Spinal Extradural Arachnoid Cyst: Significance of Intrathecal Infusion after Fistula Closure

The spinal extradural arachnoid cyst is a rare entity. Obtaining the correct diagnosis and detecting the fistula location are critical for providing effective treatment. A 41-year-old man had numbness in the soles of his feet for 2 years with accompanying gait disturbance, and a defecation disorder. Computed tomography myelography performed at another hospital revealed an epidural arachnoid cyst from Th11 to L2. He received a subarachnoid-cyst shunt at the rostral part of the cyst. However, his symptoms worsened and he was admitted to our hospital. Neuroradiological investigations revealed the correct location of the fistula at the level of Th12. We performed partial removal of the cyst wall with fistula closure via right hemilaminectomy of Th11 and 12. The complete closure of the fistula was confirmed by intrathecal infusion of artificial cerebrospinal fluid through the shunt tube. The shunt tube was removed with the sutures. The patient's symptoms improved, although numbness remained in his bilateral heels. There has been no recurrence in 15 months since the surgery. Fistula closure may work as a balanced therapeutic strategy for spinal extradural arachnoid cyst, and intrathecal cerebrospinal fluid infusion is useful for the confirmation of complete fistula closure.

Long-Term Potentiation Enhances Neuronal Differentiation in the Chronic Hypoperfusion Model of Rats

Several reports have shown that long-term potentiation (LTP) per se effectively enhances neurogenesis in the hippocampus of intact animals. If LTP can enhance neurogenesis in chronic hypoperfusion, this approach could potentially become a new therapeutic strategy for the restoration of cognitive function and for prevention from deterioration of mild cognitive impairment (MCI). Using an in vivo LTP model of rats, we examined whether LTP per se can enhance neurogenesis in hypoperfusion rats that underwent permanent bilateral common carotid artery occlusion (permanent 2-vessel occlusion, P2VO). High frequency stimulation (HFS) in the subacute phase after P2VO enhanced hippocampal cell proliferation and neurogenesis. However, most enhanced cell proliferation and neurogenesis was seen in the hypoperfusion rats that received HFS and for which LTP could finally be induced. In contrast, the same effect was not seen in the LTP induction in the chronic phase. The present findings, which reveal that most enhanced neurogenesis was seen in hypoperfusion rats for which LTP could be finally induced, could explain the ability of LTP-like activities such as learning paradigms and environmental stimuli to increase the rate of neurogenesis in the hippocampus even under hypoperfusion conditions. Moreover, the present findings, which reveal that LTP induction in the chronic phase after P2VO could not effectively enhance neurogenesis in the hypoperfusion rats, could indicate that patients with MCI and even middle-aged healthy control individuals should start LTP-like activities as early as possible and continue with these activities to prevent age-related deterioration of hippocampal function.

The Factors Affecting the Difficulty of Percutaneous Cylindrical Electrode Placement for Spinal Cord Stimulation

OBJECTIVE

Optimal placement of electrodes is important for spinal cord stimulation. Factors affecting the difficulty of percutaneous electrode placement are not well known. In this study, we retrospectively evaluated the factors affecting the difficulty of percutaneous electrode placement.

METHODS

We performed a retrospective analysis of 90 consecutive procedures of percutaneous cylindrical electrode implantation at the first author's institution. Age, sex, smoking state, body mass index, the duration of time from the beginning of pain syndrome to operation, diagnosis, the number of previous electrode placements, the previous electrode implantation period, the presence of axial low back pain, the electrode tip level, the pattern of electrode placement, and the reason for reimplantation were selected as factors associated with the success of electrode placement or the operation time of electrode placement.

RESULTS

The number of previous electrode placements and the electrode tip level were independently associated with the operation time of electrode placement. According to both univariable and multivariable regression analyses, 1 previous electrode placement lengthened the operation time by approximately 15 minutes. No factors were significantly associated with the success of electrode placement. The more frequently that previous electrode placement was performed, the more difficult electrode placement tended to be. However, electrode reimplantation can be successful given extra time.

CONCLUSIONS

This is the first study to evaluate factors affecting the difficulty of percutaneous electrode placement. A history of percutaneous cylindrical electrode placement did not affect the success of current placement, although it lengthened the operation time.

