Neuronal correlates of movement dynamics in the dorsal and ventral premotor area in the monkey

The human middle temporal cortex responds to both active leg movements and egomotion-compatible visual motion

The human middle-temporal region MT+ is highly specialized in processing visual motion. However, recent studies have shown that this region is modulated by extraretinal signals, suggesting a possible involvement in processing motion information also from non-visual modalities. Here, we used functional MRI data to investigate the influence of retinal and extraretinal signals on MT+ in a large sample of subjects. Moreover, we used resting-state functional MRI to assess how the subdivisions of MT+ (i.e., MST, FST, MT, and V4t) are functionally connected. We first compared responses in MST, FST, MT, and V4t to coherent vs. random visual motion. We found that only MST and FST were positively activated by coherent motion. Furthermore, regional analyses revealed that MST and FST were positively activated by leg, but not arm, movements, while MT and V4t were deactivated by arm, but not leg, movements. Taken together, regional analyses revealed a visuomotor role for the anterior areas MST and FST and a pure visual role for the anterior areas MT and V4t. These findings were mirrored by the pattern of functional connections between these areas and the rest of the brain. Visual and visuomotor regions showed distinct patterns of functional connectivity, with the latter preferentially connected with the somatosensory and motor areas representing leg and foot. Overall, these findings reveal a functional sensitivity for coherent visual motion and lower-limb movements in MST and FST, suggesting their possible involvement in integrating sensory and motor information to perform locomotion.

Spatial neglect treatment: The brain's spatial-motor Aiming systems

Animal and human literature supports spatial-motor "Aiming" bias, a frontal-subcortical syndrome, as a core deficit in spatial neglect. However, spatial neglect treatment studies rarely assess Aiming errors. Two knowledge gaps result: spatial neglect rehabilitation studies fail to capture the impact on motor-exploratory aspects of functional disability. Also, across spatial neglect treatment studies, discrepant treatment effects may also result from sampling different proportions of patients with Aiming bias. We review behavioural evidence for Aiming spatial neglect, and demonstrate the importance of measuring and targeting Aiming bias for treatment, by reviewing literature on Aiming spatial neglect and prism adaptation treatment, and presenting new preliminary data on bromocriptine treatment. Finally, we review neuroanatomical and network disruption that may give rise to Aiming spatial neglect. Because Aiming spatial neglect predicts prism adaptation treatment response, assessment may broaden the ability of rehabilitation research to capture functionally-relevant disability. Frontal brain lesions predict both the presence of Aiming spatial neglect, and a robust response to some spatial neglect interventions. Research is needed that co-stratifies spatial neglect patients by lesion location and Aiming spatial neglect, to personalize spatial neglect rehabilitation and perhaps even open a path to spatial retraining as a means of promoting better mobility after stroke.

A Neural Population Mechanism for Rapid Learning

Long-term learning of language, mathematics, and motor skills likely requires cortical plasticity, but behavior often requires much faster changes, sometimes even after single errors. Here, we propose one neural mechanism to rapidly develop new motor output without altering the functional connectivity within or between cortical areas. We tested cortico-cortical models relating the activity of hundreds of neurons in the premotor (PMd) and primary motor (M1) cortices throughout adaptation to reaching movement perturbations. We found a signature of learning in the "output-null" subspace of PMd with respect to M1 reflecting the ability of premotor cortex to alter preparatory activity without directly influencing M1. The output-null subspace planning activity evolved with adaptation, yet the "output-potent" mapping that captures information sent to M1 was preserved. Our results illustrate a population-level cortical mechanism to progressively adjust the output from one brain area to its downstream structures that could be exploited for rapid behavioral adaptation.

Added value of money on motor performance feedback: Increased left central beta-band power for rewards and fronto-central theta-band power for punishments

Monetary rewards and punishments have been shown to respectively enhance retention of motor memories and short-term motor performance, but their underlying neural bases in the context of motor control tasks remain unclear. Using electroencephalography (EEG), the present study tested the hypothesis that monetary rewards and punishments are respectively reflected in post-feedback beta-band (20-30 Hz) and theta-band (3-8 Hz) oscillatory power. While participants performed upper limb reaching movements toward visual targets using their right hand, the delivery of monetary rewards and punishments was manipulated as well as their probability (i.e., by changing target size). Compared to unrewarded and unpunished trials, monetary rewards and the successful avoidance of punishments both entailed greater beta-band power at left central electrodes overlaying contralateral motor areas. In contrast, monetary punishments and reward omissions both entailed increased theta-band power at fronto-central scalp sites. Additional analyses revealed that beta-band power was further increased when rewards were lowly probable. In light of previous work demonstrating similar beta-band modulations in basal ganglia during reward processing, the present results may reflect functional communication of reward-related information between the basal ganglia and motor cortical regions. In turn, the increase in fronto-central theta-band power after monetary punishments may reflect an emphasized cognitive need for behavioral adjustments. Globally, the present work identifies possible neural substrates for the growing behavioral evidence showing beneficial effects of monetary feedback on motor learning and performance.

Population coding of grasp and laterality-related information in the macaque fronto-parietal network

Preparing and executing grasping movements demands the coordination of sensory information across multiple scales. The position of an object, required hand shape, and which of our hands to extend must all be coordinated in parallel. The network formed by the macaque anterior intraparietal area (AIP) and hand area (F5) of the ventral premotor cortex is essential in the generation of grasping movements. Yet, the role of this circuit in hand selection is unclear. We recorded from 1342 single- and multi-units in AIP and F5 of two macaque monkeys (Macaca mulatta) during a delayed grasping task in which monkeys were instructed by a visual cue to perform power or precision grips on a handle presented in five different orientations with either the left or right hand, as instructed by an auditory tone. In AIP, intended hand use (left vs. right) was only weakly represented during preparation, while hand use was robustly present in F5 during preparation. Interestingly, visual-centric handle orientation information dominated AIP, while F5 contained an additional body-centric frame during preparation and movement. Together, our results implicate F5 as a site of visuo-motor transformation and advocate a strong transition between hand-independent and hand-dependent representations in this parieto-frontal circuit.

