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Musician’s dystonia is a type of focal task-specific dystonia (FTSD) characterized by abnormal muscle hypercontraction and loss of fine motor control specifically during instrument playing. Although the neuropathophysiology of musician’s dystonia remains unclear, it has been suggested that maladaptive functional abnormalities in subcortical and cortical regions may be involved. Here, we hypothesized that aberrant effective connectivity between the cerebellum (subcortical) and motor/somatosensory cortex may underlie the neuropathophysiology of musician’s dystonia. Using functional magnetic resonance imaging, we measured the brain activity of 30 pianists with or without FTSD as they played a magnetic resonance imaging-compatible piano-like keyboard, which elicited dystonic symptoms in many but not all pianists with FTSD. Pianists with FTSD showed greater activation of the right cerebellum during the task than healthy pianists. Furthermore, patients who reported dystonic symptoms during the task demonstrated greater cerebellar activation than those who did not, establishing a link between cerebellar activity and overt dystonic symptoms. Using multivoxel pattern analysis, moreover, we found that dystonic and healthy pianists differed in the task-related effective connectivity between the right cerebellum and left premotor/somatosensory cortex. The present study indicates that abnormal cerebellar activity and cerebello-cortical connectivity may underlie the pathophysiology of FTSD in musicians.

Hand choices—deciding which hand to use to reach for targets—represent continuous, daily, unconscious decisions. The posterior parietal cortex (PPC) contralateral to the selected hand is activated during a hand-choice task, and disruption of left PPC activity with a single-pulse transcranial magnetic stimulation prior to the execution of the motion suppresses the choice to use the right hand but not vice versa. These findings imply the involvement of either bilateral or left PPC in hand choice. To determine whether the effects of PPC’s activity are essential and/or symmetrical in hand choice, we increased or decreased PPC excitability in 16 healthy participants using transcranial direct current stimulation (tDCS; 10 min, 2 mA, 5 × 7 cm) and examined its online and residual effects on hand-choice probability and reaction time. After the right PPC was stimulated with an anode and the left PPC with a cathode, the probability of left-hand choice significantly increased and reaction time significantly decreased. However, no significant changes were observed with the stimulation of the right PPC with a cathode and the left PPC with an anode. These findings, thus, reveal the asymmetry of PPC-mediated regulation in hand choice.

Persistent goal-directed behaviours result in achievements in many fields. However, the underlying neural mechanisms of persistence and the methods that enhance the neuroplasticity underlying persistence, remain unclear. We here demonstrate that the structural properties of the frontal pole cortex (FPC) before tasks contain information that can classify Achievers and Non-achievers (goal-directed persistence) participating in three tasks that differ in time scale (hours to months) and task domains (cognitive, language, and motor learning). We also found that most Achievers exhibit experience-dependent neuroplastic changes in the FPC after completing language and motor learning tasks. Moreover, we confirmed that a coaching strategy that used subgoals modified goal-directed persistence and increased the likelihood of becoming an Achiever. Notably, we discovered that neuroplastic changes in the FPC were facilitated by the subgoal strategy, suggesting that goal-striving, using effective coaching, optimizes the FPC for goal persistence., Hosoda et al. study the neurobiological underpinnings of goal pursuit and persistence. They use MRI data and identify areas in the frontal pole cortex that could predict performance on various tasks. They also show that coaching results in neuroplastic remodeling that increases the likelihood of goal persistence.

Objective The aim of this study was to investigate whether anodal transcranial direct current stimulation over the left temporoparietal area improved audioverbal memory performance in stroke patients. Design Twelve stroke patients with audioverbal memory impairment participated in a single-masked, crossover, and sham-controlled experiment. The anodal or sham transcranial direct current stimulation was applied during the Rey Auditory Verbal Learning Test, which evaluates the ability to recall a list of 15 heard words over five trials. The number of correctly recalled words was compared between the anodal and sham conditions and the influence of transcranial direct current stimulation on serial position effect of the 15 words was also examined. Results The increase in the number of correctly recalled words from the first to the fifth trial was significantly greater in the anodal condition than in the sham condition (P < 0.01). There was a significant difference (P < 0.01) between the anodal and sham conditions in the number of correctly recalled words within the first five words (primacy region) over the second to fifth trial trials, but not in the middle (next five words) or recency (last five words) regions. Conclusions Anodal transcranial direct current stimulation over the left temporoparietal area improved audioverbal memory performance and induced the primacy effect in stroke patients.

Teachers often believe that they take into account learners’ ongoing learning progress in their teaching. Can behavioural data support this belief? To address this question, we investigated the interactive behavioural coordination between teachers and learners during imitation learning to solve a puzzle. The teacher manually demonstrated the puzzle solution to a learner who immediately imitated and learned it. Manual movements of teachers and learners were analysed using a bivariate autoregressive model. To identify bidirectional information exchange and information shared between the two agents, we calculated causality and noise covariance from the model. Information transfer observed from teacher to learner in the lateral component of their motion indicated imitation of the spatial information of the puzzle solution. Information transfer from learner to teacher in the vertical component of their motion indicated the monitoring process through which teachers adjust their timing of demonstration to the learner’s progress. The shared information in the lateral component increased as learning progressed, indicating the knowledge was shared between the two agents. Our findings demonstrated that the teacher interactively engaged in and contingently supported (i.e. scaffolded) imitation. We thus provide a behavioural signature of the teacher’s intention to promote learning indispensable for understanding the nature of teaching.

