Validation of Structural Equation Modeling Methods for Functional MRI Data Acquired in the Human Brainstem and Spinal Cord
Bibliographic record
Abstract
Structural equation modeling (SEM) provides a means of investigating relationships between blood oxygenation level-dependent (BOLD) signal changes in functional MRI data across neuroanatomical regions. The objectives of this study were to demonstrate adapted SEM methods for the brainstem and spinal cord, validate statistical methods and appropriate statistical thresholds, and test the methods with existing data. SEM methods were applied using an anatomical model of regions of the thalamus, brainstem, and spinal cord that are involved with pain processing. Statistical distributions (Z-scores), significance thresholds, and corrections for multiple comparisons were determined from repeated simulations using "null" data sets. SEM analyses were then applied to data from prior studies involving noxious stimulation in healthy participants. Z-score distributions were observed to vary with the number of source regions modeled, the number of time points (volumes) included in the analysis, and the time span (epoch) used for dynamic analyses. Appropriate choices of statistical thresholds and corrections for multiple comparisons were demonstrated. The results reveal consistent network features across/within studies, as well as dependences on study conditions. They show the effectiveness of a SEM method for functional MRI data from the brainstem and spinal cord.
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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 | 0.036 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".