Recording of Presentation
Speaker: Archana Venkataraman
John C. Malone Assistant Professor, Johns Hopkins University
Talk Abstract: Deep learning has disrupted nearly every major field of study from computer vision to genomics. The unrivaled power of these models offers a new and exciting way to study the intricacies of the brain via functional neuroimaging. On the flipside, deep networks lack interpretability and are notorious for overfitting on smaller datasets. My lab tackles these challenges through careful problem formulation, architectural design, and model training. This talk will highlight two ongoing projects that demonstrate both the scientific and translational potential of deep learning for fMRI. In the first project, we develop a novel deep network architecture to integrate imaging and genetics data, as guided by diagnosis. Our model consists of a coupled autoencoder and classifier. The encoder learns a non-linear subspace shared between the input data modalities. The classifier and the decoder act as regularizers to ensure that the low-dimensional space captures predictive differences between patients and controls. We use a learnable dropout layer to extract interpretable biomarkers from the data, and our unique training strategy can easily accommodate missing data modalities across subjects. In the second project, 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 in brain tumor patients. 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.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.