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Electromagnetic hypersensitivity (EHS) was the subject of many scientific studies about a decade ago [1], and several research investigations have appeared since then. The syndromes of EHS consist of nervous-system symptoms, such as headache and fatigue; skin symptoms, like facial irritations and rashes; as well as other nonspecific healthrelated symptoms. Also, there have been reports of problems with concentration, loss of memory, and dizziness. Exposure of the affected persons was generally below the recommended standard promulgated in internationally accepted guidelines. [ABSTRACT FROM PUBLISHER]

The benign epilepsy with spinous waves in the central temporal region (BECT) is the one of the most common epileptic syndromes in children, that seriously threaten the nervous system development of children. The most obvious feature of BECT is the existence of a large number of electroencephalogram (EEG) spikes in the Rolandic area during the interictal period, that is an important basis to assist neurologists in BECT diagnosis. With this regard, the paper proposes a novel BECT spike detection algorithm based on time domain EEG sequence features and the long short-term memory (LSTM) neural network. Three time domain sequence features, that can obviously characterize the spikes of BECT, are extracted for EEG representation. The synthetic minority oversampling technique (SMOTE) is applied to address the spike imbalance issue in EEGs, and the bi-directional LSTM (BiLSTM) is trained for spike detection. The algorithm is evaluated using the EEG data of 15 BECT patients recorded from the Children’s Hospital, Zhejiang University School of Medicine (CHZU). The experiment shows that the proposed algorithm can obtained an average of 88.54% F1 score, 92.04% sensitivity, and 85.75% precision, that generally outperforms several state-of-the-art spike detection methods. [ABSTRACT FROM AUTHOR]

Among the various key networks in the human body, the nervous system occupies central importance. The debilitating effects of spinal cord injuries (SCI) impact a significant number of people throughout the world, and to date, there is no satisfactory method to treat them. In this paper, we review the major treatment techniques for SCI that include promising solutions based on information and communication technology (ICT) and identify the key characteristics of such systems. We then introduce two novel ICT-based treatment approaches for SCI. The first proposal is based on neural interface systems (NIS) with enhanced feedback, where the external machines are interfaced with the brain and the spinal cord such that the brain signals are directly routed to the limbs for movement. The second proposal relates to the design of self-organizing artificial neurons (ANs) that can be used to replace the injured or dead biological neurons. Apart from SCI treatment, the proposed methods may also be utilized as enabling technologies for neural interface applications by acting as bio-cyber interfaces between the nervous system and machines. Furthermore, under the framework of Internet of Bio-Nano Things (IoBNT), experience gained from SCI treatment techniques can be transferred to nano communication research. [ABSTRACT FROM AUTHOR]

Disease-affected nervous systems exhibit anatomical or physiological impairments that degrade processing, transfer, storage, and retrieval of neural information, leading to physical or intellectual disabilities. Brain implants may potentially promote clinical means for detecting and treating neurological symptoms by establishing direct communication between the nervous and artificial systems. Current technology can modify the neural function at the supracellular level as in Parkinson’s disease, epilepsy, and depression. However, recent advances in nanotechnology, nanomaterials, and molecular communications have the potential to enable brain implants to preserve the neural function at the subcellular level, which could increase effectiveness, decrease energy consumption, and make the leadless devices chargeable from outside the body or by utilizing the body’s own energy sources. In this paper, we focus on understanding the principles of elemental processes in synapses to enable diagnosis and treatment of brain diseases with pathological conditions using biomimetic synaptically interactive brain–machine interfaces (BMIs). First, we provide an overview of the synaptic communication system, followed by an outline of brain diseases that promote dysfunction in the synaptic communication system. Then, we discuss the technologies for brain implants and propose future directions for the design and fabrication of cognitive BMIs. The overarching goal of this paper is to summarize the status of engineering research at the interface between the technology and the nervous system and direct the ongoing research toward the point where synaptically interactive BMIs can be embedded in the nervous system. [ABSTRACT FROM AUTHOR]

The muscle synergy hypothesis assumes that the nervous system controls muscles in groups to simplify behavioral tasks, which makes it possible for modularizing motor function assessment. This paper presents a new assessment method based on muscle synergy space (MSS) model to evaluate motor functions after stroke. It consists of spatiotemporal feature module, muscle activation module and synergy activation module, and focuses on the spatial and temporal characteristics of muscle synergies via synergy vectors and activation coefficients. We further applied this method to reveal spatial and temporal characteristics difference of muscle synergy between healthy controls and stroke patients. The effectiveness and accuracy of MSS model were proved by significant positive correlations between Fugl-Meyer score and the total number of optimal synergies of three modules. This measurement methodology could serve as a quantitative indicator for motor function and provide more scientific rehabilitation guidance. [ABSTRACT FROM AUTHOR]

