. Genetic, cognitive, and neuropathology data were acquired from the Religious Orders Study and Rush Memory and Aging Project. Expression of ten VEGF ligand and receptor genes was quantified using RNA sequencing of prefrontal cortex tissue. Global cognitive composite scores were calculated from 17 neuropsychological tests. beta-amyloid and tau burden were measured at autopsy. Participants (n=531) included individuals with normal cognition (n=180), mild cognitive impairment (n=148), or AD dementia (n=203). Mean age at death was 89 years and 37% were male. Higher prefrontal cortex expression of VEGFB, FLT4, FLT1, and PGF was associated with worse cognitive trajectories (p≤0.01). Increased expression of VEGFB and FLT4 was also associated with lower cognition scores at the last visit before death (p≤0.01). VEGFB, FLT4, and FLT1 were upregulated among AD dementia compared with normal cognition participants (p≤0.03). All four genes associated with cognition related to elevated beta-amyloid (p≤0.01) and/or tau burden (p≤0.03). VEGF ligand and receptor genes, specifically genes relevant to FLT4 and FLT1 receptor signaling, are associated with cognition, longitudinal cognitive decline, and AD neuropathology. Future work should confirm these observations at the protein level to better understand how changes in VEGF transcription and translation relate to neurodegenerative disease.
View details for DOI 10.1038/s41380-019-0458-5
View details for PubMedID 31332262
Performance Comparison of Individual and Ensemble CNN Models for the Classification of Brain 18F-FDG-PET Scans.
Journal of digital imaging
The high-background glucose metabolism of normal gray matter on [18F]-fluoro-2-D-deoxyglucose (FDG) positron emission tomography (PET) of the brain results in a low signal-to-background ratio, potentially increasing the possibility of missing important findings in patients with intracranial malignancies. To explore the strategy of using a deep learning classifier to aid in distinguishing normal versus abnormal findings on PET brain images, this study evaluated the performance of a two-dimensional convolutional neural network (2D-CNN) to classify FDG PET brain scans as normal (N) or abnormal (A).Two hundred eighty-nine brain FDG-PET scans (N; n = 150, A; n = 139) resulting in a total of 68,260 images were included. Nine individual 2D-CNN models with three different window settings for axial, coronal, and sagittal axes were trained and validated. The performance of these individual and ensemble models was evaluated and compared using a test dataset. Odds ratio, Akaike's information criterion (AIC), and area under curve (AUC) on receiver-operative-characteristic curve, accuracy, and standard deviation (SD) were calculated.An optimal window setting to classify normal and abnormal scans was different for each axis of the individual models. An ensembled model using different axes with an optimized window setting (window-triad) showed better performance than ensembled models using the same axis and different windows settings (axis-triad). Increase in odds ratio and decrease in SD were observed in both axis-triad and window-triad models compared with individual models, whereas improvements of AUC and AIC were seen in window-triad models. An overall model averaging the probabilities of all individual models showed the best accuracy of 82.0%.Data ensemble using different window settings and axes was effective to improve 2D-CNN performance parameters for the classification of brain FDG-PET scans. If prospectively validated with a larger cohort of patients, similar models could provide decision support in a clinical setting.
View details for DOI 10.1007/s10278-019-00289-x
View details for PubMedID 31659587
Sex differences in the association between AD biomarkers and cognitive decline.
Brain imaging and behavior
2017; 11 (1): 205–13
Women are disproportionately affected by Alzheimer's disease (AD) in terms of both disease prevalence and severity. Previous autopsy work has suggested that, in the presence of AD neuropathology, females are more susceptible to the clinical manifestation of AD. This manuscript extends that work by evaluating whether sex alters the established associations between cerebrospinal fluid (CSF) biomarker levels and brain aging outcomes (hippocampal volume, cognition). Participants were drawn from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and included individuals with normal cognition (n = 348), mild cognitive impairment (n = 565), and AD (n = 185). We leveraged mixed effects regression models to assess the interaction between sex and baseline cerebrospinal fluid biomarker levels of amyloid-β42 (Aβ-42) and total tau on cross-sectional and longitudinal brain aging outcomes. We found a significant interaction between sex and Aβ-42 on longitudinal hippocampal atrophy (p = 0.002), and longitudinal decline in memory (p = 0.017) and executive function (p = 0.025). Similarly, we observed an interaction between sex and total tau level on longitudinal hippocampal atrophy (p = 0.008), and longitudinal decline in executive function (p = 0.034). Women with Aβ-42 and total tau levels indicative of worse pathological changes showed more rapid hippocampal atrophy and cognitive decline. The sex difference was particularly pronounced among individuals with MCI, with lower education, and varied by APOE ε4 allele. These results suggest females may be more susceptible to the clinical manifestation of AD.
