Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

USGS Publications Warehouse

Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.

2007-01-01

To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.

Unfolding the neutron spectrum of a NE213 scintillator using artificial neural networks.

PubMed

Sharghi Ido, A; Bonyadi, M R; Etaati, G R; Shahriari, M

2009-10-01

Artificial neural networks technology has been applied to unfold the neutron spectra from the pulse height distribution measured with NE213 liquid scintillator. Here, both the single and multi-layer perceptron neural network models have been implemented to unfold the neutron spectrum from an Am-Be neutron source. The activation function and the connectivity of the neurons have been investigated and the results have been analyzed in terms of the network's performance. The simulation results show that the neural network that utilizes the Satlins transfer function has the best performance. In addition, omitting the bias connection of the neurons improve the performance of the network. Also, the SCINFUL code is used for generating the response functions in the training phase of the process. Finally, the results of the neural network simulation have been compared with those of the FORIST unfolding code for both (241)Am-Be and (252)Cf neutron sources. The results of neural network are in good agreement with FORIST code.

Computing single step operators of logic programming in radial basis function neural networks

NASA Astrophysics Data System (ADS)

Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong

2014-07-01

Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (Tp:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.

Kinetic Energy of Hydrocarbons as a Function of Electron Density and Convolutional Neural Networks.

PubMed

Yao, Kun; Parkhill, John

2016-03-08

We demonstrate a convolutional neural network trained to reproduce the Kohn-Sham kinetic energy of hydrocarbons from an input electron density. The output of the network is used as a nonlocal correction to conventional local and semilocal kinetic functionals. We show that this approximation qualitatively reproduces Kohn-Sham potential energy surfaces when used with conventional exchange correlation functionals. The density which minimizes the total energy given by the functional is examined in detail. We identify several avenues to improve on this exploratory work, by reducing numerical noise and changing the structure of our functional. Finally we examine the features in the density learned by the neural network to anticipate the prospects of generalizing these models.

Neural correlates of cognitive improvements following cognitive remediation in schizophrenia: a systematic review of randomized trials

PubMed Central

Isaac, Clémence; Januel, Dominique

2016-01-01

Background Cognitive impairments are a core feature in schizophrenia and are linked to poor social functioning. Numerous studies have shown that cognitive remediation can enhance cognitive and functional abilities in patients with this pathology. The underlying mechanism of these behavioral improvements seems to be related to structural and functional changes in the brain. However, studies on neural correlates of such enhancement remain scarce. Objectives We explored the neural correlates of cognitive enhancement following cognitive remediation interventions in schizophrenia and the differential effect between cognitive training and other therapeutic interventions or patients’ usual care. Method We searched MEDLINE, PsycInfo, and ScienceDirect databases for studies on cognitive remediation therapy in schizophrenia that used neuroimaging techniques and a randomized design. Search terms included randomized controlled trial, cognitive remediation, cognitive training, rehabilitation, magnetic resonance imaging, positron emission tomography, electroencephalography, magnetoencephalography, near infrared spectroscopy, and diffusion tensor imaging. We selected randomized controlled trials that proposed multiple sessions of cognitive training to adult patients with a schizophrenia spectrum disorder and assessed its efficacy with imaging techniques. Results In total, 15 reports involving 19 studies were included in the systematic review. They involved a total of 455 adult patients, 271 of whom received cognitive remediation. Cognitive remediation therapy seems to provide a neurobiological enhancing effect in schizophrenia. After therapy, increased activations are observed in various brain regions mainly in frontal – especially prefrontal – and also in occipital and anterior cingulate regions during working memory and executive tasks. Several studies provide evidence of an improved functional connectivity after cognitive training, suggesting a neuroplastic effect of therapy through mechanisms of functional reorganization. Neurocognitive and social-cognitive training may have a cumulative effect on neural networks involved in social cognition. The variety of proposed programs, imaging tasks, and techniques may explain the heterogeneity of observed neural improvements. Future studies would need to specify the effect of cognitive training depending on those variables. PMID:26993787

An improved genetic algorithm for designing optimal temporal patterns of neural stimulation

NASA Astrophysics Data System (ADS)

Cassar, Isaac R.; Titus, Nathan D.; Grill, Warren M.

2017-12-01

Objective. Electrical neuromodulation therapies typically apply constant frequency stimulation, but non-regular temporal patterns of stimulation may be more effective and more efficient. However, the design space for temporal patterns is exceedingly large, and model-based optimization is required for pattern design. We designed and implemented a modified genetic algorithm (GA) intended for design optimal temporal patterns of electrical neuromodulation. Approach. We tested and modified standard GA methods for application to designing temporal patterns of neural stimulation. We evaluated each modification individually and all modifications collectively by comparing performance to the standard GA across three test functions and two biophysically-based models of neural stimulation. Main results. The proposed modifications of the GA significantly improved performance across the test functions and performed best when all were used collectively. The standard GA found patterns that outperformed fixed-frequency, clinically-standard patterns in biophysically-based models of neural stimulation, but the modified GA, in many fewer iterations, consistently converged to higher-scoring, non-regular patterns of stimulation. Significance. The proposed improvements to standard GA methodology reduced the number of iterations required for convergence and identified superior solutions.

Structural reliability calculation method based on the dual neural network and direct integration method.

PubMed

Li, Haibin; He, Yun; Nie, Xiaobo

2018-01-01

Structural reliability analysis under uncertainty is paid wide attention by engineers and scholars due to reflecting the structural characteristics and the bearing actual situation. The direct integration method, started from the definition of reliability theory, is easy to be understood, but there are still mathematics difficulties in the calculation of multiple integrals. Therefore, a dual neural network method is proposed for calculating multiple integrals in this paper. Dual neural network consists of two neural networks. The neural network A is used to learn the integrand function, and the neural network B is used to simulate the original function. According to the derivative relationships between the network output and the network input, the neural network B is derived from the neural network A. On this basis, the performance function of normalization is employed in the proposed method to overcome the difficulty of multiple integrations and to improve the accuracy for reliability calculations. The comparisons between the proposed method and Monte Carlo simulation method, Hasofer-Lind method, the mean value first-order second moment method have demonstrated that the proposed method is an efficient and accurate reliability method for structural reliability problems.

Vertically aligned carbon nanofiber as nano-neuron interface for monitoring neural function.

PubMed

Yu, Zhe; McKnight, Timothy E; Ericson, M Nance; Melechko, Anatoli V; Simpson, Michael L; Morrison, Barclay

2012-05-01

Neural chips, which are capable of simultaneous multisite neural recording and stimulation, have been used to detect and modulate neural activity for almost thirty years. As neural interfaces, neural chips provide dynamic functional information for neural decoding and neural control. By improving sensitivity and spatial resolution, nano-scale electrodes may revolutionize neural detection and modulation at cellular and molecular levels as nano-neuron interfaces. We developed a carbon-nanofiber neural chip with lithographically defined arrays of vertically aligned carbon nanofiber electrodes and demonstrated its capability of both stimulating and monitoring electrophysiological signals from brain tissues in vitro and monitoring dynamic information of neuroplasticity. This novel nano-neuron interface may potentially serve as a precise, informative, biocompatible, and dual-mode neural interface for monitoring of both neuroelectrical and neurochemical activity at the single-cell level and even inside the cell. The authors demonstrate the utility of a neural chip with lithographically defined arrays of vertically aligned carbon nanofiber electrodes. The new device can be used to stimulate and/or monitor signals from brain tissue in vitro and for monitoring dynamic information of neuroplasticity both intracellularly and at the single cell level including neuroelectrical and neurochemical activities. Copyright © 2012 Elsevier Inc. All rights reserved.

Greater neural pattern similarity across repetitions is associated with better memory.

PubMed

Xue, Gui; Dong, Qi; Chen, Chuansheng; Lu, Zhonglin; Mumford, Jeanette A; Poldrack, Russell A

2010-10-01

Repeated study improves memory, but the underlying neural mechanisms of this improvement are not well understood. Using functional magnetic resonance imaging and representational similarity analysis of brain activity, we found that, compared with forgotten items, subsequently remembered faces and words showed greater similarity in neural activation across multiple study in many brain regions, including (but not limited to) the regions whose mean activities were correlated with subsequent memory. This result addresses a longstanding debate in the study of memory by showing that successful episodic memory encoding occurs when the same neural representations are more precisely reactivated across study episodes, rather than when patterns of activation are more variable across time.

Are individuals with higher psychopathic traits better learners at lying? Behavioural and neural evidence.

PubMed

Shao, R; Lee, T M C

2017-07-25

High psychopathy is characterized by untruthfulness and manipulativeness. However, existing evidence on higher propensity or capacity to lie among non-incarcerated high-psychopathic individuals is equivocal. Of particular importance, no research has investigated whether greater psychopathic tendency is associated with better 'trainability' of lying. An understanding of whether the neurobehavioral processes of lying are modifiable through practice offers significant theoretical and practical implications. By employing a longitudinal design involving university students with varying degrees of psychopathic traits, we successfully demonstrate that the performance speed of lying about face familiarity significantly improved following two sessions of practice, which occurred only among those with higher, but not lower, levels of psychopathic traits. Furthermore, this behavioural improvement associated with higher psychopathic tendency was predicted by a reduction in lying-related neural signals and by functional connectivity changes in the frontoparietal and cerebellum networks. Our findings provide novel and pivotal evidence suggesting that psychopathic traits are the key modulating factors of the plasticity of both behavioural and neural processes underpinning lying. These findings broadly support conceptualization of high-functioning individuals with higher psychopathic traits as having preserved, or arguably superior, functioning in neural networks implicated in cognitive executive processing, but deficiencies in affective neural processes, from a neuroplasticity perspective.

Auditory Training: Evidence for Neural Plasticity in Older Adults

PubMed Central

Anderson, Samira; Kraus, Nina

2014-01-01

Improvements in digital amplification, cochlear implants, and other innovations have extended the potential for improving hearing function; yet, there remains a need for further hearing improvement in challenging listening situations, such as when trying to understand speech in noise or when listening to music. Here, we review evidence from animal and human models of plasticity in the brain’s ability to process speech and other meaningful stimuli. We considered studies targeting populations of younger through older adults, emphasizing studies that have employed randomized controlled designs and have made connections between neural and behavioral changes. Overall results indicate that the brain remains malleable through older adulthood, provided that treatment algorithms have been modified to allow for changes in learning with age. Improvements in speech-in-noise perception and cognition function accompany neural changes in auditory processing. The training-related improvements noted across studies support the need to consider auditory training strategies in the management of individuals who express concerns about hearing in difficult listening situations. Given evidence from studies engaging the brain’s reward centers, future research should consider how these centers can be naturally activated during training. PMID:25485037

Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases

PubMed Central

Ritchie, Marylyn D; White, Bill C; Parker, Joel S; Hahn, Lance W; Moore, Jason H

2003-01-01

Background Appropriate definition of neural network architecture prior to data analysis is crucial for successful data mining. This can be challenging when the underlying model of the data is unknown. The goal of this study was to determine whether optimizing neural network architecture using genetic programming as a machine learning strategy would improve the ability of neural networks to model and detect nonlinear interactions among genes in studies of common human diseases. Results Using simulated data, we show that a genetic programming optimized neural network approach is able to model gene-gene interactions as well as a traditional back propagation neural network. Furthermore, the genetic programming optimized neural network is better than the traditional back propagation neural network approach in terms of predictive ability and power to detect gene-gene interactions when non-functional polymorphisms are present. Conclusion This study suggests that a machine learning strategy for optimizing neural network architecture may be preferable to traditional trial-and-error approaches for the identification and characterization of gene-gene interactions in common, complex human diseases. PMID:12846935

Development and Evaluation of Micro-Electrocorticography Arrays for Neural Interfacing Applications

NASA Astrophysics Data System (ADS)

Schendel, Amelia Ann

Neural interfaces have great promise for both electrophysiological research and therapeutic applications. Whether for the study of neural circuitry or for neural prosthetic or other therapeutic applications, micro-electrocorticography (micro-ECoG) arrays have proven extremely useful as neural interfacing devices. These devices strike a balance between invasiveness and signal resolution, an important step towards eventual human application. The objective of this research was to make design improvements to micro-ECoG devices to enhance both biocompatibility and device functionality. To best evaluate the effectiveness of these improvements, a cranial window imaging method for in vivo monitoring of the longitudinal tissue response post device implant was developed. Employment of this method provided valuable insight into the way tissue grows around micro-ECoG arrays after epidural implantation, spurring a study of the effects of substrate geometry on the meningeal tissue response. The results of the substrate footprint comparison suggest that a more open substrate geometry provides an easy path for the tissue to grow around to the top side of the device, whereas a solid device substrate encourages the tissue to thicken beneath the device, between the electrode sites and the brain. The formation of thick scar tissue between the recording electrode sites and the neural tissue is disadvantageous for long-term recorded signal quality, and thus future micro-ECoG device designs should incorporate open-architecture substrates for enhanced longitudinal in vivo function. In addition to investigating improvements for long-term device reliability, it was also desired to enhance the functionality of micro-ECoG devices for neural electrophysiology research applications. To achieve this goal, a completely transparent graphene-based device was fabricated for use with the cranial window imaging method and optogenetic techniques. The use of graphene as the conductive material provided the transparency necessary to image tissues directly below the micro-ECoG electrode sites, and to transmit light through the electrode sites to underlying neural tissue, for optical stimulation of neural cells. The flexibility and broad-spectrum transparency of graphene make it an ideal choice for thin-film, flexible electronic devices.

Connecting Teratogen-Induced Congenital Heart Defects to Neural Crest Cells and Their Effect on Cardiac Function

PubMed Central

Karunamuni, Ganga H.; Ma, Pei; Gu, Shi; Rollins, Andrew M.; Jenkins, Michael W.; Watanabe, Michiko

2014-01-01

Neural crest cells play many key roles in embryonic development, as demonstrated by the abnormalities that result from their specific absence or dysfunction. Unfortunately, these key cells are particularly sensitive to abnormalities in various intrinsic and extrinsic factors, such as genetic deletions or ethanol-exposure that lead to morbidity and mortality for organisms. This review discusses the role identified for a segment of neural crest is in regulating the morphogenesis of the heart and associated great vessels. The paradox is that their derivatives constitute a small proportion of cells to the cardiovascular system. Findings supporting that these cells impact early cardiac function raises the interesting possibility that they indirectly control cardiovascular development at least partially through regulating function. Making connections between insults to the neural crest, cardiac function, and morphogenesis is more approachable with technological advances. Expanding our understanding of early functional consequences could be useful in improving diagnosis and testing therapies. PMID:25220155

Neural activity during emotion recognition after combined cognitive plus social cognitive training in schizophrenia.

PubMed

Hooker, Christine I; Bruce, Lori; Fisher, Melissa; Verosky, Sara C; Miyakawa, Asako; Vinogradov, Sophia

2012-08-01

Cognitive remediation training has been shown to improve both cognitive and social cognitive deficits in people with schizophrenia, but the mechanisms that support this behavioral improvement are largely unknown. One hypothesis is that intensive behavioral training in cognition and/or social cognition restores the underlying neural mechanisms that support targeted skills. However, there is little research on the neural effects of cognitive remediation training. This study investigated whether a 50 h (10-week) remediation intervention which included both cognitive and social cognitive training would influence neural function in regions that support social cognition. Twenty-two stable, outpatient schizophrenia participants were randomized to a treatment condition consisting of auditory-based cognitive training (AT) [Brain Fitness Program/auditory module ~60 min/day] plus social cognition training (SCT) which was focused on emotion recognition [~5-15 min per day] or a placebo condition of non-specific computer games (CG) for an equal amount of time. Pre and post intervention assessments included an fMRI task of positive and negative facial emotion recognition, and standard behavioral assessments of cognition, emotion processing, and functional outcome. There were no significant intervention-related improvements in general cognition or functional outcome. fMRI results showed the predicted group-by-time interaction. Specifically, in comparison to CG, AT+SCT participants had a greater pre-to-post intervention increase in postcentral gyrus activity during emotion recognition of both positive and negative emotions. Furthermore, among all participants, the increase in postcentral gyrus activity predicted behavioral improvement on a standardized test of emotion processing (MSCEIT: Perceiving Emotions). Results indicate that combined cognition and social cognition training impacts neural mechanisms that support social cognition skills. Copyright © 2012 Elsevier B.V. All rights reserved.

Neural activity during emotion recognition after combined cognitive plus social-cognitive training in schizophrenia

PubMed Central

Hooker, Christine I.; Bruce, Lori; Fisher, Melissa; Verosky, Sara C.; Miyakawa, Asako; Vinogradov, Sophia

2012-01-01

Cognitive remediation training has been shown to improve both cognitive and social-cognitive deficits in people with schizophrenia, but the mechanisms that support this behavioral improvement are largely unknown. One hypothesis is that intensive behavioral training in cognition and/or social-cognition restores the underlying neural mechanisms that support targeted skills. However, there is little research on the neural effects of cognitive remediation training. This study investigated whether a 50 hour (10-week) remediation intervention which included both cognitive and social-cognitive training would influence neural function in regions that support social-cognition. Twenty-two stable, outpatient schizophrenia participants were randomized to a treatment condition consisting of auditory-based cognitive training (AT) [Brain Fitness Program/auditory module ~60 minutes/day] plus social-cognition training (SCT) which was focused on emotion recognition [~5–15 minutes per day] or a placebo condition of non-specific computer games (CG) for an equal amount of time. Pre and post intervention assessments included an fMRI task of positive and negative facial emotion recognition, and standard behavioral assessments of cognition, emotion processing, and functional outcome. There were no significant intervention-related improvements in general cognition or functional outcome. FMRI results showed the predicted group-by-time interaction. Specifically, in comparison to CG, AT+SCT participants had a greater pre-to-post intervention increase in postcentral gyrus activity during emotion recognition of both positive and negative emotions. Furthermore, among all participants, the increase in postcentral gyrus activity predicted behavioral improvement on a standardized test of emotion processing (MSCEIT: Perceiving Emotions). Results indicate that combined cognition and social-cognition training impacts neural mechanisms that support social-cognition skills. PMID:22695257

Intermedin improves cardiac function and sympathetic neural remodeling in a rat model of post myocardial infarction heart failure

PubMed Central

Xu, Bin; Xu, Hao; Cao, Heng; Liu, Xiaoxiao; Qin, Chunhuan; Zhao, Yanzhou; Han, Xiaolin; Li, Hongli

2017-01-01

Emerging evidence has suggested that intermedin (IMD), a novel member of the calcitonin gene-related peptide (CGRP) family, has a wide range of cardioprotective effects. The present study investigated the effects of long-term administration of IMD on cardiac function and sympathetic neural remodeling in heart failure (HF) rats, and studied potential underlying mechanism. HF was induced in rats by myocardial infarction (MI). Male Sprague Dawley rats were randomly assigned to either saline or IMD (0.6 µg/kg/h) treatment groups for 4 weeks post-MI. Another group of sham-operated rats served as controls. Cardiac function was assessed by echocardiography, cardiac catheterization and plasma level of B-type natriuretic peptide (BNP). Cardiac sympathetic neural remodeling was assessed by immunohistochemistical study of tyrosine hydroxylase (TH) and growth associated protein 43 (GAP43) immunoreactive nerve fibers. The protein expression levels of nerve growth factor (NGF), TH and GAP43 in the ventricular myocardium were studied by western blotting. Ventricular fibrillation threshold (VFT) was determined to evaluate the incidence of ventricular arrhythmia. Oxidative stress was assessed by detecting the activity of superoxide dismutase and the level of malondialdehyde. Compared with rats administrated with saline, IMD significantly improved cardiac function, decreased the plasma BNP level, attenuated sympathetic neural remodeling, increased VFT and suppressed oxidative stress. In conclusion, these results indicated that IMD prevents ventricle remodeling and improves the performance of a failing heart. In addition, IMD attenuated sympathetic neural remodeling and reduced the incidence of ventricular arrhythmia, which may contribute to its anti-oxidative property. These results implicate IMD as a potential therapeutic agent for the treatment of HF. PMID:28627670

Models of Acetylcholine and Dopamine Signals Differentially Improve Neural Representations

PubMed Central

Holca-Lamarre, Raphaël; Lücke, Jörg; Obermayer, Klaus

2017-01-01

Biological and artificial neural networks (ANNs) represent input signals as patterns of neural activity. In biology, neuromodulators can trigger important reorganizations of these neural representations. For instance, pairing a stimulus with the release of either acetylcholine (ACh) or dopamine (DA) evokes long lasting increases in the responses of neurons to the paired stimulus. The functional roles of ACh and DA in rearranging representations remain largely unknown. Here, we address this question using a Hebbian-learning neural network model. Our aim is both to gain a functional understanding of ACh and DA transmission in shaping biological representations and to explore neuromodulator-inspired learning rules for ANNs. We model the effects of ACh and DA on synaptic plasticity and confirm that stimuli coinciding with greater neuromodulator activation are over represented in the network. We then simulate the physiological release schedules of ACh and DA. We measure the impact of neuromodulator release on the network's representation and on its performance on a classification task. We find that ACh and DA trigger distinct changes in neural representations that both improve performance. The putative ACh signal redistributes neural preferences so that more neurons encode stimulus classes that are challenging for the network. The putative DA signal adapts synaptic weights so that they better match the classes of the task at hand. Our model thus offers a functional explanation for the effects of ACh and DA on cortical representations. Additionally, our learning algorithm yields performances comparable to those of state-of-the-art optimisation methods in multi-layer perceptrons while requiring weaker supervision signals and interacting with synaptically-local weight updates. PMID:28690509

Self Improving Methods for Materials and Process Design

DTIC Science & Technology

1998-08-31

using inductive coupling techniques. The first phase of the work focuses on developing an artificial neural network learning for function approximation...developing an artificial neural network learning algorithm for time-series prediction. The third phase of the work focuses on model selection. We have

Predicting protein complex geometries with a neural network.

