Mutations in the human FOXP2 gene cause impaired speech development and linguistic deficits, which have been best characterised in a large pedigree called the KE family. The encoded protein is highly conserved in many vertebrates and is expressed in homologous brain regions required for sensorimotor integration and motor-skill learning, in particular corticostriatal circuits. Independent studies in multiple species suggest that the striatum is a key site of FOXP2 action. Here, we used in vivo recordings in awake-behaving mice to investigate the effects of the KE-family mutation on the function of striatal circuits during motor-skill learning. We uncovered abnormally high ongoing striatal activity in mice carrying an identical mutation to that of the KE family. Furthermore, there were dramatic alterations in striatal plasticity during the acquisition of a motor skill, with most neurons in mutants showing negative modulation of firing rate, starkly contrasting with the predominantly positive modulation seen in control animals. We also observed striking changes in the temporal coordination of striatal firing during motor-skill learning in mutants. Our results indicate that FOXP2 is critical for the function of striatal circuits in vivo, which are important not only for speech but also for other striatal-dependent skills.
Foxp2; in vivo recording; KE family; motor-skill learning; speech and language; striatum
This article examines how independent corticostriatal loops linking basal ganglia with cerebral cortex contribute to visual categorization. The first aspect of categorization discussed is the role of the visual corticostriatal loop, which connects the visual cortex and the body/tail of the caudate, in mapping visual stimuli to categories, including evaluating the degree to which this loop may generalize across individual category members. The second aspect of categorization discussed is the selection of appropriate actions or behaviors on the basis of category membership, and the role of the visual corticostriatal loop output and the motor corticostriatal loop, which connects motor planning areas with the putamen, in action selection. The third aspect of categorization discussed is how categories are learned with the aid of feedback linked dopaminergic projections to the basal ganglia. These projections underlie corticostriatal synaptic plasticity across the basal ganglia, and also serve as input to the executive and motivational corticostriatal loops that play a role in strategic use of feedback.
Dopamine (DA)-dependent corticostriatal plasticity is thought to underlie incremental procedural learning. A primary effector of striatal DA signaling is cAMP, yet its role in corticostriatal plasticity and striatum-dependent learning remains unclear. Here, we show that genetic deletion of a striatum-enriched isoform of adenylyl cyclase, AC5 (AC5KO), impairs two forms of striatum-dependent learning and corticostriatal synaptic plasticity. AC5KO mice were severely impaired in acquisition of a response strategy in the cross maze, a striatum dependent task requiring a correct body turn to find a goal arm. In addition, AC5KO mice were impaired in acquisition of a motor skill, as assessed by the accelerated rotarod. Slice electrophysiology revealed a deficit in corticostriatal LTD following high frequency stimulation of tissue from AC5KO mice. LTD was rescued by activation of either presynaptic cannabinoid type 1 (CB1) receptors, or postsynaptic metabotropic glutamate receptors (mGluRs), suggesting a postsynaptic role of AC5-cAMP, upstream of endocannabinoid release. In striatopallidal projecting medium spiny neurons (MSNs), DA D2 receptors are negatively coupled to cAMP production and activation of these receptors is required for endocannabinoid release and corticostriatal LTD. Recordings from striatopallidal neurons indicated that this is mediated by AC5, as co-activation of D2 and mGluR receptors could induce LTD in WT, but not in AC5KO neurons. To further examine the role of cAMP in corticostriatal plasticity, we elevated cAMP in striatal neurons of wild-type mice via the recording electrode. Under these conditions corticostriatal LTD was eliminated. Together, these data suggest an AC5-cAMP-endocannabinoid-CB1 signaling pathway in corticostriatal plasticity and striatum-dependent learning.
