Bio

Professional Education


  • Doctor of Philosophy, University of Pittsburgh (2012)
  • Bachelor of Science, Carnegie Mellon University (2005)
  • Master of Science, University of Pittsburgh (2011)

Stanford Advisors


Publications

All Publications


  • Sex differences in cortical volume and gyrification in autism MOLECULAR AUTISM Schaer, M., Kochalka, J., Padmanabhan, A., Supekar, K., Menon, V. 2015; 6

    Abstract

    Male predominance is a prominent feature of autism spectrum disorders (ASD), with a reported male to female ratio of 4:1. Because of the overwhelming focus on males, little is known about the neuroanatomical basis of sex differences in ASD. Investigations of sex differences with adequate sample sizes are critical for improving our understanding of the biological mechanisms underlying ASD in females.We leveraged the open-access autism brain imaging data exchange (ABIDE) dataset to obtain structural brain imaging data from 53 females with ASD, who were matched with equivalent samples of males with ASD, and their typically developing (TD) male and female peers. Brain images were processed with FreeSurfer to assess three key features of local cortical morphometry: volume, thickness, and gyrification. A whole-brain approach was used to identify significant effects of sex, diagnosis, and sex-by-diagnosis interaction, using a stringent threshold of p?

    View details for DOI 10.1186/s13229-015-0035-y

    View details for Web of Science ID 000357384100001

    View details for PubMedID 26146534

  • Development and validation of consensus clustering-based framework for brain segmentation using resting fMRI. Journal of neuroscience methods Ryali, S., Chen, T., Padmanabhan, A., Cai, W., Menon, V. 2015; 240: 128-140

    Abstract

    Clustering methods are increasingly employed to segment brain regions into functional subdivisions using resting-state functional magnetic resonance imaging (rs-fMRI). However, these methods are highly sensitive to the (i) precise algorithms employed, (ii) their initializations, and (iii) metrics used for uncovering the optimal number of clusters from the data.To address these issues, we develop a novel consensus clustering evidence accumulation (CC-EAC) framework, which effectively combines multiple clustering methods for segmenting brain regions using rs-fMRI data. Using extensive computer simulations, we examine the performance of widely used clustering algorithms including K-means, hierarchical, and spectral clustering as well as their combinations. We also examine the accuracy and validity of five objective criteria for determining the optimal number of clusters: mutual information, variation of information, modified silhouette, Rand index, and probabilistic Rand index.A CC-EAC framework with a combination of base K-means clustering (KC) and hierarchical clustering (HC) with probabilistic Rand index as the criterion for choosing the optimal number of clusters, accurately uncovered the correct number of clusters from simulated datasets. In experimental rs-fMRI data, these methods reliably detected functional subdivisions of the supplementary motor area, insula, intraparietal sulcus, angular gyrus, and striatum.Unlike conventional approaches, CC-EAC can accurately determine the optimal number of stable clusters in rs-fMRI data, and is robust to initialization and choice of free parameters.A novel CC-EAC framework is proposed for segmenting brain regions, by effectively combining multiple clustering methods and identifying optimal stable functional clusters in rs-fMRI data.

    View details for DOI 10.1016/j.jneumeth.2014.11.014

    View details for PubMedID 25450335

  • Investigating inhibitory control in children with epilepsy: An fMRI study EPILEPSIA Triplett, R. L., Velanova, K., Luna, B., Padmanabhan, A., Gaillard, W. D., Asato, M. R. 2014; 55 (10): 1667-1676

    View details for DOI 10.1111/epi.12768

    View details for Web of Science ID 000344173800025

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