Curriculum-based humanoid robot identification using large-scale human motion database

Identifying an accurate dynamics model remains challenging for humanoid robots. The difficulty is mainly due to the following two points. First, a good initial model is required to evaluate the feasibility of motions for data acquisition. Second, a highly nonlinear optimization problem needs to be solved to design movements to acquire the identification data. To cope with the first point, in this paper, we propose a curriculum of identification to gradually learn an accurate dynamics model from an unreliable initial model. For the second point, we propose using a large-scale human motion database to efficiently design the humanoid movements for the parameter identification. The contribution of our study is developing a humanoid identification method that does not require the good initial model and does not need to solve the highly nonlinear optimization problem. We showed that our curriculum-based approach was able to more efficiently identify humanoid model parameters than a method that just randomly picked reference motions for identification. We evaluated our proposed method in a simulation experiment and demonstrated that our curriculum was led to obtain a wide variety of motion data for efficient parameter estimation. Consequently, our approach successfully identified an accurate model of an 18-DoF, simulated upper-body humanoid robot.

Proprioceptive short-term memory in passive motor learning

A physical trainer often physically guides a learner's limbs to teach an ideal movement, giving the learner proprioceptive information about the movement to be reproduced later. This instruction requires the learner to perceive kinesthetic information and store the instructed information temporarily. Therefore, (1) proprioceptive acuity to accurately perceive the taught kinesthetics and (2) short-term memory to store the perceived information are two critical functions for reproducing the taught movement. While the importance of proprioceptive acuity and short-term memory has been suggested for active motor learning, little is known about passive motor learning. Twenty-one healthy adults (mean age 25.6 years, range 19-38 years) participated in this study to investigate whether individual learning efficiency in passively guided learning is related to these two functions. Consequently, learning efficiency was significantly associated with short-term memory capacity. In particular, individuals who could recall older sensory stimuli showed better learning efficiency. However, no significant relationship was observed between learning efficiency and proprioceptive acuity. A causal graph model found a direct influence of memory on learning and an indirect effect of proprioceptive acuity on learning via memory. Our findings suggest the importance of a learner's short-term memory for effective passive motor learning.

Retrospective Study of IDegLira, a New Fixed-Ratio Combination, in Japanese Patients With Type 2 Diabetes Mellitus: Analysis of Background Factors Affecting Effectiveness After 6 Months of Treatment

Background

The aim of the study was to provide real-world data on the effectiveness and safety of a new fixed-ratio combination, insulin degludec/liraglutide (IDegLira) injection in Japanese patients with type 2 diabetes mellitus (T2DM).

Methods

The primary endpoint was the change in glycated hemoglobin (HbA1c) level 6 months after the introduction of IDegLira. We also examined the rate of achievement of target HbA1c 7% and the individualized HbA1c targets set for each patient. Baseline characteristics associated with the change in HbA1c were also assessed. Seventy-five patients with T2DM were included in the analysis.

Results

After the initiation of IDegLira, HbA1c decreased significantly from baseline with a change of -1.81% (baseline 9.61% and at 6 months 7.80%; P < 0.001). At baseline, the achievement rate of 7% HbA1c was 2.67% (n = 2), which increased to 36.0% (n = 27) after 6 months of IDegLira introduction (P < 0.05). The attainment rate of individualized HbA1c targets, which were set considering each patient's characteristics, improved from 2.67% (n = 2) to 49.3% (n = 37) (P < 0.001). Regardless of sex, body mass index, estimated glomerular filtration rate, duration of diabetes, or history of glucagon-like peptide-1 receptor agonist use, IDegLira significantly reduced HbA1c, but a higher C-peptide index was associated with a greater reduction in HbA1c.

Conclusion

In this study, initiation of IDegLira in a real-world clinical setting was beneficial in lowering HbA1c in Japanese T2DM patients with inadequate glycemic control with existing therapy.

Development of split-force-controlled body weight support (SF-BWS) robot for gait rehabilitation

This study introduces a body-weight-support (BWS) robot actuated by two pneumatic artificial muscles (PAMs). Conventional BWS devices typically use springs or a single actuator, whereas our robot has a split force-controlled BWS (SF-BWS), in which two force-controlled actuators independently support the left and right sides of the user's body. To reduce the experience of weight, vertical unweighting support forces are transferred directly to the user's left and right hips through a newly designed harness with an open space around the shoulder and upper chest area to allow freedom of movement. A motion capture evaluation with three healthy participants confirmed that the proposed harness does not impede upper-body motion during laterally identical force-controlled partial BWS walking, which is quantitatively similar to natural walking. To evaluate our SF-BWS robot, we performed a force-tracking and split-force control task using different simulated load weight setups (40, 50, and 60 kg masses). The split-force control task, providing independent force references to each PAM and conducted with a 60 kg mass and a test bench, demonstrates that our SF-BWS robot is capable of shifting human body weight in the mediolateral direction. The SF-BWS robot successfully controlled the two PAMs to generate the desired vertical support forces.

Retrospective Analysis of the Effectiveness of Oral Semaglutide in Type 2 Diabetes Mellitus and Its Effect on Cardiometabolic Parameters in Japanese Clinical Settings

Glucagon-like peptide-1 receptor agonists (GLP-1RA) have a more potent glycated hemoglobin (HbA1c)-lowering effect than existing therapies and are widely used for treating type 2 diabetes mellitus (T2DM). Once-daily oral semaglutide is the world's first oral GLP-1RA. This study aimed to provide real-world data on oral semaglutide in Japanese patients with T2DM and its effects on cardiometabolic parameters. This was a single-center retrospective observational study. We examined changes in HbA1c and body weight (BW) and the rate of achieving HbA1c < 7% after 6 months of oral semaglutide treatment in Japanese patients with T2DM. Furthermore, we examined differences in the efficacy of oral semaglutide with multiple patient backgrounds. A total of 88 patients were included in this study. Overall, the mean (standard error of the mean) HbA1c at 6 months decreased by -1.24% (0.20%) from baseline, and BW at 6 months (n = 85) also decreased by -1.44 kg (0.26 kg) from baseline. The percentage of patients who achieved HbA1c < 7% changed significantly from 14% at baseline to 48%. HbA1c decreased from baseline regardless of age, sex, body mass index, chronic kidney disease, or diabetes duration. Additionally, alanine aminotransferase, total cholesterol, triglyceride, and non-high-density lipoprotein cholesterol were significantly reduced from baseline. Oral semaglutide may be an effective option for the intensification of therapy in Japanese patients with T2DM who have inadequate glycemic control with existing therapy. It may also reduce BW and improve cardiometabolic parameters.