The Importance of Planning in Motor Learning

The addition of differentiating follow-through motions can facilitate simultaneous learning of multiple motor skills that would otherwise interfere with each other. In this issue of Neuron, Sheahan and colleagues (2016) demonstrate that it is the preparation, not execution, of different follow-through movements that separates motor memories and reduces interference.

Cortical Substrate of Supraspinal Fatigue following Exhaustive Aerobic Exercise Localizes to a Large Cluster in the Anterior Premotor Cortex

Strenuous exercise leads to a progressive reduction in the performance of voluntary physical exercise. This is due to a process described as fatigue and is defined as the failure to maintain the required or expected power output. While some of this is muscular in origin, there are data suggestive of how fatigue is modulated by cortical signals, leading to a concept of central fatigue. The previously reported fatigue-induced changes in cortical activity may have been due to blood oxygen-dependent (BOLD) signal drift and/or neural habituation alone. We implemented a functional magnetic resonance imaging paradigm to effectively isolate brain areas responsible for central (supraspinal) fatigue following exercise. Our data identify a large cluster that includes dominant the anterior ventral premotor cortex (aPMv), the insula and postcentral gyrus as critical nodes in the brain network where supraspinal fatigue might have their functional neural imprints. Findings here show that activity in the ipsilateral aPMv and the adjacent areas in the premotor cortex correlates with both localized fatigue (fatigue specific hand grip contraction), and generalized full body exhaustive fatigue. In addition, from a methodological standpoint, we have also shown that the effects of BOLD signal drift can be modeled and removed to arrive at specific brain activity patterns in our experiments. Once the loci of central fatigue are isolated in this way, treatments aimed at modulating activity in these premotor areas may reduce exercise-induced fatigue and perhaps also benefit various clinical conditions in which fatigue is a major symptom.

Disruption of M1 Activity during Performance Plateau Impairs Consolidation of Motor Memories

Upon exposure to a new sensorimotor relationship, motor behaviors iteratively change early in adaptation but eventually stabilize as adaptation proceeds. Behavioral work suggests that motor memory consolidation is initiated upon the attainment of asymptotic levels of performance. Separate lines of evidence point to a critical role of the primary motor cortex (M1) in consolidation. However, a causal relationship between M1 activity during asymptote and consolidation has yet to be demonstrated. The present study investigated this issue in male and female participants using single-pulse transcranial magnetic stimulation (TMS) to interfere with postmovement activity in M1 in two behavioral phases of a ramp-and-hold visuomotor adaptation paradigm. TMS was either provided after each trial of the ramp phase of adaptation when a gradual increase in the visuomotor rotation caused movements to be changing, or after each trial of the hold phase of adaptation when the rotation was held constant and movements tended to stabilize. Consolidation was assessed by measuring performance on the same task 24 h later. Results revealed that TMS did not influence adaptation to the new visuomotor relationship in either condition. Critically, however, TMS disruption of M1 activity selectively impaired consolidation of motor memories when it was provided during the hold phase of adaptation. This effect did not take place when TMS was delivered over adjacent dorsal premotor cortex or when motor behaviors in late adaptation were prevented from plateauing. Together, these data suggest that the impaired consolidation stemmed from interference with mechanisms of repetition-dependent plasticity in M1.SIGNIFICANCE STATEMENT The present work demonstrates that TMS disruption of M1 activity impairs the consolidation of motor memories selectively when performance reaches asymptotic levels during sensorimotor adaptation. These findings provide evidence for a causal contribution of M1 to motor memory formation when movements tend to repeat, likely through mechanisms of repetition-dependent plasticity.

The decay of motor adaptation to novel movement dynamics reveals an asymmetry in the stability of motion state-dependent learning

Motor adaptation paradigms provide a quantitative method to study short-term modification of motor commands. Despite the growing understanding of the role motion states (e.g., velocity) play in this form of motor learning, there is little information on the relative stability of memories based on these movement characteristics, especially in comparison to the initial adaptation. Here, we trained subjects to make reaching movements perturbed by force patterns dependent upon either limb position or velocity. Following training, subjects were exposed to a series of error-clamp trials to measure the temporal characteristics of the feedforward motor output during the decay of learning. The compensatory force patterns were largely based on the perturbation kinematic (e.g., velocity), but also showed a small contribution from the other motion kinematic (e.g., position). However, the velocity contribution in response to the position-based perturbation decayed at a slower rate than the position contribution to velocity-based training, suggesting a difference in stability. Next, we modified a previous model of motor adaptation to reflect this difference and simulated the behavior for different learning goals. We were interested in the stability of learning when the perturbations were based on different combinations of limb position or velocity that subsequently resulted in biased amounts of motion-based learning. We trained additional subjects on these combined motion-state perturbations and confirmed the predictions of the model. Specifically, we show that (1) there is a significant separation between the observed gain-space trajectories for the learning and decay of adaptation and (2) for combined motion-state perturbations, the gain associated to changes in limb position decayed at a faster rate than the velocity-dependent gain, even when the position-dependent gain at the end of training was significantly greater. Collectively, these results suggest that the state-dependent adaptation associated with movement velocity is relatively more stable than that based on position.

Human motor adaptation of limb movement in response to force perturbations has been shown to be motion-state dependent. That is, the compensatory response to these disturbances is correlated and proportional to the temporal changes in the position, velocity, and acceleration during the motion. Despite a growing understanding of this adaptation process, there is little information on the relative stability of this learning when based on these different temporal features of movement. Here we modified a previous computational model of motor adaptation to predict the decay of the compensatory response associated to different motion states, specifically learning based on temporal variations in limb position and velocity. We confirmed the simulated behavior by examining the decay of the temporal force output after subjects were trained to compensate for movement disturbances based on different combinations and magnitudes of these two motion states. Both simulation and behavioral results show that velocity-based learning decays at a slower rate than position-based, even when learning is significantly biased towards the latter at the end of training. Collectively, these results suggest that motion-state learning based on movement velocity is more stable than that based on limb position.

