Abstract
Attention requires filtering distractors and amplifying signals, processes classically attributed to cortico-thalamic networks. Here, we reveal that the cerebellum operates as a bidirectional “cognitive rheostat” to optimize attentional states. In mice, the anterior and posterior cerebellar vermis exert opposing control over attention. Granule cells in the anterior vermis are functionally suppressed to gate sensorimotor noise via reticular nucleus-driven feedforward inhibition. Conversely, posterior granule cells are recruited by pontine inputs to amplify cognitive signals, a process relying on Grin1 -mediated NMDA receptor plasticity. Circuit-specific manipulations targeting this push-pull mechanism, or localized pharmacological modulation, successfully rescued attentional deficits in an ADHD mouse model. These findings fundamentally expand the cerebellum’s role beyond motor coordination, identifying a topographic circuit algorithm essential for cognitive control.
Full text
1,065 characters
· extracted from
oa-doi-fallback
· click to expand
Abstract
Attention requires filtering distractors and amplifying signals, processes classically attributed to cortico-thalamic networks. Here, we reveal that the cerebellum operates as a bidirectional “cognitive rheostat” to optimize attentional states. In mice, the anterior and posterior cerebellar vermis exert opposing control over attention. Granule cells in the anterior vermis are functionally suppressed to gate sensorimotor noise via reticular nucleus-driven feedforward inhibition. Conversely, posterior granule cells are recruited by pontine inputs to amplify cognitive signals, a process relying on Grin1-mediated NMDA receptor plasticity. Circuit-specific manipulations targeting this push-pull mechanism, or localized pharmacological modulation, successfully rescued attentional deficits in an ADHD mouse model. These findings fundamentally expand the cerebellum’s role beyond motor coordination, identifying a topographic circuit algorithm essential for cognitive control.
Competing Interest Statement
The authors have declared no competing interest.
Text is read by the "Ask this paper" AI Q&A widget below.
Extraction quality varies by source — PMC NXML preserves structure
cleanly, OA-HTML may include some navigation residue, and OA-PDF can
have broken hyphenation. The publisher copy
(via DOI)
is the canonical version.