This title appears in the Scientific Report :
2022
Please use the identifier:
http://dx.doi.org/10.1126/sciadv.abj1812 in citations.
Please use the identifier: http://hdl.handle.net/2128/30862 in citations.
Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity
Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity
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...
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Personal Name(s): | Li, Jingwei (Corresponding author) |
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Bzdok, Danilo / Chen, Jianzhong / Tam, Angela / Ooi, Leon Qi Rong / Holmes, Avram J. / Ge, Tian / Patil, Kaustubh R. / Jabbi, Mbemba / Eickhoff, Simon B. / Yeo, B. T. Thomas (Corresponding author) / Genon, Sarah (Corresponding author) | |
Contributing Institute: |
Gehirn & Verhalten; INM-7 |
Published in: | Science advances, 8 (2022) 11, S. eabj1812 |
Imprint: |
Washington, DC [u.a.]
Assoc.
2022
|
PubMed ID: |
35294251 |
DOI: |
10.1126/sciadv.abj1812 |
Document Type: |
Journal Article |
Research Program: |
Neuroscientific Data Analytics and AI |
Link: |
Get full text OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/30862 in citations.
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. |