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Sample size requirement for achieving multisite harmonization using structural brain MRI features.

Pravesh Parekh1, Gaurav Vivek Bhalerao2,

  • 1NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; ADBS Neuroimaging Centre, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India; Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India.

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|November 26, 2022
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Summary
This summary is machine-generated.

Determining the minimum sample size for multisite neuroimaging data harmonization is crucial. Sample size requirements increase with greater site variability, necessitating careful consideration for effective data integration.

Keywords:
Cross-validationHarmonizationMahalanobis distanceMultisiteNeuroimagingSample size

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Area of Science:

  • Neuroimaging
  • Data Science
  • Statistical Modeling

Background:

  • Pooling neuroimaging data across sites introduces confounding site effects.
  • Harmonization methods aim to mitigate site effects while preserving biological variability.
  • Understanding sample size needs for effective harmonization is limited, especially with increasing site numbers.

Purpose of the Study:

  • To determine the minimum sample size for multisite harmonization using neuroHarmonize.
  • To investigate the relationship between sample size, site effects, and the number of sites.
  • To establish a framework for understanding site effects and optimizing harmonization.

Main Methods:

  • Utilized learning curves to assess sample size requirements for harmonization.
  • Applied univariate and multivariate approaches to remove site effects.
  • Employed both actual and simulated neuroimaging data (volumetric and surface features).
  • Investigated the impact of covariate effects and Mahalanobis distances.

Main Results:

  • Site effects were effectively removed using univariate and multivariate methods.
  • Additional regression of covariate effects on harmonized data is essential.
  • Minimum sample size for harmonization scales with the average Mahalanobis distance between sites.
  • Learning curves effectively identified sample size requirements.

Conclusions:

  • A general framework using Mahalanobis distance can elucidate site effects.
  • Optimal inter-site harmonization depends on cross-validation design factors.
  • Sample size requirements for harmonization are influenced by site variability and data structure.