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Updated: Sep 11, 2025

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

DPABI harmonization: A toolbox for harmonizing multi-site brain imaging for big-data era.

Yu-Wei Wang^1,2,3,4, Han-Lin Wang^1,3,5, Chao-Gan Yan^1,2,3,5,6,7

¹CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.

Imaging Neuroscience (Cambridge, Mass.)

|August 13, 2025

Summary

This summary is machine-generated.

This study introduces the DPABI Harmonization module, a user-friendly tool for harmonizing multi-site resting-state fMRI data. It simplifies removing site effects, enhancing neuroimaging research reproducibility.

Keywords:

data processing quality control resting-state fMRI standardization statistical analysis

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Last Updated: Sep 11, 2025

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Area of Science:

Neuroimaging
Data Science
Biostatistics

Background:

Pooling multi-site datasets increases statistical power in neuroimaging but introduces site-specific heterogeneity.
Existing methods for harmonizing multi-site resting-state fMRI data often require significant programming expertise.
Addressing site effects is crucial for robust and reproducible neuroimaging research.

Purpose of the Study:

To introduce the DPABI Harmonization module, a novel, user-friendly tool for harmonizing multi-site neuroimaging data.
To provide an accessible workflow for removing site effects without requiring advanced programming skills.
To integrate various state-of-the-art harmonization techniques into a single, versatile platform.

Main Methods:

The DPABI Harmonization module integrates multiple techniques: Subsampling Maximum-mean-distance Algorithms (SMA), ComBat/CovBat, linear models, and invariant conditional variational auto-encoder (ICVAE).
The module offers an agnostic approach, compatible with various analysis methods.
It provides a transparent workflow for neuroscientists to manage site effects.

Main Results:

The DPABI Harmonization module successfully integrates diverse site-effect harmonization techniques.
It offers a streamlined and accessible workflow for researchers.
The tool facilitates post-hoc analysis for multi-site studies, improving data utility.

Conclusions:

The DPABI Harmonization module simplifies the complex process of harmonizing multi-site neuroimaging data.
This tool enhances the feasibility and reproducibility of multi-site neuroimaging studies.
It empowers neuroscientists with an easy-to-use solution for addressing data heterogeneity.