View details for PubMedID 26843008
View details for PubMedCentralID PMC4972701
Five percent dextrose maximizes dose delivery of Yttrium-90 resin microspheres and reduces rates of premature stasis compared to sterile water.
2016; 5 (6): 745–48
Resin Yttrium-90 (Y90) microspheres have historically been infused using sterile water (H2O). In 2013, recommendations expanded to allow delivery with 5% dextrose in water (D5W). In this retrospective study, we hypothesized that D5W would improve Y90 delivery with a lower incidence of stasis. We reviewed 190 resin Y90 infusions using H2O (n=137) or D5W (n=53). Y90 dosimetry was calculated using the body surface area method. Infusion was halted if intra-arterial stasis was fluoroscopically identified prior to clearing the vial. Differences between H2O and D5W groups were calculated for activity prescription, percentage of cases reaching stasis, and percentage delivery of prescribed activity using z- and t-test comparisons, with α=0.05. Thirty-one of 137 H2O infusions developed stasis compared to 2 of 53 with D5W (z=3.07, p=1.05E-03). D5W also had a significantly higher prescribed activity than H2O [28.2 millicuries (mCi) vs. 20.4 mCi, respectively; t=5.0, p=1.1E-6]. D5W had a higher delivery percentage of the prescribed dose compared to H2O (101.5 vs. 92.7%, respectively; t=3.8, p=1.92E-4). In conclusion, resin microsphere infusion utilizing D5W has a significantly lower rate of stasis than H2O and results in more complete dose delivery. D5W is preferable to H2O for resin microsphere infusion.
View details for PubMedID 28105342
View details for PubMedCentralID PMC5228356
Procedural Impact of a Dedicated Interventional Oncology Service Line in a National Cancer Institute Comprehensive Cancer Center.
Journal of the American College of Radiology : JACR
2016; 13 (9): 1145–50
We tested the hypothesis that establishing a dedicated interventional oncology (IO) clinical service line would increase clinic visits and procedural volumes at a single quaternary care academic medical center.Two time periods were defined: July 2012 to June 2013 (pre-IO clinic) and July 2013 to June 2014 (first year of dedicated IO service). Staff was recruited, and clinic space was provided in the institution's comprehensive cancer center. Clinic visits and procedure numbers were documented using the institution's electronic medical record and billing forms. IO procedures included were transarterial chemoembolization, Y-90 radioembolization, perfusion mapping for Y-90, portal vein embolization, and bland embolization. We compared changes in clinic visit and procedure numbers using paired t tests. Changes after IO initiation were compared to 1-year changes in the Medicare 5% Limited Data Set by cross-referencing Current Procedure Terminology and International Classification of Diseases codes in 2012 and 2013.Clinic visits increased from 9 to 204 (P = .003, t = 8.89, df = 3). Procedures increased from 60 to 239 (P = .018, t = 3.85, df = 4). Procedural volumes increased at least 150% for each subtype. The volumes in the 5% Limited Data Set did not change significantly over the 2-year period (443 to 385, P > .05).The establishment of a dedicated IO service significantly increased clinic visits and procedural volumes. National trends were unchanged, suggesting that the impact of our program was not part of a sudden increase of IO procedures.
View details for PubMedID 27297700
View details for PubMedCentralID PMC5012920
Impact of family structure and common environment on heritability estimation for neuroimaging genetics studies using Sequential Oligogenic Linkage Analysis Routines.
Journal of medical imaging (Bellingham, Wash.)
2014; 1 (1): 014005
Imaging genetics is an emerging methodological field that combines genetic information with medical imaging-derived metrics to understand how genetic factors impact observable phenotypes. In order for a trait to be a reasonable phenotype in an imaging genetics study, it must be heritable: at least some proportion of its variance must be due to genetic influences. The Sequential Oligogenic Linkage Analysis Routines (SOLAR) imaging genetics software can estimate the heritability of a trait in complex pedigrees. We investigate the ability of SOLAR to accurately estimate heritability and common environmental effects on simulated imaging phenotypes in various family structures. We found that heritability is reliably estimated with small family-based studies of 40 to 80 individuals, though subtle differences remain between the family structures. In an imaging application analysis, we found that with 80 subjects in any of the family structures, estimated heritability of white matter fractional anisotropy was biased by <10% for every region of interest. Results from these studies can be used when investigators are evaluating power in planning genetic analyzes.
