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People often have opportunities to engage in social interactions with strangers, which have been reported to contribute to their well-being. Although strategies for social interaction between strangers differ from those between acquaintances, the differences in neural basis of social interaction have not been fully elucidated. In this study, we examined the geometrical properties of interpersonal neural networks in pairs of strangers and acquaintances during joint tapping using dual electroencephalography (EEG). Twenty-one pairs of participants performed antiphase joint tapping under four different conditions. Intra-brain synchronizations were calculated using the weighted phase lag index (wPLI) for all possible intra-brain pairs of the 29 channels (29C2= 406), and inter-brain synchronizations were calculated using the phase locking value (PLV) for all possible inter-brain pairs of the 29 channels (29 × 29 = 841) in the theta, alpha, and beta frequency bands. Electrode pairs with larger wPLI and PLV than their surrogates were defined as the nodes (EEG channels) and edges (connections between nodes) of the neural networks. We then calculated the global efficiency, local efficiency, clustering coefficient, and modularity derived from graph theory for the combined intra- and inter-brain networks of each pair. No significant differences in the tapping phase variance were identified between the stranger and acquaintance pairs. However, in the combined intra- and inter-brain theta EEG (4–7 Hz) networks, stranger pairs showed larger local efficiency and cluster coefficients than acquaintance pairs, indicating that the two brains of stranger pairs were more densely connected. Moreover, in the beta EEG bands, the modularity of the two brains was low in the fast condition, indicating that the two brains were coupled when the task demand was high. Our results show that weak social ties promote more extensive social interactions and result in dense brain-to-brain coupling.

In this research to assess emotions from biometric signals, participants are asked to evaluate the emotions they subjectively experienced in order to confirm whether the assumed emotions were actually elicited. However, the evaluation of emotions is not routinely performed in daily life, and it is possible that this evaluation may alter biological signals. In fMRI studies, evaluation has been shown to activate the amygdala, which is said to be related to emotional expression. However, electroencephalography (EEG) studies do not take into consideration the effects of such evaluations, and it is unclear how these evaluations affect emotion-related brain activity observed in EEG. We hypothesized that emotion evaluations would amplify emotions and c alter Frontal Alpha Asymmetry (FAA), which has been shown to be related to emotional pleasantness and unpleasantness. We suspect this is because in order to evaluate one’s emotions, one must pay attention to one’s internal state, and this self-focused attention has been found to enhance the subjective emotional experience. We measured a 29-channel EEG when presented with unpleasant and highly arousing images from the International Affective Picture System (IAPS) from 40 healthy male and female participants. The results revealed that FAA was significantly lower in the condition in which participants rated their own emotions compared to the condition in which they did not. Similar to fMRI studies, this result indicates that emotion-related brain activity is amplified on an EEG. This paper provides a cautionary note regarding the use of such evaluations in EEG emotion estimation studies.

This systematic review and meta-analysis examined the effects of neurofeedback training (NFT) on attentional performance in healthy adults. Six databases (PubMed, Scopus, Web of Science, PsycInfo, JDreamIII, and Ichu-shi) were searched up to June 2022 for randomized controlled trials (RCTs) comparing attentional performance following NFT to a control group. Risk of bias was determined using the Cochrane Collaboration’s tool. We identified 41 RCTs for qualitative synthesis and 15 RCTs without high risk of bias (including 569 participants) for meta-analysis using a random-effects model. The overall effect of NFT on attentional performance was significant (standardized mean difference = 0.27, 95% confidence interval = 0.10–0.44). However, approximately half of the studies did not compare its effects with sham-NFT, and no significant pooled effect of NFT was found in the studies compared to a sham-NFT group. Furthermore, subgroup analyses revealed variable effects on individual attentional performance subsets (executive function, spatial orientation, and arousal). Future large-scale sham-controlled RCTs are needed to confirm the efficacy of NFT for improving attentional performance.

