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001006593 1001_ $$0P:(DE-Juel1)187351$$aKomeyer, Vera$$b0
001006593 1112_ $$aGeneral Assembly of the Joint Lab Supercomputing and Modeling for the Human Brain (SMHB)$$cJülich$$d2023-04-04 - 2023-04-05$$wGermany
001006593 245__ $$aThe Confound Continuum: A 2D confounder assessment for AI in precision medicine
001006593 260__ $$c2023
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001006593 500__ $$aThis research was supported by the Joint Lab “Supercomputing and Modeling for the Human Brain”.
001006593 520__ $$aConfounding presents a major challenge in neuroimaging machine learning applications. Confounderscan influence both, brain-derived features and phenotypical targets1. Removing theirsignal from the data changes the feature-target relationship which ultimately affects the model interpretation.Additionally, confounders are not always straightforward to identify. To target this,we introduce the idea of a 2D Confound Continuum (CC). Its ordinate evaluates the strength ofthe statistical relationship between a confound and the feature(s)/target, thereby helping to betterunderstand its signal contributions to the data (statistical CC). Its abscissa defines the strength ofthe conceptual or biological relationship and hence the effects of removal on the model interpretation(conceptual CC). Sorting potential confounders within the CC can help to better understandtheir role and impact on building predictive models.
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001006593 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b1
001006593 7001_ $$0P:(DE-HGF)0$$aGrefkes, Christian$$b2
001006593 7001_ $$0P:(DE-Juel1)185083$$aRaimondo, Federico$$b3
001006593 7001_ $$0P:(DE-Juel1)172843$$aPatil, Kaustubh$$b4
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001006593 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Department of Neurology,University Hospital Cologne and Medical Faculty, University of Cologne$$b2
001006593 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)185083$$aForschungszentrum Jülich$$b3$$kFZJ
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001006593 9141_ $$y2023
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