EEG-Based Anxious States Classification Using Affective BCI-Based Closed Neurofeedback System.

PURPOSE: Anxiety disorder is one of the psychiatric disorders that involves extreme fear or worry, which can change the balance of chemicals in the brain. To the best of our knowledge, the evaluation of anxiety state is still based on some subjective questionnaires and there is no objective standard assessment yet. Unlike other methods, our approach focuses on study the neural changes to identify and classify the anxiety state using electroencephalography (EEG) signals. METHODS: We designed a closed neurofeedback experiment that contains three experimental stages to adjust subjects' mental state. The EEG resting state signal was recorded from thirty-four subjects in the first and third stages while EEG-based mindfulness recording was recorded in the second stage. At the end of each stage, the subjects were asked to fill a Visual Analogue Scale (VAS). According to their VAS score, the subjects were classified into three groups: non-anxiety, moderate or severe anxiety groups. RESULTS: After processing the EEG data of each group, support vector machine (SVM) classifiers were able to classify and identify two mental states (non-anxiety and anxiety) using the Power Spectral Density (PSD) as patterns. The highest classification accuracies using Gaussian kernel function and polynomial kernel function are 92.48 ±  1.20% and 88.60  ±  1.32%, respectively. The highest average of the classification accuracies for healthy subjects is 95.31 ±  1.97% and for anxiety subjects is 87.18 ±  3.51%. CONCLUSIONS: The results suggest that our proposed EEG neurofeedback-based classification approach is efficient for developing affective BCI system for detection and evaluation of anxiety disorder states.

G-Causality Brain Connectivity Differences of Finger Movements between Motor Execution and Motor Imagery.

Motor imagery is one of the classical paradigms which have been used in brain-computer interface and motor function recovery. Finger movement-based motor execution is a complex biomechanical architecture and a crucial task for establishing most complicated and natural activities in daily life. Some patients may suffer from alternating hemiplegia after brain stroke and lose their ability of motor execution. Fortunately, the ability of motor imagery might be preserved independently and worked as a backdoor for motor function recovery. The efficacy of motor imagery for achieving significant recovery for the motor cortex after brain stroke is still an open question. In this study, we designed a new paradigm to investigate the neural mechanism of thirty finger movements in two scenarios: motor execution and motor imagery. Eleven healthy participants performed or imagined thirty hand gestures twice based on left and right finger movements. The electroencephalogram (EEG) signal for each subject during sixty trials left and right finger motor execution and imagery were recorded during our proposed experimental paradigm. The Granger causality (G-causality) analysis method was employed to analyze the brain connectivity and its strength between contralateral premotor, motor, and sensorimotor areas. Highest numbers for G-causality trials of 37 ± 7.3, 35.5 ± 8.8, 36.3 ± 10.3, and 39.2 ± 9.0 and lowest Granger causality coefficients of 9.1 ± 3.2, 10.9 ± 3.7, 13.2 ± 0.6, and 13.4 ± 0.6 were achieved from the premotor to motor area during execution/imagination tasks of right and left finger movements, respectively. These results provided a new insight into motor execution and motor imagery based on hand gestures, which might be useful to build a new biomarker of finger motor recovery for partially or even completely plegic patients. Furthermore, a significant difference of the G-causality trial number was observed during left finger execution/imagery and right finger imagery, but it was not observed during the right finger execution phase. Significant difference of the G-causality coefficient was observed during left finger execution and imagery, but it was not observed during right finger execution and imagery phases. These results suggested that different MI-based brain motor function recovery strategies should be taken for right-hand and left-hand patients after brain stroke.

C/EBP homologous protein deficiency inhibits statin-induced myotoxicity.

