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Updated: Jan 14, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
Published on: July 24, 2019
Understanding individual neurodegenerative progression in Parkinson's disease through normative modelling
Charlotte Fraza1,2, Barbora Rehák Bučková3,4, Martin E Johansson3,5
1Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, The Netherlands. Charlotte.fraza@donders.ru.nl.
None:
Parkinson's disease (PD) is a neurodegenerative disorder with motor symptoms (e.g., bradykinesia, tremors) and non-motor symptoms (e.g., cognitive deficits). Symptom progression varies across individuals, possibly due to differences in the spread of disease pathology. This study investigates individual-level gray matter atrophy in PD patients compared to a reference cohort, modeling neurobiological trajectories to understand symptom progression. Using normative modeling, we mapped individual deviations in gray matter atrophy in PD patients (Personalized Parkinson Project, PPP; N = 408; 42% female) against a reference model (N = 58, 836) of non-diagnosed individuals. Gray matter atrophy was defined as negative deviations from the normative model in cortical thickness and subcortical volume at baseline and two-year follow-up. We correlated the deviations with clinical motor and cognitive symptoms at an individual level and compared changes across PD subtypes (mild-motor predominant, intermediate, and diffuse-malignant). Cross-sectionally, PD patients showed significant gray matter atrophy, which correlated with cognitive impairment. Longitudinally, cortical thinning and subcortical atrophy patterns showed variation amongst subtypes. Specifically, the diffuse-malignant subtype, which is characterized by more diffuse symptoms and faster clinical progression, exhibited pronounced cortical thinning and subcortical atrophy over time. In this paper, we have considered the deviation scores at three levels of granularity: cases vs. control, subtypes, and the individual level. While our findings show subgroup-level patterns of variability, they also provide a method for exploring individual-level metrics of disease progression, acknowledging that individuals may deviate from the predefined categories or groups and can exhibit large variability over time.
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