Using Structural Equation Modeling to Investigate the Neural Basis of Altered Pain Processing in Fibromyalgia with Functional Magnetic Resonance Imaging
Bibliographic record
Abstract
Participants with fibromyalgia (FM) and healthy controls (HC) experienced an identical ‘threat/safety’ experimental pain paradigm while undergoing functional magnetic resonance imaging (fMRI) to investigate the differences in pain processing between the two groups. In the ‘threat’ (Pain) imaging runs, participants were told that they would receive noxious heat stimuli to their right hands, calibrated to elicit subjectively moderate levels of pain. In the ‘safety’ (No-Pain) imaging runs, no stimulus was given. This design enabled the study of both continuous and reactive components of pain processing, as well as brain activity associated with anticipation and reward. The fMRI data were analyzed with a data-driven structural equation modeling approach, and significant group-level connectivity differences were identified in both study conditions, in both time periods of interest (Expectation, Stimulation). Group-level connectivity differences in the No-Pain condition occurred mainly during the expectation of pain, and involved regions associated with emotion and reward, suggesting FM may involve altered affective/reward processing. Group-level connectivity differences in the Pain condition occurred mainly during stimulation, with the FM group having decreased connectivity between the anterior cingulate cortex (ACC) and the amygdala, and increased connectivity between the posterior cingulate cortex (PCC) and the thalamus. The decreased ACC→Amygdala connectivity supports previous findings, suggesting FM likely involves altered responses in motivational-affective aspects of pain processing. The increased PCC→Thalamus connectivity may suggest the FM group experienced heightened saliency toward the noxious stimuli, which may contribute toward the mechanism which causes hyperalgesia in 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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".