Objective: This paper introduces the Cross-frequency Amplitude Transfer Function (CATF), a model-free method for quantifying nonlinear stimulus-response interaction based on phase-locked amplitude relationship. Method: The CATF estimates the amplitude transfer from input frequencies at stimulation signal to their harmonics/intermodulation at the response signal. We first verified the performance of CATF in simulation tests with systems containing a static nonlinear function and a linear dynamic, i.e., Hammerstein and Wiener systems. We then applied the CATF to investigate the second-order nonlinear amplitude transfer in the human proprioceptive system from the periphery to the cortex. Result: The simulation demonstrated that the CATF is a general method, which can well quantify nonlinear stimulus-response amplitude transfer for different orders of nonlinearity in Wiener or Hammerstein system configurations. Applied to the human proprioceptive system, we found a complicated nonlinear system behavior with substantial amplitude transfer from the periphery stimulation to cortical response signals in the alpha band. This complicated system behavior may be associated with the nonlinear behavior of the muscle spindle and the dynamic interaction in the thalamocortical radiation. Conclusion: This paper provides a new tool to identify nonlinear interaction in the nervous system. Significance: The results provide novel insight of nonlinear dynamics in the human proprioceptive system. [ABSTRACT FROM AUTHOR]

Pain is a complex phenomenon, involving sensory and emotional experience, that is often poorly understood, especially in infants, anesthetized patients, and others who cannot speak. Technology supporting pain assessment has the potential to help reduce suffering; however, advances are needed before it can be adopted clinically. This survey paper assesses the state of the art and provides guidance for researchers to help make such advances. First, we overview pain’s biological mechanisms, physiological and behavioral responses, emotional components, as well as assessment methods commonly used in the clinic. Next, we discuss the challenges hampering the development and validation of pain recognition technology, and we survey existing datasets together with evaluation methods. We then present an overview of all automated pain recognition publications indexed in the Web of Science as well as from the proceedings of the major conferences on biomedical informatics and artificial intelligence, to provide understanding of the current advances that have been made. We highlight progress in both non-contact and contact-based approaches, tools using face, voice, physiology, and multi-modal information, the importance of context, and discuss challenges that exist, including identification of ground truth. Finally, we identify underexplored areas such as chronic pain and connections to treatments, and describe promising opportunities for continued advances. [ABSTRACT FROM AUTHOR]

In many studies and applications that include direct human involvement-such as human-robot interaction, control of prosthetic arms, and human factor studies-hand force is needed for monitoring or control purposes. The use of inexpensive and easily portable active electromyogram (EMG) electrodes and position sensors would be advantageous in these applications compared to the use of force sensors, which are often very expensive and require bulky frames. Multilayer perceptron artificial neural networks (MLPANN) have been used commonly in the literature to model the relationship between surface EMG signals and muscle or limb forces for different anatomies. This paper investigates the use of fast orthogonal search (FOS), a time-domain method for rapid nonlinear system identification, for elbow-induced wrist force estimation. It further compares the forces estimated using FOS with the forces estimated by MLPANN for the same human anatomy under an ensemble of operational conditions. In this paper, the EMG signal readings from upper arm muscles involved in elbow joint movement and sensed elbow angular position and velocity are utilized as inputs. A single degree-of-freedom robotic experimental testbed has been constructed and used for data collection, training and validation. [ABSTRACT FROM AUTHOR]

A challenging issue for a three-dimensional (3D) unmanned aerial vehicle (UAV) network is addressed in this paper - how do we efficiently establish and maintain one or multiple routes among swarm regions (i.e., groups of UAVs), during the dynamic swarming process? Inspired by the human nervous system which can efficiently send brain signals to any tissue, we propose a 3D transformative routing (3D-TR) scheme, which consists of three novel designs: (1) Proactive routing skeleton establishment: 3D-TR first identifies the skeleton of the UAV network, which has a trunk-stem-stemlet (TSS) structure. The trunk is located in the relatively stable area of the network (i.e., the central area), and stems/stemlets reside in the outer areas with more swarming movements. Such a TSS skeleton enables the easy establishment of the path(s) with low end-to-end delay and a high packet delivery rate. (2) Reactive, transformative path selection: The 3D-TR scheme can smoothly adapt to the formation changes through dynamic TSS adjustments. (3) Bottleneck-aware route maintenance: We propose an efficient scheme to recognize the bottleneck nodes in each path and build the diversified routing pipes with different thickness/stability levels. The proposed routing scheme is distributed (only uses local message exchanges among 1-hop neighbors) and does not require the GPS (global positioning system) positions of the UAVs. Extensive simulations are conducted to validate the performance of the proposed 3D-TR scheme during the dynamic UAV swarming process. [ABSTRACT FROM AUTHOR]

The maximum allowable powers (MAPs) of current density and internal electric field shown in the above paper [1, Figs. 15 and 16] are incorrectly scaled. They should be scaled as shown here. The respective context related to these figures should be corrected as shown in this Letters. [ABSTRACT FROM PUBLISHER]

