Brain mechanisms in motor control during reaching movements: Transition of functional connectivity according to movement states

Mu-ERD reflects action understanding, but the effect is small

Since the mid-2000's, many researchers have provided evidence that mu-ERD measured at the motor cortex may reflect the collective activation of upstream brain regions associated with the human mirror system during action observation paradigms; however, several recent papers have called these findings into question. Our study represents an effort to address these criticisms. In our study, participants watched videos in which the type of grip an actor used to grasp a coffee mug either conveyed the goal with 100 % certainty (unambiguous-goal trials), or offered no predictive information (ambiguous-goal trials). If mu-ERD indexes action understanding, then we predicted that mu-ERD should increase while participants watched the actor grasp the mug for unambiguous-goal trials, but not for ambiguous-goal trials. During the intervals where participants watched the actor execute the goal, mu-ERD for unambiguous-goal trials should remain steady, whereas mu-ERD for ambiguous-goal trials should now increase. Conversely, if mu-ERD does not index action understanding, and instead reflects general motor processes associated with action (such as the activation of population vectors in M1 or planning processes), then mu-ERD should show no difference across conditions. Across most comparisons, we found that mu-ERD mostly reflected general motor processes; however, there was a small effect when participants overserved unambiguous-goal trials while watching the actor execute the goal suggesting that mu-ERD does reflect mirroring, but the effect is small.

Differential Cortical and Subcortical Activations during Different Stages of Muscle Control: A Functional Magnetic Resonance Imaging Study

Movement and muscle control are crucial for the survival of all free-living organisms. This study aimed to explore differential patterns of cortical and subcortical activation across different stages of muscle control using functional magnetic resonance imaging (fMRI). An event-related design was employed. In each trial, participants (n = 10) were instructed to gently press a button with their right index finger, hold it naturally for several seconds, and then relax the finger. Neural activation in these temporally separated stages was analyzed using a General Linear Model. Our findings revealed that a widely distributed cortical network, including the supplementary motor area and insula, was implicated not only in the pressing stage, but also in the relaxation stage, while only parts of the network were involved in the steady holding stage. Moreover, supporting the direct/indirect pathway model of the subcortical basal ganglia, their substructures played distinct roles in different stages of muscle control. The caudate nucleus exhibited greater involvement in muscle contraction, whereas the putamen demonstrated a stronger association with muscle relaxation; both structures were implicated in the pressing stage. Furthermore, the subthalamic nucleus was exclusively engaged during the muscle relaxation stage. We conclude that even the control of simple muscle movements involves intricate automatic higher sensory-motor integration at a neural level, particularly when coordinating relative muscle movements, including both muscle contraction and muscle relaxation; the cortical and subcortical regions assume distinct yet coordinated roles across different stages of muscle control.

A neuromechanical model characterizing the motor planning and posture control in the voluntary lean in Parkinson's disease

Parkinson's disease targets patients' cognitive and motor abilities, including postural control. Many studies have been carried out to introduce mathematical models for a better understanding of postural control in such patients and the relation between the model parameters and the clinical assessments. So far, these studies have addressed this connection merely in static tests, such as quiet stance. The aim of this study is to develop a model for voluntary lean, and as such, identify the model parameters for both PD patients and healthy subjects from experimental data. The proposed model comprises planning and control sections. The model parameters for the planning section were extracted from the time response characteristics. Parameters for the control section were identified based on the spatial characteristics of the center-of-pressure (COP) response using an optimization process. 24 PD patients along with 24 matched healthy subjects participated in the study. The results showed a significant difference between the two groups in terms of temporal parameters for the planning section. This difference emphasizes bradykinesia as an essential symptom of PD. Also, differences were found for the postural control section. In all directions, the proportional gain of the feedback controller was significantly larger in PD patients; however, the gain of the feedforward controller was significantly smaller in PD patients. Furthermore, the control gains were strongly correlated with the clinical scales (Functional Reach Test and Unified Parkinson's Disease Rating Scale) in certain directions. In conclusion, the new model helps to better understand and quantify some PD symptoms in voluntary lean tasks.

