MétaCan
Menu
Back to cohort

The modulatory effect of transcutaneous electrical nerve stimulation (TENS) on tonic heat pain in the human brain: topographic mapping of absolute EEG power spectra

2009· article· en· W3032087108 on OpenAlexaboutno aff
Liping Song, Li Du, Zhaoran Chen

Bibliographic record

VenueZhonghua xingwei yixue yu naokexue zazhi · 2009
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsTranscutaneous electrical nerve stimulationElectroencephalographyTonic (physiology)StimulationSensationAnesthesiaMedicinePsychologyAudiologyNeuroscience

Abstract

fetched live from OpenAlex

Objective To investigate the effect of transcutaneous electrical nerve stimulation (TENS) on baseline eyes-closed brain activation and on pain-related EEG activity when TENS is applied contralatarally to the hand of pain. Method 128-channel electroencephalography (EEG) was recorded in four conditions including baseline,heat pain test,TENS stimulation and contralateral TENS modulation in 15 right-handed healthy young males. The Short Form McGill Pain Questionnaire was employed to assess pain sensation. Fast Fourier Transformation analysis (FFT) was performed to calculate absolute EEG power spectra based on 7 bands spectrum. Results ① The fronto-central beta-1 activity was significantly enhanced by TENS compared to eyes-closed [eyes-closed:(290±201)μV2,TENS:(385±224)μV2,t=3.323,P<0.01].②TENS markedly relived pain sensation assessed by SF-MPQ and significantly increased the posterior-anterior alpha-1 power [pain:(332±221)μV2,TENS modulation:(378±243)μV2,t=3.683,P<0.01] and fronto-central and posterior beta-1 activities [pain:(348±213)μV2,TENS modulation:(397±240)μV2,t=2.362,P<0.05]. Conclusion TENS,used as a peripheral nerve stimulation,mainly activates sensorimotor cortex.The increased Alpha-1 activity implies that TENS is likely able to relieve pain affective. Key words: TENS;  Tonic heat Pain;  Absolute EEG power spectra

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)0.002
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
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 head0.014
GPT teacher head0.252
Teacher spread0.238 · 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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueZhonghua xingwei yixue yu naokexue zazhiSame topicNeuroscience and Neural EngineeringFrench-language works237,207