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

ABSTRACT: 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 classificat

SUBMITTER: Saqib M 

PROVIDER: S-EPMC11678312 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature