Journal Article

FZJ-2023-02718

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Intermediately synchronised brain states optimise trade-off between subject specificity and predictive capacity

Sasse, L.FZJ* ; Larabi, D.FZJ* ; Omidvarnia, A.FZJ* ; Jung, K.FZJ* ; Hoffstaedter, F.FZJ* ; Jocham, G. ; Eickhoff, S. B.FZJ* ; Patil, K. R. (Corresponding author)FZJ*

2023
Springer Nature London

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Please use a persistent id in citations: doi:10.1038/s42003-023-05073-w  doi:10.34734/FZJ-2023-02718

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.

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Contributing Institute(s):

1. Gehirn & Verhalten (INM-7)

Research Program(s):

1. 5251 - Multilevel Brain Organization and Variability (POF4-525) (POF4-525)

Appears in the scientific report 2023

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