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The cerebellum is emerging as a powerful regulator of cognitive and affective processing and memory in both humans and animals and has been implicated in affective disorders. How the cerebellum supports affective function remains poorly understood. The short-latency (just a few milliseconds) functional connections that were identified between the cerebellum and amygdala-a structure crucial for the processing of emotion and valence-more than four decades ago raise the exciting, yet untested, possibility that a cerebellum-amygdala pathway communicates information important for emotion. The major hurdle in rigorously testing this possibility is the lack of knowledge about the anatomy and functional connectivity of this pathway. Our initial anatomical tracing studies in mice excluded the existence of a direct monosynaptic connection between the cerebellum and amygdala. Using transneuronal tracing techniques, we have identified a novel disynaptic circuit between the cerebellar output nuclei and the basolateral amygdala. This circuit recruits the understudied intralaminar thalamus as a node. Using ex vivo optophysiology and super-resolution microscopy, we provide the first evidence for the functionality of the pathway, thus offering a missing mechanistic link between the cerebellum and amygdala. This discovery provides a connectivity blueprint between the cerebellum and a key structure of the limbic system. As such, it is the requisite first step toward obtaining new knowledge about cerebellar function in emotion, thus fundamentally advancing understanding of the neurobiology of emotion, which is perturbed in mental and autism spectrum disorders.

A meta-analysis of non-human primates performing three different tasks (Object-Match, Category-Match, and Category-Saccade associations) revealed signatures of explicit and implicit learning. Performance improved equally following correct and error trials in the Match (explicit) tasks, but it improved more after correct trials in the Saccade (implicit) task, a signature of explicit versus implicit learning. Likewise, error-related negativity, a marker for error processing, was greater in the Match (explicit) tasks. All tasks showed an increase in alpha/beta (10–30 Hz) synchrony after correct choices. However, only the implicit task showed an increase in theta (3–7 Hz) synchrony after correct choices that decreased with learning. In contrast, in the explicit tasks, alpha/beta synchrony increased with learning and decreased thereafter. Our results suggest that explicit versus implicit learning engages different neural mechanisms that rely on different patterns of oscillatory synchrony. Loonis et al. find that explicit and implicit learning use feedback about correct choices versus errors differently. Implicit learning relies more on theta synchrony (3–7 Hz) while explicit learning relies on alpha/beta synchrony (10–30 Hz). ©2017 Elsevier Inc., NIMH R37MH08702, NIMH R01MH06525, The Picower Institute Innovation Fund

In addition to the prefrontal cortex (PFC), the basal ganglia (BG) have been increasingly often reported to play a fundamental role in category learning, but the circuit mechanisms mediating their interaction remain to be explored. We developed a novel neurocomputational model of category learning that particularly addresses the BG–PFC interplay. We propose that the BG bias PFC activity by removing the inhibition of cortico–thalamo–cortical loop and thereby provide a teaching signal to guide the acquisition of category representations in the corticocortical associations to the PFC. Our model replicates key behavioral and physiological data of macaque monkey learning a prototype distortion task from Antzoulatos and Miller (2011). Our simulations allowed us to gain a deeper insight into the observed drop of category selectivity in striatal neurons seen in the experimental data and in the model. The simulation results and a new analysis of the experimental data based on the model’s predictions show that the drop in category selectivity of the striatum emerges as the variability of responses in the striatum rises when confronting the BG with an increasingly larger number of stimuli to be classified. The neurocomputational model therefore provides new testable insights of systems-level brain circuits involved in category learning that may also be generalized to better understand other cortico–BG–cortical loops., National Institute of Mental Health (Grants R01MH065252, R37MH087027)

Functional connectivity between the prefrontal cortex (PFC) and striatum (STR) is thought critical for cognition and has been linked to conditions like autism and schizophrenia. We recorded from multiple electrodes in PFC and STR while monkeys acquired new categories. Category learning was accompanied by an increase in beta band synchronization of LFPs between, but not within, the PFC and STR. After learning, different pairs of PFC-STR electrodes showed stronger synchrony for one or the other category, suggesting category-specific functional circuits. This category-specific synchrony was also seen between PFC spikes and STR LFPs, but not the reverse, reflecting the direct monosynaptic connections from the PFC to STR. However, causal connectivity analyses suggested that the polysynaptic connections from STR to the PFC exerted a stronger overall influence. This supports models positing that the basal ganglia “train” the PFC. Category learning may depend on the formation of functional circuits between the PFC and STR., National Institute of Mental Health (U.S.) (5R01MH065252-12), Picower Foundation