Goal-specific brain MRI harmonization.

Area of Science:

• Neuroimaging
• Medical Data Science
• Machine Learning in Healthcare

Background:

• Pooling magnetic resonance image (MRI) data across multiple datasets is crucial for large-scale studies (mega-analysis).
• Data heterogeneity between sites necessitates harmonization, but current methods often overlook downstream application performance.
• Ignoring downstream task relevance may limit the effectiveness of MRI harmonization techniques.

Purpose of the Study:

• To introduce a goal-specific harmonization framework that leverages downstream application performance to guide the harmonization process.
• To integrate this goal-specific approach with deep learning-based harmonization models, such as conditional variational autoencoders (cVAE).
• To evaluate the framework's ability to reduce dataset differences while preserving biologically relevant information for clinical predictions.

Main Methods:

• Development of a goal-specific harmonization framework (gcVAE) that incorporates downstream performance metrics into the regularization process.
• Integration of gcVAE with a conditional variational autoencoder (cVAE) model for MRI harmonization.
• Evaluation using three multi-continental MRI datasets (2787 participants, 10,085 scans) and assessment of downstream prediction accuracy for Mini Mental State Examination (MMSE) scores and clinical diagnoses.

Main Results:

• The standard cVAE model reduced dataset differences more effectively than the ComBat model but diminished predictive power for MMSE scores and clinical diagnoses.
• The proposed goal-specific cVAE (gcVAE) achieved comparable reduction in dataset differences to cVAE.
• gcVAE significantly improved the cross-sectional prediction accuracy of MMSE scores and clinical diagnoses compared to both ComBat and standard cVAE.

Conclusions:

• Goal-specific harmonization using gcVAE offers a superior approach for pooling MRI data by balancing data harmonization with the preservation of predictive biological information.
• This framework enhances the utility of multi-site MRI datasets for downstream clinical applications and neuroscientific research.
• Optimizing harmonization based on specific downstream tasks leads to more effective data integration and improved predictive modeling in neuroimaging.