Trial-by-Trial Motor Cortical Correlates of a Rapidly Adapting Visuomotor Internal Model

Accurate motor control is mediated by internal models of how neural activity generates movement. We examined neural correlates of an adapting internal model of visuomotor gain in motor cortex while two macaques performed a reaching task in which the gain scaling between the hand and a presented cursor was varied. Previous studies of cortical changes during visuomotor adaptation focused on preparatory and perimovement epochs and analyzed trial-averaged neural data. Here, we recorded simultaneous neural population activity using multielectrode arrays and focused our analysis on neural differences in the period before the target appeared. We found that we could estimate the monkey's internal model of the gain using the neural population state during this pretarget epoch. This neural correlate depended on the gain experienced during recent trials and it predicted the speed of the subsequent reach. To explore the utility of this internal model estimate for brain-machine interfaces, we performed an offline analysis showing that it can be used to compensate for upcoming reach extent errors. Together, these results demonstrate that pretarget neural activity in motor cortex reflects the monkey's internal model of visuomotor gain on single trials and can potentially be used to improve neural prostheses.SIGNIFICANCE STATEMENT When generating movement commands, the brain is believed to use internal models of the relationship between neural activity and the body's movement. Visuomotor adaptation tasks have revealed neural correlates of these computations in multiple brain areas during movement preparation and execution. Here, we describe motor cortical changes in a visuomotor gain change task even before a specific movement is cued. We were able to estimate the gain internal model from these pretarget neural correlates and relate it to single-trial behavior. This is an important step toward understanding the sensorimotor system's algorithms for updating its internal models after specific movements and errors. Furthermore, the ability to estimate the internal model before movement could improve motor neural prostheses being developed for people with paralysis.

Rostrocaudal functional gradient among the pre-dorsal premotor cortex, dorsal premotor cortex and primary motor cortex in goal-directed motor behaviour

The dorsal premotor cortex residing in the dorsolateral aspect of area 6 is a rostrocaudally elongated area that is rostral to the primary motor cortex (M1) and caudal to the prefrontal cortex. This region, which is subdivided into rostral [pre-dorsal premotor cortex (pre-PMd)] and caudal [dorsal premotor cortex proper (PMd)] components, probably plays a central role in planning and executing actions to achieve a behavioural goal. In the present study, we investigated the functional specializations of the pre-PMd, PMd, and M1, because the synthesis of the specific functions performed by each area is considered to be essential. Neurons were recorded while monkeys performed a conditional visuo-goal task designed to include separate processes for determining a behavioural goal (reaching towards a right or left potential target) on the basis of visual object instructions, specifying actions (direction of reaching) to be performed on the basis of the goal, and preparing and executing the action. Neurons in the pre-PMd and PMd retrieved and maintained behavioural goals without encoding the visual features of the visual object instructions, and subsequently specified the actions by multiplexing the goals with the locations of the targets. Furthermore, PMd and M1 neurons played a major role in representing the action during movement preparation and execution, whereas the contribution of the pre-PMd progressively decreased as the time of the actual execution of the movement approached. These findings revealed that the multiple processing stages necessary for the realization of an action to accomplish a goal were implemented in an area-specific manner across a functional gradient from the pre-PMd to M1 that included the PMd as an intermediary.

Flexible adaptation to an artificial recurrent connection from muscle to peripheral nerve in man

Controlling a neuroprosthesis requires learning a novel input-output transformation; however, how subjects incorporate this into limb control remains obscure. To elucidate the underling mechanisms, we investigated the motor adaptation process to a novel artificial recurrent connection (ARC) from a muscle to a peripheral nerve in healthy humans. In this paradigm, the ulnar nerve was electrically stimulated in proportion to the activation of the flexor carpi ulnaris (FCU), which is ulnar-innervated and monosynaptically innervated from Ia afferents of the FCU, defined as the "homonymous muscle," or the palmaris longus (PL), which is not innervated by the ulnar nerve and produces similar movement to the FCU, defined as the "synergist muscle." The ARC boosted the activity of the homonymous muscle and wrist joint movement during a visually guided reaching task. Participants could control muscle activity to utilize the ARC for the volitional control of wrist joint movement and then readapt to the absence of the ARC to either input muscle. Participants reduced homonymous muscle recruitment with practice, regardless of the input muscle. However, the adaptation process in the synergist muscle was dependent on the input muscle. The activity of the synergist muscle decreased when the input was the homonymous muscle, whereas it increased when it was the synergist muscle. This reorganization of the neuromotor map, which was maintained as an aftereffect of the ARC, was observed only when the input was the synergist muscle. These findings demonstrate that the ARC induced reorganization of neuromotor map in a targeted and sustainable manner.

Learning to grasp and extract affordances: the Integrated Learning of Grasps and Affordances (ILGA) model

The activity of certain parietal neurons has been interpreted as encoding affordances (directly perceivable opportunities) for grasping. Separate computational models have been developed for infant grasp learning and affordance learning, but no single model has yet combined these processes in a neurobiologically plausible way. We present the Integrated Learning of Grasps and Affordances (ILGA) model that simultaneously learns grasp affordances from visual object features and motor parameters for planning grasps using trial-and-error reinforcement learning. As in the Infant Learning to Grasp Model, we model a stage of infant development prior to the onset of sophisticated visual processing of hand-object relations, but we assume that certain premotor neurons activate neural populations in primary motor cortex that synergistically control different combinations of fingers. The ILGA model is able to extract affordance representations from visual object features, learn motor parameters for generating stable grasps, and generalize its learned representations to novel objects.

