The anterior cingulate cortex drives lateralized age-dependent modulation of claustrum circuits
Bibliographic record
Abstract
Abstract The anterior cingulate cortex (ACC) sends top-down inputs to the claustrum during sensory, motor, and cognitive processing. This ACC input is thought to drive the activation of claustrum neurons which in turn project back to the cortex to help orchestrate cortical networks during demanding cognitive states such as attention. However, the circuit mechanisms underlying ACC-claustrum signaling are not fully understood. Using in vivo single neuron recordings in mice, we show that ACC neuron activation drives a lateralized modulation of claustrum excitability that changes as a function of postnatal age. In adulthood, ACC activation evoked feed-forward inhibition of ipsilateral excitatory claustrum neurons and activation of contralateral excitatory claustrum neurons. Chemogenetic manipulation in adult mice revealed that ipsilateral claustrum inhibition by the ACC was due to feed-forward activation of claustrum parvalbumin inhibitory neurons. However, in neonatal mice, which lack mature parvalbumin interneurons, ACC inputs evoked claustrum excitation. In juvenile mice, the developmental switch from ACC-evoked claustrum excitation to inhibition occurred in parallel with the maturation of claustrum parvalbumin interneurons, thus corroborating the chemogenetic findings. Therefore, this work provides a novel mechanism of cortical control over claustrum activity that is refined during early postnatal life.
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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) | 0.002 | 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".