“Neuroinflammation”: does it have a role in chronic pain? Evidence from human imaging
Bibliographic record
Abstract
ABSTRACT: Despite hundreds of studies demonstrating the involvement of neuron-glia-immune interactions in the establishment and/or maintenance of persistent pain behaviors in animals, the role (or even occurrence) of so-called "neuroinflammation" in human pain has been an object of contention for decades. Here, I present the results of multiple positron emission tomography (PET) studies measuring the levels of the 18 kDa translocator protein (TSPO), a putative neuroimmune marker, in individuals with various pain conditions. Overall, these studies suggest that brain TSPO PET signal: (1) is elevated, compared to healthy volunteers, in individuals with chronic low back pain (with additional elevations in spinal cord and neuroforamina), fibromyalgia, migraine and other conditions characterized by persistent pain; (2) has a spatial distribution exhibiting a degree of disorder specificity; (3) is parametrically linked to pain characteristics or comorbid symptoms (eg, nociplastic pain, fatigue, depression), as well as measures of brain function (ie, functional connectivity), in a regionally-specific manner. In this narrative, I also discuss important caveats to consider in the interpretation of this work (eg, regarding the cellular source of the signal and the complexities inherent in its acquisition and analysis). While the biological and clinical significance of these findings awaits further work, this emerging preclinical literature supports a role of neuron-glia-immune interactions as possible pathophysiological underpinnings of human chronic pain. Gaining a deeper understanding of the role of neuroimmune function in human pain would likely have important practical implications, possibly paving the way for novel interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".