الفهرس 
English Cover
SummaryOnly 14 pages are availabe for public view
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Abstract
Resting-state functional Magnetic Resonance Imaging (Rs-fMRI) is a promising imaging modality to study the changes of functional brain networks in Schizophrenic patients. In this thesis, we propose a machine-learning system based on a graph-theoretic approach to investigate and differentiate the brain network alterations. The fMRI data samples are first preprocessed to reduce noise and normalize the images. An Automated Anatomical Labeling (AAL) and 264 putative functional brain area atlases are then used to parcelate the brain into 90 and 264 regions respectively and constructs region connectivity matrices. The two atlases achieve an average accuracy of 95.00% and 97.86% respectively