Investigations Of Functional Brain Networks In Different Sates

In the past decade, a multitude of human brain activity data has been acquired with the fast development of functional magnetic resonance imaging and neural electrophysiological techniques. How to find valuable and biologically meaningful knowledge and rules behind these data is a critical problem and is a hot research topic in neuroinformatics. At the same time, complex networks have attracted great attention as a compelling framework with which complex systems are being studied in many fields. Many natural and man-made networks generated from different datasets have been exhibited common principles that govern network behavior and can be quantitatively characterized by the same parameters. The human brain generates large amount of oscillatory neural activity in support of brain function. The interplay between the activity of different brain regions and their integration is crucial to the organizational principles that underlie cognitive and brain function, and one strategy is quantitative analysis and characterization of these brain activity from a network perspective.In the present dissertation, we investigate the organization rules behind multimodal experimental image data by combining computer science, neuroscience, imaging and statistics, as well as the psychology knowledge, and mainly focus on the topological properties of brain networks at rest or during tasks and meditation-related brain network plasticity. The main contents and contributions of the dissertation are as following.1. Non-random organization of resting brain networks. We investigated the topological properties of brain networks derived from resting-state functional magnetic resonance imaging. A prior anatomical automatic labeling template was first used to parcellate the brain volume into90network nodes and functional connectivity was defined by partial correlation coefficients between the mean time series of each pair of anatomically unconnected regions. The connectivity backbone represented by a maximum spanning tree was then used to visualize network layout and most brain areas of the default and attention-related network activity were observed to be distributed at the center of the network layout as important functional hubs of information processing at rest. Newman’s spectral optimization method was applied to reveal the intrinsically community architectures and the resulting nonrandom structure fit well some biologically meaningful functional systems of the brain. Both brain regions (nodes) and connections in the brain network were then classified into different categories, and several hub regions and bridge connections played pivotal roles in the global information transfer, whose lesions exhibited different impacts on network efficiency. These studies mapped the connectivity skeleton and community structure of brain networks and would help us understand functional specialization and functional integration during information processing in the human brain.2. Frequency-related differences observed in functional brain networks. We used amplitude of low frequency fluctuation, regional homogeneity, functional connectivity and complex network to reveal distinctions in the default mode network and amygdale. The new method of constructing brain networks and the effects of frequency on brain networks would provide new insights in studies of functional brain networks.3. Meditation-related brain plasticity observed in functional brain networks. We adopted meditation as a vehicle and examined changes of network topological properties due to training. The results indicated network properties of the anterior cingulate during resting state condition were altered by short-term meditation, and attempted to provide an interpretation of improvement in self-regulation. The study provided empirical support of experience-driven brain plasticity from a network perspective.4. Research of brain networks derived from event-related potential (ERP). We applied synchronization likelihood analysis and graph theory-based network analysis of ERP data in moral decision making to study the personal/impersonal distinction in organization of functional connectivity. The results indicated that the personal task had some larger long-range connections involved in frontal regions and the right hemisphere, and higher network efficiency of some frontal electrodes than the impersonal. This may be related to brain resource reorganization contributing to efficient conflict resolution mediated by distributed processing.