Back

A disinhibitory basal forebrain to cortex projection supports sustained attention

Li, S.-J.; Hangya, B.; Gupta, U.; Fischer, K. B.; Sturgill, J. F.; Callaway, E. M.; Kepecs, A.

2024-07-23 neuroscience
10.1101/2024.07.22.604711 bioRxiv
Show abstract

Sustained attention, as an essential cognitive faculty governing selective sensory processing, exhibits remarkable temporal fluctuations. However, the underlying neural circuits and computational mechanisms driving moment-to-moment attention fluctuations remain elusive. Here we demonstrate that cortex-projecting basal forebrain parvalbumin-expressing inhibitory neurons (BF-PV) mediate sustained attention in mice performing an attention task. BF-PV activity predicts the fluctuations of attentional performance metrics [-] reaction time and accuracy [-] trial-by-trial, and optogenetic activation of these neurons enhances performance. BF-PV neurons also respond to motivationally salient events, such as predictive cues, rewards, punishments, and surprises, which a computational model explains as representing motivational salience for allocating attention over time. Furthermore, we found that BF-PV neurons produce cortical disinhibition by inhibiting cortical PV+ inhibitory neurons, potentially underpinning the observed attentional gain modulation in the cortex. These findings reveal a disinhibitory BF-to-cortex projection that regulates cortical gain based on motivational salience, thereby promoting sustained attention. HIGHLIGHTSO_LIBF-PV activity predicts attentional performance metrics: reaction time and accuracy C_LIO_LIBF-PV responses reflect the computation of motivational salience-guided attention allocation C_LIO_LIOptogenetic activation of BF-PV neurons improves attentional performance C_LIO_LIBF-PV neurons produce cortical disinhibition through topographic projections and mediate gain modulation C_LI

Published in Cell (predicted rank #9) · training set

Matching journals

The top 4 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.