Adenylyl cyclase; striatum; motor learning; plasticity; dopamine; LTD
The ability to group items and events into functional categories is a fundamental characteristic of sophisticated thought. It is subserved by plasticity in many neural systems, including neocortical regions (sensory, prefrontal, parietal, and motor cortex), the medial temporal lobe, the basal ganglia, and midbrain dopaminergic systems. These systems interact during category learning. Corticostriatal loops may mediate recursive, bootstrapping interactions between fast reward-gated plasticity in the basal ganglia and slow reward-shaded plasticity in the cortex. This can provide a balance between acquisition of details of experiences and generalization across them. Interactions between the corticostriatal loops can integrate perceptual, response, and feedback-related aspects of the task and mediate the shift from novice to skilled performance. The basal ganglia and medial temporal lobe interact competitively or cooperatively, depending on the demands of the learning task.
classification; concept learning; memory systems
Motor dysfunction in Parkinson’s disease is believed to arise primarily from pathophysiology in the dorsal striatum and its related corticostriatal and thalamostriatal circuits during progressive dopamine denervation. One function of these circuits is to provide a filter that selectively facilitates or inhibits cortical activity to optimize cortical processing, making motor responses rapid and efficient. Corticostriatal synaptic plasticity mediates the learning that underlies this performance-optimizing filter. Under dopamine denervation, corticostriatal plasticity is altered, resulting in aberrant learning that induces inappropriate basal ganglia filtering that impedes rather than optimizes cortical processing. Human imaging suggests that increased cortical activity may compensate for striatal dysfunction in PD patients. In this Perspective article, we consider how aberrant learning at corticostriatal synapses may impair cortical processing and learning and undermine potential cortical compensatory mechanisms. Blocking or remediating aberrant corticostriatal plasticity may protect cortical function and support cortical compensatory mechanisms mitigating the functional decline associated with progressive dopamine denervation.
corticostriatal plasticity; striatopallidal pathway; dorsolateral striatum; cortical compensation; basal ganglia
Growing evidence suggests that the prefrontal cortex (PFC) is organized hierarchically, with more anterior regions having increasingly abstract representations. How does this organization support hierarchical cognitive control and the rapid discovery of abstract action rules? We present computational models at different levels of description. A neural circuit model simulates interacting corticostriatal circuits organized hierarchically. In each circuit, the basal ganglia gate frontal actions, with some striatal units gating the inputs to PFC and others gating the outputs to influence response selection. Learning at all of these levels is accomplished via dopaminergic reward prediction error signals in each corticostriatal circuit. This functionality allows the system to exhibit conditional if–then hypothesis testing and to learn rapidly in environments with hierarchical structure. We also develop a hybrid Bayesian-reinforcement learning mixture of experts (MoE) model, which can estimate the most likely hypothesis state of individual participants based on their observed sequence of choices and rewards. This model yields accurate probabilistic estimates about which hypotheses are attended by manipulating attentional states in the generative neural model and recovering them with the MoE model. This 2-pronged modeling approach leads to multiple quantitative predictions that are tested with functional magnetic resonance imaging in the companion paper.
basal ganglia; computational model; hierarchical reinforcement learning; prefrontal cortex
Brain-machine interfaces (BMIs) are an emerging technology with great promise for developing restorative therapies for those with disabilities. BMIs also create novel, well-defined functional circuits for action that are distinct from the natural sensorimotor apparatus. Closed-loop control of BMI systems can also actively engage learning and adaptation. These properties make BMIs uniquely suited to study learning of motor and non-physical, abstract skills. Recent work used motor BMIs to shed light on the neural representations of skill formation and motor adaptation. Emerging work in sensory BMIs, and other novel interface systems, also highlight the promise of using BMI systems to study fundamental questions in learning and sensorimotor control. This paper outlines the interpretation of BMIs as novel closed-loop systems and the benefits of these systems for studying learning. We review BMI learning studies, their relation to motor control, and propose future directions for this nascent field. Understanding learning in BMIs may both elucidate mechanisms of natural motor and abstract skill learning, and aid in developing the next generation of neuroprostheses.
