Manjari Narayan is a postdoctoral research scholar at the Etkinlab. Her current research interests combine high dimensional statistics, graphical models, network science & statistical causal inference methods to analyze interventional neuroimaging experiments as well as precision psychiatry. She received a Ph.D in Electrical Engineering from Rice University in 2016 under the supervision of Dr. Genevera Allen and a B.S in Electrical Engineering from UIUC in 2007. Her dissertation work has been recognized by numerous student paper awards including the 2016 ENAR Distinguished Student Paper Award from the International Biometrics Society and the 2013 best paper travel award in Pattern Recognition in Neuroimaging.
Honors & Awards
Distinguished Student Paper Award, International Biometrics Society, Eastern North American Region (ENAR) (2016)
R. L. Anderson Student Poster Award, Southern Regional Council on Statistics (2014)
Best Paper Travel Award, Pattern Recognition in Neuroimaging (2013)
Best Poster Award, Conference of Texas Statisticians (2013)
Google Anita Borg Memorial Scholarship (Women Techmakers), Google (2009)
- Individual Patterns of Abnormality in Resting-State Functional Connectivity Reveal Two Data-Driven PTSD Subgroups ELSEVIER SCIENCE INC. 2019: S121
- Cognitive Function Networks Vary With Quality of Life ELSEVIER SCIENCE INC. 2019: S120
Using Tolerance Intervals to Capture Heterogeneity in Neurobiological Abnormalities Within PTSD Patients
ELSEVIER SCIENCE INC. 2018: S139
View details for Web of Science ID 000432466300342
Test-retest reliability of transcranial magnetic stimulation EEG evoked potentials
Transcranial magnetic stimulation (TMS)-evoked potentials (TEPs), recorded using electroencephalography (TMS-EEG), offer a powerful tool for measuring causal interactions in the human brain. However, the test-retest reliability of TEPs, critical to their use in clinical biomarker and interventional studies, remains poorly understood.We quantified TEP reliability to: (i) determine the minimal TEP amplitude change which significantly exceeds that associated with simply re-testing, (ii) locate the most reliable scalp regions of interest (ROIs) and TEP peaks, and (iii) determine the minimal number of TEP pulses for achieving reliability.TEPs resulting from stimulation of the left dorsolateral prefrontal cortex were collected on two separate days in sixteen healthy participants. TEP peak amplitudes were compared between alternating trials, split-halves of the same run, two runs five minutes apart and two runs on separate days. Reliability was quantified using concordance correlation coefficient (CCC) and smallest detectable change (SDC).Substantial concordance was achieved in prefrontal electrodes at 40 and 60 ms, centroparietal and left parietal ROIs at 100 ms, and central electrodes at 200 ms. Minimum SDC was found in the same regions and peaks, particularly for the peaks at 100 and 200 ms. CCC, but not SDC, reached optimal values by 60-100 pulses per run with saturation beyond this number, while SDC continued to improve with increased pulse numbers.TEPs were robust and reliable, requiring a relatively small number of trials to achieve stability, and are thus well suited as outcomes in clinical biomarker or interventional studies.
View details for DOI 10.1016/j.brs.2017.12.010
Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease
ALZHEIMERS & DEMENTIA
2016; 12 (6): 645-653
Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance.
View details for DOI 10.1016/j.jalz.2016.02.006
View details for Web of Science ID 000377705600002
View details for PubMedID 27079753
Mixed Effects Models for Resampled Network Statistics Improves Statistical Power to Find Differences in Multi-Subject Functional Connectivity
FRONTIERS IN NEUROSCIENCE
Many complex brain disorders, such as autism spectrum disorders, exhibit a wide range of symptoms and disability. To understand how brain communication is impaired in such conditions, functional connectivity studies seek to understand individual differences in brain network structure in terms of covariates that measure symptom severity. In practice, however, functional connectivity is not observed but estimated from complex and noisy neural activity measurements. Imperfect subject network estimates can compromise subsequent efforts to detect covariate effects on network structure. We address this problem in the case of Gaussian graphical models of functional connectivity, by proposing novel two-level models that treat both subject level networks and population level covariate effects as unknown parameters. To account for imperfectly estimated subject level networks when fitting these models, we propose two related approaches-R (2) based on resampling and random effects test statistics, and R (3) that additionally employs random adaptive penalization. Simulation studies using realistic graph structures reveal that R (2) and R (3) have superior statistical power to detect covariate effects compared to existing approaches, particularly when the number of within subject observations is comparable to the size of subject networks. Using our novel models and methods to study parts of the ABIDE dataset, we find evidence of hypoconnectivity associated with symptom severity in autism spectrum disorders, in frontoparietal and limbic systems as well as in anterior and posterior cingulate cortices.
