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Neuroimmunology: Immune to the placebo effect

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

Nature Reviews Neuroscience 17, 535 (2016). doi:10.1038/nrn.2016.107

Author: Sian Lewis

Patient expectation and the activation of brain reward circuitry have a role in placebo-related clinical benefits, but the mechanism is unknown. Ben-Shaanan et al. show that chemogenetic activation of neurons in the ventral tegmental area followed by exposure to Escherichia coli resulted in

Decision making: Making your mind up

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

Nature Reviews Neuroscience 17, 535 (2016). doi:10.1038/nrn.2016.108

Author: Sian Lewis

During decision making, activity in several brain areas is increased, but their role in decision making is not known. Katz et al. recorded from neurons in the lateral intraparietal area (LIP) and middle temporal area (MT) of awake behaving rhesus macaques while they performed

Neurophysiology: Going with the flow

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

Nature Reviews Neuroscience 17, 535 (2016). doi:10.1038/nrn.2016.109

Author: Sian Lewis

Flow of cerebrospinal fluid (CSF) within the ventricular system of the brain is achieved by cilia on the ependyma that lines the ventricles and is important for the transport of signalling molecules. Here, 1 μm fluorescent beads were used to track cilium-generated flow in organotypic

Adhesion G protein-coupled receptors in nervous system development and disease

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

Nature Reviews Neuroscience 17, 550 (2016). doi:10.1038/nrn.2016.86

Authors: Tobias Langenhan, Xianhua Piao & Kelly R. Monk

Members of the adhesion G protein-coupled receptor (aGPCR) class have emerged as crucial regulators of nervous system development, with important implications for human health and disease. In this Review, we discuss the current understanding of aGPCR functions during key steps in neural development, including cortical

The enigmatic mossy cell of the dentate gyrus

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

Nature Reviews Neuroscience 17, 562 (2016). doi:10.1038/nrn.2016.87

Author: Helen E. Scharfman

Mossy cells comprise a large fraction of the cells in the hippocampal dentate gyrus, suggesting that their function in this region is important. They are vulnerable to ischaemia, traumatic brain injury and seizures, and their loss could contribute to dentate gyrus dysfunction in such conditions.

Meanings of self-grooming depend on an inverted U-shaped function with aversiveness

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

Nature Reviews Neuroscience 17, 591 (2016). doi:10.1038/nrn.2016.102

Authors: Alberto Fernández-Teruel & Celio Estanislau

The relationship between rodent self-grooming and stress and anxiety-like behaviour, and the regulation of such grooming by several emotion-linked brain areas, such as the amygdala–bed nucleus of the stria terminalis–hypothalamus circuit, are among the issues discussed by Kalueff et al. in their recent, excellent

'Stressing' rodent self-grooming for neuroscience research

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

Nature Reviews Neuroscience 17, 591 (2016). doi:10.1038/nrn.2016.103

Authors: Cai Song, Kent C. Berridge & Allan V. Kalueff

We appreciate the thoughtful Correspondence by Fernández-Teruel and Estanislau on our Review (Neurobiology of rodent self-grooming and its value for translational neuroscience. Nat. Rev. Neurosci.17, 45–59 (2016)), which raises the issue of the relationship between stress

Molecular mechanisms underlying alcohol-drinking behaviours

Nature on Neuroscience - Wed, 07/20/2016 - 22:00

Nature Reviews Neuroscience 17, 576 (2016). doi:10.1038/nrn.2016.85

Authors: Dorit Ron & Segev Barak

The main characteristic of alcohol use disorder is the consumption of large quantities of alcohol despite the negative consequences. The transition from the moderate use of alcohol to excessive, uncontrolled alcohol consumption results from neuroadaptations that cause aberrant motivational learning and memory processes. Here, we

Gut–brain communication: Making friends with microbes

Nature on Neuroscience - Wed, 07/06/2016 - 22:00

Nature Reviews Neuroscience 17, 533 (2016). doi:10.1038/nrn.2016.93

Author: Natasha Bray

In mice, maternal obesity induces differences in the gut microbiota of the offspring that can affect the development of social behaviour.

