Systematically comparing properties of local dynamics and pairwise coupling in the brain

COEX 

Dynamical structures of brain activity can be quantified from functional magnetic resonance imaging (fMRI), like local regional activity and functional connectivity (FC) between pairs of regions (Fig 1A). To date, most studies use only one of the two representations with a limited set of statistics, like the fractional amplitude of low-frequency fluctuations (fALFF) for regional dynamics and the Pearson correlation coefficient for FC [1]. Emerging work using comprehensive libraries of interdisciplinary time-series features [2,3] suggests that alternative statistics may be more suitable for a given application [3,4], though there is currently no unifying framework for comparing across multiple features and representation types simultaneously. Here, we introduce a systematic approach to quantify diverse types of local and pairwise dynamical structure from fMRI data, comparing the ability of multiple feature-based representations to capture meaningful differences in neuropsychiatric case–control datasets.