Objective: This paper introduces a generalized coherence framework for detecting and characterizing nonlinear interactions in the nervous system, namely cross-spectral coherence (CSC). CSC can detect different types of nonlinear interactions including harmonic and intermodulation coupling as present in static nonlinearities and also subharmonic coupling, which only occurs with dynamic nonlinearities. Methods: We verified the performance of CSC in model simulations with both static and dynamic nonlinear systems. We applied CSC to investigate nonlinear stimulus–response interactions in the human proprioceptive system. A periodic movement perturbation was imposed to the wrist when the subjects performed an isotonic wrist flexion. CSC analysis was performed between the perturbation and brain responses (electroencephalogram, EEG). Results: Both the simulation and the application demonstrated that CSC successfully detected different types of nonlinear interactions. High-order nonlinearities were revealed in the proprioceptive system, shown in harmonic and intermodulation coupling between the perturbation and EEG for all subjects. Subharmonic coupling was found in some subjects but not all. Conclusion: This paper provides a general tool to detect and characterize nonlinear nature and dynamics of the nervous system. The application of CSC on the experimental dataset indicates a complex nonlinear dynamics in the proprioceptive system. Significance: This novel framework 1) unveils the nonlinear neural dynamics in a more complete way than the existing coherence measures, and 2) is more suitable for estimating the input–output relation regarding both phase and amplitude compared to phase synchrony measures (which only consider phase coupling). Subharmonic coupling is reported in human proprioceptive system for the first time. [ABSTRACT FROM PUBLISHER]

Traditional synthesis methods usually deal with the Array Factor information or with approaches based on considering ideal radiating elements without interaction between them. Taking into account the coupling between elements increases the complexity of the synthesis tasks, so it is usually disregarded assuming a certain error in the solution. As shown in the paper, this error can be unacceptable in some antenna array designs. A neural network-based solution can exploit the "a priori" knowledge of the radiating system to relate a given radiated field distribution with the voltages that must be applied to each radiating element taking into account mutual coupling effects without increase of complexity from the designer's point of view. A set of techniques based on the use of neural networks for synthesis purposes are presented in this paper. Results of a prototype of antenna array with feeding values designed using the proposed techniques are also presented. [ABSTRACT FROM AUTHOR]

An adaptive generalized predictive control (GPC) scheme for an industrial process is designed based on a neuron adaptive splitting and merging radial basis function neural network (NASM-RBFNN) in this paper. The NASM-RBFNN is developed to identify the dynamic behaviors of the industrial process. In order to provide an accurate prediction model for the GPC, a neuron adaptive splitting and merging strategy and a weighted parameters adaptive correction approach are proposed. The neuron adaptive splitting and merging strategy can automatically add or delete the hidden neurons on-line, and the weighted parameters adaptive correction approach can update the weights based on the error of the neural network. The proposed approaches can enable the NASM-RBFNN to be adapted to the time-varying production condition and stochastic disturbance. The stability analysis and convergence of neuron adaptive splitting and merging strategy and weighted parameters adaptive correction approach are given. Finally, the NASM-RBFNN-based GPC (NASM-RBFNN-GPC) is applied to control the iron removal process in the largest zinc hydrometallurgy plant in China. The industrial experiments demonstrate that the NASM-RBFNN-GPC has a satisfactory tracking and control performance. [ABSTRACT FROM AUTHOR]

Developing new tools to better understand disorders of the nervous system, with a goal to more effectively treat them, is an active area of bioelectronic medicine research. Future tools must be flexible and configurable, given the evolving understanding of both neuromodulation mechanisms and how to configure a system for optimal clinical outcomes. We describe a system, the Summit RC+S “neural coprocessor,” that attempts to bring the capability and flexibility of a microprocessor to a prosthesis embedded within the nervous system. This paper describes the updated system architecture for the Summit RC+S system, the five custom integrated circuits required for bi-directional neural interfacing, the supporting firmware/software ecosystem, and the verification and validation activities to prepare for human implantation. Emphasis is placed on design changes motivated by experience with the CE-marked Activa PC+S research tool; specifically, enhancement of sense-stim performance for improved bi-directional communication to the nervous system, implementation of rechargeable technology to extend device longevity, and application of MICS-band telemetry for algorithm development and data management. The technology was validated in a chronic treatment paradigm for canines with naturally occurring epilepsy, including free ambulation in the home environment, which represents a typical use case for future human protocols. [ABSTRACT FROM AUTHOR]

In pursuit of the goal to understand and eventually reproduce the diverse functions of the brain, a key challenge lies in reverse engineering the peculiar biology-based “technology” that underlies the brain's remarkable ability to process and store information. The basic building block of the nervous system is the nerve cell, or “neuron,” yet after more than 100 years of neurophysiological study and 60 years of modeling, the information processing functions of individual neurons, and the parameters that allow them to engage in so many different types of computation (sensory, motor, mnemonic, executive, etc.) remain poorly understood. In this paper, we review both historical and recent findings that have led to our current understanding of the analog spatial processing capabilities of dendrites, the major input structures of neurons, with a focus on the principal cell type of the neocortex and hippocampus, the pyramidal neuron (PN). We encapsulate our current understanding of PN dendritic integration in an abstract layered model whose spatially sensitive branch-subunits compute multidimensional sigmoidal functions. Unlike the 1-D sigmoids found in conventional neural network models, multidimensional sigmoids allow the cell to implement a rich spectrum of nonlinear modulation effects directly within their dendritic trees. [ABSTRACT FROM PUBLISHER]

