%0 Electronic Article
%A Sasse, Leonard
%A Larabi, Daouia I.
%A Omidvarnia, Amir
%A Jung, Kyesam
%A Hoffstaedter, Felix
%A Jocham, Gerhard
%A Eickhoff, Simon B.
%A Patil, Kaustubh R.
%T Intermediately Synchronised Brain States optimise trade-off between Subject Identifiability and Predictive Capacity
%J bioRxiv
%M FZJ-2023-01295
%D 2022
%X 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.
%F PUB:(DE-HGF)25
%9 Preprint
%R 10.1101/2022.09.30.510304
%U https://juser.fz-juelich.de/record/1004183