Deep Imaging-Genetics to Parse Neuropsychiatric Disorders
Archana Venkataraman
December 15, 2021, Wednesday, 3:00 PM - 4:00 PM EST
Neuropsychiatric disorders, such as autism and schizophrenia, have two complementary viewpoints. On one hand, they are linked to cognitive and behavioral deficits via altered neural functionality. On the other hand, these disorders exhibit high heritability, meaning that deficits may have a genetic underpinning. Identifying the biological basis between the genetic variants and the heritable phenotypes remains an open challenge in the field. This talk will showcase two modeling frameworks that use deep learning to integrate neuroimaging, genetic, and phenotypic data, while maintaining interpretability of the extracted biomarkers. Our first framework (G-MIND) leverages a coupled autoencoder-classifier network to project the data modalities to a shared latent space that captures predictive differences between patients and controls. G-MIND uses a learnable dropout layer to extract interpretable biomarkers from the data, and our unique training strategy can easily accommodate missing data modalities across subjects. We demonstrate that G-MIND achieves better predictive performance than conventional imaging-genetics methods, and that the learned representation generalizes across sites. Our second framework (GUIDE) develops a biologically informed deep network for whole-genome analysis. Specifically, the network uses hierarchical graph convolution and pooling operations that mimic the organization of a well-established gene ontology to tracks the convergence of genetic risk across biological pathways. This ontology is coupled with an attention mechanism that automatically identifies the salient edges through the graph. We demonstrate that GUIDE can identify reproducible biomarkers that are closely associated with the deficits of schizophrenia.
Archana Venkataraman, the John C. Malone Assistant Professor of Electrical and Computer Engineering, develops new mathematical models to characterize complex processes within the brain. She is a core faculty member in the Malone Center for Engineering in Healthcare, which aims to improve the quality and efficacy of clinical interventions, and she is affiliated with the Mathematical Institute for Data Science.

Venkataraman's lab, the Neural Systems Analysis Laboratory (NSA Lab), concentrates on building a comprehensive and system-level understanding of the brain by strategically integrating computational methods, such as machine learning, signal processing, and network theory, with application-driven hypotheses about brain functionality. Based on this approach, Venkataraman and her team aim toward a greater understanding of debilitating neurological disorders, with the long-term goal of improving patient care.

Venkataraman studied electrical engineering at the Massachusetts Institute of Technology, where she received her bachelor's degree in 2006, a master's degree in 2007, and her Ph.D. in 2012. She completed postdoctoral work at MIT and the Yale School of Medicine before joining the faculty of the Whiting School of Engineering in 2016.