It has been well established that HMG-CoA reductase inhibitors (statins) cause adverse side effects in skeletal muscle ranging from mild to fatal myotoxicity upon dose, drug interaction, and exercise. However, the underlying mechanisms by which statins induce myotoxicity have not been fully addressed. Recent reports showed that statins induce endoplasmic reticulum (ER) stress and cell death in immune cells and myoblasts in vitro. Therefore, the goal of study is to investigate the molecular mechanism by which statins induce skeletal muscle cell death and myopathy via the regulation of ER stress. Biochemical data showed that TUDCA, an ER stress inhibitor, inhibited atorvastatin- and simvastatin-induced protein cleavages of PARP-1 and caspase-3, respectively. Actually, statin treatment activated marker proteins of unfolded protein responses (UPR) including ATF6, CHOP, and spliced XBP1 and these responses were inhibited by TUDCA. In addition, statin treatment induced mRNA levels of UPR marker genes, suggesting that statins activate ER stress in a transcriptional regulation. The physiological relevance of ER stress in statin-induced myopathy was demonstrated in a mouse model of myopathy, in which instillation of simvastatin and atorvastatin led to myopathy. Notably, the reduction of muscular endurance in response to statin instillation was significantly improved in TUDCA treating group compared to vehicle control group. Moreover, CHOP deficiency mice showed restoration of statin-induced reduction of muscular endurance, suggesting that statin induces myopathy via ER stress and in a CHOP-dependent manner. Taken together, these findings indicate that statins specifically induce myopathy in an ER stress-dependent manner, suggesting the therapeutic potential of ER stress regulation in preventing adverse effects of statin.

Control of a Robot Arm Using Decoded Joint Angles from Electrocorticograms in Primate.

Electrocorticogram (ECoG) is a well-known recording method for the less invasive brain machine interface (BMI). Our previous studies have succeeded in predicting muscle activities and arm trajectories from ECoG signals. Despite such successful studies, there still remain solving works for the purpose of realizing an ECoG-based prosthesis. We suggest a neuromuscular interface to control robot using decoded muscle activities and joint angles. We used sparse linear regression to find the best fit between band-passed ECoGs and electromyograms (EMG) or joint angles. The best coefficient of determination for 100 s continuous prediction was 0.6333 ± 0.0033 (muscle activations) and 0.6359 ± 0.0929 (joint angles), respectively. We also controlled a 4 degree of freedom (DOF) robot arm using only decoded 4 DOF angles from the ECoGs in this study. Consequently, this study shows the possibility of contributing to future advancements in neuroprosthesis and neurorehabilitation technology.

Electromyogram refinement using muscle synergy based regulation of uncertain information.

Electromyogram signal (EMG) measurement frequently experiences uncertainty attributed to issues caused by technical constraints such as cross talk and maximum voluntary contraction. Due to these problems, individual EMGs exhibit uncertainty in representing their corresponding muscle activations. To regulate this uncertainty, we proposed an EMG refinement, which refines EMGs with regulating the contribution redundancy of the signals from EMGs to approximating torques through EMG-driven torque estimation (EDTE) using the muscular skeletal forward dynamic model. To regulate this redundancy, we must consider the synergistic contribution redundancy of muscles, including "unmeasured" muscles, to approximating torques, which primarily causes redundancy of EDTE. To suppress this redundancy, we used the concept of muscle synergy, which is a key concept of analyzing the neurophysiological regulation of contribution redundancy of muscles to exerting torques. Based on this concept, we designed a muscle-synergy-based EDTE as a framework for EMG refinement, which regulates the abovementioned uncertainty of individual EMGs in consideration of unmeasured muscles. In achieving the proposed EMG refinement, the most considerable point is to suppress a large change such as overestimation attributed to enhancement of the contribution of particular muscles to estimating torques. Therefore it is reasonable to refine EMGs by minimizing the change in EMGs. To evaluate this model, we used a Bland-Altman plot, which quantitatively evaluates the proportional bias of refined signals to EMGs. Through this evaluation, we showed that the proposed EDTE minimizes the bias while approximating torques. Therefore this minimization optimally regulates the uncertainty of EMGs and thereby leads to optimal EMG refinement.

Arthroscopic Excision of Delayed Diagnosed Intra-articular Osteoid Osteoma of the Elbow: A Case Report.

