Virtual Reality–Guided Meditation for Chronic Pain in Patients With Cancer: Exploratory Analysis of Electroencephalograph Activity
Bibliographic record
Abstract
BACKGROUND: Mindfulness-based stress reduction has demonstrated some efficacy for chronic pain management. More recently, virtual reality (VR)-guided meditation has been used to assist mindfulness-based stress reduction. Although studies have also found electroencephalograph (EEG) changes in the brain during mindfulness meditation practices, such changes have not been demonstrated during VR-guided meditation. OBJECTIVE: This exploratory study is designed to explore the potential for recording and analyzing EEG during VR experiences in terms of the power of EEG waveforms, topographic mapping, and coherence. We examine how these measures changed during a VR-guided meditation experience in participants with cancer-related chronic pain. METHODS: A total of 10 adult patients with chronic cancer pain underwent a VR-guided meditation experience while EEG signals were recorded during the session using a BioSemi ActiveTwo system (64 channels, standard 10-20 configuration). The EEG recording session consisted of an 8-minute resting condition (pre), a 30-minute sequence of 3 VR-guided meditation conditions (med), and a final rest condition (post). Power spectral density (PSD) was compared between each condition using a cluster-based permutation test and across conditions using multivariate analysis of variance. A topographic analysis, including coherence exploration, was performed. In addition, an exploratory repeated measures correlation was used to examine possible associations between pain scores and EEG signal power. RESULTS: The predominant pattern was for increased β and γ bandwidth power in the meditation condition (P<.025), compared with both the baseline and postexperience conditions. Increased power in the δ bandwidth was evident, although not statistically significant. The pre versus post comparison also showed changes in the θ and α bands (P=.02) located around the frontal, central, and parietal cortices. Across conditions, multivariate analysis of variance tests identified 4 clusters with significant (P<.05) PSD differences in the δ, θ, β, and γ bands located around the frontal, central, and parietal cortices. Topographically, 5 peak channels were identified: AF7, FP2, FC1, CP5, and P5, and verified the changes in power in the different brain regions. Coherence changes were observed primarily between the frontal, parietal, and occipital regions in the θ, α, and γ bands (P<.0025). No significant associations were observed between pain scores and EEG PSD. CONCLUSIONS: This study demonstrates the feasibility of EEG recording in exploring neurophysiological changes in brain activity during VR-guided meditation and its effect on pain reduction. These findings suggest that distinct altered neurophysiological brain signals are detectable during VR-guided meditation. However, these changes were not necessarily associated with pain. These exploratory findings may guide further studies to investigate the highlighted regions and EEG bands with respect to VR-guided meditation. TRIAL REGISTRATION: ClinicalTrials.gov NCT00102401; http://clinicaltrials.gov/ct2/show/NCT00102401.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".