TY  - JOUR
AU  - Li, Jingwei
AU  - Bzdok, Danilo
AU  - Chen, Jianzhong
AU  - Tam, Angela
AU  - Ooi, Leon Qi Rong
AU  - Holmes, Avram J.
AU  - Ge, Tian
AU  - Patil, Kaustubh R.
AU  - Jabbi, Mbemba
AU  - Eickhoff, Simon B.
AU  - Yeo, B. T. Thomas
AU  - Genon, Sarah
TI  - Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity
JO  - Science advances
VL  - 8
IS  - 11
SN  - 2375-2548
CY  - Washington, DC [u.a.]
PB  - Assoc.
M1  - FZJ-2022-01698
SP  - eabj1812
PY  - 2022
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:35294251
UR  - <Go to ISI:>//WOS:000770280500003
DO  - DOI:10.1126/sciadv.abj1812
UR  - https://juser.fz-juelich.de/record/906797
ER  -