Journal of Neural Engineering (J Neural Eng)

Publisher: Institute of Physics (Great Britain), IOP Publishing

Journal description

Journal of Neural Engineering is a new forum for the interdisciplinary field of neural engineering, where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. Articles will cover the field of neural engineering at the molecular, cellular and systems levels.

Current impact factor: 3.42

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013 / 2014 Impact Factor 3.415
2012 Impact Factor 3.282
2011 Impact Factor 3.837
2010 Impact Factor 2.628
2009 Impact Factor 3.739
2008 Impact Factor 2.737

Impact factor over time

Impact factor

Additional details

5-year impact 4.05
Cited half-life 3.80
Immediacy index 0.67
Eigenfactor 0.01
Article influence 1.25
Website Journal of Neural Engineering website
Other titles Journal of neural engineering (Online), Neural engineering, JNE
ISSN 1741-2552
OCLC 54314172
Material type Document, Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

IOP Publishing

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Pre-print on author's personal website, repository or arXiv.
    • Pre-print can not be updated after submission
    • Post-print on author's personal website immediately
    • Post-print on institutional repository, subject-based repository, PubMed Central or third party eprint servers after 12 months embargo
    • Publisher's version/PDF cannot be used
    • Published source must be acknowledged with citation
    • Must link to publisher version with DOI
    • Set statements to accompany different versions (see policy)
    • This policy is an exception to the default policies of 'IOP Publishing'
  • Classification
    ​ green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: Ojective. Axons are guided toward desired targets through a series of choice points that they navigate by sensing cues in the cellular environment. A better understanding of how microenvironmental factors influence neurite growth during development can inform strategies to address nerve injury. Therefore, there is a need for biomimetic models to systematically investigate the influence of guidance cues at such choice points. We ran an adapted in silico biased turning axon growth model under the influence of nerve growth factor (NGF) and compared the results to corresponding in vitro experiments. We examined if growth simulations were predictive of neurite population behavior at a choice point. We used a biphasic micropatterned hydrogel system consisting of an outer cell restrictive mold that enclosed a bifurcated cell permissive region and placed a well near a bifurcating end to allow proteins to diffuse and form a gradient. Experimental diffusion profiles in these constructs were used to validate a diffusion computational model that utilized experimentally measured diffusion coefficients in hydrogels. The computational diffusion model was then used to establish defined soluble gradients within the permissive region of the hydrogels and maintain the profiles in physiological ranges for an extended period of time. Computational diffusion profiles informed the neurite growth model, which was compared with neurite growth experiments in the bifurcating hydrogel constructs. Results indicated that when applied to the constrained choice point geometry, the biased turning model predicted experimental behavior closely. Results for both simulated and in vitro neurite growth studies showed a significant chemoattractive response toward the bifurcated end containing an NGF gradient compared to the control, though some neurites were found in the end with no NGF gradient. The integrated model of neurite growth we describe will allow comparison of experimental studies against growth cone guidance computational models applied to axon pathfinding at choice points.
    Journal of Neural Engineering 08/2015; 12(4). DOI:10.1088/1741-2560/12/4/046012
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    ABSTRACT: The goal of this study was to develop a physiologically plausible, computationally robust model for muscle activation dynamics (A(t)) under physiologically relevant excitation and movement. The interaction of excitation and movement on A(t) was investigated comparing the force production between a cat soleus muscle and its Hill-type model. For capturing A(t) under excitation and movement variation, a modular modeling framework was proposed comprising of three compartments: (1) spikes-to-[Ca(2+)]; (2) [Ca(2+)]-to-A; and (3) A-to-force transformation. The individual signal transformations were modeled based on physiological factors so that the parameter values could be separately determined for individual modules directly based on experimental data. The strong dependency of A(t) on excitation frequency and muscle length was found during both isometric and dynamically-moving contractions. The identified dependencies of A(t) under the static and dynamic conditions could be incorporated in the modular modeling framework by modulating the model parameters as a function of movement input. The new modeling approach was also applicable to cat soleus muscles producing waveforms independent of those used to set the model parameters. This study provides a modeling framework for spike-driven muscle responses during movement, that is suitable not only for insights into molecular mechanisms underlying muscle behaviors but also for large scale simulations.
    Journal of Neural Engineering 06/2015; 12(4):046025. DOI:10.1088/1741-2560/12/4/046025
