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Harmonization of Structural Brain Connectivity through Distribution Matching.

Zhen Zhou1, Bruce Fischl1, Iman Aganj1

  • 1Athinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.

Biorxiv : the Preprint Server for Biology
|September 24, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel distribution-matching method to harmonize structural brain connectivity from multi-site diffusion-weighted magnetic resonance imaging (dMRI) data. The approach ensures data comparability across scanners and sites, enhancing neuroimaging research reliability.

Keywords:
Diffusion MRIconnectomedistribution matchingharmonizationstructural brain connectivity

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biostatistics

Background:

  • Multi-site diffusion-weighted magnetic resonance imaging (dMRI) studies offer increased statistical power for brain structure investigation.
  • Variations in scanner hardware and acquisition protocols present significant challenges for multi-site dMRI data harmonization.
  • Existing dMRI harmonization methods often do not specifically address structural brain connectivity.

Purpose of the Study:

  • To introduce and evaluate a novel distribution-matching approach for harmonizing structural brain connectivity across different sites and scanners.
  • To compare the performance of the proposed method against established techniques like ComBat and CovBat.
  • To assess the impact of harmonization on the correlation between structural brain connectivity and cognitive measures (Mini-Mental State Examination score) and age.

Main Methods:

  • Development of a new distribution-matching algorithm for structural brain connectivity harmonization.
  • Evaluation using three distinct dMRI datasets: OASIS-3, ADNI-2, and PREVENT-AD.
  • Comparative analysis with ComBat and CovBat harmonization methods.
  • Examination of correlations between harmonized connectivity, Mini-Mental State Examination scores, and age.

Main Results:

  • The distribution-matching technique effectively harmonizes structural brain connectivity while preserving non-negativity.
  • The method demonstrates distributional alignment across datasets, confirmed by qualitative and quantitative assessments.
  • Harmonization results in correlation strengths and significance levels competitive with existing approaches.

Conclusions:

  • The proposed distribution-matching method offers an effective solution for harmonizing structural brain connectivity in multi-site dMRI studies.
  • This technique enhances the reliability and comparability of structural connectivity data from diverse sources.
  • The findings contribute to advancing dMRI harmonization for improved neuroscientific and clinical research.