Bio

Bio

Manjari Narayan is a postdoctoral research scholar in the School of Medicine. Her current research interests combine high dimensional statistics, network science & statistical causal inference methods to analyze changes in brain networks either longitudinally or due to experimental perturbations. 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. Previously, she was a postdoctoral scholar in Psychiatry under the mentorship of Amit Etkin. 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.

Institute Affiliations

  • Member, Maternal & Child Health Research Institute (MCHRI)

Honors & Awards

  • Bio-X Travel Award, Stanford Bio-X (2020)
  • 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)

Stanford Advisors

Links

Publications

All Publications

  • Estimation of Dynamic Bivariate Correlation Using a Weighted Graph Algorithm ENTROPY John, M., Wu, Y., Narayan, M., John, A., Ikuta, T., Ferbinteanu, J. 2020; 22 (6)

    View details for DOI 10.3390/e22060617

    View details for Web of Science ID 000553500500001

  • An Electroencephalography Connectomic Profile of Posttraumatic Stress Disorder. The American journal of psychiatry Toll, R. T., Wu, W., Naparstek, S., Zhang, Y., Narayan, M., Patenaude, B., De Los Angeles, C., Sarhadi, K., Anicetti, N., Longwell, P., Shpigel, E., Wright, R., Newman, J., Gonzalez, B., Hart, R., Mann, S., Abu-Amara, D., Sarhadi, K., Cornelssen, C., Marmar, C., Etkin, A. 2020: appiajp201918080911

    Abstract

    OBJECTIVE: The authors sought to identify brain regions whose frequency-specific, orthogonalized resting-state EEG power envelope connectivity differs between combat veterans with posttraumatic stress disorder (PTSD) and healthy combat-exposed veterans, and to determine the behavioral correlates of connectomic differences.METHODS: The authors first conducted a connectivity method validation study in healthy control subjects (N=36). They then conducted a two-site case-control study of veterans with and without PTSD who were deployed to Iraq and/or Afghanistan. Healthy individuals (N=95) and those meeting full or subthreshold criteria for PTSD (N=106) underwent 64-channel resting EEG (eyes open and closed), which was then source-localized and orthogonalized to mitigate effects of volume conduction. Correlation coefficients between band-limited source-space power envelopes of different regions of interest were then calculated and corrected for multiple comparisons. Post hoc correlations of connectomic abnormalities with clinical features and performance on cognitive tasks were conducted to investigate the relevance of the dysconnectivity findings.RESULTS: Seventy-four brain region connections were significantly reduced in PTSD (all in the eyes-open condition and predominantly using the theta carrier frequency). Underconnectivity of the orbital and anterior middle frontal gyri were most prominent. Performance differences in the digit span task mapped onto connectivity between 25 of the 74 brain region pairs, including within-network connections in the dorsal attention, frontoparietal control, and ventral attention networks.CONCLUSIONS: Robust PTSD-related abnormalities were evident in theta-band source-space orthogonalized power envelope connectivity, which furthermore related to cognitive deficits in these patients. These findings establish a clinically relevant connectomic profile of PTSD using a tool that facilitates the lower-cost clinical translation of network connectivity research.

    View details for DOI 10.1176/appi.ajp.2019.18080911

    View details for PubMedID 31964161

  • Estimation of Dynamic Bivariate Correlation Using a Weighted Graph Algorithm. Entropy (Basel, Switzerland) John, M., Wu, Y., Narayan, M., John, A., Ikuta, T., Ferbinteanu, J. 2020; 22 (6)

    Abstract

    Dynamic correlation is the correlation between two time series across time. Two approaches that currently exist in neuroscience literature for dynamic correlation estimation are the sliding window method and dynamic conditional correlation. In this paper, we first show the limitations of these two methods especially in the presence of extreme values. We present an alternate approach for dynamic correlation estimation based on a weighted graph and show using simulations and real data analyses the advantages of the new approach over the existing ones. We also provide some theoretical justifications and present a framework for quantifying uncertainty and testing hypotheses.

    View details for DOI 10.3390/e22060617

    View details for PubMedID 33286389

  • Individual Patterns of Abnormality in Resting-State Functional Connectivity Reveal Two Data-Driven PTSD Subgroups. The American journal of psychiatry Maron-Katz, A., Zhang, Y., Narayan, M., Wu, W., Toll, R. T., Naparstek, S., De Los Angeles, C., Longwell, P., Shpigel, E., Newman, J., Abu-Amara, D., Marmar, C., Etkin, A. 2019: appiajp201919010060