Distinctive alterations in the mesocorticolimbic circuits in various psychiatric disorders

AIM

Increasing evidence suggests that psychiatric disorders are linked to alterations in the mesocorticolimbic dopamine-related circuits. However, the common and disease-specific alterations remain to be examined in schizophrenia (SCZ), major depressive disorder (MDD), and autism spectrum disorder (ASD). Thus, this study aimed to examine common and disease-specific features related to mesocorticolimbic circuits.

METHODS

This study included 555 participants from four institutes with five scanners: 140 individuals with SCZ (45.0% female), 127 individuals with MDD (44.9%), 119 individuals with ASD (15.1%), and 169 healthy controls (HC) (34.9%). All participants underwent resting-state functional magnetic resonance imaging. A parametric empirical Bayes approach was adopted to compare estimated effective connectivity among groups. Intrinsic effective connectivity focusing on the mesocorticolimbic dopamine-related circuits including the ventral tegmental area (VTA), shell and core parts of the nucleus accumbens (NAc), and medial prefrontal cortex (mPFC) were examined using a dynamic causal modeling analysis across these psychiatric disorders.

RESULTS

The excitatory shell-to-core connectivity was greater in the all patients than in the HC group. The inhibitory shell-to-VTA and shell-to-mPFC connectivities were greater in the ASD group than in the HC, MDD, and SCZ groups. Furthermore, the VTA-to-core and VTA-to-shell connectivities were excitatory in the ASD group, while those connections were inhibitory in the HC, MDD, and SCZ groups.

CONCLUSION

Impaired signaling in the mesocorticolimbic dopamine-related circuits could be an underlying neuropathogenesis of various psychiatric disorders. These findings will improve the understanding of unique neural alternations of each disorder and will facilitate identification of effective therapeutic targets. This article is protected by copyright. All rights reserved.

Aberrant Large-Scale Network Interactions Across Psychiatric Disorders Revealed by Large-Sample Multi-Site Resting-State Functional Magnetic Resonance Imaging Datasets

BACKGROUND AND HYPOTHESIS

Dynamics of the distributed sets of functionally synchronized brain regions, known as large-scale networks, are essential for the emotional state and cognitive processes. However, few studies were performed to elucidate the aberrant dynamics across the large-scale networks across multiple psychiatric disorders. In this paper, we aimed to investigate dynamic aspects of the aberrancy of the causal connections among the large-scale networks of the multiple psychiatric disorders.

STUDY DESIGN

We applied dynamic causal modeling (DCM) to the large-sample multi-site dataset with 739 participants from 4 imaging sites including 4 different groups, healthy controls, schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD), to compare the causal relationships among the large-scale networks, including visual network, somatomotor network (SMN), dorsal attention network (DAN), salience network (SAN), limbic network (LIN), frontoparietal network, and default mode network.

STUDY RESULTS

DCM showed that the decreased self-inhibitory connection of LIN was the common aberrant connection pattern across psychiatry disorders. Furthermore, increased causal connections from LIN to multiple networks, aberrant self-inhibitory connections of DAN and SMN, and increased self-inhibitory connection of SAN were disorder-specific patterns for SCZ, MDD, and BD, respectively.

CONCLUSIONS

DCM revealed that LIN was the core abnormal network common to psychiatric disorders. Furthermore, DCM showed disorder-specific abnormal patterns of causal connections across the 7 networks. Our findings suggested that aberrant dynamics among the large-scale networks could be a key biomarker for these transdiagnostic psychiatric disorders.

Learning acquisition of consistent leader-follower relationships depends on implicit haptic interactions

Are leaders made or born? Leader-follower roles have been well characterized in social science, but they remain somewhat obscure in sensory-motor coordination. Furthermore, it is unknown how and why leader-follower relationships are acquired, including innate versus acquired controversies. We developed a novel asymmetrical coordination task in which two participants (dyad) need to collaborate in transporting a simulated beam while maintaining its horizontal attitude. This experimental paradigm was implemented by twin robotic manipulanda, simulated beam dynamics, haptic interactions, and a projection screen. Clear leader-follower relationships were learned only when strong haptic feedback was introduced. This phenomenon occurred despite participants not being informed that they were interacting with each other and the large number of equally-valid alternative dyadic coordination strategies. We demonstrate the emergence of consistent leader-follower relationships in sensory-motor coordination, and further show that haptic interaction is essential for dyadic co-adaptation. These results provide insights into neural mechanisms responsible for the formation of leader-follower relationships in our society.

Development of a classifier for gambling disorder based on functional connections between brain regions

AIM

Recently, a machine-learning (ML) technique has been used to create generalizable classifiers for psychiatric disorders based on information of functional connections (FCs) between brain regions at resting state. These classifiers predict diagnostic labels by a weighted linear sum (WLS) of the correlation values of a small number of selected FCs. We aimed to develop a generalizable classifier for gambling disorder (GD) from the information of FCs using the ML technique and examine relationships between WLS and clinical data.