Objective Pedaling is widely used for rehabilitation of locomotion because it induces muscle activity very similar to locomotion. Afferent stimulation is important for the modulation of spinal reflexes. Furthermore, supraspinal modulation plays an important role in spinal plasticity induced by electrical stimulation. We, therefore, expected that active pedaling combined with electrical stimulation could induce strong after-effects on spinal reflexes. Design Twelve healthy adults participated in this study. They were instructed to perform 7 min of pedaling. We applied electrical stimulation to the common peroneal nerve during the extension phase of the pedaling cycle. We assessed reciprocal inhibition using a soleus H-reflex conditioning-test paradigm. The magnitude of reciprocal inhibition was measured before, immediately after, 15 and 30 min after active pedaling alone, electrical stimulation alone and active pedaling combined with electrical stimulation (pedaling + ES). Results The amount of reciprocal inhibition was significantly increased after pedaling + ES. The after-effect of pedaling + ES on reciprocal inhibition was more prominent and longer lasting compared with pedaling or electrical stimulation alone. Conclusions Pedaling + ES could induce stronger after-effects on spinal reciprocal inhibitory neurons compared with either intervention alone. Pedaling + ES might be used as a tool to improve locomotion and functional abnormalities in the patient with central nervous lesion.

Older and/or cognitively impaired patients require verbal guidance to prevent accidents during wheelchair operation, thus increasing the burden on caregivers. This study aimed to develop a new portable voice guidance device for manual wheelchairs and examine its clinical usefulness.We developed a portable voice guidance device to monitor the statuses of wheelchair brakes and footrests and automatically provide voice guidance for operation. The device comprises a microcomputer, four magnets and magnetic sensors, speaker and battery. Device operation was assessed during the transfer from a wheelchair to bed six times per day over three days for a total of 90 transfers in five stroke patients (mean age: 79.6 years) who required verbal guidance to direct wheelchair operation. Device usability was also assessed using a questionnaire.The device performed perfectly during all attempted transfers (100%). To ensure safety, the assessor needed to add verbal guidance during 33 of 90 attempted transfers (36.6%). Overall, the device usability was favourable. However, some assessors were unsatisfied with the volume of the device voice, guidance timing and burden reduction.Our device could facilitate wheelchair operation and might potentially be used to reduce fall risk in stroke patients and the burden on caregivers. Implications for Rehabilitation The acquisition of transfer independence is an important step in the rehabilitation of patients with mobility issues. Many patients require supervision and guidance regarding the operation of brakes and footrests on manual wheelchairs. This newly developed voice guidance device for manual wheelchair transfers worked well in patients with hemiplegia and might be helpful to reduce the fall risks and the burden of care.

Background. Transcranial direct current stimulation (tDCS) of the motor cortex can enhance the performance of a paretic upper extremity after stroke. Reported effects on lower limb (LL) function are sparse. Objective. The authors examined whether tDCS can increase the force production of the paretic quadriceps. Methods. In this double-blind, crossover, sham-controlled experimental design, 8 participants with chronic subcortical stroke performed knee extension using their hemiparetic leg before, during, and after anodal or sham tDCS of the LL motor cortex representation in the affected hemisphere. Affected hand-grip force was also recorded. Results. The maximal knee-extension force increased by 21 N (13.2%, P < .01) during anodal tDCS compared with baseline and sham stimulation. The increase persisted less than 30 minutes. Maximal hand-grip force did not change. Conclusions. Anodal tDCS transiently enhanced knee extensor strength. The modest increase was specific to the LL. Thus, tDCS might augment the rehabilitation of stroke patients when combined with lower extremity strengthening or functional training.

When two hands require different information in bimanual asymmetric movements, interference can occur via callosal connections and ipsilateral corticospinal pathways. This interference could potentially work as a cost-effective measure in symmetric movements, allowing the same information to be commonly available to both hands at once. Using functional magnetic resonance imaging, we investigated supra-additive and sub-additive neural interactions in bimanual movements during the initiation and continuation phases of movement. We compared activity during bimanual asymmetric and symmetric movements with the sum of activity during unimanual right and left finger-tapping. Supra-additive continuation-related activation was found in the right dorsal premotor cortex and left cerebellum (lobule V) during asymmetric movements. In addition, for unimanual movements, the right dorsal premotor cortex and left cerebellum (lobule V) showed significant activation only for left-hand (non-dominant) movements, but not for right-hand movements. These results suggest that resource-demanding interactions in bimanual asymmetric movements are involved in a non-dominant hand motor network that functions to keep non-dominant hand movements stable. We found sub-additive continuation-related activation in the supplementary motor area (SMA), bilateral cerebellum (lobule VI) in symmetric movements, and the SMA in asymmetric movements. This suggests that no extra demands were placed on these areas in bimanual movements despite the conventional notion that they play crucial roles in bimanual coordination. Sub-additive initiation-related activation in the left anterior putamen suggests that symmetric movements place lower demands on motor programming. These findings indicate that, depending on coordination patterns, the neural substrates of bimanual movements either exhibit greater effort to keep non-dominant hand movements stable, or save neural cost by sharing information commonly to both hands.