View details for PubMedID 25558465
View details for PubMedCentralID PMC4281883
Genetic interactions found between calcium channel genes modulate amyloid load measured by positron emission tomography.
2014; 133 (1): 85–93
Late-onset Alzheimer's disease (LOAD) is known to have a complex, oligogenic etiology, with considerable genetic heterogeneity. We investigated the influence of genetic interactions between genes in the Alzheimer's disease (AD) pathway on amyloid-beta (Aβ) deposition as measured by PiB or AV-45 ligand positron emission tomography (PET) to aid in understanding LOAD's genetic etiology. Subsets of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts were used for discovery and for two independent validation analyses. A significant interaction between RYR3 and CACNA1C was confirmed in all three of the independent ADNI datasets. Both genes encode calcium channels expressed in the brain. The results shown here support previous animal studies implicating interactions between these calcium channels in amyloidogenesis and suggest that the pathological cascade of this disease may be modified by interactions in the amyloid-calcium axis. Future work focusing on the mechanisms of such relationships may inform targets for clinical intervention.
View details for PubMedID 24026422
View details for PubMedCentralID PMC4045094
Genetic interactions within inositol-related pathways are associated with longitudinal changes in ventricle size.
Journal of Alzheimer's disease : JAD
2014; 38 (1): 145–54
The genetic etiology of late-onset Alzheimer's disease (LOAD) has proven complex, involving clinical and genetic heterogeneity and gene-gene interactions. Recent genome wide association studies in LOAD have led to the discovery of novel genetic risk factors; however, the investigation of gene-gene interactions has been limited. Conventional genetic studies often use binary disease status as the primary phenotype, but for complex brain-based diseases, neuroimaging data can serve as quantitative endophenotypes that correlate with disease status and closely reflect pathological changes. In the Alzheimer's Disease Neuroimaging Initiative cohort, we tested for association of genetic interactions with longitudinal MRI measurements of the inferior lateral ventricles (ILVs), which have repeatedly shown a relationship to LOAD status and progression. We performed linear regression to evaluate the ability of pathway-derived SNP-SNP pairs to predict the slope of change in volume of the ILVs. After Bonferroni correction, we identified four significant interactions in the right ILV (RILV) corresponding to gene-gene pairs SYNJ2-PI4KA, PARD3-MYH2, PDE3A-ABHD12B, and OR2L13-PRKG1 and one significant interaction in the left ILV (LILV) corresponding to SYNJ2-PI4KA. The SNP-SNP interaction corresponding to SYNJ2-PI4KA was identical in the RILV and LILV and was the most significant interaction in each (RILV: p = 9.13 × 10(-12); LILV: p = 8.17 × 10(-13)). Both genes belong to the inositol phosphate signaling pathway which has been previously associated with neurodegeneration in AD and we discuss the possibility that perturbation of this pathway results in a down-regulation of the Akt cell survival pathway and, thereby, decreased neuronal survival, as reflected by increased volume of the ventricles.
View details for PubMedID 24077433
View details for PubMedCentralID PMC3815519
Interactions between GSK3β and amyloid genes explain variance in amyloid burden.
Neurobiology of aging
2014; 35 (3): 460–65
The driving theoretical framework of Alzheimer's disease (AD) has been built around the amyloid-β (Aβ) cascade in which amyloid pathology precedes and drives tau pathology. Other evidence has suggested that tau and amyloid pathology may arise independently. Both lines of research suggest that there may be epistatic relationships between genes involved in amyloid and tau pathophysiology. In the current study, we hypothesized that genes coding glycogen synthase kinase 3 (GSK-3) and comparable tau kinases would modify genetic risk for amyloid plaque pathology. Quantitative amyloid positron emission tomography data from the Alzheimer's Disease Neuroimaging Initiative served as the quantitative outcome in regression analyses, covarying for age, gender, and diagnosis. Three interactions reached statistical significance, all involving the GSK3β single nucleotide polymorphism rs334543-2 with APBB2 (rs2585590, rs3098914) and 1 with APP (rs457581). These interactions explained 1.2%, 1.5%, and 1.5% of the variance in amyloid deposition respectively. Our results add to a growing literature on the role of GSK-3 activity in amyloid processing and suggest that combined variation in GSK3β and APP-related genes may result in increased amyloid burden.
View details for PubMedID 24112793
View details for PubMedCentralID PMC3864626
Genetic variation modifies risk for neurodegeneration based on biomarker status.