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    ABSTRACT: Visual P300-based brain-computer interface spellers offer a useful communication channel for locked-in patients, who are completely dependent in their daily lives. One of the research goals for these systems is to achieve greater communication rates by means of modifying some features of their interfaces, e.g., reducing the matrix size. However, such modifications may not work well with disabled end-users, such as patients of amyotrophic lateral sclerosis (ALS), due to a supposed reduction of their cognitive resources. The purpose of the present study was to provide a proof of concept that ALS patients could efficiently use a P300-based speller with a 4 × 3 symbol matrix based on the T9 interface developed for mobile phones. We conducted an experiment with a sample of 11 able-bodied participants and one locked-in patient with ALS. All participants tested our T9-like visual P300-based speller and also two different 7 × 6 matrix spellers based on Farwell and Donchin's classic proposal-one of them included a word predictor system like the T9-like speller did. The performance analyses indicated that the locked-in patient benefited from using a reduced matrix size as much as healthy users did, spelling words almost 1.6 times faster and equally accurately when using the T9-like speller than when using the alternative spellers. Due to counting on only one locked-in patient, the current work constitutes a feasibility study. The actual usability of systems such as the one proposed in this paper should be determined by means of studies with a greater number of end-users in real-life conditions.
    Journal of Neural Engineering 06/2015; 12(4):046023. DOI:10.1088/1741-2560/12/4/046023
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    ABSTRACT: Peripheral nerves carry neural signals that could be used to control hybrid bionic systems. Cuff electrodes provide a robust and stable interface but the recorded signal amplitude is small (<3 μVrms 700 Hz-7 kHz), thereby requiring a baseline noise of less than 1 μVrms for a useful signal-to-noise ratio (SNR). Flat interface nerve electrode (FINE) contacts alone generate thermal noise of at least 0.5 μVrms therefore the amplifier should add as little noise as possible. Since mainstream neural amplifiers have a baseline noise of 2 μVrms or higher, novel designs are required. Here we apply the concept of hardware averaging to nerve recordings obtained with cuff electrodes. An optimization procedure is developed to minimize noise and power simultaneously. The novel design was based on existing neural amplifiers (Intan Technologies, LLC) and is validated with signals obtained from the FINE in chronic dog experiments. We showed that hardware averaging leads to a reduction in the total recording noise by a factor of 1/√N or less depending on the source resistance. Chronic recording of physiological activity with FINE using the presented design showed significant improvement on the recorded baseline noise with at least two parallel operation transconductance amplifiers leading to a 46.1% reduction at N = 8. The functionality of these recordings was quantified by the SNR improvement and shown to be significant for N = 3 or more. The present design was shown to be capable of generating <1.5 μVrms total recording baseline noise when connected to a FINE placed on the sciatic nerve of an awake animal. An algorithm was introduced to find the value of N that can minimize both the power consumption and the noise in order to design a miniaturized ultralow-noise neural amplifier. These results demonstrate the efficacy of hardware averaging on noise improvement for neural recording with cuff electrodes, and can accommodate the presence of high source impedances that are associated with the miniaturized contacts and the high channel count in electrode arrays. This technique can be adopted for other applications where miniaturized and implantable multichannel acquisition systems with ultra-low noise and low power are required.
    Journal of Neural Engineering 06/2015; 12(4):046024. DOI:10.1088/1741-2560/12/4/046024
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    ABSTRACT: To explore the mechanism of lower extremity dysfunction of cerebral palsy (CP) children through muscle synergy analysis. Twelve CP children were involved in this study, ten adults (AD) and eight typically developed (TD) children were recruited as a control group. Surface electromyographic (sEMG) signals were collected bilaterally from eight lower limb muscles of the subjects during forward walking at a comfortable speed. A nonnegative matrix factorization algorithm was used to extract muscle synergies. In view of muscle synergy differences in number, structure and symmetry, a model named synergy comprehensive assessment (SCA) was proposed to quantify the abnormality of muscle synergies. There existed larger variations between the muscle synergies of the CP group and the AD group in contrast with the TD group. Fewer mature synergies were recruited in the CP group, and many abnormal synergies specific to the CP group appeared. Specifically, CP children were found to recruit muscle synergies with a larger difference in structure and symmetry between two legs of one subject and different subjects. The proposed SCA scale demonstrated its great potential to quantitatively assess the lower-limb motor dysfunction of CP children. SCA scores of the CP group (57.00 ± 16.78) were found to be significantly less (p < 0.01) than that of the control group (AD group: 95.74 ± 2.04; TD group: 84.19 ± 11.76). The innovative quantitative results of this study can help us to better understand muscle synergy abnormality in CP children, which is related to their motor dysfunction and even the physiological change in their nervous system.
