Functional Magnetic Resonance Imaging of the Human Brainstem and Cervical Spinal Cord during Cognitive Modulation of Pain
Bibliographic record
Abstract
Pain is a complex sensory experience, and cognitive factors such as attention can influence its perception. Modulation of pain involves a network of subcortical structures; however, the role and relationship of these regions in cognitive modulation of pain are not well understood. The aims of this research were to evaluate the behavioral effect of cognitive modulation of pain and investigate the neural correlates of this mechanism in the brainstem and cervical spinal cord (SC), using functional magnetic resonance imaging (fMRI) and structural equation modeling (SEM). We applied noxious thermal stimulation on the C6 dermatome to 12 healthy female participants while they performed the n-Back task. Our findings demonstrate a significant attenuation in pain perception across the group as a result of the task, along with high intersubject variability in the degree of modulation. Using fMRI, our studies characterize neural responses in subcortical regions that are involved in the modulation of pain. SEM analysis reveals connectivity between the brainstem and SC at the group and individual levels, depending on cognitive load and degree of pain modulation, respectively. All together, our research demonstrates the behavioral effect of cognitive modulation on pain and provides insight into the subcortical neural response to the process.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".