Pupil-linked arousal correlates with neural activity prior to sensorimotor decisions

Objective.Sensorimotor decisions require the brain to process external information and combine it with relevant knowledge prior to actions. In this study, we explore the neural predictors of motor actions in a novel, realistic driving task designed to study decisions while driving.Approach.Through a spatiospectral assessment of functional connectivity during the premotor period, we identified the organization of visual cortex regions of interest into a distinct scene processing network. Additionally, we identified a motor action selection network characterized by coherence between the anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (DLPFC).Main results.We show that steering behavior can be predicted from oscillatory power in the visual cortex, DLPFC, and ACC. Power during the premotor periods (specific to the theta and beta bands) correlates with pupil-linked arousal and saccade duration.Significance.We interpret our findings in the context of network-level correlations with saccade-related behavior and show that the DLPFC is a key node in arousal circuitry and in sensorimotor decisions.

Neural oscillatory activity and connectivity in children who stutter during a non-speech motor task

BACKGROUND

Neural motor control rests on the dynamic interaction of cortical and subcortical regions, which is reflected in the modulation of oscillatory activity and connectivity in multiple frequency bands. Motor control is thought to be compromised in developmental stuttering, particularly involving circuits in the left hemisphere that support speech, movement initiation, and timing control. However, to date, evidence comes from adult studies, with a limited understanding of motor processes in childhood, closer to the onset of stuttering.

METHODS

We investigated the neural control of movement initiation in children who stutter and children who do not stutter by evaluating transient changes in EEG oscillatory activity (power, phase locking to button press) and connectivity (phase synchronization) during a simple button press motor task. We compared temporal changes in these oscillatory dynamics between the left and right hemispheres and between children who stutter and children who do not stutter, using mixed-model analysis of variance.

RESULTS

We found reduced modulation of left hemisphere oscillatory power, phase locking to button press and phase connectivity in children who stutter compared to children who do not stutter, consistent with previous findings of dysfunction within the left sensorimotor circuits. Interhemispheric connectivity was weaker at lower frequencies (delta, theta) and stronger in the beta band in children who stutter than in children who do not stutter.

CONCLUSIONS

Taken together, these findings indicate weaker engagement of the contralateral left motor network in children who stutter even during low-demand non-speech tasks, and suggest that the right hemisphere might be recruited to support sensorimotor processing in childhood stuttering. Differences in oscillatory dynamics occurred despite comparable task performance between groups, indicating that an altered balance of cortical activity might be a core aspect of stuttering, observable during normal motor behavior.

Effect of transcranial photobiomodulation on electrophysiological activity of brain in healthy individuals: A scoping review

BACKGROUND OBJECTIVE

Transcranial photobiomodulation (tPBM) is a safe and non-invasive treatment that has recently emerged as an effective technique to apply near-infrared or red light to activate neural tissues. The objective is to review the literature on the effect of tPBM on electrophysiological activity in healthy individuals.

METHODS

Literature was searched through PubMed, Scopus, Web of Science, Cumulated Index to Nursing and Allied Health Literature (CINAHL), Embase, and Ovid for transcranial photobiomodulation therapy in healthy individuals age group 18-80 years of either gender having electroencephalography as an outcome. Critical appraisal of included Randomized Controlled Trials and non-randomized experimental studies was done using Joanna Briggs Institute (JBI) critical appraisal tool.

RESULTS

A database search yielded a total of 4156 results. After eliminating 2626 duplicates, 1530 records were left. 32 articles were considered for full-text screening after 1498 records were excluded through title and abstract screening. 10 articles were included in this review. tPBM has been found to increase the higher electrophysiological oscillations and there is inconclusive evidence targeting the lower oscillatory electrophysiological frequencies.

CONCLUSION

Transcranial photobiomodulation can have promising effects on the electrophysiological activity of the brain in healthy individuals.

A magnetoencephalography dataset during three-dimensional reaching movements for brain-computer interfaces

Studying the motor-control mechanisms of the brain is critical in academia and also has practical implications because techniques such as brain-computer interfaces (BCIs) can be developed based on brain mechanisms. Magnetoencephalography (MEG) signals have the highest spatial resolution (~3 mm) and temporal resolution (~1 ms) among the non-invasive methods. Therefore, the MEG is an excellent modality for investigating brain mechanisms. However, publicly available MEG data remains scarce due to expensive MEG equipment, requiring a magnetically shielded room, and high maintenance costs for the helium gas supply. In this study, we share the 306-channel MEG and 3-axis accelerometer signals acquired during three-dimensional reaching movements. Additionally, we provide analysis results and MATLAB codes for time-frequency analysis, F-value time-frequency analysis, and topography analysis. These shared MEG datasets offer valuable resources for investigating brain activities or evaluating the accuracy of prediction algorithms. To the best of our knowledge, this data is the only publicly available MEG data measured during reaching movements.