PubMed

Chae, Myong-Ho; Krull, Florian; Lorenzen, Stephan; Knapp, Ernst-Walter

2010-03-01

A major challenge of the protein docking problem is to define scoring functions that can distinguish near-native protein complex geometries from a large number of non-native geometries (decoys) generated with noncomplexed protein structures (unbound docking). In this study, we have constructed a neural network that employs the information from atom-pair distance distributions of a large number of decoys to predict protein complex geometries. We found that docking prediction can be significantly improved using two different types of polar hydrogen atoms. To train the neural network, 2000 near-native decoys of even distance distribution were used for each of the 185 considered protein complexes. The neural network normalizes the information from different protein complexes using an additional protein complex identity input neuron for each complex. The parameters of the neural network were determined such that they mimic a scoring funnel in the neighborhood of the native complex structure. The neural network approach avoids the reference state problem, which occurs in deriving knowledge-based energy functions for scoring. We show that a distance-dependent atom pair potential performs much better than a simple atom-pair contact potential. We have compared the performance of our scoring function with other empirical and knowledge-based scoring functions such as ZDOCK 3.0, ZRANK, ITScore-PP, EMPIRE, and RosettaDock. In spite of the simplicity of the method and its functional form, our neural network-based scoring function achieves a reasonable performance in rigid-body unbound docking of proteins. Proteins 2010. (c) 2009 Wiley-Liss, Inc.

A decade of imaging surgeons' brain function (part II): A systematic review of applications for technical and nontechnical skills assessment.

PubMed

Modi, Hemel Narendra; Singh, Harsimrat; Yang, Guang-Zhong; Darzi, Ara; Leff, Daniel Richard

2017-11-01

Functional neuroimaging technologies enable assessment of operator brain function and can deepen our understanding of skills learning, ergonomic optima, and cognitive processes in surgeons. Although there has been a critical mass of data detailing surgeons' brain function, this literature has not been reviewed systematically. A systematic search of original neuroimaging studies assessing surgeons' brain function and published up until November 2016 was conducted using Medline, Embase, and PsycINFO databases. Twenty-seven studies fulfilled the inclusion criteria, including 3 feasibility studies, 14 studies exploring the neural correlates of technical skill acquisition, and the remainder investigating brain function in the context of intraoperative decision-making (n = 1), neurofeedback training (n = 1), robot-assisted technology (n = 5), and surgical teaching (n = 3). Early stages of learning open surgical tasks (knot-tying) are characterized by prefrontal cortical activation, which subsequently attenuates with deliberate practice. However, with complex laparoscopic skills (intracorporeal suturing), prefrontal cortical engagement requires substantial training, and attenuation occurs over a longer time course, after years of refinement. Neurofeedback and interventions that improve neural efficiency may enhance technical performance and skills learning. Imaging surgeons' brain function has identified neural signatures of expertise that might help inform objective assessment and selection processes. Interventions that improve neural efficiency may target skill-specific brain regions and augment surgical performance. Copyright © 2017 Elsevier Inc. All rights reserved.

Cholinergic enhancement reduces functional connectivity and BOLD variability in visual extrastriate cortex during selective attention.

PubMed

Ricciardi, Emiliano; Handjaras, Giacomo; Bernardi, Giulio; Pietrini, Pietro; Furey, Maura L

2013-01-01

Enhancing cholinergic function improves performance on various cognitive tasks and alters neural responses in task specific brain regions. We have hypothesized that the changes in neural activity observed during increased cholinergic function reflect an increase in neural efficiency that leads to improved task performance. The current study tested this hypothesis by assessing neural efficiency based on cholinergically-mediated effects on regional brain connectivity and BOLD signal variability. Nine subjects participated in a double-blind, placebo-controlled crossover fMRI study. Following an infusion of physostigmine (1 mg/h) or placebo, echo-planar imaging (EPI) was conducted as participants performed a selective attention task. During the task, two images comprised of superimposed pictures of faces and houses were presented. Subjects were instructed periodically to shift their attention from one stimulus component to the other and to perform a matching task using hand held response buttons. A control condition included phase-scrambled images of superimposed faces and houses that were presented in the same temporal and spatial manner as the attention task; participants were instructed to perform a matching task. Cholinergic enhancement improved performance during the selective attention task, with no change during the control task. Functional connectivity analyses showed that the strength of connectivity between ventral visual processing areas and task-related occipital, parietal and prefrontal regions reduced significantly during cholinergic enhancement, exclusively during the selective attention task. Physostigmine administration also reduced BOLD signal temporal variability relative to placebo throughout temporal and occipital visual processing areas, again during the selective attention task only. Together with the observed behavioral improvement, the decreases in connectivity strength throughout task-relevant regions and BOLD variability within stimulus processing regions support the hypothesis that cholinergic augmentation results in enhanced neural efficiency. This article is part of a Special Issue entitled 'Cognitive Enhancers'. Copyright © 2012 Elsevier Ltd. All rights reserved.

Functional electrical stimulation-facilitated proliferation and regeneration of neural precursor cells in the brains of rats with cerebral infarction

PubMed Central

Xiang, Yun; Liu, Huihua; Yan, Tiebin; Zhuang, Zhiqiang; Jin, Dongmei; Peng, Yuan

2014-01-01

Previous studies have shown that proliferation of endogenous neural precursor cells cannot alone compensate for the damage to neurons and axons. From the perspective of neural plasticity, we observed the effects of functional electrical stimulation treatment on endogenous neural precursor cell proliferation and expression of basic fibroblast growth factor and epidermal growth factor in the rat brain on the infarct side. Functional electrical stimulation was performed in rat models of acute middle cerebral artery occlusion. Simultaneously, we set up a placebo stimulation group and a sham-operated group. Immunohistochemical staining showed that, at 7 and 14 days, compared with the placebo group, the numbers of nestin (a neural precursor cell marker)-positive cells in the subgranular zone and subventricular zone were increased in the functional electrical stimulation treatment group. Western blot assays and reverse-transcription PCR showed that total protein levels and gene expression of epidermal growth factor and basic fibroblast growth factor were also upregulated on the infarct side. Prehensile traction test results showed that, at 14 days, prehension function of rats in the functional electrical stimulation group was significantly better than in the placebo group. These results suggest that functional electrical stimulation can promote endogenous neural precursor cell proliferation in the brains of acute cerebral infarction rats, enhance expression of basic fibroblast growth factor and epidermal growth factor, and improve the motor function of rats. PMID:25206808

Neuroprostheses to treat neurogenic bladder dysfunction: current status and future perspectives.

PubMed

Rijkhoff, Nico J M

2004-02-01

Neural prostheses are a technology that uses electrical activation of the nervous system to restore function to individuals with neurological or sensory impairment. This article provides an introduction to neural prostheses and lists the most successful neural prostheses (in terms of implanted devices). The article then focuses on neurogenic bladder dysfunction and describes two clinically available implantable neural prostheses for treatment of neurogenic bladder dysfunction. Special attention is given to the usage of these neural prostheses in children. Finally, three new developments that may lead to a new generation of implantable neural prostheses for bladder control are described. They may improve the neural prostheses currently available and expand further the population of patients who can benefit from a neural prosthesis.

In vitro generation of three-dimensional substrate-adherent embryonic stem cell-derived neural aggregates for application in animal models of neurological disorders.

PubMed

Hargus, Gunnar; Cui, Yi-Fang; Dihné, Marcel; Bernreuther, Christian; Schachner, Melitta

2012-05-01

In vitro-differentiated embryonic stem (ES) cells comprise a useful source for cell replacement therapy, but the efficiency and safety of a translational approach are highly dependent on optimized protocols for directed differentiation of ES cells into the desired cell types in vitro. Furthermore, the transplantation of three-dimensional ES cell-derived structures instead of a single-cell suspension may improve graft survival and function by providing a beneficial microenvironment for implanted cells. To this end, we have developed a new method to efficiently differentiate mouse ES cells into neural aggregates that consist predominantly (>90%) of postmitotic neurons, neural progenitor cells, and radial glia-like cells. When transplanted into the excitotoxically lesioned striatum of adult mice, these substrate-adherent embryonic stem cell-derived neural aggregates (SENAs) showed significant advantages over transplanted single-cell suspensions of ES cell-derived neural cells, including improved survival of GABAergic neurons, increased cell migration, and significantly decreased risk of teratoma formation. Furthermore, SENAs mediated functional improvement after transplantation into animal models of Parkinson's disease and spinal cord injury. This unit describes in detail how SENAs are efficiently derived from mouse ES cells in vitro and how SENAs are isolated for transplantation. Furthermore, methods are presented for successful implantation of SENAs into animal models of Huntington's disease, Parkinson's disease, and spinal cord injury to study the effects of stem cell-derived neural aggregates in a disease context in vivo.

Melanoma segmentation based on deep learning.

PubMed

Zhang, Xiaoqing

2017-12-01

Malignant melanoma is one of the most deadly forms of skin cancer, which is one of the world's fastest-growing cancers. Early diagnosis and treatment is critical. In this study, a neural network structure is utilized to construct a broad and accurate basis for the diagnosis of skin cancer, thereby reducing screening errors. The technique is able to improve the efficacy for identification of normally indistinguishable lesions (such as pigment spots) versus clinically unknown lesions, and to ultimately improve the diagnostic accuracy. In the field of medical imaging, in general, using neural networks for image segmentation is relatively rare. The existing traditional machine-learning neural network algorithms still cannot completely solve the problem of information loss, nor detect the precise division of the boundary area. We use an improved neural network framework, described herein, to achieve efficacious feature learning, and satisfactory segmentation of melanoma images. The architecture of the network includes multiple convolution layers, dropout layers, softmax layers, multiple filters, and activation functions. The number of data sets can be increased via rotation of the training set. A non-linear activation function (such as ReLU and ELU) is employed to alleviate the problem of gradient disappearance, and RMSprop/Adam are incorporated to optimize the loss algorithm. A batch normalization layer is added between the convolution layer and the activation layer to solve the problem of gradient disappearance and explosion. Experiments, described herein, show that our improved neural network architecture achieves higher accuracy for segmentation of melanoma images as compared with existing processes.

Advances in two photon scanning and scanless microscopy technologies for functional neural circuit imaging.

PubMed

Schultz, Simon R; Copeland, Caroline S; Foust, Amanda J; Quicke, Peter; Schuck, Renaud

2017-01-01

Recent years have seen substantial developments in technology for imaging neural circuits, raising the prospect of large scale imaging studies of neural populations involved in information processing, with the potential to lead to step changes in our understanding of brain function and dysfunction. In this article we will review some key recent advances: improved fluorophores for single cell resolution functional neuroimaging using a two photon microscope; improved approaches to the problem of scanning active circuits; and the prospect of scanless microscopes which overcome some of the bandwidth limitations of current imaging techniques. These advances in technology for experimental neuroscience have in themselves led to technical challenges, such as the need for the development of novel signal processing and data analysis tools in order to make the most of the new experimental tools. We review recent work in some active topics, such as region of interest segmentation algorithms capable of demixing overlapping signals, and new highly accurate algorithms for calcium transient detection. These advances motivate the development of new data analysis tools capable of dealing with spatial or spatiotemporal patterns of neural activity, that scale well with pattern size.

Advances in two photon scanning and scanless microscopy technologies for functional neural circuit imaging

PubMed Central

Schultz, Simon R.; Copeland, Caroline S.; Foust, Amanda J.; Quicke, Peter; Schuck, Renaud

2017-01-01

Recent years have seen substantial developments in technology for imaging neural circuits, raising the prospect of large scale imaging studies of neural populations involved in information processing, with the potential to lead to step changes in our understanding of brain function and dysfunction. In this article we will review some key recent advances: improved fluorophores for single cell resolution functional neuroimaging using a two photon microscope; improved approaches to the problem of scanning active circuits; and the prospect of scanless microscopes which overcome some of the bandwidth limitations of current imaging techniques. These advances in technology for experimental neuroscience have in themselves led to technical challenges, such as the need for the development of novel signal processing and data analysis tools in order to make the most of the new experimental tools. We review recent work in some active topics, such as region of interest segmentation algorithms capable of demixing overlapping signals, and new highly accurate algorithms for calcium transient detection. These advances motivate the development of new data analysis tools capable of dealing with spatial or spatiotemporal patterns of neural activity, that scale well with pattern size. PMID:28757657

Regulation of endogenous neural stem/progenitor cells for neural repair—factors that promote neurogenesis and gliogenesis in the normal and damaged brain

PubMed Central

Christie, Kimberly J.; Turnley, Ann M.

2012-01-01

Neural stem/precursor cells in the adult brain reside in the subventricular zone (SVZ) of the lateral ventricles and the subgranular zone (SGZ) of the dentate gyrus in the hippocampus. These cells primarily generate neuroblasts that normally migrate to the olfactory bulb (OB) and the dentate granule cell layer respectively. Following brain damage, such as traumatic brain injury, ischemic stroke or in degenerative disease models, neural precursor cells from the SVZ in particular, can migrate from their normal route along the rostral migratory stream (RMS) to the site of neural damage. This neural precursor cell response to neural damage is mediated by release of endogenous factors, including cytokines and chemokines produced by the inflammatory response at the injury site, and by the production of growth and neurotrophic factors. Endogenous hippocampal neurogenesis is frequently also directly or indirectly affected by neural damage. Administration of a variety of factors that regulate different aspects of neural stem/precursor biology often leads to improved functional motor and/or behavioral outcomes. Such factors can target neural stem/precursor proliferation, survival, migration and differentiation into appropriate neuronal or glial lineages. Newborn cells also need to subsequently survive and functionally integrate into extant neural circuitry, which may be the major bottleneck to the current therapeutic potential of neural stem/precursor cells. This review will cover the effects of a range of intrinsic and extrinsic factors that regulate neural stem/precursor cell functions. In particular it focuses on factors that may be harnessed to enhance the endogenous neural stem/precursor cell response to neural damage, highlighting those that have already shown evidence of preclinical effectiveness and discussing others that warrant further preclinical investigation. PMID:23346046

Protease-degradable PEG-maleimide coating with on-demand release of IL-1Ra to improve tissue response to neural electrodes

PubMed Central

Gutowski, Stacie M.; Shoemaker, James T.; Templeman, Kellie L.; Wei, Yang; Latour, Robert A.; Bellamkonda, Ravi V.; LaPlaca, Michelle C.; García, Andrés J.

2015-01-01

Neural electrodes are an important part of brain-machine interface devices that can restore functionality to patients with sensory and movement disorders. Chronically implanted neural electrodes induce an unfavorable tissue response which includes inflammation, scar formation, and neuronal cell death, eventually causing loss of electrode function. We developed a poly(ethylene glycol) hydrogel coating for neural electrodes with non-fouling characteristics, incorporated an anti-inflammatory agent, and engineered a stimulus-responsive degradable portion for on-demand release of the anti-inflammatory agent in response to inflammatory stimuli. This coating reduces in vitro glial cell adhesion, cell spreading, and cytokine release compared to uncoated controls. We also analyzed the in vivo tissue response using immunohistochemistry and microarray qRT-PCR. Although no differences were observed among coated and uncoated electrodes for inflammatory cell markers, lower IgG penetration into the tissue around PEG+IL-1Ra coated electrodes indicates an improvement in blood-brain barrier integrity. Gene expression analysis showed higher expression of IL-6 and MMP-2 around PEG+IL-1Ra samples, as well as an increase in CNTF expression, an important marker for neuronal survival. Importantly, increased neuronal survival around coated electrodes compared to uncoated controls was observed. Collectively, these results indicate promising findings for an engineered coating to increase neuronal survival and improve tissue response around implanted neural electrodes. PMID:25617126

Artificial astrocytes improve neural network performance.

PubMed

Porto-Pazos, Ana B; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

2011-04-19

Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

Artificial Astrocytes Improve Neural Network Performance

PubMed Central

Porto-Pazos, Ana B.; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

2011-01-01

Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function. PMID:21526157

Improved result on stability analysis of discrete stochastic neural networks with time delay

NASA Astrophysics Data System (ADS)

Wu, Zhengguang; Su, Hongye; Chu, Jian; Zhou, Wuneng

2009-04-01

This Letter investigates the problem of exponential stability for discrete stochastic time-delay neural networks. By defining a novel Lyapunov functional, an improved delay-dependent exponential stability criterion is established in terms of linear matrix inequality (LMI) approach. Meanwhile, the computational complexity of the newly established stability condition is reduced because less variables are involved. Numerical example is given to illustrate the effectiveness and the benefits of the proposed method.

New exponential stability criteria for stochastic BAM neural networks with impulses

NASA Astrophysics Data System (ADS)

Sakthivel, R.; Samidurai, R.; Anthoni, S. M.

