Objectives: Parkinson’s disease is a multifactorial neurodegenerative disorder whose progression remains complex despiteextensive research. Our study presents an innovative approach to understanding PD progression through detailed analysis ofelectroencephalography signals. By segmenting patients based on disease duration, we uncover unique neural connectivitypatterns corresponding to different duration of PD development.
Methods: Employing advanced machine learning techniques,our methodology achieves exceptional accuracy rates in binary classification tasks compared to prior literature. Integrationof Shapley Additive Explanations values enhances model interpretability, revealing critical brain regions and connectivitypatterns implicated in PD pathophysiology.
Results: Coherence emerges as a crucial metric for capturing synchronizedsignal behaviors, aiding in discriminating PD patients from controls. Further, our analysis suggests a continuum of neuralconnectivity patterns across disease duration, with early-stage PD resembling healthy brain function and advanced durationexhibiting distinct features indicative of disease progression.
Conclusions: These findings deepen our understanding of PDpathogenesis, laying the groundwork for personalized diagnostic and therapeutic approaches tailored to different diseaseduration. Our study contributes significant insights into the complex interplay between neural dynamics, disease progression,and age-related changes in PD, offering potential for future research and clinical applications