Anti-LGI1 encephalitis preceded by psychiatric symptoms: A case report.

Background: To date, only a few reports of anti-LGI1 encephalitis with isolated psychiatric symptoms in the initial phase have been reported. We present a relatively rare case of antileucine-rich glioma-inactivated 1 (LGI1) encephalitis that developed only psychiatric symptoms at the onset. Case Presentation: The patient was a male in his 40s who developed anxiety and panic symptoms and was started on antidepressants after being diagnosed with panic disorder by a psychiatrist. He visited our hospital 2 months later presenting with hallucinations, delusions, mild cognitive decline, and faciobrachial dystonic seizures in the left upper extremity and face. Fluid-attenuated inversion recovery magnetic resonance imaging revealed swelling and hyperintensities in the right caudate nucleus and putamen. Cerebrospinal fluid analysis did not show increased protein levels or cell counts and revealed positive oligoclonal bands. Subsequently, positive results for anti-LGI1 antibodies were observed in the cerebrospinal fluid. Therefore, the patient was diagnosed with anti-LGI1 encephalitis. Conclusion: This case highlights the need to consider anti-LGI1 encephalitis therapy in patients with acute-onset psychiatric symptoms.

Enhancement of sense of ownership using virtual and haptic feedback.

Accomplishing motor function requires multimodal information, such as visual and haptic feedback, which induces a sense of ownership (SoO) over one's own body part. In this study, we developed a visual-haptic human machine interface that combines three different types of feedback (visual, haptic, and kinesthetic) in the context of passive hand-grasping motion and aimed to generate SoO over a virtual hand. We tested two conditions, both conditions the three set of feedback were synchronous, the first condition was in-phase, and the second condition was in antiphase. In both conditions, we utilized passive visual feedback (pre-recorded video of a real hand displayed), haptic feedback (balloon inflated and deflated), and kinesthetic feedback (finger movement following the balloon curvature). To quantify the SoO, the participants' reaction time was measured in response to a sense of threat. We found that most participants had a shorter reaction time under anti-phase condition, indicating that synchronous anti-phase of the multimodal system was better than in-phase condition for inducing a SoO of the virtual hand. We conclude that stronger haptic feedback has a key role in the SoO in accordance with visual information. Because the virtual hand is closing and the high pressure from the balloon against the hand creates the sensation of grasping and closing the hand, it appeared as though the person was closing his/her hand at the perceptual level.

A Case of Anti-IgLON5 Disease Showing an Improvement in Dysautonomia, Including Vocal Cord Palsy, via Combined Immunotherapy.

Anti-IgLON5 disease shows various neurological manifestations, of which dysautonomia is one of the major symptoms and is rarely improved by immunotherapy. We herein report a patient with anti-IgLON5 disease who showed several autonomic failures, including vocal cord palsy for four months. The patient presented with cognitive impairments, bulbar symptoms accompanied by myorhythmia in the pharynx and tongue, cerebellar ataxia with tremor, motor neuron symptoms in the limbs, gastrointestinal dysfunction, orthostatic hypotension, non-rapid eye movement sleep disorder on polysomnography, and severe vocal cord palsy. Combined immunotherapy improved his symptoms, including vocal cord palsy, suggesting that combined immunotherapy might improve dysautonomia in anti-IgLON5 disease.

Fluctuations in Upper and Lower Body Movement during Walking in Normal Pressure Hydrocephalus and Parkinson's Disease Assessed by Motion Capture with a Smartphone Application, TDPT-GT.

We aimed to capture the fluctuations in the dynamics of body positions and find the characteristics of them in patients with idiopathic normal pressure hydrocephalus (iNPH) and Parkinson's disease (PD). With the motion-capture application (TDPT-GT) generating 30 Hz coordinates at 27 points on the body, walking in a circle 1 m in diameter was recorded for 23 of iNPH, 23 of PD, and 92 controls. For 128 frames of calculated distances from the navel to the other points, after the Fourier transforms, the slopes (the representatives of fractality) were obtained from the graph plotting the power spectral density against the frequency in log-log coordinates. Differences in the average slopes were tested by one-way ANOVA and multiple comparisons between every two groups. A decrease in the absolute slope value indicates a departure from the 1/f noise characteristic observed in healthy variations. Significant differences in the patient groups and controls were found in all body positions, where patients always showed smaller absolute values. Our system could measure the whole body's movement and temporal variations during walking. The impaired fluctuations of body movement in the upper and lower body may contribute to gait and balance disorders in patients.