An intra-articular osteoid osteoma is a very rare cause of elbow pain, and its diagnosis and treatment remain challenging. Delayed diagnosis may lead to arthritic change of the joint. In this study, the authors present the occurrence of intra-articular osteoid osteoma in the right elbow of a 15-year-old male patient who presented with prolonged pain and limited motion owing to delayed diagnosis. After confirming the nidus of osteoid osteoma from radiographic evaluation, the lesion was completely removed arthroscopically. The patient presented a complete relief of symptoms and full range of motion. This is the first domestic report of successful arthroscopic treatment of an intra-articular osteoid osteoma of the elbow.

Mapping ECoG channel contributions to trajectory and muscle activity prediction in human sensorimotor cortex.

Studies on brain-machine interface techniques have shown that electrocorticography (ECoG) is an effective modality for predicting limb trajectories and muscle activity in humans. Motor control studies have also identified distributions of "extrinsic-like" and "intrinsic-like" neurons in the premotor (PM) and primary motor (M1) cortices. Here, we investigated whether trajectories and muscle activity predicted from ECoG were obtained based on signals derived from extrinsic-like or intrinsic-like neurons. Three participants carried objects of three different masses along the same counterclockwise path on a table. Trajectories of the object and upper arm muscle activity were predicted using a sparse linear regression. Weight matrices for the predictors were then compared to determine if the ECoG channels contributed more information about trajectory or muscle activity. We found that channels over both PM and M1 contributed highly to trajectory prediction, while a channel over M1 was the highest contributor for muscle activity prediction.

The impact of generalized joint laxity (GJL) on the posterior neck pain, cervical disc herniation, and cervical disc degeneration in the cervical spine.

BACKGROUND CONTEXT: Generalized joint laxity (GJL) can have a negative impact on lumbar spine pathology, including low back pain, disc degeneration, and disc herniation, but the relationship between GJL and cervical spine conditions remains unknown. PURPOSE: To investigate the relationship between GJL and cervical spine conditions, including the prevalence of posterior neck pain (PNP), cervical disc herniation (CDH), and cervical disc degeneration (CDD), in a young, active population. STUDY DESIGN: Retrospective 1:2 matched cohort (case-control) study from prospectively collected data PATIENT SAMPLE: Of a total of 1853 individuals reviewed, 73 individuals with GJL (study group, gruop A) and 146 without GJL (control group, Group B) were included in the study according to a 1:2 case-control matched design for age, sex, and body mass index. OUTCOME MEASURE: The primary outcome measure was the prevalence and intensity of PNP at enrollment based on a visual analogue scale score for pain. The secondary outcome measures were (1) clinical outcomes as measured with the neck disability index (NDI) and 12-item short form health survey (SF-12) at enrollment, and (2) radiological outcomes of CDH and CDD at enrollment. METHODS: We compared baseline data between groups. Descriptive statistical analyses were performed to compare the 2 groups in terms of the outcome measures. RESULTS: The prevalence and intensity of PNP were significantly greater in group A (patients with GJL) than in group B (patients without GJL) (prevalence: p=.02; intensity: p=.001). Clinical outcomes as measured with NDI and SF-12 did not differ significantly between groups. For radiologic outcomes, the prevalence of CDD was significantly greater in group A than in group B (p=.04), whereas the prevalence of CDH did not differ significantly between groups (p=.91). CONCLUSIONS: The current study revealed that GJL was closely related to the prevalence and intensity of PNP, suggesting that GJL may be a causative factor for PNP. In addition, GJL may contribute to the occurrence of CDD, but not CDH. Spine surgeons should screen for GJL in patientswith PNP and inform patients of its potential negative impact on disc degeneration of the cervical spine.

Decoding of Covert Vowel Articulation Using Electroencephalography Cortical Currents.

