Representational Models of Brain Connectivity and Behavior

We live in a world which is naturally organized into intrinsic network structures. The study of networks is thus very relevant to modern day data-science, as we gain a lot of insight into otherwise mysterious phenomena. One such complex network is the human brain. There has been a lot of interest in understanding how regions in the brain communicate with each other and how these communication patterns influence our behavior and health. This sets us up for an important, yet really challenging question in healthcare: of how to represent these interactions and relate them to meaningful patient level characterization.

My talk will briefly highlight three key projects from my PhD work which blend knowledge from the machine learning, statistics and graph signal processing worlds to analyse brain functional (rs-fMRI) and structural (DTI) connectivity data. The broad goal is to relate these representations to behavioral deficits in patient populations.

First, I will present a joint network optimization framework to predict clinical severity from resting state fMRI data. This model couples two representational terms: a generative matrix factorization to capture the data geometry of fMRI correlation matrices and a discriminative regression framework. One key novelty of this algorithm lies in jointly optimizing the representation learning and prediction, which is key to the generalization onto unseen examples. Building off of this framework, I will then introduce an extension that incorporates multimodal information from Diffusion Tensor Imaging (DTI) and dynamic functional connectivity (rs-fMRI). At a high level, the generative matrix factorization now estimates a time-varying functional decomposition guided by anatomical connections in a graph regularized setting. We couple this representation with a deep network to predict multidimensional clinical characterizations. This deep network consists of an LSTM to model temporal-attention based dynamics of scan evolution and an ANN for prediction. Based on the principles underlying these geometry-informed models, we also develop a multimodal graph convolutional network (M-GCN) designed to exploit the native topology of functional and structural connectivity data in its formulation. This framework provides us with the flexibility to exploit the complementary nature of structure and function and faithfully map to behavioral outcomes even in the presence of limited training data

These models help us construct a more holistic data-driven picture of brain connectivity and behavior. Overall, these frameworks make minimal assumptions and can potentially find a broad range of applications outside of the medical realm.