Parkinson's disease (PD) is a chronic progressive disease caused by loss of dopaminergic neurons in the substantia nigra, degenerating the nervous system of a patient over time. Freezing of gait (FOG), which is a form of akinesia, is a symptom of PD. Meanwhile, recent studies show that the gait of PD patients experiencing FOG can be significantly improved by providing the regular visual or auditory patterns for the patients. In this paper, we propose a gait-aid system built upon smart glasses. Our system continuously monitors the gait and so on of a PD patient to detect FOG, and upon detection of FOG it projects visual patterns on the glasses as if the patterns were actually on the floor. Conducting experiments involving ten PD patients, we demonstrate that our system achieves the accuracy of 92.86$\%$ in detecting FOG episodes and that it improves the gait speed and stride length of PD patients by 15.3 $\sim$ 37.2% and 18.7  $\sim$ 31.7%, respectively. [ABSTRACT FROM AUTHOR]

This paper summarizes the discussions held during the First IEEE Life Sciences Grand Challenges Conference, held on October 4–5, 2012, at the National Academy of Sciences, Washington, DC, and the grand challenges identified by the conference participants. Despite tremendous efforts to develop the knowledge and ability that are essential in addressing biomedical and health problems using engineering methodologies, the optimization of this approach toward engineering the life sciences and healthcare remains a grand challenge. The conference was aimed at high-level discussions by participants representing various sectors, including academia, government, and industry. Grand challenges were identified by the conference participants in five areas including engineering the brain and nervous system; engineering the cardiovascular system; engineering of cancer diagnostics, therapeutics, and prevention; translation of discoveries to clinical applications; and education and training. A number of these challenges are identified and summarized in this paper. [ABSTRACT FROM PUBLISHER]

Spike sorting is an important processing step in various neuroscientific and clinical studies. Energy-efficient spike-sorting ASICs are necessary to allow real-time processing of multi-channel, wireless neural recordings. Spike-sorting ASICs have to meet stringent power-density constraints and must provide significant data-rate reduction for wireless transmission. Most existing designs either provide only spike detection for multi-channel processing, or they provide detection and feature extraction only for a single channel. In this paper, we demonstrate the design of a spike-sorting DSP chip that can perform detection, alignment, and feature extraction simultaneously for 64 channels. Spike-sorting algorithms chosen based on a complexity–performance analysis were implemented on ASIC using a MATLAB/Simulink-based architecture design framework. Energy–delay tradeoffs of the design were analyzed to identify the optimal degree of interleaving. The chip was implemented with a modular architecture, and can be configured to process 16, 32, 48, or 64 channels. Inactive cores are power-gated when the chip is operated to process a reduced number of channels. The chip, implemented in a 90-nm CMOS process, has a power dissipation of 130\,\muW (power density of 30\,\muW/mm^2) when processing all 64 channels and provides a data-rate reduction of 91.25% (11.71 Mb/s to 1.02 Mb/s). [ABSTRACT FROM PUBLISHER]

Electrodermal activity (EDA) is a sensitive index of sympathetic nervous system activity. Due to the lack of sensors that can be worn comfortably during normal daily activity and over extensive periods of time, research in this area is limited to laboratory settings or artificial clinical environments. We developed a novel, unobtrusive, nonstigmatizing, wrist-worn integrated sensor, and present, for the very first time, a demonstration of long-term, continuous assessment of EDA outside of a laboratory setting. We evaluated the performance of our device against a Food and Drug Administration (FDA) approved system for the measurement of EDA during physical, cognitive, as well as emotional stressors at both palmar and distal forearm sites, and found high correlations across all the tests. We also evaluated the choice of electrode material by comparing conductive fabric with Ag/AgCl electrodes and discuss the limitations found. An important result presented in this paper is evidence that the distal forearm is a viable alternative to the traditional palmar sites for EDA measurements. Our device offers the unprecedented ability to perform comfortable, long-term, and in situ assessment of EDA. This paper opens up opportunities for future investigations that were previously not feasible, and could have far-reaching implications for diagnosis and understanding of psychological or neurological conditions. [ABSTRACT FROM AUTHOR]

This paper considers neural signal processing applied to extracellular recordings, in particular, unsupervised action potential detection at a low signal-to-noise ratio. It adopts the basic framework of the multiresolution Teager energy operator (MTEO) detector, but presents important new results including a significantly improved MTEO detector with some mathematical analyses, a new alignment technique with its effects on the whole spike sorting system, and a variety of experimental results. Specifically, the new MTEO detector employs smoothing windows normalized by noise power derived from mathematical analyses and has an improved complexity by utilizing the sampling rate. Experimental results prove that this detector achieves higher detection ratios at a fixed false alarm ratio than the TEO detector and the discrete wavelet transform detector. We also propose a method that improves the action potential alignment performance. Observing that the extreme points of the MTEO output are more robust to the background noise than those of the action potentials, we use the MTEO output for action potential alignment. This brings not only noticeable improvement in alignment performance but also quite favorable influence over the classification performance. Accordingly, the proposed detector improves the performance of the whole spike sorting system. We verified the improvement using various modeled neural signals and some real neural recordings. [ABSTRACT FROM AUTHOR]

