Increased EEG power and slowed dominant frequency in patients with neurogenic pain
Bibliographic record
Abstract
To study the mechanisms of chronic neurogenic pain, we compared the power spectra of the resting EEG of patients (n = 15, 38-75 years, median 64 years, 6 women) and healthy controls (n = 15, 41-71 years, median 60 years, 8 women). On an average, the patient group exhibited higher spectral power over the frequency range of 2-25 Hz, and the dominant peak was shifted towards lower frequencies. Maximal differences appeared in the 7-9 Hz band in all electrodes. Frontal electrodes contributed most to this difference in the 13-15 Hz band. Bicoherence analysis suggests an enhanced coupling between theta (4-9 Hz) and beta (12-25 Hz) frequencies in patients. The subgroup of six patients free from centrally acting medication showed higher spectral power in the 2-18 Hz frequency range. On an individual basis, the combination of peak height and peak frequency discriminated between patient and control groups: discriminant analysis classified 87% of all subjects correctly. After a therapeutic lesion in the thalamus (central lateral thalamotomy, CLT) we carried out follow-up for a subgroup of seven patients. Median pain relief was 70 and 95% after 3 and 12 months, respectively. The average EEG power of all seven patients gradually decreased in the theta band and approached normal values only after 12 months. The excess theta EEG power in patients and its decrease after thalamic surgery suggests that both EEG and neurogenic pain are determined by tightly coupled thalamocortical loops. The small therapeutic CLT lesion is thought to initiate a progressive normalization in the affected thalamocortical system, which is reflected in both decrease of EEG power and pain relief.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".