ComBat-Predict Enhances Generalizability of Neuroimaging Models to New Sites.

Overview

1. Department of Public Health Sciences Medical University of South Carolina Charleston South Carolina USA
2. Brain‐Gene‐Development Lab The Children's Hospital of Philadelphia and Penn Medicine Philadelphia Pennsylvania USA
3. Neuroscience Graduate Group, Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania USA
4. Department of Radiology and Medical Imaging University of Virginia Charlottesville Virginia USA
5. Department of Radiology University of Pennsylvania Philadelphia Pennsylvania USA
6. Department of Neurology Medical University of South Carolina Charleston South Carolina USA
7. Department of Neuroscience Medical University of South Carolina Charleston South Carolina USA
8. Department of Psychology University of Cambridge Cambridge UK
9. Department of Psychiatry University of Pennsylvania Philadelphia Pennsylvania USA
10. Department of Child and Adolescent Psychiatry and Behavioral Sciences The Children's Hospital of Philadelphia Philadelphia Pennsylvania USA
11. Lifespan Brain Institute of The Children's Hospital of Philadelphia and Penn Medicine Philadelphia Pennsylvania USA

Abstract

Neuroimaging is vital in quantifying brain atrophy due to typical aging and due to neurodegenerative diseases. To collect large samples necessary to model lifespan brain development, research consortiums aggregate images acquired across multiple study sites. Previous studies have demonstrated that this multi‐site study design can lead to site‐related bias, necessitating harmonization of these “site effects.” However, current methodologies are unable to generalize to new sites outside the original harmonized sample, limiting translation to new sites or clinical practice. Here, we propose a method called ComBat‐Predict (CB‐Predict) building upon the ComBat method for site effect adjustment, which extends to data from a new site with smaller sample sizes and unknown site effects. In data from the Alzheimer's Disease Neuroimaging Initiative, our proposed method mitigates bias and yields high accuracy in predicting cortical thickness measures when generalizing the model to new data. Furthermore, we demonstrate that our proposed harmonization method can reduce site‐related variance in centile scores estimated using data from the Lifespan Brain Chart Consortium. Altogether, our results demonstrate that CB‐Predict effectively harmonizes new sites and thereby enables effective translation of neuroimaging models to additional samples.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

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Data Availability Statement

Data were obtained from the publicly available ADNI database (adni.loni.usc.edu) and the LBCC consortium dataset (brainchart.shinyapps.io/brainchart), both accessed under their respective data use agreements. Links to open datasets of LBCC are listed on https://github.com/brainchart/Lifespan. The investigators within the ADNI contributed to the design and implementation of the ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at https://adni.loni.usc.edu/wp‐content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf (https://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf). ComBat‐Predict is implemented as part of the ComBatFamily R package as predict.comfam and publicly accessible via Github: https://github.com/andy1764/ComBatFamily. Code and data analysis scripts are available at: https://github.com/Munchkin‐233/ComBat‐Predict_eval (https://github.com/Munchkin-233/ComBat-Predict_eval).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 6 keywords, 10 MeSH terms, 1 funder, 40 references, 1 integrity notice.

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This paper

Xin, Y., Gardner, M., Tustison, N. J., Cook, P., Gee, J., Benitez, A., Jensen, J. H., Alzheimer's Disease Neuroimaging Initiative, Lifespan Brain Chart Consortium, Bethlehem, R., Seidlitz, J., Alexander‐Bloch, A. F., & Chen, A. A. (2026). ComBat-Predict Enhances Generalizability of Neuroimaging Models to New Sites. Human brain mapping, 47(8), e70546. https://doi.org/10.1002/hbm.70546

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