Frontiers in aging neuroscience
2014; 6: 183
While a great deal of work has gone into understanding the relationship between Cerebrospinal fluid (CSF) biomarkers, brain atrophy, and disease progression, less work has attempted to investigate how genetic variation modifies these relationships. The goal of this study was two-fold. First, we sought to identify high-risk vs. low-risk individuals based on their CSF tau and Aβ load and characterize these individuals with regard to brain atrophy in an AD-relevant region of interest. Next, we sought to identify genetic variants that modified the relationship between biomarker classification and neurodegeneration.Participants were categorized based on established cut-points for biomarker positivity. Mixed model regression was used to quantify longitudinal change in the left inferior lateral ventricle. Interaction analyses between single nucleotide polymorphisms (SNPs) and biomarker group status were performed using a genome wide association study (GWAS) approach. Correction for multiple comparisons was performed using the Bonferroni procedure.One intergenic SNP (rs4866650) and one SNP within the SPTLC1 gene (rs7849530) modified the association between amyloid positivity and neurodegeneration. A transcript variant of WDR11-AS1 gene (rs12261764) modified the association between tau positivity and neurodegeneration. These effects were consistent across the two sub-datasets and explained approximately 3% of variance in ventricular dilation. One additional SNP (rs6887649) modified the association between amyloid positivity and baseline ventricular volume, but was not observed consistently across the sub-datasets.Genetic variation modifies the association between AD biomarkers and neurodegeneration. Genes that regulate the molecular response in the brain to oxidative stress may be particularly relevant to neural vulnerability to the damaging effects of amyloid-β.
View details for PubMedID 25140149
View details for PubMedCentralID PMC4121544
Differences in age-related effects on brain volume in Down syndrome as compared to Williams syndrome and typical development.
Journal of neurodevelopmental disorders
2014; 6 (1): 8
Individuals with Down Syndrome (DS) are reported to experience early onset of brain aging. However, it is not well understood how pre-existing neurodevelopmental effects versus neurodegenerative processes might be contributing to the observed pattern of brain atrophy in younger adults with DS. The aims of the current study were to: (1) to confirm previous findings of age-related changes in DS compared to adults with typical development (TD), (2) to test for an effect of these age-related changes in a second neurodevelopmental disorder, Williams syndrome (WS), and (3) to identify a pattern of regional age-related effects that are unique to DS.High-resolution T1-weighted MRI of the brains of subjects with DS, WS, and TD controls were segmented, and estimates of regional brain volume were derived using FreeSurfer. A general linear model was employed to test for age-related effects on volume between groups. Secondary analyses in the DS group explored the relationship between brain volume and neuropsychological tests and APOE.Consistent with previous findings, the DS group showed significantly greater age-related effects relative to TD controls in total gray matter and in regions of the orbitofrontal cortex and the parietal cortex. Individuals with DS also showed significantly greater age-related effects on volume of the left and right inferior lateral ventricles (LILV and RILV, respectively). There were no significant differences in age-related effects on volume when comparing the WS and TD groups. In the DS group, cognitive tests scores measuring signs of dementia and APOE ϵ4 carrier status were associated with LILV and RILV volume.Individuals with DS demonstrated a unique pattern of age-related effects on gray matter and ventricular volume, the latter of which was associated with dementia rating scores in the DS group. Results may indicate that early onset of brain aging in DS is primarily due to DS-specific neurodegenerative processes, as opposed to general atypical neurodevelopment.
View details for PubMedID 24713364
View details for PubMedCentralID PMC4022321
Genetic modification of the relationship between phosphorylated tau and neurodegeneration.
Alzheimer's & dementia : the journal of the Alzheimer's Association
2014; 10 (6): 637–45.e1
A subset of individuals present at autopsy with the pathologic features of Alzheimer's disease having never manifest the clinical symptoms. We sought to identify genetic factors that modify the relationship between phosphorylated tau (PTau) and dilation of the lateral inferior ventricles.We used data from 700 subjects enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI). A genome-wide association study approach was used to identify PTau × single nucleotide polymorphism (SNP) interactions. Variance explained by these interactions was quantified using hierarchical linear regression.Five SNP × PTau interactions passed a Bonferroni correction, one of which (rs4728029, POT1, 2.6% of variance) was consistent across ADNI-1 and ADNI-2/GO subjects. This interaction also showed a trend-level association with memory performance and levels of interleukin-6 receptor.Our results suggest that rs4728029 modifies the relationship between PTau and both ventricular dilation and cognition, perhaps through an altered neuroinflammatory response.
View details for PubMedID 24656848
View details for PubMedCentralID PMC4169762
Epistatic genetic effects among Alzheimer's candidate genes.
