Investigating affective modulation of pain in the brainstem and spinal cord in healthy people and those with fibromyalgia using functional magnetic resonance imaging
Bibliographic record
Abstract
Pain is a multidimensional experience that involves both sensory and affective components. Because of this, understanding how affect influences pain perception can help us understand the experience of pain more comprehensively. Furthermore, investigating this influence can identify possible mechanisms of fibromyalgia (FM), a chronic pain condition which is often associated with symptoms of negative affect. FM is a debilitating condition that disproportionately affects women and affects multiple domains of well-being. Despite this, the exact physiological and neural mechanisms of FM are still unknown. The aim of this thesis was to investigate the neural signaling in the brainstem and spinal cord associated with negative affective modulation of pain in healthy people and in people with FM. This was done in two separate projects. The first project was a re-analysis of fMRI data from a previous affective modulation study done in healthy people using novel connectivity analysis methods. The results from this study suggested that people differ in the way that they respond to affective modulation of pain, in terms of pain ratings and connectivity. Building on the study done in healthy people, the second project investigated the effect of negative affective modulation of pain in FM compared to HC. The results of this study suggested that pain ratings in FM participants were not modulated by negative affect, which indicated that differences in connectivity between FM and HC were not a result of the induced negative affect. However, these results were gathered from a relatively small sample and may not have detected subtle effects of affective modulation of pain, if there were any. Nevertheless, the results from this study demonstrated that although FM is thought to be influenced strongly by affect, our results do not support this idea. Combining these results with those from the first study, we suggest that affective modulation of pain is not a strong effect, both in HC and FM.
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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) | 0.001 | 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".