EEG Dynamical Features during Variable-Intensity Cycling Exercise in Parkinson's Disease

Zahra Alizadeh¹Emad Arasteh²Maryam S. Mirian^3,4Matthew A. Sacheli^3,4,5Danielle Murray^3,4,5Silke Appel-Cresswell^3,4,5Martin J. McKeown^3,4,5,6*

• ¹University of Tehran, Tehran, Tehran, Iran
• ²Wilhelmina Children's Hospital, Utrecht, Netherlands, Netherlands
• ³Djavad Mowafaghian Centre for Brain Health, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
• ⁴Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
• ⁵Division of Neurology, Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
• ⁶University of British Columbia, Vancouver, Canada

1.Abstract1.1. BackgroundExercise is increasingly recognized as a beneficial intervention for Parkinson's disease (PD), yet the optimal type and intensity of exercise remain unclear. This study investigated the relationship between exercise intensity and neural responses in PD patients, using electroencephalography (EEG) to explore potential neural markers that could be ultimately used to guide exercise intensity.1.2. MethodEEG data were collected from 14 PD patients (5 females) and 8 healthy controls (HC) performing stationary pedaling exercises at 60 RPM with resistance adjusted to target heart rates of 30%, 40%, 50%, 60%, and 70% of maximum heart rate. Subjects pedaled for 3 minutes at each intensity level in a counterbalanced order. Canonical Time-series Characteristics (Catch-22) features and Multi-set Canonical Correlation Analysis (MCCA) were utilized to identify common profiles of EEG features at increasing exercise intensity across subjects.1.3. ResultsWe identified a statistically significant MCCA component demonstrating a monotonic relationship with pedaling intensity. We have discovered nine features which show significant trends across intensity (p-value<0.01), and the dominant feature in this component was Periodicity Wang (p-value<0.0001), related to the autocorrelation of the EEG. Analysis revealed a consistent trend across features: six features increased with intensity, indicating heightened rhythmic engagement and sustained neural activation, while three features decreased, suggesting reduced variability and enhanced predictability in neural responses. Notably, PD patients exhibited more rigid, consistent response patterns compared to healthy controls (HC), who showed greater flexibility and variability in their neural adaptation across intensities.1.4. ConclusionThis study highlights the feasibility of using EEG-derived features to track exercise intensity in PD patients, identifying specific neural markers correlating with varying intensity levels. PD subjects demonstrate less inter-subject variability in motor responses to increasing intensity. Our results suggest that EEG biomarkers can be used to assess differing brain involvement with the same exercise of increasing intensity, potentially useful for guiding targeted therapeutic strategies and maximizing the neurological benefits of exercise in PD.