METHODS

As a training dataset for ML, data from 71 GD patients and 90 healthy controls (HCs) were obtained from two magnetic resonance imaging sites. We used an ML algorithm consisting of a cascade of an L1-regularized sparse canonical correlation analysis and a sparse logistic regression to create the classifier. The generalizability of the classifier was verified using an external dataset. This external dataset consisted of six GD patients and 14 HCs, and was collected at a different site from the sites of the training dataset. Correlations between WLS and South Oaks Gambling Screen (SOGS) and duration of illness were examined.

RESULTS

The classifier distinguished between the GD patients and HCs with high accuracy in leave-one-out cross-validation (area under curve (AUC = 0.89)). This performance was confirmed in the external dataset (AUC = 0.81). There was no correlation between WLS, and SOGS and duration of illness in the GD patients.

CONCLUSION

We developed a generalizable classifier for GD based on information of functional connections between brain regions at resting state.

Simulation-Aided Handover Prediction From Video Using Recurrent Image-to-Motion Networks

Recent advances in deep neural networks have opened up new possibilities for visuomotor robot learning. In the context of human-robot or robot-robot collaboration, such networks can be trained to predict future poses and this information can be used to improve the dynamics of cooperative tasks. This is important, both in terms of realizing various cooperative behaviors, and for ensuring safety. In this article, we propose a recurrent neural architecture, capable of transforming variable-length input motion videos into a set of parameters describing a robot trajectory, where predictions can be made after receiving only a few frames. A simulation environment is utilized to expand the training database and to improve generalization capability of the network. The resulting architecture demonstrates good accuracy when predicting handover trajectories, with models trained on synthetic and real data showing better performance than when trained on real or simulated data only. The computed trajectories enable the execution of handover tasks with uncalibrated robots, which was verified in an experiment with two real robots.

Implicit Contact Dynamics Modeling With Explicit Inertia Matrix Representation for Real-Time, Model-Based Control in Physical Environment

Model-based control has great potential for use in real robots due to its high sampling efficiency. Nevertheless, dealing with physical contacts and generating accurate motions are inevitable for practical robot control tasks, such as precise manipulation. For a real-time, model-based approach, the difficulty of contact-rich tasks that requires precise movement lies in the fact that a model needs to accurately predict forthcoming contact events within a limited length of time rather than detect them afterward with sensors. Therefore, in this study, we investigate whether and how neural network models can learn a task-related model useful enough for model-based control, that is, a model predicting future states, including contact events. To this end, we propose a structured neural network model predicting a control (SNN-MPC) method, whose neural network architecture is designed with explicit inertia matrix representation. To train the proposed network, we develop a two-stage modeling procedure for contact-rich dynamics from a limited number of samples. As a contact-rich task, we take up a trackball manipulation task using a physical 3-DoF finger robot. The results showed that the SNN-MPC outperformed MPC with a conventional fully connected network model on the manipulation task.

Bayesian Estimation of Potential Performance Improvement Elicited by Robot-Guided Training

Improving human motor performance via physical guidance by an assist robot device is a major field of interest of the society in many different contexts, such as rehabilitation and sports training. In this study, we propose a Bayesian estimation method to predict whether motor performance of a user can be improved or not by the robot guidance from the user's initial skill level. We designed a robot-guided motor training procedure in which subjects were asked to generate a desired circular hand movement. We then evaluated the tracking error between the desired and actual subject's hand movement. Results showed that we were able to predict whether a novel user can reduce the tracking error after the robot-guided training from the user's initial movement performance by checking whether the initial error was larger than a certain threshold, where the threshold was derived by using the proposed Bayesian estimation method. Our proposed approach can potentially help users to decide if they should try a robot-guided training or not without conducting the time-consuming robot-guided movement training.

Spinal Surgery after Bilateral Subthalamic Stimulation for Patients with Parkinson's Disease: A Retrospective Outcome Analysis of Pain and Functional Control

Parkinson’s disease (PD) patients often suffer from spinal diseases requiring surgeries, although the risk of complications is high. There are few reports on outcomes after spinal surgery for PD patients with deep brain stimulation (DBS). The objective of this study was to explore the data on spinal surgery for PD patients with precedent DBS. We evaluated 24 consecutive PD patients with 28 spinal surgeries from 2007 to 2017 who received at least a 2-year follow-up. The characteristics and outcomes of PD patients after spinal surgery were compared to those of 156 non-PD patients with degenerative spinal diseases treated in 2013–2017. Then, the characteristics, outcomes, and spinal alignment of PD patients receiving DBS were analyzed in degenerative spinal/lumbar diseases. The mean age at the time of spinal surgery was 68 years. The Hoehn and Yahr score regarding PD was stage 1 for 8 patients, stage 2 for 2 patients, stage 3 for 8 patients, stage 4 for 10 patients, and stage 5 for 0 patient. The median preoperative L-DOPA equivalent daily dose was 410 mg. Thirteen patients (46%) received precedent subthalamic nucleus (STN) DBS. Lumbar lesions with pain were common, and operation and anesthesia times were long in PD patients. Pain and functional improvement of PD patients persisted for 2 years after surgery with a higher complication rate than for non-PD patients. PD patients with STN DBS maintained better lumbar lordosis for 2 years after spinal surgery. STN DBS significantly maintained spinal alignment with subsequent pain and functional amelioration 2 years after surgery. The outcomes of spinal surgery for PD patients might be favorably affected by thorough treatment for PD including DBS.

Parallel and hierarchical neural mechanisms for adaptive and predictive behavioral control

Our brain can be recognized as a network of largely hierarchically organized neural circuits that operate to control specific functions, but when acting in parallel, enable the performance of complex and simultaneous behaviors. Indeed, many of our daily actions require concurrent information processing in sensorimotor, associative, and limbic circuits that are dynamically and hierarchically modulated by sensory information and previous learning. This organization of information processing in biological organisms has served as a major inspiration for artificial intelligence and has helped to create in silico systems capable of matching or even outperforming humans in several specific tasks, including visual recognition and strategy-based games. However, the development of human-like robots that are able to move as quickly as humans and respond flexibly in various situations remains a major challenge and indicates an area where further use of parallel and hierarchical architectures may hold promise. In this article we review several important neural and behavioral mechanisms organizing hierarchical and predictive processing for the acquisition and realization of flexible behavioral control. Then, inspired by the organizational features of brain circuits, we introduce a multi-timescale parallel and hierarchical learning framework for the realization of versatile and agile movement in humanoid robots.