2013; 8 (11): e80839
Novel risk variants for late-onset Alzheimer's disease (AD) have been identified and replicated in genome-wide association studies. Recent work has begun to address the relationship between these risk variants and biomarkers of AD, though results have been mixed. The aim of the current study was to characterize single marker and epistatic genetic effects between the top candidate Single Nucleotide Polymorphisms (SNPs) in relation to amyloid deposition.We used a combined dataset across ADNI-1 and ADNI-2, and looked within each dataset separately to validate identified genetic effects. Amyloid was quantified using data acquired by Positron Emission Tomography (PET) with (18)F-AV-45.Two SNP-SNP interactions reached significance when correcting for multiple comparisons, BIN1 (rs7561528, rs744373) x PICALM (rs7851179). Carrying the minor allele in BIN1 was related to higher levels of amyloid deposition, however only in non-carriers of the protective PICALM minor allele.Our results support previous research suggesting these candidate SNPs do not show single marker associations with amyloid pathology. However, we provide evidence for a novel interaction between PICALM and BIN1 in relation to amyloid deposition. Risk related to the BIN1 minor allele appears to be mitigated in the presence of the PICALM protective variant. In that way, variance in amyloid plaque burden can be better classified within the context of a complex genetic background. Efforts to model cumulative risk for AD should explicitly account for this epistatic effect, and future studies should explicitly test for such effects whenever statistically feasible.
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Associations between KCNJ6 (GIRK2) gene polymorphisms and pain-related phenotypes.
2013; 154 (12): 2853–59
G-protein coupled inwardly rectifying potassium (GIRK) channels are effectors determining degree of analgesia experienced upon opioid receptor activation by endogenous and exogenous opioids. The impact of GIRK-related genetic variation on human pain responses has received little research attention. We used a tag single nucleotide polymorphism (SNP) approach to comprehensively examine pain-related effects of KCNJ3 (GIRK1) and KCNJ6 (GIRK2) gene variation. Forty-one KCNJ3 and 69 KCNJ6 tag SNPs were selected, capturing the known variability in each gene. The primary sample included 311 white patients undergoing total knee arthroplasty in whom postsurgical oral opioid analgesic medication order data were available. Primary sample findings were then replicated in an independent white sample of 63 healthy pain-free individuals and 75 individuals with chronic low back pain (CLBP) who provided data regarding laboratory acute pain responsiveness (ischemic task) and chronic pain intensity and unpleasantness (CLBP only). Univariate quantitative trait analyses in the primary sample revealed that 8 KCNJ6 SNPs were significantly associated with the medication order phenotype (P < .05); overall effects of the KCNJ6 gene (gene set-based analysis) just failed to reach significance (P = .054). No significant KCNJ3 effects were observed. A continuous GIRK Related Risk Score (GRRS) was derived in the primary sample to summarize each individual's number of KCNJ6 "pain risk" alleles. This GRRS was applied to the replication sample, which revealed significant associations (P < .05) between higher GRRS values and lower acute pain tolerance and higher CLBP intensity and unpleasantness. Results suggest further exploration of the impact of KCNJ6 genetic variation on pain outcomes is warranted.
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Genetic interactions associated with 12-month atrophy in hippocampus and entorhinal cortex in Alzheimer's Disease Neuroimaging Initiative.
Neurobiology of aging
2013; 34 (5): 1518.e9–18
Missing heritability in late onset Alzheimer disease can be attributed, at least in part, to heterogeneity in disease status and to the lack of statistical analyses exploring genetic interactions. In the current study, we use quantitative intermediate phenotypes derived from magnetic resonance imaging data available from the Alzheimer's Disease Neuroimaging Initiative, and we test for association with gene-gene interactions within biological pathways. Regional brain volumes from the hippocampus (HIP) and entorhinal cortex (EC) were estimated from baseline and 12-month magnetic resonance imaging scans. Approximately 560,000 single nucleotide polymorphisms (SNPs) were available genome-wide. We tested all pairwise SNP-SNP interactions (approximately 151 million) within 212 Kyoto Encyclopedia of Genes and Genomes pathways for association with 12-month regional atrophy rates using linear regression, with sex, APOE ε4 carrier status, age, education, and clinical status as covariates. A total of 109 SNP-SNP interactions were associated with right HIP atrophy, and 125 were associated with right EC atrophy. Enrichment analysis indicated significant SNP-SNP interactions were overrepresented in the calcium signaling and axon guidance pathways for both HIP and EC atrophy and in the ErbB signaling pathway for HIP atrophy.
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