Funtional Magnetic Resonance Imaging (fMRI) offers a rich source of data for understanding brain structure and function. There is an urgent need for computational approaches to eliciting brain structure-function relationships from such data. While current approaches that rely on network representations of fMRI data offer useful insights into brain connectivity, they have significant limitations in terms of discovering and validating complex functional mechanisms that underly brain function: the very notion of a node in the functional network is difficult to define, inter- and intra- subject variability leads to network representations that vary a great deal within and among subjects; and static representations of correlations between activities of different brain regions offer only a partial picture of brain activity which is inherently dynamic.
This exploratory research project aims to introduce dynamic network analysis techniques to discover functional brain regions and study their evolution over time and across subjects. It leverages the research team's experience in the analysis of continuous and highly variable spatio-temporal climate data to address several of the outstanding challenges in analyzing brain networks built from fMRI data. A key goal of this exploratory study is to explore the feasibility of analyzing network representations of fMRI data to answer questions such as the following: Do nodes changes dynamically with time during an fMRI scan? What community detection techniques work best for dynamic networks? What patterns arise in dynamic brain networks? What is the statistical validity of the observed patterns?
Broader Impact: If successful, the project could establish the feasibility of research directions that could eventually lead to computational tools that enable better characterization of normal and abnormal brain function; better understanding of the variation of brain function within and across individuals over time, including patterns that characterize the brain activities of different populations e.g., adolescents, those suffering from specific brain disorders, etc. The project offers enhanced opportunities for interdisciplinary collaborations between neuroscientists and computer scientists, and research-based advanced training of graduate and postdoctoral students at the University of Minnesota. Free dissemination of open source implementations of the algorithms resulting from the project to the larger research community contribute to its broader impact.