Markov Switching Model for Quick Detection of Event Related Desynchronization in EEG

Quick detection of motor intentions is critical in order to minimize the time required to activate a neuroprosthesis. We propose a Markov Switching Model (MSM) to achieve quick detection of an event related desynchronization (ERD) elicited by motor imagery (MI) and recorded by electroencephalography (EEG). Conventional brain computer interfaces (BCI) rely on sliding window classifiers in order to perform online continuous classification of the rest vs. MI classes. Based on this approach, the detection of abrupt changes in the sensorimotor power suffers from an intrinsic delay caused by the necessity of computing an estimate of variance across several tenths of a second. Here we propose to avoid explicitly computing the EEG signal variance, and estimate the ERD state directly from the voltage information, in order to reduce the detection latency. This is achieved by using a model suitable in situations characterized by abrupt changes of state, the MSM. In our implementation, the model takes the form of a Gaussian observation model whose variance is governed by two latent discrete states with Markovian dynamics. Its objective is to estimate the brain state (i.e., rest vs. ERD) given the EEG voltage, spatially filtered by common spatial pattern (CSP), as observation. The two variances associated with the two latent states are calibrated using the variance of the CSP projection during rest and MI, respectively. The transition matrix of the latent states is optimized by the “quickest detection” strategy that minimizes a cost function of detection latency and false positive rate. Data collected by a dry EEG system from 50 healthy subjects, was used to assess performance and compare the MSM with several logistic regression classifiers of different sliding window lengths. As a result, the MSM achieves a significantly better tradeoff between latency, false positive and true positive rates. The proposed model could be used to achieve a more reactive and stable control of a neuroprosthesis. This is a desirable property in BCI-based neurorehabilitation, where proprioceptive feedback is provided based on the patient's brain signal. Indeed, it is hypothesized that simultaneous contingent association between brain signals and proprioceptive feedback induces superior associative learning.

An optimal control strategy for hybrid actuator systems: Application to an artificial muscle with electric motor assist

Humans use multiple muscles to generate such joint movements as an elbow motion. With multiple lightweight and compliant actuators, joint movements can also be efficiently generated. Similarly, robots can use multiple actuators to efficiently generate a one degree of freedom movement. For this movement, the desired joint torque must be properly distributed to each actuator. One approach to cope with this torque distribution problem is an optimal control method. However, solving the optimal control problem at each control time step has not been deemed a practical approach due to its large computational burden. In this paper, we propose a computationally efficient method to derive an optimal control strategy for a hybrid actuation system composed of multiple actuators, where each actuator has different dynamical properties. We investigated a singularly perturbed system of the hybrid actuator model that subdivided the original large-scale control problem into smaller subproblems so that the optimal control outputs for each actuator can be derived at each control time step and applied our proposed method to our pneumatic-electric hybrid actuator system. Our method derived a torque distribution strategy for the hybrid actuator by dealing with the difficulty of solving real-time optimal control problems.

Soft humanoid motor learning

A combination of compliant mechanical components and data-driven approaches may improve humanoid motor control.

Hippocampal neurogenesis of Wistar Kyoto rats is congenitally impaired and correlated with stress resistance

The hippocampus is thought to be an important region for depression. However, the relationship between hippocampal neurogenesis and depression is still controversial. Wistar Kyoto (WKY) rats are frequently used as a depression model. WKY rats are known to show physiologically abnormal features, and these features resemble abnormalities seen in depressed patients. However, the neurogenesis of WKY rats is still unknown. In this study, we first evaluated the neurogenesis of WKY rats and compared it to that of Wistar (WIS) rats. No strain effect was observed in the number of cells positive for 5-bromo-2'-deoxyuridine (BrdU) and BrdU/Doublecortin (Dcx) in the subventricular zone (SVZ). However, the number of BrdU- and BrdU/Dcx-positive cells in the dentate gyrus (DG) of the hippocampus was significantly lower in WKY rats than in WIS rats. Next, we evaluated the correlation between neurogenesis and behavior tests. Behavior tests did not affect neurogenesis in either strain. Hippocampal neurogenesis correlated negatively with the results of a forced swim test (FST) on day 2 in each strain. That is, rats with a lower level of native neurogenesis in the DG showed a higher level of learned helplessness induced by the inescapable stress of the FST on day 1. Our findings indicate that hippocampal neurogenesis in WKY rats is congenitally impaired in contrast to that in WIS rats. Native cell proliferation and neurogenesis in the DG are correlated with stress resistance. These findings may be useful for developing new targets for depression treatment.