2010-10-01

In this paper, we study the global exponential stability of time-delayed stochastic bidirectional associative memory neural networks with impulses and Markovian jumping parameters. A generalized activation function is considered, and traditional assumptions on the boundedness, monotony and differentiability of activation functions are removed. We obtain a new set of sufficient conditions in terms of linear matrix inequalities, which ensures the global exponential stability of the unique equilibrium point for stochastic BAM neural networks with impulses. The Lyapunov function method with the Itô differential rule is employed for achieving the required result. Moreover, a numerical example is provided to show that the proposed result improves the allowable upper bound of delays over some existing results in the literature.

Three-dimensional co-culture of C2C12/PC12 cells improves skeletal muscle tissue formation and function.

PubMed

Ostrovidov, Serge; Ahadian, Samad; Ramon-Azcon, Javier; Hosseini, Vahid; Fujie, Toshinori; Parthiban, S Prakash; Shiku, Hitoshi; Matsue, Tomokazu; Kaji, Hirokazu; Ramalingam, Murugan; Bae, Hojae; Khademhosseini, Ali

2017-02-01

Engineered muscle tissues demonstrate properties far from native muscle tissue. Therefore, fabrication of muscle tissues with enhanced functionalities is required to enable their use in various applications. To improve the formation of mature muscle tissues with higher functionalities, we co-cultured C2C12 myoblasts and PC12 neural cells. While alignment of the myoblasts was obtained by culturing the cells in micropatterned methacrylated gelatin (GelMA) hydrogels, we studied the effects of the neural cells (PC12) on the formation and maturation of muscle tissues. Myoblasts cultured in the presence of neural cells showed improved differentiation, with enhanced myotube formation. Myotube alignment, length and coverage area were increased. In addition, the mRNA expression of muscle differentiation markers (Myf-5, myogenin, Mefc2, MLP), muscle maturation markers (MHC-IId/x, MHC-IIa, MHC-IIb, MHC-pn, α-actinin, sarcomeric actinin) and the neuromuscular markers (AChE, AChR-ε) were also upregulated. All these observations were amplified after further muscle tissue maturation under electrical stimulation. Our data suggest a synergistic effect on the C2C12 differentiation induced by PC12 cells, which could be useful for creating improved muscle tissue. Copyright © 2014 John Wiley & Sons, Ltd. Copyright © 2014 John Wiley & Sons, Ltd.

Neural Effects of Short-Term Training on Working Memory

PubMed Central

Buschkuehl, Martin; Garcia, Luis Hernandez; Jaeggi, Susanne M.; Bernard, Jessica A.; Jonides, John

2014-01-01

Working memory training has been the focus of intense research interest. Despite accumulating behavioral work, knowledge about the neural mechanisms underlying training effects is scarce. Here we show that seven days of training on an n back task lead to substantial performance improvements in the trained task; furthermore, the experimental group shows cross modal transfer as compared to an active control group. In addition, there are two neural effects that emerged as a function of training: first, increased perfusion during task performance in selected regions, reflecting a neural response to cope with high task demand; second, increased blood flow at rest in regions where training effects were apparent. We also found that perfusion at rest was correlated with task proficiency, probably reflecting an improved neural readiness to perform. Our findings are discussed within the context of the available neuroimaging literature on n back training. PMID:24496717

Kernel Temporal Differences for Neural Decoding

PubMed Central

Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2015-01-01

We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504

Transplantation of motoneurons derived from MASH1-transfected mouse ES cells reconstitutes neural networks and improves motor function in hemiplegic mice.

PubMed

Ikeda, Ritsuko; Kurokawa, Manae S; Chiba, Shunmei; Yoshikawa, Hideshi; Hashimoto, Takuo; Tadokoro, Mamoru; Suzuki, Noboru

2004-10-01

Mouse embryonic stem (ES) cells were transfected with a MASH1 expression vector and G418-resistant cells were selected. The MASH1-transfected cells became neuron-like appearance and expressed betaIIItubulin and panNCAM. Glial fibrillary acidic protein (GFAP) and galactocerebroside (GalC)-expressing cells were rarely detected. Half of the neural cells differentiated into the Islet1+ motoneuron lineage. Thus, we obtained motoneuron lineage-enriched neuronal cells by transfection of ES cells with MASH1. A hemiplegic model of mice was developed by cryogenic injury of the motor cortex, and motoneuron lineage-enriched neuronal cells were transplanted underneath the injured motor cortex neighboring the periventricular region. The motor function of the recipients was assessed by a beam walking and rotarod tests, whereby the results gradually improved, but little improvement was observed in vehicle injected control mice. We found that the grafted cells not only remained close to the implantation site, but also exhibited substantial migration, penetrating into the damaged lesion in a directed manner up to the cortical region. Grafted neuronal cells that had migrated into the cortex were elongated axon-positive for neurofilament middle chain (NFM). Synaptophysin immunostaining showed a positive staining pattern around the graft, suggesting that the transplanted neurons interacted with the recipient neurons to form a neural network. Our study suggests that the motoneuron lineage can be induced from ES cells, and grafted cells adapt to the host environment and can reconstitute a neural network to improve motor function of a paralyzed limb.

Neural and behavioural changes in male periadolescent mice after prolonged nicotine-MDMA treatment.

PubMed

Adeniyi, Philip A; Ishola, Azeez O; Laoye, Babafemi J; Olatunji, Babawale P; Bankole, Oluwamolakun O; Shallie, Philemon D; Ogundele, Olalekan M

2016-02-01

The interaction between MDMA and Nicotine affects multiple brain centres and neurotransmitter systems (serotonin, dopamine and glutamate) involved in motor coordination and cognition. In this study, we have elucidated the effect of prolonged (10 days) MDMA, Nicotine and a combined Nicotine-MDMA treatment on motor-cognitive neural functions. In addition, we have shown the correlation between the observed behavioural change and neural structural changes induced by these treatments in BALB/c mice. We observed that MDMA (2 mg/Kg body weight; subcutaneous) induced a decline in motor function, while Nicotine (2 mg/Kg body weight; subcutaneous) improved motor function in male periadolescent mice. In combined treatment, Nicotine reduced the motor function decline observed in MDMA treatment, thus no significant change in motor function for the combined treatment versus the control. Nicotine or MDMA treatment reduced memory function and altered hippocampal structure. Similarly, a combined Nicotine-MDMA treatment reduced memory function when compared with the control. Ultimately, the metabolic and structural changes in these neural systems were seen to vary for the various forms of treatment. It is noteworthy to mention that a combined treatment increased the rate of lipid peroxidation in brain tissue.

Delay-slope-dependent stability results of recurrent neural networks.

PubMed

Li, Tao; Zheng, Wei Xing; Lin, Chong

2011-12-01

By using the fact that the neuron activation functions are sector bounded and nondecreasing, this brief presents a new method, named the delay-slope-dependent method, for stability analysis of a class of recurrent neural networks with time-varying delays. This method includes more information on the slope of neuron activation functions and fewer matrix variables in the constructed Lyapunov-Krasovskii functional. Then some improved delay-dependent stability criteria with less computational burden and conservatism are obtained. Numerical examples are given to illustrate the effectiveness and the benefits of the proposed method.

Application of artificial neural networks to composite ply micromechanics

NASA Technical Reports Server (NTRS)

Brown, D. A.; Murthy, P. L. N.; Berke, L.

1991-01-01

Artificial neural networks can provide improved computational efficiency relative to existing methods when an algorithmic description of functional relationships is either totally unavailable or is complex in nature. For complex calculations, significant reductions in elapsed computation time are possible. The primary goal is to demonstrate the applicability of artificial neural networks to composite material characterization. As a test case, a neural network was trained to accurately predict composite hygral, thermal, and mechanical properties when provided with basic information concerning the environment, constituent materials, and component ratios used in the creation of the composite. A brief introduction on neural networks is provided along with a description of the project itself.

Developing a hippocampal neural prosthetic to facilitate human memory encoding and recall.

PubMed

Hampson, Robert E; Song, Dong; Robinson, Brian S; Fetterhoff, Dustin; Dakos, Alexander S; Roeder, Brent M; She, Xiwei; Wicks, Robert T; Witcher, Mark R; Couture, Daniel E; Laxton, Adrian W; Munger-Clary, Heidi; Popli, Gautam; Sollman, Myriam J; Whitlow, Christopher T; Marmarelis, Vasilis Z; Berger, Theodore W; Deadwyler, Sam A

2018-06-01

We demonstrate here the first successful implementation in humans of a proof-of-concept system for restoring and improving memory function via facilitation of memory encoding using the patient's own hippocampal spatiotemporal neural codes for memory. Memory in humans is subject to disruption by drugs, disease and brain injury, yet previous attempts to restore or rescue memory function in humans typically involved only nonspecific, modulation of brain areas and neural systems related to memory retrieval. We have constructed a model of processes by which the hippocampus encodes memory items via spatiotemporal firing of neural ensembles that underlie the successful encoding of short-term memory. A nonlinear multi-input, multi-output (MIMO) model of hippocampal CA3 and CA1 neural firing is computed that predicts activation patterns of CA1 neurons during the encoding (sample) phase of a delayed match-to-sample (DMS) human short-term memory task. MIMO model-derived electrical stimulation delivered to the same CA1 locations during the sample phase of DMS trials facilitated short-term/working memory by 37% during the task. Longer term memory retention was also tested in the same human subjects with a delayed recognition (DR) task that utilized images from the DMS task, along with images that were not from the task. Across the subjects, the stimulated trials exhibited significant improvement (35%) in both short-term and long-term retention of visual information. These results demonstrate the facilitation of memory encoding which is an important feature for the construction of an implantable neural prosthetic to improve human memory.

Developing a hippocampal neural prosthetic to facilitate human memory encoding and recall

NASA Astrophysics Data System (ADS)

Hampson, Robert E.; Song, Dong; Robinson, Brian S.; Fetterhoff, Dustin; Dakos, Alexander S.; Roeder, Brent M.; She, Xiwei; Wicks, Robert T.; Witcher, Mark R.; Couture, Daniel E.; Laxton, Adrian W.; Munger-Clary, Heidi; Popli, Gautam; Sollman, Myriam J.; Whitlow, Christopher T.; Marmarelis, Vasilis Z.; Berger, Theodore W.; Deadwyler, Sam A.

2018-06-01

Objective. We demonstrate here the first successful implementation in humans of a proof-of-concept system for restoring and improving memory function via facilitation of memory encoding using the patient’s own hippocampal spatiotemporal neural codes for memory. Memory in humans is subject to disruption by drugs, disease and brain injury, yet previous attempts to restore or rescue memory function in humans typically involved only nonspecific, modulation of brain areas and neural systems related to memory retrieval. Approach. We have constructed a model of processes by which the hippocampus encodes memory items via spatiotemporal firing of neural ensembles that underlie the successful encoding of short-term memory. A nonlinear multi-input, multi-output (MIMO) model of hippocampal CA3 and CA1 neural firing is computed that predicts activation patterns of CA1 neurons during the encoding (sample) phase of a delayed match-to-sample (DMS) human short-term memory task. Main results. MIMO model-derived electrical stimulation delivered to the same CA1 locations during the sample phase of DMS trials facilitated short-term/working memory by 37% during the task. Longer term memory retention was also tested in the same human subjects with a delayed recognition (DR) task that utilized images from the DMS task, along with images that were not from the task. Across the subjects, the stimulated trials exhibited significant improvement (35%) in both short-term and long-term retention of visual information. Significance. These results demonstrate the facilitation of memory encoding which is an important feature for the construction of an implantable neural prosthetic to improve human memory.

Research of converter transformer fault diagnosis based on improved PSO-BP algorithm

NASA Astrophysics Data System (ADS)

Long, Qi; Guo, Shuyong; Li, Qing; Sun, Yong; Li, Yi; Fan, Youping

2017-09-01

To overcome those disadvantages that BP (Back Propagation) neural network and conventional Particle Swarm Optimization (PSO) converge at the global best particle repeatedly in early stage and is easy trapped in local optima and with low diagnosis accuracy when being applied in converter transformer fault diagnosis, we come up with the improved PSO-BP neural network to improve the accuracy rate. This algorithm improves the inertia weight Equation by using the attenuation strategy based on concave function to avoid the premature convergence of PSO algorithm and Time-Varying Acceleration Coefficient (TVAC) strategy was adopted to balance the local search and global search ability. At last the simulation results prove that the proposed approach has a better ability in optimizing BP neural network in terms of network output error, global searching performance and diagnosis accuracy.

Modelling and prediction for chaotic fir laser attractor using rational function neural network.

PubMed

Cho, S

2001-02-01

Many real-world systems such as irregular ECG signal, volatility of currency exchange rate and heated fluid reaction exhibit highly complex nonlinear characteristic known as chaos. These chaotic systems cannot be retreated satisfactorily using linear system theory due to its high dimensionality and irregularity. This research focuses on prediction and modelling of chaotic FIR (Far InfraRed) laser system for which the underlying equations are not given. This paper proposed a method for prediction and modelling a chaotic FIR laser time series using rational function neural network. Three network architectures, TDNN (Time Delayed Neural Network), RBF (radial basis function) network and the RF (rational function) network, are also presented. Comparisons between these networks performance show the improvements introduced by the RF network in terms of a decrement in network complexity and better ability of predictability.

MEG and fMRI Fusion for Non-Linear Estimation of Neural and BOLD Signal Changes

PubMed Central

Plis, Sergey M.; Calhoun, Vince D.; Weisend, Michael P.; Eichele, Tom; Lane, Terran

2010-01-01

The combined analysis of magnetoencephalography (MEG)/electroencephalography and functional magnetic resonance imaging (fMRI) measurements can lead to improvement in the description of the dynamical and spatial properties of brain activity. In this paper we empirically demonstrate this improvement using simulated and recorded task related MEG and fMRI activity. Neural activity estimates were derived using a dynamic Bayesian network with continuous real valued parameters by means of a sequential Monte Carlo technique. In synthetic data, we show that MEG and fMRI fusion improves estimation of the indirectly observed neural activity and smooths tracking of the blood oxygenation level dependent (BOLD) response. In recordings of task related neural activity the combination of MEG and fMRI produces a result with greater signal-to-noise ratio, that confirms the expectation arising from the nature of the experiment. The highly non-linear model of the BOLD response poses a difficult inference problem for neural activity estimation; computational requirements are also high due to the time and space complexity. We show that joint analysis of the data improves the system's behavior by stabilizing the differential equations system and by requiring fewer computational resources. PMID:21120141

Neural Mechanisms of Improvements in Social Motivation after Pivotal Response Treatment: Two Case Studies

ERIC Educational Resources Information Center

Voos, Avery C.; Pelphrey, Kevin A.; Tirrell, Jonathan; Bolling, Danielle Z.; Vander Wyk, Brent; Kaiser, Martha D.; McPartland, James C.; Volkmar, Fred R.; Ventola, Pamela

2013-01-01

Pivotal response treatment (PRT) is an empirically validated behavioral treatment that has widespread positive effects on communication, behavior, and social skills in young children with autism spectrum disorder (ASD). For the first time, functional magnetic resonance imaging was used to identify the neural correlates of successful response to…

Functional Multichannel Poly(Propylene Fumarate)-Collagen Scaffold with Collagen-Binding Neurotrophic Factor 3 Promotes Neural Regeneration After Transected Spinal Cord Injury.

PubMed

Chen, Xi; Zhao, Yannan; Li, Xing; Xiao, Zhifeng; Yao, Yuanjiang; Chu, Yun; Farkas, Balázs; Romano, Ilaria; Brandi, Fernando; Dai, Jianwu

2018-06-19

Many factors contribute to the poor axonal regrowth and ineffective functional recovery after spinal cord injury (SCI). Biomaterials have been used for SCI repair by promoting bridge formation and reconnecting the neural tissue at the lesion site. The mechanical properties of biomaterials are critical for successful design to ensure the stable support as soon as possible when compressed by the surrounding spine and musculature. Poly(propylene fumarate) (PPF) scaffolds with high mechanical strength have been shown to provide firm spatial maintenance and to promote repair of tissue defects. A multichannel PPF scaffold is combined with collagen biomaterial to build a novel biocompatible delivery system coated with neurotrophin-3 containing an engineered collagen-binding domain (CBD-NT3). The parallel-aligned multichannel structure of PPF scaffolds guide the direction of neural tissue regeneration across the lesion site and promote reestablishment of bridge connectivity. The combinatorial treatment consisting of PPF and collagen loaded with CBD-NT3 improves the inhibitory microenvironment, facilitates axonal and neuronal regeneration, survival of various types of functional neurons and remyelination and synapse formation of regenerated axons following SCI. This novel treatment strategy for SCI repair effectively promotes neural tissue regeneration after transected spinal injury by providing a regrowth-supportive microenvironment and eventually induces functional improvement. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Cholinergic enhancement reduces functional connectivity and BOLD variability in visual extrastriate cortex during selective attention

PubMed Central

Ricciardi, Emiliano; Handjaras, Giacomo; Bernardi, Giulio; Pietrini, Pietro; Furey, Maura L.

2012-01-01

Enhancing cholinergic function improves performance on various cognitive tasks and alters neural responses in task specific brain regions. Previous findings by our group strongly suggested that the changes in neural activity observed during increased cholinergic function may reflect an increase in neural efficiency that leads to improved task performance. The current study was designed to assess the effects of cholinergic enhancement on regional brain connectivity and BOLD signal variability. Nine subjects participated in a double-blind, placebo-controlled crossover functional magnetic resonance imaging (fMRI) study. Following an infusion of physostigmine (1mg/hr) or placebo, echo-planar imaging (EPI) was conducted as participants performed a selective attention task. During the task, two images comprised of superimposed pictures of faces and houses were presented. Subjects were instructed periodically to shift their attention from one stimulus component to the other and to perform a matching task using hand held response buttons. A control condition included phase-scrambled images of superimposed faces and houses that were presented in the same temporal and spatial manner as the attention task; participants were instructed to perform a matching task. Cholinergic enhancement improved performance during the selective attention task, with no change during the control task. Functional connectivity analyses showed that the strength of connectivity between ventral visual processing areas and task-related occipital, parietal and prefrontal regions was reduced significantly during cholinergic enhancement, exclusively during the selective attention task. Cholinergic enhancement also reduced BOLD signal temporal variability relative to placebo throughout temporal and occipital visual processing areas, again during the selective attention task only. Together with the observed behavioral improvement, the decreases in connectivity strength throughout task-relevant regions and BOLD variability within stimulus processing regions provide further support to the hypothesis that cholinergic augmentation results in enhanced neural efficiency. PMID:22906685

NeuroMEMS: Neural Probe Microtechnologies

PubMed Central

HajjHassan, Mohamad; Chodavarapu, Vamsy; Musallam, Sam

2008-01-01

Neural probe technologies have already had a significant positive effect on our understanding of the brain by revealing the functioning of networks of biological neurons. Probes are implanted in different areas of the brain to record and/or stimulate specific sites in the brain. Neural probes are currently used in many clinical settings for diagnosis of brain diseases such as seizers, epilepsy, migraine, Alzheimer's, and dementia. We find these devices assisting paralyzed patients by allowing them to operate computers or robots using their neural activity. In recent years, probe technologies were assisted by rapid advancements in microfabrication and microelectronic technologies and thus are enabling highly functional and robust neural probes which are opening new and exciting avenues in neural sciences and brain machine interfaces. With a wide variety of probes that have been designed, fabricated, and tested to date, this review aims to provide an overview of the advances and recent progress in the microfabrication techniques of neural probes. In addition, we aim to highlight the challenges faced in developing and implementing ultra-long multi-site recording probes that are needed to monitor neural activity from deeper regions in the brain. Finally, we review techniques that can improve the biocompatibility of the neural probes to minimize the immune response and encourage neural growth around the electrodes for long term implantation studies. PMID:27873894

Classifying the molecular functions of Rab GTPases in membrane trafficking using deep convolutional neural networks.

