Functional and molecular properties of learning-related neuroplasticity in perirhinal cortex
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Abstract
Learning is actuated by plasticity, the remarkable ability of the brain to change and adapt in response to experience. A robust neural circuit level correlate of plasticity is a change in network activity, while on a molecular level, plasticity is mediated by immediate early gene (IEG) programs and finely tuned, highly specific downstream structural adaptations. Although plasticity occurs in all areas of the brain, the perirhinal cortex (Prh) in the medial temporal lobe (MTL) is a zone of convergence for higher order sensory association areas and a number of subcortical structures related to learning, memory consolidation and valence, and is therefore a particularly relevant structure for understanding the plasticity responses related to sensory- and goal-directed learning. However, it is currently unknown how Prh processes information from diverse regions of the brain to generate a flexible internal model related learning. Additionally, there is a gap in knowledge regarding the direct relationship between changes in gene expression and adaptations in network activity patterns that accompany complex, goal-directed learning in this region of the brain. To address these gaps in knowledge, we performed chronic two-photon calcium imaging of Prh layer 2/3 neurons while animals learned a whisker-based, go-no go delayed non-match to sample task. At the conclusion of behavior experiments, spatial transcriptomics was performed on functionally imaged neurons to identify molecularly defined, Prh-specific cell types and quantify expression of cell type marker genes and immediate early genes (IEGs) associated with neuronal plasticity. Using population analysis and generalized linear models, we analyzed the relationship between task-related responses, cell type identity, and IEG expression. We found that Prh encodes stimulus features as sensory prediction errors, and forms stable, stimulus-outcome associations that expand retrospectively from outcome back to the time of stimulus delivery and generalize as animals learn new contingencies. Additionally, we found that task-related responses were best explained by IEG expression patterns that spanned cell types. To confirm this role of IEGs, we perturbed the expression of brain-derived neurotrophic factor (Bdnf), a known regulator of task-related IEGs. We found that reward representations in Prh showed increased stability on a session-to-session basis in Bdnf conditional knockout (cKO) mice compared to control animals. Whereas, stimulus-reward associations emerged over sessions in control animals, those associations failed to form in Bdnf cKO animals. Taken together, this work demonstrates that Prh combines error-driven and map-like properties to generate a predictive map of learned task behavior and delineates the specificity in which gene expression participates in Prh-dependent task learning.
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2024