brain-machine interfaces; motor learning; neural plasticity; volitional control; sensorimotor systems
Skilled performers such as athletes or musicians can improve their performance by imagining the actions or sensory outcomes associated with their skill. Performers vary widely in their auditory and motor imagery abilities, and these individual differences influence sensorimotor learning. It is unknown whether imagery abilities influence both memory encoding and retrieval. We examined how auditory and motor imagery abilities influence musicians' encoding (during Learning, as they practiced novel melodies), and retrieval (during Recall of those melodies). Pianists learned melodies by listening without performing (auditory learning) or performing without sound (motor learning); following Learning, pianists performed the melodies from memory with auditory feedback (Recall). During either Learning (Experiment 1) or Recall (Experiment 2), pianists experienced either auditory interference, motor interference, or no interference. Pitch accuracy (percentage of correct pitches produced) and temporal regularity (variability of quarter-note interonset intervals) were measured at Recall. Independent tests measured auditory and motor imagery skills. Pianists' pitch accuracy was higher following auditory learning than following motor learning and lower in motor interference conditions (Experiments 1 and 2). Both auditory and motor imagery skills improved pitch accuracy overall. Auditory imagery skills modulated pitch accuracy encoding (Experiment 1): Higher auditory imagery skill corresponded to higher pitch accuracy following auditory learning with auditory or motor interference, and following motor learning with motor or no interference. These findings suggest that auditory imagery abilities decrease vulnerability to interference and compensate for missing auditory feedback at encoding. Auditory imagery skills also influenced temporal regularity at retrieval (Experiment 2): Higher auditory imagery skill predicted greater temporal regularity during Recall in the presence of auditory interference. Motor imagery aided pitch accuracy overall when interference conditions were manipulated at encoding (Experiment 1) but not at retrieval (Experiment 2). Thus, skilled performers' imagery abilities had distinct influences on encoding and retrieval of musical sequences.
sensorimotor learning; auditory imagery; motor imagery; individual differences; music performance
Most of our motor skills are not innately programmed, but are learned by a combination of motor exploration and performance evaluation, suggesting that they proceed through a reinforcement learning (RL) mechanism. Songbirds have emerged as a model system to study how a complex behavioral sequence can be learned through an RL-like strategy. Interestingly, like motor sequence learning in mammals, song learning in birds requires a basal ganglia (BG)-thalamocortical loop, suggesting common neural mechanisms. Here we outline a specific working hypothesis for how BG-forebrain circuits could utilize an internally computed reinforcement signal to direct song learning. Our model includes a number of general concepts borrowed from the mammalian BG literature, including a dopaminergic reward prediction error and dopamine mediated plasticity at corticostriatal synapses. We also invoke a number of conceptual advances arising from recent observations in the songbird. Specifically, there is evidence for a specialized cortical circuit that adds trial-to-trial variability to stereotyped cortical motor programs, and a role for the BG in ‘biasing’ this variability to improve behavioral performance. This BG-dependent ‘premotor bias’ may in turn guide plasticity in downstream cortical synapses to consolidate recently-learned song changes. Given the similarity between mammalian and songbird BG-thalamocortical circuits, our model for the role of the BG in this process may have broader relevance to mammalian BG function.
We value skills we have learned intentionally, but equally important are skills acquired incidentally without ability to describe how or what is learned, referred to as implicit. Randomized practice schedules are superior to grouped schedules for long-term skill gained intentionally, but its relevance for implicit learning is not known. In a parallel design, we studied healthy subjects who learned a motor sequence implicitly under randomized or grouped practice schedule and obtained diffusion-weighted images to identify white matter microstructural correlates of long-term skill. Randomized practice led to superior long-term skill compared with grouped practice. Whole-brain analyses relating interindividual variability in fractional anisotropy (FA) to long-term skill demonstrated that 1) skill in randomized learners correlated with FA within the corticostriatal tract connecting left sensorimotor cortex to posterior putamen, while 2) skill in grouped learners correlated with FA within the right forceps minor connecting homologous regions of the prefrontal cortex (PFC) and the corticostriatal tract connecting lateral PFC to anterior putamen. These results demonstrate first that randomized practice schedules improve long-term implicit skill more than grouped practice schedules and, second, that the superior skill acquired through randomized practice can be related to white matter microstructure in the sensorimotor corticostriatal network.
consolidation; contextual interference; diffusion tensor imaging; magnetic resonance imaging; motor learning; motor sequence; online learning
In this article, the authors show that the neural representation for control of a neuroprosthetic device undergoes a process of consolidation, after which it is stable, readily recalled, and resistant to interference.