PubMed

Le, Nguyen-Quoc-Khanh; Ho, Quang-Thai; Ou, Yu-Yen

2018-06-13

Deep learning has been increasingly used to solve a number of problems with state-of-the-art performance in a wide variety of fields. In biology, deep learning can be applied to reduce feature extraction time and achieve high levels of performance. In our present work, we apply deep learning via two-dimensional convolutional neural networks and position-specific scoring matrices to classify Rab protein molecules, which are main regulators in membrane trafficking for transferring proteins and other macromolecules throughout the cell. The functional loss of specific Rab molecular functions has been implicated in a variety of human diseases, e.g., choroideremia, intellectual disabilities, cancer. Therefore, creating a precise model for classifying Rabs is crucial in helping biologists understand the molecular functions of Rabs and design drug targets according to such specific human disease information. We constructed a robust deep neural network for classifying Rabs that achieved an accuracy of 99%, 99.5%, 96.3%, and 97.6% for each of four specific molecular functions. Our approach demonstrates superior performance to traditional artificial neural networks. Therefore, from our proposed study, we provide both an effective tool for classifying Rab proteins and a basis for further research that can improve the performance of biological modeling using deep neural networks. Copyright © 2018 Elsevier Inc. All rights reserved.

Increasing Spontaneous Retinal Activity before Eye Opening Accelerates the Development of Geniculate Receptive Fields

PubMed Central

Davis, Zachary W.; Chapman, Barbara

2015-01-01

Visually evoked activity is necessary for the normal development of the visual system. However, little is known about the capacity for patterned spontaneous activity to drive the maturation of receptive fields before visual experience. Retinal waves provide instructive retinotopic information for the anatomical organization of the visual thalamus. To determine whether retinal waves also drive the maturation of functional responses, we increased the frequency of retinal waves pharmacologically in the ferret (Mustela putorius furo) during a period of retinogeniculate development before eye opening. The development of geniculate receptive fields after receiving these increased neural activities was measured using single-unit electrophysiology. We found that increased retinal waves accelerate the developmental reduction of geniculate receptive field sizes. This reduction is due to a decrease in receptive field center size rather than an increase in inhibitory surround strength. This work reveals an instructive role for patterned spontaneous activity in guiding the functional development of neural circuits. SIGNIFICANCE STATEMENT Patterned spontaneous neural activity that occurs during development is known to be necessary for the proper formation of neural circuits. However, it is unknown whether the spontaneous activity alone is sufficient to drive the maturation of the functional properties of neurons. Our work demonstrates for the first time an acceleration in the maturation of neural function as a consequence of driving patterned spontaneous activity during development. This work has implications for our understanding of how neural circuits can be modified actively to improve function prematurely or to recover from injury with guided interventions of patterned neural activity. PMID:26511250

The neural correlates of agrammatism: Evidence from aphasic and healthy speakers performing an overt picture description task

PubMed Central

Schönberger, Eva; Heim, Stefan; Meffert, Elisabeth; Pieperhoff, Peter; da Costa Avelar, Patricia; Huber, Walter; Binkofski, Ferdinand; Grande, Marion

2014-01-01

Functional brain imaging studies have improved our knowledge of the neural localization of language functions and the functional reorganization after a lesion. However, the neural correlates of agrammatic symptoms in aphasia remain largely unknown. The present fMRI study examined the neural correlates of morpho-syntactic encoding and agrammatic errors in continuous language production by combining three approaches. First, the neural mechanisms underlying natural morpho-syntactic processing in a picture description task were analyzed in 15 healthy speakers. Second, agrammatic-like speech behavior was induced in the same group of healthy speakers to study the underlying functional processes by limiting the utterance length. In a third approach, five agrammatic participants performed the picture description task to gain insights in the neural correlates of agrammatism and the functional reorganization of language processing after stroke. In all approaches, utterances were analyzed for syntactic completeness, complexity, and morphology. Event-related data analysis was conducted by defining every clause-like unit (CLU) as an event with its onset-time and duration. Agrammatic and correct CLUs were contrasted. Due to the small sample size as well as heterogeneous lesion sizes and sites with lesion foci in the insula lobe, inferior frontal, superior temporal and inferior parietal areas the activation patterns in the agrammatic speakers were analyzed on a single subject level. In the group of healthy speakers, posterior temporal and inferior parietal areas were associated with greater morpho-syntactic demands in complete and complex CLUs. The intentional manipulation of morpho-syntactic structures and the omission of function words were associated with additional inferior frontal activation. Overall, the results revealed that the investigation of the neural correlates of agrammatic language production can be reasonably conducted with an overt language production paradigm. PMID:24711802

Polarity-specific high-level information propagation in neural networks.

PubMed

Lin, Yen-Nan; Chang, Po-Yen; Hsiao, Pao-Yueh; Lo, Chung-Chuan

2014-01-01

Analyzing the connectome of a nervous system provides valuable information about the functions of its subsystems. Although much has been learned about the architectures of neural networks in various organisms by applying analytical tools developed for general networks, two distinct and functionally important properties of neural networks are often overlooked. First, neural networks are endowed with polarity at the circuit level: Information enters a neural network at input neurons, propagates through interneurons, and leaves via output neurons. Second, many functions of nervous systems are implemented by signal propagation through high-level pathways involving multiple and often recurrent connections rather than by the shortest paths between nodes. In the present study, we analyzed two neural networks: the somatic nervous system of Caenorhabditis elegans (C. elegans) and the partial central complex network of Drosophila, in light of these properties. Specifically, we quantified high-level propagation in the vertical and horizontal directions: the former characterizes how signals propagate from specific input nodes to specific output nodes and the latter characterizes how a signal from a specific input node is shared by all output nodes. We found that the two neural networks are characterized by very efficient vertical and horizontal propagation. In comparison, classic small-world networks show a trade-off between vertical and horizontal propagation; increasing the rewiring probability improves the efficiency of horizontal propagation but worsens the efficiency of vertical propagation. Our result provides insights into how the complex functions of natural neural networks may arise from a design that allows them to efficiently transform and combine input signals.

Polarity-specific high-level information propagation in neural networks

PubMed Central

Lin, Yen-Nan; Chang, Po-Yen; Hsiao, Pao-Yueh; Lo, Chung-Chuan

2014-01-01

Analyzing the connectome of a nervous system provides valuable information about the functions of its subsystems. Although much has been learned about the architectures of neural networks in various organisms by applying analytical tools developed for general networks, two distinct and functionally important properties of neural networks are often overlooked. First, neural networks are endowed with polarity at the circuit level: Information enters a neural network at input neurons, propagates through interneurons, and leaves via output neurons. Second, many functions of nervous systems are implemented by signal propagation through high-level pathways involving multiple and often recurrent connections rather than by the shortest paths between nodes. In the present study, we analyzed two neural networks: the somatic nervous system of Caenorhabditis elegans (C. elegans) and the partial central complex network of Drosophila, in light of these properties. Specifically, we quantified high-level propagation in the vertical and horizontal directions: the former characterizes how signals propagate from specific input nodes to specific output nodes and the latter characterizes how a signal from a specific input node is shared by all output nodes. We found that the two neural networks are characterized by very efficient vertical and horizontal propagation. In comparison, classic small-world networks show a trade-off between vertical and horizontal propagation; increasing the rewiring probability improves the efficiency of horizontal propagation but worsens the efficiency of vertical propagation. Our result provides insights into how the complex functions of natural neural networks may arise from a design that allows them to efficiently transform and combine input signals. PMID:24672472

Development of Neural Sensitivity to Face Identity Correlates with Perceptual Discriminability

PubMed Central

Barnett, Michael A.; Hartley, Jake; Gomez, Jesse; Stigliani, Anthony; Grill-Spector, Kalanit

2016-01-01

Face perception is subserved by a series of face-selective regions in the human ventral stream, which undergo prolonged development from childhood to adulthood. However, it is unknown how neural development of these regions relates to the development of face-perception abilities. Here, we used functional magnetic resonance imaging (fMRI) to measure brain responses of ventral occipitotemporal regions in children (ages, 5–12 years) and adults (ages, 19–34 years) when they viewed faces that parametrically varied in dissimilarity. Since similar faces generate lower responses than dissimilar faces due to fMRI adaptation, this design objectively evaluates neural sensitivity to face identity across development. Additionally, a subset of subjects participated in a behavioral experiment to assess perceptual discriminability of face identity. Our data reveal three main findings: (1) neural sensitivity to face identity increases with age in face-selective but not object-selective regions; (2) the amplitude of responses to faces increases with age in both face-selective and object-selective regions; and (3) perceptual discriminability of face identity is correlated with the neural sensitivity to face identity of face-selective regions. In contrast, perceptual discriminability is not correlated with the amplitude of response in face-selective regions or of responses of object-selective regions. These data suggest that developmental increases in neural sensitivity to face identity in face-selective regions improve perceptual discriminability of faces. Our findings significantly advance the understanding of the neural mechanisms of development of face perception and open new avenues for using fMRI adaptation to study the neural development of high-level visual and cognitive functions more broadly. SIGNIFICANCE STATEMENT Face perception, which is critical for daily social interactions, develops from childhood to adulthood. However, it is unknown what developmental changes in the brain lead to improved performance. Using fMRI in children and adults, we find that from childhood to adulthood, neural sensitivity to changes in face identity increases in face-selective regions. Critically, subjects' perceptual discriminability among faces is linked to neural sensitivity: participants with higher neural sensitivity in face-selective regions demonstrate higher perceptual discriminability. Thus, our results suggest that developmental increases in face-selective regions' sensitivity to face identity improve perceptual discrimination of faces. These findings significantly advance understanding of the neural mechanisms underlying the development of face perception and have important implications for assessing both typical and atypical development. PMID:27798143

Neural Correlates of Math Gains Vary Depending on Parental Socioeconomic Status (SES)

PubMed Central

Demir-Lira, Özlem Ece; Prado, Jérôme; Booth, James R.

2016-01-01

We used functional magnetic resonance imaging (fMRI) to examine the neural predictors of math development, and asked whether these predictors vary as a function of parental socioeconomic status (SES) in children ranging in age from 8 to 13 years. We independently localized brain regions subserving verbal versus spatial processing in order to characterize relations between activation in these regions during an arithmetic task and long-term change in math skill (up to 3 years). Neural predictors of math gains encompassed brain regions subserving both verbal and spatial processing, but the relation between relative reliance on these regions and math skill growth varied depending on parental SES. Activity in an area of the left inferior frontal gyrus (IFG) identified by the verbal localizer was related to greater growth in math skill at the higher end of the SES continuum, but lesser improvements at the lower end. Activity in an area of the right superior parietal cortex identified by the spatial localizer was related to greater growth in math skill at the lower end of the SES continuum, but lesser improvements at the higher end. Results highlight early neural mechanisms as possible neuromarkers of long-term arithmetic learning and suggest that neural predictors of math gains vary with parental SES. PMID:27378987

EEG-fMRI Bayesian framework for neural activity estimation: a simulation study

NASA Astrophysics Data System (ADS)

Croce, Pierpaolo; Basti, Alessio; Marzetti, Laura; Zappasodi, Filippo; Del Gratta, Cosimo

2016-12-01

Objective. Due to the complementary nature of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), and given the possibility of simultaneous acquisition, the joint data analysis can afford a better understanding of the underlying neural activity estimation. In this simulation study we want to show the benefit of the joint EEG-fMRI neural activity estimation in a Bayesian framework. Approach. We built a dynamic Bayesian framework in order to perform joint EEG-fMRI neural activity time course estimation. The neural activity is originated by a given brain area and detected by means of both measurement techniques. We have chosen a resting state neural activity situation to address the worst case in terms of the signal-to-noise ratio. To infer information by EEG and fMRI concurrently we used a tool belonging to the sequential Monte Carlo (SMC) methods: the particle filter (PF). Main results. First, despite a high computational cost, we showed the feasibility of such an approach. Second, we obtained an improvement in neural activity reconstruction when using both EEG and fMRI measurements. Significance. The proposed simulation shows the improvements in neural activity reconstruction with EEG-fMRI simultaneous data. The application of such an approach to real data allows a better comprehension of the neural dynamics.

EEG-fMRI Bayesian framework for neural activity estimation: a simulation study.

PubMed

Croce, Pierpaolo; Basti, Alessio; Marzetti, Laura; Zappasodi, Filippo; Gratta, Cosimo Del

2016-12-01

Due to the complementary nature of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), and given the possibility of simultaneous acquisition, the joint data analysis can afford a better understanding of the underlying neural activity estimation. In this simulation study we want to show the benefit of the joint EEG-fMRI neural activity estimation in a Bayesian framework. We built a dynamic Bayesian framework in order to perform joint EEG-fMRI neural activity time course estimation. The neural activity is originated by a given brain area and detected by means of both measurement techniques. We have chosen a resting state neural activity situation to address the worst case in terms of the signal-to-noise ratio. To infer information by EEG and fMRI concurrently we used a tool belonging to the sequential Monte Carlo (SMC) methods: the particle filter (PF). First, despite a high computational cost, we showed the feasibility of such an approach. Second, we obtained an improvement in neural activity reconstruction when using both EEG and fMRI measurements. The proposed simulation shows the improvements in neural activity reconstruction with EEG-fMRI simultaneous data. The application of such an approach to real data allows a better comprehension of the neural dynamics.

Improving the performance of the amblyopic visual system

PubMed Central

Levi, Dennis M.; Li, Roger W.

2008-01-01

Experience-dependent plasticity is closely linked with the development of sensory function; however, there is also growing evidence for plasticity in the adult visual system. This review re-examines the notion of a sensitive period for the treatment of amblyopia in the light of recent experimental and clinical evidence for neural plasticity. One recently proposed method for improving the effectiveness and efficiency of treatment that has received considerable attention is ‘perceptual learning’. Specifically, both children and adults with amblyopia can improve their perceptual performance through extensive practice on a challenging visual task. The results suggest that perceptual learning may be effective in improving a range of visual performance and, importantly, the improvements may transfer to visual acuity. Recent studies have sought to explore the limits and time course of perceptual learning as an adjunct to occlusion and to investigate the neural mechanisms underlying the visual improvement. These findings, along with the results of new clinical trials, suggest that it might be time to reconsider our notions about neural plasticity in amblyopia. PMID:19008199

Neural Network Optimization of Ligament Stiffnesses for the Enhanced Predictive Ability of a Patient-Specific, Computational Foot/Ankle Model.

PubMed

Chande, Ruchi D; Wayne, Jennifer S

2017-09-01

Computational models of diarthrodial joints serve to inform the biomechanical function of these structures, and as such, must be supplied appropriate inputs for performance that is representative of actual joint function. Inputs for these models are sourced from both imaging modalities as well as literature. The latter is often the source of mechanical properties for soft tissues, like ligament stiffnesses; however, such data are not always available for all the soft tissues nor is it known for patient-specific work. In the current research, a method to improve the ligament stiffness definition for a computational foot/ankle model was sought with the greater goal of improving the predictive ability of the computational model. Specifically, the stiffness values were optimized using artificial neural networks (ANNs); both feedforward and radial basis function networks (RBFNs) were considered. Optimal networks of each type were determined and subsequently used to predict stiffnesses for the foot/ankle model. Ultimately, the predicted stiffnesses were considered reasonable and resulted in enhanced performance of the computational model, suggesting that artificial neural networks can be used to optimize stiffness inputs.

[Methylphenidate and short-term memory in young females with attention deficit hyperactivity disorder. A study using functional magnetic resonance imaging].

PubMed

Gonzalez-Garrido, A A; Barrios, F A; de la Serna-Tuya, J M; Cocula-León, H; Gómez-Velázquez, F R

Attention deficit/hyperactivity disorder (ADHD) is a common behavioral disorder found mainly in males, thus current knowledge on its clinical expression in female adults is extremely limited. AIM. To evaluate the behavioral and neural substrates associated with the performance of a short-term memory task in female ADHD adults, with and without methylphenidate exposure, with respect to a control group. Two groups of eight young right-handed, female, university students with ADHD and healthy controls matched by age, gender, handedness and academic level, voluntarily participated. All subjects performed twice an easy auditory short-term memory task (ADHD group without, and 90 minutes post-intake of methylphenidate 0.4 mg/kg in a counterbalanced order). The BOLD-fMRI response was used as a measure of neural activity during task performance. ADHD subjects showed a tendency to improve their performances under medication, showing an increased widespread functional activation, especially relevant over left frontal and cerebellar areas, in comparison with control subjects. Methylphenidate slightly improves short-term memory task performance in adult female ADHD subjects by modifying underlying neural functioning patterns.

Therapeutic intraspinal stimulation to generate activity and promote long-term recovery.

PubMed

Mondello, Sarah E; Kasten, Michael R; Horner, Philip J; Moritz, Chet T

2014-01-01

Neuroprosthetic approaches have tremendous potential for the treatment of injuries to the brain and spinal cord by inducing appropriate neural activity in otherwise disordered circuits. Substantial work has demonstrated that stimulation applied to both the central and peripheral nervous system leads to immediate and in some cases sustained benefits after injury. Here we focus on cervical intraspinal microstimulation (ISMS) as a promising method of activating the spinal cord distal to an injury site, either to directly produce movements or more intriguingly to improve subsequent volitional control of the paretic extremities. Incomplete injuries to the spinal cord are the most commonly observed in human patients, and these injuries spare neural tissue bypassing the lesion that could be influenced by neural devices to promote recovery of function. In fact, recent results have demonstrated that therapeutic ISMS leads to modest but sustained improvements in forelimb function after an incomplete spinal cord injury (SCI). This therapeutic spinal stimulation may promote long-term recovery of function by providing the necessary electrical activity needed for neuron survival, axon growth, and synaptic stability.

The importance of task design and behavioral control for understanding the neural basis of cognitive functions.

PubMed

Fetsch, Christopher R

2016-04-01

The success of systems neuroscience depends on the ability to forge quantitative links between neural activity and behavior. Traditionally, this process has benefited from the rigorous development and testing of hypotheses using tools derived from classical psychophysics and computational motor control. As our capacity for measuring neural activity improves, accompanied by powerful new analysis strategies, it seems prudent to remember what these traditional approaches have to offer. Here I present a perspective on the merits of principled task design and tight behavioral control, along with some words of caution about interpretation in unguided, large-scale neural recording studies. I argue that a judicious combination of new and old approaches is the best way to advance our understanding of higher brain function in health and disease. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Barnes Maze for Juvenile Rats Delineates the Emergence of Spatial Navigation Ability

ERIC Educational Resources Information Center

McHail, Daniel G.; Valibeigi, Nazanin; Dumas, Theodore C.

2018-01-01

The neural bases of cognition may be greatly informed by relating temporally defined developmental changes in behavior with concurrent alterations in neural function. A robust improvement in performance in spatial learning and memory tasks occurs at 3 wk of age in rodents. We reported that the developmental increase of spontaneous alternation in a…

Based on BP Neural Network Stock Prediction

ERIC Educational Resources Information Center

Liu, Xiangwei; Ma, Xin

2012-01-01

The stock market has a high profit and high risk features, on the stock market analysis and prediction research has been paid attention to by people. Stock price trend is a complex nonlinear function, so the price has certain predictability. This article mainly with improved BP neural network (BPNN) to set up the stock market prediction model, and…

Forecasting of the electrical actuators condition using stator’s current signals

NASA Astrophysics Data System (ADS)

Kruglova, T. N.; Yaroshenko, I. V.; Rabotalov, N. N.; Melnikov, M. A.

2017-02-01

This article describes a forecasting method for electrical actuators realized through the combination of Fourier transformation and neural network techniques. The method allows finding the value of diagnostic functions in the iterating operating cycle and the number of operational cycles in time before the BLDC actuator fails. For forecasting of the condition of the actuator, we propose a hierarchical structure of the neural network aiming to reduce the training time of the neural network and improve estimation accuracy.

Adaptive exponential synchronization of complex-valued Cohen-Grossberg neural networks with known and unknown parameters.

PubMed

Hu, Jin; Zeng, Chunna

2017-02-01

The complex-valued Cohen-Grossberg neural network is a special kind of complex-valued neural network. In this paper, the synchronization problem of a class of complex-valued Cohen-Grossberg neural networks with known and unknown parameters is investigated. By using Lyapunov functionals and the adaptive control method based on parameter identification, some adaptive feedback schemes are proposed to achieve synchronization exponentially between the drive and response systems. The results obtained in this paper have extended and improved some previous works on adaptive synchronization of Cohen-Grossberg neural networks. Finally, two numerical examples are given to demonstrate the effectiveness of the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

Research on wind field algorithm of wind lidar based on BP neural network and grey prediction

NASA Astrophysics Data System (ADS)

Chen, Yong; Chen, Chun-Li; Luo, Xiong; Zhang, Yan; Yang, Ze-hou; Zhou, Jie; Shi, Xiao-ding; Wang, Lei

2018-01-01

This paper uses the BP neural network and grey algorithm to forecast and study radar wind field. In order to reduce the residual error in the wind field prediction which uses BP neural network and grey algorithm, calculating the minimum value of residual error function, adopting the residuals of the gray algorithm trained by BP neural network, using the trained network model to forecast the residual sequence, using the predicted residual error sequence to modify the forecast sequence of the grey algorithm. The test data show that using the grey algorithm modified by BP neural network can effectively reduce the residual value and improve the prediction precision.

Community-based benchmarking improves spike rate inference from two-photon calcium imaging data.

PubMed

Berens, Philipp; Freeman, Jeremy; Deneux, Thomas; Chenkov, Nikolay; McColgan, Thomas; Speiser, Artur; Macke, Jakob H; Turaga, Srinivas C; Mineault, Patrick; Rupprecht, Peter; Gerhard, Stephan; Friedrich, Rainer W; Friedrich, Johannes; Paninski, Liam; Pachitariu, Marius; Harris, Kenneth D; Bolte, Ben; Machado, Timothy A; Ringach, Dario; Stone, Jasmine; Rogerson, Luke E; Sofroniew, Nicolas J; Reimer, Jacob; Froudarakis, Emmanouil; Euler, Thomas; Román Rosón, Miroslav; Theis, Lucas; Tolias, Andreas S; Bethge, Matthias

2018-05-01

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong

Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T{sub p}:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed amore » new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.« less

Physical Exercise Promotes Recovery of Neurological Function after Ischemic Stroke in Rats

PubMed Central

Zheng, Hai-Qing; Zhang, Li-Ying; Luo, Jing; Li, Li-Li; Li, Menglin; Zhang, Qingjie; Hu, Xi-Quan

2014-01-01

Although physical exercise is an effective strategy for treatment of ischemic stroke, the underlying protective mechanisms are still not well understood. It has been recently demonstrated that neural progenitor cells play a vital role in the recovery of neurological function (NF) through differentiation into mature neurons. In the current study, we observed that physical exercise significantly reduced the infarct size and improved damaged neural functional recovery after an ischemic stroke. Furthermore, we found that the treatment not only exhibited a significant increase in the number of neural progenitor cells and neurons but also decreased the apoptotic cells in the peri-infarct region, compared to a control in the absence of exercise. Importantly, the insulin-like growth factor-1 (IGF-1)/Akt signaling pathway was dramatically activated in the peri-infarct region of rats after physical exercise training. Therefore, our findings suggest that physical exercise directly influences the NF recovery process by increasing neural progenitor cell count via activation of the IGF-1/Akt signaling pathway. PMID:24945308

Dual adaptive dynamic control of mobile robots using neural networks.

PubMed

Bugeja, Marvin K; Fabri, Simon G; Camilleri, Liberato

2009-02-01

This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.

Peptide therapy with pentadecapeptide BPC 157 in traumatic nerve injury.

PubMed

Gjurasin, Miroslav; Miklic, Pavle; Zupancic, Bozidar; Perovic, Darko; Zarkovic, Kamelija; Brcic, Luka; Kolenc, Danijela; Radic, Bozo; Seiwerth, Sven; Sikiric, Predrag

2010-02-25

We focused on the healing of rat transected sciatic nerve and improvement made by stable gastric pentadecapeptide BPC 157 (10 microg, 10ng/kg) applied shortly after injury (i) intraperitoneally/intragastrically/locally, at the site of anastomosis, or after (ii) non-anastomozed nerve tubing (7 mm nerve segment resected) directly into the tube. Improvement was shown clinically (autotomy), microscopically/morphometrically and functionally (EMG, one or two months post-injury, walking recovery (sciatic functional index (SFI)) at weekly intervals). BPC 157-rats exhibited faster axonal regeneration: histomorphometrically (improved presentation of neural fascicles, homogeneous regeneration pattern, increased density and size of regenerative fibers, existence of epineural and perineural regeneration, uniform target orientation of regenerative fibers, and higher proportion of neural vs. connective tissue, all fascicles in each nerve showed increased diameter of myelinated fibers, thickness of myelin sheet, number of myelinated fibers per area and myelinated fibers as a percentage of the nerve transected area and the increased blood vessels presentation), electrophysiologically (increased motor action potentials), functionally (improved SFI), the autotomy absent. Thus, BPC 157 markedly improved rat sciatic nerve healing. Copyright 2009 Elsevier B.V. All rights reserved.

Improved Classification of Lung Cancer Using Radial Basis Function Neural Network with Affine Transforms of Voss Representation.

PubMed

Adetiba, Emmanuel; Olugbara, Oludayo O

2015-01-01

Lung cancer is one of the diseases responsible for a large number of cancer related death cases worldwide. The recommended standard for screening and early detection of lung cancer is the low dose computed tomography. However, many patients diagnosed die within one year, which makes it essential to find alternative approaches for screening and early detection of lung cancer. We present computational methods that can be implemented in a functional multi-genomic system for classification, screening and early detection of lung cancer victims. Samples of top ten biomarker genes previously reported to have the highest frequency of lung cancer mutations and sequences of normal biomarker genes were respectively collected from the COSMIC and NCBI databases to validate the computational methods. Experiments were performed based on the combinations of Z-curve and tetrahedron affine transforms, Histogram of Oriented Gradient (HOG), Multilayer perceptron and Gaussian Radial Basis Function (RBF) neural networks to obtain an appropriate combination of computational methods to achieve improved classification of lung cancer biomarker genes. Results show that a combination of affine transforms of Voss representation, HOG genomic features and Gaussian RBF neural network perceptibly improves classification accuracy, specificity and sensitivity of lung cancer biomarker genes as well as achieving low mean square error.

Recent Advances in Neural Electrode-Tissue Interfaces.

PubMed

Woeppel, Kevin; Yang, Qianru; Cui, Xinyan Tracy

2017-12-01

Neurotechnology is facing an exponential growth in the recent decades. Neural electrode-tissue interface research has been well recognized as an instrumental component of neurotechnology development. While satisfactory long-term performance was demonstrated in some applications, such as cochlear implants and deep brain stimulators, more advanced neural electrode devices requiring higher resolution for single unit recording or microstimulation still face significant challenges in reliability and longevity. In this article, we review the most recent findings that contribute to our current understanding of the sources of poor reliability and longevity in neural recording or stimulation, including the material failure, biological tissue response and the interplay between the two. The newly developed characterization tools are introduced from electrophysiology models, molecular and biochemical analysis, material characterization to live imaging. The effective strategies that have been applied to improve the interface are also highlighted. Finally, we discuss the challenges and opportunities in improving the interface and achieving seamless integration between the implanted electrodes and neural tissue both anatomically and functionally.

Construction of a Piezoresistive Neural Sensor Array

NASA Technical Reports Server (NTRS)

Carlson, W. B.; Schulze, W. A.; Pilgrim, P. M.

1996-01-01

The construction of a piezoresistive - piezoelectric sensor (or actuator) array is proposed using 'neural' connectivity for signal recognition and possible actuation functions. A closer integration of the sensor and decision functions is necessary in order to achieve intrinsic identification within the sensor. A neural sensor is the next logical step in development of truly 'intelligent' arrays. This proposal will integrate 1-3 polymer piezoresistors and MLC electroceramic devices for applications involving acoustic identification. The 'intelligent' piezoresistor -piezoelectric system incorporates printed resistors, composite resistors, and a feedback for the resetting of resistances. A model of a design is proposed in order to simulate electromechanical resistor interactions. The goal of optimizing a sensor geometry for improving device reliability, training, & signal identification capabilities is the goal of this work. At present, studies predict performance of a 'smart' device with a significant control of 'effective' compliance over a narrow pressure range due to a piezoresistor percolation threshold. An interesting possibility may be to use an array of control elements to shift the threshold function in order to change the level of resistance in a neural sensor array for identification, or, actuation applications. The proposed design employs elements of: (1) conductor loaded polymers for a 'fast' RC time constant response; and (2) multilayer ceramics for actuation or sensing and shifting of resistance in the polymer. Other material possibilities also exist using magnetoresistive layered systems for shifting the resistance. It is proposed to use a neural net configuration to test and to help study the possible changes required in the materials design of these devices. Numerical design models utilize electromechanical elements, in conjunction with structural elements in order to simulate piezoresistively controlled actuators and changes in resistance of sensors. The construction of these devices may show significant improvement in ability to interrogate signals and in the control of effective compliance. This work focuses on the development a variety of series/parallel interconnected piezoresistive control elements for the neural sensing function.

Advancing Clinical Outcomes, Biomarkers and Treatments for Severe TBI

DTIC Science & Technology

2017-08-01

determining the neurobehavioral and neural effects of repetitive transcranial magnetic stimulation (rTMS), which is a non-invasive technique to stimulate the...examined to determine effectiveness in inducing structural and functional neural plasticity and improving neurobehavioral recovery after severe TBI...Specific Aims: Aim I will determine presence, direction and sustainability of rTMS-induced neurobehavioral effects measured with the Disability Rating

Oscillatory neural representations in the sensory thalamus predict neuropathic pain relief by deep brain stimulation.

PubMed

Huang, Yongzhi; Green, Alexander L; Hyam, Jonathan; Fitzgerald, James; Aziz, Tipu Z; Wang, Shouyan

2018-01-01

Understanding the function of sensory thalamic neural activity is essential for developing and improving interventions for neuropathic pain. However, there is a lack of investigation of the relationship between sensory thalamic oscillations and pain relief in patients with neuropathic pain. This study aims to identify the oscillatory neural characteristics correlated with pain relief induced by deep brain stimulation (DBS), and develop a quantitative model to predict pain relief by integrating characteristic measures of the neural oscillations. Measures of sensory thalamic local field potentials (LFPs) in thirteen patients with neuropathic pain were screened in three dimensional feature space according to the rhythm, balancing, and coupling neural behaviours, and correlated with pain relief. An integrated approach based on principal component analysis (PCA) and multiple regression analysis is proposed to integrate the multiple measures and provide a predictive model. This study reveals distinct thalamic rhythms of theta, alpha, high beta and high gamma oscillations correlating with pain relief. The balancing and coupling measures between these neural oscillations were also significantly correlated with pain relief. The study enriches the series research on the function of thalamic neural oscillations in neuropathic pain and relief, and provides a quantitative approach for predicting pain relief by DBS using thalamic neural oscillations. Copyright © 2017 Elsevier Inc. All rights reserved.

Enhancing neural activity to drive respiratory plasticity following cervical spinal cord injury

PubMed Central

Hormigo, Kristiina M.; Zholudeva, Lyandysha V.; Spruance, Victoria M.; Marchenko, Vitaliy; Cote, Marie-Pascale; Vinit, Stephane; Giszter, Simon; Bezdudnaya, Tatiana; Lane, Michael A.

2016-01-01

Cervical spinal cord injury (SCI) results in permanent life-altering sensorimotor deficits, among which impaired breathing is one of the most devastating and life-threatening. While clinical and experimental research has revealed that some spontaneous respiratory improvement (functional plasticity) can occur post-SCI, the extent of the recovery is limited and significant deficits persist. Thus, increasing effort is being made to develop therapies that harness and enhance this neuroplastic potential to optimize long-term recovery of breathing in injured individuals. One strategy with demonstrated therapeutic potential is the use of treatments that increase neural and muscular activity (e.g. locomotor training, neural and muscular stimulation) and promote plasticity. With a focus on respiratory function post-SCI, this review will discuss advances in the use of neural interfacing strategies and activity-based treatments, and highlights some recent results from our own research. PMID:27582085

New bioactive motifs and their use in functionalized self-assembling peptides for NSC differentiation and neural tissue engineering

NASA Astrophysics Data System (ADS)

Gelain, F.; Cigognini, D.; Caprini, A.; Silva, D.; Colleoni, B.; Donegá, M.; Antonini, S.; Cohen, B. E.; Vescovi, A.

2012-04-01

Developing functionalized biomaterials for enhancing transplanted cell engraftment in vivo and stimulating the regeneration of injured tissues requires a multi-disciplinary approach customized for the tissue to be regenerated. In particular, nervous tissue engineering may take a great advantage from the discovery of novel functional motifs fostering transplanted stem cell engraftment and nervous fiber regeneration. Using phage display technology we have discovered new peptide sequences that bind to murine neural stem cell (NSC)-derived neural precursor cells (NPCs), and promote their viability and differentiation in vitro when linked to LDLK12 self-assembling peptide (SAPeptide). We characterized the newly functionalized LDLK12 SAPeptides via atomic force microscopy, circular dichroism and rheology, obtaining nanostructured hydrogels that support human and murine NSC proliferation and differentiation in vitro. One functionalized SAPeptide (Ac-FAQ), showing the highest stem cell viability and neural differentiation in vitro, was finally tested in acute contusive spinal cord injury in rats, where it fostered nervous tissue regrowth and improved locomotor recovery. Interestingly, animals treated with the non-functionalized LDLK12 had an axon sprouting/regeneration intermediate between Ac-FAQ-treated animals and controls. These results suggest that hydrogels functionalized with phage-derived peptides may constitute promising biomimetic scaffolds for in vitro NSC differentiation, as well as regenerative therapy of the injured nervous system. Moreover, this multi-disciplinary approach can be used to customize SAPeptides for other specific tissue engineering applications.Developing functionalized biomaterials for enhancing transplanted cell engraftment in vivo and stimulating the regeneration of injured tissues requires a multi-disciplinary approach customized for the tissue to be regenerated. In particular, nervous tissue engineering may take a great advantage from the discovery of novel functional motifs fostering transplanted stem cell engraftment and nervous fiber regeneration. Using phage display technology we have discovered new peptide sequences that bind to murine neural stem cell (NSC)-derived neural precursor cells (NPCs), and promote their viability and differentiation in vitro when linked to LDLK12 self-assembling peptide (SAPeptide). We characterized the newly functionalized LDLK12 SAPeptides via atomic force microscopy, circular dichroism and rheology, obtaining nanostructured hydrogels that support human and murine NSC proliferation and differentiation in vitro. One functionalized SAPeptide (Ac-FAQ), showing the highest stem cell viability and neural differentiation in vitro, was finally tested in acute contusive spinal cord injury in rats, where it fostered nervous tissue regrowth and improved locomotor recovery. Interestingly, animals treated with the non-functionalized LDLK12 had an axon sprouting/regeneration intermediate between Ac-FAQ-treated animals and controls. These results suggest that hydrogels functionalized with phage-derived peptides may constitute promising biomimetic scaffolds for in vitro NSC differentiation, as well as regenerative therapy of the injured nervous system. Moreover, this multi-disciplinary approach can be used to customize SAPeptides for other specific tissue engineering applications. Electronic supplementary information (ESI) available: Supporting methods and data about CD spectral analysis of SAPeptide solutions (Fig. S1), neural differentiation of murine and human NSCs (Fig. S2) on SAPeptide scaffolds, and their statistical analysis (Table S1). See DOI: 10.1039/c2nr30220a

Future trends in Neuroimaging: Neural processes as expressed within real-life contexts

PubMed Central

Hasson, Uri; Honey, Christopher J.

2012-01-01

Human neuroscience research has changed dramatically with the proliferation and refinement of functional magnetic resonance imaging (fMRI) technologies. The early years of the technique were largely devoted to methods development and validation, and to the coarse-grained mapping of functional topographies. This paper will cover three emerging trends that we believe will be central to fMRI research in the coming decade. In the first section of this paper, we argue in favor of a shift from fine-grained functional labeling toward the characterization of underlying neural processes. In the second section, we examine three methodological developments that have improved our ability to characterize underlying neural processes using fMRI. In the last section, we highlight the trend towards more ecologically valid fMRI experiments, which engage neural circuits in real life conditions. We note that many of our cognitive faculties emerge from interpersonal interactions, and that a complete understanding of the cognitive processes within a single individual's brain cannot be achieved without understanding the interactions among individuals. Looking forward to the future of human fMRI, we conclude that the major constraint on new discoveries will not be related to the spatiotemporal resolution of the BOLD signal, which is constantly improving, but rather to the precision of our hypotheses and the creativity of our methods for testing them. PMID:22348879

Evolvable synthetic neural system

NASA Technical Reports Server (NTRS)

Curtis, Steven A. (Inventor)

2009-01-01

An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

Training-specific functional, neural, and hypertrophic adaptations to explosive- vs. sustained-contraction strength training.