Artificial Intelligence Distinguishes Pathological Gait: The Analysis of Markerless Motion Capture Gait Data Acquired by an iOS Application (TDPT-GT).

Distinguishing pathological gait is challenging in neurology because of the difficulty of capturing total body movement and its analysis. We aimed to obtain a convenient recording with an iPhone and establish an algorithm based on deep learning. From May 2021 to November 2022 at Yamagata University Hospital, Shiga University, and Takahata Town, patients with idiopathic normal pressure hydrocephalus (n = 48), Parkinson's disease (n = 21), and other neuromuscular diseases (n = 45) comprised the pathological gait group (n = 114), and the control group consisted of 160 healthy volunteers. iPhone application TDPT-GT captured the subjects walking in a circular path of about 1 meter in diameter, a markerless motion capture system, with an iPhone camera, which generated the three-axis 30 frames per second (fps) relative coordinates of 27 body points. A light gradient boosting machine (Light GBM) with stratified k-fold cross-validation (k = 5) was applied for gait collection for about 1 min per person. The median ability model tested 200 frames of each person's data for its distinction capability, which resulted in the area under a curve of 0.719. The pathological gait captured by the iPhone could be distinguished by artificial intelligence.

Quantitative measurement of finger usage in stroke hemiplegia using ring-shaped wearable devices.

BACKGROUND: In post-stroke rehabilitation, positive use of affected limbs in daily life is important to improve affected upper-limb function. Several studies have quantitatively evaluated the amount of upper-limb activity, but few have measured finger usage. In this study, we used a ring-shaped wearable device to measure upper-limb and finger usage simultaneously in hospitalized patients with hemiplegic stroke and investigated the association between finger usage and general clinical evaluation. METHODS: Twenty patients with hemiplegic stroke in an inpatient hospital participated in this study. All patients wore a ring-shaped wearable device on both hands for 9 h on the day of the intervention, and their finger and upper-limb usage were recorded. For the rehabilitation outcome assessments, the Fugl-Meyer Assessment of the Upper Extremity (FMA-UE), Simple Test for Evaluating Hand Function (STEF), Action Research Arm Test (ARAT), Motor Activity Log-14 (MAL), and Functional Independence Measure Motor (FIM-m) were performed and evaluated on the same day as the intervention. RESULTS: Finger usage of the affected hand was moderately correlated with STEF ([Formula: see text], [Formula: see text]) and STEF ratio ([Formula: see text], [Formula: see text]). The finger-usage ratio was moderately correlated with FMA-UE ([Formula: see text], [Formula: see text]) and ARAT ([Formula: see text], [Formula: see text]), and strongly correlated with STEF ([Formula: see text], [Formula: see text]) and STEF ratio ([Formula: see text], [Formula: see text]). The upper-limb usage of the affected side was moderately correlated with FMA-UE ([Formula: see text], [Formula: see text]), STEF ([Formula: see text], [Formula: see text]) and STEF ratio ([Formula: see text], [Formula: see text]), and strongly correlated with ARAT ([Formula: see text], [Formula: see text]). The upper-limb usage ratio was moderately correlated with ARAT ([Formula: see text], [Formula: see text]) and STEF ([Formula: see text], [Formula: see text]), and strongly correlated with the STEF ratio ([Formula: see text], [Formula: see text]). By contrast, there was no correlation between MAL and any of the measurements. CONCLUSIONS: This measurement technique provided useful information that was not biased by the subjectivity of the patients and therapists.

Quantitative Gait Feature Assessment on Two-Dimensional Body Axis Projection Planes Converted from Three-Dimensional Coordinates Estimated with a Deep Learning Smartphone App.

