Coupling between motor cortex and striatum increases during sleep over long-term skill learning

The strength of cortical connectivity to the striatum influences the balance between behavioral variability and stability. Learning to consistently produce a skilled action requires plasticity in corticostriatal connectivity associated with repeated training of the action. However, it remains unknown whether such corticostriatal plasticity occurs during training itself or 'offline' during time away from training, such as sleep. Here, we monitor the corticostriatal network throughout long-term skill learning in rats and find that non-REM (NREM) sleep is a relevant period for corticostriatal plasticity. We first show that the offline activation of striatal NMDA receptors is required for skill learning. We then show that corticostriatal functional connectivity increases offline, coupled to emerging consistent skilled movements and coupled cross-area neural dynamics. We then identify NREM sleep spindles as uniquely poised to mediate corticostriatal plasticity, through interactions with slow oscillations. Our results provide evidence that sleep shapes cross-area coupling required for skill learning.

Exercise decreases aberrant corticostriatal plasticity in an animal model of L-DOPA-induced dyskinesia

Physical exercise attenuates the development of L-DOPA-induced dyskinesia (LID) in 6-hydroxydopamine-induced hemiparkinsonian mice through unknown mechanisms. We now tested if exercise normalizes the aberrant corticostriatal neuroplasticity associated with experimental murine models of LID. C57BL/6 mice received two unilateral intrastriatal injections of 6-hydroxydopamine (12 μg) and were treated after three weeks with L-DOPA/benserazide (25/12.5 mg/kg) for four weeks, with individualized moderate-intensity running (60-70% V̇O2peak) or not (untrained). L-DOPA converted the pattern of plasticity in corticostriatal synapses from a long-term depression (LTD) into a long-term potentiation (LTP). Exercise reduced LID severity and decreased aberrant LTP. These results suggest that exercise attenuates abnormal corticostriatal plasticity to decrease LID.

Sleep spindles coordinate corticostriatal reactivations during the emergence of automaticity

Plasticity within the corticostriatal network is known to regulate the balance between behavioral flexibility and automaticity. Repeated training of an action has been shown to bias behavior towards automaticity, suggesting that training may trigger activity-dependent corticostriatal plasticity. However, surprisingly little is known about the natural activity patterns that may drive plasticity or when they occur during long-term training. Here we chronically monitored neural activity from primary motor cortex (M1) and the dorsolateral striatum (DLS) during both training and offline periods, i.e., time away from training including sleep, throughout the development of an automatic reaching action. We first show that blocking striatal NMDA receptors during offline periods prevents the emergence of behavioral consistency, a hallmark of automaticity. We then show that, throughout the development of an automatic reaching action, corticostriatal functional connectivity increases during offline periods. Such increases track the emergence of consistent behavior and predictable cross-area neural dynamics. We then identify sleep spindles during non-REM sleep (NREM) as uniquely poised to mediate corticostriatal plasticity during offline periods. We show that sleep spindles are periods of maximal corticostriatal transmission within offline periods, that sleep spindles in post-training NREM reactivate neurons across areas, and that sleep-spindle modulation in post-training NREM is linked to observable changes in spiking relationships between individual pairs of M1 and DLS neurons. Our results indicate that offline periods, in general, and sleep spindles, specifically, play an important role in regulating behavioral flexibility through corticostriatal network plasticity.

Corticostriatal Plasticity Established by Initial Learning Persists After Behavioral Reversal

The neural mechanisms that allow animals to adapt their previously learned associations in response to changes in the environment remain poorly understood. To probe the synaptic mechanisms that mediate such adaptive behavior, we trained mice on an auditory-motor reversal task, and tracked changes in the strength of corticostriatal synapses associated with the formation of learned associations. Using a ChR2-based electrophysiological assay in acute striatal slices, we measured the strength of these synapses after animals learned to pair auditory stimuli with specific actions. Here we report that the pattern of synaptic strength initially established by learning remains unchanged even when the task contingencies are reversed. Our results suggest that synaptic changes associated with the initial acquisition of this task are not erased or over-written, and that behavioral reversal of learned associations may recruit a separate neural circuit.

Amygdala-cortical control of striatal plasticity drives the acquisition of goal-directed action

SummaryThe acquisition of goal-directed action requires the encoding of specific action-outcome associations involving plasticity in the posterior dorsomedial striatum (pDMS). We first investigated the relative involvement of the major inputs to the pDMS argued to be involved in this learning-related plasticity, from prelimbic prefrontal cortex (PL) and from the basolateral amygdala (BLA). Using ex vivo optogenetic stimulation of PL or BLA terminals in pDMS, we found that goal-directed learning potentiated the PL input to direct pathway spiny projection neurons (dSPNs) bilaterally but not to indirect pathway neurons (iSPNs). In contrast, learning-related plasticity was not observed in the direct BLA-pDMS pathway. Using toxicogenetics, we ablated BLA projections to either pDMS or PL and found that only the latter was necessary for goal-directed learning. Importantly, transient inactivation of the BLA during goal-directed learning prevented the PL-pDMS potentiation of dSPNs, establishing that the BLA input to the PL is necessary for the corticostriatal plasticity underlying goal-directed learning.