With the goal of providing assistive technology for the communication impaired, we proposed electroencephalography (EEG) cortical currents as a new approach for EEG-based brain-computer interface spellers. EEG cortical currents were estimated with a variational Bayesian method that uses functional magnetic resonance imaging (fMRI) data as a hierarchical prior. EEG and fMRI data were recorded from ten healthy participants during covert articulation of Japanese vowels /a/ and /i/, as well as during a no-imagery control task. Applying a sparse logistic regression (SLR) method to classify the three tasks, mean classification accuracy using EEG cortical currents was significantly higher than that using EEG sensor signals and was also comparable to accuracies in previous studies using electrocorticography. SLR weight analysis revealed vertices of EEG cortical currents that were highly contributive to classification for each participant, and the vertices showed discriminative time series signals according to the three tasks. Furthermore, functional connectivity analysis focusing on the highly contributive vertices revealed positive and negative correlations among areas related to speech processing. As the same findings were not observed using EEG sensor signals, our results demonstrate the potential utility of EEG cortical currents not only for engineering purposes such as brain-computer interfaces but also for neuroscientific purposes such as the identification of neural signaling related to language processing.

Real-Time Control of a Video Game Using Eye Movements and Two Temporal EEG Sensors.

EEG-controlled gaming applications range widely from strictly medical to completely nonmedical applications. Games can provide not only entertainment but also strong motivation for practicing, thereby achieving better control with rehabilitation system. In this paper we present real-time control of video game with eye movements for asynchronous and noninvasive communication system using two temporal EEG sensors. We used wavelets to detect the instance of eye movement and time-series characteristics to distinguish between six classes of eye movement. A control interface was developed to test the proposed algorithm in real-time experiments with opened and closed eyes. Using visual feedback, a mean classification accuracy of 77.3% was obtained for control with six commands. And a mean classification accuracy of 80.2% was obtained using auditory feedback for control with five commands. The algorithm was then applied for controlling direction and speed of character movement in two-dimensional video game. Results showed that the proposed algorithm had an efficient response speed and timing with a bit rate of 30 bits/min, demonstrating its efficacy and robustness in real-time control.

Α-glucosidase inhibitor isolated from coffee.

The potent α-glucosidase inhibitor (compound I) was isolated from coffee brews by the activity-based fractionation and identified as a ß-carboline alkaloid norharman (9H-pyrido[ 3.4-b]indole) on the basis of mass spectroscopy and nuclear magnetic resonance spectra ((1)H NMR, (13)C NMR, and COSY). The norharman showed a potent inhibition against α-glucosidase enzyme in a concentration dependent manner with an IC50 value of 0.27 mM for maltase and 0.41 mM for sucrase, respectively. A Lineweaver-Burk plot revealed that norharman inhibited α-glucosidase enzyme uncompetitively, with a Ki value of 0.13 mM.

Decoding fingertip trajectory from electrocorticographic signals in humans.

Seeking to apply brain-machine interface technology in neuroprosthetics, a number of methods for predicting trajectory of the elbow and wrist have been proposed and have shown remarkable results. Recently, the prediction of hand trajectory and classification of hand gestures or grasping types have attracted considerable attention. However, trajectory prediction for precise finger motion has remained a challenge. We proposed a method for the prediction of fingertip motions from electrocorticographic signals in human cortex. A patient performed extension/flexion tasks with three fingers. Average Pearson's correlation coefficients and normalized root-mean-square errors between decoded and actual trajectories were 0.83-0.90 and 0.24-0.48, respectively. To confirm generalizability to other users, we applied our method to the BCI Competition IV open data sets. Our method showed that the prediction accuracy of fingertip trajectory could be equivalent to that of other results in the competition.

Dissociable neural representations of wrist motor coordinate frames in human motor cortices.

There is a growing interest in how the brain transforms body part positioning in the extrinsic environment into an intrinsic coordinate frame during motor control. To explore the human brain areas representing intrinsic and extrinsic coordinate frames, this fMRI study examined neural representation of motor cortices while human participants performed isometric wrist flexions and extensions in different forearm postures, thereby applying the same wrist actions (representing the intrinsic coordinate frame) to different movement directions (representing the extrinsic coordinate frame). Using sparse logistic regression, critical voxels involving pattern information that specifically discriminates wrist action (flexion vs. extension) and movement direction (upward vs. downward) were identified within the primary motor and premotor cortices. Analyses of classifier weights further identified contributions of the primary motor cortex to the intrinsic coordinate frame and the ventral and dorsal premotor cortex and supplementary motor area proper to the extrinsic coordinate frame. These results are consistent with existing findings using non-human primates and demonstrate the distributed representations of independent coordinate frames in the human brain.