PubMed

Balshaw, Thomas G; Massey, Garry J; Maden-Wilkinson, Thomas M; Tillin, Neale A; Folland, Jonathan P

2016-06-01

Training specificity is considered important for strength training, although the functional and underpinning physiological adaptations to different types of training, including brief explosive contractions, are poorly understood. This study compared the effects of 12 wk of explosive-contraction (ECT, n = 13) vs. sustained-contraction (SCT, n = 16) strength training vs. control (n = 14) on the functional, neural, hypertrophic, and intrinsic contractile characteristics of healthy young men. Training involved 40 isometric knee extension repetitions (3 times/wk): contracting as fast and hard as possible for ∼1 s (ECT) or gradually increasing to 75% of maximum voluntary torque (MVT) before holding for 3 s (SCT). Torque and electromyography during maximum and explosive contractions, torque during evoked octet contractions, and total quadriceps muscle volume (QUADSVOL) were quantified pre and post training. MVT increased more after SCT than ECT [23 vs. 17%; effect size (ES) = 0.69], with similar increases in neural drive, but greater QUADSVOL changes after SCT (8.1 vs. 2.6%; ES = 0.74). ECT improved explosive torque at all time points (17-34%; 0.54 ≤ ES ≤ 0.76) because of increased neural drive (17-28%), whereas only late-phase explosive torque (150 ms, 12%; ES = 1.48) and corresponding neural drive (18%) increased after SCT. Changes in evoked torque indicated slowing of the contractile properties of the muscle-tendon unit after both training interventions. These results showed training-specific functional changes that appeared to be due to distinct neural and hypertrophic adaptations. ECT produced a wider range of functional adaptations than SCT, and given the lesser demands of ECT, this type of training provides a highly efficient means of increasing function. Copyright © 2016 the American Physiological Society.

Neural Markers of the Development of Executive Function: Relevance for Education

PubMed Central

Shanmugan, Sheila; Satterthwaite, Theodore D.

2016-01-01

Executive functions are involved in the development of academic skills and are critical for functioning in school settings. The relevance of executive functions to education begins early and continues throughout development, with clear impact on achievement. Diverse efforts increasingly suggest ways in which facilitating development of executive function may be used to improve academic performance. Such interventions seek to alter the trajectory of executive development, which exhibits a protracted course of maturation that stretches into young adulthood. As such, it may be useful to understand how the executive system develops normally and abnormally in order to tailor interventions within educational settings. Here we review recent work investigating the neural basis for executive development during childhood and adolescence. PMID:27182537

Implantable neurotechnologies: bidirectional neural interfaces--applications and VLSI circuit implementations.

PubMed

Greenwald, Elliot; Masters, Matthew R; Thakor, Nitish V

2016-01-01

A bidirectional neural interface is a device that transfers information into and out of the nervous system. This class of devices has potential to improve treatment and therapy in several patient populations. Progress in very large-scale integration has advanced the design of complex integrated circuits. System-on-chip devices are capable of recording neural electrical activity and altering natural activity with electrical stimulation. Often, these devices include wireless powering and telemetry functions. This review presents the state of the art of bidirectional circuits as applied to neuroprosthetic, neurorepair, and neurotherapeutic systems.

Bidirectional Neural Interfaces

PubMed Central

Masters, Matthew R.; Thakor, Nitish V.

2016-01-01

A bidirectional neural interface is a device that transfers information into and out of the nervous system. This class of devices has potential to improve treatment and therapy in several patient populations. Progress in very-large-scale integration (VLSI) has advanced the design of complex integrated circuits. System-on-chip (SoC) devices are capable of recording neural electrical activity and altering natural activity with electrical stimulation. Often, these devices include wireless powering and telemetry functions. This review presents the state of the art of bidirectional circuits as applied to neuroprosthetic, neurorepair, and neurotherapeutic systems. PMID:26753776

Development of Neural Sensitivity to Face Identity Correlates with Perceptual Discriminability.

PubMed

Natu, Vaidehi S; Barnett, Michael A; Hartley, Jake; Gomez, Jesse; Stigliani, Anthony; Grill-Spector, Kalanit

2016-10-19

Face perception is subserved by a series of face-selective regions in the human ventral stream, which undergo prolonged development from childhood to adulthood. However, it is unknown how neural development of these regions relates to the development of face-perception abilities. Here, we used functional magnetic resonance imaging (fMRI) to measure brain responses of ventral occipitotemporal regions in children (ages, 5-12 years) and adults (ages, 19-34 years) when they viewed faces that parametrically varied in dissimilarity. Since similar faces generate lower responses than dissimilar faces due to fMRI adaptation, this design objectively evaluates neural sensitivity to face identity across development. Additionally, a subset of subjects participated in a behavioral experiment to assess perceptual discriminability of face identity. Our data reveal three main findings: (1) neural sensitivity to face identity increases with age in face-selective but not object-selective regions; (2) the amplitude of responses to faces increases with age in both face-selective and object-selective regions; and (3) perceptual discriminability of face identity is correlated with the neural sensitivity to face identity of face-selective regions. In contrast, perceptual discriminability is not correlated with the amplitude of response in face-selective regions or of responses of object-selective regions. These data suggest that developmental increases in neural sensitivity to face identity in face-selective regions improve perceptual discriminability of faces. Our findings significantly advance the understanding of the neural mechanisms of development of face perception and open new avenues for using fMRI adaptation to study the neural development of high-level visual and cognitive functions more broadly. Face perception, which is critical for daily social interactions, develops from childhood to adulthood. However, it is unknown what developmental changes in the brain lead to improved performance. Using fMRI in children and adults, we find that from childhood to adulthood, neural sensitivity to changes in face identity increases in face-selective regions. Critically, subjects' perceptual discriminability among faces is linked to neural sensitivity: participants with higher neural sensitivity in face-selective regions demonstrate higher perceptual discriminability. Thus, our results suggest that developmental increases in face-selective regions' sensitivity to face identity improve perceptual discrimination of faces. These findings significantly advance understanding of the neural mechanisms underlying the development of face perception and have important implications for assessing both typical and atypical development. Copyright © 2016 the authors 0270-6474/16/3610893-15$15.00/0.

Human Neural Stem Cell Extracellular Vesicles Improve Recovery in a Porcine Model of Ischemic Stroke

PubMed Central

Webb, Robin L.; Kaiser, Erin E.; Jurgielewicz, Brian J.; Spellicy, Samantha; Scoville, Shelley L.; Thompson, Tyler A.; Swetenburg, Raymond L.; Hess, David C.; West, Franklin D.

2018-01-01

Background and Purpose— Recent work from our group suggests that human neural stem cell–derived extracellular vesicle (NSC EV) treatment improves both tissue and sensorimotor function in a preclinical thromboembolic mouse model of stroke. In this study, NSC EVs were evaluated in a pig ischemic stroke model, where clinically relevant end points were used to assess recovery in a more translational large animal model. Methods— Ischemic stroke was induced by permanent middle cerebral artery occlusion (MCAO), and either NSC EV or PBS treatment was administered intravenously at 2, 14, and 24 hours post-MCAO. NSC EV effects on tissue level recovery were evaluated via magnetic resonance imaging at 1 and 84 days post-MCAO. Effects on functional recovery were also assessed through longitudinal behavior and gait analysis testing. Results— NSC EV treatment was neuroprotective and led to significant improvements at the tissue and functional levels in stroked pigs. NSC EV treatment eliminated intracranial hemorrhage in ischemic lesions in NSC EV pigs (0 of 7) versus control pigs (7 of 8). NSC EV–treated pigs exhibited a significant decrease in cerebral lesion volume and decreased brain swelling relative to control pigs 1-day post-MCAO. NSC EVs significantly reduced edema in treated pigs relative to control pigs, as assessed by improved diffusivity through apparent diffusion coefficient maps. NSC EVs preserved white matter integrity with increased corpus callosum fractional anisotropy values 84 days post-MCAO. Behavior and mobility improvements paralleled structural changes as NSC EV–treated pigs exhibited improved outcomes, including increased exploratory behavior and faster restoration of spatiotemporal gait parameters. Conclusions— This study demonstrated for the first time that in a large animal model novel NSC EVs significantly improved neural tissue preservation and functional levels post-MCAO, suggesting NSC EVs may be a paradigm changing stroke therapeutic. PMID:29650593

Error-based analysis of optimal tuning functions explains phenomena observed in sensory neurons.

PubMed

Yaeli, Steve; Meir, Ron

2010-01-01

Biological systems display impressive capabilities in effectively responding to environmental signals in real time. There is increasing evidence that organisms may indeed be employing near optimal Bayesian calculations in their decision-making. An intriguing question relates to the properties of optimal encoding methods, namely determining the properties of neural populations in sensory layers that optimize performance, subject to physiological constraints. Within an ecological theory of neural encoding/decoding, we show that optimal Bayesian performance requires neural adaptation which reflects environmental changes. Specifically, we predict that neuronal tuning functions possess an optimal width, which increases with prior uncertainty and environmental noise, and decreases with the decoding time window. Furthermore, even for static stimuli, we demonstrate that dynamic sensory tuning functions, acting at relatively short time scales, lead to improved performance. Interestingly, the narrowing of tuning functions as a function of time was recently observed in several biological systems. Such results set the stage for a functional theory which may explain the high reliability of sensory systems, and the utility of neuronal adaptation occurring at multiple time scales.

Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: three-class classification of rest, right-, and left-hand motor execution.

PubMed

Trakoolwilaiwan, Thanawin; Behboodi, Bahareh; Lee, Jaeseok; Kim, Kyungsoo; Choi, Ji-Woong

2018-01-01

The aim of this work is to develop an effective brain-computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI. In this study, the hemodynamic responses evoked by performing rest, right-, and left-hand motor execution tasks were measured on eight healthy subjects to compare performances. Our CNN-based method provided improvements in classification accuracy over conventional methods employing the most commonly used features of mean, peak, slope, variance, kurtosis, and skewness, classified by support vector machine (SVM) and artificial neural network (ANN). Specifically, up to 6.49% and 3.33% improvement in classification accuracy was achieved by CNN compared with SVM and ANN, respectively.

Nuclear charge radii: density functional theory meets Bayesian neural networks

NASA Astrophysics Data System (ADS)

Utama, R.; Chen, Wei-Chia; Piekarewicz, J.

2016-11-01

The distribution of electric charge in atomic nuclei is fundamental to our understanding of the complex nuclear dynamics and a quintessential observable to validate nuclear structure models. The aim of this study is to explore a novel approach that combines sophisticated models of nuclear structure with Bayesian neural networks (BNN) to generate predictions for the charge radii of thousands of nuclei throughout the nuclear chart. A class of relativistic energy density functionals is used to provide robust predictions for nuclear charge radii. In turn, these predictions are refined through Bayesian learning for a neural network that is trained using residuals between theoretical predictions and the experimental data. Although predictions obtained with density functional theory provide a fairly good description of experiment, our results show significant improvement (better than 40%) after BNN refinement. Moreover, these improved results for nuclear charge radii are supplemented with theoretical error bars. We have successfully demonstrated the ability of the BNN approach to significantly increase the accuracy of nuclear models in the predictions of nuclear charge radii. However, as many before us, we failed to uncover the underlying physics behind the intriguing behavior of charge radii along the calcium isotopic chain.

Neural Correlates for Intrinsic Motivational Deficits of Schizophrenia; Implications for Therapeutics of Cognitive Impairment

PubMed Central

Takeda, Kazuyoshi; Sumiyoshi, Tomiki; Matsumoto, Madoka; Murayama, Kou; Ikezawa, Satoru; Matsumoto, Kenji; Nakagome, Kazuyuki

2018-01-01

The ultimate goal of the treatment of schizophrenia is recovery, a notion related to improvement of cognitive and social functioning. Cognitive remediation therapies (CRT), one of the most effective cognition enhancing methods, have been shown to moderately improve social functioning. For this purpose, intrinsic motivation, related to internal values such as interest and enjoyment, has been shown to play a key role. Although the impairment of intrinsic motivation is one of the characteristics of schizophrenia, its neural mechanisms remain unclear. This is related to the lack of feasible measures of intrinsic motivation, and its response to treatment. According to the self-determination theory (SDT), not only intrinsic motivation, but extrinsic motivation has been reported to enhance learning and memory in healthy subjects to some extent. This finding suggests the contribution of different types of motivation to potentiate the ability of the CRT to treat cognitive impairment of schizophrenia. In this paper, we provide a review of psychological characteristics, assessment methods, and neural correlates of intrinsic motivation in healthy subjects and patients with schizophrenia. Particularly, we focus on neuroimaging studies of intrinsic motivation, including our own. These considerations are relevant to enhancement of functional outcomes of schizophrenia. PMID:29922185

Neural Correlates for Intrinsic Motivational Deficits of Schizophrenia; Implications for Therapeutics of Cognitive Impairment.

PubMed

Takeda, Kazuyoshi; Sumiyoshi, Tomiki; Matsumoto, Madoka; Murayama, Kou; Ikezawa, Satoru; Matsumoto, Kenji; Nakagome, Kazuyuki

2018-01-01

The ultimate goal of the treatment of schizophrenia is recovery, a notion related to improvement of cognitive and social functioning. Cognitive remediation therapies (CRT), one of the most effective cognition enhancing methods, have been shown to moderately improve social functioning. For this purpose, intrinsic motivation, related to internal values such as interest and enjoyment, has been shown to play a key role. Although the impairment of intrinsic motivation is one of the characteristics of schizophrenia, its neural mechanisms remain unclear. This is related to the lack of feasible measures of intrinsic motivation, and its response to treatment. According to the self-determination theory (SDT), not only intrinsic motivation, but extrinsic motivation has been reported to enhance learning and memory in healthy subjects to some extent. This finding suggests the contribution of different types of motivation to potentiate the ability of the CRT to treat cognitive impairment of schizophrenia. In this paper, we provide a review of psychological characteristics, assessment methods, and neural correlates of intrinsic motivation in healthy subjects and patients with schizophrenia. Particularly, we focus on neuroimaging studies of intrinsic motivation, including our own. These considerations are relevant to enhancement of functional outcomes of schizophrenia.

Neural networks: What non-linearity to choose

NASA Technical Reports Server (NTRS)

Kreinovich, Vladik YA.; Quintana, Chris

1991-01-01

Neural networks are now one of the most successful learning formalisms. Neurons transform inputs (x(sub 1),...,x(sub n)) into an output f(w(sub 1)x(sub 1) + ... + w(sub n)x(sub n)), where f is a non-linear function and w, are adjustable weights. What f to choose? Usually the logistic function is chosen, but sometimes the use of different functions improves the practical efficiency of the network. The problem of choosing f as a mathematical optimization problem is formulated and solved under different optimality criteria. As a result, a list of functions f that are optimal under these criteria are determined. This list includes both the functions that were empirically proved to be the best for some problems, and some new functions that may be worth trying.

Neural correlates underlying micrographia in Parkinson’s disease

PubMed Central

Zhang, Jiarong; Hallett, Mark; Feng, Tao; Hou, Yanan; Chan, Piu

2016-01-01

Micrographia is a common symptom in Parkinson’s disease, which manifests as either a consistent or progressive reduction in the size of handwriting or both. Neural correlates underlying micrographia remain unclear. We used functional magnetic resonance imaging to investigate micrographia-related neural activity and connectivity modulations. In addition, the effect of attention and dopaminergic administration on micrographia was examined. We found that consistent micrographia was associated with decreased activity and connectivity in the basal ganglia motor circuit; while progressive micrographia was related to the dysfunction of basal ganglia motor circuit together with disconnections between the rostral supplementary motor area, rostral cingulate motor area and cerebellum. Attention significantly improved both consistent and progressive micrographia, accompanied by recruitment of anterior putamen and dorsolateral prefrontal cortex. Levodopa improved consistent micrographia accompanied by increased activity and connectivity in the basal ganglia motor circuit, but had no effect on progressive micrographia. Our findings suggest that consistent micrographia is related to dysfunction of the basal ganglia motor circuit; while dysfunction of the basal ganglia motor circuit and disconnection between the rostral supplementary motor area, rostral cingulate motor area and cerebellum likely contributes to progressive micrographia. Attention improves both types of micrographia by recruiting additional brain networks. Levodopa improves consistent micrographia by restoring the function of the basal ganglia motor circuit, but does not improve progressive micrographia, probably because of failure to repair the disconnected networks. PMID:26525918

A Systematic and Meta-analytic Review of Neural Correlates of Functional Outcome in Schizophrenia.

PubMed

Wojtalik, Jessica A; Smith, Matthew J; Keshavan, Matcheri S; Eack, Shaun M

2017-10-21

Individuals with schizophrenia are burdened with impairments in functional outcome, despite existing interventions. The lack of understanding of the neurobiological correlates supporting adaptive function in the disorder is a significant barrier to developing more effective treatments. This research conducted a systematic and meta-analytic review of all peer-reviewed studies examining brain-functional outcome relationships in schizophrenia. A total of 53 (37 structural and 16 functional) brain imaging studies examining the neural correlates of functional outcome across 1631 individuals with schizophrenia were identified from literature searches in relevant databases occurring between January, 1968 and December, 2016. Study characteristics and results representing brain-functional outcome relationships were systematically extracted, reviewed, and meta-analyzed. Results indicated that better functional outcome was associated with greater fronto-limbic and whole brain volumes, smaller ventricles, and greater activation, especially during social cognitive processing. Thematic observations revealed that the dorsolateral prefrontal cortex, anterior cingulate, posterior cingulate, parahippocampal gyrus, superior temporal sulcus, and cerebellum may have role in functioning. The neural basis of functional outcome and disability is infrequently studied in schizophrenia. While existing evidence is limited and heterogeneous, these findings suggest that the structural and functional integrity of fronto-limbic brain regions is consistently related to functional outcome in individuals with schizophrenia. Further research is needed to understand the mechanisms and directionality of these relationships, and the potential for identifying neural targets to support functional improvement. © The Author 2017. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) and its application to predicting key process variables.

PubMed

He, Yan-Lin; Xu, Yuan; Geng, Zhi-Qiang; Zhu, Qun-Xiong

2016-03-01

In this paper, a hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) is proposed. Firstly, an improved functional link neural network with small norm of expanded weights and high input-output correlation (SNEWHIOC-FLNN) was proposed for enhancing the generalization performance of FLNN. Unlike the traditional FLNN, the expanded variables of the original inputs are not directly used as the inputs in the proposed SNEWHIOC-FLNN model. The original inputs are attached to some small norm of expanded weights. As a result, the correlation coefficient between some of the expanded variables and the outputs is enhanced. The larger the correlation coefficient is, the more relevant the expanded variables tend to be. In the end, the expanded variables with larger correlation coefficient are selected as the inputs to improve the performance of the traditional FLNN. In order to test the proposed SNEWHIOC-FLNN model, three UCI (University of California, Irvine) regression datasets named Housing, Concrete Compressive Strength (CCS), and Yacht Hydro Dynamics (YHD) are selected. Then a hybrid model based on the improved FLNN integrating with partial least square (IFLNN-PLS) was built. In IFLNN-PLS model, the connection weights are calculated using the partial least square method but not the error back propagation algorithm. Lastly, IFLNN-PLS was developed as an intelligent measurement model for accurately predicting the key variables in the Purified Terephthalic Acid (PTA) process and the High Density Polyethylene (HDPE) process. Simulation results illustrated that the IFLNN-PLS could significant improve the prediction performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Viability and neuronal differentiation of neural stem cells encapsulated in silk fibroin hydrogel functionalized with an IKVAV peptide.

