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Cannabinoids exert their psychomotor actions through the CB1 cannabinoid receptor in the brain. Genetic deletion of CB1 in mice causes various symptoms, including changes in locomotor activity, increased ring catalepsy, supraspinal hypoalgesia, and impaired memory extinction. Although the cerebellar cortex contains the highest level of CB1, severe cerebellum-related functional deficits have not been reported in CB1 knock-out mice. To clarify the roles of CB1 in cerebellar function, we subjected CB1 knock-out mice to a delay version of classical eyeblink conditioning. This paradigm is a test for cerebellum-dependent discrete motor learning, in which conditioned stimulus (CS) (352 ms tone) and unconditioned stimulus (US) (100 ms periorbital electrical shock) are coterminated. We found that delay eyeblink conditioning performance was severely impaired in CB1 knock-out mice. In contrast, they exhibited normal performance in a trace version of eyeblink conditioning with 500 ms stimulus-free interval intervened between the CS offset and the US onset. This paradigm is a test for hippocampus-dependent associative learning. Sensitivity of CB1 knock-out mice to CS or US was normal, suggesting that impaired delay eyeblink conditioning is attributable to defects in association of responses to CS and US. We also found that intraperitoneal injection of the CB1 antagonist SR141716A [N-piperidino-5-(4-chlorophenyl)-1-(2,4-dichlorophenyl)-4-methyl-3-pyrazole carboxamide] to wild-type mice caused severe impairment in acquisition but not extinction of delay eyeblink conditioning. SR141716A treatment had no effect on trace eyeblink conditioning with a 500 or 750 ms trace interval. These results indicate that endogenous cannabinoid signaling through CB1 is essential for cerebellum-dependent discrete motor learning, especially for its acquisition.

Endogenous Cannabinoid Signaling through the CB1Receptor Is Essential for Cerebellum-Dependent Discrete Motor Learning

Endogenous Cannabinoid Signaling through the CB₁Receptor Is Essential for Cerebellum-Dependent Discrete Motor Learning