Multimodal human brain connectivity analysis based on graph theory

Billions of people worldwide are affected by neurological disorders. Recent studies indicate that many neurological disorders can be described as dysconnectivity syndromes, and associated with changes in the brain networks prior to the development of clinical symptoms. This thesis presents contributions towards improving brain connectivity analysis based on graph theory representation of the human brain network. We propose novel multimodal techniques to analyze brain imaging data to better understand its structure, function and connectivity, i.e., brain connectomics. Our first contribution is towards improving parcellation, \ie brain network node definition, in terms of reproducibility, functional homogeneity, leftout data likelihood and overlaps with cytoarchitecture, by utilizing the neighbourhood information and multi-modality integration techniques. Specifically, we embed neighborhood connectivity information into the affinity matrix for parcellation to ameliorate the adverse effects of noise. We further integrate the connectivity information from both anatomical and functional modalities based on adaptive weighting for an improved parcellation. Our second contribution is to propose noise reduction techniques for brain network edge definition. We propose a matrix completion based technique to combat false negatives by recovering missing connections. We also present a local thresholding method which can address the regional bias issue when suppressing the false positives in connectivity estimates. Our third contribution is to improve the brain subnetwork extraction by using multi-pronged graphical metric guided methods. We propose a connection-fingerprint based modularity reinforcement model which reflects the putative modular structure of a brain graph. Inspired by the brain subnetwork's biological nature, we propose a provincial hub guided feedback optimization model for more reproducible subnetwork extraction. Our fourth contribution is to develop multimodal integration techniques to further improve brain subnetwork extraction. We propose a provincial hub guided subnetwork extraction model to fuse anatomical and functional data by propagating the modular structure information across different modalities. We further propose to fuse the task and rest functional data based on hypergraphs for non-overlapping and overlapping subnetwork extraction. Our results collectively indicate that combing multimodal information and applying graphical metric guided strategies outperform classical unimodal brain connectivity analysis methods. The resulting methods could provide important insights into cognitive and clinical neuroscience.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat