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VISE Fall Seminar – Archana Venkataraman 9.23.21

Posted by on Monday, September 13, 2021 in News.

VISE Fall Seminar
to be led by

Archana Venkataraman, PhD
John C. Malone Assistant Professor of Electrical and Computer Engineering,
Johns Hopkins University

Date: September 23, 2021
Time: 12:05pm start, Noon Lunch-ticket hand out
Location: Stevenson Center 5326

Title:  Unlocking the Translational Potential of Resting-State Data

Abstract:  Resting-state data has become ubiquitous in computational neuroscience by allowing us to parse the intrinsic organization of the brain, identify biomarkers of different neural processes, and discriminate between patient and control groups. While such methods have yielded novel and valuable insights into the brain, there remains a notable gap between the data and models we are analyzing and the patient care pathway. This talk will showcase recent work in my lab that attempts to bridge this translational gap. The first project focuses on resting-state EEG. I will describe a deep-generative hybrid model for epileptic seizure detection. The latent variables in this model capture the spatiotemporal spread of a seizure across the scalp; they are complemented by a nonparametric likelihood based on convolutional neural networks. I will also highlight our current end-to-end extensions of this work focused on seizure onset localization. The second project turns to resting-state fMRI. Here, we develop an end-to-end deep learning framework that uses dynamic functional connectivity to simultaneously localize the language and motor areas of the eloquent cortex for preoperative mapping. Our model leverages specialized convolutional filters that extract graph-based features from the dynamic connectivity matrices, an LSTM attention network to weigh the relevant time points and multitask classification to simultaneously localize different eloquent subsystems. The final project circles back to epilepsy, where we have developed a multimodal deep neural network resting-state fMRI and diffusion MRI connectivity. The graph-based interactions in this model can accurately localize the seizure onset zone across a heterogeneous cohort of temporal and extratemporal lobe epilepsy patients.

Speaker Bio: Archana Venkataraman is a John C. Malone Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University. She directs the Neural Systems Analysis Laboratory and is a core faculty member of the Malone Center for Engineering in Healthcare. Dr. Venkataraman’s research lies at the intersection of artificial intelligence, network modeling and clinical neuroscience. Her work has yielded novel insights in to debilitating neurological disorders, such as autism, schizophrenia, and epilepsy, with the long-term goal of improving patient care. Dr. Venkataraman completed her B.S., M.Eng. and Ph.D. in Electrical Engineering at MIT in 2006, 2007 and 2012, respectively. She is a recipient of the MIT Provost Presidential Fellowship, the Siebel Scholarship, the National Defense Science and Engineering Graduate Fellowship, the NIH Advanced Multimodal Neuroimaging Training Grant, the CHDI Grant on network models for Huntington’s Disease, and the National Science Foundation CAREER award. Dr. Venkataraman was also named by MIT Technology Review as one of 35 Innovators Under 35 in 2019.