Temporal evolution of both premotor and motor cortical tuning properties reflect changes in limb biomechanics

A prevailing theory in the cortical control of limb movement posits that premotor cortex initiates a high-level motor plan that is transformed by the primary motor cortex (MI) into a low-level motor command to be executed. This theory implies that the premotor cortex is shielded from the motor periphery, and therefore, its activity should not represent the low-level features of movement. Contrary to this theory, we show that both dorsal (PMd) and ventral premotor (PMv) cortexes exhibit population-level tuning properties that reflect the biomechanical properties of the periphery similar to those observed in M1. We recorded single-unit activity from M1, PMd, and PMv and characterized their tuning properties while six rhesus macaques performed a reaching task in the horizontal plane. Each area exhibited a bimodal distribution of preferred directions during execution consistent with the known biomechanical anisotropies of the muscles and limb segments. Moreover, these distributions varied in orientation or shape from planning to execution. A network model shows that such population dynamics are linked to a change in biomechanics of the limb as the monkey begins to move, specifically to the state-dependent properties of muscles. We suggest that, like M1, neural populations in PMd and PMv are more directly linked with the motor periphery than previously thought.

Learning and generalization in an isometric visuomotor task

Adaptation is a prominent feature of the human motor system and has been studied extensively in reaching movements. This study characterizes adaptation and generalization during isometric reaching in which the arm remains stationary and the participant controls a virtual cursor via force applied by the hand. We measured how learning of a visual cursor rotation generalizes across workspace 1) to determine the coordinate system that predominates visual rotation learning, and 2) to ascertain whether mapping type, namely position or velocity control, influences transfer. Participants performed virtual reaches to one of two orthogonal training targets with the applied rotation. In a new workspace, participants reached to a single target, similar to the training target in either hand or joint space. Furthermore, a control experiment measured within-workspace generalization to an orthogonal target. Across position and velocity mappings, learning transferred predominantly in intrinsic (joint) space, although the transfer was incomplete. The velocity mapping resulted in significantly larger aftereffects and broader within-workspace generalization than the position mapping, potentially due to slower peak speeds, longer trial times, greater target overshoot, or other factors. Although we cannot rule out a mixed reference frame in our task, the predominance of intrinsic coding of cursor kinematics in the isometric environment opposes the extrinsic coding of arm kinematics in real reaching but matches the intrinsic coding of dynamics found in prior work. These findings have implications for the design of isometric control systems in human-machine interaction or in rehabilitation when coordinated multi-degree-of-freedom movement is difficult to achieve.

Motor cortex single-neuron and population contributions to compensation for multiple dynamic force fields

To elucidate how primary motor cortex (M1) neurons contribute to the performance of a broad range of different and even incompatible motor skills, we trained two monkeys to perform single-degree-of-freedom elbow flexion/extension movements that could be perturbed by a variety of externally generated force fields. Fields were presented in a pseudorandom sequence of trial blocks. Different computer monitor background colors signaled the nature of the force field throughout each block. There were five different force fields: null field without perturbing torque, assistive and resistive viscous fields proportional to velocity, a resistive elastic force field proportional to position and a resistive viscoelastic field that was the linear combination of the resistive viscous and elastic force fields. After the monkeys were extensively trained in the five field conditions, neural recordings were subsequently made in M1 contralateral to the trained arm. Many caudal M1 neurons altered their activity systematically across most or all of the force fields in a manner that was appropriate to contribute to the compensation for each of the fields. The net activity of the entire sample population likewise provided a predictive signal about the differences in the time course of the external forces encountered during the movements across all force conditions. The neurons showed a broad range of sensitivities to the different fields, and there was little evidence of a modular structure by which subsets of M1 neurons were preferentially activated during movements in specific fields or combinations of fields.

Co-registering kinematics and evoked related potentials during visually guided reach-to-grasp movements

Background

In non-human primates grasp-related sensorimotor transformations are accomplished in a circuit involving the anterior intraparietal sulcus (area AIP) and both the ventral and the dorsal sectors of the premotor cortex (vPMC and dPMC, respectively). Although a human homologue of such a circuit has been identified, the time course of activation of these cortical areas and how such activity relates to specific kinematic events has yet to be investigated.

Methodology/Principal Findings

We combined kinematic and event-related potential techniques to explicitly test how activity within human grasping-related brain areas is modulated in time. Subjects were requested to reach towards and grasp either a small stimulus using a precision grip (i.e., the opposition of index finger and thumb) or a large stimulus using a whole hand grasp (i.e., the flexion of all digits around the stimulus). Results revealed a time course of activation starting at the level of parietal regions and continuing at the level of premotor regions. More specifically, we show that activity within these regions was tuned for specific grasps well before movement onset and this early tuning was carried over - as evidenced by kinematic analysis - during the preshaping period of the task.

Conclusions/Significance

Data are discussed in terms of recent findings showing a marked differentiation across different grasps during premovement phases which was carried over into subsequent movement phases. These findings offer a substantial contribution to the current debate about the nature of the sensorimotor transformations underlying grasping. And provide new insights into the detailed movement information contained in the human preparatory activity for specific hand movements.

Changes in corticospinal excitability during reach adaptation in force fields

Both abrupt and gradually imposed perturbations produce adaptive changes in motor output, but the neural basis of adaptation may be distinct. Here, we measured the state of the primary motor cortex (M1) and the corticospinal network during adaptation by measuring motor-evoked potentials (MEPs) before reach onset using transcranial magnetic stimulation of M1. Subjects reached in a force field in a schedule in which the field was introduced either abruptly or gradually over many trials. In both groups, by end of the training, muscles that countered the perturbation in a given direction increased their activity during the reach (labeled as the on direction for each muscle). In the abrupt group, in the period before the reach toward the on direction, MEPs in these muscles also increased, suggesting a direction-specific increase in the excitability of the corticospinal network. However, in the gradual group, these MEP changes were missing. After training, there was a period of washout. The MEPs did not return to baseline. Rather, in the abrupt group, off direction MEPs increased to match on direction MEPs. Therefore, we observed changes in corticospinal excitability in the abrupt but not gradual condition. Abrupt training includes the repetition of motor commands, and repetition may be the key factor that produces this plasticity. Furthermore, washout did not return MEPs to baseline, suggesting that washout engaged a new network that masked but did not erase the effects of previous adaptation. Abrupt but not gradual training appears to induce changes in M1 and/or corticospinal networks.