Surgery in the Standing Position by a Surgeon with Achilles Tendon Rupture

Unexpected injuries can have a profound effect on a surgeon's performance and thus on patients and surgical departments. Here we describe a technique for performing surgery in the standing position, as done by a surgeon with an Achilles tendon rupture. During his prescribed 45-day non-weight-bearing period for the left ankle after surgery for an Achilles tendon rupture, the surgeon was able to participate in 15 surgeries as an operator or assistant, due to his use of a combination of injured-leg genuflection on a stool and a 'Surgical Body Support' device. Similarly injured surgeons may benefit from such support.

Neuroimaging Biomarker of Major Depressive Disorder

Introduction

Recent studies have shown that it is important to understand the brain mechanism specifically by focusing on the common and unique functional connectivity in each disorder including depression.

Objectives

To specify the biomarker of major depressive disorder (MDD), we applied the sparse machine learning algorithm to classify several types of affective disorders using the resting state fMRI data collected in multiple sites, and this study shows the results of depression as a part of those results.

Aims

The aim of this study is to understand some specific pattern of functional connectivity in MDD, which would support diagnosis of depression and development of focused and personalized treatments in the future.

Methods

The neuroimaging data from patients with major depressive disorder (MDD, n = 100) and healthy control adults (HC: n = 100) from multiple sites were used for the training dataset. A completely separate dataset (n = 16) was kept aside for testing. After all preprocessing of fMRI data, based on one hundred and forty anatomical region of interests (ROIs), 9730 functional connectivities during resting states were prepared as the input of the sparse machine-learning algorithm.

Results

As results, 20 functional connectivities were selected with the classification performance of Accuracy: 83.0% (Sensitivity: 81.0%, Specificity: 85.0%). The test data, which was completely separate from the training data, showed the performance accuracy of 83.3%.

Conclusions

The selected functional connectivities based on the sparse machine learning algorithm included the brain regions which have been associated with depression.

Disclosure of interest

The authors have not supplied their declaration of competing interest.

Adaptive Control of Exoskeleton Robots for Periodic Assistive Behaviours Based on EMG Feedback Minimisation

In this paper we propose an exoskeleton control method for adaptive learning of assistive joint torque profiles in periodic tasks. We use human muscle activity as feedback to adapt the assistive joint torque behaviour in a way that the muscle activity is minimised. The user can then relax while the exoskeleton takes over the task execution. If the task is altered and the existing assistive behaviour becomes inadequate, the exoskeleton gradually adapts to the new task execution so that the increased muscle activity caused by the new desired task can be reduced. The advantage of the proposed method is that it does not require biomechanical or dynamical models. Our proposed learning system uses Dynamical Movement Primitives (DMPs) as a trajectory generator and parameters of DMPs are modulated using Locally Weighted Regression. Then, the learning system is combined with adaptive oscillators that determine the phase and frequency of motion according to measured Electromyography (EMG) signals. We tested the method with real robot experiments where subjects wearing an elbow exoskeleton had to move an object of an unknown mass according to a predefined reference motion. We further evaluated the proposed approach on a whole-arm exoskeleton to show that it is able to adaptively derive assistive torques even for multiple-joint motion.

Spatiotemporal synchronization of biped walking patterns with multiple external inputs by style-phase adaptation

In this paper, we propose a framework for generating coordinated periodic movements of robotic systems with multiple external inputs. We developed an adaptive pattern generator model that is composed of a two-factor observation model with a style parameter and phase dynamics with a phase variable. The style parameter controls the spatial patterns of the generated trajectories, and the phase variable manages its temporal profiles. By exploiting the style-phase separation in the pattern generation, we can independently design adaptation schemes for the spatial and temporal profiles of the pattern generator to multiple external inputs. To validate the effectiveness of our proposed method, we applied it to a user-exoskeleton model to achieve user-adaptive walking assistance for which the exoskeleton robot's movements need to be coordinated with the user walking patterns and environment. As a result, the exoskeleton robot successfully performed stable biped walking behaviors for walking assistance even when the style of the observed walking pattern and the period were suddenly changed.

Anti-high mobility group box 1 antibody exerts neuroprotection in a rat model of Parkinson's disease

