Objective: To track Parkinson’s progression over 2-6 years using global MRI measures.

Background: Parkinson’s is associated with accelerated brain atrophy, but the relative contribution of gray matter volume, cortical thickess, and white matter lesions has not been described. The identification of global changes may provide fast, objective assessment for disease-modifying treatments in the future.

Methods: Sixty-nine Parkinson’s participants and 41 controls, aged between 46 and 84, had multiple assessments over 2-6 years (195 follow up scans). Each assessment included a neuropsychological battery and MRI scan. Global cognition was expressed as an aggregate z score derived from multiple tests within each of five cognitive domains, which also helped define mild cognitive impairment or dementia using International Parkinson and Movement Disorder Society Task Force criteria. MR data were acquired on a 3T GE HDxt scanner and included a T1-weighted 3D SPGR image and a T2-weighted FLAIR image at each timepoint. Brain integrity was assessed using global gray matter (GM), average cortical thickness (CTh), and white matter lesion (WML) volume. Linear mixed effects models assessed the relationship between global MRI metrics (GM, CTh, and WMLs) and subject group (Parkinson’s/Control), global cognitive ability, time from baseline, and the interaction between time and subject group, accounting for baseline age, sex, and intracranial volume.

Results: Only GM volume showed a significant group-by-time interaction, with the Parkinson’s group exhibiting greater GM atrophy over time. Across all participants, there was a decrease in GM volume and CTh, and an increase (p<0.05) in WML volume. Compared to controls, Parkinson’s participants had higher WML volumes, smaller GM volumes, and thinner cortices. In addition, global cognitive ability was significantly (p<0.001) associated with global MRI metrics (GM volume, CTh, WML).

Conclusions: Despite older participants showing cortical decline and increased WML, only global GM volume loss was specifically associated with significant changes over time due to progression in established Parkinson’s. This group-level change was not sufficiently sensitive to track individual patients. More advanced techniques, such as machine learning approaches that accommodate integrated information across multiple measures together with regional variations, may be more useful when tracking individuals with Parkinson’s.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/tracking-parkinsons-disease-over-2-6-years-with-mri/