In vivo multi-target and selective concentration monitoring of neurotransmitters can help to unravel the brain chemical complex signaling interplay. This paper presents a dedicated integrated potentiostat transducer circuit and its selective electrode interface. A custom 2-electrode time-based potentiostat circuit was fabricated with 0.13 μm CMOS technology and provides a wide dynamic input current range of 20 pA to 600 nA with 56 μ W, for a minimum sampling frequency of 1.25 kHz. A multi-working electrode chip is functionalized with carbon nanotubes (CNT)-based chemical coatings that offer high sensitivity and selectivity towards electroactive dopamine and non-electroactive glutamate. The prototype was experimentally tested with different concentrations levels of both neurotransmitter types, and results were similar to measurements with a commercially available potentiostat. This paper validates the functionality of the proposed biosensor, and demonstrates its potential for the selective detection of a large number of neurochemicals.

Brain–machine–body interfaces (BMBIs) aim to create an artificial connection in the nervous system by converting neural activity recorded from one cortical region to electrical stimuli delivered to another cortical region, spinal cord, or muscles in real-time. In particular, conditioning-mode BMBIs utilize such activity-dependent stimulation strategies to induce functional re-organization in the nervous system and promote functional recovery after injury by exploiting mechanisms underlying neuroplasticity. This paper reports on reconfigurable, field-programmable gate array (FPGA)-based implementation of a translation algorithm to extract multichannel stimulus trigger signals from intracortical neural spike activity. The approach features digital spike discrimination based on user-set thresholding and time-amplitude windowing, decision making to support different triggering patterns for various stimulation scenarios, as well as trigger-pattern-dependent blanking schemes for robust operation in the presence of stimulus artifacts. Readily lending itself to low-power, low-area implementation for future integration, the algorithm has been synthesized on a Cyclone II FPGA using Altera’s Quartus II design software and validated experimentally with prerecorded intracortical neural spike activity from an anesthetized laboratory rat. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM PUBLISHER]

Pain is a subjective phenomenon that causes suffering and that everyone experiences at some point. However, non-communicative patients, they can not express the experience of pain in the same way that conscious people. In this paper are discussed the ethical guidelines that should be considered for pain testing in non-communicative patients with research purposes, for pain assistance and improving of quality life of these kind of patients. [ABSTRACT FROM PUBLISHER]

Recently, there has been a renovated interest in functional dependencies due to the possibility of employing them in several advanced database operations, such as data cleaning, query relaxation, record matching, and so forth. In particular, the constraints defined for canonical functional dependencies have been relaxed to capture inconsistencies in real data, patterns of semantically related data, or semantic relationships in complex data types. In this paper, we have surveyed 35 of such functional dependencies, providing a classification criteria, motivating examples, and a systematic analysis of them. [ABSTRACT FROM PUBLISHER]

Automatic localization of the standard plane containing complicated anatomical structures in ultrasound (US) videos remains a challenging problem. In this paper, we present a learning-based approach to locate the fetal abdominal standard plane (FASP) in US videos by constructing a domain transferred deep convolutional neural network (CNN). Compared with previous works based on low-level features, our approach is able to represent the complicated appearance of the FASP and hence achieve better classification performance. More importantly, in order to reduce the overfitting problem caused by the small amount of training samples, we propose a transfer learning strategy, which transfers the knowledge in the low layers of a base CNN trained from a large database of natural images to our task-specific CNN. Extensive experiments demonstrate that our approach outperforms the state-of-the-art method for the FASP localization as well as the CNN only trained on the limited US training samples. The proposed approach can be easily extended to other similar medical image computing problems, which often suffer from the insufficient training samples when exploiting the deep CNN to represent high-level features. [ABSTRACT FROM PUBLISHER]

Hammersmith Infant Neurological Examination (HINE) is a set of tests used for grading neurological development of infants on a scale of 0 to 3. These tests help in assessing neurophysiological development of babies, especially preterm infants who are born before (the fetus reaches) the gestational age of 36 weeks. Such tests are often conducted in the follow-up clinics of hospitals for grading infants with suspected disabilities. Assessment based on HINE depends on the expertise of the physicians involved in conducting the examinations. It has been noted that some of these tests, especially pulled-to-sit and lateral tilting, are difficult to assess solely based on visual observation. For example, during the pulled-to-sit examination, the examiner needs to observe the relative movement of the head with respect to torso while pulling the infant by holding wrists. The examiner may find it difficult to follow the head movement from the coronal view. Video object tracking based automatic or semi-automatic analysis can be helpful in this case. In this paper, we present a video based method to automate the analysis of pulled-to-sit examination. In this context, a dynamic programming and node pruning based efficient video object tracking algorithm has been proposed. Pulled-to-sit event detection is handled by the proposed tracking algorithm that uses a 2-D geometric model of the scene. The algorithm has been tested with normal as well as marker based videos of the examination recorded at the neuro-development clinic of the SSKM Hospital, Kolkata, India. It is found that the proposed algorithm is capable of estimating the pulled-to-sit score with sensitivity (80%-92%) and specificity (89%-96%).