Several functional magnetic resonance imaging (fMRI) studies have demonstrated that resting-state brain activity consists of multiple components, each corresponding to the spatial pattern of brain activity induced by performing a task. Especially in a movement task, such components have been shown to correspond to the brain activity pattern of the relevant anatomical region, meaning that the voxels of pattern that are cooperatively activated while using a body part (e.g., foot, hand, and tongue) also behave cooperatively in the resting state. However, it is unclear whether the components involved in resting-state brain activity correspond to those induced by the movement of discrete body parts. To address this issue, in the present study, we focused on wrist and finger movements in the hand, and a cross-decoding technique trained to discriminate between the multi-voxel patterns induced by wrist and finger movement was applied to the resting-state fMRI. We found that the multi-voxel pattern in resting-state brain activity corresponds to either wrist or finger movements in the motor-related areas of each hemisphere of the cerebrum and cerebellum. These results suggest that resting-state brain activity in the motor-related areas consists of the components corresponding to the elementary movements of individual body parts. Therefore, the resting-state brain activity possibly has a finer structure than considered previously.

[Purpose] Arm choice is an unconscious action selection performed in daily life. Even if hemiparetic stroke patients can use their paretic arm, they compensate for their movements with their non-paretic arm, leading to decreased function of their paretic arm. Therefore, we need to encourage stroke patients to actively use their paretic arm. For this purpose, it is imperative to understand the process of selection of the left or right hand by patients. Here, we conducted a scoping review to summarize the findings of previous studies on factors and brain regions related to choice of arm. [Methods] We used PubMed/Medline, EBSCO, and the Cochrane Library to obtain research literature according to the PRISMA Extension for Scoping Reviews guidelines. [Results] Twenty-five of the 81 articles obtained from the search met the defined criteria. Cost, success, and dominance were investigated as relevant factors for arm choice. We also extracted articles examining the relationship between the posterior parietal and premotor cortex activity and arm choice. [Conclusion] From these results, we considered ways to facilitate the use of the paretic arm, such as the use of virtual reality systems or exoskeletal robots to modulate the reaching cost and success rates, or non-invasive brain stimulation methods to modulate brain activity.

[Purpose] Arm choice is an unconscious action selection performed in daily life. Even if stroke hemiparetic patients can use their paretic arm, they compensate for their movements with their non-paretic arm, leading to decreased function of their paretic arm. Therefore, stroke patients need to use their paretic arm actively. Hence, we need to understand how the left and right hands are selected. Therefore, we conducted a scoping review to summarize findings of previous studies on factors and brain regions related to arm choice.[Methods] We used PubMed/Medline, EBSCO, and Cochran Library to obtain research literature according to the PRISMA Extension for Scoping Reviews guidelines. [Results] Through consultation with co-authors, 25 articles which met the defined criteria were identified from the 81 articles obtained from the search. The cost, success, and dominance were investigated as relevant factors for arm choice. We also extracted articles examining the relationship between posterior parietal and premotor cortex activity and arm choice. [Conclusion] From these results, we considered ways to facilitate the use of the paretic arm, such as using virtual reality systems or exoskeletal robots to modulate the reaching cost and success rate or using non-invasive brain stimulation methods to modulate brain activity.

People solve insight problems that they encounter daily with a sudden sense of ‘aha!’ to reach a solution. Chunk decomposition, which decomposes the factors of the problem, and constraint relaxation, which manipulates filters to organize the information necessary to solve the problem, are important in insight problem-solving. Although there have been many studies on brain activity during chunk decomposition, few have examined brain activity during constraint relaxation. Particularly, no study has observed the changes over time due to constraint relaxation during insight problem-solving. This study used a slot machine task as an insight problem to measure brain activity using functional Near-Infrared Spectroscopy (fNIRS), and eye movements to estimate constraint relaxation. The results showed that the right dorsolateral prefrontal cortex (DLPFC) and right superior temporal gyrus (STG) were activated over time in participants in whom constraint relaxation was induced. The right inferior temporal gyrus (ITG) of participants who were able to solve the insight problem after constraint relaxation was also activated. The analysis of brain activity during constraint relaxation in insight problem-solving may be useful for advancing research on the ‘aha!’ phenomenon and creativity.

As humans, we constantly change our movement strategies to adapt to changes in physical functions and the external environment. We have to walk very slowly in situations with a high risk of falling, such as walking on slippery ice, carrying an overflowing cup of water, or muscle weakness owing to aging or motor deficit. However, previous studies have shown that a normal gait pattern at low speeds results in reduced efficiency and stability in comparison with those at a normal speed. Another possible strategy is to change the gait pattern from normal to step-to gait, in which the other foot is aligned with the first swing foot. However, the efficiency and stability of the step-to gait pattern at low speeds have not been investigated yet. Therefore, in this study, we compared the efficiency and stability of the normal and step-to gait patterns at intermediate, low, and very low speeds. Eleven healthy participants were asked to walk with a normal gait and step-to gait on a treadmill at five different speeds (i.e., 10, 20, 30, 40, and 60 m/min), ranging from very low to normal walking speed. The efficiency parameters (percent recovery and walk ratio) and stability parameters (center of mass lateral displacement) were analyzed from the motion capture data and then compared for the two gait patterns. The results suggested that step-to gait had a more efficient gait pattern at very low speeds of 10–30 m/min, with a larger percent recovery, and was more stable at 10–60 m/min in comparison with a normal gait. However, the efficiency of the normal gait was better than that of the step-to gait pattern at 60 m/min. Therefore, step-to gait is effective in improving gait efficiency and stability when faced with situations that force us to walk slowly or hinder quick walking because of muscle weakness owing to aging or motor deficit along with a high risk of falling.

Objective This study aimed to develop the Motivation in stroke patients for rehabilitation scale (MORE scale), following the Consensus-based standards for the selection of health measurement instruments (COSMIN). Method Study participants included rehabilitation professionals working at the convalescent rehabilitation hospital and stroke patients admitted to the hospital. The original MORE scale was developed from an item pool, which was created through discussions of nine rehabilitation professionals. After the content validity of the scale was verified using the Delphi method with 61 rehabilitation professionals and 22 stroke patients, the scale’s validity and reliability were examined for 201 stroke patients. The construct validity of the scale was investigated using exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and item response theory analysis. Cronbach’s alpha confirmed its internal consistency. Regarding convergent, discriminant, and criterion validity, Spearman’s rho was calculated between the MORE scale and the Apathy Scale (AS), Self-rating Depression Scale (SDS), and Visual Analogue Scale (VAS), which rates the subjective feelings of motivation. Results Using the Delphi method, 17 items were incorporated into the MORE scale. According to EFA and CFA, a one-factor model was suggested. All MORE scale items demonstrated satisfactory item response, with item slopes ranging from 0.811 to 2.142, and item difficulty parameters ranging from -3.203 to 0.522. Cronbach’s alpha was 0.948. Regarding test-retest reliability, a moderate correlation was found between scores at the beginning and one month after hospitalization (rho = 0.612. p < 0.001). The MORE scale showed significant correlation with AS (rho = -0.536, p < 0.001), SDS (rho = -0.347, p < 0.001), and VAS (rho = 0.536, p < 0.001), confirming the convergent, discriminant, and criterion validity, respectively. Conclusions The MORE scale was verified as a valid and reliable scale for evaluating stroke patients’ motivation for rehabilitation.

Background:Motivation is essential for patients with subacute stroke undergoing intensive rehabilitation. Although it is known that motivation induces behavioral changes toward rehabilitation, detailed description has been lacking. Motivation can be intrinsic or extrinsic; however, it is unclear which type of factors mainly motivates patients' daily rehabilitation.Purpose:This study aimed to examine the factors influencing patients' motivation and to explore the behavioral changes induced by motivation, especially age-related differences.Method:Twenty participants (mean age 65.8 years [standard deviation 13.7]) who had a subacute stroke and underwent rehabilitation at a convalescent hospital were recruited using convenience sampling. Semi-structured interviews were conducted by an occupational therapist with an interview topic guide regarding factors influencing motivation and how it affects behavioral change. Interviews were recorded, transcribed to text, and analyzed by three occupational therapists using thematic analysis. The participants were divided into two groups: aged patients (aged ≥ 65 years) and middle-aged patients (aged < 65 years), and data were analyzed according to the groups. This study was conducted according to the consolidated criteria for reporting qualitative research.Results:Seven core categories were identified as factors influencing patients' motivation: patients' goals, experiences of success and failure, physical condition and cognitive function, resilience, influence of rehabilitation professionals, relationships between patients, and patients' supporters. The first four and last three core categories were further classified as personal and social-relationship factors, respectively. The categories related to intrinsic motivation such as enjoyment of rehabilitation itself were not derived. In both age-groups, motivation affected the frequency of self-training and activity in daily lives. In some aged patients, however, high motivation restrained their self-training to conserve their physical strength for rehabilitation by professionals. Some aged patients do not express their high motivation through their facial expressions and conversations compared to middle-aged patients; therefore, motivation is not always observable in aged patients.Conclusions:Interventions tailored to extrinsic factors are important for maintaining patients' motivation. Observational evaluation may lead to mislabeling of their motivation, especially for aged patients. Rehabilitation professionals should use validated evaluation scales or patients' narratives to assess patients' motivation.

