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Cortical integration of bilateral nociceptive signals: when more is less

2018· article· en· W2902115439 on OpenAlexaff
Stéphane Northon, Nabi Rustamov, Mathieu Piché

Bibliographic record

VenuePain · 2018
Typearticle
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsNociceptionSensory systemNeurosciencePsychologyElectroencephalographyPerceptionMedicineAudiologyInternal medicineReceptor

Abstract

fetched live from OpenAlex

Integration of nociceptive information is essential to produce adapted responses, to promote body integrity and survival. However, how the brain integrates nociceptive inputs from different body areas remains unknown. The aim of this study was to examine the cortical integration of bilateral nociceptive inputs evoked by laser heat stimuli. Sixteen healthy volunteers (8 F, 8 M; age: 25.5 ± 4.3) were recruited to participate in one session during which painful laser stimuli were applied to their hands with 2 Nd:YAP laser systems. Electroencephalographic activity was recorded to measure laser-evoked potentials and event-related spectral perturbations. Twenty nociceptive stimuli were applied in each of the 4 counterbalanced conditions: (1) right hand, (2) left hand, and both hands with (3) attention to the right or (4) attention to the left. Compared with unilateral conditions, N2 and P2 peak amplitude as well as gamma oscillation power were decreased in bilateral conditions (P < 0.05), but these effects were not affected by the direction of attention (P > 0.1). By contrast, pain was not significantly different in any condition (P > 0.05). These findings show that although more nociceptive inputs reach the brain with multiple nociceptive stimuli, their sensory representation is decreased while pain perception remains unchanged. These interactions between cerebral processing of nociceptive information from different body regions could support coordinated behavioral responses when pain origins from multiple sources.

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)0.005
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.059
GPT teacher head0.310
Teacher spread0.251 · 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

Citations18
Published2018
Admission routes1
Has abstractyes

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