Journal Article

FZJ-2022-01698

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity

Li, J. (Corresponding author)FZJ* ; Bzdok, D. ; Chen, J. ; Tam, A. ; Ooi, L. Q. R. ; Holmes, A. J. ; Ge, T. ; Patil, K. R.FZJ* ; Jabbi, M. ; Eickhoff, S. B.FZJ* ; Yeo, B. T. T. (Corresponding author) ; Genon, S. (Corresponding author)FZJ*

2022
Assoc. Washington, DC [u.a.]

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Please use a persistent id in citations: http://hdl.handle.net/2128/30862  doi:10.1126/sciadv.abj1812

Abstract: Algorithmic 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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Contributing Institute(s):

1. Gehirn & Verhalten (INM-7)

Research Program(s):

1. 5254 - Neuroscientific Data Analytics and AI (POF4-525) (POF4-525)

Appears in the scientific report 2022

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