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Modelling microglial function with induced pluripotent stem cells: an update

Nature on Neuroscience - Wed, 07/04/2018 - 22:00

Modelling microglial function with induced pluripotent stem cells: an update

Modelling microglial function with induced pluripotent stem cells: an update, Published online: 05 July 2018; doi:10.1038/s41583-018-0030-3

In recent years, several studies have reported the production of microglia-like cells from induced pluripotent stem cells. Pocock and Piers describe the methods used to produce and analyse these cells and their potential to improve our understanding of microglial function.

The role of engram cells in the systems consolidation of memory

Nature on Neuroscience - Mon, 07/02/2018 - 22:00

The role of engram cells in the systems consolidation of memory

The role of engram cells in the systems consolidation of memory, Published online: 03 July 2018; doi:10.1038/s41583-018-0031-2

Long-term episodic memory storage has been proposed to require a reorganization of neural circuits and networks in a process known as systems consolidation. Tonegawa and colleagues discuss recent advances in our understanding of the contribution of engram cells to this process.

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IEEE Transactions on Neural Systems and Rehabilitation Engineering publication information

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Two-Stage Frequency Recognition Method Based on Correlated Component Analysis for SSVEP-Based BCI

A canonical correlation analysis (CCA) is a state-of-the-art method for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) systems. Various extended methods have been developed, and among such methods, a combination method of CCA and individual-template-based CCA has achieved the best performance. However, the CCA requires the canonical vectors to be orthogonal, which may not be a reasonable assumption for the EEG analysis. In this paper, we propose using the correlated component analysis (CORRCA) rather than CCA to implement frequency recognition. CORRCA can relax the constraint of canonical vectors in CCA and generate the same projection vector for two multichannel EEG signals. Furthermore, we propose a two-stage method based on the basic CORRCA method (termed TSCORRCA). Evaluated on a benchmark data set of 35 subjects, the experimental results demonstrate that CORRCA significantly outperformed CCA, and TSCORRCA obtained the best performance among the compared methods. This paper demonstrates that CORRCA-based methods have a great potential for implementing high-performance SSVEP-based BCI systems.

Intersession Instability in fNIRS-Based Emotion Recognition

Emotion recognition based on neural signals is a promising technique for the detection of patients’ emotions for enhancing healthcare. However, emotion-related neural signals, such as from functional near infrared spectroscopy (fNIRS), can be affected by various psychophysiological and environmental factors. There is a paucity of literature regarding data instability and classification instability in fNIRS-based emotion recognition systems, phenomenon which may lead to user dissatisfaction and abandonment. We collected data in an fNIRS-based 2-class emotion recognition test-retest experiment (3 week interval) with visual stimuli emotion induction to examine data instability and its impact on classification accuracy. We found a 22.2% average deterioration of emotion classification accuracy between the two sessions, suggesting that classification instability is a serious problem. We found that the changes in the distributions of the selected neural signal features, as evaluated by Kullback–Leibler (KL) divergence, were a likely cause of the accuracy decline. We analyzed the data instability and our results showed that instability of spatial activation patterns and instability of the hemodynamic response in the most activated region are correlated with accuracy decline. Finally, we propose a method for mitigating classification instability in fNIRS-based emotion recognition based on feature selection for stable features, the first such method to our knowledge. This new feature selection criterion considers not only the separability of features (evaluated by Fisher Score) but also their stability over time (evaluated by KL divergence between feature distributions at different time points). Testing showed that this method led to an approximately 5% improvement in cross-session generalization accuracy.

