Neurotransmitter receptor densities are associated with changes in regional Cerebral blood flow during clinical ongoing pain
Bibliographic record
Abstract
Arterial spin labelling (ASL) plays an increasingly important role in neuroimaging pain research but does not provide molecular insights regarding how regional cerebral blood flow (rCBF) relates to underlying neurotransmission. Here, we integrate ASL with positron emission tomography (PET) and brain transcriptome data to investigate the molecular substrates of rCBF underlying clinically relevant pain states. Two data sets, representing acute and chronic ongoing pain respectively, were utilised to quantify changes in rCBF; one examining pre-surgical versus post-surgical pain, and the second comparing patients with painful hand Osteoarthritis to a group of matched controls. We implemented a whole-brain spatial correlation analysis to explore associations between change in rCBF (ΔCBF) and neurotransmitter receptor distributions derived from normative PET templates. Additionally, we utilised transcriptomic data from the Allen Brain Atlas to inform distributions of receptor expression. Both datasets presented significant correlations of ΔCBF with the μ-opioid and dopamine-D2 receptor expressions, which play fundamental roles in brain activity associated with pain experiences. ΔCBF also correlated with the gene expression distributions of several receptors involved in pain processing. Overall, this is the first study illustrating the molecular basis of ongoing pain ASL indices and emphasises the potential of rCBF as a biomarker in pain research.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".