CIF:Small: A Signal Processing Approach to the Analysis of Time-Varying Functional Networks of the Brain

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Complex network theory has proved to be a versatile framework to represent and analyze relational data in many disciplines including the social sciences, biology and information systems. One particular application that has benefited from these developments is cognitive neuroscience. Contemporary neuroimaging techniques provide neural recordings with increasing spatial and temporal resolution yielding rich multichannel datasets that can be exploited for detailed description of functional connectivity patterns in the brain. Recent research provides evidence that neural integration across various spatial and temporal scales plays an important role in a wide range of cognitive and executive processes as well as in the manifestation of neural diseases and psychopathologies. The current characterizations of functional brain networks are limited to global and static measures that quantify average activity across subjects, brain regions and time. This research addresses this problem by developing a signal processing framework to study the time-varying nature of the functional brain networks based on multichannel electroencephalogram (EEG) data for a better understanding of cognitive control. This research develops three major approaches to study the dynamic nature of functional connectivity in the brain. First, time-varying measures of synchrony along with statistical hypothesis testing are implemented to construct sparse weighted graphs across time and frequency. Second, hierarchical multiple subject clustering algorithms are developed with information theoretic criteria to identify time-varying community structure. Third, a statistical signal processing framework is developed to summarize dynamic network activity with a few representative networks and to identify transient and stationary activity. Finally, this research is applied to the study of cognitive control for identifying the pathways that control cognition and perception, and in understanding the basic network causes of psychopathologies.