Provides instructions and guidelines to prospective authors who wish to submit manuscripts. [ABSTRACT FROM AUTHOR]

This paper quantifies and comparatively validates functional connectivity between neurons by measuring the statistical dependence between their firing rates. Based on statistical analysis of the pairwise functional connectivity, we estimate, exclusively from neural data, the neural assembly functional connectivity given a behavior task, which provides a quantifiable representation of the dynamic nature during the behavioral task. Because of the time scale of behavior (100-1000 ms), a statistical method that yields robust estimators for this small sample size is desirable. In this work, the temporal resolutions of four estimators of functional connectivity are compared on both simulated data and real neural ensemble recordings. The comparison highlights how the properties and assumptions of statistical-based and phase-based metrics affect the interpretation of connectivity. Simulation results show that mean square contingency (MSC) and mutual information (MI) create more robust quantification of functional connectivity under identical conditions than cross correlation (CC) and phase synchronization (PhS) when the sample size is 1 s. The results of the simulated analysis are extended to real neuronal recordings to assess the functional connectivity in monkey's cortex corresponding to three movement states in a food reaching task and construct the assembly graph given a movement state and the activation degree of a state-related assembly over time using the statistical test exclusively from neural data dependencies. The activation degree of a given state-related assembly reaches the peak repeatedly when the specific movement states occur, which also reveals the network of interactions among the neurons are key for the operation of a specific behavior. [ABSTRACT FROM AUTHOR]

Biodynamic feedthrough (BDFT) refers to a phenomenon where accelerations cause involuntary limb motions, which can result in unintentional control inputs that can substantially degrade manual control. It is known that humans can adapt the dynamics of their limbs by adjusting their neuromuscular settings, and it is likely that these adaptations have a large influence on BDFT. The goal of this paper is to present a method that can provide evidence for this hypothesis. Limb dynamics can be described by admittance, which is the causal dynamic relation between a force input and a position output. This paper presents a method to simultaneously measure BDFT and admittance in a motion-based simulator. The method was validated in an experiment. Admittance was measured by applying a force disturbance signal to the control device; BDFT was measured by applying a motion disturbance signal to the motion simulator. To allow distinguishing between the operator's responses to each disturbance signal, the perturbation signals were separated in the frequency domain. To show the impact of neuromuscular adaptation, subjects were asked to perform three different control tasks, each requiring a different setting of the neuromuscular system (NMS). Results show a dependence of BDFT on neuromuscular admittance: A change in neuromuscular admittance results in a change in BDFT dynamics. This dependence is highly relevant when studying BDFT. The data obtained with the proposed measuring method provide insight in how exactly the settings of the NMS influence the level of BDFT. This information can be used to gain fundamental knowledge on BDFT and also, for example, in the development of a canceling controller. [ABSTRACT FROM PUBLISHER]

A pupillary light reflex (PLR) model was proposed in this paper by considering the iris muscle mechanical properties and modulation inputs from both parasympathetic and sympathetic systems. The model can describe very well the experimental PLR responses induced by a short light flash of various intensities. In addition, an inverse method was developed to fit numerically this model to experimental PLR data. The model was tested in experimental human PLR data to extract separately the parasympathetic and sympathetic modulations during PLR. The results indicated a higher parasympathetic and a lower sympathetic activity in females than in males, which was consistent with previous findings in cardiovascular studies. This new model may help improve our understanding of the PLR process and could be applied to analyze autonomic nervous interaction during pupillary responses. [ABSTRACT FROM AUTHOR]

Reach-to-grasp tasks are composed of several actions that are more and more considered as simultaneously controlled by the central nervous system in a feedforward manner (at least for well-known activities). If this hypothesis is correct, during prehension tasks, the activity of proximal muscles (and not only of the distal ones used to control finger movements) is modulated according to the kind of object to be grasped and its position. This means that different objects could be identified by processing the electromyographic (EMG) signals recorded from proximal muscles. In this paper, specific experiments have been carried out to support this hypothesis in able-bodied subjects. The results achieved seem to confirm this possibility by showing that the activation of proximal muscles can be statistically different for different grip types. This finding supports the hypothesis that proximal and distal muscles are simultaneously controlled during reaching and grasping. Moreover, this kind of information could allow the development of an EMG-based control strategy based on the natural muscular activities selected by the central nervous system. [ABSTRACT FROM AUTHOR]

The Expanded Disability Status Scale (EDSS) has been the most widely used measure of disability in multiple sclerosis (MS) clinical trials. Although EDSS has the advantage of familiarity with respect to recent proposals, and remains the de facto standard, it is difficult to use consistently between evaluators. Automatic EDSS (AEDSS) is an expert system designed to overcome this problem. It constrains the neurologist to follow precise reasoning steps, enhancing EDSS reliability. In this paper, we show how a deep analysis of the neurological knowledge involved has been essential for adopting AEDSS in routine clinical use. We present an ontology for the EDSS domain and highlight the enhancements to AEDSS due to this additional knowledge. A validation experiment in four MS centers in Italy showed that AEDSS reduces interrater variability, and in many cases, is able to correct errors of neurologists. [ABSTRACT FROM AUTHOR]