Neural correlates of driving and of decision making have been investigated separately, but little is known about the underlying neural mechanisms of decision making in driving. Previous research discusses two types of decision making: reward‐weighted decision making and cost‐weighted decision making. There are many reward‐weighted decision making neuroimaging studies but there are few cost‐weighted studies. Considering that driving involves serious risk, it is assumed that decision making in driving is cost weighted. Therefore, neural substrates of cost‐weighted decision making can be assessed by investigation of driver's decision making. In this study, neural correlates of resolving uncertainty in driver's decision making were investigated. Turning right in left‐hand traffic at a signalized intersection was simulated by computer graphic animation based videos. When the driver's view was occluded by a big truck, the uncertainty of the oncoming traffic was resolved by an in‐car video assist system that presented the driver's occluded view. Resolving the uncertainty reduced activity in a distributed area including the amygdala and anterior cingulate. These results implicate the amygdala and anterior cingulate as serving a role in cost‐weighted decision making. Hum Brain Mapp 2009. © 2008 Wiley‐Liss, Inc.

Humans are capable of achieving complex tasks with redundant degrees of freedom. Much attention has been paid to task relevant variance modulation as an indication of online feedback control strategies to cope with motor variability. Meanwhile, it has been discussed that the brain learns internal models of environments to realize feedforward control with nominal trajectories. Here we examined trajectory variance in both spatial and temporal domains to elucidate the relative contribution of these control schemas. We asked subjects to learn reaching movements with multiple via-points and found that hand trajectories converged to stereotyped trajectories with the reduction of task relevant variance modulation as learning proceeded. Furthermore, variance reduction was not always associated with task constraints but was highly correlated with the velocity profile. A model assuming noise both on the nominal trajectory and motor command was able to reproduce the observed variance modulation, supporting an expression of nominal trajectories in the brain. The learning-related decrease in task-relevant modulation revealed a reduction in the influence of optimal feedback around the task constraints. After practice, the major part of computation seems to be taken over by the feedforward controller around the nominal trajectory with feedback added only when it becomes necessary.

How do humans choose one arm or the other to reach single targets in front of the body? Current theories of reward-driven decisionmaking predict that choice results from a comparison of “action values,” which are the expected rewards for possible actions in a given state. In addition, current theories of motor control predict that in planning arm movements, humans minimize an expected motor cost that balances motor effort and endpoint accuracy. Here, we test the hypotheses that arm choice is determined by comparison of action values comprising expected effort and expected task success for each arm, as well as a handedness bias. Right-handed subjects, in either a large or small target condition, were first instructed to use each hand in turn to shoot through an array of targets and then to choose either hand to shoot through the same targets. Effort was estimated via inverse kinematics and dynamics. A mixed-effects logistic-regression analysis showed that, as predicted, both expected effort and expected success predicted choice, as did arm use in the preceding trial. Finally, individual parameter estimation showed that the handedness bias correlated with mean difference between right- and left-arm success, leading to overall lower use of the left arm. We discuss our results in light of arm nonuse in individuals' poststroke.

Rhythmic movements, such as walking, chewing or scratching, are phylogenetically old motor behaviors found in many organisms, ranging from insects to primates. In contrast, discrete movements, such as reaching, grasping or kicking, are behaviors that have reached sophistication primarily in younger species, particularly primates. Neurophysiological and computational research on arm motor control has focused almost exclusively on discrete movements, essentially assuming similar neural circuitry for rhythmic tasks. In contrast, many behavioral studies have focused on rhythmic models, subsuming discrete movement as a special case. Here, using a human functional neuroimaging experiment, we show that in addition to areas activated in rhythmic movement, discrete movement involves several higher cortical planning areas, even when both movement conditions are confined to the same single wrist joint. These results provide neuroscientific evidence that rhythmic arm movement cannot be part of a more general discrete movement system and may require separate neurophysiological and theoretical treatment.

Recently, we demonstrated that humans can learn to make accurate movements in an unstable environment by controlling magnitude, shape, and orientation of the endpoint impedance. Although previous studies of human motor learning suggest that the brain acquires an inverse dynamics model of the novel environment, it is not known whether this control mechanism is operative in unstable environments. We compared learning of multijoint arm movements in a “velocity-dependent force field” (VF), which interacted with the arm in a stable manner, and learning in a “divergent force field” (DF), where the interaction was unstable. The characteristics of error evolution were markedly different in the 2 fields. The direction of trajectory error in the DF alternated to the left and right during the early stage of learning; that is, signed error was inconsistent from movement to movement and could not have guided learning of an inverse dynamics model. This contrasted sharply with trajectory error in the VF, which was initially biased and decayed in a manner that was consistent with rapid feedback error learning. EMG recorded before and after learning in the DF and VF are also consistent with different learning and control mechanisms for adapting to stable and unstable dynamics, that is, inverse dynamics model formation and impedance control. We also investigated adaptation to a rotated DF to examine the interplay between inverse dynamics model formation and impedance control. Our results suggest that an inverse dynamics model can function in parallel with an impedance controller to compensate for consistent perturbing force in unstable environments.

This study compared the mechanisms of adaptation to stable and unstable dynamics from the perspective of changes in joint mechanics. Subjects were instructed to make point to point movements in force fields generated by a robotic manipulandum which interacted with the arm in either a stable or an unstable manner. After subjects adjusted to the initial disturbing effects of the force fields they were able to produce normal straight movements to the target. In the case of the stable interaction, subjects modified the joint torques in order to appropriately compensate for the force field. No change in joint torque or endpoint force was required or observed in the case of the unstable interaction. After adaptation, the endpoint stiffness of the arm was measured by applying displacements to the hand in eight different directions midway through the movements. This was compared to the stiffness measured similarly during movements in a null force field. After adaptation, the endpoint stiffness under both the stable and unstable dynamics was modified relative to the null field. Adaptation to unstable dynamics was achieved by selective modification of endpoint stiffness in the direction of the instability. To investigate whether the change in endpoint stiffness could be accounted for by change in joint torque or endpoint force, we estimated the change in stiffness on each trial based on the change in joint torque relative to the null field. For stable dynamics the change in endpoint stiffness was accurately predicted. However, for unstable dynamics the change in endpoint stiffness could not be reproduced. In fact, the predicted endpoint stiffness was similar to that in the null force field. Thus, the change in endpoint stiffness seen after adaptation to stable dynamics was directly related to changes in net joint torque necessary to compensate for the dynamics in contrast to adaptation to unstable dynamics, where a selective change in endpoint stiffness occurred without any modification of net joint torque.

Motor memory is updated to generate ideal movements in a novel environment. When the environment changes every trial randomly, how does the brain incorporate this uncertainty into motor memory? To investigate how the brain adapts to an uncertain environment, we considered a reach adaptation protocol where individuals practiced moving in a force field where a noise was injected. After they had adapted, we measured the trial-to-trial variability in the temporal profiles of the produced hand force. We found that the motor variability was significantly magnified by the adaptation to the random force field. Temporal profiles of the motor variance were significantly dissociable between two different types of random force fields experienced. A model-based analysis suggests that the variability is generated by noise in the gains of the internal model. It further suggests that the trial-to-trial motor variability magnified by the adaptation in a random force field is generated by the uncertainty of the internal model formed in the brain as a result of the adaptation.

How do physical interactions with others change our own motor behavior? Utilizing a novel motor learning paradigm in which the hands of two - individuals are physically connected without their conscious awareness, we investigated how the interaction forces from a partner adapt the motor behavior in physically interacting humans. We observed the motor adaptations during physical interactions to be mutually beneficial such that both the worse and better of the interacting partners improve motor performance during and after interactive practice. We show that these benefits cannot be explained by multi-sensory integration by an individual, but require physical interaction with a reactive partner. Furthermore, the benefits are determined by both the interacting partner's performance and similarity of the partner's behavior to one's own. Our results demonstrate the fundamental neural processes underlying human physical interactions and suggest advantages of interactive paradigms for sport-training and physical rehabilitation.

Quantitative examinations of internal representations for arm trajectory planning: minimum commanded torque change model. A number of invariant features of multijoint planar reaching movements have been observed in measured hand trajectories. These features include roughly straight hand paths and bell-shaped speed profiles where the trajectory curvatures between transverse and radial movements have been found to be different. For quantitative and statistical investigations, we obtained a large amount of trajectory data within a wide range of the workspace in the horizontal and sagittal planes (400 trajectories for each subject). A pair of movements within the horizontal and sagittal planes was set to be equivalent in the elbow and shoulder flexion/extension. The trajectory curvatures of the corresponding pair in these planes were almost the same. Moreover, these curvatures can be accurately reproduced with a linear regression from the summation of rotations in the elbow and shoulder joints. This means that trajectory curvatures systematically depend on the movement location and direction represented in the intrinsic body coordinates. We then examined the following four candidates as planning spaces and the four corresponding computational models for trajectory planning. The candidates were as follows: the minimum hand jerk model in an extrinsic-kinematic space, the minimum angle jerk model in an intrinsic-kinematic space, the minimum torque change model in an intrinsic-dynamic-mechanical space, and the minimum commanded torque change model in an intrinsic-dynamic-neural space. The minimum commanded torque change model, which is proposed here as a computable version of the minimum motor command change model, reproduced actual trajectories best for curvature, position, velocity, acceleration, and torque. The model’s prediction that the longer the duration of the movement the larger the trajectory curvature was also confirmed. Movements passing through via-points in the horizontal plane were also measured, and they converged to those predicted by the minimum commanded torque change model with training. Our results indicated that the brain may plan, and learn to plan, the optimal trajectory in the intrinsic coordinates considering arm and muscle dynamics and using representations for motor commands controlling muscle tensions.

