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: |
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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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