In this paper, we study the spontaneous cortical neuronal network in hopes of finding a reliable indicator of burst initiation pathway, which would allow us to study burst initiation in conjunction with burst propagation in future research. Electrical activity is recorded using a 96-electrode microelectrode array on a weekly batch culture (half of the medium was replaced twice every week). We hypothesize that the first-spike onset sequence, which we call first-spike rank order (FSRO) is a reliable indicator i of burst initiation, and verified our hypothesis by studying evoked bursts using rearranged rank probability matrices. Under similar conditions, stimulating the same site reliably reproduces the same FSRO. Spontaneous bursts can be classified based on their FSRO using dendrogram clustering. Bursts with different first-spike sequences showed evidence of sharing common early-to-fire neurons, but early-to-fire neurons only consist of a minority of neuronal activity during burst initiation, which is in partial accordance with existing literature. In the study of early-to-fire neurons, we also noticed that our batch-cultured network did not show clear preburst activity, which may indicate fundamental difference compared to continuous perfusion culture. [ABSTRACT FROM AUTHOR]

Multivariate point processes are increasingly being used to model neuronal response properties in the cortex. Estimating the conditional intensity functions underlying these processes is important to characterize and decode the firing patterns of cortical neurons. This paper proposes a new approach for estimating these intensity functions directly from a compressed representation of the neurons' extracellular recordings. The approach is based on exploiting a sparse representation of the extracellular spike waveforms, previously demonstrated to yield near-optimal denoising and compression properties. We show that by restricting this sparse representation to a subset of projections that simultaneously preserve features of the spike waveforms in addition to the temporal characteristics of the underlying intensity functions, we can reasonably approximate the instantaneous firing rates of the recorded neurons with variable tuning characteristics across a multitude of time scales. Such feature is highly desirable to detect subtle temporal differences in neuronal firing characteristics from single-trial data. An added advantage of this approach is that it eliminates multiple steps from the typical processing path of neural signals that are customarily performed for instantaneous neural decoding. We demonstrate the decoding performance of the approach using a stochastic cosine tuning model of motor cortical activity during a natural, nongoal-directed 2-D arm movement. [ABSTRACT FROM AUTHOR]

In this paper, we present an assessment of the practical value of existing traditional and nonstandard measures for discriminating healthy people from people with Parkinson's disease (PD) by detecting dysphonia. We introduce a new measure of dysphonia, pitch period entropy (PPE), which is robust to many uncontrollable confounding effects including noisy acoustic environments and normal, healthy variations in voice frequency. We collected sustained phonations from 31 people, 23 with PD. We then selected ten highly uncorrelated measures, and an exhaustive search of all possible combinations of these measures finds four that in combination lead to overall correct classification performance of 91.4%, using a kernel support vector machine. In conclusion, we find that nonstandard methods in combination with traditional harmonics-to noise ratios are best able to separate healthy from PD subjects. The selected nonstandard methods are robust to many uncontrollable variations in acoustic environment and individual subjects, and are thus well suited to telemonitoring applications. [ABSTRACT FROM AUTHOR]

Several treatment modalities for neurodegenerative diseases or tumors of the central nervous system involve invasive delivery of large molecular weight drugs to the brain. Despite the ample record of experimental studies, accurate drug targeting for the human brain remains a challenge. This paper proposes a systematic design method of administering drugs to specific locations in the human brain based on first principles transport in porous media. The proposed mathematical framework predicts achievable treatment volumes in target regions as a function of brain anatomy and infusion catheter position. A systematic procedure to deter- mine the optimal infusion and catheter design parameters that maximize the penetration depth and volumes of distribution will be discussed. The computer simulations are validated with agarose gel phantom experiments and rat data. The rigorous computational approach will allow physicians and scientists to better plan the administration of therapeutic drugs to the central nervous system. [ABSTRACT FROM AUTHOR]

The cochlear implant is the most successful neural prosthesis to date and may serve as a paradigm for the development or further development of other systems to interface sensors with the nervous system, e.g., visual or vestibular prostheses. This paper traces the history of cochlear implants and describes how the current levels of performance have been achieved. Lessons and insights from this experience are presented in concluding sections. [ABSTRACT FROM PUBLISHER]

A bionic neuron (BION) is an inductively powered, miniature implant developed for functional electric stimulation (FES) to reanimate paralyzed limbs. This paper investigates the possibility of reusing the BION antenna coil as a magnetic sensor to provide meaningful posture information for feedback control of FES. A variety of techniques have been developed to model and cancel nonideal effects caused by the shapes of the internal and external coils, ferrite material, and electronic connections. Field warping has been employed to both amplitude and direction to achieve more accurate description of the dipole magnetic field generated by external coils suitable for generating a reference magnetic frame in the environment of a wheelchair. Models of the transmitting coil and the receiving BION coil were validated against experimental data, providing a solid foundation for implementing a sensor system. Based on the established model, a magnetic sensing system combined with customized microelectro-mechanical systems (MEMS) accelerometer has been designed and tested as a prototype on the bench. The sensor output can be employed to compute 6-D position and orientation. A two-step algorithm integrated with multiple error-cancelling techniques demonstrated sufficient accuracy in bench tests to appear promising for control of reach-and-grasp tasks. A sensor fusion step is proposed to estimate the position and orientation of a limb segment using data from multiple implants in muscles, where they will also function as neuromuscular stimulators to produce the movements to be controlled. [ABSTRACT FROM AUTHOR]

