A novel electroencephalography‐based tool for objective assessment of network dynamics activated by nociceptive stimuli
Bibliographic record
Abstract
BACKGROUND: Pain perception is typically assessed using subjective measures; an objective measure of the response to pain would be valuable. In this study, Brain Network Activation (BNA), a novel multivariate pattern analysis and scoring algorithm, was applied to event-related potentials (ERPs) elicited by cortical responses to brief heat stimuli. Objectives of this study were to evaluate the utility of BNA as a quantitative and qualitative measure of cortical response to pain. METHODS: Contact Heat Evoked Potentials (CHEPs) data were collected from 17 healthy, right-handed volunteers (10 M, 7F) using 5 different temperatures (35, 41, 46, 49 and 52 °C). A set of spatio-temporal activity patterns common to all the subjects in the group (Reference Brain Network Model; RBNM) was generated using the BNA algorithm, based on evoked responses at 52 °C. RESULTS: Frame by frame 'unfolding' of the brain network across time showed qualitative differences between responses to painful and non-painful stimuli. Brain network activation scores were shown to be a better indicator of the individual's sensitivity to pain when compared to subjective pain ratings. Additionally, BNA scores correlated significantly with temperature, demonstrated good test-retest reliability, as well as a high degree of sensitivity, specificity and accuracy in correctly categorizing subjects who reported stimuli as painful. CONCLUSIONS: These results may provide evidence that the multivariate analysis performed with BNA may be useful as a quantitative, temporally sensitive tool for assessment of pain perception.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".