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

Abstract

Background and Objective

Parkinson’s disease (PD) alters the brain’s neurodynamic properties, contributing to both motor and non-motor symptoms. Although advances in neuroimaging techniques—such as resting-state functional MRI (rsfMRI), diffusion tensor imaging (DTI), and structural MRI (sMRI)—have enhanced our understanding of brain structure and function, they remain limited in detecting subtle, region-specific dynamic alterations associated with functional deficits. This study aims to apply the relaxed mean field dynamic modeling (rMFM) to identify microscale dynamic abnormalities in PD and to link these changes with network topology and clinical characteristics.

Methods

We employed the rMFM, a biophysically informed computational framework that integrates structural and functional imaging data with microstructural features to simulate local dynamics of brain regions. Unlike traditional models, rMFM allows the optimization of regional recurrent connection strength w and subcortical input I, thereby capturing inter-regional heterogeneity more effectively. Separate rMFM models were constructed for the PD and healthy control (HC) groups. Group differences in model parameters were assessed, followed by graph-theoretical analysis to examine alterations in brain network topology. Correlation analyses were also performed to investigate the relationships between model parameters, network metrics, and clinical variables.

Results

Significant alterations in w and I were observed in regions such as the middle temporal gyrus and banks of the superior temporal sulcus (bankssts) in the PD group, suggesting localized dynamic disruptions related to language, memory, and cognitive impairments. Corresponding alterations in brain network topology accompanied these parameter changes. At the same time, the results of graph theory analysis suggest that in early PD, functional disorders may appear before obvious structural changes.

Conclusions

This study introduces rMFM as an innovative approach for modeling local brain dynamics by integrating multimodal MRI data with microscale neural features. The findings highlight distinctive microscale dynamic abnormalities in PD and their linkage to large-scale network changes. This approach enhances our understanding of PD pathophysiology and provids a basis for identifying potential disease-specific biomarkers.