To assess pathological gaits quantitatively, three-dimensional coordinates estimated with a deep learning model were converted into body axis plane projections. First, 15 healthy volunteers performed four gait patterns; that is, normal, shuffling, short-stepped, and wide-based gaits, with the Three-Dimensional Pose Tracker for Gait Test (TDPT-GT) application. Second, gaits of 47 patients with idiopathic normal pressure hydrocephalus (iNPH) and 92 healthy elderly individuals in the Takahata cohort were assessed with the TDPT-GT. Two-dimensional relative coordinates were calculated from the three-dimensional coordinates by projecting the sagittal, coronal, and axial planes. Indices of the two-dimensional relative coordinates associated with a pathological gait were comprehensively explored. The candidate indices for the shuffling gait were the angle range of the hip joint < 30° and relative vertical amplitude of the heel < 0.1 on the sagittal projection plane. For the short-stepped gait, the angle range of the knee joint < 45° on the sagittal projection plane was a candidate index. The candidate index for the wide-based gait was the leg outward shift > 0.1 on the axial projection plane. In conclusion, the two-dimensional coordinates on the body axis projection planes calculated from the 3D relative coordinates estimated by the TDPT-GT application enabled the quantification of pathological gait features.

Development of Smartphone Application for Markerless Three-Dimensional Motion Capture Based on Deep Learning Model.

To quantitatively assess pathological gait, we developed a novel smartphone application for full-body human motion tracking in real time from markerless video-based images using a smartphone monocular camera and deep learning. As training data for deep learning, the original three-dimensional (3D) dataset comprising more than 1 million captured images from the 3D motion of 90 humanoid characters and the two-dimensional dataset of COCO 2017 were prepared. The 3D heatmap offset data consisting of 28 × 28 × 28 blocks with three red-green-blue colors at the 24 key points of the entire body motion were learned using the convolutional neural network, modified ResNet34. At each key point, the hottest spot deviating from the center of the cell was learned using the tanh function. Our new iOS application could detect the relative tri-axial coordinates of the 24 whole-body key points centered on the navel in real time without any markers for motion capture. By using the relative coordinates, the 3D angles of the neck, lumbar, bilateral hip, knee, and ankle joints were estimated. Any human motion could be quantitatively and easily assessed using a new smartphone application named Three-Dimensional Pose Tracker for Gait Test (TDPT-GT) without any body markers or multipoint cameras.

Mutual Skill Learning and Adaptability to Others via Haptic Interaction.

When learning a new skill through an unknown environment, should we practice alone, or together with another beginner, or learn from the expert? It is normally helpful to have an expert guiding through unknown environmental dynamics. The guidance from the expert is fundamentally based on mutual interactions. From the perspective of the beginner, one needs to face dual unknown dynamics of the environment and motor coordination of the expert. In a cooperative visuo-haptic motor task, we asked novice participants to bring a virtual mass onto the specified target location under an unknown external force field. The task was completed by an individual or with an expert or another novice. In addition to evaluation of the motor performance, we evaluated the adaptability of the novice participants to a new partner while attempting to achieve a common goal together. The experiment was set in five phases; baseline for skill transfer and adaptability, learning and evaluation for adaptability and skill transfer respectively. The performance of the participants was characterized by using the time to target, effort index, and length of the trajectory. Experimental results suggested that (1) peer-to-peer interactions among paired beginners enhanced the motor learning most, (2) individuals practicing on their own (learning as a single) showed better motor learning than practicing under the expert's guidance, and (3) regarding the adaptability, peer-to-peer interactions induced higher adaptability to a new partner than the novice-to-expert interactions while attempting to achieve a common goal together. Thus, we conclude that the peer-to-peer interactions under a collaborative task can realize the best motor learning of the motor skills through the new environmental dynamics, and adaptability to others in order to achieve a goal together. We suggest that the peer-to-peer learning can induce both adaptability to others and learning of motor skills through the unknown environmental dynamics under mutual interactions. On the other hand, during the peer-to-peer interactions, the novice can learn how to coordinate motion with his/her partner (even though one is a new partner), and thus, is able to learn the motor skills through new environmental dynamics.

Brain Processes Involved in Motor Planning Are a Dominant Factor for Inducing Event-Related Desynchronization.

