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Basal Ganglia: Pathways for action

Nature on Neuroscience - Wed, 08/17/2016 - 22:00

Nature Reviews Neuroscience 17, 534 (2016). doi:10.1038/nrn.2016.118

Author: Darran Yates

Patterns of coordinated activity in the direct, striatonigral pathway and the indirect, striatopallidal pathway regulate action performance.

Cerebral cortex: Multi-modal mapping

Nature on Neuroscience - Wed, 08/17/2016 - 22:00

Nature Reviews Neuroscience 17, 536 (2016). doi:10.1038/nrn.2016.115

Author: Darran Yates

Using data from the Human Connectome Project and a semi-automated neuroanatomical approach, a study has generated a new multi-modal parcellation of the human cerebral cortex.

Neuroimmunology: Social support from the immune system

Nature on Neuroscience - Wed, 08/03/2016 - 22:00

Nature Reviews Neuroscience 17, 534 (2016). doi:10.1038/nrn.2016.112

Author: Yvonne Bordon

Interferon-γ acts on inhibitory neurons to regulate social behaviour in mice.

Emotion: Exciting extinction

Nature on Neuroscience - Wed, 08/03/2016 - 22:00

Nature Reviews Neuroscience 17, 536 (2016). doi:10.1038/nrn.2016.110

Author: Katherine Whalley

GABAB receptors drive presynaptic excitation in habenula cholinergic neurons to regulate the extinction of fear memories in mice.

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Ambulatory Gait Behavior in Patients With Dementia: A Comparison With Parkinson’s Disease

Accelerometry-based gait analysis is a promising approach in obtaining insightful information on the gait characteristics of patients with neurological disorders such as dementia and Parkinson’s disease (PD). In order to improve its practical use outside the laboratory or hospital, it is required to design new metrics capable of quantifying ambulatory gait and their extraction procedures from long-term acceleration data. This paper presents a gait analysis method developed for such a purpose. Our system is based on a single trunk-mounted accelerometer and analytical algorithm for the assessment of gait behavior that may be context dependent. The algorithm consists of the detection of gait peaks from acceleration data and the analysis of multimodal patterns in the relationship between gait cycle and vertical gait acceleration. A set of six new measures can be obtained by applying the algorithm to a 24-h motion signal. To examine the performance and utility of our method, we recorded acceleration data from 13 healthy, 26 PD, and 26 mild cognitive impairment or dementia subjects. Each patient group was further classified into two, comprising 13 members each, according to the severity of the disease, and the gait behavior of the five groups was compared. We found that the normal, PD, and MCI/dementia groups show characteristic walking patterns which can be distinguished from one another by the developed gait measure set. We also examined conventional parameters such as gait acceleration, gait cycle, and gait variability, but failed to reproduce the distinct differences among the five groups. These findings suggest that the proposed gait analysis may be useful in capturing disease-specific gait features in a community setting.

Effects of Different Tactile Feedback on Myoelectric Closed-Loop Control for Grasping Based on Electrotactile Stimulation

Closed-loop control is important for amputees to manipulate myoelectric prostheses intuitively and dexterously. Tactile feedback can help amputees improve myoelectric control performance for grasping objects. To investigate the effects of different tactile feedback, we performed experiments on six amputees and six able-bodied subjects via electrotactile stimulation. Using a virtual environment, six kinds of objects with different weights and stiffnesses were used for grasping tasks. Five feedback conditions (no feedback, pressure feedback, slip feedback, pressure slip feedback, and vision feedback) were considered. Nine evaluation indexes and three control objectives (rapidity, economy, and stability) were proposed. Under the five feedback conditions, our study investigated four issues: 1) three types of grasping-related failures; 2) four types of grasping-related time measures; 3) average grasping force; 4) standard deviation of the grasping force. Results indicate that: 1) slip feedback is better than pressure feedback; 2) pressure slip feedback can improve grasping rapidity; 3) slip feedback significantly contributes to grasping economy and stability; and 4) pressure slip feedback can perform as well as vision feedback.

