Explicitly Nonlinear Connectivity-Matrix Independent Component Analysis in Resting fMRI DataDownload PDF

02 Oct 2022, 19:07 (modified: 21 Nov 2022, 07:03)InfoCog @ NeurIPS 2022 PosterReaders: Everyone
Keywords: Mutual information, functional network connectivity, time courses, nonlinear functional network connectivity, spatial domain
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Abstract: Connectivity-matrix independent component analysis (cmICA) is a data-driven method to calculate brain voxel maps of functional connectivity. It is a powerful approach, but one limitation is that it can only capture linear relationships. In this work, we focus on measuring the explicitly nonlinear relationships between the voxel connectivity to identify brain spatial map in which demonstrate explicitly nonlinear dependencies. We expand cmICA using normalized mutual information (NMI) after removing the linear relationships and find highly structured resting networks which would be completely missed by existing functional connectivity approaches.
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