EEG decoding with spatiotemporal convolutional neural network for visualization and closed-loop control of sensorimotor activities: A simultaneous EEG-fMRI study.

Closed-loop neurofeedback training utilizes neural signals such as scalp electroencephalograms (EEG) to manipulate specific neural activities and the associated behavioral performance. A spatiotemporal filter for high-density whole-head scalp EEG using a convolutional neural network can overcome the ambiguity of the signaling source because each EEG signal includes information on the remote regions. We simultaneously acquired EEG and functional magnetic resonance images in humans during the brain-computer interface (BCI) based neurofeedback training and compared the reconstructed and modeled hemodynamic responses of the sensorimotor network. Filters constructed with a convolutional neural network captured activities in the targeted network with spatial precision and specificity superior to those of the EEG signals preprocessed with standard pipelines used in BCI-based neurofeedback paradigms. The middle layers of the trained model were examined to characterize the neuronal oscillatory features that contributed to the reconstruction. Analysis of the layers for spatial convolution revealed the contribution of distributed cortical circuitries to reconstruction, including the frontoparietal and sensorimotor areas, and those of temporal convolution layers that successfully reconstructed the hemodynamic response function. Employing a spatiotemporal filter and leveraging the electrophysiological signatures of the sensorimotor excitability identified in our middle layer analysis would contribute to the development of a further effective neurofeedback intervention.

Investigating proficiency using a lift-type transfer support device for effective care: comparison of skilled and unskilled nursing homes.

PURPOSE: The purpose of the study was to investigate whether the sustained use of the "Hug," a "hugging" type robotic transfer support device, could increase the level of quality of care. METHODS: The effect of proficiency on using the device was examined in terms of time spent for transfer, ratio of transfers using the device, and range of targets. The results were compared between skilled care facilities that had used the device for >24 months and unskilled facilities. RESULTS: The time spent for transfer at the unskilled facility was 4.6 min (2nd week after introduction), was reduced to 3.0 min (5th week), and 1.5 min at the skilled facility. The usage ratio at the unskilled facility was 13% and 30% (2nd and 5th week, respectively), while it was 97% in the skilled facility. Further, we identified wider in the range of target care recipients in the skilled facility. CONCLUSION: It takes time to master the use of Hug; however, its use was associated with many positive aspects, especially from the perspective of care recipients, e.g., better care, use of their own legs, and reduced time for transfers. These findings suggest that the widespread use of Hug would improve the quality of care.IMPLICATIONS FOR REHABILITATIONLess physically burdened on the caregivers using Hug, they can afford to talk to the care recipients.Using recipient's own legs during transfers, it prevents leg muscle disuse.As reduced time for transfers, recipients will have more opportunities to get out of bed.

Epileptic discharges initiate from brain areas with elevated accumulation of α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors.

Presurgical identification of the epileptogenic zone is a critical determinant of seizure control following surgical resection in epilepsy. Excitatory glutamate α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor is a major component of neurotransmission. Although elevated α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor levels are observed in surgically resected brain areas of patients with epilepsy, it remains unclear whether increased α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor-mediated currents initiate epileptic discharges. We have recently developed the first PET tracer for α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor, [11C]K-2, to visualize and quantify the density of α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors in living human brains. Here, we detected elevated [11C]K-2 uptake in the epileptogenic temporal lobe of patients with mesial temporal lobe epilepsy. Brain areas with high [11C]K-2 uptake are closely colocalized with the location of equivalent current dipoles estimated by magnetoencephalography or with seizure onset zones detected by intracranial electroencephalogram. These results suggest that epileptic discharges initiate from brain areas with increased α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors, providing a biological basis for epileptic discharges and an additional non-invasive option to identify the epileptogenic zone in patients with mesial temporal lobe epilepsy.

Recognition capability of one's own skilled movement is dissociated from acquisition of motor skill memory.

When we have rehearsed a movement using an object, we can reproduce the movement without holding the object. However, the reproduced movement sometimes differs from the movement holding a real object, likely because movement recognition is inaccurate. In the present study, we tested whether the recognition capability was dissociated from the acquisition of motor skill memory. Twelve novices were asked to rotate two balls with their right hand as quickly as possible; they practiced the task for 29 days. To evaluate recognition capability, we calculated the difference in coordination pattern of all five digits between the ball-rotation movement and the reproduced movement without holding balls. The recognition capability did not change within the first day, but improved after one week of practice. On the other hand, performance of the ball rotation significantly improved within the first day. Since improvement of performance is likely associated with acquisition of motor skill memory, we suggest that recognition capability, which reflects the capability to cognitively access motor skill memory, was dissociated from the acquisition of motor skill memory. Therefore, recognition of one's own skilled movement would rely on a hierarchical structure of acquisition of motor skill memory and cognitive access to that memory.

Identification of care tasks for the use of wearable transfer support robots - an observational study at nursing facilities using robots on a daily basis.

BACKGROUND: To reduce the physical burden of caregivers, wearable transfer support robots are highly desirable. Although these robots are reportedly effective for specific tasks in experimental environments, there is little information about their effectiveness at nursing care facilities. The aim of this study was to identify care tasks and operations suitable for the use of these robots among caregivers in nursing facilities where these robots have been in use on a daily basis. METHODS: A 1-min observational time-motion analysis was conducted to examine care tasks and operations in two nursing facilities where wearable transfer support robots, namely Muscle Suit or HAL® Lumbar Type for Care Support, have been used routinely on a daily basis for more than 24 months. RESULTS: Analysis of the care tasks and their time ratio while wearing the equipment revealed that both robots were used conspicuously for direct care in over 70% of transits, especially during transfer assistance and toileting care. Furthermore, these robots were used intensively in the morning along with wake-up calls to care recipients, where pre-assigned wearers used them as part of their "routine work." CONCLUSIONS: We found that these wearable transfer support robots enabled effective performance of care tasks and operations in nursing facilities where these robots have been used on a daily basis for an extended period of time. These results may lead to the effective implementation and sustained operation of other types of care robots in the future. TRIAL REGISTRATION: UMIN Clinical Trials Registry no. UMIN000039204 . Trial registration date: January 21, 2020. Interventional study. Parallel, non-randomized, single blinded.

Use of common average reference and large-Laplacian spatial-filters enhances EEG signal-to-noise ratios in intrinsic sensorimotor activity.

BACKGROUND: Oscillations in the resting-state scalp electroencephalogram (EEG) represent various intrinsic brain activities. One of the characteristic EEG oscillations is the sensorimotor rhythm (SMR)-with its arch-shaped waveform in alpha- and betabands-that reflect sensorimotor activity. The representation of sensorimotor activity by the SMR depends on the signal-to-noise ratio achieved by EEG spatial filters. NEW METHOD: We employed simultaneous recording of EEG and functional magnetic resonance imaging, and 10-min resting-state brain activities were recorded in 19 healthy volunteers. To compare the EEG spatial-filtering methods commonly used for extracting sensorimotor cortical activities, we assessed nine different spatial-filters: a default reference of EEG amplifier system, a common average reference (CAR), small-, middle- and large-Laplacian filters, and four types of bipolar manners (C3-Cz, C3-F3, C3-P3, and C3-T7). We identified the brain region that correlated with the EEG-SMR power obtained after each spatial-filtering method was applied. Subsequently, we calculated the proportion of the significant voxels in the sensorimotor cortex as well as the sensorimotor occupancy in all significant regions to examine the sensitivity and specificity of each spatial-filter. RESULTS: The CAR and large-Laplacian spatial-filters were superior at improving the signal-to-noise ratios for extracting sensorimotor activity from the EEG-SMR signal. COMPARISON WITH EXISTING METHODS: Our results are consistent with the spatial-filter selection to extract task-dependent activation for better control of EEG-SMR-based interventions. Our approach has the potential to identify the optimal spatial-filter for EEG-SMR. CONCLUSIONS: Evaluating spatial-filters for extracting spontaneous sensorimotor activity from the EEG is a useful procedure for constructing more effective EEG-SMR-based interventions.