Event-related desynchronization (ERD) is a relative attenuation in the spectral power of an electroencephalogram (EEG) observed over the sensorimotor area during motor execution and motor imagery. It is a well-known EEG feature and is commonly employed in brain-computer interfaces. However, its underlying neural mechanisms are not fully understood, as ERD is a single variable correlated with external events involving numerous pathways, such as motor intention, planning, and execution. In this study, we aimed to identify a dominant factor for inducing ERD. Participants were instructed to grasp their right hand with three different (10, 25, or 40%MVF: maximum voluntary force) levels under two distinct experimental conditions: a closed-loop condition involving real-time visual force feedback (VF) or an open-loop condition in a feedforward (FF) manner. In each condition, participants were instructed to repeat the grasping task a certain number of times with a timeline of Rest (10.0 s), Preparation (1.0 s), and Motor Execution (4.0 s) periods, respectively. EEG signals were recorded simultaneously with the motor task to evaluate the time-course of the event-related spectrum perturbation for each condition and dissect the modulation of EEG power. We performed statistical analysis of mu and beta-ERD under the instructed grasping force levels and the feedback conditions. In the FF condition (i.e., no force feedback), mu and beta-ERD were significantly attenuated in the contralateral motor cortex during the middle of the motor execution period, while ERD in the VF condition was maintained even during keep grasping. Only mu-ERD at the somatosensory cortex tended to be slightly stronger in high load conditions. The results suggest that the extent of ERD reflects neural activity involved in the motor planning process for changing virtual equilibrium point rather than the motor control process for recruiting motor neurons to regulate grasping force.

Enhanced Visual Feedback Using Immersive VR Affects Decision Making Regarding Hand Use With a Simulated Impaired Limb.

The long-term effects of impairment have a negative impact on the quality of life of stroke patients in terms of not using the affected limb even after some recovery (i.e., learned non-use). Immersive virtual reality (IVR) has been introduced as a new approach for the treatment of stroke rehabilitation. We propose an IVR-based therapeutic approach to incorporate positive reinforcement components in motor coordination as opposed to constraint-induced movement therapy (CIMT). This study aimed to investigate the effect of IVR-reinforced physical therapy that incorporates positive reinforcement components in motor coordination. To simulate affected upper limb function loss in patients, a wrist weight was attached to the dominant hand of participant. Participants were asked to choose their right or left hand to reach toward a randomly allocated target. The movement of the virtual image of the upper limb was reinforced by visual feedback to participants, that is, the participants perceived their motor coordination as if their upper limb was moving to a greater degree than what was occurring in everyday life. We found that the use of the simulated affected limb was increased after the visual feedback enhancement intervention, and importantly, the effect was maintained even after gradual withdrawal of the visual amplification. The results suggest that positive reinforcement within the IVR could induce an effect on decision making in hand usage.

Statistical Learning Model of the Sense of Agency.

A sense of agency (SoA) is the experience of subjective awareness regarding the control of one's actions. Humans have a natural tendency to generate prediction models of the environment and adapt their models according to changes in the environment. The SoA is associated with the degree of the adaptation of the prediction models, e.g., insufficient adaptation causes low predictability and lowers the SoA over the environment. Thus, identifying the mechanisms behind the adaptation process of a prediction model related to the SoA is essential for understanding the generative process of the SoA. In the first half of the current study, we constructed a mathematical model in which the SoA represents a likelihood value for a given observation (sensory feedback) in a prediction model of the environment and in which the prediction model is updated according to the likelihood value. From our mathematical model, we theoretically derived a testable hypothesis that the prediction model is updated according to a Bayesian rule or a stochastic gradient. In the second half of our study, we focused on the experimental examination of this hypothesis. In our experiment, human subjects were repeatedly asked to observe a moving square on a computer screen and press a button after a beep sound. The button press resulted in an abrupt jump of the moving square on the screen. Experiencing the various stochastic time intervals between the action execution (button-press) and the consequent event (square jumping) caused gradual changes in the subjects' degree of their SoA. By comparing the above theoretical hypothesis with the experimental results, we concluded that the update (adaptation) rule of the prediction model based on the SoA is better described by a Bayesian update than by a stochastic gradient descent.

Collective almost synchronization-based model to extract and predict features of EEG signals.

Understanding the brain is important in the fields of science, medicine, and engineering. A promising approach to better understand the brain is through computing models. These models were adjusted to reproduce data collected from the brain. One of the most commonly used types of data in neuroscience comes from electroencephalography (EEG), which records the tiny voltages generated when neurons in the brain are activated. In this study, we propose a model based on complex networks of weakly connected dynamical systems (Hindmarsh-Rose neurons or Kuramoto oscillators), set to operate in a dynamic regime recognized as Collective Almost Synchronization (CAS). Our model not only successfully reproduces EEG data from both healthy and epileptic EEG signals, but it also predicts EEG features, the Hurst exponent, and the power spectrum. The proposed model is able to forecast EEG signals 5.76 s in the future. The average forecasting error was 9.22%. The random Kuramoto model produced the outstanding result for forecasting seizure EEG with an error of 11.21%.