    Journal of Neural Engineering 06/2015; 12(4):046017. DOI:10.1088/1741-2560/12/4/046017
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    ABSTRACT: The P300 speller is a common brain-computer interface (BCI) application designed to communicate language by detecting event related potentials in a subject's electroencephalogram signal. Information about the structure of natural language can be valuable for BCI communication, but attempts to use this information have thus far been limited to rudimentary n-gram models. While more sophisticated language models are prevalent in natural language processing literature, current BCI analysis methods based on dynamic programming cannot handle their complexity. Sampling methods can overcome this complexity by estimating the posterior distribution without searching the entire state space of the model. In this study, we implement sequential importance resampling, a commonly used particle filtering (PF) algorithm, to integrate a probabilistic automaton language model. This method was first evaluated offline on a dataset of 15 healthy subjects, which showed significant increases in speed and accuracy when compared to standard classification methods as well as a recently published approach using a hidden Markov model (HMM). An online pilot study verified these results as the average speed and accuracy achieved using the PF method was significantly higher than that using the HMM method. These findings strongly support the integration of domain-specific knowledge into BCI classification to improve system performance.
    Journal of Neural Engineering 06/2015; 12(4):046018. DOI:10.1088/1741-2560/12/4/046018
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    ABSTRACT: Infection, inflammation, and neuronal loss are common issues that seriously affect the functionality and longevity of chronically implanted neural prostheses. Minocycline hydrochloride (MH) is a broad-spectrum antibiotic and effective anti-inflammatory drug that also exhibits potent neuroprotective activities. In this study, we investigated the development of biocompatible thin film coatings capable of sustained release of MH for improving the long term performance of implanted neural electrodes. We developed a novel magnesium binding-mediated drug delivery mechanism for controlled and sustained release of MH from an ultrathin hydrophilic layer-by-layer (LbL) coating and characterized the parameters that control MH loading and release. The anti-biofilm, anti-inflammatory and neuroprotective potencies of the LbL coating and released MH were also examined. Sustained release of physiologically relevant amount of MH for 46 days was achieved from the Mg(2+)-based LbL coating at a thickness of 1.25 μm. In addition, MH release from the LbL coating is pH-sensitive. The coating and released MH demonstrated strong anti-biofilm, anti-inflammatory, and neuroprotective potencies. This study reports, for the first time, the development of a bioactive coating that can target infection, inflammation, and neuroprotection simultaneously, which may facilitate the translation of neural interfaces to clinical applications.
    Journal of Neural Engineering 06/2015; 12(4):046015. DOI:10.1088/1741-2560/12/4/046015
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    ABSTRACT: Transcranial magnetic stimulation (TMS) represents a powerful technique to noninvasively modulate cortical neurophysiology in the brain. However, the relationship between the magnetic fields created by TMS coils and neuronal activation in the cortex is still not well-understood, making predictable cortical activation by TMS difficult to achieve. Our goal in this study was to investigate the relationship between induced electric fields and cortical activation measured by blood flow response. Particularly, we sought to discover the E-field characteristics that lead to cortical activation. Subject-specific finite element models (FEMs) of the head and brain were constructed for each of six subjects using magnetic resonance image scans. Positron emission tomography (PET) measured each subject's cortical response to image-guided robotically-positioned TMS to the primary motor cortex. FEM models that employed the given coil position, orientation, and stimulus intensity in experimental applications of TMS were used to calculate the electric field (E-field) vectors within a region of interest for each subject. TMS-induced E-fields were analyzed to better understand what vector components led to regional cerebral blood flow (CBF) responses recorded by PET. This study found that decomposing the E-field into orthogonal vector components based on the cortical surface geometry (and hence, cortical neuron directions) led to significant differences between the regions of cortex that were active and nonactive. Specifically, active regions had significantly higher E-field components in the normal inward direction (i.e., parallel to pyramidal neurons in the dendrite-to-axon orientation) and in the tangential direction (i.e., parallel to interneurons) at high gradient. In contrast, nonactive regions had higher E-field vectors in the outward normal direction suggesting inhibitory responses. These results provide critical new understanding of the factors by which TMS induces cortical activation necessary for predictive and repeatable use of this noninvasive stimulation modality.
    Journal of Neural Engineering 06/2015; 12(4):046014. DOI:10.1088/1741-2560/12/4/046014
