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@INPROCEEDINGS{Komeyer:1006593,
      author       = {Komeyer, Vera and Eickhoff, Simon and Grefkes, Christian
                      and Raimondo, Federico and Patil, Kaustubh},
      title        = {{T}he {C}onfound {C}ontinuum: {A} 2{D} confounder
                      assessment for {AI} in precision medicine},
      reportid     = {FZJ-2023-01734},
      year         = {2023},
      note         = {This research was supported by the Joint Lab
                      “Supercomputing and Modeling for the Human Brain”.},
      abstract     = {Confounding presents a major challenge in neuroimaging
                      machine learning applications. Confounderscan influence
                      both, brain-derived features and phenotypical targets1.
                      Removing theirsignal from the data changes the
                      feature-target relationship which ultimately affects the
                      model interpretation.Additionally, confounders are not
                      always straightforward to identify. To target this,we
                      introduce the idea of a 2D Confound Continuum (CC). Its
                      ordinate evaluates the strength ofthe statistical
                      relationship between a confound and the feature(s)/target,
                      thereby helping to betterunderstand its signal contributions
                      to the data (statistical CC). Its abscissa defines the
                      strength ofthe conceptual or biological relationship and
                      hence the effects of removal on the model
                      interpretation(conceptual CC). Sorting potential confounders
                      within the CC can help to better understandtheir role and
                      impact on building predictive models.},
      month         = {Apr},
      date          = {2023-04-04},
      organization  = {General Assembly of the Joint Lab
                       Supercomputing and Modeling for the
                       Human Brain (SMHB), Jülich (Germany),
                       4 Apr 2023 - 5 Apr 2023},
      subtyp        = {After Call},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / JL SMHB - Joint Lab Supercomputing and Modeling
                      for the Human Brain (JL SMHB-2021-2027)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(DE-Juel1)JL SMHB-2021-2027},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1006593},
}