EEG dynamical network analysis method reveals the neural signature of visual-motor coordination.

Human visual-motor coordination is an essential function of movement control, which requires interactions of multiple brain regions. Understanding the cortical-motor coordination is important for improving physical therapy for motor disabilities. However, its underlying transient neural dynamics is still largely unknown. In this study, we applied an eigenvector-based dynamical network analysis method to investigate the functional connectivity calculated from electroencephalography (EEG) signals under visual-motor coordination task and to identify its meta-stable states dynamics. We first tested this signal processing on a simulated network to evaluate it in comparison with other dynamical methods, demonstrating that the eigenvector-based dynamical network analysis was able to correctly extract the dynamical features of the evolving networks. Subsequently, the eigenvector-based analysis was applied to EEG data collected under a visual-motor coordination experiment. In the EEG study with participants, the results of both topological analysis and the eigenvector-based dynamical analysis were able to distinguish different experimental conditions of visual tracking task. With the dynamical analysis, we showed that different visual-motor coordination states can be distinguished by investigating the meta-stable states dynamics of the functional connectivity.

Assessment and Rating of Motor Cerebellar Ataxias With the Kinect v2 Depth Sensor: Extending Our Appraisal.

Current assessment of patients with cerebellar disorders is based on conventional neurological examination that is dependent on subjective judgements. Quantitative measurement of cerebellar ataxias (CAs) is essential for assessment of evidence-based treatments and the monitoring of the progress or recovery of diseases. It may provide us a useful tool to navigate future treatments for ataxia. We developed a Kinect v2. sensor system with a novel algorithm to measure and evaluate movements for two tests of Scale for the Assessment and Rating of Ataxia (SARA): the nose-finger test and gait. For the nose-finger test, we evaluated and compared accuracy, regularities and smoothness in the movements of the index finger and the proximal limbs between cerebellar patients and control subjects. For the task of walking, we evaluated and compared stability between the two groups. The precision of the system for evaluation of movements was smaller than 2 mm. For the nose-finger test, the mildly affected patients tended to show more instability than the control subjects. For a severely affected patient, our system quantified the instability of movements of the index finger using kinematic parameters, such as fluctuations and average speed. The average speed appears to be the most sensitive parameter that contrasts between patients with CAs and control subjects. Furthermore, our system also detected the adventitious movements of more proximal body parts, such as the elbow, shoulder and head. Assessment of walking was possible only in patients with mild CAs. They demonstrated large sways and compensatory wide stances. These parameters appeared to show higher accuracy than SARA. This examiner-independent device measures movements of the points of interest of SARA more accurately than eye and further provides additional information about the ataxic movements (e.g., the adventitious movements of the elbow, shoulder and head in the nose-finger test and the wide-based walking with large oscillation in the gait task), which is out of the scope of SARA. Our new system enables more accurate scoring of SARA and further provides additional information that is not currently evaluated with SARA. Therefore, it provides an easier, more accurate and more systematic description of CAs.

Temporal Structure in Haptic Signaling Under a Cooperative Task.

Haptic communication between humans plays an important role in society. Although this form of communication is ubiquitous at all levels of society and of human development, little is known about how synchronized coordination of motion between two persons leads to higher-order cognitive functions used in communication. In this study, we developed a novel experimental paradigm of a coin-collecting task in which participants used their hands to control a rod to jointly collect the coins on the screen. We characterized the haptic interactions between paired participants while they were taking part in a cooperative task. The individual participants first completed this task on their own and then with a randomly assigned partner for the cooperative task. Single participant experiments were used as a baseline to compare results of the paired participants. Forces applied to the rod were translated to four possible haptic states which encode the combination of the haptic interactions. As a next step, pairs of consecutive haptic states were then combined into 16 possible haptic signals which were classified in terms of their temporal patterns using a Tsallis q-exponential function. For paired participants, 80% of the haptic signals could be fit by the Tsallis q-exponential. On the other hand, only 30% of the signals found in the single-participant trials could be fit by the Tsallis q-exponential. This shows a clear difference in the temporal structures of haptic signals when participants are interacting with each other and when they are not. We also found a large difference in the number of haptic signals used by paired participants and singles. Single participants only used 1/4 of the possible haptic signals. Paired participants, on the other hand, used more than half of the possible signals. These results suggest that temporal structures present in haptic communication could be linked to the emergence of language at an evolutionary level.

