Bio

Honors & Awards


  • Electrical and Computer Engineering Departmental Fellowship, University of California San Diego (2013-14)

Professional Education


  • Doctor of Philosophy, University of California San Diego, Electrical Engineering (Signal and Image Processing) (2018)
  • Master of Science, University of California San Diego, Electrical Engineering (Signal and Image Processing) (2015)
  • Bachelor of Technology, Indian Institute of Technology Guwahati, Electronics and Communication Engineering (2013)

Stanford Advisors


Research & Scholarship

Current Research and Scholarly Interests


Development and implementation of advanced computational methods to investigate human brain functional organization and development

Publications

All Publications


  • Characterizing Brain Connectivity from Human Electrocorticography Recordings with Unobserved Inputs during Epileptic Seizures. Neural computation Das, A., Sexton, D., Lainscsek, C., Cash, S. S., Sejnowski, T. J. 2019: 1–56

    Abstract

    Epilepsy is a neurological disorder characterized by the sudden occurrence of unprovoked seizures. There is extensive evidence of significantly altered brain connectivity during seizure periods in the human brain. Research on analyzing human brain functional connectivity during epileptic seizures has been limited predominantly to the use of the correlation method. However, spurious connectivity can be measured between two brain regions without having direct connection or interaction between them. Correlations can be due to the apparent interactions of the two brain regions resulting from common input from a third region, which may or may not be observed. Hence, researchers have recently proposed a sparse-plus-latent-regularized precision matrix (SLRPM) when there are unobserved or latent regions interacting with the observed regions. The SLRPM method yields partial correlations of the conditional statistics of the observed regions given the latent regions, thus identifying observed regions that are conditionally independent of both the observed and latent regions. We evaluate the performance of the methods using a spring-mass artificial network and assuming that some nodes cannot be observed, thus constituting the latent variables in the example. Several cases have been considered, including both sparse and dense connections, short-range and long-range connections, and a varying number of latent variables. The SLRPM method is then applied to estimate brain connectivity during epileptic seizures from human ECoG recordings. Seventy-four clinical seizures from five patients, all having complex partial epilepsy, were analyzed using SLRPM, and brain connectivity was quantified using modularity index, clustering coefficient, and eigenvector centrality. Furthermore, using a measure of latent inputs estimated by the SLRPM method, it was possible to automatically detect 72 of the 74 seizures with four false positives and find six seizures that were not marked manually.

    View details for DOI 10.1162/neco_a_01205

    View details for PubMedID 31113298

  • Comparison of Two Hyperparameter-Free Sparse Signal Processing Methods for Direction-of-Arrival Tracking in the HF97 Ocean Acoustic Experiment IEEE JOURNAL OF OCEANIC ENGINEERING Das, A., Zachariah, D., Stoica, P. 2018; 43 (3): 725–34
  • Narrowband and Wideband Off-Grid Direction-of-Arrival Estimation via Sparse Bayesian Learning IEEE JOURNAL OF OCEANIC ENGINEERING Das, A., Sejnowski, T. J. 2018; 43 (1): 108–18
  • Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings NEURAL COMPUTATION Lin, T. W., Das, A., Krishnan, G. P., Bazhenov, M., Sejnowski, T. J. 2017; 29 (10): 2581–2632

    Abstract

    With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship of multiple neural signals. Correlation-based methods are a set of currently well-used techniques for functional connectivity estimation. However, due to explaining away and unobserved common inputs (Stevenson, Rebesco, Miller, & Körding, 2008 ), they produce spurious connections. The general linear model (GLM), which models spike trains as Poisson processes (Okatan, Wilson, & Brown, 2005 ; Truccolo, Eden, Fellows, Donoghue, & Brown, 2005 ; Pillow et al., 2008 ), avoids these confounds. We develop here a new class of methods by using differential signals based on simulated intracellular voltage recordings. It is equivalent to a regularized AR(2) model. We also expand the method to simulated local field potential recordings and calcium imaging. In all of our simulated data, the differential covariance-based methods achieved performance better than or similar to the GLM method and required fewer data samples. This new class of methods provides alternative ways to analyze neural signals.

    View details for DOI 10.1162/NECO_a_01008

    View details for Web of Science ID 000417405600001

    View details for PubMedID 28777719

    View details for PubMedCentralID PMC5726979

  • A Bayesian Sparse-Plus-Low-Rank Matrix Decomposition Method for Direction-of-Arrival Tracking IEEE SENSORS JOURNAL Das, A. 2017; 17 (15): 4894–4902
  • Peer-Reviewed Technical Communication-Coherent Multipath Direction-of-Arrival Resolution Using Compressed Sensing IEEE JOURNAL OF OCEANIC ENGINEERING Das, A., Hodgkiss, W. S., Gerstoft, P. 2017; 42 (2): 494-505
  • Interpretation of the Precision Matrix and Its Application in Estimating Sparse Brain Connectivity during Sleep Spindles from Human Electrocorticography Recordings NEURAL COMPUTATION Das, A., Sampson, A. L., Lainscsek, C., Muller, L., Lin, W., Doyle, J. C., Cash, S. S., Halgren, E., Sejnowski, T. J. 2017; 29 (3): 603-642

    Abstract

    The correlation method from brain imaging has been used to estimate functional connectivity in the human brain. However, brain regions might show very high correlation even when the two regions are not directly connected due to the strong interaction of the two regions with common input from a third region. One previously proposed solution to this problem is to use a sparse regularized inverse covariance matrix or precision matrix (SRPM) assuming that the connectivity structure is sparse. This method yields partial correlations to measure strong direct interactions between pairs of regions while simultaneously removing the influence of the rest of the regions, thus identifying regions that are conditionally independent. To test our methods, we first demonstrated conditions under which the SRPM method could indeed find the true physical connection between a pair of nodes for a spring-mass example and an RC circuit example. The recovery of the connectivity structure using the SRPM method can be explained by energy models using the Boltzmann distribution. We then demonstrated the application of the SRPM method for estimating brain connectivity during stage 2 sleep spindles from human electrocorticography (ECoG) recordings using an [Formula: see text] electrode array. The ECoG recordings that we analyzed were from a 32-year-old male patient with long-standing pharmaco-resistant left temporal lobe complex partial epilepsy. Sleep spindles were automatically detected using delay differential analysis and then analyzed with SRPM and the Louvain method for community detection. We found spatially localized brain networks within and between neighboring cortical areas during spindles, in contrast to the case when sleep spindles were not present.

    View details for DOI 10.1162/NECO_a_00936

    View details for Web of Science ID 000395564100003

    View details for PubMedID 28095202

    View details for PubMedCentralID PMC5424817

  • Theoretical and Experimental Comparison of Off-Grid Sparse Bayesian Direction-of-Arrival Estimation Algorithms IEEE ACCESS Das, A. 2017; 5: 18075–87