From 1D to 5D: Data-driven Discovery of Whole-brain Dynamic Connectivity in fMRI Data
The analysis of functional magnetic resonance imaging (fMRI) data can greatly benefit from flexible analytic approaches. In particular, the advent of data-driven approaches to identify whole-brain time-varying connectivity and activity has revealed a number of interesting relevant variation in the data which, when ignored, can provide misleading information. In this lecture Dr. Vince Calhoun will provide a comparative introduction of a range of data-driven approaches to estimating time-varying connectivity. Dr. Calhoun will also present detailed examples where studies of both brain health and disorder have been advanced by approaches designed to capture and estimate time-varying information in resting fMRI data. Dr. Calhoun will review several exemplar data sets analyzed in different ways to demonstrate the complementarity as well as trade-offs of various modeling approaches to answer questions about brain function. Finally, Dr. Calhoun will review and provide examples of strategies for validating time-varying connectivity including simulations, multimodal imaging, and comparative prediction within clinical populations, among others. As part of the interactive aspect Dr. Calhoun will provide a hands-on guide to the dynamic functional network connectivity toolbox within the GIFT software, including an online didactic analytic decision tree to introduce the various concepts and decisions that need to be made when using such tools.
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Dr. Vince Calhoun
Dr. Vince Calhoun is Founding Director of the Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) at Georgia State, Georgia Tech, Emory, Atlanta, GA.
Publication Year: 2021