Decoding grasp force profile from electrocorticography signals in non-human primate sensorimotor cortex.

The relatively low invasiveness of electrocorticography (ECoG) has made it a promising candidate for the development of practical, high-performance neural prosthetics. Recent ECoG-based studies have shown success in decoding hand and finger movements and muscle activity in reaching and grasping tasks. However, decoding of force profiles is still lacking. Here, we demonstrate that lateral grasp force profile can be decoded using a sparse linear regression from 15 and 16 channel ECoG signals recorded from sensorimotor cortex in two non-human primates. The best average correlation coefficients of prediction after 10-fold cross validation were 0.82±0.09 and 0.79±0.15 for our monkeys A and B, respectively. These results show that grasp force profile was successfully decoded from ECoG signals in reaching and grasping tasks and may potentially contribute to the development of more natural control methods for grasping in neural prosthetics.

Characterization of an antibiotic produced by Bacillus subtilis JW-1 that suppresses Ralstonia solanacearum.

Bacillus subtilis JW-1 was isolated from rhizosphere soil as a potential biocontrol agent of bacterial wilt caused by Ralstonia solanacearum. Seed treatment followed by a soil drench application with this strain resulted in >80% reduction in bacterial wilt disease compared with that in the untreated control under greenhouse conditions. The antibacterial compound produced by strain JW-1 was purified by bioactivity-guided fractionation. Based on mass spectroscopy and nuclear magnetic resonance spectral data ((1)H, (13)C, (1)H-(1)H correlation spectroscopies, rotating frame nuclear Overhauser effect spectroscopy, and heteronuclear multiple-bond correlation spectroscopy), the structure of this compound was elucidated as a cyclic lipopeptide composed of a heptapeptide (Gln-Leu-Leu-Val-Asp-Leu-Leu) bonded to a ß-hydroxy-iso-hexadecanoic acid arranged in a lactone ring system.

Muscle synergy control model-tuned EMG driven torque estimation system with a musculo-skeletal model.

Muscle activity is the final signal for motion control from the brain. Based on this biological characteristic, Electromyogram (EMG) signals have been applied to various systems that interface human with external environments such as external devices. In order to use EMG signals as input control signal for this kind of system, the current EMG driven torque estimation models generally employ the mathematical model that estimates the nonlinear transformation function between the input signal and the output torque. However, these models need to estimate too many parameters and this process cause its estimation versatility in various conditions to be poor. Moreover, as these models are designed to estimate the joint torque, the input EMG signals are tuned out of consideration for the physiological synergetic contributions of multiple muscles for motion control. To overcome these problems of the current models, we proposed a new tuning model based on the synergy control mechanism between multiple muscles in the cortico-spinal tract. With this synergetic tuning model, the estimated contribution of multiple muscles for the motion control is applied to tune the EMG signals. Thus, this cortico-spinal control mechanism-based process improves the precision of torque estimation. This system is basically a forward dynamics model that transforms EMG signals into the joint torque. It should be emphasized that this forward dynamics model uses a musculo-skeletal model as a constraint. The musculo-skeletal model is designed with precise musculo-skeletal data, such as origins and insertions of individual muscles or maximum muscle force. Compared with the mathematical model, the proposed model can be a versatile model for the torque estimation in the various conditions and estimates the torque with improved accuracy. In this paper, we also show some preliminary experimental results for the discussion about the proposed model.

Prediction of three-dimensional arm trajectories based on ECoG signals recorded from human sensorimotor cortex.

Brain-machine interface techniques have been applied in a number of studies to control neuromotor prostheses and for neurorehabilitation in the hopes of providing a means to restore lost motor function. Electrocorticography (ECoG) has seen recent use in this regard because it offers a higher spatiotemporal resolution than non-invasive EEG and is less invasive than intracortical microelectrodes. Although several studies have already succeeded in the inference of computer cursor trajectories and finger flexions using human ECoG signals, precise three-dimensional (3D) trajectory reconstruction for a human limb from ECoG has not yet been achieved. In this study, we predicted 3D arm trajectories in time series from ECoG signals in humans using a novel preprocessing method and a sparse linear regression. Average Pearson's correlation coefficients and normalized root-mean-square errors between predicted and actual trajectories were 0.44~0.73 and 0.18~0.42, respectively, confirming the feasibility of predicting 3D arm trajectories from ECoG. We foresee this method contributing to future advancements in neuroprosthesis and neurorehabilitation technology.

