Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.
Title: Mapping Brain Connectivity to Behavior: from Network Optimization Frameworks to Deep-Generative Hybrid Models
Abstract: 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 thesis 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. My talk is organised into two main parts, as follows:
Network Optimization Models for rs-fMRI connectomics and clinical severity:
Analysis of a multi-subject rs-fMRI imaging study often begins at the group level, for example, estimating group-averaged functional connectivity across all subjects. The failure of data-driven machine learning techniques such as PCA, k-PCA, SVMs etc. are largely attributed to their failure at capturing both the group structure and the individual patient variability, due to which they fail to generalize to unseen patients. To overcome these limitations, we developed a matrix factorization technique to represent the rs-fMRI correlation matrices by decomposing them into a sparse set of representative subnetworks modeled by rank one outer products. The subnetworks are combined using patient-specific non-negative coefficients. The network representations are fixed across the entire group, however, the strength of the subnetworks can vary across individuals. We significantly extend prior work in the area by using these very network coefficients to simultaneously predict behavioral measures via techniques ranging from simple linear regression models to parametric kernel methods, to Artificial Neural Networks (ANNs). The main novelty of the algorithms lies in jointly optimizing for the regression/ANN weights in conjunction with the rs-fMRI matrix factors. By leveraging techniques from convex and non-convex optimization, these frameworks significantly outperform several state-of-the art machine learning, graph theoretic and deep learning baselines at generalization to unseen patients.
Deep-Generative Hybrid Frameworks for Integrating Multimodal and Dynamic Connectivity with Behavior:
There is now growing evidence that functional connectivity between regions is a dynamically process evolving over a static anatomical connectivity profile, and that modeling this evolution is crucial to understanding ASD. Thus, we propose 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. Once again, our coupled optimization procedure 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.
In recent years, graph neural networks have shown great promise in brain connectivity research due to their ability to underscore subtle interactions between communicating brain regions while exploiting the underlying hierarchy of brain organization. To conclude, I will present some ongoing explorations based on end-to-end graph convolutional networks that directly model the evolution of the rs-fMRI signals/connectivity patterns over the underlying anatomical DTI graphs.
Committee Members
Archana Venkataraman, Department of Electrical and Computer Engineering
Rene Vidal, Department of Biomedical Engineering
Carey E. Priebe, Department of Applied Mathematics & Statistics
Stewart Mostofsky, Director of Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute
Kilian Pohl, Program Director, Image Analysis, Center for Health Sciences,and Biomedical Computing, SRI International; Associate Professor of Psychiatry and Behavioral Sciences, Stanford University