A multi-site, multi-disorder resting-state magnetic resonance image database

Machine learning classifiers for psychiatric disorders using resting-state functional magnetic resonance imaging (rs-fMRI) have recently attracted attention as a method for directly examining relationships between neural circuits and psychiatric disorders. To develop accurate and generalizable classifiers, we compiled a large-scale, multi-site, multi-disorder neuroimaging database. The database comprises resting-state fMRI and structural images of the brain from 993 patients and 1,421 healthy individuals, as well as demographic information such as age, sex, and clinical rating scales. To harmonize the multi-site data, nine healthy participants (“traveling subjects”) visited the sites from which the above datasets were obtained and underwent neuroimaging with 12 scanners. All participants consented to having their data shared and analyzed at multiple medical and research institutions participating in the project, and 706 patients and 1,122 healthy individuals consented to having their data disclosed. Finally, we have published four datasets: 1) the SRPBS Multi-disorder Connectivity Dataset 2), the SRPBS Multi-disorder MRI Dataset (restricted), 3) the SRPBS Multi-disorder MRI Dataset (unrestricted), and 4) the SRPBS Traveling Subject MRI Dataset.

Measurement(s)	mental or behavioural disorder • brain measurement • Demographic Data
Technology Type(s)	functional magnetic resonance imaging • magnetic resonance imaging • Resting State Functional Connectivity Magnetic Resonance Imaging
Factor Type(s)	age • sex • site • disorder
Sample Characteristic - Organism	Homo sapiens

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14716329

Overlapping but Asymmetrical Relationships Between Schizophrenia and Autism Revealed by Brain Connectivity

Although the relationship between schizophrenia spectrum disorder (SSD) and autism spectrum disorder (ASD) has long been debated, it has not yet been fully elucidated. The authors quantified and visualized the relationship between ASD and SSD using dual classifiers that discriminate patients from healthy controls (HCs) based on resting-state functional connectivity magnetic resonance imaging. To develop a reliable SSD classifier, sophisticated machine-learning algorithms that automatically selected SSD-specific functional connections were applied to Japanese datasets from Kyoto University Hospital (N = 170) including patients with chronic-stage SSD. The generalizability of the SSD classifier was tested by 2 independent validation cohorts, and 1 cohort including first-episode schizophrenia. The specificity of the SSD classifier was tested by 2 Japanese cohorts of ASD and major depressive disorder. The weighted linear summation of the classifier's functional connections constituted the biological dimensions representing neural classification certainty for the disorders. Our previously developed ASD classifier was used as ASD dimension. Distributions of individuals with SSD, ASD, and HCs s were examined on the SSD and ASD biological dimensions. We found that the SSD and ASD populations exhibited overlapping but asymmetrical patterns in the 2 biological dimensions. That is, the SSD population showed increased classification certainty for the ASD dimension but not vice versa. Furthermore, the 2 dimensions were correlated within the ASD population but not the SSD population. In conclusion, using the 2 biological dimensions based on resting-state functional connectivity enabled us to discover the quantified relationships between SSD and ASD.

Sodium-glucose cotransporter 2 inhibitors improved time-in-range without increasing hypoglycemia in Japanese patients with type 1 diabetes: A retrospective, single-center, pilot study

AIMS/INTRODUCTION

Studies have shown that sodium-glucose cotransporter 2 (SGLT2) inhibitors increased time-in-range (TIR; percentage of time glucose level remains between 3.9 and 10.0 mmol/L [70-180 mg/dL]) and decreased glycemic variability in patients with type 1 diabetes. The aim of this study was to investigate the effects of SGLT2 inhibitors on TIR, glycemic variability and glucose control in Japanese patients with type 1 diabetes in a real clinical setting.

MATERIALS AND METHODS

We designed a single-arm, retrospective cohort study to analyze data from patients starting to use ipragliflozin or dapagliflozin and who used a sensor-based flash glucose monitoring system between February 2019 and August 2019. We measured TIR, time above range >180 mg/dL (percentage of time with glucose level of >180 mg/dL or >10.0 mmol/L), time below range <70 mg/dL (percentage of time with glucose level of <70 mg/dL or <3.9 mmol/L), mean glucose and standard deviation, and coefficient of variation for glycemic variability, and then compared the data before and after SGLT2 inhibitors treatments.

RESULTS

We enrolled 15 patients in the study. The total dosages of basal insulin decreased significantly, but the total doses of bolus insulin did not change significantly. TIR increased significantly by approximately 11.6%; the time below range <70 mg/dL remained unchanged; and the mean glucose and standard deviation decreased significantly, whereas the coefficients of variation did not.

CONCLUSIONS

SGLT2 inhibitors improved TIR and the mean glucose level and standard deviation without increasing the time below range <70 mg/dL in patients with type 1 diabetes.

Phase portraits as movement primitives for fast humanoid robot control

Currently, usual approaches for fast robot control are largely reliant on solving online optimal control problems. Such methods are known to be computationally intensive and sensitive to model accuracy. On the other hand, animals plan complex motor actions not only fast but seemingly with little effort even on unseen tasks. This natural sense to infer temporal dynamics and coordination motivates us to approach robot control from a motor skill learning perspective to design fast and computationally light controllers that can be learned autonomously by the robot under mild modeling assumptions. This article introduces Phase Portrait Movement Primitives (PPMP), a primitive that predicts dynamics on a low dimensional phase space which in turn is used to govern the high dimensional kinematics of the task. The stark difference with other primitive formulations is a built-in mechanism for phase prediction in the form of coupled oscillators that replaces model-based state estimators such as Kalman filters. The policy is trained by optimizing the parameters of the oscillators whose output is connected to a kinematic distribution in the form of a phase portrait. The drastic reduction in dimensionality allows us to efficiently train and execute PPMPs on a real human-sized, dual-arm humanoid upper body on a task involving 20 degrees-of-freedom. We demonstrate PPMPs in interactions requiring fast reactions times while generating anticipative pose adaptation in both discrete and cyclic tasks.

