Brain oscillatory signal analyses in chronic pain

Pain lasting three months or longer is generally referred to as Chronic Pain (CP). According to Health Canada, approximately 20% of the Canadian population experience CP within their lifespan. CP also greatly burdens society. For example, in 2019 alone, $40 billion was spent on CP. CP research includes the search for diagnostic tools and pain intervention methods. My thesis examined using EEG/MEG brain rhythms in CP and explored non-traditional intervention, which focused on two main goals. The first goal was to explore the inclusion of age in CP analysis, as age can influence brain rhythm properties. The second goal was to use virtual reality (VR)-guided meditation as a mindfulness-based stress reduction alternative intervention for CP. To study CP and aging, a MEG database of 474 healthy controls and 22 CP participants was used. The methods included finding the participants' brain rhythm properties in four alpha generation regions of interest (ROIs) and the rhythm property correlations with age. A CP case study demonstrated how to include age using support vector regression (SVR). The results showed the brain rhythm properties in the four ROIs were significantly independent, and negative correlations with medium effect size were found between peak alpha frequency (PAF) and age in the frontal, temporal, and parieto-occipital ROIs. In the case study, SVR prediction helped identify slowed PAF in the posterior brain area of the CP participants. This novel SVR prediction approach helps to include age in CP analysis without using age-matched controls. To study the VR-guided meditation, ten adult patients with chronic cancer pain were recruited. The patients underwent a VR-guided meditation experience in a specially designed therapy sequence. Brain rhythm changes measured in EEG before, during, and after meditation were compared using topography, coherence, and cluster-based permutation techniques. EEG power changes were compared with the patient-reported numerical pain rating scores before, during and after meditation. During the therapy, increased power was found in β and γ bandwidths, and coherence changes between the frontal, parietal, and occipital regions in the θ, α, and γ bands were observed. No significant relationships between pain scores and EEG power variations were found. The novel EEG recording and analysis methods help reveal specific therapy-related EEG changes and can be used to investigate neurophysiological changes in VR pain applications.