Effects of Virtual Reality and Augmented Reality on Induced Anxiety

To explore the effects of virtual reality (VR) and augmented reality (AR) in the treatment of claustrophobia, the potential effects of VR and AR on induced anxiety were investigated in this paper. During the experiment, 34 subjects were randomly selected and distributed in AR and VR scenes in a sequence. The skin conductance and heart rates of the subjects were measured throughout the entire process, and the anxiety scale was used to assess the subjective anxiety when the task in each scene was completed. The results showed the following: (1) AR and VR scenes led to feelings of discomfort, but the subjective anxiety scores obtained in the two scenes were not significantly different; (2) the skin conductance level of the subjects significantly increased from the baseline when the subjects entered the experimental scene but remained active in the two scenes without showing significant difference between the scenes; and (3) the heart rate index significantly increased from the baseline after the subjects entered the scene and then gradually decreased. The heart rates of the subjects significantly increased again when the anxiety-induced event was triggered. However, no significant difference was observed between AR and VR scenes. AR and VR have induced obvious anxiety, which was reflected in the subjective and objective physiological indicators. However, no significant difference was found in the effects of AR and VR on the induced anxiety. Considering the cost of building two scenes and other factors, AR was more suitable for the treatment of claustrophobia than VR.

A Novel Technique for Selecting EMG-Contaminated EEG Channels in Self-Paced Brain–Computer Interface Task Onset

Electromyography artifacts are a well-known problem in electroencephalography studies [brain–computer interfaces (BCIs), brain mapping, and clinical areas]. Blind source separation (BSS) techniques are commonly used to handle artifacts. However, these may remove not only the EMG artifacts but also some useful electroencephalography (EEG) sources. To reduce this useful information loss, we propose a new technique for statistically selecting EEG channels that are contaminated with class-dependent EMG (henceforth called EMG-CCh). The EMG-CCh is selected based on the correlation between EEG and facial EMG channels. They were compared (using a Wilcoxon test) to determine whether the artifacts played a significant role in class separation. To ensure that the promising results are not due to the weak EMG removal, reliability tests were done In our data set, the comparison results between BSS artifact removal applied in two ways, to all channels and only to EMG-CCh showed that ICA, PCA, and BSS-CCA can yield significantly better ( ${p}<0.05$ ) class separation with the proposed method (79% of the cases for ICA, 53% for PCA, and 11% for BSS-CCA). With BCI competition data, we saw improvement in 60% of the cases for ICA and BSS-CCA. The simple method proposed in this paper showed improvement in class separation with both our data and the BCI competition data. There are no existing methods for removing EMG artifacts based on the correlation between the EEG and EMG channels. Also, the EMG-CCh selection can be used on its own or it can be combined with pre-existing artifact handling methods. For these reasons, we believe that this method can be useful for other EEG studies.

Reduced Effort Does Not Imply Slacking: Responsiveness to Error Increases With Robotic Assistance

In both neurorehabilitation and functional augmentation, the patient or the user’s muscular effort diminishes when the movement of their limb is supported by a robot. Is this relaxation a result of “slacking” by letting the robot take-over the movement, resulting in less responsiveness in the task? To address this question, we tested subjects who controlled a virtual cursor isometrically to track a moving target without and with different assistants. We measured the force applied by the subject as a metric for effort and estimated their control gain as the metric for responsiveness in the task. Although subjects applied less force with position assistance, the norm of the control gain increased with all assistants, i.e., they applied proportionately larger forces for the same difference between the cursor and the target states. Furthermore, assisting velocity errors improved baseline performance without reducing effort. Though all assistants improved task performance, the control gain adapted differently to position and velocity assistance. Position assistance was exploited to accurately track the target, whereas velocity assistance was treated as a disturbance, and was effectively nullified as it prevented submovements that minimized positional error. Our results show that robotic assistance increases task responsiveness in healthy individuals and that assisting velocity errors could boost patient performance without reducing their motor effort.