The high mobility group box-1 (HMGB1) exists as an architectural nuclear protein in the normal state, but displays an inflammatory cytokine-like activity in the extracellular space under pathological condition. Inflammation in the pathogenesis of Parkinson's disease (PD) has been documented. In this study, we investigated the involvement of HMGB1 in the pathology and the neuroprotective effects of neutralizing anti-HMGB1 monoclonal antibody (mAb) on an animal model of PD. Adult female Sprague-Dawley rats were initially injected with 6-hydroxydopmaine (6-OHDA, 20 μg/4 μl) into the right striatum, then anti-HMGB1 mAb (1 mg/kg), or control mAb was intravenously administered immediately, at 6 and 24 h after 6-OHDA injection. The treatment with anti-HMGB1 mAb significantly preserved dopaminergic neurons in substantia nigra pars compacta and dopaminergic terminals inherent in the striatum, and attenuated PD behavioral symptoms compared to the control mAb-treated group. HMGB1 was retained in the nucleus of neurons and astrocytes by inhibiting the proinflammation-induced oxidative stress in the anti-HMGB1 mAb-treated group, whereas HMGB1 translocation was observed in neurons at 1 day and astrocytes at 7 days after 6-OHDA injection in the control mAb-treated group. Anti-HMGB1 mAb inhibited the activation of microglia, disruption of blood-brain-barrier (BBB), and the expression of inflammation cytokines such as IL-1β and IL-6. These results suggested that HMGB1 released from neurons and astrocytes was at least partly involved in the mechanism and pathway of degeneration of dopaminergic neurons induced by 6-OHDA exposure. Intravenous administration of anti-HMGB1 mAb stands as a novel therapy for PD possibly acting through the suppression of neuroinflammation and the attenuation of disruption of BBB associated with the disease.

Creating the brain and interacting with the brain: an integrated approach to understanding the brain

In the past two decades, brain science and robotics have made gigantic advances in their own fields, and their interactions have generated several interdisciplinary research fields. First, in the 'understanding the brain by creating the brain' approach, computational neuroscience models have been applied to many robotics problems. Second, such brain-motivated fields as cognitive robotics and developmental robotics have emerged as interdisciplinary areas among robotics, neuroscience and cognitive science with special emphasis on humanoid robots. Third, in brain-machine interface research, a brain and a robot are mutually connected within a closed loop. In this paper, we review the theoretical backgrounds of these three interdisciplinary fields and their recent progress. Then, we introduce recent efforts to reintegrate these research fields into a coherent perspective and propose a new direction that integrates brain science and robotics where the decoding of information from the brain, robot control based on the decoded information and multimodal feedback to the brain from the robot are carried out in real time and in a closed loop.

EEG Single-Trial Detection of Gait Speed Changes during Treadmill Walk

In this study, we analyse the electroencephalography (EEG) signal associated with gait speed changes (i.e. acceleration or deceleration). For data acquisition, healthy subjects were asked to perform volitional speed changes between 0, 1, and 2 Km/h, during treadmill walk. Simultaneously, the treadmill controller modified the speed of the belt according to the subject’s linear speed. A classifier is trained to distinguish between the EEG signal associated with constant speed gait and with gait speed changes, respectively. Results indicate that the classification performance is fair to good for the majority of the subjects, with accuracies always above chance level, in both batch and pseudo-online approaches. Feature visualisation and equivalent dipole localisation suggest that the information used by the classifier is associated with increased activity in parietal areas, where mu and beta rhythms are suppressed during gait speed changes. Specifically, the parietal cortex may be involved in motor planning and visuomotor transformations throughout the online gait adaptation, which is in agreement with previous research. The findings of this study may help to shed light on the cortical involvement in human gait control, and represent a step towards a BMI for applications in post-stroke gait rehabilitation.

Brain-machine interfacing control of whole-body humanoid motion

We propose to tackle in this paper the problem of controlling whole-body humanoid robot behavior through non-invasive brain-machine interfacing (BMI), motivated by the perspective of mapping human motor control strategies to human-like mechanical avatar. Our solution is based on the adequate reduction of the controllable dimensionality of a high-DOF humanoid motion in line with the state-of-the-art possibilities of non-invasive BMI technologies, leaving the complement subspace part of the motion to be planned and executed by an autonomous humanoid whole-body motion planning and control framework. The results are shown in full physics-based simulation of a 36-degree-of-freedom humanoid motion controlled by a user through EEG-extracted brain signals generated with motor imagery task.

