Virtual Reality–Guided Meditation for Chronic Pain in Patients With Cancer: Exploratory Analysis of Electroencephalograph Activity
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».