A Model to Estimate the Optimal Layout for Assistive Communication Touchscreen Devices in Children With Dyskinetic Cerebral Palsy

Excess involuntary movements and slowness of movement in children with dyskinetic cerebral palsy often result in the inability to properly interact with augmentative and alternative communication (AAC) devices. This significantly limits communication. It is, therefore, essential to know how to adjust the device layout in order to maximize each child’s rate of communication. The aim of this paper was to develop a mathematical model to estimate the information rate in children with dyskinetic cerebral palsy and to determine the optimal AAC layout for a touchscreen tablet that results in enhanced speed of communication. The model predicts information rate based on button size, number, spacing between buttons, and the probability of making an error or missing target buttons. Estimation of the information rate confirmed our hypothesis of lower channel capacity in children with dyskinetic cerebral palsy compared with age-matched healthy children. Information rate increased when the AAC layout was customized based on the optimal parameters predicted by the model. In conclusion, this paper quantifies the effect of motor impairments on communication with assistive communication devices and shows that communication performance can be improved by optimally matching the parameters of the AAC touchscreen device to the abilities of the child.

Tactile Sensor-Based Steering as a Substitute of the Attendant Joystick in Powered Wheelchairs

Attendant joysticks of powered wheelchairs are devices oriented to help caregivers. Diseases and disabilities such as dementia, spinal cord injuries or blindness make the user unable to drive the chair by his or her own. However, this device is not intuitive to use, especially for old people. Proper processing of the information provided by two tactile sensors in the handlebar achieves control signals that allow an easy and intuitive driving. This is done in this paper, where the performance of this approach is evaluated in comparison with that of the joystick by means of objective measurements as well as questionnaires to obtain the subjective perception of the participants in the experiments. The results show a better performance of the handlebar in terms of error in following a trajectory, collisions with the surrounding furniture, and user feeling related to ease of use, comfort, required training, usefulness, safety, and fatigue.

Design and Functional Evaluation of a Dexterous Myoelectric Hand Prosthesis With Biomimetic Tactile Sensor

This paper presents the design, tactile sensor, characterization, and control system of a new dexterous myoelectric hand prosthesis to overcome the limitations of state-of-the-art myoelectric prostheses (e.g., limited functionality, controllability, and sensory feedback). Our dexterous myoelectric hand allows independent finger movement and thumb abduction/adduction, with a motor for each finger and an additional motor for the thumb (i.e., six total motors). Each fingertip has a biomimetic tactile sensor with 13 tactile units, each of which can detect normal and tangential forces. The hand controller uses an electromyography pattern recognition controller and a tactile sensor feedback-based grasping controller to automatically and dynamically adjust the finger grasp force to prevent objects from slipping. This closed-loop controller structure will allow users to safely and effectively grasp complex objects with varying densities and shapes. In addition, the electronic hardware is integrated into the hand, and the pattern recognition controller can be implemented in the hand embedded system.

Validity of the Nintendo Wii Balance Board for the Assessment of Balance Measures in the Functional Reach Test

The functional reach test (FRT) is widely used for assessing dynamic balance stability in elderly and pathological subjects. Force platforms (FPs) represent a fundamental part of the instrumented FRT experimental setup due to the central role of center-of-pressure (COP) displacement in FRT analysis. Recently, the nintendo wii balance board (NBB) has been suggested as a low-cost and reliable device for ground reaction force and COP measurement in poorly dynamic motor tasks. Therefore, this paper aimed to compare NBB-COP data with those obtained from a laboratory-grade platform during FRT. Data from 48 healthy subjects were simultaneously acquired from both devices. FP-COP and NBB-COP trajectories showed a remarkable correlation in both directions ( ${r}>textsf {0.990}$ ) and low root-mean-square error values (1.14 ± 0.88 mm and 0.55 ± 0.28 mm for anterior–posterior and medial–lateral direction). Fixed biases between COP-based parameters did not exceed 2% of the FP outcomes with high consistency throughout the present measurement range (ICC consistency always >0.950). Only the COP mean velocity exhibited a tendency toward proportional errors, which can be adjusted by a calibration of NBB data. Findings of this paper confirmed the NBB validity for COP measurement in a widely used motor task as the functional reach, supporting the feasibility of NBB in research scenarios.

