Personalized progression modelling and prediction in Parkinson’s disease with a novel multi-modal graph approach

Lian, J., Luo, X., et al. (2024).

Npj Parkinson S Disease, 10(1).

doi.org/10.1038/s41531-024-00832-w

Abstract

Parkinson’s disease (PD) is a complex neurological disorder characterized by dopaminergic neuron degeneration, leading to diverse motor and non-motor impairments. This variability complicates accurate progression modelling and early-stage prediction. Traditional classification methods based on clinical symptoms are often limited by disease heterogeneity. This study introduces an graph-based interpretable personalized progression method, utilizing data from the Parkinson’s Progression Markers Initiative (PPMI) and Stroke Parkinson’s Disease Biomarker Program (PDBP). Our approach integrates multimodal inter-individual and intra-individual data, including clinical assessments, MRI, and genetic information to make multi-dimension predictions. Validated using the PDBP dataset from 12 to 36 months, our AdaMedGraph method demonstrated strong performance, achieving AUC values of 0.748 and 0.714 for the 12-month Hoehn and Yahr Scale and Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) III on the PPMI test set. Ablation analysis reveals the importance of baseline clinical assessment predictors. This novel framework improves personalized care and offers insights into unique disease trajectories in PD patients.

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The research introduces AdaMedGraph, an innovative tool designed to model and predict the progression of Parkinson’s Disease (PD) using personalized, multimodal graph-based methods. This approach integrates clinical assessments, MRI imaging, and genetic data to create individualized disease trajectories. AdaMedGraph demonstrates superior predictive performance compared to traditional machine learning methods, achieving high accuracy for progression markers like the Hoehn and Yahr Scale (e.g., AUC 0.748) and effectively addressing the challenges of disease heterogeneity. Its ability to incorporate diverse data sources allows for detailed predictions across motor (e.g., rigidity, tremors) and non-motor symptoms (e.g., cognition, sleep patterns), which are critical for comprehensive PD management.

For psychologists, this research has significant implications. The integration of cognitive and behavioral assessments into the predictive framework underscores the importance of psychological evaluations in understanding PD progression. The model’s ability to identify personalized trajectories enables psychologists to tailor interventions for both motor and non-motor symptoms, enhancing patient-centered care. Furthermore, the study’s exploration of medication effects on symptoms provides valuable insights into the interaction between treatments and behavioral outcomes, informing therapeutic adjustments. By identifying critical predictive features and offering interpretable patient similarity analyses, AdaMedGraph aligns well with the emphasis on individualized care in psychological practice. This tool has the potential to advance multidisciplinary collaboration, enabling psychologists to combine behavioral insights with advanced predictive analytics to improve outcomes for individuals with Parkinson’s Disease.