This paper reports the development and in-vivo testing of a compact multitransducer microsystem intended for neuroethology experiments, including studies of gait dynamics in free-running insects. The system incorporates a combination of custom and off-the-shelf components. Its suite of measurement devices comprises leg-mounted strain gauges, electromyogram (EMG) and extracellular electrodes for the central nervous system, and a two-axis accelerometer. For signal conditioning and selection, the microsystem implements off-the-shelf electronics in a custom chip-on-board configuration. The microsystem measures 16 mm × 19 mm, supports 40 components and 56 I/O leads, and is assembled on a four-layer printed-circuit board. The entire system occupies only 0.65 cm³ and weighs less than 5 g. It has been successfully used to monitor leg-strain and EMG signals on walking cockroaches and for stimulation in the insect central nervous and muscular systems. [ABSTRACT FROM AUTHOR]

Robot-mediated neurorehabilitation is a rapidly advancing field that seeks to use advances in robotics, virtual realities, and haptic interfaces, coupled with theories in neuroscience and rehabilitation to define new methods for treating neurological injuries such as stroke, spinal cord injury, and traumatic brain injury. The field is nascent and much work is needed to identify efficient hardware, software, and control system designs alongside the most effective methods for delivering treatment in home and hospital settings. This paper identifies the need for robots in neurorehabilitation and identifies important goals that will allow this field to advance. [ABSTRACT FROM AUTHOR]

Spike sorting is the mandatory first step in analyzing multiunit recording signals for studying information processing mechanisms within the nervous system. Extracellular recordings usually contain overlapped spikes produced by a number of neurons adjacent to the electrode, together with unknown background noise, which in turn induce some difficulties in neural signal identification. In this paper, we propose a robust method to deal with these problems, which employs an automatic overlap decomposition technique based on the relaxation algorithm that requires simple fast Fourier transforms. The performance of the presented system was tested at various signal-to-noise ratio levels based on synthetic data that were generated from real recordings. [ABSTRACT FROM AUTHOR]

The amount of experimental data concerning physiology and anatomy of the nervous system is growing very fast, challenging our capacity to make comprehensive syntheses of the plethora of data available. Computer models of neuronal networks provide useful tools to construct such syntheses. They can be used to interpret experimental data, generate experimentally testable predictions, and formulate new hypotheses regarding the function of the neural systems. Models can also act as a bridge between different levels of neuronal organization. The ultimate aim of computational neuroscience is to provide a link between behavior and underlying neural mechanisms. Depending on the specific aim of the model, there are different levels of neuronal organization at which the model can be set. Models are constructed at the microscopic (molecular and cellular), macroscopic level (local populations or systems), or dynamical systems level. Apart from purely computational models, hybrid networks are being developed in which biological neurons are connected in vitro to computer simulated neurons. Also, neuromorphic systems are recently being created using silicon chips that mimic computational operations in the brain. This paper reviews various computational models of the brain and insights obtained through their simulations. [ABSTRACT FROM AUTHOR]

There have been numerous studies presented in the literature related to the simulation of the interaction between biological neurons and electronic devices. A complicating factor associated with these simulations is the algebraic complexity involved in implementation. This complication has impeded simulation of more involved neural-electronic circuitry and consequently has limited potential advancements in the integration of biological neurons with synthetic electronics. In this paper, we describe a modification to a previously proposed SPICE based Hodgkin-Huxley neuron model that demonstrates more physiologically relevant electrical behavior. We utilize this SPICE based neuron model in conjunction with an external circuit that allows for artificial selective inhibition of neural spiking. The neural firing control scheme proposed herein would allow for action potential frequency modulation of neural activity that, if developed further, could potentially be applied to suppress undesirable neural activity that manifests symptomatically as the tremors or seizures associated with specific pathologies of the nervous system. [ABSTRACT FROM AUTHOR]

The effects of spinal cord injuries are likely to be ameliorated with the help of functional electrical stimulation of the spinal cord, a technique that may benefit from a new style of electrode: the cylindrical multielectrode. This paper describes the specifications for, fabrication techniques for, and in vitro evaluation of cylindrical multielectrodes. Four tip shapes were tested to determine which shape required the lowest peak force and would, therefore, be expected to minimize dimpling during implantation. The impedance of the electrode interface was monitored for changes due to insertion as well as repetitive delivery of current pulses. The charge delivery capacity was determined by testing with safe (≤ 0.6 mC/cm²) and damaging levels (≥ 0.8 mC/cm²) of charge density. The results of these tests suggest that this electrode design could be used to stimulate neurons in the ventral horn of the spinal cord. [ABSTRACT FROM AUTHOR]