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@ARTICLE{Sasse:1009258,
      author       = {Sasse, Leonard and Larabi, Daouia and Omidvarnia, Amir and
                      Jung, Kyesam and Hoffstaedter, Felix and Jocham, Gerhard and
                      Eickhoff, Simon B. and Patil, Kaustubh R.},
      title        = {{I}ntermediately synchronised brain states optimise
                      trade-off between subject specificity and predictive
                      capacity},
      journal      = {Communications biology},
      volume       = {6},
      number       = {1},
      issn         = {2399-3642},
      address      = {London},
      publisher    = {Springer Nature},
      reportid     = {FZJ-2023-02718},
      pages        = {705},
      year         = {2023},
      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.},
      cin          = {INM-7},
      ddc          = {570},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37429937},
      UT           = {WOS:001025904900005},
      doi          = {10.1038/s42003-023-05073-w},
      url          = {https://juser.fz-juelich.de/record/1009258},
}