Learning assessment-aware brain representations from multimodal neuroimaging data by Dr. Ishaan Batta

Venue: SIT001

Online joining: https://teams.microsoft.com/meet/48853006918605?p=788lqF84K1ykLq1rtg

Abstract: Standard supervised learning on neuroimaging data optimizes for diagnostic prediction while yielding feature-level importance scores that lack network-level, assessment-specific interpretability required for biomarker discovery; while unsupervised methods reduce data dimensions leading to loss of assessment-specific information. This talk presents frameworks developed towards addressing these gaps via biologically interpretable methodologies for neuroimaging data analysis. First, a multimodal active subspace analysis framework to compute multiple salient directions that define the gradient space of a prediction function learned on brain features, followed by repeated analysis to extract consistent and robust assessment-oriented subspace centers: compact multimodal representations of co-varying brain regions and functional connections maximally associated with a target clinical assessment. Second, an interpretable deep learning framework, constrained source-based salience, that embeds active subspace learning and spatially constrained ICA directly into the saliency space of trained deep learning architectures, producing network-level full-brain visualizations anchored around spatial brain templates. Lastly, it will include some of the ongoing work on a conditional graph variational autoencoder that encodes static functional network connectivity in the brain into a structured latent space conditioned on demographic and cognitive variables, enabling condition-specific reconstruction and identification of discriminative patterns of biological sex and fluid intelligence. Collectively, these frameworks establish a principled methodology for learning brain representations that are simultaneously predictive, network-interpretable, and account for clinical observations.

Bio: Ishaan Batta is a postdoctoral research associate at the tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), GSU/GAtech/Emory, Atlanta, USA. His research lies at the intersection of machine learning and neuroscience, with a focus on developing interpretable representation learning methods for high-dimensional multimodal neuroimaging data to uncover biologically meaningful signatures of brain disorders and cognitive function.
Ishaan completed his Ph.D. in Electrical and Computer Engineering at the Georgia Institute of Technology (Georgia Tech), USA in 2023, advised by Dr. Vince Calhoun. His doctoral and postdoctoral work has introduced a suite of novel frameworks spanning active subspace learning, deep learning-based interpretation, and conditional generative modeling, aimed at ensuring both predictive performance as well as neurobiological interpretability in brain imaging studies. Prior to his graduate studies, Ishaan received a dual degree (B.Tech. and M.Tech.) in Computer Science and Engineering from the Indian Institute of Technology Delhi (IIT Delhi) in 2017