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Enregistrement W4388643845 · doi:10.33425/2692-7918.1054

Cognitive Network Changes After Exposure to Haptic Vibrotactile Trigger Technology: Results From The ENHANCE Study

2023· article· en· W4388643845 sur OpenAlexaboutno aff
John J. Haddad, Mark Debrincat, Duane M. North, Jay Dhaliwal, Peter Hurwitz

Notice bibliographique

RevueNeurology and Neuroscience · 2023
Typearticle
Langueen
DomaineNeuroscience
ThématiqueTactile and Sensory Interactions
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCognitionSomatosensory systemComputer scienceStimulus modalityPerceptionHaptic technologyModalitiesSensory systemNeuroscienceHuman–computer interactionPsychologyArtificial intelligence

Résumé

récupéré en direct d'OpenAlex

The conceptual framework of cognitive networks, or cognits, represents a system of working memory, especially long-term memory arrays that are intrinsically designed to attain certain behavioral ends and that are activated by a neural structure. Despite the fact that cognitions can be used in a plethora of systems, current technologies allow manipulations of the central nervous system (CNS) to execute certain sensory and motor functions. Sensation and tactual perception by the skin are an innate mechanism for human survival and represent our adaptive somatosensorial ability to apprehend information via haptics—the active touch for object recognition and perception by higher centers of the brain. The somatosensation, which is identified by a set of channels and receptors sensitive to a variety of stimuli (thermal, tactile, and mechanical), is critical to survival, balance control, cognition, and pain modulation. Cognits are cutting-edge tools and modalities that provide a landscape of theoretical assets, evidencebased experimental protocols, computational intelligence schemes, and direct empirical modalities that facilitate the understanding of the complex functionalities of the human brain. By evaluating neuroimaging data after somatosensory stimulation and collected via electroencephalogram (EEG), cognition response and change can be obtained that allows researchers to gain a better understanding of emerging scientific approaches aimed at understanding human behavioral outcomes. An emerging technology, haptic vibrotactile trigger technology (VTT), incorporates somatosensory patterns in compression sleeves. eSmartr Smart Compression Sleeves (Srysty Holdings Inc., Mississauga, ON, Canada) with VTT and its Cognitive Boost Technology (CBT) pattern is designed to optimize neural communications for improved mindful wellness. This technology has also been incorporated into patches, braces, apparel (socks), wrist bands, and other routes of delivery. Mindful wellness is considered an outcome of somatosensory intervention that modulates the behavioral responses associated with cognitive networks. Currently, there is limited research exploring these modalities, exposing the need to study new technologies and their influence on somatosensory pathways and cognitive networks. The purpose of this IRB-approved study was to explore the effects of forearm VTT stimulation patterns on cognitive networks by comparing a baseline EEG to an EEG after placing a sleeve incorporating VTT on the right or left forearm of adult healthy individuals. Materials and methods: A baseline EEG was recorded over 5 minutes from 19 scalp locations on 20 subjects ranging in age from 17.6 years to 41.9 years (n=7 females, 13 males). The subject’s dominant arm was then fitted with the eSmartr Smart Compression Sleeve for 20 minutes and another 5-minute EEG was recorded. Both the LORETA (Low Resolution Electromagnetic Tomography Analysis) inverse solution and a power spectral analysis of the surface EEG were calculated. Additionally, for 10 distinct networks, the current sources from 88 Brodmann areas were computed. The variables were absolute power and absolute current density in 1 Hz increments in 10 frequency bands (delta, theta, alpha-1, alpha-2, beta-1, beta-2, beta-3, and hi-beta). Paired t-tests were computed for each individual for all EEG parameters, as well as group paired t-tests, between the baseline EEG and followup EEG. Results: The results showed statistically significant t-test differences (P < 0.01) in both the surface EEG and the LORETA current sources between the baseline measurement and the follow-up ‘sleeve-on’ measurement. The largest differences were detected with a prominent downregulation of alpha and beta frequency powers at both the surface EEG and the LORETA current sources with the “sleeve-on,” as compared to baseline. In addition, the maximal effects of the “sleeve-on” condition were in the left frontal and left temporal surface EEGs and on the medial bank of the somatosensory cortex in the range of the alpha frequency. Changes in the default network and attention network were also prominent. Conclusions: Study results indicate that these non-pharmacologic, non-invasive, haptic vibrotactile trigger technology (VTT) patterned compression sleeves elicited a response in multiple cognitive networks. These networks play a key role in executive function, memory, attention, mood, and information flow. There was a prominent effect of the haptic vibrotactile trigger technology with the CBT- patterned sleeves on the EEG that was primarily located in alpha and beta frequency bands. The substantial impacts on the homuncular projection of the arm to the medial somatosensory cortex as well as the default network demonstrated activity influenced by the patterned sleeve. The mechanisms of action of the VTT sleeve on the brain, neuropathways, and the EEG spectrum are still being investigated. If results are confirmed with further research, this novel VTT technology could be a promising addition as a non-invasive and non-drug treatment approach for a variety of conditions and therapeutic applications.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,747
Score d'incertitude au seuil0,722

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,002
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,038
Tête enseignante GPT0,302
Écart entre enseignants0,264 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2023
Routes d'admission1
Résumé présentoui

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