Connectome Analyses of Functional Brain Networks in Patients with Psychiatric Disorders

The human connectome is a connectivity matrix which maps the neural connection in the brain network. Functional connectivity matrix could be acquired by functional magnetic resonance imaging (fMRI) or other non-invasive neural monitoring approaches such as Electroencephalography (EEG) or Magnetoencephalography (MEG). FMRI based connectivity has high spatial resolution but limited task availability. EEG based connectivity gives more freedom to set the task, to analyze select frequency domain with high temporal resolution, but the spatial resolution is limited by the numbers of electrode. Graph theory metrics were able to characterize these networks’ properties including small-wordless and modularity quantitatively, by which could provide vital physiological information to understand the brains dysfunctions in neuropsychiatric disorders. However the brain network is highly dynamic even the subject is in resting state, so it is crucial to capture the both temporal and spatial information of the network. Therefore the overall goal of the thesis is to make attempt to build a pipeline that could process the dynamic functional connectivity matrix, and identify distinct patterns from the subject with psychiatric disorders. Three aims are proposed to achieve the goal. Aim 1 will develop a strategy to characterize the network properties while preserving the dynamic of the functional network for both EEG based network and fMRI based network. Aim 2 will determine in EEG based functional network, whether the pipeline will be able to identify differences of the network for same group at different states, or the different group (anxiety disorder patients and controls) at the same state. Aim 3 will expend the understanding with psychiatric disorders like catatonia by applying same analyses for fMRI based network.