Differential entrainment and learning-related dynamics of spike and local field potential activity in the sensorimotor and associative striatum

Preserving cortico-striatal function: deep brain stimulation in Huntington's disease

Huntington's disease (HD) is an incurable neurodegenerative disease characterized by the triad of chorea, cognitive dysfunction and psychiatric disturbances. Since the discovery of the HD gene, the pathogenesis has been outlined, but to date a cure has not been found. Disease modifying therapies are needed desperately to improve function, alleviate suffering, and provide hope for symptomatic patients. Deep brain stimulation (DBS), a proven therapy for managing the symptoms of some neurodegenerative movement disorders, including Parkinson's disease, has been reported as a palliative treatment in select cases of HD with debilitating chorea with variable success. New insights into the mechanism of action of DBS suggest it may have the potential to circumvent other manifestations of HD including cognitive deterioration. Furthermore, because DBS is already widely used, reversible, and has a risk profile that is relatively low, new studies can be initiated. In this article we contend that new clinical trials be considered to test the effects of DBS for HD.

Functional relationships between the hippocampus and dorsomedial striatum in learning a visual scene-based memory task in rats

The hippocampus is important for contextual behavior, and the striatum plays key roles in decision making. When studying the functional relationships with the hippocampus, prior studies have focused mostly on the dorsolateral striatum (DLS), emphasizing the antagonistic relationships between the hippocampus and DLS in spatial versus response learning. By contrast, the functional relationships between the dorsomedial striatum (DMS) and hippocampus are relatively unknown. The current study reports that lesions to both the hippocampus and DMS profoundly impaired performance of rats in a visual scene-based memory task in which the animals were required to make a choice response by using visual scenes displayed in the background. Analysis of simultaneous recordings of local field potentials revealed that the gamma oscillatory power was higher in the DMS, but not in CA1, when the rat performed the task using familiar scenes than novel ones. In addition, the CA1-DMS networks increased coherence at γ, but not at θ, rhythm as the rat mastered the task. At the single-unit level, the neuronal populations in CA1 and DMS showed differential firing patterns when responses were made using familiar visual scenes than novel ones. Such learning-dependent firing patterns were observed earlier in the DMS than in CA1 before the rat made choice responses. The present findings suggest that both the hippocampus and DMS process memory representations for visual scenes in parallel with different time courses and that flexible choice action using background visual scenes requires coordinated operations of the hippocampus and DMS at γ frequencies.

A new framework for cortico-striatal plasticity: behavioural theory meets in vitro data at the reinforcement-action interface

A computational model yields new insights into the bewildering complexity of cortico-striatal plasticity and its rationale for supporting operant learning.

Operant learning requires that reinforcement signals interact with action representations at a suitable neural interface. Much evidence suggests that this occurs when phasic dopamine, acting as a reinforcement prediction error, gates plasticity at cortico-striatal synapses, and thereby changes the future likelihood of selecting the action(s) coded by striatal neurons. But this hypothesis faces serious challenges. First, cortico-striatal plasticity is inexplicably complex, depending on spike timing, dopamine level, and dopamine receptor type. Second, there is a credit assignment problem—action selection signals occur long before the consequent dopamine reinforcement signal. Third, the two types of striatal output neuron have apparently opposite effects on action selection. Whether these factors rule out the interface hypothesis and how they interact to produce reinforcement learning is unknown. We present a computational framework that addresses these challenges. We first predict the expected activity changes over an operant task for both types of action-coding striatal neuron, and show they co-operate to promote action selection in learning and compete to promote action suppression in extinction. Separately, we derive a complete model of dopamine and spike-timing dependent cortico-striatal plasticity from in vitro data. We then show this model produces the predicted activity changes necessary for learning and extinction in an operant task, a remarkable convergence of a bottom-up data-driven plasticity model with the top-down behavioural requirements of learning theory. Moreover, we show the complex dependencies of cortico-striatal plasticity are not only sufficient but necessary for learning and extinction. Validating the model, we show it can account for behavioural data describing extinction, renewal, and reacquisition, and replicate in vitro experimental data on cortico-striatal plasticity. By bridging the levels between the single synapse and behaviour, our model shows how striatum acts as the action-reinforcement interface.