PubMed

Sun, Wei; Incitti, Tania; Migliaresi, Claudio; Quattrone, Alessandro; Casarosa, Simona; Motta, Antonella

2017-05-01

Three-dimensional (3D) porous scaffolds combined with therapeutic stem cells play vital roles in tissue engineering. The adult brain has very limited regeneration ability after injuries such as trauma and stroke. In this study, injectable 3D silk fibroin-based hydrogel scaffolds with encapsulated neural stem cells were developed, aiming at supporting brain regeneration. To improve the function of the hydrogel towards neural stem cells, silk fibroin was modified by an IKVAV peptide through covalent binding. Both unmodified and modified silk fibroin hydrogels were obtained, through sonication, with mechanical stiffness comparable to that of brain tissue. Human neural stem cells were encapsulated in both hydrogels and the effects of IKVAV peptide conjugation on cell viability and neural differentiation were assessed. The silk fibroin hydrogel modified by IKVAV peptide showed increased cell viability and an enhanced neuronal differentiation capability, which contributed to understanding the effects of IKVAV peptide on the behaviour of neural stem cells. For these reasons, IKVAV-modified silk fibroin is a promising material for brain tissue engineering. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Establishing a Dynamic Self-Adaptation Learning Algorithm of the BP Neural Network and Its Applications

NASA Astrophysics Data System (ADS)

Li, Xiaofeng; Xiang, Suying; Zhu, Pengfei; Wu, Min

2015-12-01

In order to avoid the inherent deficiencies of the traditional BP neural network, such as slow convergence speed, that easily leading to local minima, poor generalization ability and difficulty in determining the network structure, the dynamic self-adaptive learning algorithm of the BP neural network is put forward to improve the function of the BP neural network. The new algorithm combines the merit of principal component analysis, particle swarm optimization, correlation analysis and self-adaptive model, hence can effectively solve the problems of selecting structural parameters, initial connection weights and thresholds and learning rates of the BP neural network. This new algorithm not only reduces the human intervention, optimizes the topological structures of BP neural networks and improves the network generalization ability, but also accelerates the convergence speed of a network, avoids trapping into local minima, and enhances network adaptation ability and prediction ability. The dynamic self-adaptive learning algorithm of the BP neural network is used to forecast the total retail sale of consumer goods of Sichuan Province, China. Empirical results indicate that the new algorithm is superior to the traditional BP network algorithm in predicting accuracy and time consumption, which shows the feasibility and effectiveness of the new algorithm.

The application of improved neural network in hydrocarbon reservoir prediction

NASA Astrophysics Data System (ADS)

Peng, Xiaobo

2013-03-01

This paper use BP neural network techniques to realize hydrocarbon reservoir predication easier and faster in tarim basin in oil wells. A grey - cascade neural network model is proposed and it is faster convergence speed and low error rate. The new method overcomes the shortcomings of traditional BP neural network convergence slow, easy to achieve extreme minimum value. This study had 220 sets of measured logging data to the sample data training mode. By changing the neuron number and types of the transfer function of hidden layers, the best work prediction model is analyzed. The conclusion is the model which can produce good prediction results in general, and can be used for hydrocarbon reservoir prediction.

Two-Stage Approach to Image Classification by Deep Neural Networks

NASA Astrophysics Data System (ADS)

Ososkov, Gennady; Goncharov, Pavel

2018-02-01

The paper demonstrates the advantages of the deep learning networks over the ordinary neural networks on their comparative applications to image classifying. An autoassociative neural network is used as a standalone autoencoder for prior extraction of the most informative features of the input data for neural networks to be compared further as classifiers. The main efforts to deal with deep learning networks are spent for a quite painstaking work of optimizing the structures of those networks and their components, as activation functions, weights, as well as the procedures of minimizing their loss function to improve their performances and speed up their learning time. It is also shown that the deep autoencoders develop the remarkable ability for denoising images after being specially trained. Convolutional Neural Networks are also used to solve a quite actual problem of protein genetics on the example of the durum wheat classification. Results of our comparative study demonstrate the undoubted advantage of the deep networks, as well as the denoising power of the autoencoders. In our work we use both GPU and cloud services to speed up the calculations.

Modeling task-specific neuronal ensembles improves decoding of grasp

NASA Astrophysics Data System (ADS)

Smith, Ryan J.; Soares, Alcimar B.; Rouse, Adam G.; Schieber, Marc H.; Thakor, Nitish V.

2018-06-01

Objective. Dexterous movement involves the activation and coordination of networks of neuronal populations across multiple cortical regions. Attempts to model firing of individual neurons commonly treat the firing rate as directly modulating with motor behavior. However, motor behavior may additionally be associated with modulations in the activity and functional connectivity of neurons in a broader ensemble. Accounting for variations in neural ensemble connectivity may provide additional information about the behavior being performed. Approach. In this study, we examined neural ensemble activity in primary motor cortex (M1) and premotor cortex (PM) of two male rhesus monkeys during performance of a center-out reach, grasp and manipulate task. We constructed point process encoding models of neuronal firing that incorporated task-specific variations in the baseline firing rate as well as variations in functional connectivity with the neural ensemble. Models were evaluated both in terms of their encoding capabilities and their ability to properly classify the grasp being performed. Main results. Task-specific ensemble models correctly predicted the performed grasp with over 95% accuracy and were shown to outperform models of neuronal activity that assume only a variable baseline firing rate. Task-specific ensemble models exhibited superior decoding performance in 82% of units in both monkeys (p  <  0.01). Inclusion of ensemble activity also broadly improved the ability of models to describe observed spiking. Encoding performance of task-specific ensemble models, measured by spike timing predictability, improved upon baseline models in 62% of units. Significance. These results suggest that additional discriminative information about motor behavior found in the variations in functional connectivity of neuronal ensembles located in motor-related cortical regions is relevant to decode complex tasks such as grasping objects, and may serve the basis for more reliable and accurate neural prosthesis.

Body weight-supported training in Becker and limb girdle 2I muscular dystrophy.

PubMed

Jensen, Bente R; Berthelsen, Martin P; Husu, Edith; Christensen, Sofie B; Prahm, Kira P; Vissing, John

2016-08-01

We studied the functional effects of combined strength and aerobic anti-gravity training in severely affected patients with Becker and Limb-Girdle muscular dystrophies. Eight patients performed 10-week progressive combined strength (squats, calf raises, lunges) and aerobic (walk/run, jogging in place or high knee-lift) training 3 times/week in a lower-body positive pressure environment. Closed-kinetic-chain leg muscle strength, isometric knee strength, rate of force development (RFD), and reaction time were evaluated. Baseline data indicated an intact neural activation pattern but showed compromised muscle contractile properties. Training (compliance 91%) improved functional leg muscle strength. Squat series performance increased 30%, calf raises 45%, and lunges 23%. Anti-gravity training improved closed-kinetic-chain leg muscle strength despite no changes in isometric knee extension strength and absolute RFD. The improved closed-kinetic-chain performance may relate to neural adaptation involving motor learning and/or improved muscle strength of other muscles than the weak knee extensors. Muscle Nerve 54: 239-243, 2016. © 2016 Wiley Periodicals, Inc.

Monte Carlo point process estimation of electromyographic envelopes from motor cortical spikes for brain-machine interfaces

NASA Astrophysics Data System (ADS)

Liao, Yuxi; She, Xiwei; Wang, Yiwen; Zhang, Shaomin; Zhang, Qiaosheng; Zheng, Xiaoxiang; Principe, Jose C.

2015-12-01

Objective. Representation of movement in the motor cortex (M1) has been widely studied in brain-machine interfaces (BMIs). The electromyogram (EMG) has greater bandwidth than the conventional kinematic variables (such as position, velocity), and is functionally related to the discharge of cortical neurons. As the stochastic information of EMG is derived from the explicit spike time structure, point process (PP) methods will be a good solution for decoding EMG directly from neural spike trains. Previous studies usually assume linear or exponential tuning curves between neural firing and EMG, which may not be true. Approach. In our analysis, we estimate the tuning curves in a data-driven way and find both the traditional functional-excitatory and functional-inhibitory neurons, which are widely found across a rat’s motor cortex. To accurately decode EMG envelopes from M1 neural spike trains, the Monte Carlo point process (MCPP) method is implemented based on such nonlinear tuning properties. Main results. Better reconstruction of EMG signals is shown on baseline and extreme high peaks, as our method can better preserve the nonlinearity of the neural tuning during decoding. The MCPP improves the prediction accuracy (the normalized mean squared error) 57% and 66% on average compared with the adaptive point process filter using linear and exponential tuning curves respectively, for all 112 data segments across six rats. Compared to a Wiener filter using spike rates with an optimal window size of 50 ms, MCPP decoding EMG from a point process improves the normalized mean square error (NMSE) by 59% on average. Significance. These results suggest that neural tuning is constantly changing during task execution and therefore, the use of spike timing methodologies and estimation of appropriate tuning curves needs to be undertaken for better EMG decoding in motor BMIs.

Selective Estrogen Receptor Modulation Increases Hippocampal Activity during Probabilistic Association Learning in Schizophrenia

PubMed Central

Kindler, Jochen; Weickert, Cynthia Shannon; Skilleter, Ashley J; Catts, Stanley V; Lenroot, Rhoshel; Weickert, Thomas W

2015-01-01

People with schizophrenia show probabilistic association learning impairment in conjunction with abnormal neural activity. The selective estrogen receptor modulator (SERM) raloxifene preserves neural activity during memory in healthy older men and improves memory in schizophrenia. Here, we tested the extent to which raloxifene modifies neural activity during learning in schizophrenia. Nineteen people with schizophrenia participated in a twelve-week randomized, double-blind, placebo-controlled, cross-over adjunctive treatment trial of the SERM raloxifene administered orally at 120 mg daily to assess brain activity during probabilistic association learning using functional magnetic resonance imaging (fMRI). Raloxifene improved probabilistic association learning and significantly increased fMRI BOLD activity in the hippocampus and parahippocampal gyrus relative to placebo. A separate region of interest confirmatory analysis in 21 patients vs 36 healthy controls showed a positive association between parahippocampal neural activity and learning in patients, but no such relationship in the parahippocampal gyrus of healthy controls. Thus, selective estrogen receptor modulation by raloxifene concurrently increases activity in the parahippocampal gyrus and improves probabilistic association learning in schizophrenia. These results support a role for estrogen receptor modulation of mesial temporal lobe neural activity in the remediation of learning disabilities in both men and women with schizophrenia. PMID:25829142

Temporal plasticity in auditory cortex improves neural discrimination of speech sounds

PubMed Central

Engineer, Crystal T.; Shetake, Jai A.; Engineer, Navzer D.; Vrana, Will A.; Wolf, Jordan T.; Kilgard, Michael P.

2017-01-01

Background Many individuals with language learning impairments exhibit temporal processing deficits and degraded neural responses to speech sounds. Auditory training can improve both the neural and behavioral deficits, though significant deficits remain. Recent evidence suggests that vagus nerve stimulation (VNS) paired with rehabilitative therapies enhances both cortical plasticity and recovery of normal function. Objective/Hypothesis We predicted that pairing VNS with rapid tone trains would enhance the primary auditory cortex (A1) response to unpaired novel speech sounds. Methods VNS was paired with tone trains 300 times per day for 20 days in adult rats. Responses to isolated speech sounds, compressed speech sounds, word sequences, and compressed word sequences were recorded in A1 following the completion of VNS-tone train pairing. Results Pairing VNS with rapid tone trains resulted in stronger, faster, and more discriminable A1 responses to speech sounds presented at conversational rates. Conclusion This study extends previous findings by documenting that VNS paired with rapid tone trains altered the neural response to novel unpaired speech sounds. Future studies are necessary to determine whether pairing VNS with appropriate auditory stimuli could potentially be used to improve both neural responses to speech sounds and speech perception in individuals with receptive language disorders. PMID:28131520

Carbon nanotubes in neural interfacing applications

NASA Astrophysics Data System (ADS)

Voge, Christopher M.; Stegemann, Jan P.

2011-02-01

Carbon nanotubes (CNT) are remarkable materials with a simple and inert molecular structure that gives rise to a range of potentially valuable physical and electronic properties, including high aspect ratio, high mechanical strength and excellent electrical conductivity. This review summarizes recent research on the application of CNT-based materials to study and control cells of the nervous system. It includes the use of CNT as cell culture substrates, to create patterned surfaces and to study cell-matrix interactions. It also summarizes recent investigations of CNT toxicity, particularly as related to neural cells. The application of CNT-based materials to directing the differentiation of progenitor and stem cells toward neural lineages is also discussed. The emphasis is on how CNT surface chemistry and nanotopography can be altered, and how such changes can affect neural cell function. This knowledge can be applied to creating improved neural interfaces and devices, as well as providing new approaches to neural tissue engineering and regeneration.

Optogenetics and the future of neuroscience.

PubMed

Boyden, Edward S

2015-09-01

Over the last 10 years, optogenetics has become widespread in neuroscience for the study of how specific cell types contribute to brain functions and brain disorder states. The full impact of optogenetics will emerge only when other toolsets mature, including neural connectivity and cell phenotyping tools and neural recording and imaging tools. The latter tools are rapidly improving, in part because optogenetics has helped galvanize broad interest in neurotechnology development.

Improved selectivity from a wavelength addressable device for wireless stimulation of neural tissue

PubMed Central

Seymour, Elif Ç.; Freedman, David S.; Gökkavas, Mutlu; Özbay, Ekmel; Sahin, Mesut; Ünlü, M. Selim

2014-01-01

Electrical neural stimulation with micro electrodes is a promising technique for restoring lost functions in the central nervous system as a result of injury or disease. One of the problems related to current neural stimulators is the tissue response due to the connecting wires and the presence of a rigid electrode inside soft neural tissue. We have developed a novel, optically activated, microscale photovoltaic neurostimulator based on a custom layered compound semiconductor heterostructure that is both wireless and has a comparatively small volume (<0.01 mm3). Optical activation provides a wireless means of energy transfer to the neurostimulator, eliminating wires and the associated complications. This neurostimulator was shown to evoke action potentials and a functional motor response in the rat spinal cord. In this work, we extend our design to include wavelength selectivity and thus allowing independent activation of devices. As a proof of concept, we fabricated two different microscale devices with different spectral responsivities in the near-infrared region. We assessed the improved addressability of individual devices via wavelength selectivity as compared to spatial selectivity alone through on-bench optical measurements of the devices in combination with an in vivo light intensity profile in the rat cortex obtained in a previous study. We show that wavelength selectivity improves the individual addressability of the floating stimulators, thus increasing the number of devices that can be implanted in close proximity to each other. PMID:24600390

Dynamic boundary layer based neural network quasi-sliding mode control for soft touching down on asteroid

NASA Astrophysics Data System (ADS)

Liu, Xiaosong; Shan, Zebiao; Li, Yuanchun

2017-04-01

Pinpoint landing is a critical step in some asteroid exploring missions. This paper is concerned with the descent trajectory control for soft touching down on a small irregularly-shaped asteroid. A dynamic boundary layer based neural network quasi-sliding mode control law is proposed to track a desired descending path. The asteroid's gravitational acceleration acting on the spacecraft is described by the polyhedron method. Considering the presence of input constraint and unmodeled acceleration, the dynamic equation of relative motion is presented first. The desired descending path is planned using cubic polynomial method, and a collision detection algorithm is designed. To perform trajectory tracking, a neural network sliding mode control law is given first, where the sliding mode control is used to ensure the convergence of system states. Two radial basis function neural networks (RBFNNs) are respectively used as an approximator for the unmodeled term and a compensator for the difference between the actual control input with magnitude constraint and nominal control. To improve the chattering induced by the traditional sliding mode control and guarantee the reachability of the system, a specific saturation function with dynamic boundary layer is proposed to replace the sign function in the preceding control law. Through the Lyapunov approach, the reachability condition of the control system is given. The improved control law can guarantee the system state move within a gradually shrinking quasi-sliding mode band. Numerical simulation results demonstrate the effectiveness of the proposed control strategy.

Human neural stem cell-derived cultures in three-dimensional substrates form spontaneously functional neuronal networks.

PubMed

Smith, Imogen; Silveirinha, Vasco; Stein, Jason L; de la Torre-Ubieta, Luis; Farrimond, Jonathan A; Williamson, Elizabeth M; Whalley, Benjamin J

2017-04-01

Differentiated human neural stem cells were cultured in an inert three-dimensional (3D) scaffold and, unlike two-dimensional (2D) but otherwise comparable monolayer cultures, formed spontaneously active, functional neuronal networks that responded reproducibly and predictably to conventional pharmacological treatments to reveal functional, glutamatergic synapses. Immunocytochemical and electron microscopy analysis revealed a neuronal and glial population, where markers of neuronal maturity were observed in the former. Oligonucleotide microarray analysis revealed substantial differences in gene expression conferred by culturing in a 3D vs a 2D environment. Notable and numerous differences were seen in genes coding for neuronal function, the extracellular matrix and cytoskeleton. In addition to producing functional networks, differentiated human neural stem cells grown in inert scaffolds offer several significant advantages over conventional 2D monolayers. These advantages include cost savings and improved physiological relevance, which make them better suited for use in the pharmacological and toxicological assays required for development of stem cell-based treatments and the reduction of animal use in medical research. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Functional Neuroimaging in Psychopathy.

PubMed

Del Casale, Antonio; Kotzalidis, Georgios D; Rapinesi, Chiara; Di Pietro, Simone; Alessi, Maria Chiara; Di Cesare, Gianluigi; Criscuolo, Silvia; De Rossi, Pietro; Tatarelli, Roberto; Girardi, Paolo; Ferracuti, Stefano

2015-01-01

Psychopathy is associated with cognitive and affective deficits causing disruptive, harmful and selfish behaviour. These have considerable societal costs due to recurrent crime and property damage. A better understanding of the neurobiological bases of psychopathy could improve therapeutic interventions, reducing the related social costs. To analyse the major functional neural correlates of psychopathy, we reviewed functional neuroimaging studies conducted on persons with this condition. We searched the PubMed database for papers dealing with functional neuroimaging and psychopathy, with a specific focus on how neural functional changes may correlate with task performances and human behaviour. Psychopathy-related behavioural disorders consistently correlated with dysfunctions in brain areas of the orbitofrontal-limbic (emotional processing and somatic reaction to emotions; behavioural planning and responsibility taking), anterior cingulate-orbitofrontal (correct assignment of emotional valence to social stimuli; violent/aggressive behaviour and challenging attitude) and prefrontal-temporal-limbic (emotional stimuli processing/response) networks. Dysfunctional areas more consistently included the inferior frontal, orbitofrontal, dorsolateral prefrontal, ventromedial prefrontal, temporal (mainly the superior temporal sulcus) and cingulated cortices, the insula, amygdala, ventral striatum and other basal ganglia. Emotional processing and learning, and several social and affective decision-making functions are impaired in psychopathy, which correlates with specific changes in neural functions. © 2015 S. Karger AG, Basel.

Dynamic stability analysis of fractional order leaky integrator echo state neural networks

NASA Astrophysics Data System (ADS)

Pahnehkolaei, Seyed Mehdi Abedi; Alfi, Alireza; Tenreiro Machado, J. A.

2017-06-01

The Leaky integrator echo state neural network (Leaky-ESN) is an improved model of the recurrent neural network (RNN) and adopts an interconnected recurrent grid of processing neurons. This paper presents a new proof for the convergence of a Lyapunov candidate function to zero when time tends to infinity by means of the Caputo fractional derivative with order lying in the range (0, 1). The stability of Fractional-Order Leaky-ESN (FO Leaky-ESN) is then analyzed, and the existence, uniqueness and stability of the equilibrium point are provided. A numerical example demonstrates the feasibility of the proposed method.