Neurofeedback of scalp bi-hemispheric EEG sensorimotor rhythm guides hemispheric activation of sensorimotor cortex in the targeted hemisphere.

Oscillatory electroencephalographic (EEG) activity is associated with the excitability of cortical regions. Visual feedback of EEG-oscillations may promote sensorimotor cortical activation, but its spatial specificity is not truly guaranteed due to signal interaction among interhemispheric brain regions. Guiding spatially specific activation is important for facilitating neural rehabilitation processes. Here, we tested whether users could explicitly guide sensorimotor cortical activity to the contralateral or ipsilateral hemisphere using a spatially bivariate EEG-based neurofeedback that monitors bi-hemispheric sensorimotor cortical activities for healthy participants. Two different motor imageries (shoulder and hand MIs) were selected to see how differences in intrinsic corticomuscular projection patterns might influence activity lateralization. We showed sensorimotor cortical activities during shoulder, but not hand MI, can be brought under ipsilateral control with guided EEG-based neurofeedback. These results are compatible with neuroanatomy; shoulder muscles are innervated bihemispherically, whereas hand muscles are mostly innervated contralaterally. We demonstrate the neuroanatomically-inspired approach enables us to investigate potent neural remodeling functions that underlie EEG-based neurofeedback via a BCI.

Scalp electroencephalograms over ipsilateral sensorimotor cortex reflect contraction patterns of unilateral finger muscles.

A variety of neural substrates are implicated in the initiation, coordination, and stabilization of voluntary movements underpinned by adaptive contraction and relaxation of agonist and antagonist muscles. To achieve such flexible and purposeful control of the human body, brain systems exhibit extensive modulation during the transition from resting state to motor execution and to maintain proper joint impedance. However, the neural structures contributing to such sensorimotor control under unconstrained and naturalistic conditions are not fully characterized. To elucidate which brain regions are implicated in generating and coordinating voluntary movements, we employed a physiologically inspired, two-stage method to decode relaxation and three patterns of contraction in unilateral finger muscles (i.e., extension, flexion, and co-contraction) from high-density scalp electroencephalograms (EEG). The decoder consisted of two parts employed in series. The first discriminated between relaxation and contraction. If the EEG data were discriminated as contraction, the second stage then discriminated among the three contraction patterns. Despite the difficulty in dissociating detailed contraction patterns of muscles within a limb from scalp EEG signals, the decoder performance was higher than chance-level by 2-fold in the four-class classification. Moreover, weighted features in the trained decoders revealed EEG features differentially contributing to decoding performance. During the first stage, consistent with previous reports, weighted features were localized around sensorimotor cortex (SM1) contralateral to the activated fingers, while those during the second stage were localized around ipsilateral SM1. The loci of these weighted features suggested that the coordination of unilateral finger muscles induced different signaling patterns in ipsilateral SM1 contributing to motor control. Weighted EEG features enabled a deeper understanding of human sensorimotor processing as well as of a more naturalistic control of brain-computer interfaces.

Depth Sensor-Based Assessment of Reachable Work Space for Visualizing and Quantifying Paretic Upper Extremity Motor Function in People With Stroke.

