Abnormal Affective Modulation of Somatosensory Brain Processing Among Patients With Fibromyalgia
Bibliographic record
Abstract
Objective: It is well established that subjective pain perception can be modulated by negative mood states and that patients with chronic pain are characterized by high levels of depression and anxiety. Nevertheless, very little is known about the effects of negative mood induction on brain processing of somatosensory information in fibromyalgia. The objective of the present study was to examine the influence of two emotional states (pleasant and unpleasant) on brain activity of patients with fibromyalgia (FM; n = 27) and with musculoskeletal (MSK) pain resulting from identifiable somatic lesions (n = 16). Methods: For this purpose, somatosensory-evoked potentials (SEPs) elicited by nonpainful pneumatic stimuli, delivered to the right and left hand following an oddball paradigm, were recorded when patients were viewing affective slides. Results: As compared with patients with MSK pain, patients with FM displayed overall larger P50 amplitude to tactile stimuli. In addition, significantly larger P50 and smaller N80 amplitudes were found within patients with FM when they were viewing the unpleasant rather than the pleasant slides. Conclusion: Our data suggest an abnormal processing of nonpainful somatosensory information in FM, especially when somatic signals are arising from the body within an aversive stimulus context. These findings provide further support for the use of biopsychosocial models for understanding FM and other chronic pain states. ACR = American College of Rheumatology; ANOVA = analysis of variance; BDI = Beck Depression Inventory; EEG = electroencephalogram; FM = fibromyalgia; fMRI=functional magnetic resonance imaging; IAPS = International Affective Picture System; MPI = West Haven–Yale Multidimensional Pain Inventory; MPQ = McGill Pain Questionnaire; MSK = musculoskeletal; SE = standard error of the mean; SEP = somatosensory-evoked potentials; STAI = State-Trait Anxiety Inventory.
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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 | 0.000 | 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) | 0.002 | 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".