Extracting interpretable signatures of whole-brain dynamics through systematic comparison

Annie G Bryant, Kevin Aquino, Linden Parkes, Alex Fornito and Ben D Fulcher

PLOS Computational Biology, 2024, vol. 20, issue 12, 1-46

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.Author summary: Neuroimaging techniques, like resting-state functional magnetic resonance imaging (rs-fMRI), provide a window into complex brain dynamics in health and disease. Much existing work has involved manually distilling the complexity of a neural time-series dataset down to a set of hand-selected summary statistics, an approach that is prone to over-complicating or missing the most clearly interpretable and informative dynamical structures in the data. To overcome these methodological limitations, in this study, we introduce a systematic approach to capturing informative dynamical structure from neural time-series data that compares across a broad range of interpretable analysis methods. Our framework encompasses five different representations with increasing complexity, from the localized activity of a single brain region up to the distributed activity of all brain regions and their pairwise interactions. We demonstrate our method in the context of four distinct neuropsychiatric disorders to discover and compare the types of brain activity dynamics that are most informative of each diagnostic group. We find that simpler techniques, like quantifying activity within a single brain region, perform surprisingly well in classifying schizophrenia and autism spectrum disorder cases from clinically normal controls—supporting continued investigations into region-specific alterations in neuropsychiatric disorders. Furthermore, combining region-specific metrics with inter-regional interactions generally provides a more informative understanding of how brain dynamics are altered in these conditions, demonstrating the benefit of combining local dynamics with pairwise coupling. Importantly, our systematic and comprehensive approach to quantifying interpretable patterns from complex time-series data shows promise for studying signatures of brain dynamics across domains, from functional fingerprinting to developmental trajectory analysis to dementia research.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1012692

DOI: 10.1371/journal.pcbi.1012692

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