BACKGROUND: Quantitative evaluation of upper extremity (UE) motor function is important in people with hemiparetic stroke. A depth sensor-based assessment of reachable work space (RWS) was applied to visualize and quantify paretic UE motor function. OBJECTIVE: The objectives of this study were to examine the characteristics of RWS and to assess its validity, reliability, measurement error, and responsiveness in people with hemiparetic stroke. DESIGN: This was a descriptive, repeated-measures, observational study. METHODS: Fifty-eight people with stroke participated. RWS was assessed on both paretic and nonparetic UEs, and the RWS ratio was determined by dividing the RWS of the paretic UE by that of the nonparetic UE. The concurrent validity of the RWS was determined by examining the relationship with the Fugl-Meyer Assessment UE motor score. Test-retest reproducibility was examined in 40 participants. Responsiveness was determined by examining the RWS results before and after 3 weeks of intensive training of the paretic UE in 32 participants. RESULTS: The lower area of RWS bordering shoulder was significantly larger than the upper area, and the medial-lower area of RWS bordering shoulder was significantly larger than the lateral-lower area. The RWS ratio was highly correlated with the Fugl-Meyer Assessment UE motor score (r = 0.81). The RWS ratio showed good intrarater relative reliability (intraclass correlation coefficient = 0.94) and no fixed or proportional bias. The minimal detectable change of the RWS ratio was 16.6. The responsiveness of the RWS ratio was large (standardized response mean = 0.83). LIMITATIONS: Interexaminer reliability was not assessed. CONCLUSIONS: The RWS assessment showed sufficient validity, reliability, and responsiveness in people with hemiparetic stroke. A depth sensor-based RWS evaluation is useful for visualizing and quantifying paretic UE motor function in the clinical setting.

White matter microstructural organizations in patients with severe treatment-resistant schizophrenia: A diffusion tensor imaging study.

Previous diffusion tensor imaging (DTI) studies have reported white matter alterations in patients with schizophrenia. Notably, one third of this population does not respond to first-line antipsychotics and is thus referred to as treatment-resistant schizophrenia (TRS). Despite potentially distinct neural bases between TRS and non-TRS, few studies have compared white matter integrity between these groups. In order to reflect clinical picture of TRS, we enrolled TRS patients who had severe symptoms. According to the consensus criteria for TRS. TRS was defined by severe positive symptomatology despite optimal antipsychotic treatment. Fractional anisotropy (FA), an index of white matter integrity, was examined by DTI and analyzed with tract-based spatial statistics in 24 TRS patients (mean PANSS = 108.9), 28 non-TRS patients (mean PANSS = 50.0), and 27 healthy controls (HCs) for group comparison. Additionally, correlation analyses were conducted between FA values and symptomatology. The TRS group had lower FA values in multiple tracts (cerebral peduncle, corona radiata, corpus callosum, external and internal capsules, posterior thalamic radiation, sagittal stratum, superior longitudinal fasciculus, tapetum, and uncinate fasciculus) compared to the HC group as well as the non-TRS group (p < .05; family-wise error-corrected), while no differences were found between the non-TRS and HC groups. In the TRS group, FA values in most of the tracts (other than the left anterior limb of internal capsule, left cerebral peduncle, and right uncinate fasciculus) were negatively correlated with the Positive and Negative Syndrome Scale total scores, and negative and general symptom scores. No such relationships were found in the non-TRS group. The identified white matter integrity deficits may reflect the pathophysiology of TRS.

Sensorimotor Connectivity after Motor Exercise with Neurofeedback in Post-Stroke Patients with Hemiplegia.

Impaired finger motor function in post-stroke hemiplegia is a debilitating condition with no evidence-based or accessible treatments. Here, we evaluated the neurophysiological effectiveness of direct brain control of robotic exoskeleton that provides movement support contingent with brain activity. To elucidate the mechanisms underlying the neurofeedback intervention, we assessed resting-state functional connectivity with functional magnetic resonance imaging (rsfcMRI) between the ipsilesional sensory and motor cortices before and after a single 1-h intervention. Eighteen stroke patients were randomly assigned to crossover interventions in a double-blind and sham-controlled design. One patient dropped out midway through the study, and 17 patients were included in this analysis. Interventions involved motor imagery, robotic assistance, and neuromuscular electrical stimulation administered to a paretic finger. The neurofeedback intervention delivered stimulations contingent on desynchronized ipsilesional electroencephalographic (EEG) oscillations during imagined movement, and the control intervention delivered sensorimotor stimulations that were independent of EEG oscillations. There was a significant time × intervention interaction in rsfcMRI in the ipsilesional sensorimotor cortex. Post-hoc analysis showed a larger gain in increased functional connectivity during the neurofeedback intervention. Although the neurofeedback intervention delivered fewer total sensorimotor stimulations compared to the sham-control, rsfcMRI in the ipsilesional sensorimotor cortices was increased during the neurofeedback intervention compared to the sham-control. Higher coactivation of the sensory and motor cortices during neurofeedback intervention enhanced rsfcMRI in the ipsilesional sensorimotor cortices. This study showed neurophysiological evidence that EEG-contingent neurofeedback is a promising strategy to induce intrinsic ipsilesional sensorimotor reorganization, supporting the importance of integrating closed-loop sensorimotor processing at a neurophysiological level.

