Simultaneous progression modeling of neurodegeneration and clinical symptoms in Parkinson’s disease
Poster No:
208
Submission Type:
Abstract Submission
Authors:
Emile d'Angremont1, Alessandro Viani2, Ysbrand van der Werf1, Marco Lorenzi2, Boris Gutman3
Institutions:
1Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, Amsterdam, The Netherlands, 2Inria Center of Université Côte d'Azur, Sophia Antipolis, France, 3Illinois Institute of Technology, Chicago, IL
First Author:
Emile d'Angremont
Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences
Amsterdam, The Netherlands
Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences
Amsterdam, The Netherlands
Co-Author(s):
Ysbrand van der Werf
Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences
Amsterdam, The Netherlands
Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences
Amsterdam, The Netherlands
Introduction:
Parkinson's disease (PD) is a progressive neurodegenerative disorder. Modelling the disease progression is pivotal for detection, prevention, and treatment, yet it remains challenging due to the heterogeneity in disease trajectories among individuals. In the current study, we applied Disease Progression Modelling and Stratification (DP-MoSt) (Viani, 2024) to longitudinal data in the Parkinson's Progression Markers Initiative (PPMI) dataset, including cortical thickness, motor score and global cognition score. DP-MoSt is a novel probabilistic method that optimizes clusters of continuous trajectories over a long-term disease time-axis while estimating the confidence of trajectory sub-types for each biomarker.
Methods:
T1-weighted MRI images of all PD patients and visits within PPMI were processed using FreeSurfer 5.3. Cortical thickness of 5 bilateral brain regions that were found to be affected in early stages of PD (fusiform gyrus, inferior temporal gyrus, superior and inferior parietal gyrus and precuneus [2]) were included in the model. Additional biomarkers were Montreal Cognitive Assessment (MoCA) and the Unified Parkinson's Disease Rating Scale (UPDRS) parts 2 and 3. Tremor and postural instability and gait disorder (PIGD) subscores were derived from UPDRS part 3 and also included in the model. The model was applied using a monotonicity constraint on all biomarkers. DP-MoSt estimates the trajectories of biomarkers as they progress, while also identifying subtypes in disease progression and assessing the specificity of biomarkers in defining these subtypes.
Results:
Overall, two distinct subgroups were identified in disease progression. The subtypes had estimated p>0.5 probability of group difference in MoCA, UPDRS parts 2 and 3 total scores and PIGD subscore, with the smaller subgroup showing faster progression (Figure below). No differences between the two subtypes exceeded 0.5 in any of the neuroimaging biomarkers or the tremor score.
·Figure
Conclusions:
Clinical features appear more sensitive for the identification of subtypes in progression of PD compared to our selected set of structural neuroimaging in the form of cortical thickness. We plan to include more neuroimaging features as well as data from other cohorts within the ENIGMA-PD consortium.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
Keywords:
Cognition
Cortex
Movement Disorder
MRI
Neurological
1|2Indicates the priority used for review
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Laansma, M. A., Bright, J. K., Al‐Bachari, S., Anderson, T. J., Ard, T., Assogna, F., ... & ENIGMA‐Parkinson's Study. (2021). International multicenter analysis of brain structure across clinical stages of Parkinson's disease. Movement disorders, 36(11), 2583-2594.