Multijoint muscle regulation mechanisms examined by measured human arm stiffness and EMG signals. Stiffness properties of the musculo-skeletal system can be controlled by regulating muscle activation and neural feedback gain. To understand the regulation of multijoint stiffness, we examined the relationship between human arm joint stiffness and muscle activation during static force control in the horizontal plane by means of surface electromyographic (EMG) studies. Subjects were asked to produce a specified force in a specified direction without cocontraction or they were asked to keep different cocontractions while producing or not producing an external force. The stiffness components of shoulder, elbow, and their cross-term and the EMG of six related muscles were measured during the tasks. Assuming that the EMG reflects the corresponding muscle stiffness, the joint stiffness was predicted from the EMG by using a two-link six-muscle arm model and a constrained least-square-error regression method. Using the parameters estimated in this regression, single-joint stiffness (diagonal terms of the joint-stiffness matrix) was decomposed successfully into biarticular and monoarticular muscle components. Although biarticular muscles act on both shoulder and elbow, they were found to covary strongly with elbow monoarticular muscles. The preferred force directions of biarticular muscles were biased to the directions of elbow monoarticular muscles. Namely, the elbow joint is regulated by the simultaneous activation of monoarticular and biarticular muscles, whereas the shoulder joint is regulated dominantly by monoarticular muscles. These results suggest that biarticular muscles are innervated mainly to control the elbow joint during static force-regulation tasks. In addition, muscle regulation mechanisms for static force control tasks were found to be quite different from those during movements previously reported. The elbow single-joint stiffness was always higher than cross-joint stiffness (off-diagonal terms of the matrix) in static tasks while elbow single-joint stiffness is reported to be sometimes as small as cross-joint stiffness during movement. That is, during movements, the elbow monoarticular muscles were occasionally not activated when biarticular muscles were activated. In static tasks, however, monoarticular muscle components in single-joint stiffness were increased considerably whenever biarticular muscle components in single- and cross-joint stiffness increased. These observations suggest that biarticular muscles are not simply coupled with the innervation of elbow monoarticular muscles but also are regulated independently according to the required task. During static force-regulation tasks, covariation between biarticular and elbow monoarticular muscles may be required to increase stability and/or controllability or to distribute effort among the appropriate muscles.

Background. Arm nonuse, defined as the difference between what the individual can do when constrained to use the paretic arm and what the individual does when given a free choice to use either arm, has not yet been quantified in individuals poststroke. Objectives. (1) To quantify nonuse poststroke and (2) to develop and test a novel, simple, objective, reliable, and valid instrument, the Bilateral Arm Reaching Test (BART), to quantify arm use and nonuse poststroke. Methods. First, we quantify nonuse with the Quality of Movement (QOM) subscale of the Actual Amount of Use Test (AAUT) by subtracting the AAUT QOM score in the spontaneous use condition from the AAUT QOM score in a subsequent constrained use condition. Second, we quantify arm use and nonuse with BART by comparing reaching performance to visual targets projected over a 2D horizontal hemi–work space in a spontaneous-use condition (in which participants are free to use either arm at each trial) with reaching performance in a constrained-use condition. Results. All participants (N = 24) with chronic stroke and with mild to moderate impairment exhibited nonuse with the AAUT QOM. Nonuse with BART had excellent test-retest reliability and good external validity. Conclusions. BART is the first instrument that can be used repeatedly and practically in the clinic to quantify the effects of neurorehabilitation on arm use and nonuse and in the laboratory for advancing theoretical knowledge about the recovery of arm use and the development of nonuse and “learned nonuse” after stroke.

Background We developed an electroencephalogram-based brain computer interface system to modulate functional electrical stimulation (FES) to the affected tibialis anterior muscle in a stroke patient. The intensity of FES current increased in a stepwise manner when the event-related desynchronization (ERD) reflecting motor intent was continuously detected from the primary cortical motor area. Methods We tested the feasibility of the ERD-modulated FES system in comparison with FES without ERD modulation. The stroke patient who presented with severe hemiparesis attempted to perform dorsiflexion of the paralyzed ankle during which FES was applied either with or without ERD modulation. Results After 20 minutes of training, the range of movement at the ankle joint and the electromyography amplitude of the affected tibialis anterior muscle were significantly increased following the ERD-modulated FES compared with the FES alone. Conclusions The proposed rehabilitation technique using ERD-modulated FES for stroke patients was feasible. The system holds potentials to improve the limb function and to benefit stroke patients.

For the recovery of hemiparetic hand function, a therapy was developed called contralateral homonymous muscle activity stimulated electrical stimulation (CHASE), which combines electrical stimulation and bilateral movements, and its feasibility was studied in three chronic stroke patients with severe hand hemiparesis.Patients with a subcortical lesion were asked to extend their wrist and fingers bilaterally while an electromyogram (EMG) was recorded from the extensor carpi radialis (ECR) muscle in the unaffected hand. Electric stimulation was applied to the homonymous wrist and finger extensors of the affected side. The intensity of the electrical stimulation was computed based on the EMG and scaled so that the movements of the paretic hand looked similar to those of the unaffected side. The patients received 30-minutes of therapy per day for 2 weeks.Improvement in the active range of motion of wrist extension was observed for all patients. There was a decrease in the scores of modified Ashworth scale in the flexors. Fugl-Meyer assessment scores of motor function of the upper extremities improved in two of the patients.The results suggest a positive outcome can be obtained using the CHASE system for upper extremity rehabilitation of patients with severe hemiplegia.

Movement error is a driving force behind motor learning. For motor learning with discrete movements, such as point-to-point reaching, it is believed that the brain uses error information of the immediately preceding movement only. However, in the case of continuous and repetitive movements (i.e., rhythmic movements), there is a ceaseless inflow of performance information. Thus, an accurate temporal association of the motor commands with the resultant movement errors is not necessarily guaranteed. We investigated how the brain overcomes this challenging situation. Human participants adapted rhythmic movements between two targets to visuomotor rotations, the amplitudes of which changed randomly from cycle to cycle (the duration of one cycle was ∼400 ms). A system identification technique revealed that the motor adaptation was affected not just by the preceding movement error, but also by a history of errors from the previous cycles. Error information obtained from more than one previous cycle tended to increase, rather than decrease, movement error. This result led to a counterintuitive prediction: providing visual error feedback for only a fraction of cycles should enhance visuomotor adaptation. As predicted, we observed that motor adaptation to a constant visual rotation (30°) was significantly enhanced by providing visual feedback once every fourth or fifth cycle rather than for every cycle. These results suggest that the brain requires a specific processing time to modify the motor command, based on the error information, and so is unable to deal appropriately with the overwhelming flow of error information generated during rhythmic movements.