Modulations of Cortical Power and Connectivity in Alpha and Beta Bands during the Preparation of Reaching Movements

Planning goal-directed movements towards different targets is at the basis of common daily activities (e.g., reaching), involving visual, visuomotor, and sensorimotor brain areas. Alpha (8-13 Hz) and beta (13-30 Hz) oscillations are modulated during movement preparation and are implicated in correct motor functioning. However, how brain regions activate and interact during reaching tasks and how brain rhythms are functionally involved in these interactions is still limitedly explored. Here, alpha and beta brain activity and connectivity during reaching preparation are investigated at EEG-source level, considering a network of task-related cortical areas. Sixty-channel EEG was recorded from 20 healthy participants during a delayed center-out reaching task and projected to the cortex to extract the activity of 8 cortical regions per hemisphere (2 occipital, 2 parietal, 3 peri-central, 1 frontal). Then, we analyzed event-related spectral perturbations and directed connectivity, computed via spectral Granger causality and summarized using graph theory centrality indices (in degree, out degree). Results suggest that alpha and beta oscillations are functionally involved in the preparation of reaching in different ways, with the former mediating the inhibition of the ipsilateral sensorimotor areas and disinhibition of visual areas, and the latter coordinating disinhibition of the contralateral sensorimotor and visuomotor areas.

Decoding trajectories of imagined hand movement using electrocorticograms for brain-machine interface

OBJECTIVE

Reaching hand movement is an important motor skill actively examined in brain-computer interface (BCI). Among various components of movement analyzed is the hand's trajectory, which describes the hand's continuous positions in three-dimensional space. While a large body of studies have investigated the decoding of real movements and the reconstruction of real hand movement trajectories from neural signals, fewer studies have attempted to decode the trajectory of imagined hand movement. To develop BCI systems for patients with hand motor dysfunctions, the systems essentially require to achieve movement-free control of external devices, which is only possible through successful decoding of purely imagined hand movement.

APPROACH

To achieve this goal, this study used a machine learning technique (i.e., the variational Bayesian least square) to analyze the electrocorticogram (ECoG) of eighteen epilepsy patients obtained from when they performed movement execution (ME) and kinesthetic movement imagination (KMI) of the reach-and-grasp hand action.

MAIN RESULTS

The variational Bayesian decoding model was able to successfully predict the imagined trajectories of hand movement significantly above chance level. The Pearson's correlation coefficient between imagined and predicted trajectories was 0.3393 and 0.4936 for the KMI (KMI trials only) and MEKMI paradigm (alternating trials of ME and KMI) respectively.

SIGNIFICANCE

This study demonstrated a high accuracy of prediction for trajectories of imagined hand movement, and more importantly, higher decoding accuracy of imagined trajectories in the MEKMI paradigm than in the KMI paradigm solely.

Influence of the number of trials on evoked motor cortical activity in EEG recordings

OBJECTIVE

Improvements in electroencephalography enable the study of the localization of active brain regions during motor tasks. Movement-related cortical potentials (MRCPs), and event-related desynchronization (ERD) and synchronization (ERS) are the main motor-related cortical phenomena/neural correlates observed when a movement is elicited. When assessing neurological diseases, averaging techniques are commonly applied to characterize motor related processes better. In this case, a large number of trials is required to obtain a motor potential that is representative enough of the subject's condition. This study aimed to assess the effect of a limited number of trials on motor-related activity corresponding to different upper limb movements (elbow flexion/extension, pronation/supination and hand open/close).

APPROACH

An open dataset consisting on 15 healthy subjects was used for the analysis. A Monte Carlo simulation approach was applied to analyse, in a robust way, different typical time- and frequency-domain features, topography, and low-resolution tomography (LORETA).

MAIN RESULTS

Grand average potentials, and topographic and tomographic maps showed few differences when using fewer trials, but shifts in the localization of motor-related activity were found for several individuals. MRCP and beta ERD features were more robust to a limited number of trials, yielding differences lower than 20% for cases with 50 trials or more. Strong correlations between features were obtained for subsets above 50 trials. However, the inter-subject variability increased as the number of trials decreased. The elbow flexion/extension movement showed a more robust performance for a limited number of trials, both in population and in individual-based analysis.

SIGNIFICANCE

Our findings suggested that 50 trials can be an appropriate number to obtain stable motor-related features in terms of differences in the averaged motor features, correlation, and changes in topography and tomography.

Optimal reaching trajectories based on feedforward control

In human upper-arm reaching movements, the variance of the hand position increases until the middle of the movement and then decreases toward the endpoint. Such decrease in positional variance has been suggested as an evidence to support the hypothesis that our nervous system uses feedback control, rather than feedforward control, for arm reaching tasks. In this study, we computed the optimal trajectories based on feedforward control under several criteria for a one-link two-muscle arm model with considering the stochastic property of muscle activities in order to reexamine the hypothesis. The results showed that the feedforward control also represents the decrease in positional variance in the latter half of the movement when the control signal is planned under the minimum energy cost and minimum variance models. Furthermore, the optimal trajectory that minimizes energy cost represents not only the decrease in positional variance but also many other characteristics of the human reaching movements, e.g., the three-phasic activity of antagonistic muscle, bell-shaped speed curve, N-shaped equilibrium trajectory, and bimodal profile of joint stiffness. These results suggest that minimum energy cost model well expresses the characteristics of hand reaching movements, and our central nervous system would make use of not only a feedback control but also feedforward control.

Transcranial Direct Current Stimulation (tDCS) modulates motor execution in a limb reaching task

The majority of human activities show a trade-off between movement speed and accuracy. Here we tested 16 participants in a quick pointing action after 20 minutes (2mA) of transcranial Direct Current Stimulation (tDCS) delivered at the Supplementary Motor Area (SMA) in a single-blind crossover design study for testing the feedforward components in the control of action. tDCS stimuli were delivered in three randomized sessions of stimulations as anodal, cathodal and sham as a control. The task performed Pre and Post tDCS stimulation, was to point as fast and as precise as possible with the big toe to targets having different sizes (2 and 8 cm; Width) and positioned at different Distances (20 and 60 cm; Distance). An optoelectronic motion capture system was used to collect the kinematics of movement. Result indicates that individuals after receiving anodal stimulation decreased their movement time and increased their movement speed while the opposite happened after receiving a cathodal stimulation. The scarcity of studies in this area invites us to plan a research that aims at the trade-off especially in the clinical settings.

Planning and Executing Aiming Movements in Middle Childhood

Harmonious voluntary movements require efficiency in their planning and execution. Throughout middle childhood structural changes in the central nervous and musculoskeletal systems influence these processes and resultant motor behavior. In this study, we evaluated the characteristics of the motor planning and executing of aiming movements directed at targets located in different positions in space in children aged 7, 9, and 11 years. We divided 43 right-handed children, into three age groups and instructed them to perform aiming movements directed at targets using a stylus on a digital tablet. The children performed the movement with their dominant upper limbs from a starting point towards targets positioned ipsilaterally or contralaterally to this dominant limb. We analyzed temporal and spatial variables of motor performance. Younger (7-year-old) children made more errors in the initial movement direction and more frequently corrected their movements during task execution when compared to 9- and 11-year-old children who did not differ from each other. All age groups were similar in movement accuracy and precision. Movements toward contralateral targets were slower and more accurate than movements toward ipsilateral targets for all groups. These results show that performing aiming movements develop with the onset of middle childhood.

Neural Activation in the Ventromedial Prefrontal Cortex Precedes Conscious Experience of Being in or out of a Transient Hallucinatory State

BACKGROUND AND HYPOTHESES

Auditory verbal hallucinations (AVHs) is not only a common symptom in schizophrenia but also observed in individuals in the general population. Despite extensive research, AVHs are poorly understood, especially their underlying neuronal architecture. Neuroimaging methods have been used to identify brain areas and networks that are activated during hallucinations. A characteristic feature of AVHs is, however, that they fluctuate over time, with varying frequencies of starts and stops. An unanswered question is, therefore, what neuronal events co-occur with the initiation and inhibition of an AVH episode.

STUDY DESIGN

We investigated brain activation with fMRI in 66 individuals who experienced multiple AVH-episodes while in the scanner. We extracted time-series fMRI-data and monitored changes second-by-second from 10 s before to 15 s after participants indicated the start and stop of an episode, respectively, by pressing a hand-held response-button.

STUDY RESULTS

We found a region in the ventromedial prefrontal cortex (VMPFC) which showed a significant increase in activation initiated a few seconds before participants indicated the start of an episode, and a corresponding decrease in activation initiated a few seconds before the end of an episode.

CONCLUSIONS

The consistent increase and decrease in activation in this area in advance of the consciously experienced presence or absence of the "voice" imply that this region may act as a switch in turning episodes on and off. The activation is unlikely to be confounded by motor responses. The findings could have clinical implications for brain stimulation treatments, like transcranial magnetic stimulation.

Cross-frequency phase-amplitude coupling in repetitive movements in patients with Parkinson's disease

Bradykinesia is a cardinal motor symptom in Parkinson’s disease (PD), the pathophysiology of which is not fully understood. We analyzed the role of cross-frequency coupling of oscillatory cortical activity in motor impairment in patients with PD and healthy controls. High-density EEG signals were recorded during various motor activities and at rest. Patients performed a repetitive finger-pressing task normally, but were slower than controls during tapping. Phase-amplitude coupling (PAC) between β (13–30 Hz) and broadband γ (50–150 Hz) was computed from individual EEG source signals in the premotor, primary motor, and primary somatosensory cortices, and the primary somatosensory complex. In all four regions, averaging the entire movement period resulted in higher PAC in patients than in controls for the resting condition and the pressing task (similar performance between groups). However, this was not the case for the tapping tasks where patients performed slower. This suggests the strength of state-related β-γ PAC does not determine Parkinsonian bradykinesia. Examination of the dynamics of oscillatory EEG signals during motor transitions revealed a distinctive motif of PAC rise and decay around press onset. This pattern was also present at press offset and slow tapping onset, linking such idiosyncratic PAC changes to transitions between different movement states. The transition-related PAC modulation in patients was similar to controls in the pressing task but flattened during slow tapping, which related to normal and abnormal performance, respectively. These findings suggest that the dysfunctional evolution of neuronal population dynamics during movement execution is an important component of the pathophysiology of Parkinsonian bradykinesia.

NEW & NOTEWORTHY Our findings using noninvasive EEG recordings provide evidence that PAC dynamics might play a role in the physiological cortical control of movement execution and may encode transitions between movement states. Results in patients with Parkinson’s disease suggest that bradykinesia is related to a deficit of the dynamic regulation of PAC during movement execution rather than its absolute strength. Our findings may contribute to the development of a new concept of the pathophysiology of bradykinesia.

F-Value Time-Frequency Analysis: Between-Within Variance Analysis

Studies on brain mechanisms enable us to treat various brain diseases and develop diverse technologies for daily life. Therefore, an analysis method of neural signals is critical, as it provides the basis for many brain studies. In many cases, researchers want to understand how neural signals change according to different conditions. However, it is challenging to find distinguishing characteristics, and doing so requires complex statistical analysis. In this study, we propose a novel analysis method, FTF (F-value time-frequency) analysis, that applies the F-value of ANOVA to time-frequency analysis. The proposed method shows the statistical differences among conditions in time and frequency. To evaluate the proposed method, electroencephalography (EEG) signals were analyzed using the proposed FTF method. The EEG signals were measured during imagined movement of the left hand, right hand, foot, and tongue. The analysis revealed the important characteristics which were different among different conditions and similar within the same condition. The FTF analysis method will be useful in various fields, as it allows researchers to analyze how frequency characteristics vary according to different conditions.

Versatile GCH Control Software for Correction of Loads Applied to Forearm Crutches During Gait Recovery Through Technological Feedback: Development and Implementation Study

Background

Measuring weight bearing is an essential aspect of clinical care for lower limb injuries such as sprains or meniscopathy surgeries. This care often involves the use of forearm crutches for partial loads progressing to full loads. Therefore, feasible methods of load monitoring for daily clinical use are needed.

Objective

The main objective of this study was to design an innovative multifunctional desktop load-measuring software that complements GCH System 2.0–instrumented forearm crutches and monitors the applied loads, displaying real-time graphical and numerical information, and enabling the correction of inaccuracies through feedback technology during assisted gait. The secondary objective was to perform a preliminary implementation trial.

