A novel MRI-based volumetric index for monitoring the motor symptoms in Parkinson's disease

• Published in: Journal of the neurological sciences 2023-10, Vol.453, p.120813-120813, Article 120813
• Main Authors: Vijayakumari, Anupa A., Mandava, Nymisha, Hogue, Olivia, Fernandez, Hubert H., Walter, Benjamin L.
• Format: Article
• Language: English
• Subjects: Imaging biomarker; Linear mixed-effects models; Magnetic resonance imaging; Mahalanobis distance; Motor symptom progression; Parkinson's disease
• ISSN: 0022-510X; 1878-5883
• DOI: 10.1016/j.jns.2023.120813

Conventional MRI scans have limited usefulness in monitoring Parkinson's disease as they typically do not show any disease-specific brain abnormalities. This study aimed to identify an imaging biomarker for tracking motor symptom progression by using a multivariate statistical approach that can combine gray matter volume information from multiple brain regions into a single score specific to each PD patient. A cohort of 150 patients underwent MRI at baseline and had their motor symptoms tracked for up to 10 years using MDS-UPDRS-III, with motor symptoms focused on total and subscores, including rigidity, bradykinesia, postural instability, and gait disturbances, resting tremor, and postural-kinetic tremor. Gray matter volume extracted from MRI data was summarized into a patient-specific summary score using Mahalanobis distance, MGMV. MDS-UPDRS-III's progression and its association with MGMV were modeled via linear mixed-effects models over 5- and 10-year follow-up periods. Over the 5-year follow-up, there was a significant increase (P