Cortical control of neuroprosthetic devices is known to require neuronal adaptations. It remains unclear whether a stable cortical representation for prosthetic function can be stored and recalled in a manner that mimics our natural recall of motor skills. Especially in light of the mixed evidence for a stationary neuron-behavior relationship in cortical motor areas, understanding this relationship during long-term neuroprosthetic control can elucidate principles of neural plasticity as well as improve prosthetic function. Here, we paired stable recordings from ensembles of primary motor cortex neurons in macaque monkeys with a constant decoder that transforms neural activity to prosthetic movements. Proficient control was closely linked to the emergence of a surprisingly stable pattern of ensemble activity, indicating that the motor cortex can consolidate a neural representation for prosthetic control in the presence of a constant decoder. The importance of such a cortical map was evident in that small perturbations to either the size of the neural ensemble or to the decoder could reversibly disrupt function. Moreover, once a cortical map became consolidated, a second map could be learned and stored. Thus, long-term use of a neuroprosthetic device is associated with the formation of a cortical map for prosthetic function that is stable across time, readily recalled, resistant to interference, and resembles a putative memory engram.
Brain–machine interfaces (BMIs) have the potential to revolutionize the care of neurologically impaired patients. Numerous studies have now shown the feasibility of direct “brain control” of a neuroprosthetic device, yet it remains unclear whether the neural representation for prosthetic control can become consolidated and remain stable over time. This question is especially intriguing given the evidence demonstrating that the neural representation for natural movements can be unstable: BMIs provide a window into the plasticity of cortical circuits in awake-behaving subjects. Here, we show that long-term neuroprosthetic control leads to the formation of a remarkably stable cortical map. Interestingly, this map has the putative attributes of a memory trace, namely, it is stable across time, readily recalled, and resistant to the storage of a second map. The demonstration of such a cortical map for prosthetic control indicates that neuroprosthetic devices could eventually be controlled through the effortless recall of motor memory in a manner that mimics natural skill acquisition and motor control.
Dopamine (DA) is critical for motor performance, motor learning, and corticostriatal plasticity. The relationship between motor performance and learning, and the role of DA in the mediation of them, however, remain unclear.
To examine this question, we took advantage of PITx3-deficient mice (aphakia mice), in which DA in the dorsal striatum is reduced by 90%. PITx3-deficient mice do not display obvious motor deficits in their home cage, but are impaired in motor tasks that require new motor skills. We used the accelerating rotarod as a motor learning task.
We show that the deficiency in motor skill learning in PITx3(−/−) is dramatic and can be rescued with levodopa treatment. In addition, cessation of levodopa treatment after acquisition of the motor skill does not result in an immediate drop in performance. Instead, there is a gradual decline of performance that lasts for a few days, which is not related to levodopa pharmacokinetics. We show that this gradual decline is dependent on the retesting experience.
This observation resembles the long-duration response to levodopa therapy in its slow buildup of improvement after the initiation of therapy and gradual degradation. We hypothesize that motor learning may play a significant, underappreciated role in the symptomatology of Parkinson disease as well as in the therapeutic effects of levodopa. We suggest that the important, yet enigmatic long-duration response to chronic levodopa treatment is a manifestation of rescued motor learning.