Activation and connectivity patterns of the presupplementary and dorsal premotor areas during free improvisation of melodies and rhythms

Free, i.e. non-externally cued generation of movement sequences is fundamental to human behavior. We have earlier hypothesized that the dorsal premotor cortex (PMD), which has been consistently implicated in cognitive aspects of planning and selection of spatial motor sequences may be particularly important for the free generation of spatial movement sequences, whereas the pre-supplementary motor area (pre-SMA), which shows increased activation during perception, learning and reproduction of temporal sequences, may contribute more to the generation of temporal structures. Here we test this hypothesis using fMRI and musical improvisation in professional pianists as a model behavior. We employed a 2 × 2 factorial design with the factors Melody (Specified/Improvised) and Rhythm (Specified/Improvised). The main effect analyses partly confirmed our hypothesis: there was a main effect of Melody in the PMD; the pre-SMA was present in the main effect of Rhythm, as predicted, as well as in the main effect of Melody. A psychophysiological interaction analysis of functional connectivity demonstrated that the correlation in activity between the pre-SMA and cerebellum was higher during rhythmic improvisation than during the other conditions. In summary, there were only subtle differences in activity level between the pre-SMA and PMD during improvisation, regardless of condition. Consequently, the free generation of rhythmic and melodic structures, appears to be largely integrated processes but the functional connectivity between premotor areas and other regions may change during free generation in response to sequence-specific spatiotemporal demands.

Activity of the same motor cortex neurons during repeated experience with perturbed movement dynamics

Neurons in the primary motor cortex (M1) have been shown to have persistent, memory-like activity following adaptation to altered movement dynamics. However, the techniques used to study these memory traces limited recordings to only single sessions lasting no more than a few hours. Here, chronically implanted microelectrode arrays were used to study the long-term neuronal responses to repeated experience with perturbing, velocity-dependent force fields. Force-field-related neuronal activity within each session was similar to that found previously. That is, the directional tuning curves of the M1 neurons shifted in a manner appropriate to compensate for the forces. Next, the across-session behavior was examined. Long-term learning was evident in the performance improvements across multiple force-field sessions. Correlated with this change, the neuronal population had smaller within-session spike rate changes as experience with the force field increased. The smaller within-session changes were a result of persistent across-session shifts in directional tuning. The results extend the observation of memory traces of newly learned dynamics and provide further evidence for the role of M1 in early motor memory formation.

Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials

Neural activity in motor cortex during reach and grasp movements shows modulations in a broad range of signals from single-neuron spiking activity (SA) to various frequency bands in broadband local field potentials (LFPs). In particular, spatiotemporal patterns in multiband LFPs are thought to reflect dendritic integration of local and interareal synaptic inputs, attentional and preparatory processes, and multiunit activity (MUA) related to movement representation in the local motor area. Nevertheless, the relationship between multiband LFPs and SA, and their relationship to movement parameters and their relative value as brain-computer interface (BCI) control signals, remain poorly understood. Also, although this broad range of signals may provide complementary information channels in primary (MI) and ventral premotor (PMv) areas, areal differences in information have not been systematically examined. Here, for the first time, the amount of information in SA and multiband LFPs was compared for MI and PMv by recording from dual 96-multielectrode arrays while monkeys made naturalistic reach and grasp actions. Information was assessed as decoding accuracy for 3D arm end point and grip aperture kinematics based on SA or LFPs in MI and PMv, or combinations of signal types across areas. In contrast with previous studies with ≤16 simultaneous electrodes, here ensembles of >16 units (on average) carried more information than multiband, multichannel LFPs. Furthermore, reach and grasp information added by various LFP frequency bands was not independent from that in SA ensembles but rather typically less than and primarily contained within the latter. Notably, MI and PMv did not show a particular bias toward reach or grasp for this task or for a broad range of signal types. For BCIs, our results indicate that neuronal ensemble spiking is the preferred signal for decoding, while LFPs and combined signals from PMv and MI can add robustness to BCI control.

Enhanced locomotor adaptation aftereffect in the "broken escalator" phenomenon using anodal tDCS

The everyday experience of stepping onto a stationary escalator causes a stumble, despite our full awareness that the escalator is broken. In the laboratory, this "broken escalator" phenomenon is reproduced when subjects step onto an obviously stationary platform (AFTER trials) that was previously experienced as moving (MOVING trials) and attests to a process of motor adaptation. Given the critical role of M1 in upper limb motor adaptation and the potential for transcranial direct current stimulation (tDCS) to increase cortical excitability, we hypothesized that anodal tDCS over leg M1 and premotor cortices would increase the size and duration of the locomotor aftereffect. Thirty healthy volunteers received either sham or real tDCS (anodal bihemispheric tDCS; 2 mA for 15 min at rest) to induce excitatory effects over the primary motor and premotor cortex before walking onto the moving platform. The real tDCS group, compared with sham, displayed larger trunk sway and increased gait velocity in the first AFTER trial and a persistence of the trunk sway aftereffect into the second AFTER trial. We also used transcranial magnetic stimulation to probe changes in cortical leg excitability using different electrode montages and eyeblink conditioning, before and after tDCS, as well as simulating the current flow of tDCS on the human brain using a computational model of these different tDCS montages. Our data show that anodal tDCS induces excitability changes in lower limb motor cortex with resultant enhancement of locomotor adaptation aftereffects. These findings might encourage the use of tDCS over leg motor and premotor regions to improve locomotor control in patients with neurological gait disorders.