The Time-Varying Networks in P300: <?Pub _newline ?> A Task-Evoked EEG Study

P300 is an important event-related potential that can be elicited by external visual, auditory, and somatosensory stimuli. Various cognition-related brain functions (i.e., attention, intelligence, and working memory) and multiple brain regions (i.e., prefrontal, frontal, and parietal) are reported to be involved in the elicitation of P300. However, these studies do not investigate the instant interactions across the neural cortices from the hierarchy of milliseconds. Importantly, time-varying network analysis among these brain regions can uncover the detailed and dynamic information processing in the corresponding cognition process. In the current study, we utilize the adaptive directed transfer function to construct the time-varying networks of P300 based on scalp electroencephalographs, investigating the time-varying information processing in P300 that can depict the deeper neural mechanism of P300 from the network. Our analysis found that different stages of P300 evoked different brain networks, i.e., the center area performs as the central source during the decision process stage, while the source region is transferred to the right prefrontal cortex (rPFC) in the neuronal response stage. Moreover, during the neuronal response stage, the directed information that flows from the rPFC to the parietal cortex are remarkably important. These findings indicate that the two brain hemispheres exhibit asymmetrical functions in processing related information for different P300 stages, and this work may provide new evidence for our better understanding of the neural mechanism of P300 generation.

Single-Channel EMG Classification With Ensemble-Empirical-Mode-Decomposition-Based ICA for Diagnosing Neuromuscular Disorders

An accurate and computationally efficient quantitative analysis of electromyography (EMG) signals plays an inevitable role in the diagnosis of neuromuscular disorders, prosthesis, and several related applications. Since it is often the case that the measured signals are the mixtures of electric potentials that emanate from surrounding muscles (sources), many EMG signal processing approaches rely on linear source separation techniques such as the independent component analysis (ICA). Nevertheless, naive implementations of ICA algorithms do not comply with the task of extracting the underlying sources from a single-channel EMG measurement. In this respect, the present work focuses on a classification method for neuromuscular disorders that deals with the data recorded using a single-channel EMG sensor. The ensemble empirical mode decomposition algorithm decomposes the single-channel EMG signal into a set of noise-canceled intrinsic mode functions, which in turn are separated by the FastICA algorithm. A reduced set of five time domain features extracted from the separated components are classified using the linear discriminant analysis, and the classification results are fine-tuned with a majority voting scheme. The performance of the proposed method has been validated with a clinical EMG database, which reports a higher classification accuracy (98%). The outcome of this study encourages possible extension of this approach to real settings to assist the clinicians in making correct diagnosis of neuromuscular disorders.

Context-Dependent Upper Limb Prosthesis Control for Natural and Robust Use

Pattern recognition and regression methods applied to the surface EMG have been used for estimating the user intended motor tasks across multiple degrees of freedom (DOF), for prosthetic control. While these methods are effective in several conditions, they are still characterized by some shortcomings. In this study we propose a methodology that combines these two approaches for mutually alleviating their limitations. This resulted in a control method capable of context-dependent movement estimation that switched automatically between sequential (one DOF at a time) or simultaneous (multiple DOF) prosthesis control, based on an online estimation of signal dimensionality. The proposed method was evaluated in scenarios close to real-life situations, with the control of a physical prosthesis in applied tasks of varying difficulties. Test prostheses were individually manufactured for both able-bodied and transradial amputee subjects. With these prostheses, two amputees performed the Southampton Hand Assessment Procedure test with scores of 58 and 71 points. The five able-bodied individuals performed standardized tests, such as the box&block and clothes pin test, reducing the completion times by up to 30%, with respect to using a state-of-the-art pure sequential control algorithm. Apart from facilitating fast simultaneous movements, the proposed control scheme was also more intuitive to use, since human movements are predominated by simultaneous activations across joints. The proposed method thus represents a significant step towards intelligent, intuitive and natural control of upper limb prostheses.

A Mobile Kalman-Filter Based Solution for the Real-Time Estimation of Spatio-Temporal <?Pub _newline ?>Gait Parameters

Gait impairments are among the most disabling symptoms in several musculoskeletal and neurological conditions, severely limiting personal autonomy. Wearable gait sensors have been attracting attention as diagnostic tool for gait and are emerging as promising tool for tutoring and guiding gait execution. If their popularity is continuously growing, still there is room for improvement, especially towards more accurate solutions for spatio-temporal gait parameters estimation. We present an implementation of a zero-velocity-update gait analysis system based on a Kalman filter and off-the-shelf shoe-worn inertial sensors. The algorithms for gait events and step length estimation were specifically designed to comply with pathological gait patterns. More so, an Android app was deployed to support fully wearable and stand-alone real-time gait analysis. Twelve healthy subjects were enrolled to preliminarily tune the algorithms; afterwards sixteen persons with Parkinson's disease were enrolled for a validation study. Over the 1314 strides collected on patients at three different speeds, the total root mean square difference on step length estimation between this system and a gold standard was 2.9%. This shows that the proposed method allows for an accurate gait analysis and paves the way to a new generation of mobile devices usable anywhere for monitoring and intervention.

