We assess the potential application of RMT-based features for the analysis of functional MRI (fMRI) across diverse datasets. As novel contributions, we (1) assess the potential for RMT-inspired, whole-brain features extracted from voxel-wise functional connectivity, (2) assess these features’ predictive—rather than explanatory—value, (3) investigate the effect of varying RMT analysis methods on the robustness of study findings, and (4) make general-purpose code publicly available for users to extract these features from a wide variety of data. We find preliminary evidence suggesting that RMT-inspired features may have unique potential in analyses of fMRI functional connectivity.
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