Decoding the ERD/ERS: influence of afferent input induced by a leg assistive robot

This paper investigates the influence of the leg afferent input, induced by a leg assistive robot, on the decoding performance of a BMI system. Specifically, it focuses on a decoder based on the event-related (de)synchronization (ERD/ERS) of the sensorimotor area. The EEG experiment, performed with healthy subjects, is structured as a 3 × 2 factorial design, consisting of two factors: "finger tapping task" and "leg condition." The former is divided into three levels (BMI classes), being left hand finger tapping, right hand finger tapping and no movement (Idle); while the latter is composed by two levels: leg perturbed (Pert) and leg not perturbed (NoPert). Specifically, the subjects' leg was periodically perturbed by an assistive robot in 5 out of 10 sessions of the experiment and not moved in the remaining sessions. The aim of this study is to verify that the decoding performance of the finger tapping task is comparable between the two conditions NoPert and Pert. Accordingly, a classifier is trained to output the class of the finger tapping, given as input the features associated with the ERD/ERS. Individually for each subject, the decoding performance is statistically compared between the NoPert and Pert conditions. Results show that the decoding performance is notably above chance, for all the subjects, under both conditions. Moreover, the statistical comparison do not highlight a significant difference between NoPert and Pert in any subject, which is confirmed by feature visualization.

The impact of the number of occult metastatic lymph nodes on postoperative relapse of resectable esophageal cancer

Clinical stage II/III esophageal cancer (EC), as defined by the Japanese Classification, relapses at a moderately high rate even after curative resection. The number of lymph node metastases is known to be associated with tumor relapse. Recently, the prognostic significance of occult metastatic lymph nodes (MLNs), as well as that of overt MLNs, has been reported. The aim of this study was to investigate the impact of the total number of MLNs including occult MLNs on postoperative relapse in clinical stage II/III EC. One hundred and five patients with clinical stage II/III EC who underwent esophagectomy accompanied by radical lymphadenectomy at the Department of Surgical Oncology in Osaka City University Hospital between January 2000 and October 2008 were included in this study. Occult MLNs, metastases not detected by hematoxylin-eosin staining, were identified by immunohistochemistry (IHC) using antipancytokeratin antibody AE1/AE3. The clinicopathological features of occult MLNs were compared between the relapse and no relapse groups. A total of 6558 lymph nodes (1357 from two-field dissection and 5201 from three-field dissection) were examined by IHC staining; 362 overt MLNs and 143 occult MLNs were detected. The number of occult MLNs increased in proportion to the International Union Against Cancer pathological (p)N-status and pStage. When the number of occult MLNs was added to the number of pNs, the number of total MLNs was associated with postoperative relapse. With respect to tumor, node, metastasis stage, 6 of 22 patients (27%) who were pathological node-negative converted to node-positive by considering total MLNs. The number of N3 patients with relapse increased markedly with restaging by total MLNs. The number of total MLNs, but not overt MLNs, was an independent prognostic factor on multivariate analysis. These results suggest that occult MLNs were often found, and they were associated with postoperative relapse of resectable esophageal cancer. The total number of MLNs including occult MLNs could contribute to evaluating the precise stage of patients with esophageal cancer.

Efficient Sample Reuse in Policy Gradients with Parameter-Based Exploration

The policy gradient approach is a flexible and powerful reinforcement learning method particularly for problems with continuous actions such as robot control. A common challenge is how to reduce the variance of policy gradient estimates for reliable policy updates. In this letter, we combine the following three ideas and give a highly effective policy gradient method: (1) policy gradients with parameter-based exploration, a recently proposed policy search method with low variance of gradient estimates; (2) an importance sampling technique, which allows us to reuse previously gathered data in a consistent way; and (3) an optimal baseline, which minimizes the variance of gradient estimates with their unbiasedness being maintained. For the proposed method, we give a theoretical analysis of the variance of gradient estimates and show its usefulness through extensive experiments.

Bilinear modeling of EMG signals to extract user-independent features for multiuser myoelectric interface

In this study, we propose a multiuser myoelectric interface that can easily adapt to novel users. When a user performs different motions (e.g., grasping and pinching), different electromyography (EMG) signals are measured. When different users perform the same motion (e.g., grasping), different EMG signals are also measured. Therefore, designing a myoelectric interface that can be used by multiple users to perform multiple motions is difficult. To cope with this problem, we propose for EMG signals a bilinear model that is composed of two linear factors: 1) user dependent and 2) motion dependent. By decomposing the EMG signals into these two factors, the extracted motion-dependent factors can be used as user-independent features. We can construct a motion classifier on the extracted feature space to develop the multiuser interface. For novel users, the proposed adaptation method estimates the user-dependent factor through only a few interactions. The bilinear EMG model with the estimated user-dependent factor can extract the user-independent features from the novel user data. We applied our proposed method to a recognition task of five hand gestures for robotic hand control using four-channel EMG signals measured from subject forearms. Our method resulted in 73% accuracy, which was statistically significantly different from the accuracy of standard nonmultiuser interfaces, as the result of a two-sample t -test at a significance level of 1%.