Compliant Prosthetic Wrists Entail More Natural Use Than Stiff Wrists During Reaching, Not (Necessarily) During Manipulation

Developing an artificial arm with functions equivalent to those of the human arm is one of the challenging goals of bioengineering. State-of-the-artprostheses lack several degrees of freedom and force the individuals to compensate for them by means of compensatory movements, which often result in residual limb pain and overuse syndromes. Passive wristsmay reduce such compensatory actions, nonethelessto date their actual efficacy, associated to conventional myoelectric hands is a matter of debate. We hypothesized that a transradial prosthesiswould allow a simpler operation if its wrist behaved compliant during the reaching and grasping phase, and stiff during the holding andmanipulation phase. To assess this, we compared a stiff and a compliant wrist and evaluating the extent of compensatory movements in the trunk and shoulder, with unimpaired subjects wearing orthoses, while performing nine activities of daily living taken from the southampton hand assessment procedure. Our findings show indeed that the optimal compliance for a prosthetic wrist is specific to the phase of the motor task: the compliant wrist outperforms the stiff wrist during the reaching phase, whereas the stiff wrist exhibits more natural movements during the manipulation phase of heavy objects. Hence, this paper invites rehabilitation engineers to develop wrists with switchable compliance.

Objective Assessment of Spasticity With a Method Based on a Human Upper Limb Model

This paper presents a method based on a human upper limb model that assesses the severity of spasticity in patients with stroke objectively. The kinematic model consists of four moving segments connected by four joints. The joint torques are computed using inverse dynamics with measurements from three inertial measurement units (IMUs) attached to the participant’s upper limb. The muscle activations are estimated using the joint torques via a musculoskeletal model which consists of 22 muscles. The severity of spasticity is then quantified by measuring the tonic stretch reflex threshold (TSRT) of the participant. 15 patient participants participated in the experiments where they were assessed by two qualified therapists using modified Ashworth scale (MAS), and their motions and EMG signals were captured at the same time. Using the upper limb model, the TSRT of each patient was measured and ranked. The estimated muscle activation profiles have a high correlation (0.707) to the EMG signal profiles. The null hypothesis that the rankings of the severity using the model and the MAS assessment have no correlation has been tested, and was rejected convincingly ( ${text p} approx 0.0003$ ). These findings suggest that the model has the potential to complement the existing practices by providing an alternative evaluation method.

Spasticity Measurement Based on the HHT Marginal Spectrum Entropy of sEMG Using a Portable System: A Preliminary Study

To facilitate stretch reflex onset (SRO) detection and improve accuracy and reliability of spasticity assessment in clinical settings, a new method to measure dynamic stretch reflex threshold (DSRT) based on Hilbert–Huang transform marginal spectrum entropy (HMSEN) of surface electromyography (sEMG) signals and a portable system to quantify modified Ashworth scale (MAS) for spasticity assessment were developed. The sEMG signals were divided into frames using a fixed-length sliding window, and the HMSEN of each frame was calculated. An adaptive threshold was set to measure the DSRT. The HMSEN based method can quantify muscle activity through time-frequency and nonlinear dynamics analysis, therefore providing deeper insight about the spastic muscle mechanisms during stretching and a reliable SRO detection method. Experimental results revealed that the HMSEN based method could reliably detect the SRO and measure the DSRT (recognition rate: 95.45%), and could achieve improved performance over the time-domain based method. There was a strong correlation ( ${r} = -0.824$ to −0.900) between the MAS scores and the DSRT index, and the test-retest reliability was high. Additionally, limitations of the MAS were analyzed. This paper indicates that the presented framework can provide a promising tool to measure DSRT and a clinical quantitative approach for spasticity assessment.

Myoelectric Control Based on a Generic Musculoskeletal Model: Toward a Multi-User Neural-Machine Interface

This paper aimed to develop a novel electromyography (EMG)-based neural-machine interface (NMI) that is user-generic for continuously predicting coordinated motion betweenmuscle contractionmetacarpophalangeal (MCP) and wrist flexion/extension. The NMI requires a minimum calibration procedure that only involves capturing maximal voluntary muscle contraction for themonitoredmuscles for individual users. At the center of the NMI is a user-generic musculoskeletal model based on the experimental data collected from six able-bodied (AB) subjects and nine different upper limb postures. The generic model was evaluated on-line on both AB subjects and a transradial amputee. The subjectswere instructed to performa virtual hand/wrist posture matching task with different upper limb postures. The on-line performanceof the genericmodelwas also compared with that of the musculoskeletal model customized to each individual user (called “specific model”). All subjects accomplished the assigned virtual tasks while using the user-generic NMI, although the AB subjects produced better performance than the amputee subject. Interestingly, compared with the specific model, the generic model produced comparable completion time, a reduced number of overshoots, and improved path efficiency in the virtual hand/wrist posture matching task. The results suggested that it is possible to design an EMG-driven NMI based on a musculoskeletalmodelthat could fit multiple users, including upper limb amputees, for predicting coordinated MCP and wrist motion. The present new method might address the challenges of existing advanced EMG-based NMI that require frequent and lengthy customization and calibration. Our future research will focus on evaluating the developed NMI for powered prosthetic arms.

A High Performance Spelling System based on EEG-EOG Signals With Visual Feedback

In this paper, we propose a highly accurate and fast spelling system that employs multi-modal electroencephalography-electrooculography (EEG-EOG) signals and visual feedback technology. Over the last 20 years, various types of speller systems have been developed in brain-computer interface and EOG/eye-tracking research; however, these conventional systems have a tradeoff between the spelling accuracy (or decoding) and typing speed. Healthy users and physically challenged participants, in particular, may become exhausted quickly; thus, there is a need for a speller system with fast typing speed while retaining a high level of spelling accuracy. In this paper, we propose the first hybrid speller system that combines EEG and EOG signals with visual feedback technology so that the user and the speller system can act cooperatively for optimal decision-making. The proposed spelling system consists of a classic row-column event-related potential (ERP) speller, an EOG command detector, and visual feedback modules. First, the online ERP speller calculates classification probabilities for all candidate characters from the EEG epochs. Second, characters are sorted by their probability, and the characters with the highest probabilities are highlighted as visual feedback within the row-column spelling layout. Finally, the user can actively select the character as the target by generating an EOG command. The proposed system shows 97.6% spelling accuracy and an information transfer rate of 39.6 (±13.2) [bits/min] across 20 participants. In our extended experiment, we redesigned the visual feedback and minimized the number of channels (four channels) in order to enhance the speller performance and increase usability. Most importantly, a new weighted strategy resulted in 100% accuracy and a 57.8 (±23.6) [bits/min] information transfer rate across six participants. This paper demonstr- tes that the proposed system can provide a reliable communication channel for practical speller applications and may be used to supplement existing systems.

Towards Real-Time, Continuous Decoding of Gripping Force From Deep Brain Local Field Potentials

Lack of force information and longevity issues are impediments to the successful translation of brain–computer interface systems for prosthetic control from experimental settings to widespread clinical application. The ability to decode force using deep brain stimulation electrodes in the subthalamic nucleus (STN) of the basal ganglia provides an opportunity to address these limitations. This paper explores the use of various classes of algorithms (Wiener filter, Wiener-Cascade model, Kalman filter, and dynamic neural networks) and recommends the use of a Wiener-Cascade model for decoding force from STN. This recommendation is influenced by a combination of accuracy and practical considerations to enable real-time, continuous operation. This paper demonstrates an ability to decode a continuous signal (force) from the STN in real time, allowing the possibility of decoding more than two states from the brain at low latency.

HIVE is supported by the European Commission under the Future and Emerging Technologies program.

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