The striatum is the major input nucleus of basal ganglia, an ensemble of interconnected sub-cortical nuclei associated with fundamental processes of action-selection and procedural learning and memory. The striatum receives afferents from the cerebral cortex and the thalamus. In turn, it relays the integrated information towards the basal ganglia output nuclei through which it operates a selected activation of behavioral effectors. The striatal output neurons, the GABAergic medium-sized spiny neurons (MSNs), are in charge of the detection and integration of behaviorally relevant information. This property confers to the striatum the ability to extract relevant information from the background noise and select cognitive-motor sequences adapted to environmental stimuli. As long-term synaptic efficacy changes are believed to underlie learning and memory, the corticostriatal long-term plasticity provides a fundamental mechanism for the function of the basal ganglia in procedural learning. Here, we reviewed the different forms of spike-timing dependent plasticity (STDP) occurring at corticostriatal synapses. Most of the studies have focused on MSNs and their ability to develop long-term plasticity. Nevertheless, the striatal interneurons (the fast-spiking GABAergic, NO-synthase and cholinergic interneurons) also receive monosynaptic afferents from the cortex and tightly regulated corticostriatal information processing. Therefore, it is important to take into account the variety of striatal neurons to fully understand the ability of striatum to develop long-term plasticity. Corticostriatal STDP with various spike-timing dependence have been observed depending on the neuronal sub-populations and experimental conditions. This complexity highlights the extraordinary potentiality in term of plasticity of the corticostriatal pathway.
spike-timing dependent plasticity; corticostriatal; striatum; GABAergic interneurons; cholinergic interneurons; LTP; LTD; basal ganglia
Learning a novel motor skill is associated with well characterized structural and functional plasticity in the rodent motor cortex. Furthermore, neuroimaging studies of visuomotor learning in humans have suggested that structural plasticity can occur in white matter (WM), but the biological basis for such changes is unclear. We assessed the influence of motor skill learning on WM structure within sensorimotor cortex using both diffusion MRI fractional anisotropy (FA) and quantitative immunohistochemistry. Seventy-two adult (male) rats were randomly assigned to one of three conditions (skilled reaching, unskilled reaching, and caged control). After 11 d of training, postmortem diffusion MRI revealed significantly higher FA in the skilled reaching group compared with the control groups, specifically in the WM subjacent to the sensorimotor cortex contralateral to the trained limb. In addition, within the skilled reaching group, FA across widespread regions of WM in the contralateral hemisphere correlated significantly with learning rate. Immunohistological analysis conducted on a subset of 24 animals (eight per group) revealed significantly increased myelin staining in the WM underlying motor cortex in the hemisphere contralateral (but not ipsilateral) to the trained limb for the skilled learning group versus the control groups. Within the trained hemisphere (but not the untrained hemisphere), myelin staining density correlated significantly with learning rate. Our results suggest that learning a novel motor skill induces structural change in task-relevant WM pathways and that these changes may in part reflect learning-related increases in myelination.
Recent behavioral studies in both humans and rodents have found evidence that performance in decision-making tasks depends on two different learning processes; one encoding the relationship between actions and their consequences and a second involving the formation of stimulus–response associations. These learning processes are thought to govern goal-directed and habitual actions, respectively, and have been found to depend on homologous corticostriatal networks in these species. Thus, recent research using comparable behavioral tasks in both humans and rats has implicated homologous regions of cortex (medial prefrontal cortex/medial orbital cortex in humans and prelimbic cortex in rats) and of dorsal striatum (anterior caudate in humans and dorsomedial striatum in rats) in goal-directed action and in the control of habitual actions (posterior lateral putamen in humans and dorsolateral striatum in rats). These learning processes have been argued to be antagonistic or competing because their control over performance appears to be all or none. Nevertheless, evidence has started to accumulate suggesting that they may at times compete and at others cooperate in the selection and subsequent evaluation of actions necessary for normal choice performance. It appears likely that cooperation or competition between these sources of action control depends not only on local interactions in dorsal striatum but also on the cortico-basal ganglia network within which the striatum is embedded and that mediates the integration of learning with basic motivational and emotional processes. The neural basis of the integration of learning and motivation in choice and decision-making is still controversial and we review some recent hypotheses relating to this issue.