Passive training with upper extremity exoskeleton robot affects proprioceptive acuity and performance of motor learning

Sports trainers often grasp and move trainees' limbs to give instructions on desired movements, and a merit of this passive training is the transferring of instructions via proprioceptive information. However, it remains unclear how passive training affects the proprioceptive system and improves learning. This study examined changes in proprioceptive acuity due to passive training to understand the underlying mechanisms of upper extremity training. Participants passively learned a trajectory of elbow-joint movement as per the instructions of a single-arm upper extremity exoskeleton robot, and the performance of the target movement and proprioceptive acuity were assessed before and after the training. We found that passive training improved both the reproduction performance and proprioceptive acuity. We did not identify a significant transfer of the training effect across arms, suggesting that the learning effect is specific to the joint space. Furthermore, we found a significant improvement in learning performance in another type of movement involving the trained elbow joint. These results suggest that participants form a representation of the target movement in the joint space during the passive training, and intensive use of proprioception improves proprioceptive acuity.

Long-Term Continuous Cervical Spinal Cord Stimulation Exerts Neuroprotective Effects in Experimental Parkinson's Disease

BACKGROUND

Spinal cord stimulation (SCS) exerts neuroprotective effects in animal models of Parkinson's disease (PD). Conventional stimulation techniques entail limited stimulation time and restricted movement of animals, warranting the need for optimizing the SCS regimen to address the progressive nature of the disease and to improve its clinical translation to PD patients.

OBJECTIVE

Recognizing the limitations of conventional stimulation, we now investigated the effects of continuous SCS in freely moving parkinsonian rats.

METHODS

We developed a small device that could deliver continuous SCS. At the start of the experiment, thirty female Sprague-Dawley rats received the dopamine (DA)-depleting neurotoxin, 6-hydroxydopamine, into the right striatum. The SCS device was fixed below the shoulder area of the back of the animal, and a line from this device was passed under the skin to an electrode that was then implanted epidurally over the dorsal column. The rats were divided into three groups: control, 8-h stimulation, and 24-h stimulation, and behaviorally tested then euthanized for immunohistochemical analysis.

RESULTS

The 8- and 24-h stimulation groups displayed significant behavioral improvement compared to the control group. Both SCS-stimulated groups exhibited significantly preserved tyrosine hydroxylase (TH)-positive fibers and neurons in the striatum and substantia nigra pars compacta (SNc), respectively, compared to the control group. Notably, the 24-h stimulation group showed significantly pronounced preservation of the striatal TH-positive fibers compared to the 8-h stimulation group. Moreover, the 24-h group demonstrated significantly reduced number of microglia in the striatum and SNc and increased laminin-positive area of the cerebral cortex compared to the control group.

CONCLUSIONS

This study demonstrated the behavioral and histological benefits of continuous SCS in a time-dependent manner in freely moving PD animals, possibly mediated by anti-inflammatory and angiogenic mechanisms.

Cell encapsulation enhances antidepressant effect of the mesenchymal stem cells and counteracts depressive-like behavior of treatment-resistant depressed rats

Despite the advances in pharmacological therapies, only the half of depressed patients respond to currently available treatment. Thus, the need for further investigation and development of effective therapies, especially those designed for treatment-resistant depression, has been sorely needed. Although antidepressant effects of mesenchymal stem cells (MSCs) have been reported, the potential benefit of this cell therapy on treatment-resistant depression is unknown. Cell encapsulation may enhance the survival rate of grafted cells, but the therapeutic effects and mechanisms mediating encapsulation of MSCs remain unexplored. Here, we showed that encapsulation enhanced the antidepressant effects of MSCs by attenuating depressive-like behavior of Wistar Kyoto (WKY) rats, which are considered as a promising animal model of treatment-resistant depression. The implantation of encapsulated MSCs (eMSCs) into the lateral ventricle counteracted depressive-like behavior and enhanced the endogenous neurogenesis in the subventricular zone (SVZ) and the dentate gyrus (DG) of the hippocampus, whereas the implantation of MSCs without encapsulation or the implantation of eMSCs into the striatum did not show such ameliorative effects. eMSCs displayed robust and stable secretion of vascular endothelial growth factor (VEGF), brain-derived neurotrophic factor, fibroblast growth factor-2, and ciliary neurotrophic factor (CNTF), and the implantation of eMSCs into the lateral ventricle activated relevant pathways associated with these growth factors. Additionally, eMSCs upregulated intrinsic expression of VEGF and CNTF and their receptors. This study suggests that the implantation of eMSCs into the lateral ventricle exerted antidepressant effects likely acting via neurogenic pathways, supporting their utility for depression treatment.

Training of deep neural networks for the generation of dynamic movement primitives

Dynamic movement primitives (DMPs) have proven to be an effective movement representation for motor skill learning. In this paper, we propose a new approach for training deep neural networks to synthesize dynamic movement primitives. The distinguishing property of our approach is that it can utilize a novel loss function that measures the physical distance between movement trajectories as opposed to measuring the distance between the parameters of DMPs that have no physical meaning. This was made possible by deriving differential equations that can be applied to compute the gradients of the proposed loss function, thus enabling an effective application of backpropagation to optimize the parameters of the underlying deep neural network. While the developed approach is applicable to any neural network architecture, it was evaluated on two different architectures based on encoder-decoder networks and convolutional neural networks. Our results show that the minimization of the proposed loss function leads to better results than when more conventional loss functions are used.

