Abstract
The brain’s complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case–control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case–control analyses, while also identifying new ways to quantify informative dynamical fMRI structures. While simple statistical representations of fMRI dynamics performed surprisingly well (e.g., properties within a single brain region), combining intra-regional properties with inter-regional coupling generally improved performance, underscoring the distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The comprehensive, data-driven method introduced here enables systematic identification and interpretation of quantitative dynamical signatures of multivariate time-series data, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.
Competing Interest Statement
K.A. is an employee and shareholder of BrainKey Inc., a medical image analysis software company, with all contributions to the present work made prior to commencing this position.
Footnotes
Results Sections 2.1 through 2.5 updated to reflect p-values with Benjamini--Hochberg correction for multiple comparisons; Results Section 2.6 added to directly compare performance across rs-fMRI dynamics representation types for each disorder; Figures 1 through 6 in the main text updated; Supplemental figures updated; Supplemental tables updated.
