Processing of pain and itch information by modality-specific neurons within the anterior cingulate cortex in mice
Bibliographic record
Abstract
Pain and itch are aversive sensations with distinct qualities, processed in overlapping pathways and brain regions, including the anterior cingulate cortex (ACC), which is critical for their affective dimensions. However, the cellular mechanisms underlying their processing in the ACC remain unclear. Here, we identify modality-specific neuronal populations in layer II/III of the ACC in mice involved in pain and itch processing. Using a synapse labeling tool, we show that pain- and itch-related neurons selectively receive synaptic inputs from mediodorsal thalamic neurons activated by pain and itch stimuli, respectively. Chemogenetic inhibition of these neurons reduced pruriception or nociception without affecting the opposite modality. Conversely, activation of these neurons did not enhance stimulus-specific responses but commonly increased freezing-like behavior. These findings reveal that the processing of itch and pain information in the ACC involves activity-dependent and modality-specific neuronal populations, and that pain and itch are processed by functionally distinct ACC neuronal subsets. How itch and pain, which are similar but distinct, are processed in the brain remains unclear. Here, the authors show modality-specific neural circuits between the anterior cingulate cortex (ACC) and mediodorsal thalamus (MD) are in charge of each sensation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".