Primary functional brain connections associated with melancholic major depressive disorder and modulation by antidepressants

The limited efficacy of available antidepressant therapies may be due to how they affect the underlying brain network. The purpose of this study was to develop a melancholic MDD biomarker to identify critically important functional connections (FCs), and explore their association to treatments. Resting state fMRI data of 130 individuals (65 melancholic major depressive disorder (MDD) patients, 65 healthy controls) were included to build a melancholic MDD classifier, and 10 FCs were selected by our sparse machine learning algorithm. This biomarker generalized to a drug-free independent cohort of melancholic MDD, and did not generalize to other MDD subtypes or other psychiatric disorders. Moreover, we found that antidepressants had a heterogeneous effect on the identified FCs of 25 melancholic MDDs. In particular, it did impact the FC between left dorsolateral prefrontal cortex (DLPFC)/inferior frontal gyrus (IFG) and posterior cingulate cortex (PCC)/precuneus, ranked as the second 'most important' FC based on the biomarker weights, whilst other eight FCs were normalized. Given that left DLPFC has been proposed as an explicit target of depression treatments, this suggest that the limited efficacy of antidepressants might be compensated by combining therapies with targeted treatment as an optimized approach in the future.

P1518 Relationship between post-operative asymptomatic status and reverse remodeling of large left atrium in patients with aortic stenosis who underwent aortic valve replacement

Introduction

Recently, we have reported that large left atrial volume (minimum left atrial volume index : LAVImin ≥30ml/m²) at end-diastole determined by direct exposure of left ventricular (LV) end-diastolic pressure can predict post-operative symptomatic status after aortic valve replacement (AVR) in aortic stenosis (AS) patients with high sensitivity and modest specificity. Reverse remodeling of large LAVImin after AVR may contribute to false positive for the prediction of post-operative symptomatic status in patients with AS.

Purpose

The purpose of this study was to evaluate relationship between post-operative symptomatic status and reverse remodeling of large LAVImin in patients with AS who underwent AVR.

Methods

The study population consisted of 75 patients with AS who underwent AVR and were followed up for 600 days after AVR, after the exclusion of the followings; atrial fibrillation, significant coronary artery disease, significant mitral valve disease, pacemaker rhythm, and inadequate echocardiographic images. We measured LAVImin by biplane Simpson"s method before and after AVR. Preoperative large LAVImin (≥30ml/m²) according to the previous study was observed in 32 (43%) of 75 patients. We divided these 32 patients into two groups according to the post-operative symptomatic status during the follow-up period.

Results

There was no significant difference in pre-operative LAVImin between patients with and without post-operative symptom (46.5 ± 13.4 vs 40.4 ± 8.6 ml/m²). On the other hand, post-operative LAVImin in patients without post-operative symptom was significantly smaller than that in patients with post-operative symptom (31.5 ± 8.6 vs 54.8 ± 14.0 ml/m², p &lt; 0.01). While significant regression in LAVImin after AVR was observed in patients without post-operative symptom (40.4 ± 8.6 to 31.5 ± 8.6 ml/m², p &lt; 0.05), no regression in LAVImin after AVR was observed in patients with post-operative symptom (46.5 ± 13.4 to 54.8 ± 14.0 ml/m²).

Conclusions

Reverse remodeling of large LAVmin in patients with AS who underwent AVR was observed in post-operative asymptomatic group, but not in symptomatic group. These results suggest that reverse remodeling of large LAVImin after AVR could contribute to the post-operative asymptomatic status in patients with AS who underwent AVR.

Cell therapy for central nervous system disorders: Current obstacles to progress

Cell therapy for disorders of the central nervous system has progressed to a new level of clinical application. Various clinical studies are underway for Parkinson's disease, stroke, traumatic brain injury, and various other neurological diseases. Recent biotechnological developments in cell therapy have taken advantage of the technology of induced pluripotent stem (iPS) cells. The advent of iPS cells has provided a robust stem cell donor source for neurorestoration via transplantation. Additionally, iPS cells have served as a platform for the discovery of therapeutics drugs, allowing breakthroughs in our understanding of the pathology and treatment of neurological diseases. Despite these recent advances in iPS, adult tissue‐derived mesenchymal stem cells remain the widely used donor for cell transplantation. Mesenchymal stem cells are easily isolated and amplified toward the cells' unique trophic factor‐secretion property. In this review article, the milestone achievements of cell therapy for central nervous system disorders, with equal consideration on the present translational obstacles for clinic application, are described.

P2453Value of pre-operative left atrial minimum volume as a surrogate for post-operative symptoms in patients with aortic stenosis who underwent aortic valve replacement

Background

Previous reports have shown that symptoms after aortic valve replacement (AVR) are not uncommon depending on severity of myocardial fibrosis in patients with severe aortic stenosis (AS). Pre-operative minimum left atrial volume (LAVmin) at end-diastole determined by direct exposure of left ventricular end-diastolic pressure may be used as a surrogate for post-operative symptoms in patients with severe AS undergoing AVR.

Purpose

The purpose of this study was to examine the value of pre-operative echocardiographic LAVmin index (LAVImin) to predict post-operative symptomatic status after AVR in patients with severe AS.

Methods

The study population consisted of 219 patients with severe AS who underwent AVR and were followed up for 1000 days after AVR. Pre-operative maximum LAV index (LAVImax), LAVImin, LA emptying fraction (LAEF), LV volume indexes, LV ejection fraction (LVEF) by biplane Simpson's method, aortic valve area index (AVAI), mean aortic valve pressure gradient (mAV-PG), E/A, mean E/e' from LV inflow and mitral annular velocity, and pulmonary artery systolic pressure (PASP) were evaluated by Doppler echocardiography.