Gain field encoding of the kinematics of both arms in the internal model enables flexible bimanual action

Bimanual action requires the neural controller (internal model) for each arm to predictively compensate for mechanical interactions resulting from movement of both that arm and its counterpart on the opposite side of the body. Here, we demonstrate that the brain may accomplish this by constructing the internal model with primitives multiplicatively encoding information from the kinematics of both arms. We had human participants adapt to a novel force field imposed on one arm while both arms were moving in particular directions and examined the generalization pattern of motor learning when changing the movement directions of both arms. The generalization pattern was consistent with the pattern predicted from the multiplicative encoding scheme. As proposed by previous theoretical studies, the strength of multiplicative encoding was manifested in the observation that participants could adapt reaching movements to complicated force fields depending nonlinearly on the movement directions of both arms. These results indicate that multiplicative neuronal influence of the kinematics of the opposing arm on the internal models enables the brain to control bimanual movement by providing great flexible ability to handle arbitrary dynamical environments resulting from the interactions of both arms.

Cortical correlates of fitts' law

Fitts’ law describes the fundamental trade-off between movement accuracy and speed: it states that the duration of reaching movements is a function of target size (TS) and distance. While Fitts’ law has been extensively studied in ergonomics and has guided the design of human–computer interfaces, there have been few studies on its neuronal correlates. To elucidate sensorimotor cortical activity underlying Fitts’ law, we implanted two monkeys with multielectrode arrays in the primary motor (M1) and primary somatosensory (S1) cortices. The monkeys performed reaches with a joystick-controlled cursor toward targets of different size. The reaction time (RT), movement time, and movement velocity changed with TS, and M1 and S1 activity reflected these changes. Moreover, modifications of cortical activity could not be explained by changes of movement parameters alone, but required TS as an additional parameter. Neuronal representation of TS was especially prominent during the early RT period where it influenced the slope of the firing rate rise preceding movement initiation. During the movement period, cortical activity was correlated with movement velocity. Neural decoders were applied to simultaneously decode TS and motor parameters from cortical modulations. We suggest that sensorimotor cortex activity reflects the characteristics of both the movement and the target. Classifiers that extract these parameters from cortical ensembles could improve neuroprosthetic control.

The neuronal basis of long-term sensorimotor learning

The brain has a remarkable ability to learn and adjust behavior. For instance, the brain can adjust muscle activation to cope with changes in the environment. However, the neuronal mechanisms behind this adaptation are not clear. To address this fundamental question, this study examines the neuronal basis of long-term sensorimotor learning by recording neuronal activity in the primary motor cortex of monkeys during a long-term adaptation to a force-field perturbation. For 5 consecutive days, the same perturbation was applied to the monkey's hand when reaching to a single target, whereas movements to all other targets were not perturbed. The gradual improvement in performance over these 5 days was correlated to the evolvement in the population neuronal signal, with two timescales of changes in single-cell activity. Specifically, one subgroup of cells showed a relatively fast increase in activity, whereas the other showed a gradual, slower decrease. These adapted patterns of neuronal activity did not involve changes in directional tuning of single cells, suggesting that adaptation was the result of adjustments of the required motor plan by a population of neurons rather than changes in single-cell properties. Furthermore, generalization was mostly expressed in the direction of the required compensatory force during adaptation. Altogether, the neuronal activity and its generalization accord with the adapted motor plan.

Combined adaptiveness of specific motor cortical ensembles underlies learning

Learning motor skills entails adaptation of neural computations that can generate or modify associations between sensations and actions. Indeed, humans can use different strategies when adapting to dynamic loads depending on available sensory feedback. Here, we examined how neural activity in motor cortex was modified when monkeys made arm reaches to a visual target and locally adapted to curl force field with or without visual trajectory feedback. We found that firing rates of a large subpopulation of cells were consistently modulated depending on the distance of their preferred direction from the learned movement direction. The newly acquired activity followed a cosine-like function, with maximal increase in directions that opposed the perturbing force and decrease in opposite directions. As a result, the combined neuronal activity generated an adapted population vector. The results suggest that this could be achieved without changing the tuning properties of the cells. This population directional signal was however altered in the absence of visual feedback; while the cosine pattern of modulation was maintained, the population distributions of modulated cells differed across feedback consistent with the different trajectory shapes. Finally, we predicted generalization patterns of force-field learning based on the cosine-like modulation. These conformed to reported features of generalization in humans, suggesting that the generalization function was related to the observed rate modulations in the motor cortex. Overall, the findings suggest that the new combined activation of neuronal ensembles could underlie the change in the internal model of movement dynamics in a way that depends on available sensory feedback and chosen strategy.

Representation of internal models of action in the autistic brain

Children with autism spectrum disorder (ASD) have deficits in motor control, imitation and social function. Does a dysfunction in the neural basis of representing internal models of action contribute to these problems? We measured patterns of generalization as children learned to control a novel tool and found that the autistic brain built a stronger than normal association between self-generated motor commands and proprioceptive feedback; furthermore, the greater the reliance on proprioception, the greater the child's impairments in social function and imitation.

Signaling of grasp dimension and grasp force in dorsal premotor cortex and primary motor cortex neurons during reach to grasp in the monkey

A fundamental question is how the CNS controls the hand with its many degrees of freedom. Several motor cortical areas, including the dorsal premotor cortex (PMd) and primary motor cortex (M1), are involved in reach to grasp. Although neurons in PMd are known to modulate in relation to the type of grasp and neurons in M1 in relation to grasp force and finger movements, whether specific parameters of whole hand shaping are encoded in the discharge of these cells has not been studied. In this study, two monkeys were trained to reach and grasp 16 objects varying in shape, size, and orientation. Grasp force was explicitly controlled, requiring the monkeys to exert either three or five levels of grasp force on each object. The animals were unable to see the objects or their hands. Single PMd and M1 neurons were recorded during the task, and cell firing was examined for modulation with object properties and grasp force. The firing of the vast majority of PMd and M1 neurons varied significantly as a function of the object presented as well as the object grasp dimension. Grasp dimension of the object was an important determinant of the firing of cells in both PMd and M1. A smaller percentage of PMd and M1 neurons were modulated by grasp force. Linear encoding was prominent with grasp force but less so with grasp dimension. The correlations with grasp dimension and grasp force were stronger in the firing of M1 than PMd neurons and across both regions the modulation with these parameters increased as reach to grasp proceeded. All PMd and M1 neurons that signaled grasp force also signaled grasp dimension, yet the two signals showed limited interactions, providing a neural substrate for the independent control of these two parameters at the behavioral level.