Transradial Amputee Gesture Classification Using an Optimal Number of sEMG Sensors: An Approach Using ICA Clustering

Surface electromyography (sEMG)-based pattern recognition studies have been widely used to improve the classification accuracy of upper limb gestures. Information extracted from multiple sensors of the sEMG recording sites can be used as inputs to control powered upper limb prostheses. However, usage of multiple EMG sensors on the prosthetic hand is not practical and makes it difficult for amputees due to electrode shift/movement, and often amputees feel discomfort in wearing sEMG sensor array. Instead, using fewer numbers of sensors would greatly improve the controllability of prosthetic devices and it would add dexterity and flexibility in their operation. In this paper, we propose a novel myoelectric control technique for identification of various gestures using the minimum number of sensors based on independent component analysis (ICA) and Icasso clustering. The proposed method is a model-based approach where a combination of source separation and Icasso clustering was utilized to improve the classification performance of independent finger movements for transradial amputee subjects. Two sEMG sensor combinations were investigated based on the muscle morphology and Icasso clustering and compared to Sequential Forward Selection (SFS) and greedy search algorithm. The performance of the proposed method has been validated with five transradial amputees, which reports a higher classification accuracy ( "> 95%). The outcome of this study encourages possible extension of the proposed approach to real time prosthetic applications.

Children With Dystonia Can Learn a Novel Motor Skill: Strategies That are Tolerant to High Variability

Children with dystonia are characterized by highly variable and seemingly uncontrolled movements. An important question for any rehabilitative effort is whether these children can learn and improve their performance. This study compared children with dystonia due to cerebral palsy, typically developing children, and healthy adults in their ability to acquire a novel sensorimotor skill. Using a virtual setup, subjects threw a virtual ball tethered to a post to hit a virtual target. Multiple combinations of release angle and velocity of the arm at ball release could achieve a target hit—the task was redundant and afforded solutions with different sensitivity to variability. Subjects performed 200 trials for two target locations that presented different types of redundancy. We hypothesized that children with dystonia develop strategies that are tolerant to their high variability. Estimating this variability highlighted the insufficiency of traditional outcome measures. Therefore, additional analyses of data distributions and of ball release timing were applied. Results showed that: 1) children with dystonia reduced their performance error despite their high variability; 2) this improvement was brought about by finding error-tolerant solutions; and 3) they generated arm trajectories that created time windows for ball release that were tolerant to timing variability. While reduced in magnitude, the performance improvements in children with dystonia paralleled those in healthy children and adults. These findings demonstrate that children with dystonia are able to adapt their behavior to their high variability, an important basis for any rehabilitative intervention.

Parameters Selection for Bivariate Multiscale Entropy Analysis of Postural Fluctuations in Fallers and Non-Fallers Older Adults

Entropy measures are often used to quantify the regularity of postural sway time series. Recent methodological developments provided both multivariate and multiscale approaches allowing the extraction of complexity features from physiological signals; see “Dynamical complexity of human responses: A multivariate data-adaptive framework,” in Bulletin of Polish Academy of Science and Technology, vol. 60, p. 433, 2012. The resulting entropy measures are good candidates for the analysis of bivariate postural sway signals exhibiting nonstationarity and multiscale properties. These methods are dependant on several input parameters such as embedding parameters. Using two data sets collected from institutionalized frail older adults, we numerically investigate the behavior of a recent multivariate and multiscale entropy estimator; see “Multivariate multiscale entropy: A tool for complexity analysis of multichannel data,” <?Pub _bookmark Command="[Quick Mark]"?>Physics Review E, vol. 84, p. 061918, 2011. We propose criteria for the selection of the input parameters. Using these optimal parameters, we statistically compare the multivariate and multiscale entropy values of postural sway data of non-faller subjects to those of fallers. These two groups are discriminated by the resulting measures over multiple time scales. We also demonstrate that the typical parameter settings proposed in the literature lead to entropy measures that do not distinguish the two groups. This last result confirms the importance of the selection of appropriate input parameters.

Locomotor Adaptation to an Asymmetric Force on the Human Pelvis Directed Along the Right Leg

In this work, we study locomotor adaptation in healthy adults when an asymmetric force vector is applied to the pelvis directed along the right leg. A cable-driven Active Tethered Pelvic Assist Device (A-TPAD) is used to apply an external force on the pelvis, specific to a subject's gait pattern. The force vector is intended to provide external weight bearing during walking and modify the durations of limb supports. The motivation is to use this paradigm to improve weight bearing and stance phase symmetry in individuals with hemiparesis. An experiment with nine healthy subjects was conducted. The results show significant changes in the gait kinematics and kinetics while the healthy subjects developed temporal and spatial asymmetry in gait pattern in response to the applied force vector. This was followed by after effects once the applied force vector was removed. The adaptation to the applied force resulted in asymmetry in stance phase timing and lower limb muscle activity. We believe this paradigm, when extended to individuals with hemiparesis, can show improvements in weight bearing capability with positive effects on gait symmetry and walking speed.