Prior-to- and Post-Impact Fall Detection Using Inertial and Barometric Altimeter Measurements

This paper investigates a fall detection system based on the integration of an inertial measurement unit with a barometric altimeter (BIMU). The vertical motion of the body part the BIMU was attached to was monitored on-line using a method that delivered drift-free estimates of the vertical velocity and estimates of the height change from the floor. The experimental study included activities of daily living of seven types and falls of five types, simulated by a cohort of 25 young healthy adults. The downward vertical velocity was thresholded at 1.38 m/s, yielding 80% sensitivity (SE), 100% specificity (SP) and a mean prior-to-impact time of 157 ms (range 40–300 ms). The soft falls, i.e., those with downward vertical velocity above 0.55 m/s and below 1.38 m/s were analyzed post-impact. Six fall detection methods, tuned to achieve 100% SE, were considered to include features of impact, change of posture and height, singularly or in association with one another. No single feature allowed for 100% SP. The detection accuracy marginally improved when the height change was considered in association with either the impact or the change of posture; the post-impact fall detection method that analyzed the impact and the change of posture together achieved 100% SP.

Measurement of Contact Behavior Including Slippage of Cuff When Using Wearable Physical Assistant Robot

Continuous use of wearable robots can cause skin injuries beneath the cuffs of robots. To prevent such injuries, understanding the contact behavior of the cuff is important. Thus far, this contact behavior has not been studied because of the difficulty involved in measuring the slippage under the cuff. In this study, for the first time, the relative displacement, slippage, and interaction force and moment at the thigh cuff of a robot during sit-to-stand motion were measured using an instrumented cuff, which was developed for this purpose. The results indicated that the slippage and relative displacement under the cuff was uneven because of the rotation of the cuff, which suggests that the risk of skin injuries is different at different positions. Especially, the skin closer to the hip showed larger dynamism, with a maximum slippage of approximately 10 mm and a displacement of 20 mm during motion. Another important phenomenon was the individual difference among subjects. During motion, the interaction force, moment, and slippage of some subjects suddenly increased. Such behavior results in stress concentration, which increases the risk of skin injuries. These analyses are intended to understand how skin injuries are caused and to design measures to prevent such injuries.

The Effects of Stimulation Strategy on Joint Movement Elicited by Intraspinal Microstimulation

The goal of this study was to characterize the effects of stimulation parameters and multielectrode stimulation on selectivity, range of motion, recruitment characteristics, and fatigue during intraspinal microstimulation (ISMS). A custom-made multielectrode array was implanted into the activation pool of the rat dorsiflexor muscle where the stimulation produced the highest movement range on the ankle joint and the least effect on the other joints. The results show that the selectivity could be significantly enhanced using multielectrode stimulation strategy. Moreover, the fatigue was significantly reduced using multielectrode synchronous stimulation with respect to single-electrode stimulation. For a given charge, stimulation with higher current amplitude and shorter pulse duration produced greater range of motion than that with lower amplitude and longer pulse duration. However, the stimulation with shorter duration caused greater fatigue than that with longer. In addition, there was a significant difference in time constant of spinal response obtained with different pulse amplitudes during pulse width (PW) modulation. The time constant decreased with increasing pulse amplitude. However, there was no significant effect of pulse duration on time constant during pulse amplitude (PA) modulation. The results suggest that the motor neurons (MNs) within the spinal cord can be recruited according to size principle by appropriate selection of stimulation parameters. Based on these results an efficient stimulation strategy can be designed for control of movement performance (i.e., speed of movement, fatigue, range of motion, and selectivity) during ISMS.

An Inflatable and Wearable Wireless System for Making 32-Channel Electroencephalogram Measurements

Potable electroencephalography (EEG) devices have become critical for important research. They have various applications, such as in brain–computer interfaces (BCI). Numerous recent investigations have focused on the development of dry sensors, but few concern the simultaneous attachment of high-density dry sensors to different regions of the scalp to receive qualified EEG signals from hairy sites. An inflatable and wearable wireless 32-channel EEG device was designed, prototyped, and experimentally validated for making EEG signal measurements; it incorporates spring-loaded dry sensors and a novel gasbag design to solve the problem of interference by hair. The cap is ventilated and incorporates a circuit board and battery with a high-tolerance wireless (Bluetooth) protocol and low power consumption characteristics. The proposed system provides a 500/250 Hz sampling rate, and 24 bit EEG data to meet the BCI system data requirement. Experimental results prove that the proposed EEG system is effective in measuring audio event-related potential, measuring visual event-related potential, and rapid serial visual presentation. Results of this work demonstrate that the proposed EEG cap system performs well in making EEG measurements and is feasible for practical applications.

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

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