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Related Experiment Video

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Comprehensive evaluation of harmonization on functional brain imaging for multisite data-fusion.

Yu-Wei Wang1, Xiao Chen2, Chao-Gan Yan3

  • 1CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China; International Big-Data Center for Depression Research, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.

Neuroimage
|April 22, 2023
PubMed
Summary

Harmonizing resting-state functional MRI (R-fMRI) data is crucial for big-data neuroimaging. The Subsampling Maximum-mean-distance based distribution shift correction Algorithm (SMA) shows superior performance in individual identification, reliability, and replicability across sites.

Keywords:
ComparisonHarmonizationMulti-site poolingResting-state fMRI

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Area of Science:

  • Neuroimaging
  • Data Science
  • Biostatistics

Background:

  • Harmonizing resting-state functional magnetic resonance imaging (R-fMRI) data is essential for large-scale neuroimaging studies.
  • Existing harmonization strategies for R-fMRI metrics lack comprehensive evaluation, especially with specifically collected datasets.

Purpose of the Study:

  • To comprehensively evaluate R-fMRI data harmonization strategies across multiple perspectives.
  • To assess methods for residual site effect reduction, individual identifiability, test-retest reliability, and group-level result replicability.

Main Methods:

  • Evaluated various harmonization methods including SMA, CovBat, linear models, and VAEs on diverse R-fMRI datasets.
  • Assessed performance based on clustering accuracy, test-retest reliability (overlapped voxels), and replicability (Dice coefficient).
  • Investigated optimal target site features for SMA and proposed a heuristic site selection formula.

Main Results:

  • SMA and parametric unadjusted CovBat excelled in individual identifiability compared to other methods.
  • SMA demonstrated superior test-retest reliability and replicability of group-level findings, including sex differences.
  • SMA effectively detected reproducible sex differences in ALFF even with site-sex confounding.

Conclusions:

  • SMA emerges as a highly effective harmonization strategy for R-fMRI data, outperforming other methods in key metrics.
  • The study provides practical guidelines for selecting target sites to optimize harmonization.
  • Findings will inform advancements in harmonizing methodologies for big R-fMRI data.