Background To more accurately evaluate rehabilitation outcomes in stroke patients, movement irregularities should be quantified. Previous work in stroke patients has revealed a reduction in the trajectory smoothness and segmentation of continuous movements. Clinically, the Stroke Impairment Assessment Set (SIAS) evaluates the clumsiness of arm movements using an ordinal scale based on the examiner's observations. In this study, we focused on three-dimensional curvature of hand trajectory to quantify movement, and aimed to establish a novel measurement that is independent of movement duration. We compared the proposed measurement with the SIAS score and the jerk measure representing temporal smoothness. Methods Sixteen stroke patients with SIAS upper limb proximal motor function (Knee-Mouth test) scores ranging from 2 (incomplete performance) to 4 (mild clumsiness) were recruited. Nine healthy participant with a SIAS score of 5 (normal) also participated. Participants were asked to grasp a plastic glass and repetitively move it from the lap to the mouth and back at a conformable speed for 30 s, during which the hand movement was measured using OPTOTRAK. The position data was numerically differentiated and the three-dimensional curvature was computed. To compare against a previously proposed measure, the mean squared jerk normalized by its minimum value was computed. Age-matched healthy participants were instructed to move the glass at three different movement speeds. Results There was an inverse relationship between the curvature of the movement trajectory and the patient's SIAS score. The median of the -log of curvature (MedianLC) correlated well with the SIAS score, upper extremity subsection of Fugl-Meyer Assessment, and the jerk measure in the paretic arm. When the healthy participants moved slowly, the increase in the jerk measure was comparable to the paretic movements with a SIAS score of 2 to 4, while the MedianLC was distinguishable from paretic movements. Conclusions Measurement based on curvature was able to quantify movement irregularities and matched well with the examiner's observations. The results suggest that the quality of paretic movements is well characterized using spatial smoothness represented by curvature. The smaller computational costs associated with this measurement suggest that this method has potential clinical utility.

Previous simulation and experimental studies have demonstrated that the application of Variational Bayesian Multimodal EncephaloGraphy (VBMEG) to magnetoencephalography (MEG) data can be used to estimate cortical currents with high spatio-temporal resolution, by incorporating functional magnetic resonance imaging (fMRI) activity as a hierarchical prior. However, the use of combined MEG and fMRI is restricted by the high costs involved, a lack of portability and high sensitivity to body-motion artifacts. One possible solution for overcoming these limitations is to use a combination of electroencephalography (EEG) and near-infrared spectroscopy (NIRS). This study therefore aimed to extend the possible applications of VBMEG to include EEG data with NIRS activity as a hierarchical prior. Using computer simulations and real experimental data, we evaluated the performance of VBMEG applied to EEG data under different conditions, including different numbers of EEG sensors and different prior information. The results suggest that VBMEG with NIRS prior performs well, even with as few as 19 EEG sensors. These findings indicate the potential value of clinically applying VBMEG using a combination of EEG and NIRS.

The present study investigated a skill-level-dependent interaction between gravity and muscular force when striking piano keys. Kinetic analysis of the arm during the downswing motion performed by expert and novice piano players was made using an inverse dynamic technique. The corresponding activities of the elbow agonist and antagonist muscles were simultaneously recorded using electromyography (EMG). Muscular torque at the elbow joint was computed while excluding the effects of gravitational and motion-dependent interaction torques. During descending the forearm to strike the keys, the experts kept the activation of the triceps (movement agonist) muscle close to the resting level, and decreased anti-gravity activity of the biceps muscle across all loudness levels. This suggested that elbow extension torque was produced by gravity without the contribution of agonist muscular work. For the novices, on the other hand, a distinct activity in the triceps muscle appeared during the middle of the downswing, and its amount and duration were increased with increasing loudness. Therefore, for the novices, agonist muscular force was the predominant contributor to the acceleration of elbow extension during the downswing. We concluded that a balance shift from muscular force dependency to gravity dependency for the generation of a target joint torque occurs with long-term piano training. This shift would support the notion of non-muscular force utilization for improving physiological efficiency of limb movement with respect to the effective use of gravity.

Near-infrared spectroscopy (NIRS) has recently been used to measure human motor-cortical activation, enabling the classification of the content of a sensory-motor event such as whether the left or right hand was used. Here, we advance this NIRS application by demonstrating quantitative estimates of multiple sensory-motor events from single-trial NIRS signals. It is known that different degrees of sensory-motor activation are required to generate various hand/finger force levels. Thus, using a sparse linear regression method, we examined whether the temporal changes in different force levels could be reconstructed from NIRS signals. We measured the relative changes in oxyhemoglobin concentrations in the bilateral sensory-motor cortices while participants performed an isometric finger-pinch force production with their thumb and index finger by repeatedly exerting one of three target forces (25, 50, or 75% of the maximum voluntary contraction) for 12 s. To reconstruct the generated forces, we determined the regression parameters from the training datasets and applied these parameters to new test datasets to validate the parameters in the single-trial reconstruction. The temporal changes in the three different levels of generated forces, as well as the baseline resting state, could be reconstructed, even for the test datasets. The best reconstruction was achieved when using only the selected NIRS channels dominantly located in the contralateral sensory-motor cortex, and with a four second hemodynamic delay. These data demonstrate the potential for reconstructing different levels of external loads (forces) from those of the internal loads (activation) in the human brain using NIRS.