Coordination of human movements resulting in motor strategies exploited by skilled players during a throwing task.

In this study, we investigated the underlying mechanisms of a motor system that affects skills and strategies of expert dart throwers. Eight experts participated in our experiment and each subject performed 42 throws. Kinematics of the shoulder, elbow, wrist, and dart were recorded by six high-speed cameras (200 Hz). The vertical error curve over time was calculated based on both hand and dart trajectories to clarify their relationship and interaction, which could attribute to their skills. Moreover, the kinematics of the dart (speed and direction) and angular kinematics of the elbow and wrist at the time of release were investigated to elucidate which parameters constitute the throwing strategies of experts. Experimental results showed that expert's throwing can be classified into two strategies, i.e., reducing timing sensitivity and reducing timing error. These strategies were derived from the spatial and temporal controls of the hand trajectory. Moreover, we confirmed that the speed of the dart and angular acceleration of the wrist joint at the time of release were highly correlated with the time-window for successful release. These results imply that the two strategies are characterized not only by a spatiotemporal relationship between the hand and dart trajectories, but also by relationships with release kinematic parameters of the proximal joint and the dart. Understanding characteristics which lead to strategies of skilled throwers would provide effective training methodology for beginners.

[Recurrent ataxia and respiratory failure with probable paraneoplastic syndrome responsive to plasma exchange therapy].

An 80-year-old male with prostatic adenocarcinoma who was treated with orchiectomy presented dysarthria and difficulty in walking. His symptoms subacutely progressed. Seven days later, he was non-ambulatory and was admitted to our hospital. He had poor vision and cerebellar ataxia of the lower extremities; however, his muscle strength, tendon reflexes, and sensory functions were preserved. Paraneoplastic retinopathy was diagnosed based on electroretinographic and visual field defect. Further, brain and spinal MRI, cerebral spinal fluid, and nerve conduction assessments were normal. These symptoms were followed by consciousness disturbance and respiratory failure; consequently, he required non-invasive positive pressure ventilation (NPPV) and tube feeding. Steroid pulse therapy and plasma exchange (PE) were performed. In response to the therapy, all these symptoms were relieved, and NPPV and tube feeding were withdrawn. However, the same symptoms occurred additional three times throughout the course of approximately 1 year. Each time, PE was the most effective treatment. Although paraneoplastic neurological syndrome associated with prostatic cancer is rare, immunotherapy could be a therapeutic choice to relive symptoms.

On-Body Sensor Positions Hierarchical Classification.

Many motion sensor-based applications have been developed in recent years because they provide useful information about daily activities and current health status of users. However, most of these applications require knowledge of sensor positions. Therefore, this research focused on the problem of detecting sensor positions. We collected standing-still and walking sensor data at various body positions from ten subjects. The offset values were removed by subtracting the sensor data of standing-still phase from the walking data for each axis of each sensor unit. Our hierarchical classification technique is based on optimizing local classifiers. Many common features are computed, and informative features are selected for specific classifications. In this approach, local classifiers such as arm-side and hand-side discriminations yielded F1-scores of 0.99 and 1.00, correspondingly. Overall, the proposed method achieved an F1-score of 0.81 and 0.84 using accelerometers and gyroscopes, respectively. Furthermore, we also discuss contributive features and parameter tuning in this analysis.

Muscle synergy analysis in dart throwing.

This study aims to analyze muscle synergies in darts throwing movement to clarify how the central nervous system (CNS) controls muscle activation to produce complicated motion. Activities of 10 different muscles on upper limb were recorded through surface electromyography (EMG) electrodes from four participants with less experience of dart throwing. Subsequently, non-negative matrix factorization (NNMF) was applied to the collected EMG signals for muscle synergy analysis. Results showed that three muscle synergies could explain sufficiently the observed EMG data, with determination coefficient r2 = 0.91 ± 0.02 across four subjects. However, it was found that there was significant difference in terms of patterns of both muscle synergies and activation coefficients between participants, which might reflect different neuromuscular control strategies in dart throwing.