Two-stage regression of high-density scalp electroencephalograms visualizes force regulation signaling during muscle contraction.

OBJECTIVE: A critical feature for the maintenance of precise skeletal muscle force production by the human brain is its ability to configure motor function activity dynamically and adaptively in response to visual and somatosensory information. Existing studies have concluded that not only the sensorimotor area but also distributed cortical areas act cooperatively in the generation of motor commands for voluntary force production to the desired level. However, less attention has been paid to such physiological mechanisms in conventional brain-computer interface (BCI) design and implementation. We proposed a new, physiologically inspired two-stage decoding method to see its contribution on accuracy improvement of BCI. APPROACH: We performed whole-head high-density scalp electroencephalographic (EEG) recording during a right finger force-matching task at three strength levels (20%, 40%, and 60% maximal voluntary contraction following a resting state). A two-stage regression approach was employed that decodes muscle contraction level from EEG signals in the multi-level force-matching task and translates them into: (1) presence/absence of muscle contraction as a first stage; and (2) muscle contraction level as a second stage. Dimensionality reduction of the EEG signals, using principal component analysis, avoided multicollinearity during multiple regression, and data-driven stepwise multiple regression identified EEG components that were involved in the multi-level force-matching task. MAIN RESULTS: An alternatively tuned two-stage regressor accurately decoded muscle contraction level with online processing rather than the conventional decoders, and identified EEG components that were related to voluntary force production. Relaxation/contraction state-dependent EEG components were localized dominantly in the contralateral parieto-temporal regions, whereas multi-level force regulation-dependent EEG components came from the fronto-parietal regions. SIGNIFICANCE: Our findings identify respective cortical signalings during relaxation/contraction and multi-level force regulation using a sensor-based approach with EEG. Simulation-based assessment of the current physiologically inspired decoding technique proved improved accuracy in online BCI control.

Resting-State Fluctuations of EEG Sensorimotor Rhythm Reflect BOLD Activities in the Pericentral Areas: A Simultaneous EEG-fMRI Study.

Blockade of the scalp electroencephalographic (EEG) sensorimotor rhythm (SMR) is a well-known phenomenon following attempted or executed motor functions. Such a frequency-specific power attenuation of the SMR occurs in the alpha and beta frequency bands and is spatially registered at primary somatosensory and motor cortices. Here, we hypothesized that resting-state fluctuations of the SMR in the alpha and beta frequency bands also covary with resting-state sensorimotor cortical activity, without involving task-related neural dynamics. The present study employed functional magnetic resonance imaging (fMRI) to investigate the neural regions whose activities were correlated with the simultaneously recorded SMR power fluctuations. The SMR power fluctuations were convolved with a canonical hemodynamic response function and correlated with blood-oxygen-level dependent (BOLD) signals obtained from the entire brain. Our findings show that the alpha and beta power components of the SMR correlate with activities of the pericentral area. Furthermore, brain regions with correlations between BOLD signals and the alpha-band SMR fluctuations were located posterior to those with correlations between BOLD signals and the beta-band SMR. These results are consistent with those of event-related studies of SMR modulation induced by sensory input or motor output. Our findings may help to understand the role of the sensorimotor cortex activity in contributing to the amplitude modulation of SMR during the resting state. This knowledge may be applied to the diagnosis of pathological conditions in the pericentral areas or the refinement of brain-computer interfaces using SMR in the future.