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Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model.

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  • 1Department of Bio-Convergence Engineering, Korea University, Seoul, South Korea.

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Summary

This study developed a harmonized w-score to correct center effects in multicenter neuroimaging data, improving classification accuracy for neurological diseases. The method effectively harmonizes data, preserving disease-specific patterns for better diagnostic insights.

Keywords:
Alzheimer’s diseaseParkinson’s diseasecortical thicknesslinear mixed effect modelmagnetic resonance imagingmulticenter data harmonization

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Area of Science:

  • Neuroscience
  • Neurology
  • Medical Imaging

Background:

  • Analyzing neuroimages is crucial in neuroscience and neurology.
  • Incompatibilities across protocols and vendors, termed "center effects," pose a major challenge in multicenter studies.
  • Correcting these center effects is vital for reliable neuroimage analysis.

Purpose of the Study:

  • To correct center effects in cortical features from multicenter magnetic resonance images (MRIs).
  • To develop a harmonized w-score method for neuroimage data standardization.
  • To evaluate the effectiveness of the proposed method in preserving disease-specific information.

Main Methods:

  • Calculated a harmonized w-score for 4,321 multicenter subjects using MRI data.
  • Corrected biological covariates (age, sex, education, ICV) as fixed effects and center information as a random effect.
  • Employed classification tasks with PCA and LDA to assess center effect correction.

Main Results:

  • Linear mixed effect (LME) model-based w-score showed significantly better prediction accuracy than raw cortical thickness.
  • Classification accuracy for normal versus patient groups was higher with LME model-based w-score.
  • Intrasubject comparison confirmed reduced differences between centers and successful data harmonization.

Conclusions:

  • The LME model-based w-score effectively corrects center effects while preserving disease-specific patterns.
  • Preserved disease effects align with known atrophy patterns in conditions like Alzheimer's and Parkinson's diseases.
  • The model successfully harmonizes data not used during training, demonstrating broad applicability.