decision-making; instrumental conditioning; prefrontal cortex; dorsal striatum; nucleus accumbens; amygdala
Action potentials are thought to be determinant for the induction of long-term synaptic plasticity, the cellular basis of learning and memory. However, neuronal activity does not lead systematically to an action potential but also, in many cases, to synaptic depolarizing subthreshold events. This is particularly exemplified in corticostriatal information processing. Indeed, the striatum integrates information from the whole cerebral cortex and, due to the membrane properties of striatal medium spiny neurons, cortical inputs do not systematically trigger an action potential but a wide range of subthreshold postsynaptic depolarizations. Accordingly, we have addressed the following question: does a brief subthreshold event act as a Hebbian signal and induce long-term synaptic efficacy changes?
Here, using perforated patch-clamp recordings on rat brain corticostriatal slices, we demonstrate, that brief (30 ms) subthreshold depolarizing events in quasi-coincidence with presynaptic activity can act as Hebbian signals and are sufficient to induce long-term synaptic plasticity at corticostriatal synapses. This “subthreshold-depolarization dependent plasticity” (SDDP) induces strong, significant and bidirectional long-term synaptic efficacy changes at a very high occurrence (81%) for time intervals between pre- and postsynaptic stimulations (Δt) of −110<Δt<+110 ms. Such subthreshold depolarizations are able to induce robust long-term depression (cannabinoid type-1 receptor-activation dependent) as well as long-term potentiation (NMDA receptor-activation dependent).
Our data show the existence of a robust, reliable and timing-dependent bidirectional long-term plasticity induced by brief subthreshold events paired with presynaptic activity. The existence of a subthreshold-depolarization dependent plasticity extends considerably, beyond the action potential, the neuron's capabilities to express long-term synaptic efficacy changes.
Two dissociable learning processes underlie instrumental behaviour. Whereas goal-directed behaviour is controlled by knowledge of the consequences, habitual behaviour is elicited directly by antecedent Pavlovian stimuli without knowledge of the consequences. Predominance of habitual control is thought to underlie psychopathological conditions associated with corticostriatal abnormalities, such as impulsivity and drug dependence. To explore this claim, smokers were assessed for nicotine dependence, impulsivity, and capacity for goal-directed control over instrumental performance in an outcome devaluation procedure. Reduced goal-directed control was selectively associated with the Motor Impulsivity factor of Barrett's Impulsivity Scale (BIS), which reflects propensity for action without thought. These data support the claim that human impulsivity is marked by impaired use of causal knowledge to make adaptive decisions. The predominance of habit learning may play a role in psychopathological conditions that are associated with trait impulsivity.
Outcome specific devaluation; Goal-directed learning; Habit; Drug dependence; Nicotine; Impulsivity
Humans are capable of learning numerous motor skills, but newly acquired skills may be abolished by subsequent learning. Here we ask what factors determine whether interference occurs in motor learning. We speculated that interference requires competing processes of synaptic plasticity in overlapping circuits and predicted specificity. To test this, subjects learned a ballistic motor task. Interference was observed following subsequent learning of an accuracy-tracking task, but only if the competing task involved the same muscles and movement direction. Interference was not observed from a non-learning task suggesting that interference requires competing learning. Subsequent learning of the competing task 4 h after initial learning did not cause interference suggesting disruption of early motor memory consolidation as one possible mechanism underlying interference. Repeated transcranial magnetic stimulation (rTMS) of corticospinal motor output at intensities below movement threshold did not cause interference, whereas suprathreshold rTMS evoking motor responses and (re)afferent activation did. Finally, the experiments revealed that suprathreshold repetitive electrical stimulation of the agonist (but not antagonist) peripheral nerve caused interference. The present study is, to our knowledge, the first to demonstrate that peripheral nerve stimulation may cause interference. The finding underscores the importance of sensory feedback as error signals in motor learning. We conclude that interference requires competing plasticity in overlapping circuits. Interference is remarkably specific for circuits involved in a specific movement and it may relate to sensory error signals.
