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| Home > Publications database > Intermediately Synchronised Brain States optimise trade-off between Subject Identifiability and Predictive Capacity |
| Home > Publications database > Intermediately Synchronised Brain States optimise trade-off between Subject Identifiability and Predictive Capacity |
| Preprint | FZJ-2023-01295 |
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2022
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Please use a persistent id in citations: http://hdl.handle.net/2128/34186 doi:10.1101/2022.09.30.510304
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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