Harmonization for Parkinson’s Disease Multi-Dataset T1 MRI Morphometry Classification

• Published in: NeuroSci Vol. 5; no. 4; pp. 600 - 613
• Main Authors: Saqib, Mohammed, Horovitz, Silvina G.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.11.2024
• Subjects: batch effect; brain morphometry; classifier; data harmonization; magnetic resonance imaging; Parkinson’s disease; batch effect; classifier; magnetic resonance imaging; brain morphometry; data harmonization; Parkinson’s disease
• ISSN: 2673-4087; 2673-4087
• DOI: 10.3390/neurosci5040042

Classification of disease and healthy volunteer cohorts provides a useful clinical alternative to traditional group statistics due to individualized, personalized predictions. Classifiers for neurodegenerative disease can be trained on structural MRI morphometry, but require large multi-scanner datasets, introducing confounding batch effects. We test ComBat, a common harmonization model, in an example application to classify subjects with Parkinson’s disease from healthy volunteers and identify common pitfalls, including data leakage. We used a multi-dataset cohort of 372 subjects (216 with Parkinson’s disease, 156 healthy volunteers) from 11 identified scanners. We extracted both FreeSurfer and the determinant of Jacobian morphometry to compare single-scanner and multi-scanner classification pipelines. We confirm the presence of batch effects by running single scanner classifiers which could achieve wildly divergent AUCs on scanner-specific datasets (mean:0.651 ± 0.144). Multi-scanner classifiers that considered neurobiological batch effects between sites could easily achieve a test AUC of 0.902, though pipelines that prevented data leakage could only achieve a test AUC of 0.550. We conclude that batch effects remain a major issue for classification problems, such that even impressive single-scanner classifiers are unlikely to generalize to multiple scanners, and that solving for batch effects in a classifier problem must avoid circularity and reporting overly optimistic results.