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.

Inter-brain synchronization is enhanced when individuals perform rhythmic interpersonal coordination tasks, such as playing instruments in music ensembles. Experimentally, synchronization has been shown to correlate with the performance of joint tapping tasks. However, it is unclear whether inter-brain synchronization is related to the stability of interpersonal coordination represented as the standard deviation of relative phase (SDRP). In this study, we simultaneously recorded electroencephalograms of two paired individuals during anti-phase tapping in three interactive tapping conditions: slow (reference inter-tap interval [ITI]: 0.5 s), fast (reference ITI: 0.25 s), and free (preferred ITI), and pseudo tapping where each participant tapped according to the metronome sounds without interaction. We calculated the inter-brain synchronization between pairs of six regions of interest (ROI): frontal, central, left/right temporal, parietal, and occipital regions. During the fast tapping, the inter-brain synchronization significantly increased in multiple ROI pairs including temporoparietal junction in comparison to pseudo tapping. Synchronization between the central and left-temporal regions was positively correlated with SDRP in the theta in the fast condition. These results demonstrate that inter-brain synchronization occurs when task requirements are high and increases with the instability of the coordination.

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.

Humans have the amazing ability to learn the dynamics of the body and environment to develop motor skills. Traditional motor studies using arm reaching paradigms have viewed this ability as the process of ‘internal model adaptation’. However, the behaviors have not been fully explored in the case when reaches fail to attain the intended target. Here we examined human reaching under two force fields types; one that induces failures (i.e., target errors), and the other that does not. Our results show the presence of a distinct failure-driven adaptation process that enables quick task success after failures, and before completion of internal model adaptation, but that can result in persistent changes to the undisturbed trajectory. These behaviors can be explained by considering a hierarchical interaction between internal model adaptation and the failure-driven adaptation of reach direction. Our findings suggest that movement failure is negotiated using hierarchical motor adaptations by humans., Author summary How do we improve actions after a movement failure? Although negotiating movement failures is obviously crucial, previous motor-control studies have predominantly examined human movement adaptations in the absence of failures, and it remains unclear how failures affect subsequent movement adaptations. Here we examined this issue by developing a novel force field adaptation task where the hand movement during an arm reaching is perturbed by novel forces that induce a large target error, that is a failure. Our experimental observation and computational modeling show that, in addition to the popular ‘internal model learning’ process of motor adaptations, humans also utilize a ‘failure-negotiating’ process, that enables them to quickly improve movements in the presence of failure, even at the expense of increased arm trajectory deflections, which are subsequently reduced gradually with training after the achievement of the task success. Our results suggest that a hierarchical interaction between these two processes is a key for humans to negotiate movement failures.

This research investigated the difference in aspects of gaze control between esports experts (Expert) and players with lower skills (Low Skill) while playing the real-time strategy game called StarCraft. Three versions of this game at different difficulty levels were made with the StarCraft Editor, and the gaze movements of seven Expert and nine Low Skill players were analyzed while they played the games. The gaze of Expert players covered a significantly larger area in the horizontal direction than the gaze of Low Skill players. Furthermore, the magnitude and number of saccadic eye movements were greater, and saccade velocity was faster in the Expert than in the Low Skill players. In conclusion, StarCraft experts have a specific gaze control ability that enables them to quickly and widely take visual information from all over the monitor. This could be one of the factors enabling StarCraft experts to perform better than players with lower skills when playing games that require task-switching ability.

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.