Results

After exclusion of 136 patients who met the exclusion criteria (atrial fibrillation, significant coronary artery disease, significant mitral valve diseases, pacemaker rhythm, and inadequate echocardiographic images), the final study population consisted of 75 patients (75±7 years old, 46 female). During a follow-up, 19 patients (25%) complained post-operative symptoms. There were no significant differences in pre-operative serum hemoglobin, creatinine, BNP, chronic obstructive pulmonary disease, hypertension, diabetes, LV volume indexes, LVEF, AVA, mAV-PG between patients with and without post-operative symptoms. There were significant differences in pre-operative LAVImax, LAVImin, and LAEF between patients with and without post-operative symptoms. (60±15 vs 47±15 ml/m2, 45±15 vs 28±1 ml/m2, and 29±12 vs 42±11 ml/m2, respectively). E/A, mean E/e', and PASP in patients with symptoms were significantly greater compared with patients without symptoms (1.0±0.3 vs 0.7±0.2, 25±3 vs 18±2, 44±17 vs 32±9 mmHg, respectively). In the multivariate analysis, pre-operative LAVImin was the independent predictor of the post-operative symptomatic status after AVR (odds ratio: 1.11, 95% confidence interval: 1.04 - 1.18). Receiver operating characteristic analysis revealed that area under the curve (AUC) of LAVImin (cutoff: 30ml/m2) for post-operative symptoms was the largest (0.84) among the other echocardiographic parameters, and significantly larger than that of mean E/e' (0.67, *p<0.01) and LVEF (0.53, **p<0.05) (figure).

Figure 1. ROC analysis

Conclusions

The present results suggest that pre-operative echocardiographic LAVImin may be used as a surrogate for post-operative symptomatic status after AVR in patients with severe AS.

EEG Sensorimotor Correlates of Speed During Forearm Passive Movements

Although passive movement therapy has been widely adopted to recover lost motor functions of impaired body parts, the underlying neural mechanisms are still unclear. In this context, fully understanding how the proprioceptive input modulates the brain activity may provide valuable insights. Specifically, it has not been investigated how the speed of motions, passively guided by a haptic device, affects the sensorimotor rhythms (SMR). On the grounds that faster passive motions elicit larger quantity of afferent input, we hypothesize a proportional relationship between localized SMR features and passive movement speed. To address this hypothesis, we conducted an experiment where healthy subjects received passive forearm oscillations at different speed levels while their electroencephalogram was recorded. The mu and beta event related desynchronization (ERD) and beta rebound of both left and right sensorimotor areas are analyzed by linear mixed-effects models. Results indicate that passive movement speed is correlated with the contralateral beta rebound and ipsilateral mu ERD. The former has been previously linked with the processing of proprioceptive afferent input quantity, while the latter with speed-dependent inhibitory processes. This suggests the existence of functionally-distinct frequency-specific neuronal populations associated with passive movements. In future, our findings may guide the design of novel rehabilitation paradigms.

Design of physical user–robot interactions for model identification of soft actuators on exoskeleton robots

Recent breakthroughs in wearable robots, such as exoskeleton robots with soft actuators and soft exosuits, have enabled the use of safe and comfortable movement assistance. However, modeling and identification methods for soft actuators used in wearable robots have yet to be sufficiently explored. In this study, we propose a novel approach for obtaining accurate soft actuator models through the design of physical user–robot interactions for wearable robots, in which the user applies external forces to the robot. To obtain an accurate soft actuator model from the limited amount of data acquired through an interaction, we leverage an active learning framework based on Gaussian process regression. We conducted experiments using a two-degree-of-freedom upper-limb exoskeleton robot with four pneumatic artificial muscles (PAMs). Experimental results showed that physical interactions between the exoskeleton robot and the user were successfully designed to allow PAM models to be identified. Furthermore, we found that data acquired through an interaction could result in more accurate soft actuator models for the exoskeleton robots than data acquired without a physical interaction between the exoskeleton robot and the user.

Assistive Arm-Exoskeleton Control Based on Human Muscular Manipulability

This paper introduces a novel control framework for an arm exoskeleton that takes into account force of the human arm. In contrast to the conventional exoskeleton controllers where the assistance is provided without considering the human arm biomechanical force manipulability properties, we propose a control approach based on the arm muscular manipulability. The proposed control framework essentially reshapes the anisotropic force manipulability into the endpoint force manipulability that is invariant with respect to the direction in the entire workspace of the arm. This allows users of the exoskeleton to perform tasks effectively in the whole range of the workspace, even in areas that are normally unsuitable due to the low force manipulability of the human arm. We evaluated the proposed control framework with real robot experiments where subjects wearing an arm exoskeleton were asked to move a weight between several locations. The results show that the proposed control framework does not affect the normal movement behavior of the users while effectively reduces user effort in the area of low manipulability. Particularly, the proposed approach augments the human arm force manipulability to execute tasks equally well in the entire workspace of the arm.

Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias

When collecting large amounts of neuroimaging data associated with psychiatric disorders, images must be acquired from multiple sites because of the limited capacity of a single site. However, site differences represent a barrier when acquiring multisite neuroimaging data. We utilized a traveling-subject dataset in conjunction with a multisite, multidisorder dataset to demonstrate that site differences are composed of biological sampling bias and engineering measurement bias. The effects on resting-state functional MRI connectivity based on pairwise correlations because of both bias types were greater than or equal to psychiatric disorder differences. Furthermore, our findings indicated that each site can sample only from a subpopulation of participants. This result suggests that it is essential to collect large amounts of neuroimaging data from as many sites as possible to appropriately estimate the distribution of the grand population. Finally, we developed a novel harmonization method that removed only the measurement bias by using a traveling-subject dataset and achieved the reduction of the measurement bias by 29% and improvement of the signal-to-noise ratios by 40%. Our results provide fundamental knowledge regarding site effects, which is important for future research using multisite, multidisorder resting-state functional MRI data.

