How fMRI Analysis Using Structural Equation Modeling Techniques Can Improve Our Understanding of Pain Processing in Fibromyalgia
Bibliographic record
Abstract
Howard JM Warren,1 Gabriela Ioachim,1 Jocelyn M Powers,1 Patrick W Stroman1– 3 1Centre for Neuroscience Studies, Queen’s University, Kingston, Ontario, Canada; 2Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, Ontario, Canada; 3Department of Physics, Queen’s University, Kingston, Ontario, CanadaCorrespondence: Patrick W StromanQueen’s University, Centre for Neuroscience Studies, 2nd Floor, Botterell Hall, 18 Stuart Street, Kingston, K7L 3N6, Ontario, CanadaTel +1-613-533-3245Email stromanp@queensu.caPurpose: The purpose of this study was to investigate the utility of data-driven analyses of functional magnetic resonance imaging (fMRI) data, by means of structural equation modeling, for the investigation of pain processing in fibromyalgia (FM).Patients and Methods: Datasets from two separate pain fMRI studies involving healthy controls (HC) and participants with FM were re-analyzed using both a conventional model-driven approach and a data-driven approach, and the results from these analyses were compared. The first dataset contained 15 women with FM and 15 women as healthy controls. The second dataset contained 15 women with FM and 11 women as healthy controls.Results: Consistent with previous studies, the model-driven analyses did not identify differences in pain processing between the HC and FM study groups in both datasets. On the other hand, the data-driven analyses identified significant group differences in both datasets.Conclusion: Data-driven analyses can enhance our understanding of pain processing in healthy controls and in clinical populations by identifying activity associated with pain processing specific to the clinical groups that conventional model-driven analyses may miss.Keywords: human, brain, pain, neuroimaging, fibromyalgia, chronic pain
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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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".