To characterize each cognitive function per se and to understand the brain as an aggregate of those functions, it is vital to relate dozens of these functions to each other. Knowledge about the relationships among cognitive functions is informative not only for basic neuroscientific research but also for clinical applications and developments of brain-inspired artificial intelligence. In the present study, we propose an exhaustive data mining approach to reveal relationships among cognitive functions based on functional brain mapping and network analysis. We began our analysis with 109 pseudo-activation maps (cognitive function maps; CFM) that were reconstructed from a functional magnetic resonance imaging meta-analysis database, each of which corresponds to one of 109 cognitive functions such as ‘emotion,’ ‘attention,’ ‘episodic memory,’ etc. Based on the resting-state functional connectivity between the CFMs, we mapped the cognitive functions onto a two-dimensional space where the relevant functions were located close to each other, which provided a rough picture of the brain as an aggregate of cognitive functions. Then, we conducted so-called conceptual analysis of cognitive functions using clustering of voxels in each CFM connected to the other 108 CFMs with various strengths. As a result, a CFM for each cognitive function was subdivided into several parts, each of which is strongly associated with some CFMs for a subset of the other cognitive functions, which brought in sub-concepts (i.e., sub-functions) of the cognitive function. Moreover, we conducted network analysis for the network whose nodes were parcels derived from whole-brain parcellation based on the whole-brain voxel-to-CFM resting-state functional connectivities. Since each parcel is characterized by associations with the 109 cognitive functions, network analyses using them are expected to inform about relationships between cognitive and network characteristics. Indeed, we found that informational diversities of interaction between parcels and densities of local connectivity were dependent on the kinds of associated functions. In addition, we identified the homogeneous and inhomogeneous network communities about the associated functions. Altogether, we suggested the effectiveness of our approach in which we fused the large-scale meta-analysis of functional brain mapping with the methods of network neuroscience to investigate the relationships among cognitive functions.

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.

The cortical motor areas are activated not only during contralateral limb movements but also during ipsilateral limb movements. Although these ipsilateral activities have been observed in several brain imaging studies, their functional role is poorly understood. Due to its high temporal resolution and low susceptibility to artifacts from body movements, the electrocorticogram (ECoG) is an advantageous measurement method for assessing the human brain function of motor behaviors. Here, we demonstrate that contra- and ipsilateral movements share a similarity in the high-frequency band of human ECoG signals. The ECoG signals were measured from the unilateral sensorimotor cortex while patients conducted self-paced movements of different body parts, contra- or ipsilateral to the measurement side. The movement categories (wrist, shoulder, or ankle) of ipsilateral movements were decoded as accurately as those of contralateral movements from spatial patterns of the high-frequency band of the precentral motor area (the primary motor and premotor areas). The decoder, trained in the high-frequency band of ipsilateral movements generalized to contralateral movements, and vice versa, confirmed that the activity patterns related to ipsilateral limb movements were similar to contralateral ones in the precentral motor area. Our results suggest that the high-frequency band activity patterns of ipsilateral and contralateral movements might be functionally coupled to control limbs, even during unilateral movements.

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.

In a conventional view of motor control, the human brain might employ an optimization principle that leads a stereotypical motor behavior which we observe as an averaged behavioral data over subjects. In this scenario, the inter-individual motor variability is considered as an observation noise. Here, we challenged this view. We considered a motor control task where the human participants manipulated arm force by coordinating shoulder and elbow torques and investigated the muscle-tuning function that represents how the brain distributed the ideal joint torques to multiple muscles. In the experimental data, we observed large inter-individual variability in the profile of a muscle-tuning function. This contradicts with a well-established optimization theory that is based on minimization of muscle energy consumption and minimization of motor variability. We then hypothesized the inter-subject differences in the structure of the motor cortical areas might be the source of the across-subjects variability of the motor behavior. This was supported by a voxel-based morphometry analysis of magnetic resonance imaging; The inter-individual variability of the muscle tuning profile was correlated with that of the gray matter volume in the premotor cortex which is ipsilateral to the used arm (i.e., right hemisphere for the right arm). This study suggests that motor individuality may originate from inter-individual variation in the cortical structure.

