Supervised Domain Adaptation Mitigates Cross-Ethnicity Prediction Error in Neuroimaging-Based Cognitive Prediction
Year: 2026
Type: preprint
Abstract: Machine-learning models are increasingly used to predict cognitive and clinical outcomes from neuroimaging data, yet challenges in fairness and generalizability remain. Large-scale datasets are often racially and ethnically imbalanced, leading to systematic performance disparities, with models typically achieving higher accuracy for majority populations represented in the training data. In this study, we evaluated whether supervised domain adaptation methods—including balanced weighting, two-stage TrAdaBoost, feature augmentation with SrcOnly prediction, and linear interpolation—can mitigate these biases. Using the ABCD dataset, we assessed whether models trained on 80 different MRI measures from White American participants could generalize more effectively to African American participants. All domain adaptation methods reduced prediction error for African American participants, particularly for MRI modalities with large baseline disparities (e.g., structural MRI), while offering limited improvements where initial gaps were smaller (e.g., functional connectivity). Among the approaches, balanced weighting performed best and remained stable and beneficial even when only 10 African American participants were used to adapt the original model trained exclusively on White American participants. These findings suggest that simple, low-cost strategies can effectively reduce cross-ethnic performance gaps and improve equity in predictive neuroimaging, offering a practical path forward for future neuroimaging predictive biomarkers. Significant Statement Large-scale neuroimaging datasets increasingly enable machine-learning models to predict cognitive and clinical outcomes; however, these datasets are often ethnically/racially imbalanced. As a result, predictive models tend to generalize poorly to underrepresented populations. We demonstrate that, across 80 MRI phenotypes, a class of machine-learning approaches collectively known as supervised domain adaptation can substantially reduce cross-ethnicity disparities in neuroimaging-based cognitive prediction, even when only limited data from underrepresented groups are available. Among the methods evaluated, balanced weighting achieved the best performance while maintaining low computational cost. Together, these findings provide a practical and scalable framework for improving fairness and generalizability in neuroimaging-based machine learning under realistic conditions of ethnic/racial imbalance.
Institution: University of Otago
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G778140590, G8852411848, 9/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT U MICHIGAN, 20/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT VCU, 3/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT U MINNESOTA, 4/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT FIU, 7/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT YALE, 17/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT UCLA, 15/21 ABCD-USA Consortium: Research Project Site at LIBR, 16/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT UNIVERSITY OF ROCHESTER, 8/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT CHLA, ABCD-USA Consortium: Coordinating Center, 6/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT OHSU, 10/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT SRI, 12/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT U PITTSBURGH, 19/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT UVM, 1/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT U UTAH, 11/21 ABCD-USA Consortium: Research Project Site at UWM, ABCD-USA Consortium: Data Analysis, Informatics and Resource Center, 21/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT WUSTL, 13/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT MUSC, 5/21 ABCD-USA Consortium: Research Project Site at UC San Diego, 18/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT THE UNIVERSITY OF FLORIDA, 2/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT UMB, 14/21 ABCD-USA Consortium: Research Project Site at CU Boulder
OpenAlex ID: W7162797170