001004183 001__ 1004183
001004183 005__ 20240226075522.0
001004183 0247_ $$2doi$$a10.1101/2022.09.30.510304
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001004183 037__ $$aFZJ-2023-01295
001004183 1001_ $$0P:(DE-Juel1)190306$$aSasse, Leonard$$b0
001004183 245__ $$aIntermediately Synchronised Brain States optimise trade-off between Subject Identifiability and Predictive Capacity
001004183 260__ $$c2022
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001004183 520__ $$aFunctional connectivity (FC) refers to the statistical dependencies between activity of distinct brain areas. To study temporal fluctuations in FC within the duration of a functional magnetic resonance imaging (fMRI) scanning session, researchers have proposed the computation of an edge time series (ETS) and their derivatives. Evidence suggests that FC is driven by a few time points of high-amplitude co-fluctuation (HACF) in the ETS, which may also contribute disproportionately to interindividual differences. However, it remains unclear to what degree different time points actually contribute to brain-behaviour associations. Here, we systematically evaluate this question by assessing the predictive utility of FC estimates at different levels of co-fluctuation using machine learning (ML) approaches. We demonstrate that time points of lower and intermediate co-fluctuation levels provide overall highest subject specificity as well as highest predictive capacity of individual-level phenotypes.
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001004183 7001_ $$0P:(DE-Juel1)180372$$aLarabi, Daouia I.$$b1
001004183 7001_ $$0P:(DE-Juel1)188339$$aOmidvarnia, Amir$$b2
001004183 7001_ $$0P:(DE-Juel1)178611$$aJung, Kyesam$$b3
001004183 7001_ $$0P:(DE-Juel1)131684$$aHoffstaedter, Felix$$b4
001004183 7001_ $$0P:(DE-HGF)0$$aJocham, Gerhard$$b5
001004183 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b6
001004183 7001_ $$0P:(DE-Juel1)172843$$aPatil, Kaustubh R.$$b7$$eCorresponding author
001004183 773__ $$a10.1101/2022.09.30.510304$$tbioRxiv$$y2022
001004183 8564_ $$uhttps://juser.fz-juelich.de/record/1004183/files/2022.09.30.510304.docx$$yOpenAccess
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001004183 9141_ $$y2023
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