Gait Analysis From a Single Ear-Worn Sensor: Reliability and Clinical Evaluation for Orthopaedic Patients

Objective assessment of detailed gait patterns after orthopaedic surgery is important for post-surgical follow-up and rehabilitation. The purpose of this paper is to assess the use of a single ear-worn sensor for clinical gait analysis. A reliability measure is devised for indicating the confidence level of the estimated gait events, allowing it to be used in free-walking environments and for facilitating clinical assessment of orthopaedic patients after surgery. Patient groups prior to or following anterior cruciate ligament (ACL) reconstruction and knee replacement were recruited to assess the proposed method. The ability of the sensor for detailed longitudinal analysis is demonstrated with a group of patients after lower limb reconstruction by considering parameters such as temporal and force-related gait asymmetry derived from gait events. The results suggest that the ear-worn sensor can be used for objective gait assessments of orthopaedic patients without the requirement and expense of an elaborate laboratory setup for gait analysis. It significantly simplifies the monitoring protocol and opens the possibilities for home-based remote patient assessment.

Classifying Regularized Sensor Covariance Matrices: An Alternative to CSP

Common spatial patterns (CSP) is a commonly used technique for classifying imagined movement type brain–computer interface (BCI) datasets. It has been very successful with many extensions and improvements on the basic technique. However, a drawback of CSP is that the signal processing pipeline contains two supervised learning stages: the first in which class- relevant spatial filters are learned and a second in which a classifier is used to classify the filtered variances. This may lead to potential overfitting issues, which are generally avoided by limiting CSP to only a few filters.

Endogenous sensory discrimination and selection by a fast brain switch for a high transfer rate brain-computer interface

We present a novel multi-class brain-computer interface (BCI) for communication and control. In this system, the information processing is shared by the algorithm (computer) and the user (human). Specifically, an electro-tactile cycle was presented to the user, providing the choice (class) by delivering timely sensory input. The user discriminated these choices by his/her endogenous sensory ability and selected the desired choice with an intuitive motor task. This selection was detected by a fast brain switch based on real-time detection of movement-related cortical potentials from scalp EEG. We demonstrated the feasibility of such a system with a four-class BCI, yielding a true positive rate of $sim {hbox{80}}%$ and $sim {hbox{70}}%$, and an information transfer rate of $sim {hbox{7}}~{hbox{bits}}/{hbox{min}}$ and $sim {hbox{5}}~{hbox{bits}}/{hbox{min}}$, for the movement and imagination selection command, respectively. Furthermore, when the system was extended to eight classes, the throughput of the system was improved, demonstrating the capability of accommodating a large number of classes. Combining the endogenous sensory discrimination with the fast brain switch, the proposed system could be an effective, multi-class, gaze-independent BCI system for communication and control applications.

Movement Anticipation and EEG: Implications for BCI-Contingent Robot Therapy

Brain–computer interfacing is a technology that has the potential to improve patient engagement in robot-assisted rehabilitation therapy. For example, movement intention reduces mu (8–13 Hz) oscillation amplitude over the sensorimotor cortex, a phenomenon referred to as event-related desynchronization (ERD). In an ERD-contingent assistance paradigm, initial BCI-enhanced robotic therapy studies have used ERD to provide robotic assistance for movement. Here we investigated how ERD changed as a function of audio-visual stimuli, overt movement from the participant, and robotic assistance. Twelve unimpaired subjects played a computer game designed for rehabilitation therapy with their fingers using the FINGER robotic exoskeleton. In the game, the participant and robot matched movement timing to audio-visual stimuli in the form of notes approaching a target on the screen set to the consistent beat of popular music. The audio-visual stimulation of the game alone did not cause ERD, before or after training. In contrast, overt movement by the subject caused ERD, whether or not the robot assisted the finger movement. Notably, ERD was also present when the subjects remained passive and the robot moved their fingers to play the game. This ERD occurred in anticipation of the passive finger movement with similar onset timing as for the overt movement conditions. These results demonstrate that ERD can be contingent on expectation of robotic assistance; that is, the brain generates an anticipatory ERD in expectation of a robot-imposed but predictable movement. This is a caveat that should be considered in designing BCIs for enhancing patient effort in robotically-assisted therapy.

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Spatial processing: Location, location, location

Nature on Neuroscience - Wed, 07/27/2016 - 22:00

Nature Reviews Neuroscience 17, 535 (2016). doi:10.1038/nrn.2016.106

Author: Sian Lewis

How individual CA1 pyramidal cells (PCs) contribute to spatial memory is not well understood. Changes in intracellular Ca2+ levels in specific PCs (indicating their place field) in superficial and deep layers of CA1 in mice were measured during a spatial-navigation task and a

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

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