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A quadruple dissociation of reward-related behaviour in mice across excitatory inputs to the nucleus accumbens shell

The nucleus accumbens shell (NAcSh) is critically important for reward valuations, yet it remains unclear how valuation information is integrated in this region to drive behaviour during reinforcement learning. Using an optogenetic spatial self-stimulation task in mice, here we show that contingent...

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Detalles Bibliográficos
Autores principales: Lind, Erin B., Sweis, Brian M., Asp, Anders J., Esguerra, Manuel, Silvis, Keelia A., David Redish, A., Thomas, Mark J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9886947/
https://www.ncbi.nlm.nih.gov/pubmed/36717646
http://dx.doi.org/10.1038/s42003-023-04429-6
Descripción
Sumario:The nucleus accumbens shell (NAcSh) is critically important for reward valuations, yet it remains unclear how valuation information is integrated in this region to drive behaviour during reinforcement learning. Using an optogenetic spatial self-stimulation task in mice, here we show that contingent activation of different excitatory inputs to the NAcSh change expression of different reward-related behaviours. Our data indicate that medial prefrontal inputs support place preference via repeated actions, ventral hippocampal inputs consistently promote place preferences, basolateral amygdala inputs produce modest place preferences but as a byproduct of increased sensitivity to time investments, and paraventricular inputs reduce place preferences yet do not produce full avoidance behaviour. These findings suggest that each excitatory input provides distinct information to the NAcSh, and we propose that this reflects the reinforcement of different credit assignment functions. Our finding of a quadruple dissociation of NAcSh input-specific behaviours provides insights into how types of information carried by distinct inputs to the NAcSh could be integrated to help drive reinforcement learning and situationally appropriate behavioural responses.