Human neuroimaging studies suggest that aberrant neural connectivity underlies behavioural deficits in autism spectrum disorders (ASDs), but the molecular and neural circuit mechanisms underlying ASDs remain elusive. Here, we describe a complete knockout mouse model of the autism-associated Shank3 gene, with a deletion of exons 4–22 (Δe4–22). Both mGluR5-Homer scaffolds and mGluR5-mediated signalling are selectively altered in striatal neurons. These changes are associated with perturbed function at striatal synapses, abnormal brain morphology, aberrant structural connectivity and ASD-like behaviour. In vivo recording reveals that the cortico-striatal-thalamic circuit is tonically hyperactive in mutants, but becomes hypoactive during social behaviour. Manipulation of mGluR5 activity attenuates excessive grooming and instrumental learning differentially, and rescues impaired striatal synaptic plasticity in Δe4–22−/− mice. These findings show that deficiency of Shank3 can impair mGluR5-Homer scaffolding, resulting in cortico-striatal circuit abnormalities that underlie deficits in learning and ASD-like behaviours. These data suggest causal links between genetic, molecular, and circuit mechanisms underlying the pathophysiology of ASDs.

Summary Circuits distributed across cortico-limbic brain regions compose the networks that mediate emotional behavior. The prefrontal cortex (PFC) regulates ultraslow (<1Hz) dynamics across these networks, and PFC dysfunction is implicated in stress-related illnesses including major depressive disorder (MDD). To uncover the mechanism whereby stress-induced changes in PFC circuitry alter emotional networks to yield pathology, we used a multi-disciplinary approach including in vivo recordings in mice and chronic social-defeat stress. Our network model, inferred using machine learning, linked stress-induced behavioral pathology to the capacity of PFC to synchronize amygdala and VTA activity. Direct stimulation of PFC-amygdala circuitry with DREADDs normalized PFC-dependent limbic synchrony in stress-susceptible animals and restored normal behavior. In addition to providing insights into MDD mechanisms, our findings demonstrate an interdisciplinary approach that can be used to identify the large-scale network changes that underlie complex emotional pathologies and the specific network nodes that can be used to develop targeted interventions.

Background The prefrontal cortex (PFC) plays a critical role in regulating emotional behaviors, and dysfunction of PFC-dependent networks has been broadly implicated in mediating stress-induced behavioral disorders including major depressive disorder (MDD). Methods Here we acquire multi-circuit in vivo activity from eight cortical and limbic brain regions as mice are subjected to the tail suspension test (TST) and an open field test (OFT). We use a linear decoder to determine whether cellular responses across each of the cortical and limbic areas signal movement during the TST and OFT. We then perform repeat behavioral testing to identify which brain areas show cellular adaptations that signal the increase in immobility induced by repeat TST exposure. Results The increase in immobility observed during repeat TST exposure is linked to a selective functional upregulation of cellular activity in infralimbic cortex (IL) and medial dorsal thalamic (Thal), and an increase in the spatiotemporal dynamic interaction between these structures. Inducing this spatiotemporal dynamic using “closed-loop” optogenetic stimulation is sufficient to increase movement in the TST in stress-naïve mice, while stimulating above the carrier frequency of this circuit suppressed movement. This demonstrates that the adaptations in IL-Thal circuitry observed after stress reflect a compensatory mechanism whereby the brain drives neural systems to counterbalance stress effects. Conclusion Our findings provide evidence that targeting endogenous spatiotemporal dynamics is a potential therapeutic approach for treating stress-induced behavioral disorders, and that dynamics are a critical axis of manipulation for causal optogenetic studies.

Anticipation of an upcoming stimulus induces neural activity across cortical and subcortical regions and influences subsequent behavior. Nevertheless, the network mechanism whereby the brain integrates this information to signal the anticipation of rewards remains relatively unexplored. Here we employ multi-circuit electrical recordings from six brain regions as mice perform a sample-to-match task in which reward anticipation is operationalized as their progress towards obtaining a potential reward. We then use machine learning to discover the naturally occurring network patterns that integrate this neural activity across timescales. Only one of the networks that we uncovered signals responses linked to reward anticipation, specifically relative proximity and reward magnitude. Activity in this Electome (electrical functional connectivity) network is dominated by theta oscillations leading from prelimbic cortex and striatum that converge on ventral tegmental area, and by beta oscillations leading from striatum that converge on prelimbic cortex. Network activity is also synchronized with brain-wide cellular firing. Critically, this network generalizes to new groups of healthy mice, as well as a mouse line that models aberrant neural circuitry observed in brain disorders that show altered reward anticipation. Thus, our findings reveal the network-level architecture whereby the brain integrates spatially distributed activity across timescales to signal reward anticipation.

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