Human dexterity with tools is believed to stem from our ability to incorporate and use tools as parts of our body. However tool incorporation, evident as extensions in our body representation and peri-personal space, has been observed predominantly after extended tool exposures and does not explain our immediate motor behaviours when we change tools. Here we utilize two novel experiments to elucidate the presence of additional immediate tool incorporation effects that determine motor planning with tools. Interestingly, tools were observed to immediately induce a trial-by-trial, tool length dependent shortening of the perceived limb lengths, opposite to observations of elongations after extended tool use. Our results thus exhibit that tools induce a dual effect on our body representation; an immediate shortening that critically affects motor planning with a new tool, and the slow elongation, probably a consequence of skill related changes in sensory-motor mappings with the repeated use of the tool.

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.

Functional near-infrared spectroscopy (fNIRS) is a widely utilized neuroimaging tool in fundamental neuroscience research and clinical investigation. Previous research has revealed that task-evoked systemic artifacts mainly originating from the superficial-tissue may preclude the identification of cerebral activation during a given task. We examined the influence of such artifacts on event-related brain activity during a brisk squeezing movement. We estimated task-evoked superficial-tissue hemodynamics from short source–detector distance channels (15 mm) by applying principal component analysis. The estimated superficial-tissue hemodynamics exhibited temporal profiles similar to the canonical cerebral hemodynamic model. Importantly, this task-evoked profile was also observed in data from a block design motor experiment, suggesting a transient increase in superficial-tissue hemodynamics occurs following motor behavior, irrespective of task design. We also confirmed that estimation of event-related cerebral hemodynamics was improved by a simple superficial-tissue hemodynamic artifact removal process using 15-mm short distance channels, compared to the results when no artifact removal was applied. Thus, our results elucidate task design-independent characteristics of superficial-tissue hemodynamics and highlight the need for the application of superficial-tissue hemodynamic artifact removal methods when analyzing fNIRS data obtained during event-related motor tasks.

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.

It has been suggested that resting-state brain activity reflects task-induced brain activity patterns. In this study, we examined whether neural representations of specific movements can be observed in the resting-state brain activity patterns of motor areas. First, we defined two regions of interest (ROIs) to examine brain activity associated with two different behavioral tasks. Using multi-voxel pattern analysis with regularized logistic regression, we designed a decoder to detect voxel-level neural representations corresponding to the tasks in each ROI. Next, we applied the decoder to resting-state brain activity. We found that the decoder discriminated resting-state neural activity with accuracy comparable to that associated with task-induced neural activity. The distribution of learned weighted parameters for each ROI was similar for resting-state and task-induced activities. Large weighted parameters were mainly located on conjunctive areas. Moreover, the accuracy of detection was higher than that for a decoder whose weights were randomly shuffled, indicating that the resting-state brain activity includes multi-voxel patterns similar to the neural representation for the tasks. Therefore, these results suggest that the neural representation of resting-state brain activity is more finely organized and more complex than conventionally considered.

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.

Despite the existence of neural noise, which leads variability in motor commands, the central nervous system can effectively reduce movement variance at the end effector to meet task requirements. Although online correction based on feedback information is essential for reducing error, feedforward impedance control is another way to regulate motor variability. This Update Article reviews key studies examining the relation between task constraints and impedance control for human arm movement. When a smaller reaching target is given as a task constraint, flexor and extensor muscles are co-activated, and positional variance is decreased around the task constraint. Trial-by-trial muscle activations revealed no on-line feedback correction, indicating that humans are able to regulate their impedance in advance. These results demonstrate that not only on-line feedback correction, but also feedforward impedance control, helps reduce the motor variability caused by internal noise to realize dexterous movements of human arms. A computational model of movement planning considering the presence of signal-dependent noise provides a unifying framework that potentially accounts for optimizing impedance to maximize accuracy. A recently proposed learning algorism formulated as a V-shaped learning function explains how the central nervous system acquires impedance to optimize accuracy as well as stability and efficiency.

Although human motor variability inevitably exists because of neural noises, it is not clear how humans can effectively reduce this variability for task accuracy. We examined the ability of humans to compensate for this variability during task-constraint reaching movements. The positional variance and muscle activations of reaching movements with task-constraints were compared with those without task-constraints. Flexor and extensor muscles were co-activated for the movements with task-constraints, and the positional variance was decreased to obtain accuracy. The results indicated that subjects were able to regulate their muscle impedance and to modulate the variability to meet the task requirements.

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.

