04. ComBat-Predict Improves Generalizability of Traditional and Normative Cortical Thickness Modeling to a New Site

Neuroimaging is vital for the screening of atypical brain development and the diagnosis of neurodegenerative diseases at an early stage. 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) extending 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 (ADNI), our proposed method mitigates bias 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 (LBCC). Altogether, our results demonstrate that CB-Predict effectively harmonizes new sites and thereby enables effective translation of neuroimaging models to additional samples.

ComBat

Cortical Thickness

Harmonization

Multi-site analysis

Site effect

Alzheimer’s Disease Neuroimaging Initiative (ADNI) 

Beginner

Influence

Women in Statistics and Data Science 2025