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    ABSTRACT: To describe a toolkit of components for mathematical models of the relationship between cortical neural activity and space-resolved and time-resolved flows and volumes of oxygenated and deoxygenated hemoglobin motivated by optical intrinsic signal imaging (OISI). Both blood flow and blood volume and both oxygenated and deoxygenated hemoglobin and their interconversion are accounted for. Flow and volume are described by including analogies to both resistive and capacitive electrical circuit elements. Oxygenated and deoxygenated hemoglobin and their interconversion are described by generalization of Kirchhoff's laws based on well-mixed compartments. Mathematical models built from this toolkit are able to reproduce experimental single-stimulus OISI results that are described in papers from other research groups and are able to describe the response to multiple-stimuli experiments as a sublinear superposition of responses to the individual stimuli. The same assembly of tools from the toolkit but with different parameter values is able to describe effects that are considered distinctive, such as the presence or absence of an initial decrease in oxygenated hemoglobin concentration, indicating that the differences might be due to unique parameter values in a subject rather than different fundamental mechanisms.
    Journal of Neural Engineering 06/2015; 12(4):046013. DOI:10.1088/1741-2560/12/4/046013
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    ABSTRACT: Aligned nanofibers (AFs) are regarded as promising biomaterials in nerve tissue engineering. However, a full understanding of the biocompatibility of AFs at the molecular level is still challenging. Therefore, the present study focused on identifying the microRNA (miRNA)-mediated regulatory mechanism by which poly-L-lactic acid (PLLA) AFs influence PC12 cell differentiation. Firstly, the effects of PLLA random nanofibers (RFs)/AFs and PLLA films (control) on the biological responses of PC12 cells that are associated with neuronal differentiation were examined. Then, SOLiD sequencing and cDNA microarray were employed to profile the expressions of miRNAs and mRNAs. The target genes of the misregulated miRNAs were predicted and compared with the mRNA profile data. Functions of the matched target genes (the intersection between the predicted target genes and the experimentally-determined, misregulated genes) were analyzed. The results revealed that neurites spread in various directions in control and RF groups. In the AF group, most neurites extended in parallel with each other. The glucose consumption and lactic acid production in the RF and AF groups were higher than those in the control group. Compared with the control group, 42 and 94 miRNAs were significantly dysregulated in the RF and AF groups, respectively. By comparing the predicted target genes with the mRNA profile data, five and 87 matched target genes were found in the RF and AF groups, respectively. Three of the matched target genes in the AF group were found to be associated with neuronal differentiation, whereas none had this association in the RF group. The PLLA AFs induced the dysregulation of miRNAs that regulate many biological functions, including axonal guidance, lipid metabolism and long-term potentiation. In particular, two miRNA-matched target gene-biological function modules associated with neuronal differentiation were identified as follows: (1) miR-23b, miR-18a, miR-107 and miR-103 regulate the Rras2 and Nf1 gene and thereby, affect cytoskeleton regulation and MAPK pathway; (2) miR-92a, miR-339-5p, miR-25, miR-125a-5p, miR-351 and miR-19b co-regulate the Pafah1b1 gene, affecting PC12 cell migration and differentiation. This work demonstrates a bioinformatic approach to accomplish miRNA-mRNA profile integrative analysis and provides more insights for understanding the regulatory mechanism of miRNA in AFs affecting neuronal differentiation. These findings will be greatly beneficial for the application and design of AFs in nerve tissue engineering.
    Journal of Neural Engineering 06/2015; 12(4):046010. DOI:10.1088/1741-2560/12/4/046010
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    ABSTRACT: Recent studies have reported that the classification performance of electromyographic (EMG) signals degrades over time without proper classification retraining. This problem is relevant for the applications of EMG pattern recognition in the control of active prostheses. In this study we investigated the changes in EMG classification performance over 11 consecutive days in eight able-bodied subjects and two amputees. It was observed that, when the classifier was trained on data from one day and tested on data from the following day, the classification error decreased exponentially but plateaued after four days for able-bodied subjects and six to nine days for amputees. The between-day performance became gradually closer to the corresponding within-day performance. These results indicate that the relative changes in EMG signal features over time become progressively smaller when the number of days during which the subjects perform the pre-defined motions are increased. The performance of the motor tasks is thus more consistent over time, resulting in more repeatable EMG patterns, even if the subjects do not have any external feedback on their performance. The learning curves for both able-bodied subjects and subjects with limb deficiencies could be modeled as an exponential function. These results provide important insights into the user adaptation characteristics during practical long-term myoelectric control applications, with implications for the design of an adaptive pattern recognition system.
    Journal of Neural Engineering 06/2015; 12(4):046005. DOI:10.1088/1741-2560/12/4/046005