Back

Claustral Projections to Anterior Cingulate Cortex Modulate Engagement with the External World

Atlan, G.; Matosevich, N.; Peretz-Rivlin, N.; Yvgi, I.; Chen, E.; Kleinman, T.; Bleistein, N.; Sheinbach, E.; Groysman, M.; Nir, Y.; Citri, A.

2021-06-19 neuroscience
10.1101/2021.06.17.448649 bioRxiv
Show 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.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.