Coordinated Human Brainstem and Spinal Cord Networks during the Expectation of Pain Have Elements Unique from Resting-State Effects
Bibliographic record
Abstract
Functional magnetic resonance imaging (fMRI) research on the human brainstem (BS) and spinal cord (SC) has identified extensive BS/SC resting-state networks (RSNs) by showing spontaneous coordinated blood oxygenation-level dependent (BOLD) signal fluctuations in the absence of a stimulus. Studies have shown that these networks can be influenced by participants’ level of arousal or attention (e.g., watching a video), and linked network function to autonomic homeostatic regulation. Here we explore how the cognitive state of expecting pain can influence connectivity in these networks. Data from two studies (a predictable pain stimulus study, and a resting-state study) were compared to show the effects of expecting pain on BS/SC networks, and how networks differed from networks associated with the resting-state. In each study, BOLD fMRI data were obtained from the cervical SC and brainstem in healthy participants at 3 tesla using a T2-weighted single-shot fast spin-echo imaging method. Functional connectivity was investigated within the entire 3D volume by means of structural equation modeling (SEM) and analyses of covariance (ANCOVA). Results showed extensive connectivity within/across BS and SC regions during the expectation of pain, and ANCOVA analyses showed that connectivity in specific components of these networks varied with individual pain sensitivity. Comparing these results to RSN fluctuations revealed commonalities in coordination between BS and SC regions, and specific BS–BS connectivity fluctuations unique to the expectation of pain. Based on the regions involved, these results provide evidence of brainstem regulation specific to the expectation of 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 | 0.000 | 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".