We propose a new model of motor learning to explain the exceptional dexterity and rapid adaptation to change, which characterize human motor control. It is based on the brain simultaneously optimizing stability, accuracy and efficiency. Formulated as a V-shaped learning function, it stipulates precisely how feedforward commands to individual muscles are adjusted based on error. Changes in muscle activation patterns recorded in experiments provide direct support for this control scheme. In simulated motor learning of novel environmental interactions, muscle activation, force and impedance evolved in a manner similar to humans, demonstrating its efficiency and plausibility. This model of motor learning offers new insights as to how the brain controls the complex musculoskeletal system and iteratively adjusts motor commands to improve motor skills with practice.

There is an infinity of impedance parameter values, and thus different co-contraction levels, that can produce similar movement kinematics from which the CNS must select one. Although signal-dependent noise (SDN) predicts larger motor-command variability during higher co-contraction, the relationship between impedance and task performance is not theoretically obvious and thus was examined here. Subjects made goal-directed, single-joint elbow movements to either move naturally to different target sizes or voluntarily co-contract at different levels. Stiffness was estimated as the weighted summation of rectified EMG signals through the index of muscle co-contraction around the joint (IMCJ) proposed previously. When subjects made movements to targets of different sizes, IMCJ increased with the accuracy requirements, leading to reduced endpoint deviations. Therefore without the need for great accuracy, subjects accepted worse performance with lower co-contraction. When subjects were asked to increase co-contraction, the variability of EMG and torque both increased, suggesting that noise in the neuromotor command increased with muscle activation. In contrast, the final positional error was smallest for the highest IMCJ level. Although co-contraction increases the motor-command noise, the effect of this noise on the task performance is reduced. Subjects were able to regulate their impedance and control endpoint variance as the task requirements changed, and they did not voluntarily select the high impedance that generated the minimum endpoint error. These data contradict predictions of the SDN-based theory, which postulates minimization of only endpoint variance and thus require its revision.

This study compared adaptation in novel force fields where trajectories were initially either stable or unstable to elucidate the processes of learning novel skills and adapting to new environments. Subjects learned to move in a null force field (NF), which was unexpectedly changed either to a velocity-dependent force field (VF), which resulted in perturbed but stable hand trajectories, or a position-dependent divergent force field (DF), which resulted in unstable trajectories. With practice, subjects learned to compensate for the perturbations produced by both force fields. Adaptation was characterized by an initial increase in the activation of all muscles followed by a gradual reduction. The time course of the increase in activation was correlated with a reduction in hand-path error for the DF but not for the VF. Adaptation to the VF could have been achieved solely by formation of an inverse dynamics model and adaptation to the DF solely by impedance control. However, indices of learning, such as hand-path error, joint torque, and electromyographic activation and deactivation suggest that the CNS combined these processes during adaptation to both force fields. Our results suggest that during the early phase of learning there is an increase in endpoint stiffness that serves to reduce hand-path error and provides additional stability, regardless of whether the dynamics are stable or unstable. We suggest that the motor control system utilizes an inverse dynamics model to learn the mean dynamics and an impedance controller to assist in the formation of the inverse dynamics model and to generate needed stability.

In the field of motor control, two hypotheses have been controversial: whether the brain acquires internal models that generate accurate motor commands, or whether the brain avoids this by using the viscoelasticity of musculoskeletal system. Recent observations on relatively low stiffness during trained movements support the existence of internal models. However, no study has revealed the decrease in viscoelasticity associated with learning that would imply improvement of internal models as well as synergy between the two hypothetical mechanisms. Previously observed decreases in electromyogram (EMG) might have other explanations, such as trajectory modifications that reduce joint torques. To circumvent such complications, we required strict trajectory control and examined only successful trials having identical trajectory and torque profiles. Subjects were asked to perform a hand movement in unison with a target moving along a specified and unusual trajectory, with shoulder and elbow in the horizontal plane at the shoulder level. To evaluate joint viscoelasticity during the learning of this movement, we proposed an index of muscle co-contraction around the joint (IMCJ). The IMCJ was defined as the summation of the absolute values of antagonistic muscle torques around the joint and computed from the linear relation between surface EMG and joint torque. The IMCJ during isometric contraction, as well as during movements, was confirmed to correlate well with joint stiffness estimated using the conventional method, i.e., applying mechanical perturbations. Accordingly, the IMCJ during the learning of the movement was computed for each joint of each trial using estimated EMG-torque relationship. At the same time, the performance error for each trial was specified as the root mean square of the distance between the target and hand at each time step over the entire trajectory. The time-series data of IMCJ and performance error were decomposed into long-term components that showed decreases in IMCJ in accordance with learning with little change in the trajectory and short-term interactions between the IMCJ and performance error. A cross-correlation analysis and impulse responses both suggested that higher IMCJs follow poor performances, and lower IMCJs follow good performances within a few successive trials. Our results support the hypothesis that viscoelasticity contributes more when internal models are inaccurate, while internal models contribute more after the completion of learning. It is demonstrated that the CNS regulates viscoelasticity on a short- and long-term basis depending on performance error and finally acquires smooth and accurate movements while maintaining stability during the entire learning process.