Computational characteristics of the striatal dopamine system described by reinforcement learning with fast generalization

Generalization enables applying past experience to similar but nonidentical situations. Therefore, it may be essential for adaptive behaviors. Recent neurobiological observation indicates that the striatal dopamine system achieves generalization and subsequent discrimination by updating corticostriatal synaptic connections in differential response to reward and punishment. To analyze how the computational characteristics in this system affect behaviors, we proposed a novel reinforcement learning model with multilayer neural networks in which the synaptic weights of only the last layer are updated according to the prediction error. We set fixed connections between the input and hidden layers so as to maintain the similarity of inputs in the hidden-layer representation. This network enabled fast generalization, and thereby facilitated safe and efficient exploration in reinforcement learning tasks, compared to algorithms which do not show generalization. However, disturbance in the network induced aberrant valuation. In conclusion, the unique computation suggested by corticostriatal plasticity has the advantage of providing safe and quick adaptations to unknown environments, but on the other hand has the potential defect which can induce maladaptive behaviors like delusional symptoms of psychiatric disorders.Author summaryThe brain has an ability to generalize knowledge obtained from reward- and punishment-related learning. Animals that have been trained to associate a stimulus with subsequent reward or punishment respond not only to the same stimulus but also to resembling stimuli. How does generalization affect behaviors in situations where individuals are required to adapt to unknown environments? It may enable efficient learning and promote adaptive behaviors, but inappropriate generalization may disrupt behaviors by associating reward or punishment with irrelevant stimuli. The effect of generalization here should depend on computational characteristics of underlying biological basis in the brain, namely, the striatal dopamine system. In this research, we made a novel computational model based on the characteristics of the striatal dopamine system. Our model enabled fast generalization and showed its advantage of providing safe and quick adaptation to unknown environments. By contrast, disturbance of our model induced abnormal behaviors. The results suggested the advantage and the shortcoming of generalization by the striatal dopamine system.

A Brain to Spine Interface for Transferring Artificial Sensory Information

Lack of sensory feedback is a major obstacle in the rapid absorption of prosthetic devices by the brain. While electrical stimulation of cortical and subcortical structures provides unique means to deliver sensory information to higher brain structures, these approaches require highly invasive surgery and are dependent on accurate targeting of brain structures. Here, we propose a semi-invasive method, Dorsal Column Stimulation (DCS) as a tool for transferring sensory information to the brain. Using this new approach, we show that rats can learn to discriminate artificial sensations generated by DCS and that DCS-induced learning results in corticostriatal plasticity. We also demonstrate a proof of concept brain-to-spine interface (BTSI), whereby tactile and artificial sensory information are decoded from the brain of an “encoder” rat, transformed into DCS pulses, and delivered to the spinal cord of a second “decoder” rat while the latter performs an analog-to-digital conversion during a tactile discrimination task. These results suggest that DCS can be used as an effective sensory channel to transmit prosthetic information to the brain or between brains, and could be developed as a novel platform for delivering tactile and proprioceptive feedback in clinical applications of brain-machine interfaces.

BDNF Controls Bidirectional Endocannabinoid Plasticity at Corticostriatal Synapses

The dorsal striatum exhibits bidirectional corticostriatal synaptic plasticity, NMDAR and endocannabinoids (eCB) mediated, necessary for the encoding of procedural learning. Therefore, characterizing factors controlling corticostriatal plasticity is of crucial importance. Brain-derived neurotrophic factor (BDNF) and its receptor, the tropomyosine receptor kinase-B (TrkB), shape striatal functions, and their dysfunction deeply affects basal ganglia. BDNF/TrkB signaling controls NMDAR plasticity in various brain structures including the striatum. However, despite cross-talk between BDNF and eCBs, the role of BDNF in eCB plasticity remains unknown. Here, we show that BDNF/TrkB signaling promotes eCB-plasticity (LTD and LTP) induced by rate-based (low-frequency stimulation) or spike-timing–based (spike-timing–dependent plasticity, STDP) paradigm in striatum. We show that TrkB activation is required for the expression and the scaling of both eCB-LTD and eCB-LTP. Using 2-photon imaging of dendritic spines combined with patch-clamp recordings, we show that TrkB activation prolongs intracellular calcium transients, thus increasing eCB synthesis and release. We provide a mathematical model for the dynamics of the signaling pathways involved in corticostriatal plasticity. Finally, we show that TrkB activation enlarges the domain of expression of eCB-STDP. Our results reveal a novel role for BDNF/TrkB signaling in governing eCB-plasticity expression in striatum and thus the engram of procedural learning.