A key component of survival is the ability to learn which actions, in what contexts, yield useful and rewarding outcomes. Actions are encoded in the brain in the cortex but, as many actions are possible at any one time, there needs to be a mechanism to select which one is to be performed. This problem of action selection is mediated by a set of nuclei known as the basal ganglia, which receive convergent “action requests” from all over the cortex and select the one that is currently most important. Working out which is most important is determined by the strength of the input from each action request: the stronger the connection, the more important that action. Understanding learning thus requires understanding how that strength is changed by the outcome of each action. We built a computational model that demonstrates how the brain's internal signal for outcome (carried by the neurotransmitter dopamine) changes the strength of these cortical connections to learn the selection of rewarded actions, and the suppression of unrewarded ones. Our model shows how several known signals in the brain work together to shape the influence of cortical inputs to the basal ganglia at the interface between our actions and their outcomes.

Cell-type-specific resonances shape the responses of striatal neurons to synaptic input

Neurons respond to synaptic inputs in cell-type-specific ways. Each neuron type may thus respond uniquely to shared patterns of synaptic input. We applied statistically identical barrages of artificial synaptic inputs to four striatal cell types to assess differences in their responses to a realistic input pattern. Each interneuron type fired in phase with a specific input-frequency component. The fast-spiking interneuron fired in relation to the gamma-band (and higher) frequencies, the low-threshold spike interneuron to the beta-band frequencies, and the cholinergic neurons to the delta-band frequencies. Low-threshold spiking and cholinergic interneurons showed input impedance resonances at frequencies matching their spiking resonances. Fast-spiking interneurons showed resonance of input impedance but at lower than gamma frequencies. The spiny projection neuron's frequency preference did not have a fixed frequency but instead tracked its own firing rate. Spiny cells showed no input impedance resonance. Striatal interneurons are each tuned to a specific frequency band corresponding to the major frequency components of local field potentials. Their influence in the circuit may fluctuate along with the contribution of that frequency band to the input. In contrast, spiny neurons may tune to any of the frequency bands by a change in firing rate.

The Aristotelian conception of habit and its contribution to human neuroscience

The notion of habit used in neuroscience is an inheritance from a particular theoretical origin, whose main source is William James. Thus, habits have been characterized as rigid, automatic, unconscious, and opposed to goal-directed actions. This analysis leaves unexplained several aspects of human behavior and cognition where habits are of great importance. We intend to demonstrate the utility that another philosophical conception of habit, the Aristotelian, may have for neuroscientific research. We first summarize the current notion of habit in neuroscience, its philosophical inspiration and the problems that arise from it, mostly centered on the sharp distinction between goal-directed actions and habitual behavior. We then introduce the Aristotelian view and we compare it with that of William James. For Aristotle, a habit is an acquired disposition to perform certain types of action. If this disposition involves an enhanced cognitive control of actions, it can be considered a “habit-as-learning”. The current view of habit in neuroscience, which lacks cognitive control and we term “habit-as-routine”, is also covered by the Aristotelian conception. He classifies habits into three categories: (1) theoretical, or the retention of learning understood as “knowing that x is so”; (2) behavioral, through which the agent achieves a rational control of emotion-permeated behavior (“knowing how to behave”); and (3) technical or learned skills (“knowing how to make or to do”). Finally, we propose new areas of research where this “novel” conception of habit could serve as a framework concept, from the cognitive enrichment of actions to the role of habits in pathological conditions. In all, this contribution may shed light on the understanding of habits as an important feature of human action. Habits, viewed as a cognitive enrichment of behavior, are a crucial resource for understanding human learning and behavioral plasticity.

