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Record 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 on OpenAlexaboutno aff
John J. Haddad, Mark Debrincat, Duane M. North, Jay Dhaliwal, Peter Hurwitz

Bibliographic record

VenueNeurology and Neuroscience · 2023
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionSomatosensory systemComputer scienceStimulus modalityPerceptionHaptic technologyModalitiesSensory systemNeuroscienceHuman–computer interactionPsychologyArtificial intelligence

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.722

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.302
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations3
Published2023
Admission routes1
Has abstractyes

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