SF-36 scores predict postoperative delirium after surgery for cervical spondylotic myelopathy

OBJECTIVECervical spondylotic myelopathy (CSM) is one of the most common causes of spinal cord dysfunction. Surgery for CSM is generally effective, but postoperative delirium is a potential complication. Although there have been some studies that investigated postoperative delirium after spine surgery, no useful tool for identifying high-risk patients has been established, and it is unknown if 36-Item Short Form Health Survey (SF-36) scores can predict postoperative delirium. The objective of this study was to evaluate the correlation between preoperative SF-36 scores and postoperative delirium after surgery for CSM.METHODSSixty-seven patients who underwent surgery for CSM at the authors' institution were enrolled in this study. Medical records of these patients were retrospectively reviewed. Patient background, preoperative laboratory data, preoperative SF-36 scores, the preoperative Japanese Orthopaedic Association (JOA) score for the evaluation of cervical myelopathy, and perioperative factors were selected as potential risk factors for postoperative delirium. These factors were evaluated using univariable and multivariable logistic regression analysis.RESULTSTen patients were diagnosed with postoperative delirium. Univariable analysis revealed that the physical functioning score (p = 0.01), general health perception score (p < 0.01), and vitality score (p < 0.01) of the SF-36 were significantly lower in patients with postoperative delirium than in those without. The total number of medications was significantly higher in the delirium group compared with the no-delirium group (p = 0.02). In contrast, there were no significant differences between the delirium group and the no-delirium group in cervical JOA scores (p = 0.20). Multivariable analysis revealed that a low general health perception score was an independent risk factor for postoperative delirium (p = 0.02; odds ratio 0.810, 95% confidence interval 0.684-0.960).CONCLUSIONSSome of the SF-36 scores were significantly lower in patients with postoperative delirium than in those without. In particular, the general health perception score was independently correlated with postoperative delirium. SF-36 scores could help identify patients at high risk for postoperative delirium and aid in the development of prevention strategies.

Human-In-The-Loop Control and Task Learning for Pneumatically Actuated Muscle Based Robots

Pneumatically actuated muscles (PAMs) provide a low cost, lightweight, and high power-to-weight ratio solution for many robotic applications. In addition, the antagonist pair configuration for robotic arms make it open to biologically inspired control approaches. In spite of these advantages, they have not been widely adopted in human-in-the-loop control and learning applications. In this study, we propose a biologically inspired multimodal human-in-the-loop control system for driving a one degree-of-freedom robot, and realize the task of hammering a nail into a wood block under human control. We analyze the human sensorimotor learning in this system through a set of experiments, and show that effective autonomous hammering skill can be readily obtained through the developed human-robot interface. The results indicate that a human-in-the-loop learning setup with anthropomorphically valid multi-modal human-robot interface leads to fast learning, thus can be used to effectively derive autonomous robot skills for ballistic motor tasks that require modulation of impedance.

Shoulder Glenohumeral Elevation Estimation based on Upper Arm Orientation

In this paper, the shoulder glenohumeral displacement during the movement of the upper arm is studied. Four modeling approaches were examined and compared to estimate the humeral head elevation (vertical displacement) and translation (horizontal displacement). A biomechanics-inspired method was used firstly to model the glenohumeral displacement in which a least squares method was implemented for parameter identification. Then, three Gaussian process regression models were used in which the following variable sets were employed: i) shoulder adduction/abduction angle, ii) combination of shoulder adduction/abduction and flexion/extension angles, iii) overall upper arm orientation in the form of quaternions. In order to test the respective performances of these four models, we collected motion capture data and compared the models' representative capabilities. As a result, Gaussian process regression that considered the overall upper arm orientation outperformed the other modeling approaches; however, it should be noted that the other methods also provided accuracy levels that may be sufficient depending on task requirements.

Electrical Stimulation Enhances Migratory Ability of Transplanted Bone Marrow Stromal Cells in a Rodent Ischemic Stroke Model

BACKGROUND/AIMS

Bone marrow stromal cells (BMSCs) transplantation is an important strategy for the treatment of ischemic stroke. Currently, there are no effective methods to guide BMSCs toward the targeted site. In this study, we investigated the effect of electrical stimulation on BMSCs migration in an ischemic model of rats.

METHODS

Adult male Wistar rats weighing 200 to 250 g received right middle cerebral artery occlusion (MCAO) for 90 minutes. BMSCs (2.5×105 cells/ 4 µl PBS) were stereotaxically injected into the left corpus callosum at 1 day after MCAO. After BMSCs injection, a plate electrode with a diameter of 3 mm connected to an implantable electrical stimulator was placed on the right frontal epidural space and a counter electrode was placed in the extra-cranial space. Electrical stimulation at preset current (100 µA) and frequency (100 Hz) was performed for two weeks. Behavioral tests were performed at 1, 4, 8, and 15 days after MCAO using the modified Neurological Severity Score (mNSS) and cylinder test. Rats were euthanized at 15 days after MCAO for evaluation of infarction area and the migration distance and area of BMSCs found in the brain tissue. After evaluating cell migration, we proceeded to explore the mechanisms guiding these observations. MCAO rats without BMSCs transplantation were stimulated with same current and frequency. At 1 and 2 weeks after MCAO, rats were euthanized to evaluate stromal cell-derived factor 1 alpha (SDF-1α) level of brain tissues in the bilateral cortex and striatum.

RESULTS

Behavioral tests at 4, 8, and 15 days after MCAO revealed that stimulation group displayed significant amelioration in mNSS and cylinder test compared to control group (p<0.05). Similarly, the infarction areas of stroke rats in stimulation group were significantly decreased compared to control group (p<0.05). Migration distance and area of transplanted BMSCs were significantly longer and wider respectively in stimulation group. An increased concentration gradient of SDF-1α in stimulation group accompanied this enhanced migration of transplanted cells.

CONCLUSIONS

These results suggest that electrical stimulation enhances migratory ability of transplanted BMSCs in ischemic stroke model of rats. If we can direct the implanted BMSCs to the site of interest, it may lead to a greater therapeutic effect.