The feasibility of Mobile-EEG to profile cognitive impairment in Parkinson’s disease

Student thesis: Doctoral ThesisDoctor of Philosophy

Abstract

Background

Cognitive impairment is prevalent in Parkinson’s disease (PD), with 50% of patients developing dementia within 10 years (Williams-Gray et al., 2013; Aarsland and Kurz, 2010). The presence of mild cognitive impairment (MCI) has previously been associated with increased dementia risk (Pederson et al. 2013). Electroencephalography (EEG) is an inexpensive and non-invasive tool used to assess cognition in aging clinical groups. EEG has previously been shown to have prognostic value in MCI associated with Alzheimer’s disease (AD-MCI), yet there is little information on the electrophysiological correlates of PD-MCI. In this study, the feasibility of using a ‘dry-EEG’ mobile headset to assess cognitive impairment in Parkinson’s disease (PD) was investigated. Dry-EEG headsets offer many benefits as the set-up time is significantly shortened, the equipment is cheaper and the headset is portable, which should improve patient access to neuroimaging technologies as called for by a World Health Organisation report (WHO, 2017).

Aim

To determine if dry-EEG can adequately record well-known EEG components and to evaluate if these recordings enhance PD-MCI patient classification.

Method

47 PD patients and 37 age and sex-matched controls were recruited to complete a battery of neuropsychological and EEG tasks. PD-MCI status was assigned based on the Movement Disorders Society PD-MCI level I criteria. A comprehensive battery of neuropsychological tests and EEG tasks were conducted. Resting state EEG was also recorded. A mix of both event-related potential (ERP) component analysis and time-frequency spectral analysis was conducted. Differences between groups were assessed using randomisation tests with FDR-corrected pairwise comparisons. Machine learning models were employed to investigate if the addition of EEG recordings improved PD-MCI classification accuracy.

Results

A significant proportion of PD participants were classified as PD-MCI according to the Movement Disorders Society PD-MCI criteria. The majority of PD-MCI patients were impaired in at least three cognitive domains, demonstrating that cognitive impairment is pervasive in this PD cohort. Significant differences were found between PD participants and controls on a range of EEG tasks. Machine learning models provided interesting insights on what features were most discriminative for participant classification.

Conclusion

The findings of this study show that cognitive impairment is prevalent in PD. The addition of EEG did not aid differential discrimination between cognitively normal and cognitively impaired PD patients at an early stage. The most discriminative features selected by machine learning models are in agreement with the dual syndrome hypothesis of cognition (Kehagia et al., 2013) and those identified in a 10 year follow-up study (Williams-Grey et al., 2013). This suggests that it may not be feasible to use dry-EEG to profile cognition in PD. Longitudinal study of this cohort would investigate the ability of these EEG recordings to predict change to PD cognitive status over time. These findings could also inform the development of a larger trial which uses EEG as an outcome measure to evaluate the success of an intervention, be it pharmacological, cognitive training or exercise-based to improve cognitive outcomes in PD-MCI.an intervention, be it pharmacological, cognitive training or exercise-based to improve cognitive outcomes in PD-MCI.

Thesis is embargoed until 31 July 2027
Date of AwardJul 2023
Original languageEnglish
Awarding Institution
  • Queen's University Belfast
SponsorsNorthern Ireland Department for the Economy & Dr Cecelia Williamson Special Research Scholarship
SupervisorAnthony Passmore (Supervisor), Barry Devereux (Supervisor) & Bernadette McGuinness (Supervisor)

Keywords

  • Parkinson's disease
  • Electroencephalography
  • EEG
  • mild cognitive impairment
  • Dementia
  • Parkinson's disease dementia
  • machine learning

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