Journal Article

ComBat‐Predict Enhances Generalizability of Neuroimaging Models to New Sites

Xin, Yao, Bethlehem, Richard, Benitez, Andreana, Seidlitz, Jakob, Gardner, Margaret, Tustison, Nicholas J., Chen, Andrew A., Alexander‐Bloch, Aaron F., Gee, James, Jensen, Jens H., Cook, Philip

2026

Overview

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. ComBat‐Predict effectively harmonizes data from a new site, improving the applicability of normative models to private data. ComBat‐Predict is available for R in the ComBatFamily package.

• Publisher: John Wiley & Sons, Inc
• Subject: ADNI / Aging - pathology / Alzheimer Disease - diagnostic imaging / Alzheimer Disease - pathology / Alzheimer's disease / Atrophy / Bias / Brain / Brain - pathology / Brain Cortical Thickness / Cerebral Cortex - diagnostic imaging / Cerebral Cortex - pathology / ComBat / Consortia / cortical thickness / Design / Estimates / Female / Humans / Image acquisition / Life span / Machine learning / Magnetic resonance imaging / Magnetic Resonance Imaging - methods / Magnetic Resonance Imaging - standards / Male / Medical imaging / Neurodegenerative diseases / Neuroimaging / Neuroimaging - methods / Neuroimaging - standards / normative modeling / site effects / Statistical power / Thickness measurement / Translation