Proof‐of‐concept of a novel structural equation modelling approach for the analysis of functional magnetic resonance imaging data applied to investigate individual differences in human pain responses
Bibliographic record
Abstract
A novel network analysis method is demonstrated for applications with functional magnetic resonance imaging (fMRI) data. The method is based on structural equation modeling (SEM) plus modeling of physiological responses in order to explain blood oxygenation-level dependent (BOLD) responses across interconnected regions. The method, termed structural and physiological modeling (SAPM) aims to overcome a weakness of previous analysis methods by estimating both input and output signaling of every region of a network. The results also provide weighting factors (B) which describe the influence of each input signal to a region on its output signaling to another region. The SAPM method is demonstrated by applying it to fMRI data from the brainstem and spinal cord in 55 healthy participants undergoing repeated applications of a heat pain stimulation paradigm. Data are also analyzed using our established SEM method for comparison. The results with both methods indicate that individual differences in nociceptive processing are mediated by differences in descending regulation of spinal cord neurons under the influence of both the nucleus tractus solitarius and periaqueductal gray region. The SAPM results show that BOLD responses in the entire network can be explained during all periods of the stimulation paradigm based on two latent (unobserved) input signaling sources, and a model of the predicted BOLD responses to the heat stimulus. The results demonstrate the concept of our novel SAPM method and provide evidence for its validity. Additional studies are needed to further develop the method and its applications to investigations of complex neural processes across networks.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".