Synergistic deficits in parvalbumin interneurons and dopamine signaling drive ACC dysfunction in chronic pain
Bibliographic record
Abstract
Chronic pain arises from maladaptive changes in both peripheral and central nervous systems, including the anterior cingulate cortex (ACC), a key region implicated in descending pain modulation. Chronic pain increases the excitability of pyramidal neurons in the ACC. Although a reduction in inhibitory inputs onto pyramidal neurons has been observed in neuropathic conditions, the identity of the specific interneurons responsible remains unclear. We show that chronic pain selectively impairs parvalbumin (PV), but not somatostatin, interneurons in the rostral ACC. This is characterized by a decrease in the density of PV interneuron processes, a reduction in their surrounding perineuronal net, and a lower expression of PV. Functionally, PV interneurons display diminished inhibitory efficacy in vitro and reduced phasic activation in response to aversive stimuli in vivo. Dopamine (DA) fibers preferentially contact PV interneurons and excite them via D1 dopamine receptor activation, increasing their excitability and enhancing the frequency of inhibitory postsynaptic currents on pyramidal neurons in healthy, but not neuropathic, conditions. Furthermore, we show that this pathway is involved in hunger-induced analgesia: Food deprivation increases DA release in the ACC and consequently decreases pain thresholds in neuropathic mice. Conversely, when mice are not food deprived, neuropathic pain significantly reduces DA release in the ACC. We conclude that the loss of PV interneuron inhibitory efficacy, alongside convergent hypodopaminergic signaling, synergistically contributes to pathological ACC dysfunction and associated symptoms of 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".