Neural activity changes associated with impulsive responding in the sustained attention to response task.

Humans can anticipate and prepare for uncertainties to achieve a goal. However, it is difficult to maintain this effort over a prolonged period of time. Inappropriate behavior is impulsively (or mindlessly) activated by an external trigger, which can result in serious consequences such as traffic crashes. Thus, we examined the neural mechanisms underlying such impulsive responding using functional magnetic resonance imaging (fMRI). Twenty-two participants performed a block-designed sustained attention to response task (SART), where each task block was composed of consecutive Go trials followed by a NoGo trial at the end. This task configuration enabled us to measure compromised preparation for NoGo trials during Go responses using reduced Go reaction times. Accordingly, parametric modulation analysis was conducted on fMRI data using block-based mean Go reaction times as an online marker of impulsive responding in the SART. We found that activity in the right dorsolateral prefrontal cortex (DLPFC) and the bilateral intraparietal sulcus (IPS) was positively modulated with mean Go reaction times. In addition, activity in the medial prefrontal cortex (MPFC) and the posterior cingulate cortex (PCC) was negatively modulated with mean Go reaction times, albeit statistically weakly. Taken together, spontaneously reduced activity in the right DLPFC and the IPS and spontaneously elevated activity in the MPFC and the PCC were associated with impulsive responding in the SART. These results suggest that such a spontaneous transition of brain activity pattern results in impulsive responding in monotonous situations, which in turn, might cause human errors in actual work environments.

Imprint cytology of soft tissue myoepithelioma: a case study.

Soft tissue myoepithelioma is a rare neoplasm composed of myoepithelial cells. Here, we describe the cytologic features of soft tissue myoepithelioma arising on the right forearm in an 18-year-old man. The excised tumor (3.0×1.8×1.5 cm) was well-demarcated, yellow-gray, soft, and myxoid. The cytologic smears showed round to spindle, epithelioid, and plasmacytoid cells in the myxoid background. The nuclei were uniform, round to ovoid, with finely distributed chromatin and eosinophilic or pale cytoplasm. The tumor cells demonstrated immunoreactivity for cytokeratin (AE1/AE3), epithelial membrane antigen, S100 protein, and glial fibrillary acidic protein. Electron microscopy showed intermediate filaments, desmosomes, and basal lamina.

The effect of temporal perception on weight perception.

A successful catch of a falling ball requires an accurate estimation of the timing for when the ball hits the hand. In a previous experiment in which participants performed ball-catching task in virtual reality environment, we accidentally found that the weight of a falling ball was perceived differently when the timing of ball load force to the hand was shifted from the timing expected from visual information. Although it is well known that spatial information of an object, such as size, can easily deceive our perception of its heaviness, the relationship between temporal information and perceived heaviness is still not clear. In this study, we investigated the effect of temporal factors on weight perception. We conducted ball-catching experiments in a virtual environment where the timing of load force exertion was shifted away from the visual contact timing (i.e., time when the ball hit the hand in the display). We found that the ball was perceived heavier when force was applied earlier than visual contact and lighter when force was applied after visual contact. We also conducted additional experiments in which participants were conditioned to one of two constant time offsets prior to testing weight perception. After performing ball-catching trials with 60 ms advanced or delayed load force exertion, participants' subjective judgment on the simultaneity of visual contact and force exertion changed, reflecting a shift in perception of time offset. In addition, timing of catching motion initiation relative to visual contact changed, reflecting a shift in estimation of force timing. We also found that participants began to perceive the ball as lighter after conditioning to 60 ms advanced offset and heavier after the 60 ms delayed offset. These results suggest that perceived heaviness depends not on the actual time offset between force exertion and visual contact but on the subjectively perceived time offset between them and/or estimation error in force timing.