Microglia Disrupt Mesolimbic Reward Circuitry in Chronic Pain
Bibliographic record
Abstract
Chronic pain attenuates midbrain dopamine (DA) transmission, as evidenced by a decrease in opioid-evoked DA release in the ventral striatum, suggesting that the occurrence of chronic pain impairs reward-related behaviors. However, mechanisms by which pain modifies DA transmission remain elusive. Using in vivo microdialysis and microinjection of drugs into the mesolimbic DA system, we demonstrate in mice and rats that microglial activation in the VTA compromises not only opioid-evoked release of DA, but also other DA-stimulating drugs, such as cocaine. Our data show that loss of stimulated extracellular DA is due to impaired chloride homeostasis in midbrain GABAergic interneurons. Treatment with minocycline or interfering with BDNF signaling restored chloride transport within these neurons and recovered DA-dependent reward behavior. Our findings demonstrate that a peripheral nerve injury causes activated microglia within reward circuitry that result in disruption of dopaminergic signaling and reward behavior. These results have broad implications that are not restricted to the problem of pain, but are also relevant to affective disorders associated with disruption of reward circuitry. Because chronic pain causes glial activation in areas of the CNS important for mood and affect, our findings may translate to other disorders, including anxiety and depression, that demonstrate high comorbidity with chronic 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".