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| Home > Publications database > Intermediately Synchronised Brain States optimise trade-off between Subject Identifiability and Predictive Capacity > print |
| 001 | 1004183 | ||
| 005 | 20240226075522.0 | ||
| 024 | 7 | _ | |a 10.1101/2022.09.30.510304 |2 doi |
| 024 | 7 | _ | |a 2128/34186 |2 Handle |
| 037 | _ | _ | |a FZJ-2023-01295 |
| 100 | 1 | _ | |a Sasse, Leonard |0 P:(DE-Juel1)190306 |b 0 |
| 245 | _ | _ | |a Intermediately Synchronised Brain States optimise trade-off between Subject Identifiability and Predictive Capacity |
| 260 | _ | _ | |c 2022 |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1679557417_31832 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 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. |
| 536 | _ | _ | |a 5251 - Multilevel Brain Organization and Variability (POF4-525) |0 G:(DE-HGF)POF4-5251 |c POF4-525 |f POF IV |x 0 |
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| 700 | 1 | _ | |a Larabi, Daouia I. |0 P:(DE-Juel1)180372 |b 1 |
| 700 | 1 | _ | |a Omidvarnia, Amir |0 P:(DE-Juel1)188339 |b 2 |
| 700 | 1 | _ | |a Jung, Kyesam |0 P:(DE-Juel1)178611 |b 3 |
| 700 | 1 | _ | |a Hoffstaedter, Felix |0 P:(DE-Juel1)131684 |b 4 |
| 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 10.1101/2022.09.30.510304 |y 2022 |t bioRxiv |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/1004183/files/2022.09.30.510304.docx |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/1004183/files/2022.09.30.510304.full-1.pdf |
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| 914 | 1 | _ | |y 2023 |
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