The role of dopaminergic modulation of the anterior cingulate cortex in chronic pain
Bibliographic record
Abstract
Chronic pain is a highly prevalent debilitating condition resulting from maladaptive changes following nerve injury or neurodegeneration.Those suffering from chronic pain experience allodynia, a pathological sensitivity to innocuous stimuli, and hyperalgesia, an increased sensitivity to painful stimuli.Opioids are potent analgesics but they lose efficacy due to tolerance and commonly result in side effects and use disorders.The current opioid epidemic shines a spotlight on the critical need for alternative treatment options with less damaging side effects.The development of novel analgesic approaches is contingent on our understanding of the dysregulated cortical circuits involved in abnormal pain processing.Symptoms of chronic pain are linked to an increase in pyramidal cell excitability in the anterior cingulate cortex (ACC), a cortical region involved in the processing of affective components of pain.Reducing ACC hyperexcitability alleviates allodynia and hyperalgesia, confirming its role in top-down modulation of pain and identifying a promising druggable pathway for the development of novel treatments for chronic pain.Modulation of ACC activity by dopamine (DA) is of particular interest in this search given the presence of a dense mesocortical dopaminergic projection and the expression of DA receptors across all cortical layers.Additionally, the high comorbidity between chronic pain and hypodopaminergic pathologies, such as Parkinson's Disease (PD) and major depression, suggests that the mesocortical dopaminergic pathway and cortical pain circuits are firmly linked.This thesis focuses on the modulatory role of DA on pyramidal cell excitability in ACC, and consequently on its role in regulating symptoms of pain in healthy and chronic conditions.We provide convincing evidence that DA, specifically acting on the dopamine D1 receptor (D1R), is a potent inhibitory neuromodulator of pyramidal excitability via two independent mechanisms: one linked to the opening of postsynaptic hyperpolarization-activated Chapters 1 and 4
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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) | 0.005 | 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".