Modulation of internal model formation during force field-induced motor learning by anodal transcranial direct current stimulation of primary motor cortex

Human subjects can quickly adapt and maintain performance of arm reaching when experiencing novel physical environments such as robot-induced velocity-dependent force fields. Using anodal transcranial direct current stimulation (tDCS) this study showed that the primary motor cortex may play a role in motor adaptation of this sort. Subjects performed arm reaching movement trials in three phases: in a null force field (baseline), in a velocity-dependent force field (adaptation; 25 N s m(-1)) and once again in a null force field (de-adaptation). Active or sham tDCS was directed to the motor cortex representation of biceps brachii muscle during the adaptation phase of the motor learning protocol. During the adaptation phase, the global error in arm reaching (summed error from an ideal trajectory) was similar in both tDCS conditions. However, active tDCS induced a significantly greater global reaching (overshoot) error during the early stage of de-adaptation compared to the sham tDCS condition. The overshoot error may be representative of the development of a greater predictive movement to overcome the expected imposed force. An estimate of the predictive, initial movement trajectory (signed error in the first 150 ms of movement) was significantly augmented during the adaptation phase with active tDCS compared to sham tDCS. Furthermore, this increase was linearly related to the change of the overshoot summed error in the de-adaptation process. Together the results suggest that anodal tDCS augments the development of an internal model of the novel adapted movement and suggests that the primary motor cortex is involved in adaptation of reaching movements of healthy human subjects.

Cortical Processing during Dynamic Motor Adaptation

In this chapter we investigate the role of motor cortex in adapting movements to novel dynamic environments. We present results from two experiments in which monkey or human subjects learned to make two-dimensional reaching movements while holding a robotic manipulandum that applied a predictable pattern of forces (a curl field) to their hand. In the first study, we analyzed electrophysiological data recorded in motor cortex while monkeys adapted or readapted to the novel forces on each day of the experiment. In the second study, we perturbed the excitability of motor cortex using repetitive transcranial magnetic stimulation (rTMS) as human participants adapted to the forces. From the first experiment, we present qualitative evidence that a network of cortical areas including the supplementary motor area, premotor cortex, and primary motor cortex (M1) not only encodes kinematic and dynamic parameters of motor execution, but also registers changes in encoding that could provide a substrate for motor memory. Based on the second experiment, we qualify the role of M1 in motor memory, by showing that its disruption by rTMS does not interfere with the process of initial motor adaptation, but rather with offline improvement as measured at retest on the following day.

Neural basis of sensorimotor learning: modifying internal models

The neural basis of the internal models used in sensorimotor transformations is beginning to be uncovered. Sensorimotor learning involves the modification of such models. Different stages of sensory-motor processing have been explored with a continuum of experimental tasks, from learning arbitrary associations of sensory cues to movements, to adapting to altered kinematic and dynamic environments. Several groups have been studying changes in neuronal activity in cortical and subcortical areas that may be related to the acquisition and consolidation processes. We discuss the progress and challenges in understanding how these learning-related neural changes are involved in the modification of internal models, and offer future directions.

Neuronal activity in the cingulate motor areas during adaptation to a new dynamic environment

Neurons in the cingulate motor areas (CMA) have been shown to be involved in many aspects of sensorimotor behavior, although their role in motor learning has received less attention. Here, we recorded single-cell activity in the CMA of monkeys while they adapted reaching movements to different dynamic environments. Specifically, we analyzed CMA activity during normal reaching to visual targets and during reaching in the presence of an applied velocity-dependent force field. We found that the cingulate neuronal activity was modulated during each phase of the task and in response to the applied forces. The neurons' involvement in the visuomotor transformation was influenced by their rostrocaudal location in the cingulate sulcus. Rostral CMA (CMAr) neurons were modulated by the visual instruction to a greater extent than caudal CMA (CMAc) neurons. In contrast, CMAc neurons had a greater amount of phasic and directionally tuned activity during movement than CMAr cells. Furthermore, compared with CMAr cells, the movement-related activity of CMAc cells was more frequently modulated by the applied force fields. The magnitude of the force-field-related neuronal response scaled with the amount of perturbation in each reaching direction. However, contrary to previous results from other cortical motor areas, force-field adaptation was not correlated with a shift in directional tuning of the CMA population. Based on these results, we suggest that although the CMA is clearly sensitive to applied forces, it is less involved in generating anticipatory responses to predictable forces than other cortical motor areas.

Impairment of retention but not acquisition of a visuomotor skill through time-dependent disruption of primary motor cortex

Learning a visuomotor skill involves a distributed network which includes the primary motor cortex (M1). Despite multiple lines of evidence supporting the role of M1 in motor learning and memory, it is unclear whether M1 plays distinct roles in different aspects of learning such as acquisition and retention. Here, we investigated the nature and chronometry of that processing through a temporally specific disruption of M1 activity using single-pulse transcranial magnetic stimulation (TMS). We applied single-pulse TMS to M1 or dorsal premotor cortex (PMd) during adaptation of rapid arm movements (approximately 150 ms duration) to a visuomotor rotation. When M1 was stimulated either immediately after the end of each trial or with a 700 ms delay, subjects exhibited normal adaptation. However, whereas the memory of the subjects who received delayed-TMS showed normal rates of forgetting during deadaptation, the memory of those who received immediate TMS was more fragile: in the deadaptation period, they showed a faster rate of forgetting. Stimulation of PMd with adjusted (reduced) intensity to rule out the possibility of coactivation of this structure caused by the current spread from M1 stimulation did not affect adaptation or retention. The data suggest that, during the short time window after detection of movement errors, neural processing in M1 plays a crucial role in formation of motor memories. This processing in M1 may represent a slow component of motor memory which plays a significant role in retention.