Preliminary evidence indicates that dopamine given by mouth facilitates the learning of motor skills and improves the recovery of movement after stroke. The mechanism of these phenomena is unknown. Here, we describe a mechanism by demonstrating in rat that dopaminergic terminals and receptors in primary motor cortex (M1) enable motor skill learning and enhance M1 synaptic plasticity. Elimination of dopaminergic terminals in M1 specifically impaired motor skill acquisition, which was restored upon DA substitution. Execution of a previously acquired skill was unaffected. Reversible blockade of M1 D1 and D2 receptors temporarily impaired skill acquisition but not execution, and reduced long-term potentiation (LTP) within M1, a form of synaptic plasticity critically involved in skill learning. These findings identify a behavioral and functional role of dopaminergic signaling in M1. DA in M1 optimizes the learning of a novel motor skill.
This article stresses the plasticity of the
adult sensorimotor cortex in response to
various injuries or environmental changes. The
dominant role of sensory input is discussed. A
number of studies are presented that show how
input may lead to learning and change. Learning
is discussed in relation to recovery. It is shown
how concepts from the field of motor control
and learning may be used for improving neurological
rehabilitation. Specific attention is given
to the variability of input, the meaningfulness
of input, and the role of the learning context.
The learning context and the application
context should have essential characteristics in
common, otherwise transfer of learning will be
non-optimal. It is argued that learning landscapes
are necessary in order to treat patients
in such a way that he learned skills are
transferable to situations outside the hospital.
This essay summarizes recent advances in the field of brain-machine interfaces, with a focus on the learning and acquisition of neuroprosthetic skills.
Significant progress has occurred in the field of brain–machine interfaces (BMI) since the first demonstrations with rodents, monkeys, and humans controlling different prosthetic devices directly with neural activity. This technology holds great potential to aid large numbers of people with neurological disorders. However, despite this initial enthusiasm and the plethora of available robotic technologies, existing neural interfaces cannot as yet master the control of prosthetic, paralyzed, or otherwise disabled limbs. Here I briefly discuss recent advances from our laboratory into the neural basis of BMIs that should lead to better prosthetic control and clinically viable solutions, as well as new insights into the neurobiology of action.
Complex skill learning at a joint initiates competition between its representation in the primary motor cortex (M1) and that of the neighboring untrained joint. This process of representational plasticity has been mapped by cortically-evoking simple movements. We investigated, following skill learning at a joint, 1) whether comparable processes of representational plasticity are observed when mapping is based on volitionally produced complex movements and 2) the consequence on the skill of the adjacent untrained joint. Twenty-four healthy subjects were assigned to either finger- or elbow-skill training or no-training control group. At pretest and posttest, subjects performed complex skill movements at finger, elbow and ankle concurrent with functional magnetic resonance imaging (fMRI) to define learning and allow mapping of corresponding activation-based representations in M1. Skill following both finger- and elbow- training transferred to the ankle (remote joint) (p=0.05 and 0.05); however, finger training did not transfer to the elbow and elbow training did not transfer to the finger. Following finger training, location of the trained finger representation showed a trend (p=0.08) for medial shift towards the representation of adjacent untrained elbow joint; the change in intensity of the latter representation was associated with elbow skill (Spearman's ρ=–0.71, p=0.07). Following elbow training, the trained elbow representation and the adjacent untrained finger representation increased their overlap (p=0.02), which was associated with finger skill (Spearman's ρ=–0.83, p=0.04). Thus, our pilot study reveals comparable processes of representational plasticity with fMRI mapping of complex skill movements as have been demonstrated with cortically-evoked methods. Importantly, these processes may limit the degree of transfer of skill between trained and adjacent untrained joints. These pilot findings that await confirmation in large-scale studies have significant implications for neuro-rehabilitation. For instance, techniques, such as motor cortical stimulation, that can potentially modulate processes of representational plasticity between trained and adjacent untrained representations, may optimize transfer of skill.
