Changes in Pain Processing in the Spinal Cord and Brainstem after Spinal Cord Injury Characterized by Functional Magnetic Resonance Imaging
Bibliographic record
Abstract
Traumatic spinal cord injury (SCI) has a number of devastating consequences, including high prevalence of chronic pain and altered pain sensitivity. The causes of altered pain states vary depending on the injury and are difficult to diagnose and treat. A better understanding of pain mechanisms after SCI is expected to lead to better diagnostic capabilities and improved treatments. We therefore applied functional magnetic resonance imaging (fMRI) of the brainstem and spinal cord in a group of participants with previous traumatic SCI to characterize changes in pain processing as a result of their injuries. The same thermal stimulus was applied to the medial palm (C8 dermatome) as a series of repeated brief noxious thermal pulses in a group of 16 participants with a cervical (n = 14) and upper thoracic (n = 2) injuries. Functional MRI of the brainstem and spinal cord was used to determine the neuronal activity evoked by the noxious stimulation, and connectivity between regions was characterized with structural equation modeling (SEM). The results show that pain ratings, the location and magnitude of blood oxygenation-level dependent fMRI results, and connectivity assessed with SEM varied widely across participants. However, the results varied in relation to the perceived pain and the level/severity of injuries, particularly in terms of hypothalamus connectivity with other regions, and descending modulation via the periaqueductal gray matter-rostral ventromedial medulla-cord pathway. The results, therefore, appear to provide sensitive indicators of each individual's pain response, and information about the mechanisms of altered pain sensitivity. The ability to characterize changes in pain processing in individuals with SCI represents a significant technological advance.
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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 | 0.001 |
| 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".