Methods

The software was designed for indoor use (clinics/laboratories). This software translates the crutch sensor signal in millivolts into force units, records and analyzes data (10-80 Hz), and provides real-time effective curves of the loads exerted on crutches. It covers numerous types of extrinsic feedback, including visual, acoustic (verbal/beeps), concurrent, terminal, and descriptive feedback, and includes a clinical and research use database. An observational descriptive pilot study was performed with 10 healthy subjects experienced in bilateral assisted gait. The Wilcoxon matched-pairs signed-rank test was used to evaluate the load accuracy evolution of each subject (ie, changes in the loads exerted on crutches for each support) among various walks, which was interpreted at the 95% confidence level.

Results

GCH Control Software was developed as a multifunctional desktop tool complementing GCH System 2.0–instrumented forearm crutches. The pilot implementation of the feedback mechanism observed 96/100 load errors at baseline (walk 0, no feedback) with 7/10 subjects exhibiting crutch overloading. Errors ranged from 61.09% to 203.98%, demonstrating heterogeneity. The double-bar feedback found 54/100 errors in walk 1, 28/100 in walk 2, and 14/100 in walk 3. The first walk with double-bar feedback (walk 1) began with errors similar to the baseline walk, generally followed by attempts at correction. The Wilcoxon matched-pairs signed-rank test used to evaluate each subject’s progress showed that all participants steadily improved the accuracy of the loads applied to the crutches. In particular, Subject 9 required extra feedback with two single-bar walks to focus on the total load. The participants also corrected the load balance between crutches and fluency errors. Three subjects made one error of load balance and one subject made six fluctuation errors during the three double-bar walks. The latter subject performed additional feedback with two balance-bar walks to focus on the load balance.

Conclusions

GCH Control Software proved to be useful for monitoring the loads exerted on forearm crutches, providing a variety of feedback for correcting load accuracy, load balance between crutches, and fluency. The findings of the complementary implementation were satisfactory, although clinical trials with larger samples are needed to assess the efficacy of the different feedback mechanisms and to select the best alternatives in each case.

Articulatory Gain Predicts Motor Cortex and Subthalamic Nucleus Activity During Speech

Speaking precisely is important for effective verbal communication, and articulatory gain is one component of speech motor control that contributes to achieving this goal. Given that the basal ganglia have been proposed to regulate the speed and size of limb movement, that is, movement gain, we explored the basal ganglia contribution to articulatory gain, through local field potentials (LFP) recorded simultaneously from the subthalamic nucleus (STN), precentral gyrus, and postcentral gyrus. During STN deep brain stimulation implantation for Parkinson's disease, participants read aloud consonant-vowel-consonant syllables. Articulatory gain was indirectly assessed using the F2 Ratio, an acoustic measurement of the second formant frequency of/i/vowels divided by/u/vowels. Mixed effects models demonstrated that the F2 Ratio correlated with alpha and theta activity in the precentral gyrus and STN. No correlations were observed for the postcentral gyrus. Functional connectivity analysis revealed that higher phase locking values for beta activity between the STN and precentral gyrus were correlated with lower F2 Ratios, suggesting that higher beta synchrony impairs articulatory precision. Effects were not related to disease severity. These data suggest that articulatory gain is encoded within the basal ganglia-cortical loop.

Characterization of kinesthetic motor imagery compared with visual motor imageries

Motor imagery (MI) is the only way for disabled subjects to robustly use a robot arm with a brain-machine interface. There are two main types of MI. Kinesthetic motor imagery (KMI) is proprioceptive (OR somato-) sensory imagination and Visual motor imagery (VMI) represents a visualization of the corresponding movement incorporating the visual network. Because these imagery tactics may use different networks, we hypothesized that the connectivity measures could characterize the two imageries better than the local activity. Electroencephalography data were recorded. Subjects performed different conditions, including motor execution (ME), KMI, VMI, and visual observation (VO). We tried to classify the KMI and VMI by conventional power analysis and by the connectivity measures. The mean accuracies of the classification of the KMI and VMI were 98.5% and 99.29% by connectivity measures (alpha and beta, respectively), which were higher than those by the normalized power (p < 0.01, Wilcoxon paired rank test). Additionally, the connectivity patterns were correlated between the ME-KMI and between the VO-VMI. The degree centrality (DC) was significantly higher in the left-S1 at the alpha-band in the KMI than in the VMI. The MI could be well classified because the KMI recruits a similar network to the ME. These findings could contribute to MI training methods.