Extracting interpretable signatures of whole-brain dynamics through systematic comparison

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 systematica...

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Bibliographic Details
Published in:PLoS computational biology 2024-12, Vol.20 (12), p.e1012692
Main Authors: Bryant, Annie G, Aquino, Kevin, Parkes, Linden, Fornito, Alex, Fulcher, Ben D
Format: Article
Language:English
Subjects:
Adult
Biology and Life Sciences
Brain
Brain - diagnostic imaging
Brain - physiology
Brain Mapping - methods
Case-Control Studies
Computational Biology
Diagnosis
Engineering and Technology
Female
Health aspects
Humans
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
Medicine and Health Sciences
Mental illness
Nervous system diseases
Physical Sciences
Physiological aspects
Research and Analysis Methods
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Summary: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.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1012692