Motor cortex; Learning; Skill; Representation; Plasticity; functional Magnetic Resonance Imaging (fMRI)
The striatum has important roles in motor control and action learning and, like many brain regions, receives multiple monoaminergic inputs. We have examined serotonergic modulation of rat and mouse corticostriatal neurotransmission and find that serotonin (5-HT) activates 5-HT1b receptors resulting in a long-term depression (LTD) of glutamate release and striatal output that we have termed 5-HT-LTD. 5-HT-LTD is presynaptically mediated, cAMP pathway-dependent, and inducible by endogenous striatal 5-HT, as revealed by application of a selective 5-HT reuptake inhibitor (SSRI). 5-HT-LTD is mutually occlusive with dopamine/endocannabinoid-dependent LTD, suggesting that these two forms of LTD act on the same corticostriatal terminals. Thus, serotonergic and dopaminergic mechanisms exist that may interact to persistently sculpt corticostriatal circuits, potentially influencing action learning and striatal-based disorders.
‘Learning to learn’ phenomena have been widely investigated in cognition, perception and more recently also in action. During concept learning tasks, for example, it has been suggested that characteristic features are abstracted from a set of examples with the consequence that learning of similar tasks is facilitated—a process termed ‘learning to learn’. From a computational point of view such an extraction of invariants can be regarded as learning of an underlying structure. Here we review the evidence for structure learning as a ‘learning to learn’ mechanism, especially in sensorimotor control where the motor system has to adapt to variable environments. We review studies demonstrating that common features of variable environments are extracted during sensorimotor learning and exploited for efficient adaptation in novel tasks. We conclude that structure learning plays a fundamental role in skill learning and may underlie the unsurpassed flexibility and adaptability of the motor system.
Structure learning; Adaptive motor control; Learning-to-learn; Visuomotor learning; Dimensionality reduction; Variability
Children with autism spectrum disorder (ASD) show deficits in development of motor skills, in addition to core deficits in social skill development. In a previous study (Haswell et al., 2009) we found that children with autism show a key difference in how they learn motor actions, with a bias for relying on joint position rather than visual feedback; further, this pattern of motor learning predicted impaired motor, imitation and social abilities. We were interested in finding out whether this altered motor learning pattern was specific to autism. To do so, we examined children with Attention Deficit Hyperactivity Disorder (ADHD), who also show deficits in motor control. Children learned a novel movement and we measured rates of motor learning, generalization patterns of motor learning, and variability of motor speed during learning. We found children with ASD show a slower rate of learning and, consistent with previous findings, an altered pattern of generalization that was predictive of impaired motor, imitation, and social impairment. In contrast, children with ADHD showed a normal rate of learning and a normal pattern of generalization; instead, they (and they alone), showed excessive variability in movement speed. The findings suggest that there is a specific pattern of altered motor learning associated with autism.
The brain builds an association between action and sensory feedback to predict the sensory consequence of self-generated motor commands. This internal model of action is central to our ability to adapt movements, and may also play a role in our ability to learn from observing others. Recently we reported that the spatial generalization patterns that accompany adaptation of reaching movements were distinct in children with Autism Spectrum Disorder (ASD) as compared to typically developing (TD) children. To test whether the generalization patterns are specific to ASD, here we compared the patterns of adaptation to those in children with Attention Deficit Hyperactivity Disorder (ADHD). Consistent with our previous observations, we found that in ASD the motor memory showed greater than normal generalization in proprioceptive coordinates compared with both TD children and children with ADHD; children with ASD also showed slower rates of adaptation compared with both control groups. Children with ADHD did not show this excessive generalization to the proprioceptive target, but did show excessive variability in the speed of movements with an increase in the exponential distribution of responses (τ) as compared with both TD children and children with ASD. The results suggest that slower rate of adaptation and anomalous bias towards proprioceptive feedback during motor learning is characteristic of autism; whereas increased variability in execution is characteristic of ADHD.