CAREER: Integrated Research and Education in Functional Brain Networks

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The advance in the neuroimaging technology has brought with itself many challenges. One key challenge is the identification of dynamic functional networks underlying the observed neural activity. The current imaging modalities reflect solely the local neural activity, and do not provide a measure of the interaction between electrical or magnetic activity recorded at different sites. In order to gain a better understanding of how the brain processes information, it is crucial to quantify these interactions across the brain. This research addresses this problem by developing a comprehensive signal processing framework to study the functionality of the brain as a complex system based on electroencephalogram (EEG) data for a better understanding of psychopathologies.This research develops three major signal processing methods to improve the current understanding of the brain as an integrated information processing system. First, data reduction and source separation methods that use the sparsity of the EEG signals in the time-frequency plane are developed to reduce large amounts of data into a few neuronal sources.Second, synchrony and information based functional connectivity measures are developed to quantify the time-varying dependence between different neuronal sources. Third, methods from graph theory are adopted to build network models based on the functional connectivity measures and to identify the neuronal pathways and modules. Finally, this framework is applied to the study of the brain related to different psychopathologies including schizophrenia and impulse control problems. Several educational programs are tightly integrated with these research activities to emphasize the societal benefits of engineering. In particular, the investigator is designing outreach activities for K-12 women students and developing an undergraduate signal processing course with a focus on neuroscience applications.