A Signal Processing Framework for Studying Dynamic Functional Brain Networks
Department of Electrical and Computer Engineering
Michigan State University
Monday, July 1, 13:40-14:30
Increasingly sophisticated neuroimaging methods have opened up important areas of basic research in psychiatry, psychology, and neurology. These neuroimaging modalities pose new challenges and opportunities for the signal processing community to analyze highly complex, multi-dimensional and dynamic data. One particular challenge is the identification of dynamic functional networks underlying observed neural activity. Current imaging modalities index local neural activity very well, but there is an increasing need for methods that provide measures of the interaction between regional activations. In this talk, I will focus on signal processing methods to quantify this interaction between different parts of the brain based on the electroencephalogram (EEG) measurement of the brain activity. In the first part of the talk, I will present phase synchronization as a plausible mechanism for modeling the reciprocal interactions between local networks of the brain. In order to quantify the time varying nature of interactions through phase synchronization, a new time-frequency phase synchrony (TFPS) measure based on Rihaczek distribution will be introduced. After the functional connectivity is quantified through the proposed TFPS measure, new graph theoretic measures will be proposed to characterize the topology of the networks. In the second part of the talk, I will present some work on information-theoretic approaches for understanding the causality of the interactions in the brain. Finally, application of the proposed methods to EEG data containing the error-related negativity (ERN), a brain potential response that indexes endogenous action monitoring, will be presented.
Selin Aviyente received her B.S. degree with high honors in electrical and electronics engineering from Bogazici University, Istanbul in 1997. She received her M.S. and Ph.D. degrees, both in Electrical Engineering: Systems, from the University of Michigan, Ann Arbor, in 1999 and 2002, respectively. Currently, she is an associate professor in the Department of Electrical and Computer Engineering at Michigan State University. Her research focuses on statistical signal processing, in particular non-stationary signal analysis, with applications to biological signals. Her most recent research focuses on the study of the functional networks in the brain. She is the recipient of 2005 Withrow Teaching Excellence Award and 2008 NSF CAREER Award.