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Related Experiment Video

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Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA.

Joaquim Radua1, Eduard Vieta2, Russell Shinohara3

  • 1Imaging of Mood- and Anxiety-Related Disorders (IMARD) Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; CIBERSAM, Madrid, Spain; Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Department of Clinical Neuroscience, Stockholm Health Care Services, Stockholm County Council, Karolinska Institutet, Stockholm, Sweden.

Neuroimage
|May 30, 2020
PubMed
Summary
This summary is machine-generated.

The ComBat method effectively reduces site-related heterogeneity in neuroimaging data, enhancing statistical significance and power in multi-site studies like those by the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium.

Keywords:
BrainCortical thicknessGray matterMega-analysisNeuroimagingSchizophreniaVolume

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Area of Science:

  • Neuroimaging
  • Psychiatric Genetics
  • Data Harmonization

Background:

  • Neuroimaging studies often face limitations due to small sample sizes.
  • The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium addresses this by pooling global data, but site-specific variations (heterogeneity) arise from different scanners and protocols.
  • Existing methods like random-effects meta-analysis and mixed-effects mega-analysis are used to manage this heterogeneity.

Purpose of the Study:

  • To evaluate the effectiveness of the ComBat batch adjustment method in reducing site-related heterogeneity in multi-site neuroimaging data.
  • To determine if ComBat can increase statistical power compared to traditional meta-analysis and mega-analysis approaches.
  • To assess ComBat's performance across different neuroimaging measures (cortical thickness, surface area, subcortical volumes) and its utility for large-scale consortia.

Main Methods:

  • Conducted random-effects meta-analyses, mixed-effects mega-analyses, and ComBat mega-analyses on structural neuroimaging data from 2897 individuals with schizophrenia and 3141 healthy controls across 33 sites.
  • Compared cortical thickness, surface area, and subcortical volumes between groups, controlling for age and sex.
  • Applied ComBat harmonization to the data and compared results with traditional methods, including analyses with reduced site numbers.

Main Results:

  • ComBat significantly increased the statistical significance of findings compared to random-effects meta-analyses.
  • ComBat demonstrated slightly improved statistical significance over mixed-effects mega-analysis.
  • ComBat enhanced statistical power, particularly when analyses were repeated with fewer sites, and yielded consistent results across different neuroimaging measures.

Conclusions:

  • The ComBat batch adjustment method is a valuable tool for reducing site effects in multi-site neuroimaging studies, including ENIGMA projects.
  • Applying ComBat can lead to increased statistical significance and power in detecting neuroimaging differences between clinical and control groups.
  • The study provides accessible R functions for implementing ComBat, supporting its adoption in large-scale structural imaging research and machine learning applications.