Movement variability plays a vital role in motor control. Although previous studies have examined the size and direction of the variability at end points, little research has examined how the variability changes during the time of move. The time course of the variability on point-to-point movements seems to be composed of two different properties: one increases monotonically and the other has an increasing–decreasing property. The first one can be explained by neural noise at the control level, such as the signal-dependent noise (SDN). However, how do we explain the latter one? Our numerical experiment hypothesized the time-jitter noise at trajectory planning level, which represents local advance or delay time of the reference trajectory for reaching movements. The simulation result could well reproduce the feature of the behavioral results.

Humans have exceptional abilities to learn new skills, manipulate tools and objects, and interact with our environment. In order to be successful at these tasks, our brain has become exceptionally well adapted to learning to deal not only with the complex dynamics of our own limbs but also with novel dynamics in the external world. While learning of these dynamics includes learning the complex time-varying forces at the end of limbs through the updating of internal models, it must also include learning the appropriate mechanical impedance in order to stabilize both the limb and any objects contacted in the environment. This article reviews the field of human learning by examining recent experimental evidence about adaptation to novel unstable dynamics and explores how this knowledge about the brain and neuro-muscular system can expand the learning capabilities of robotics and prosthetics.

In this study, two different viscous force fields are generated by using a PFM (parallel link direct-drive air-magnet floating manipulandum), and the responding human learning process for multi-joint reaching motion is observed. In this experiment, the subject is given a certain cue for the force field. The cues are provided as follows: (1) audiovisual cue (a color and a peep sound corresponding to the force field, and a feather pattern representing the direction and magnitude of the force) and (2) movement cue (two trials are defined as a set, and the first movement is used as the cue). The task of the experiment is to learn the movement in force fields which are switched at random. Whichever cue (1) or (2) is used, there is a tendency that the area error from the desired straight trajectory decreases with an increasing number of trials. It is also observed that when the force field is eliminated at random after learning, a marked aftereffect arises in the group to which the audiovisual cue is given. The aftereffect is also observed in the group to which the movement cue is given, but is not as marked as in the group to which the audiovisual cue is given. The above results suggest that the internal models for two different viscous force fields can be acquired simultaneously without time separation by providing an appropriate cue to indicate the force field and learning in the environment in which the force field is switched at random. © 2006 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 89(7): 29– 39, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.20279

In control, stability captures the reproducibility of motions and the robustness to environmental and internal perturbations. This paper examines how stability can be evaluated in human movements, and possible mechanisms by which humans ensure stability. First, a measure of stability is introduced, which is simple to apply to human movements and corresponds to Lyapunov exponents. Its application to real data shows that it is able to distinguish effectively between stable and unstable dynamics. A computational model is then used to investigate stability in human arm movements, which takes into account motor output variability and computes the force to perform a task according to an inverse dynamics model. Simulation results suggest that even a large time delay does not affect movement stability as long as the reflex feedback is small relative to muscle elasticity. Simulations are also used to demonstrate that existing learning schemes, using a monotonic antisymmetric update law, cannot compensate for unstable dynamics. An impedance compensation algorithm is introduced to learn unstable dynamics, which produces similar adaptation responses to those found in experiments.

An influential idea in human motor learning is that there is a consolidation period during which motor memories are transformed from a fragile to a permanent state, no longer susceptible to interference from new learning. The evidence supporting this idea comes from studies showing that the motor memory of a task (A) is lost when an opposing task (B) is experienced soon after, but not if sufficient time is allowed to pass (∼6 hr). We report results from three laboratories challenging this consolidation idea. We used an ABA paradigm in the context of a reaching task to assess the influence of experiencing B after A on the retention of A. In two experiments using visuomotor rotations, we found that B fully interferes with the retention of A even when B is experienced 24 hr after A. Contrary to previous reports, in four experiments on learning force fields, we also observed full interference between A and B when they are separated by 24 hr or even 1 week. This latter result holds for both position-dependent and velocity-dependent force fields. For both the visuomotor and force-field tasks, complete interference is still observed when the possible affects of anterograde interference are controlled through the use of washout trials. Our results fail to support the idea that motor memories become consolidated into a protected state. Rather, they are consistent with recent ideas of memory formation, which propose that memories can shift between active and inactive states.

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.