Whole-brain computational modeling reveals disruption of microscale brain dynamics in Parkinson’s disease

Motivation: Previous studies on brain damage caused by Parkinson's disease were mostly based on single imaging modality analysis. Goal(s): Large-scale whole-brain dynamic modeling can integrate structural and functional connectivity. Approach: In this study, we applied relaxed mean field dynamic modeling (rMFM) to analyze microscale brain dynamics in 54 PD patients and 57 age-matched healthy controls (HC). Results: Results revealed significant differences in recurrent connection strength and subcortical input between groups. Additionally, these parameters correlated with network topology in the HC group but not in PD, suggesting disrupted local dynamics in PD. This work highlights the relevance of multimodal imaging in understanding PD-related brain disruptions. Impact: The findings underscore the value of multimodal dynamic modeling in the PD research, providing insight into altered brain dynamics and network topology associations. This approach could inform future studies on neurodegenerative disease mechanisms, potentially aiding early diagnosis and targeted interventions.