Parieto-frontal connectivity during visually guided grasping

Grasping an object requires processing visuospatial information about the extrinsic features (spatial location) and intrinsic features (size, shape, orientation) of the object. Accordingly, manual prehension has been subdivided into a reach component, guiding the hand toward the object on the basis of its extrinsic features, and a grasp component, preshaping the fingers around the center of mass of the object on the basis of its intrinsic features. In neural terms, this distinction has been linked to a dedicated dorsomedial "reaching" circuit and a dorsolateral "grasping" circuit that process extrinsic and intrinsic features, linking occipital areas via parietal regions with the dorsal and ventral premotor cortex, respectively. We have tested an alternative possibility, namely that the relative contribution of the two circuits is related to the degree of on-line control required by the prehension movement. We used dynamic causal modeling of functional magnetic resonance imaging time series to assess how parieto-frontal connectivity is modulated by planning and executing prehension movements toward objects of different size and width. This experimental manipulation evoked different movements, with different planning and execution phases for the different objects. Crucially, grasping large objects increased inter-regional couplings within the dorsomedial circuit, whereas grasping small objects increased the effective connectivity of a mainly dorsolateral circuit, with a degree of overlap between these circuits. These results argue against the presence of dedicated cerebral circuits for reaching and grasping, suggesting that the contributions of the dorsolateral and the dorsomedial circuits are a function of the degree of on-line control required by the movement.

Motor learning with unstable neural representations

It is often assumed that learning takes place by changing an otherwise stable neural representation. To test this assumption, we studied changes in the directional tuning of primate motor cortical neurons during reaching movements performed in familiar and novel environments. During the familiar task, tuning curves exhibited slow random drift. During learning of the novel task, random drift was accompanied by systematic shifts of tuning curves. Our analysis suggests that motor learning is based on a surprisingly unstable neural representation. To explain these results, we propose that motor cortex is a redundant neural network, i.e., any single behavior can be realized by multiple configurations of synaptic strengths. We further hypothesize that synaptic modifications underlying learning contain a random component, which causes wandering among synaptic configurations with equivalent behaviors but different neural representations. We use a simple model to explore the implications of these assumptions.

The relationship between brain activity and peak grip force is modulated by corticospinal system integrity after subcortical stroke

In healthy human subjects, the relative contribution of cortical regions to motor performance varies with the task parameters. Additionally, after stroke, recruitment of cortical areas during a simple motor task varies with corticospinal system integrity. We investigated whether the pattern of motor system recruitment in a task involving increasingly forceful hand grips is influenced by the degree of corticospinal system damage. Nine chronic subcortical stroke patients and nine age-matched controls underwent functional magnetic brain imaging whilst performing repetitive isometric hand grips. Target grip forces were varied between 15% and 45% of individual maximum grip force. Corticospinal system functional integrity was assessed with transcranial magnetic stimulation. Averaged across all forces, there was more task-related activation compared with rest in the secondary motor areas of patients with greater corticospinal system damage, confirming previous reports. However, here we were primarily interested in regional brain activation, which covaried with the amount of force generated, implying a prominent executive role in force production. We found that in control subjects and patients with lesser corticospinal system damage, signal change increased linearly with increasing force output in contralateral primary motor cortex, supplementary motor area and ipsilateral cerebellum. In contrast, in patients with greater corticospinal system damage, force-related signal changes were seen mainly in contralesional dorsolateral premotor cortex, bilateral ventrolateral premotor cortices and contralesional cerebellum, but not ipsilesional primary motor cortex. These findings suggest that the premotor cortices might play a new and functionally relevant role in controlling force production in patients with more severe corticospinal system disruption.

Distinctions between dorsal and ventral premotor areas: anatomical connectivity and functional properties

The dorsal and ventral premotor areas, together with the primary motor cortex, are believed to have major roles in preparing and executing limb movements. Recent studies have expanded our knowledge of the dorsal and ventral premotor areas, which occupy the lateral part of area 6 in the frontal cortex. It is becoming clear that these two premotor areas, through involvement in distinct cortical networks, take part in unique aspects of motor planning and decision making. New lines of evidence also implicate the lateral premotor areas in planning motor behavior and selecting actions.

Disruption of primary motor cortex before learning impairs memory of movement dynamics

Although multiple lines of evidence implicate the primary motor cortex (M1) in motor learning, the precise role of M1 in the adaptation to novel movement dynamics and in the subsequent consolidation of a memory of those dynamics remains unclear. Here we used repetitive transcranial magnetic stimulation (rTMS) to dissociate the contribution of M1 to these distinct aspects of motor learning. Subjects performed reaching movements in velocity-dependent force fields over three epochs: a null-field baseline epoch, a clockwise-field learning epoch (15 min after the baseline epoch), and a clockwise-field retest epoch (24 h after the learning epoch). Half of the subjects received 15 min of 1 Hz rTMS to M1 between the baseline and learning epochs. Subjects given rTMS performed identically to control subjects during the learning epoch. However, control subjects performed with significantly less error than rTMS subjects in the retest epoch on the following day. These results suggest that M1 is not critical to the network supporting motor adaptation per se but that, within this network, M1 may be important for initiating the development of long-term motor memories.

Force field effects on cerebellar Purkinje cell discharge with implications for internal models

The cerebellum has been hypothesized to provide internal models for limb movement control. If the cerebellum is the site of an inverse dynamics model, then cerebellar neural activity should signal limb dynamics and be coupled to arm muscle activity. To address this, we recorded from 166 task-related Purkinje cells in two monkeys performing circular manual tracking under varying viscous and elastic loads. Hand forces and arm muscle activity increased with the load, and their spatial tuning differed markedly between the viscous and elastic fields. In contrast, the simple spike firing of 91.0% of the Purkinje cells was not significantly modulated by the force nor was their spatial tuning affected. For the 15 cells with a significant force effect, changes were small and isolated. These results do not support the hypothesis that Purkinje cells represent the output of an inverse dynamics model of the arm. Instead these neurons provide a kinematic representation of arm movements.