Assessing ageing, cognitive ability and freezing of gait in Parkinson’s disease through integrated brain–heart network dynamics

Parkinson’s Disease (PD) is a complex neurodegenerative disorder that manifests through systemic, large-scale physiological reorganizations. While research often focuses on region-specific neural changes, there is a growing need for multidomain approaches capable of capturing the complexity of the disease and its clinical heterogeneity. Here, we propose an analytical pipeline to evaluate Brain–heart Interactions (BHI) as a systemic biomarker of neurodegeneration and healthy ageing.

In this study, we assessed BHI across three open-source datasets combining EEG and ECG recordings. We compared Healthy Young adults, Healthy Elderly participants, and early-stage PD patients during resting state to investigate the effects of ageing and neurodegeneration on large-scale physiological organization and cognition. In addition, we examined BHI dynamics surrounding episodes of freezing of gait (FOG) in PD patients. Methodologically, brain network organization was quantified using coherence-based EEG functional connectivity and graph-theoretical metrics, while cardiac dynamics were characterized through Poincaré plot-derived measures of autonomic activity. Coupling between the two systems was estimated using the Maximal Information Coefficient, enabling the detection of both linear and non-linear dependencies between cortical network organization and cardiac autonomic outflow.

Our results demonstrate that brain–heart networks are sensitive to systemic physiological changes associated with both healthy ageing and early PD. Specifically, we observed distinct BHI profiles differentiating Healthy Young, Healthy Elderly, and PD participants, suggesting that the proposed framework captures progressive alterations in integrated neurophysiological regulation. Importantly, resting-state BHI metrics were associated with cognitive performance in early PD, supporting the relevance of brain–heart coupling as a marker of clinical heterogeneity beyond motor symptoms alone. Furthermore, the analysis of FOG episodes revealed the emergence of specific BHI interaction clusters preceding and during gait freezing, highlighting coordinated alterations in cortical and autonomic dynamics linked to motor dysfunction. Together, these findings suggest that brain–heart networks provide a promising systems-level framework for understanding PD symptomatology and detecting early multisystem dysfunction in neurodegeneration and ageing. Our proposed pipeline offers a scalable and clinically relevant tool for large-scale physiological assessment in translational and clinical neuroscience research.

Analytical pipeline to evaluate Brain–heart Interaction (BHI)

Simultaneous electroencephalogram (EEG) and electrocardiogram (ECG) recordings undergo advanced signal preprocessing, followed by the extraction of brain and cardiac autonomic indices. For the EEG, graph-theory-based network organization metrics are computed, including Global Efficiency (E _g ) and Modularity (Q). For the ECG, Poincaré analysis of inter-beat interval (IBI) sequences is used to derive cardiac sympathetic (CSI) and parasympathetic (CPI) indices. Coupling between EEG and ECG time series is then quantified using the Maximal Information Coefficient (MIC). This multimodal framework provides informative markers related to ageing, cognitive ability, and freezing of gait in patients with Parkinson’s disease.

Highlights

• We propose a pipeline based on EEG-ECG to assess ageing and neurodegeneration

• Brain–heart networks detect systemic changes in ageing and early PD

• Resting brain–heart networks relate to cognitive performance in early PD

• Specific brain–heart interaction clusters emerge during freezing of gait

• Brain–heart networks offer a promising tool to understand PD’s symptomatology