    Abstract

    A major challenge in understanding and treating posttraumatic stress disorder (PTSD) is its clinical heterogeneity, which is likely determined by various neurobiological perturbations. This heterogeneity likely also reduces the effectiveness of standard group comparison approaches. The authors tested whether a statistical approach aimed at identifying individual-level neuroimaging abnormalities that are more prevalent in case subjects than in control subjects could reveal new clinically meaningful insights into the heterogeneity of PTSD.Resting-state functional MRI data were recorded from 87 unmedicated PTSD case subjects and 105 war zone-exposed healthy control subjects. Abnormalities were modeled using tolerance intervals, which referenced the distribution of healthy control subjects as the "normative population." Out-of-norm functional connectivity values were examined for enrichment in cases and then used in a clustering analysis to identify biologically defined PTSD subgroups based on their abnormality profiles.The authors identified two subgroups among PTSD cases, each with a distinct pattern of functional connectivity abnormalities with respect to healthy control subjects. Subgroups differed clinically on levels of reexperiencing symptoms and improved case-control discriminability and were detectable using independently recorded resting-state EEG data.The results provide proof of concept for the utility of abnormality-based approaches for studying heterogeneity within clinical populations. Such approaches, applied not only to neuroimaging data, may allow detection of subpopulations with distinct biological signatures so that further clinical and mechanistic investigations can be focused on more biologically homogeneous subgroups.

    View details for DOI 10.1176/appi.ajp.2019.19010060

    View details for PubMedID 31838870

  • Using Tolerance Intervals to Capture Heterogeneity in Neurobiological Abnormalities Within PTSD Patients Maron-Katz, A., Narayan, M., Shpigel, E., Longwell, P., De Los Angeles, C., Marmar, C., Etkin, A. ELSEVIER SCIENCE INC. 2018: S139

    View details for Web of Science ID 000432466300342

  • Test-retest reliability of transcranial magnetic stimulation EEG evoked potentials Brain Stimulation Kerwin, L. J., Keller, C., Wu, W., Narayan, M., Etkin, A. 2017

    Abstract

    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 Allen, G. I., Amoroso, N., Anghel, C., Balagurusamy, V., Bare, C. J., Beaton, D., Bellotti, R., Bennett, D. A., Boehme, K. L., Boutros, P. C., Caberlotto, L., Caloian, C., Campbell, F., Chaibub Neto, E., Chang, Y., Chen, B., Chen, C., Chien, T., Clark, T., Das, S., Davatzikos, C., Deng, J., Dillenberger, D., Dobson, R. J., Dong, Q., Doshi, J., Duma, D., Errico, R., Erus, G., Everett, E., Fardo, D. W., Friend, S. H., Froehlich, H., Gan, J., St George-Hyslop, P., Ghosh, S. S., Glaab, E., Green, R. C., Guan, Y., Hong, M., Huang, C., Hwang, J., Ibrahim, J., Inglese, P., Iyappan, A., Jiang, Q., Katsumata, Y., Kauwe, J. S., Klein, A., Kong, D., Krause, R., Lalonde, E., Lauria, M., Lee, E., Lin, X., Liu, Z., Livingstone, J., Logsdon, B. A., Lovestone, S., Ma, T., Malhotra, A., Mangravite, L. M., Maxwell, T. J., Merrill, E., Nagorski, J., Namasivayam, A., Narayan, M., Naz, M., Newhouse, S. J., Norman, T. C., Nurtdinov, R. N., Oyang, Y., Pawitan, Y., Peng, S., Peters, M. A., Piccolo, S. R., Praveen, P., Priami, C., Sabelnykova, V. Y., Senger, P., Shen, X., Simmons, A., Sotiras, A., Stolovitzky, G., Tangaro, S., Tateo, A., Tung, Y., Tustison, N. J., Varol, E., Vradenburg, G., Weiner, M. W., Xiao, G., Xie, L., Xie, Y., Xu, J., Yang, H., Zhan, X., Zhou, Y., Zhu, F., Zhu, H., Zhu, S. 2016; 12 (6): 645-653

    Abstract

    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 Narayan, M., Allen, G. I. 2016; 10

    Abstract

    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 Tomson, S. N., Schreiner, M. J., Narayan, M., Rosser, T., Enrique, N., Silva, A. J., Allen, G. I., Bookheimer, S. Y., Bearden, C. E. 2015; 36 (11): 4566-4581

    Abstract

    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 Narayan, M., Allen, G. I. https://arxiv.org/abs/1502.03853. 2015
  • Anisotropic nonlocal means denoising APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS Maleki, A., Narayan, M., Baraniuk, R. G. 2013; 35 (3): 452-482

    View details for DOI 10.1016/j.acha.2012.11.003

    View details for Web of Science ID 000324848400006

  • Neural Networks of Colored Sequence Synesthesia JOURNAL OF NEUROSCIENCE Tomson, S. N., Narayan, M., Allen, G. I., Eagleman, D. M. 2013; 33 (35): 14098-14106

    Abstract

    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 Maleki, A., Narayan, M., Baraniuk, R. G. 2012; 33 (3): 370-387

    View details for DOI 10.1016/j.acha.2012.02.003

    View details for Web of Science ID 000307372800004

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