Intermediately synchronised brain states optimise trade-off between subject specificity and predictive capacity

Autor: Leonard Sasse, Daouia I. Larabi, Amir Omidvarnia, Kyesam Jung, Felix Hoffstaedter, Gerhard Jocham, Simon B. Eickhoff, Kaustubh R. Patil
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Communications Biology, Vol 6, Iss 1, Pp 1-14 (2023)
Druh dokumentu: article
ISSN: 2399-3642
DOI: 10.1038/s42003-023-05073-w
Popis: Abstract Functional connectivity (FC) refers to the statistical dependencies between activity of distinct brain areas. To study temporal fluctuations in FC within the duration of a functional magnetic resonance imaging (fMRI) scanning session, researchers have proposed the computation of an edge time series (ETS) and their derivatives. Evidence suggests that FC is driven by a few time points of high-amplitude co-fluctuation (HACF) in the ETS, which may also contribute disproportionately to interindividual differences. However, it remains unclear to what degree different time points actually contribute to brain-behaviour associations. Here, we systematically evaluate this question by assessing the predictive utility of FC estimates at different levels of co-fluctuation using machine learning (ML) approaches. We demonstrate that time points of lower and intermediate co-fluctuation levels provide overall highest subject specificity as well as highest predictive capacity of individual-level phenotypes.
Databáze: Directory of Open Access Journals
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