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

Abstract

BackgroundConventional 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. MethodsA 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. ResultsOver the 5-year follow-up, there was a significant increase (P < 0.05) in MDS-UPDRS-III total and subscores, except for postural-kinetic tremor. Over the 10-year follow-up, all MDS-UPDRS-III scores increased significantly (P < 0.05). A higher baseline MGMV was associated with a significant increase in MDS-UPDRS-III total, bradykinesia, postural instability and gait disturbances, and resting tremor (P < 0.05) over the 5-year follow-up, but only with total, bradykinesia, and postural instability and gait disturbances during the 10-year follow-up (P < 0.05). ConclusionsHigher MGMV scores were linked to faster motor symptom progression, suggesting it could be a valuable marker for clinicians monitoring Parkinson's disease over time.