View details for DOI 10.3389/fnins.2016.00108
View details for Web of Science ID 000373757400001
View details for PubMedID 27147940
Resting state functional MRI reveals abnormal network connectivity in neurofibromatosis 1
HUMAN BRAIN MAPPING
2015; 36 (11): 4566-4581
Neurofibromatosis type I (NF1) is a genetic disorder caused by mutations in the neurofibromin 1 gene at locus 17q11.2. Individuals with NF1 have an increased incidence of learning disabilities, attention deficits, and autism spectrum disorders. As a single-gene disorder, NF1 represents a valuable model for understanding gene-brain-behavior relationships. While mouse models have elucidated molecular and cellular mechanisms underlying learning deficits associated with this mutation, little is known about functional brain architecture in human subjects with NF1. To address this question, we used resting state functional connectivity magnetic resonance imaging (rs-fcMRI) to elucidate the intrinsic network structure of 30 NF1 participants compared with 30 healthy demographically matched controls during an eyes-open rs-fcMRI scan. Novel statistical methods were employed to quantify differences in local connectivity (edge strength) and modularity structure, in combination with traditional global graph theory applications. Our findings suggest that individuals with NF1 have reduced anterior-posterior connectivity, weaker bilateral edges, and altered modularity clustering relative to healthy controls. Further, edge strength and modular clustering indices were correlated with IQ and internalizing symptoms. These findings suggest that Ras signaling disruption may lead to abnormal functional brain connectivity; further investigation into the functional consequences of these alterations in both humans and in animal models is warranted.
View details for DOI 10.1002/hbm.22937
View details for Web of Science ID 000364219500024
View details for PubMedID 26304096
- Two Sample Inference for Populations of Graphical Models with Applications to Functional Connectivity https://arxiv.org/abs/1502.03853. 2015
- Anisotropic nonlocal means denoising APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS 2013; 35 (3): 452-482
Neural Networks of Colored Sequence Synesthesia
JOURNAL OF NEUROSCIENCE
2013; 33 (35): 14098-14106
Synesthesia is a condition in which normal stimuli can trigger anomalous associations. In this study, we exploit synesthesia to understand how the synesthetic experience can be explained by subtle changes in network properties. Of the many forms of synesthesia, we focus on colored sequence synesthesia, a form in which colors are associated with overlearned sequences, such as numbers and letters (graphemes). Previous studies have characterized synesthesia using resting-state connectivity or stimulus-driven analyses, but it remains unclear how network properties change as synesthetes move from one condition to another. To address this gap, we used functional MRI in humans to identify grapheme-specific brain regions, thereby constructing a functional "synesthetic" network. We then explored functional connectivity of color and grapheme regions during a synesthesia-inducing fMRI paradigm involving rest, auditory grapheme stimulation, and audiovisual grapheme stimulation. Using Markov networks to represent direct relationships between regions, we found that synesthetes had more connections during rest and auditory conditions. We then expanded the network space to include 90 anatomical regions, revealing that synesthetes tightly cluster in visual regions, whereas controls cluster in parietal and frontal regions. Together, these results suggest that synesthetes have increased connectivity between grapheme and color regions, and that synesthetes use visual regions to a greater extent than controls when presented with dynamic grapheme stimulation. These data suggest that synesthesia is better characterized by studying global network dynamics than by individual properties of a single brain region.
View details for DOI 10.1523/JNEUROSCI.5131-12.2013
View details for Web of Science ID 000323727000017
View details for PubMedID 23986245
- Suboptimality of nonlocal means for images with sharp edges APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS 2012; 33 (3): 370-387