eLife

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   10.64898/2026.02.02.703236

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   10.64898/2026.07.01.735788

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   10.64898/2026.06.17.26354096

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   10.64898/2026.02.11.705448

5. Supervised Domain Adaptation Mitigates Cross-Ethnicity Prediction Error in Neuroimaging Based Cognitive Prediction

   This article has 5 authors:

   1. Farzane Lal Khakpoor
   2. William van der Vliet
   3. Jeremiah Deng
   4. Yue Wang
   5. Narun Pat

   This article has been curated by 1 group:

   • Curated by eLife

     eLife Assessment

     This study provides a useful investigation of machine learning approaches that can lessen potential gaps in the prediction of behaviour from brain imaging data across majority and minority samples. The authors provide incomplete evidence to suggest that domain adaptation methods can mitigate these gaps. The analysis would benefit from further testing of model generalisability and the inclusion of recommended workflows for how the tested approaches can be used in future research. This work will be of interest to scientists using machine learning in brain imaging.

   Reviewed by eLife

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   10.1101/2025.08.14.670329

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   10.64898/2026.06.26.734820

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   10.64898/2026.04.28.721452

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   10.64898/2026.05.07.723469

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    10.64898/2026.06.01.729353