[an error occurred while processing this directive] [an error occurred while processing this directive] [an error occurred while processing this directive] [an error occurred while processing this directive] PhD Candidate [an error occurred while processing this directive] Johns Hopkins University [an error occurred while processing this directive] [an error occurred while processing this directive] [an error occurred while processing this directive] [an error occurred while processing this directive]

Signal Processing

Machine Learning

Computational Neuroscience

[an error occurred while processing this directive] Mapping Brain Connectivity to Behavior: A Deep-Generative Hybrid Framework [an error occurred while processing this directive] Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by multiple impairments and levels of disability that vary widely across the ASD spectrum. Currently, the most common methods of quantifying symptom severity are almost solely based on a trained clinician's evaluation. Recently, neuroimaging techniques such as resting state functional MRI (rs-fMRI) and Diffusion Tensor Imaging (DTI) have been gaining popularity for studying aberrant brain connectivity in ASD. My work aims at linking the symptomatic characterization of ASD with the functional and structural organization of a typical patient's brain as given by rs-fMRI and DTI respectively.

We have developed an integrated deep-generative framework, that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography to extract predictive biomarkers of a disease. The generative part of our framework is a structurally-regularized Dynamic Dictionary Learning (sr-DDL) model that decomposes the dynamic rs-fMRI correlation matrices into a collection of shared basis networks and time varying patient-specific loadings. This matrix factorization is guided by the DTI tractography matrices to learn anatomically informed connectivity profiles. The deep part of our framework is an LSTM-ANN block, which models the temporal evolution of the patient sr-DDL loadings to predict multidimensional clinical severity. Our main novelty lies in our coupled optimization procedure, which collectively estimates the basis networks, the patient-specific dynamic loadings, and the neural network weights. Our hybrid model outperforms state-of-the-art baselines in a cross validated setting and extracts interpretable multimodal neural signatures of brain dysfunction in ASD. [an error occurred while processing this directive] Niharika is a PhD candidate in the department of Electrical and Computer Engineering. Her research interests lie at the intersection of deep learning, non-convex optimization and graph signal processing applied to neuroimaging data. She has developed novel machine learning algorithms that predict behavioral deficits in patients with Autism by decoding their brain organization from their functional and structural neuroimaging scans. Prior to joining Hopkins, she obtained a bachelor’s degree (B. Tech with Hons.) in Electrical Engineering with a minor in Electronics and Electrical Communications Engineering from the Indian Institute of Technology, Kharagpur. [an error occurred while processing this directive] Personal home page [an error occurred while processing this directive] [an error occurred while processing this directive]