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@ARTICLE{Wiersch:1026172,
      author       = {Wiersch, Lisa and Friedrich, Patrick and Hamdan, Sami and
                      Komeyer, Vera and Hoffstaedter, Felix and Patil, Kaustubh R.
                      and Eickhoff, Simon B. and Weis, Susanne},
      title        = {{S}ex classification from functional brain connectivity:
                      {G}eneralization to multiple datasets},
      journal      = {Human brain mapping},
      volume       = {45},
      number       = {6},
      issn         = {1065-9471},
      address      = {New York, NY},
      publisher    = {Wiley-Liss},
      reportid     = {FZJ-2024-03325},
      pages        = {e26683},
      year         = {2024},
      note         = {Funding informationDeutsche Forschungsgemeinschaft
                      (DFG),Grant/Award Numbers: 491111487,431549029; National
                      Institute of MentalHealth, Grant/Award Number:R01-MH074457;
                      the Helmholtz PortfolioTheme “Supercomputing and Modeling
                      for theHuman Brain”; European Union's Horizon2020 Research
                      and Innovation Programme,Grant/Award Number: 945539},
      abstract     = {Machine learning (ML) approaches are increasingly being
                      applied to neuroimaging data. Studies in neuroscience
                      typically have to rely on a limited set of training data
                      which may impair the generalizability of ML models. However,
                      it is still unclear which kind of training sample is best
                      suited to optimize generalization performance. In the
                      present study, we systematically investigated the
                      generalization performance of sex classification models
                      trained on the parcelwise connectivity profile of either
                      single samples or compound samples of two different sizes.
                      Generalization performance was quantified in terms of mean
                      across-sample classification accuracy and spatial
                      consistency of accurately classifying parcels. Our results
                      indicate that the generalization performance of parcelwise
                      classifiers (pwCs) trained on single dataset samples is
                      dependent on the specific test samples. Certain datasets
                      seem to "match" in the sense that classifiers trained on a
                      sample from one dataset achieved a high accuracy when tested
                      on the respected other one and vice versa. The pwCs trained
                      on the compound samples demonstrated overall highest
                      generalization performance for all test samples, including
                      one derived from a dataset not included in building the
                      training samples. Thus, our results indicate that both a
                      large sample size and a heterogeneous data composition of a
                      training sample have a central role in achieving
                      generalizable results.Keywords: big data; generalizability;
                      machine learning; neuroimaging; resting‐state functional
                      connectivity; sex classification.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / DFG project 491111487 -
                      Open-Access-Publikationskosten / 2022 - 2024 /
                      Forschungszentrum Jülich (OAPKFZJ) (491111487)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(GEPRIS)491111487},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {38647035},
      UT           = {WOS:001206019500001},
      doi          = {10.1002/hbm.26683},
      url          = {https://juser.fz-juelich.de/record/1026172},
}