Claustral Projections to Anterior Cingulate Cortex Modulate Engagement with the External World
Bibliographic record
Abstract
Abstract Engagement is a major determinant of performance. Hyper-engagement risks impulsivity and is fatiguing over time, while hypo-engagement could lead to missed opportunities. Even in sleep, when engagement levels are minimal, sensory responsiveness varies. Thus, maintaining an optimal engagement level with the environment is a fundamental cognitive ability. The claustrum, and in particular its reciprocal connectivity with executive regions in the frontal cortex, has been associated with salience, attention and sleep. These apparently disparate roles can be consolidated within the context of engagement. Here we describe the activity of claustro-frontal circuits in a task imposing a tradeoff between response inhibition and sensory acuity (‘ENGAGE’). Recording calcium fiber photometry during >80,000 trials, we characterize claustrum recruitment during salient behavioral events, and find that a moderate level of activity in claustro-cingulate projections defines optimal engagement. Low activity of this pathway is associated with impulsive actions, while high activity is associated with behavioral lapses. Chemogenetic activation of cingulate-projecting claustrum neurons suppressed impulsive behavior and reduced the engagement of mice in the task. This relationship became even clearer upon addressing individual variability in the strategy mice employed during the ENGAGE task. Furthermore, this association of claustrum activity and engagement extends into sleep. Using simultaneous EEG and photometry recordings in the claustrum, we find that cingulate projecting claustrum neurons are most active during deep unresponsive slow-wave sleep, when mice are less prone to awakening by sensory stimuli.
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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 | 0.000 |
| 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".