Diurnal influences on electrophysiological oscillations and coupling in the dorsal striatum and cerebellar cortex of the anesthetized rat

Circadian rhythms modulate behavioral processes over a 24 h period through clock gene expression. What is largely unknown is how these molecular influences shape neural activity in different brain areas. The clock gene Per2 is rhythmically expressed in the striatum and the cerebellum and its expression is linked with daily fluctuations in extracellular dopamine levels and D2 receptor activity. Electrophysiologically, dopamine depletion enhances striatal local field potential (LFP) oscillations. We investigated if LFP oscillations and synchrony were influenced by time of day, potentially via dopamine mechanisms. To assess the presence of a diurnal effect, oscillatory power and coherence were examined in the striatum and cerebellum of rats under urethane anesthesia at four different times of day zeitgeber time (ZT1, 7, 13 and 19—indicating number of hours after lights turned on in a 12:12 h light-dark cycle). We also investigated the diurnal response to systemic raclopride, a D2 receptor antagonist. Time of day affected the proportion of LFP oscillations within the 0–3 Hz band and the 3–8 Hz band. In both the striatum and the cerebellum, slow oscillations were strongest at ZT1 and weakest at ZT13. A 3–8 Hz oscillation was present when the slow oscillation was lowest, with peak 3–8 Hz activity occurring at ZT13. Raclopride enhanced the slow oscillations, and had the greatest effect at ZT13. Within the striatum and with the cerebellum, 0–3 Hz coherence was greatest at ZT1, when the slow oscillations were strongest. Coherence was also affected the most by raclopride at ZT13. Our results suggest that neural oscillations in the cerebellum and striatum, and the synchrony between these areas, are modulated by time of day, and that these changes are influenced by dopamine manipulation. This may provide insight into how circadian gene transcription patterns influence network electrophysiology. Future experiments will address how these network alterations are linked with behavior.

Humanized Foxp2 accelerates learning by enhancing transitions from declarative to procedural performance

The acquisition of language and speech is uniquely human, but how genetic changes might have adapted the nervous system to this capacity is not well understood. Two human-specific amino acid substitutions in the transcription factor forkhead box P2 (FOXP2) are outstanding mechanistic candidates, as they could have been positively selected during human evolution and as FOXP2 is the sole gene to date firmly linked to speech and language development. When these two substitutions are introduced into the endogenous Foxp2 gene of mice (Foxp2(hum)), cortico-basal ganglia circuits are specifically affected. Here we demonstrate marked effects of this humanization of Foxp2 on learning and striatal neuroplasticity. Foxp2(hum/hum) mice learn stimulus-response associations faster than their WT littermates in situations in which declarative (i.e., place-based) and procedural (i.e., response-based) forms of learning could compete during transitions toward proceduralization of action sequences. Striatal districts known to be differently related to these two modes of learning are affected differently in the Foxp2(hum/hum) mice, as judged by measures of dopamine levels, gene expression patterns, and synaptic plasticity, including an NMDA receptor-dependent form of long-term depression. These findings raise the possibility that the humanized Foxp2 phenotype reflects a different tuning of corticostriatal systems involved in declarative and procedural learning, a capacity potentially contributing to adapting the human brain for speech and language acquisition.

Investigating habits: strategies, technologies and models

Understanding habits at a biological level requires a combination of behavioral observations and measures of ongoing neural activity. Theoretical frameworks as well as definitions of habitual behaviors emerging from classic behavioral research have been enriched by new approaches taking account of the identification of brain regions and circuits related to habitual behavior. Together, this combination of experimental and theoretical work has provided key insights into how brain circuits underlying action-learning and action-selection are organized, and how a balance between behavioral flexibility and fixity is achieved. New methods to monitor and manipulate neural activity in real time are allowing us to have a first look “under the hood” of a habit as it is formed and expressed. Here we discuss ideas emerging from such approaches. We pay special attention to the unexpected findings that have arisen from our own experiments suggesting that habitual behaviors likely require the simultaneous activity of multiple distinct components, or operators, seen as responsible for the contrasting dynamics of neural activity in both cortico-limbic and sensorimotor circuits recorded concurrently during different stages of habit learning. The neural dynamics identified thus far do not fully meet expectations derived from traditional models of the structure of habits, and the behavioral measures of habits that we have made also are not fully aligned with these models. We explore these new clues as opportunities to refine an understanding of habits.