Exploring Neural Signaling Patterns and Their Physiological Origins in Fibromyalgia by Means of Functional MRI Guided by a Review of the Literature
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Fibromyalgia (FM) is a chronic pain condition that includes symptoms of hyperalgesia and has an unknown etiology. This study aimed to further investigate the underlying neural signaling mechanisms and their relation to observed blood oxygenation-level dependent (BOLD) signal increases at the onset of functional magnetic resonance imaging (fMRI) runs. METHODS: The possible neural mechanisms were first explored by reviewing the current literature. The second component of this study involved a voxel-by-voxel analysis of BOLD responses in all regions of the brain. The fMRI data were obtained from a previous study of participants with and without fibromyalgia during fMRI runs involving either a noxious heat pain stimulus or no stimulus. RESULTS: The literature review indicates that no single factor can explain the initial BOLD signal rise observed in FM but that there are likely multiple interacting influences. These include physiological dysregulation via mechanisms, such as oxidative stress, mitochondrial dysfunction, and cytokine activity, and may involve the sympathetic nervous system. The analysis of BOLD responses demonstrated that the initial BOLD rises occur specifically in gray matter regions and are largest in regions involved with pain processing, including the right insular cortex and periaqueductal gray region. Moreover, the BOLD rise is significantly larger in people with FM prior to the application of a noxious stimulus. CONCLUSIONS: The initial rise in BOLD response demonstrates heightened metabolic demand that is exaggerated in people with FM. It appears to be influenced by cognitive factors such as anticipation and may reflect neural dysregulation, possibly involving autonomic signaling.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".