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| 001 | 1009258 | ||
| 005 | 20231127201901.0 | ||
| 024 | 7 | _ | |a 10.1038/s42003-023-05073-w |2 doi |
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| 100 | 1 | _ | |a Sasse, Leonard |0 P:(DE-Juel1)190306 |b 0 |
| 245 | _ | _ | |a Intermediately synchronised brain states optimise trade-off between subject specificity and predictive capacity |
| 260 | _ | _ | |a London |c 2023 |b Springer Nature |
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| 520 | _ | _ | |a 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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| 700 | 1 | _ | |a Jocham, Gerhard |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Eickhoff, Simon B. |0 P:(DE-Juel1)131678 |b 6 |
| 700 | 1 | _ | |a Patil, Kaustubh R. |0 P:(DE-Juel1)172843 |b 7 |e Corresponding author |
| 773 | _ | _ | |a 10.1038/s42003-023-05073-w |g Vol. 6, no. 1, p. 705 |0 PERI:(DE-600)2919698-X |n 1 |p 705 |t Communications biology |v 6 |y 2023 |x 2399-3642 |
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