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000906797 1001_ $$0P:(DE-Juel1)164828$$aLi, Jingwei$$b0$$eCorresponding author
000906797 245__ $$aCross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity
000906797 260__ $$aWashington, DC [u.a.]$$bAssoc.$$c2022
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000906797 520__ $$aAlgorithmic biases that favor majority populations pose a key challenge to the application of machine learning for precision medicine. Here, we assessed such bias in prediction models of behavioral phenotypes from brain functional magnetic resonance imaging. We examined the prediction bias using two independent datasets (preadolescent versus adult) of mixed ethnic/racial composition. When predictive models were trained on data dominated by white Americans (WA), out-of-sample prediction errors were generally higher for African Americans (AA) than for WA. This bias toward WA corresponds to more WA-like brain-behavior association patterns learned by the models. When models were trained on AA only, compared to training only on WA or an equal number of AA and WA participants, AA prediction accuracy improved but stayed below that for WA. Overall, the results point to the need for caution and further research regarding the application of current brain-behavior prediction models in minority populations.
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000906797 7001_ $$0P:(DE-Juel1)136848$$aBzdok, Danilo$$b1
000906797 7001_ $$00000-0001-5676-979X$$aChen, Jianzhong$$b2
000906797 7001_ $$00000-0001-6752-5707$$aTam, Angela$$b3
000906797 7001_ $$00000-0002-3546-4580$$aOoi, Leon Qi Rong$$b4
000906797 7001_ $$00000-0001-6583-803X$$aHolmes, Avram J.$$b5
000906797 7001_ $$0P:(DE-HGF)0$$aGe, Tian$$b6
000906797 7001_ $$0P:(DE-Juel1)172843$$aPatil, Kaustubh R.$$b7
000906797 7001_ $$0P:(DE-HGF)0$$aJabbi, Mbemba$$b8
000906797 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b9
000906797 7001_ $$00000-0002-0119-3276$$aYeo, B. T. Thomas$$b10$$eCorresponding author
000906797 7001_ $$0P:(DE-Juel1)161225$$aGenon, Sarah$$b11$$eCorresponding author
000906797 773__ $$0PERI:(DE-600)2810933-8$$a10.1126/sciadv.abj1812$$gVol. 8, no. 11, p. eabj1812$$n11$$peabj1812$$tScience advances$$v8$$x2375-2548$$y2022
000906797 8564_ $$uhttps://juser.fz-juelich.de/record/906797/files/Invoice_APC600284027.pdf
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