Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A combination of ketones and NAD<sup>+</sup> precursor preserves white matter integrity in mild cognitive impairment.

Alzheimer's & dementia (New York, N. Y.)·2026

Same author

Automated Segmentation of Brainstem and Subcortical White Matter: Mapping the Deep Tegmental Core with BundleParc.

bioRxiv : the preprint server for biology·2026

Same author

Anatomy and Structural Connectivity of the Opercula.

Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society·2026

Same author

Disentangling crossing fibers with advanced dMRI methods reveals bundle-specific degeneration across the visual system in asymmetric glaucoma.

PloS one·2026

Same author

When little brain goes to school: Impact of pedagogy on cerebellar peduncles' development.

Developmental cognitive neuroscience·2026

Same author

Reinforcement Learning for Unsupervised Domain Adaptation in Spatio-Temporal Echocardiography Segmentation.

IEEE transactions on medical imaging·2026

Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026

Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026

Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026

Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026

Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026

Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 10, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Challenges and best practices when using ComBAT to harmonize diffusion MRI data.

Pierre-Marc Jodoin^1,2, Manon Edde^3,4, Gabriel Girard³

¹VitaLab, Dep of Computer Science, University of Sherbrooke, Sherbrooke, Qc, J1K 2R1, Canada. Pierre-Marc.Jodoin@usherbrooke.ca.

Scientific Reports

|November 24, 2025

Summary

This summary is machine-generated.

ComBAT is a standard for harmonizing MRI data, but its assumptions can lead to errors. This study reviews ComBAT

More Related Videos

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Published on: August 14, 2019

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

Related Experiment Videos

Last Updated: Jan 10, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Published on: August 14, 2019

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

Area of Science:

Neuroimaging
Biostatistics
Medical Informatics

Background:

ComBAT is widely used for harmonizing MRI data, correcting site-specific biases.
Violated ComBAT assumptions can compromise harmonization accuracy and data integrity.
Understanding these assumptions is crucial for reliable neuroimaging analysis.

Purpose of the Study:

To critically review ComBAT's mathematical underpinnings and assumptions.
To investigate the impact of demographic factors on ComBAT harmonization performance.
To provide recommendations for improving harmonization consistency and reproducibility.

Main Methods:

Mathematical review of ComBAT's foundational principles.
Experimental assessment using Pairwise-ComBAT with simulated population variations.
Analysis of effects of population size, age distribution, covariates, and bias magnitudes.

Main Results:

ComBAT's performance is sensitive to its underlying assumptions and population characteristics.
Deviations from assumptions can lead to suboptimal harmonization outcomes.
Specific demographic factors significantly influence the reliability of ComBAT harmonization.

Conclusions:

Adherence to ComBAT's assumptions is critical for accurate MRI data harmonization.
Recommendations are provided to enhance consistency and reproducibility in neuroimaging studies.
Optimized ComBAT application supports open science, collaborative research, and clinical deployment.