EEG Source-Space Functional Connectivity Predicts Cognition in Parkinson's Disease
Poster No:
234
Submission Type:
Abstract Submission
Authors:
Alina Tetereva1, Grace Hall-McMaster1, Nicola Slater2, Abbie Harris1, Reza Shoorangiz1, Tracy Melzer3, Campbell Le Heron2, Ross Keenan4, Ian Kirk5, Wassilios Meissner6, Tim Anderson2, Narun Pat7, John Dalrymple-Alford1
Institutions:
1New Zealand Brain Research Institute, Christchurch, New Zealand, 2University of Otago, Christchurch, New Zealand, 3University of Canterbury, Christchurch, New Zealand, 4Pacific Radiology Canterbury, Christchurch, New Zealand, 5University of Auckland, Auckland, New Zealand, 6Service de Neurologie des Maladies Neurodégénératives, Bordeaux, France, 7University of Otago, Dunedin, Otago
First Author:
Co-Author(s):
Introduction:
Parkinson's disease (PD) is a progressive neurodegenerative disorder that involves both motor and nonmotor symptoms, including cognitive impairments that progress to dementia in the majority of patients (Svenningsson et al., 2012; Aarsland et al., 2021). The mechanisms underlying cognitive disturbances in PD remain unclear; however, these impairments likely result from ongoing neurodegenerative processes that disrupt structural and functional connectivity (FC) within the brain (Droby et al., 2022). Previous studies have primarily focused on the alpha band to explain cognitive decline in PD (Morita et al., 2011). In this study, we aimed to investigate source-space functional connectivity measures in all frequency bands and evaluate their ability to capture cognitive differences in PD.
Methods:
We used data from the Christchurch Longitudinal Parkinson's Study, including 235 participants (169 PD (all taking medications), 61 controls, 5 atypical parkinsonism; 71.4±7.8 years old). Cognitive evaluations assessed attention, executive function, visuospatial ability, and episodic memory, with a global cognitive score calculated as the average (MacAskill et al., 2023). A 64-channel EEG data were recorded during a 10-minute eyes-closed resting session. Preprocessing (MNE-BIDS-Pipeline, custom Python scripts) included filtering, noise removal, re-referencing, ICA-based artifact correction, and bad epoch and channel interpolation. We computed inverse solutions using a standard head model (fsaverage) and extracted two FC indices, Amplitude Envelope Correlation (AEC) and debiased weighted Phase Lag Index (dwPLI), for delta, theta, alpha, beta, and gamma bands, using Glasser atlas. Elastic Net regression predicted global cognitive scores, with separate models for indices/bands and stacked models for three combinations (AEC only, dwPLI only, AEC+dwPLI), evaluated via Pearson correlation.
Results:
We found that some FC indices and frequency bands performed better than others in predicting cognition. Specifically, AEC showed superior predictive performance compared to dwPLI (average r: 0.32 vs 0.18). Among the frequency bands, theta, beta, and delta (AEC) demonstrated the best performance (see Figure 1a). Combining AEC and dwPLI indices in a stacked model did not improve predictions. However, stacking only AEC indices across all five frequency bands significantly boosted prediction accuracy (r = 0.50) compared to the best single modality, AEC theta (r = 0.41). We also examined feature importance for the best non-stacked model, AEC theta. Feature importance reflects the relative contribution of each feature in predicting the target variable, based on the magnitude of its regression coefficient. The most important features corresponded to connections between the frontal and parieto-occipital areas, which were the strongest predictors of cognitive performance (see Figure 1b).
Conclusions:
In this study, we tested the ability of FC measures to capture individual differences in global cognitive scores among PD subjects and healthy controls. Our findings demonstrate that not all FC indices or frequency bands are equally effective for predicting cognition. Specifically, the AEC in the theta, beta, and delta bands showed the best predictive performance, while alpha band FC, despite its prominence, was not a strong predictor. These results align with previous findings of altered connectivity in these bands in PD (Ponsen et al., 2013; Cai et al., 2021; Maggioni et al., 2021; Ding et al., 2024). Although this study presents preliminary findings and includes certain limitations, such as the use of a standard head model, it highlights the potential of FC measures-particularly AEC-as promising biomarkers for cognitive dysfunction in PD. These results provide a solid foundation for future research aimed at improving biomarker accuracy and understanding the mechanisms underlying cognitive disturbances in Parkinson's disease.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Higher Cognitive Functions:
Higher Cognitive Functions Other
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
EEG/MEG Modeling and Analysis 2
Task-Independent and Resting-State Analysis
Keywords:
Cognition
Electroencephaolography (EEG)
Machine Learning
Other - Parkinson's Disease
1|2Indicates the priority used for review
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Cai, M., Dang, G., Su, X., Zhu, L., Shi, X., Che, S., ... & Guo, Y. (2021). Identifying mild cognitive impairment in Parkinson’s disease with electroencephalogram functional connectivity. Frontiers in aging neuroscience, 13, 701499.
Ding, H., Weng, X., Xu, M., Shen, J., & Wu, Z. (2024). Dynamic channelwise functional-connectivity states extracted from resting-state EEG signals of patients with Parkinson’s disease. The Egyptian Journal of Neurology, Psychiatry and Neurosurgery, 60(1), 62.
Droby, A., Nosatzki, S., Edry, Y., Thaler, A., Giladi, N., Mirelman, A., & Maidan, I. (2022). The interplay between structural and functional connectivity in early stage Parkinson's disease patients. Journal of the Neurological Sciences, 442, 120452.
MacAskill, M. R., Pitcher, T. L., Melzer, T. R., Myall, D. J., Horne, K. L., Shoorangiz, R., ... & Anderson, T. J. (2023). The New Zealand Parkinson’s progression programme. Journal of the Royal Society of New Zealand, 53(4), 466-488.
Maggioni, E., Arienti, F., Minella, S., Mameli, F., Borellini, L., Nigro, M., ... & Ardolino, G. (2021). Effective connectivity during rest and music listening: An eeg study on parkinson’s disease. Frontiers in aging neuroscience, 13, 657221.
Morita, A., Kamei, S., & Mizutani, T. (2011). Relationship between slowing of the EEG and cognitive impairment in Parkinson disease. Journal of Clinical Neurophysiology, 28(4), 384-387.
Ponsen, M. M., Stam, C. J., Bosboom, J. L. W., Berendse, H. W., & Hillebrand, A. (2013). A three dimensional anatomical view of oscillatory resting-state activity and functional connectivity in Parkinson's disease related dementia: An MEG study using atlas-based beamforming. NeuroImage: Clinical, 2, 95-102.
Svenningsson, P., Westman, E., Ballard, C., & Aarsland, D. (2012). Cognitive impairment in patients with Parkinson's disease: diagnosis, biomarkers, and treatment. The Lancet Neurology, 11(8), 697-707.