We used fMRI to identify the brain areas related to the perception of biological motion (4 T EPI; whole brain). In experiment 1, 10 subjects viewed biological motion (a human figure jumping up and down, composed of 21 dots), alternating with a control stimulus created by applying autoregressive models to the biological motion stimulus (such that the dots’ speeds and amplitudes were preserved whereas their linking structure was not). The lengths of the stimulus bouts varied, and therefore the transitions between biological motion and control stimuli were unpredictable. Subjects had to indicate with a button press when each transition occurred. In a related biological motion task, subjects detected short (1 s) disturbances within these displays. We also examined the neural substrates of motion and shape perception, as well as motor imagery, to determine whether or not the cortical regions involved in these processes are also recruited during biological motion perception. Subjects viewed linear motion displays alternating with static dots and a series of common objects alternating with band-limited white noise patterns. Subjects also generated imagery of their own arm movements alternating with visual imagery of common objects. Biological motion specific BOLD signal was found within regions of the lingual gyrus at the cuneus border, showing little overlap with object recognition, linear motion or motion imagery areas. The lingual gyrus activation was replicated in a second experiment that also mapped retinotopic visual areas in three subjects. The results suggest that a region of the lingual gyrus within VP is involved in higher-order processing of motion information.

To manipulate objects or to use tools we must compensate for any forces arising from interaction with the physical environment. Recent studies indicate that this compensation is achieved by learning an internal model of the dynamics1,2,3,4,5,6, that is, a neural representation of the relation between motor command and movement5,7. In these studies interaction with the physical environment was stable, but many common tasks are intrinsically unstable8,9. For example, keeping a screwdriver in the slot of a screw is unstable because excessive force parallel to the slot can cause the screwdriver to slip and because misdirected force can cause loss of contact between the screwdriver and the screw. Stability may be dependent on the control of mechanical impedance in the human arm because mechanical impedance can generate forces which resist destabilizing motion. Here we examined arm movements in an unstable dynamic environment created by a robotic interface. Our results show that humans learn to stabilize unstable dynamics using the skilful and energy-efficient strategy of selective control of impedance geometry.

The learning process of reaching movements was examined under novel environments whose kinematic and dynamic properties were altered. We used a kinematic transformation (visuomotor rotation), a dynamic transformation (viscous curl field), and a combination of these transformations. When the subjects learned the combined transformation, reaching errors were smaller if the subject first learned the separate kinematic and dynamic transformations. Reaching errors under the kinematic (but not the dynamic) transformation were smaller if subjects first learned the combined transformation. These results suggest that the brain learns multiple internal models to compensate for each transformation and has some ability to combine and decompose these internal models as called for by the occasion.

Functional near-infrared spectroscopy (fNIRS) is used to measure cerebral activity because it is simple and portable. However, scalp-hemodynamics often contaminates fNIRS signals, leading to detection of cortical activity in regions that are actually inactive. Methods for removing these artifacts using standard source–detector distance channels (Long-channel) tend to over-estimate the artifacts, while methods using additional short source–detector distance channels (Short-channel) require numerous probes to cover broad cortical areas, which leads to a high cost and prolonged experimental time. Here, we propose a new method that effectively combines the existing techniques, preserving the accuracy of estimating cerebral activity and avoiding the disadvantages inherent when applying the techniques individually. Our new method accomplishes this by estimating a global scalp-hemodynamic component from a small number of Short-channels, and removing its influence from the Long-channels using a general linear model (GLM). To demonstrate the feasibility of this method, we collected fNIRS and functional magnetic resonance imaging (fMRI) measurements during a motor task. First, we measured changes in oxygenated hemoglobin concentration (∆ Oxy-Hb) from 18 Short-channels placed over motor-related areas, and confirmed that the majority of scalp-hemodynamics was globally consistent and could be estimated from as few as four Short-channels using principal component analysis. We then measured ∆ Oxy-Hb from 4 Short- and 43 Long-channels. The GLM identified cerebral activity comparable to that measured separately by fMRI, even when scalp-hemodynamics exhibited substantial task-related modulation. These results suggest that combining measurements from four Short-channels with a GLM provides robust estimation of cerebral activity at a low cost.

Many evidences suggest that the central nervous system (CNS) acquires and switches internal models for adaptive control in various environments. However, little is known about the neural mechanisms responsible for the switching. A recent computational model for simultaneous learning and switching of internal models proposes two separate switching mechanisms: a predictive mechanism purely based on contextual information and a postdictive mechanism based on the difference between actual and predicted sensorimotor feedbacks. This model can switch internal models solely based on contextual information in a predictive fashion immediately after alteration of the environment. Here we show that when subjects simultaneously adapted to alternating blocks of opposing visuomotor rotations, explicit contextual information about the rotations improved the initial performance at block alternations and asymptotic levels of performance within each block but not readaptation speeds. Our simulations using separate switching mechanisms duplicated these effects of contextual information on subject performance and suggest that improvement of initial performance was caused by improved accuracy of the predictive switch while adaptation speed corresponds to a switch dependent on sensorimotor feedback. Simulations also suggested that a slow change in output signals from the switching mechanisms causes contamination of motor commands from an internal model used in the previous context (anterograde interference) and partial destruction of internal models (retrograde interference). Explicit contextual information prevents destruction and assists memory retention by improving the changes in output signals. Thus, the asymptotic levels of performance improved.