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ComBatLS: A Location- and Scale-Preserving Method for Multi-Site Image Harmonization.

Margaret Gardner^1,2, Russell T Shinohara^3,4, Richard A I Bethlehem⁵

¹Brain-Gene-Development Lab, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, Pennsylvania, USA.

Human Brain Mapping

|June 11, 2025

Summary

This summary is machine-generated.

This study introduces ComBatLS, a new harmonization method that preserves biological sex and age effects in neuroimaging data. ComBatLS improves the accuracy of normative scores and reduces sex biases, enhancing data harmonization across studies.

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Area of Science:

Neuroimaging data analysis
Biostatistics
Computational neuroscience

Background:

Massive datasets and harmonization methods are used for neuroanatomical normative models.
Current harmonization tools fail to preserve biological covariates like sex and age, leading to errors in normative scores.

Purpose of the Study:

Introduce ComBatLS, an extension of ComBat, to preserve biological variance in neuroanatomical features.
Improve the accuracy of normative scores and reduce sex biases in neuroimaging data.
Successfully harmonize multi-site neuroimaging data.

Main Methods:

Developed ComBatLS, an extension of the ComBat harmonization method.
Utilized UK Biobank data for validation.
Tested ComBatLS on consortium data from over 50 studies.

Main Results:

ComBatLS robustly replicates normative scores better than other ComBat methods in sex-imbalanced datasets.
ComBatLS significantly reduces sex biases in normative scores compared to traditional methods.
ComBatLS successfully harmonizes consortium data across diverse studies.

Conclusions:

ComBatLS is a superior harmonization method for neuroimaging data, preserving biological variance.
This method enhances the reliability of normative scores and reduces systematic biases.
ComBatLS offers a valuable tool for large-scale neuroimaging data analysis and harmonization.