Improving Synchronization and Functional Connectivity in Autism Spectrum Disorders Through Plasticity-Induced Rehabilitation

DTIC Science & Technology

2013-08-01

Waiter GD, Gilchrist A, Perrett DI, Murray AD, Whiten A. Neural mechanisms of imitation and ‘mirror neuron’ functioning in autistic spectrum...or risk factor associated with the onset of ASD, the inherent heterogeneity of endophenotypical presentation makes clinical management challenging. In

Individual Differences in Neural Regions Functionally Related to Real and Imagined Stuttering

ERIC Educational Resources Information Center

Wymbs, Nicholas F.; Ingham, Roger J.; Ingham, Janis C.; Paolini, Katherine E.; Grafton, Scott T.

2013-01-01

Recent brain imaging investigations of developmental stuttering show considerable disagreement regarding which regions are related to stuttering. These divergent findings have been mainly derived from group studies. To investigate functional neurophysiology with improved precision, an individual-participant approach (N = 4) using event-related…

Neural functional architecture and modulation during decision making under uncertainty in individuals with generalized anxiety disorder.

PubMed

Assaf, Michal; Rabany, Liron; Zertuche, Luis; Bragdon, Laura; Tolin, David; Goethe, John; Diefenbach, Gretchen

2018-06-21

Recent evidence suggests that repetitive transcranial magnetic stimulation (rTMS) might be effective in treating generalized anxiety disorder (GAD). Cognitive models of GAD highlight the role of intolerance of uncertainty (IU) in precipitating and maintaining worry, and it has been hypothesized that patients with GAD exhibit decision-making deficits under uncertain conditions. Improving understanding of the neural mechanisms underlying cognitive deficits associated with IU may lead to the identification of novel rTMS treatment targets and optimization of treatment parameters. The current report describes two interrelated studies designed to identify and verify a potential neural target for rTMS treatment of GAD. Study I explored the integrity of prefrontal cortex (PFC) and amygdala neural networks, which underlie decision making under conditions of uncertainty, in GAD. Individuals diagnosed with GAD (n = 31) and healthy controls (n = 20) completed a functional magnetic resonance imaging (fMRI) gambling task that manipulated uncertainty using high versus low error rates. In a subsequent randomized-controlled trial (Study II), a subset of the GAD sample (n = 16) completed the fMRI gambling task again after 30 sessions of active versus sham rTMS (1 Hz, right dorsolateral prefrontal cortex) to investigate the modulation of functional networks and symptoms. In Study I, participants with GAD demonstrated impairments in PFC-PFC and PFC-amygdala functional connectivity (FC) mostly during the high uncertainty condition. In Study II, one region of interest pair, dorsal anterior cingulate (ACC) - subgenual ACC, showed "normalization" of FC following active, but not sham, rTMS, and neural changes were associated with improvement in worry symptoms. These results outline a possible treatment mechanism of rTMS in GAD, and pave the way for future studies of treatment optimization. © 2018 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.

Improved Neural Networks with Random Weights for Short-Term Load Forecasting

PubMed Central

Lang, Kun; Zhang, Mingyuan; Yuan, Yongbo

2015-01-01

An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting. PMID:26629825

Improved Neural Networks with Random Weights for Short-Term Load Forecasting.

PubMed

Lang, Kun; Zhang, Mingyuan; Yuan, Yongbo

2015-01-01

An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting.

Adaptively combined FIR and functional link artificial neural network equalizer for nonlinear communication channel.

PubMed

Zhao, Haiquan; Zhang, Jiashu

2009-04-01

This paper proposes a novel computational efficient adaptive nonlinear equalizer based on combination of finite impulse response (FIR) filter and functional link artificial neural network (CFFLANN) to compensate linear and nonlinear distortions in nonlinear communication channel. This convex nonlinear combination results in improving the speed while retaining the lower steady-state error. In addition, since the CFFLANN needs not the hidden layers, which exist in conventional neural-network-based equalizers, it exhibits a simpler structure than the traditional neural networks (NNs) and can require less computational burden during the training mode. Moreover, appropriate adaptation algorithm for the proposed equalizer is derived by the modified least mean square (MLMS). Results obtained from the simulations clearly show that the proposed equalizer using the MLMS algorithm can availably eliminate various intensity linear and nonlinear distortions, and be provided with better anti-jamming performance. Furthermore, comparisons of the mean squared error (MSE), the bit error rate (BER), and the effect of eigenvalue ratio (EVR) of input correlation matrix are presented.

Adaptive Backstepping-Based Neural Tracking Control for MIMO Nonlinear Switched Systems Subject to Input Delays.

PubMed

Niu, Ben; Li, Lu

2018-06-01

This brief proposes a new neural-network (NN)-based adaptive output tracking control scheme for a class of disturbed multiple-input multiple-output uncertain nonlinear switched systems with input delays. By combining the universal approximation ability of radial basis function NNs and adaptive backstepping recursive design with an improved multiple Lyapunov function (MLF) scheme, a novel adaptive neural output tracking controller design method is presented for the switched system. The feature of the developed design is that different coordinate transformations are adopted to overcome the conservativeness caused by adopting a common coordinate transformation for all subsystems. It is shown that all the variables of the resulting closed-loop system are semiglobally uniformly ultimately bounded under a class of switching signals in the presence of MLF and that the system output can follow the desired reference signal. To demonstrate the practicability of the obtained result, an adaptive neural output tracking controller is designed for a mass-spring-damper system.

Stimulatory effect of icariin on the proliferation of neural stem cells from rat hippocampus.

PubMed

Fu, Xiaolong; Li, Shujun; Zhou, Shaoyu; Wu, Qin; Jin, Feng; Shi, Jingshan

2018-01-29

Icariin (ICA), a major ingredient of Epimediumbrevicornum, has various pharmacological activities including central nervous system protective functions such as the improvement of learning and memory function in mice models of Alzheimer's disease. It has been reported that ICA can promote regeneration of peripheral nerve and functional recovery. The purpose of this study was to investigate the potentiating effect of ICA on the proliferation of rat hippocampal neural stem cells, and explore the possible mechanism involved. Primary neural stem cells were prepared from the hippocampus of newly born SD rats, and cells were cultured in special stem cell culture medium. Neural stem cells were confirmed by immunofluorescence detection of nestin, NSE and GFAP expression. The effect of ICA on the growth and proliferation of the neural stem cells was evaluated by 5-ethynyl-2-deoxyuridine (EdU) labeling of proliferating cells, and photomicrographic images of the cultured neural stem cells. Further, the mechanism of ICA-induced cell proliferation of neural stem cells was investigated by analyzing the gene and protein expression of cell cycle related genes cyclin D1 and p21. The present study showed that icariin promotes the growth and proliferation of neural stem cells from rat hippocampus in a dose-dependent manner. Incubation of cells with icariin resulted in significant increase in the number of stem cell spheres as well as the increased incorporation of EdU when compared with cells exposed to control vehicle. In addition, it was found that icariin-induced effect on neural stem cells is associated with increased mRNA and protein expression of cell cycle genes cyclin D1 and p21. This study evidently demonstrates the potentiating effect of ICA on neural stem cell growth and proliferation, which might be mediated through regulation of cell cycle gene and protein expression promoting cell cycle progression.

Alterations in Brain Activation During Cognitive Empathy Are Related to Social Functioning in Schizophrenia

PubMed Central

Smith, Matthew J.; Schroeder, Matthew P.; Abram, Samantha V.; Goldman, Morris B.; Parrish, Todd B.; Wang, Xue; Derntl, Birgit; Habel, Ute; Decety, Jean; Reilly, James L.; Csernansky, John G.; Breiter, Hans C.

2015-01-01

Impaired cognitive empathy (ie, understanding the emotional experiences of others) is associated with poor social functioning in schizophrenia. However, it is unclear whether the neural activity underlying cognitive empathy relates to social functioning. This study examined the neural activation supporting cognitive empathy performance and whether empathy-related activation during correctly performed trials was associated with self-reported cognitive empathy and measures of social functioning. Thirty schizophrenia outpatients and 24 controls completed a cognitive empathy paradigm during functional magnetic resonance imaging. Neural activity corresponding to correct judgments about the expected emotional expression in a social interaction was compared in schizophrenia subjects relative to control subjects. Participants also completed a self-report measure of empathy and 2 social functioning measures (social competence and social attainment). Schizophrenia subjects demonstrated significantly lower accuracy in task performance and were characterized by hypoactivation in empathy-related frontal, temporal, and parietal regions as well as hyperactivation in occipital regions compared with control subjects during accurate cognitive empathy trials. A cluster with peak activation in the supplementary motor area (SMA) extending to the anterior midcingulate cortex (aMCC) correlated with social competence and social attainment in schizophrenia subjects but not controls. These results suggest that neural correlates of cognitive empathy may be promising targets for interventions aiming to improve social functioning and that brain activation in the SMA/aMCC region could be used as a biomarker for monitoring treatment response. PMID:24583906

Neural Basis of Enhanced Executive Function in Older Video Game Players: An fMRI Study.

PubMed

Wang, Ping; Zhu, Xing-Ting; Qi, Zhigang; Huang, Silin; Li, Hui-Jie

2017-01-01

Video games have been found to have positive influences on executive function in older adults; however, the underlying neural basis of the benefits from video games has been unclear. Adopting a task-based functional magnetic resonance imaging (fMRI) study targeted at the flanker task, the present study aims to explore the neural basis of the improved executive function in older adults with video game experiences. Twenty video game players (VGPs) and twenty non-video game players (NVGPs) of 60 years of age or older participated in the present study, and there are no significant differences in age ( t = 0.62, p = 0.536), gender ratio ( t = 1.29, p = 0.206) and years of education ( t = 1.92, p = 0.062) between VGPs and NVGPs. The results show that older VGPs present significantly better behavioral performance than NVGPs. Older VGPs activate greater than NVGPs in brain regions, mainly in frontal-parietal areas, including the right dorsolateral prefrontal cortex, the left supramarginal gyrus, the right angular gyrus, the right precuneus and the left paracentral lobule. The present study reveals that video game experiences may have positive influences on older adults in behavioral performance and the underlying brain activation. These results imply the potential role that video games can play as an effective tool to improve cognitive ability in older adults.

Developmental improvements in dynamic control of fingertip forces last throughout childhood and into adolescence

PubMed Central

Dayanidhi, Sudarshan; Hedberg, Åsa; Forssberg, Hans

2013-01-01

While it is clear that the development of dexterous manipulation in children exhibits dramatic improvements over an extended period, it is difficult to separate musculoskeletal from neural contributors to these important functional gains. This is in part due to the inability of current methods to disambiguate improvements in hand strength from gains in finger dexterity (i.e., the dynamic control of fingertip force vectors at low magnitudes). We adapted our novel instrumentation to evaluate finger dexterity in 130 typically developing children between the ages of 4 and 16 yr. We find that finger dexterity continues to develop well into late adolescence and musculoskeletal growth and strength are poorly correlated with the improvements in dexterity. Importantly, because these behavioral results seem to mirror the known timelines of neuroanatomical development up to adolescence, we speculate that they reflect the functional benefits of such continual neural maturation. This novel perspective now enables the systematic study of the functional roles of specific neuroanatomical structures and their connectivity, maturity, and plasticity. Moreover, the temporal dynamics of the fingertip force vectors shows improvements in stability that provide a novel way to look at the maturation of finger control. From a clinical perspective, our results provide a practical means to chart functional development of dexterous manipulation in typically developing children and could be adapted for clinical use and for use in children with developmental disorders. PMID:23864371

Emergence of a Stable Cortical Map for Neuroprosthetic Control

PubMed Central

Ganguly, Karunesh; Carmena, Jose M.

2009-01-01

Cortical control of neuroprosthetic devices is known to require neuronal adaptations. It remains unclear whether a stable cortical representation for prosthetic function can be stored and recalled in a manner that mimics our natural recall of motor skills. Especially in light of the mixed evidence for a stationary neuron-behavior relationship in cortical motor areas, understanding this relationship during long-term neuroprosthetic control can elucidate principles of neural plasticity as well as improve prosthetic function. Here, we paired stable recordings from ensembles of primary motor cortex neurons in macaque monkeys with a constant decoder that transforms neural activity to prosthetic movements. Proficient control was closely linked to the emergence of a surprisingly stable pattern of ensemble activity, indicating that the motor cortex can consolidate a neural representation for prosthetic control in the presence of a constant decoder. The importance of such a cortical map was evident in that small perturbations to either the size of the neural ensemble or to the decoder could reversibly disrupt function. Moreover, once a cortical map became consolidated, a second map could be learned and stored. Thus, long-term use of a neuroprosthetic device is associated with the formation of a cortical map for prosthetic function that is stable across time, readily recalled, resistant to interference, and resembles a putative memory engram. PMID:19621062

Constructing general partial differential equations using polynomial and neural networks.

PubMed

Zjavka, Ladislav; Pedrycz, Witold

2016-01-01

Sum fraction terms can approximate multi-variable functions on the basis of discrete observations, replacing a partial differential equation definition with polynomial elementary data relation descriptions. Artificial neural networks commonly transform the weighted sum of inputs to describe overall similarity relationships of trained and new testing input patterns. Differential polynomial neural networks form a new class of neural networks, which construct and solve an unknown general partial differential equation of a function of interest with selected substitution relative terms using non-linear multi-variable composite polynomials. The layers of the network generate simple and composite relative substitution terms whose convergent series combinations can describe partial dependent derivative changes of the input variables. This regression is based on trained generalized partial derivative data relations, decomposed into a multi-layer polynomial network structure. The sigmoidal function, commonly used as a nonlinear activation of artificial neurons, may transform some polynomial items together with the parameters with the aim to improve the polynomial derivative term series ability to approximate complicated periodic functions, as simple low order polynomials are not able to fully make up for the complete cycles. The similarity analysis facilitates substitutions for differential equations or can form dimensional units from data samples to describe real-world problems. Copyright © 2015 Elsevier Ltd. All rights reserved.

Subliminal versus supraliminal stimuli activate neural responses in anterior cingulate cortex, fusiform gyrus and insula: a meta-analysis of fMRI studies.

PubMed

Meneguzzo, Paolo; Tsakiris, Manos; Schioth, Helgi B; Stein, Dan J; Brooks, Samantha J

2014-01-01

Non-conscious neural activation may underlie various psychological functions in health and disorder. However, the neural substrates of non-conscious processing have not been entirely elucidated. Examining the differential effects of arousing stimuli that are consciously, versus unconsciously perceived will improve our knowledge of neural circuitry involved in non-conscious perception. Here we conduct preliminary analyses of neural activation in studies that have used both subliminal and supraliminal presentation of the same stimulus. We use Activation Likelihood Estimation (ALE) to examine functional Magnetic Resonance Imaging (fMRI) studies that uniquely present the same stimuli subliminally and supraliminally to healthy participants during functional magnetic resonance imaging (fMRI). We included a total of 193 foci from 9 studies representing subliminal stimulation and 315 foci from 10 studies representing supraliminal stimulation. The anterior cingulate cortex is significantly activated during both subliminal and supraliminal stimulus presentation. Subliminal stimuli are linked to significantly increased activation in the right fusiform gyrus and right insula. Supraliminal stimuli show significantly increased activation in the left rostral anterior cingulate. Non-conscious processing of arousing stimuli may involve primary visual areas and may also recruit the insula, a brain area involved in eventual interoceptive awareness. The anterior cingulate is perhaps a key brain region for the integration of conscious and non-conscious processing. These preliminary data provide candidate brain regions for further study in to the neural correlates of conscious experience.

Computational Models of Neuron-Astrocyte Interactions Lead to Improved Efficacy in the Performance of Neural Networks

PubMed Central

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B.

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem. PMID:22649480

Computational models of neuron-astrocyte interactions lead to improved efficacy in the performance of neural networks.

PubMed

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem.

Reorganization of functional brain networks mediates the improvement of cognitive performance following real-time neurofeedback training of working memory.

PubMed

Zhang, Gaoyan; Yao, Li; Shen, Jiahui; Yang, Yihong; Zhao, Xiaojie

2015-05-01

Working memory (WM) is essential for individuals' cognitive functions. Neuroimaging studies indicated that WM fundamentally relied on a frontoparietal working memory network (WMN) and a cinguloparietal default mode network (DMN). Behavioral training studies demonstrated that the two networks can be modulated by WM training. Different from the behavioral training, our recent study used a real-time functional MRI (rtfMRI)-based neurofeedback method to conduct WM training, demonstrating that WM performance can be significantly improved after successfully upregulating the activity of the target region of interest (ROI) in the left dorsolateral prefrontal cortex (Zhang et al., [2013]: PloS One 8:e73735); however, the neural substrate of rtfMRI-based WM training remains unclear. In this work, we assessed the intranetwork and internetwork connectivity changes of WMN and DMN during the training, and their correlations with the change of brain activity in the target ROI as well as with the improvement of post-training behavior. Our analysis revealed an "ROI-network-behavior" correlation relationship underlying the rtfMRI training. Further mediation analysis indicated that the reorganization of functional brain networks mediated the effect of self-regulation of the target brain activity on the improvement of cognitive performance following the neurofeedback training. The results of this study enhance our understanding of the neural basis of real-time neurofeedback and suggest a new direction to improve WM performance by regulating the functional connectivity in the WM related networks. © 2014 Wiley Periodicals, Inc.

The Potential of Stem Cells in Treatment of Traumatic Brain Injury.

PubMed

Weston, Nicole M; Sun, Dong

2018-01-25

Traumatic brain injury (TBI) is a global public health concern, with limited treatment options available. Despite improving survival rate after TBI, treatment is lacking for brain functional recovery and structural repair in clinic. Recent studies have suggested that the mature brain harbors neural stem cells which have regenerative capacity following brain insults. Much progress has been made in preclinical TBI model studies in understanding the behaviors, functions, and regulatory mechanisms of neural stem cells in the injured brain. Different strategies targeting these cell population have been assessed in TBI models. In parallel, cell transplantation strategy using a wide range of stem cells has been explored for TBI treatment in pre-clinical studies and some in clinical trials. This review summarized strategies which have been explored to enhance endogenous neural stem cell-mediated regeneration and recent development in cell transplantation studies for post-TBI brain repair. Thus far, neural regeneration through neural stem cells either by modulating endogenous neural stem cells or by stem cell transplantation has attracted much attention. It is highly speculated that targeting neural stem cells could be a potential strategy to repair and regenerate the injured brain. Neuroprotection and neuroregeneration are major aspects for TBI therapeutic development. With technique advancement, it is hoped that stem cell-based therapy targeting neuroregeneration will be able to translate to clinic in not so far future.

Enhanced neural activation with blueberry supplementation in mild cognitive impairment.

PubMed

Boespflug, Erin L; Eliassen, James C; Dudley, Jonathan A; Shidler, Marcelle D; Kalt, Wilhelmina; Summer, Suzanne S; Stein, Amanda L; Stover, Amanda N; Krikorian, Robert

2018-05-01

Preclinical studies have shown that blueberry supplementation can improve cognitive performance and neural function in aged animals and have identified associations between anthocyanins and such benefits. Preliminary human trials also suggest cognitive improvement in older adults, although direct evidence of enhancement of brain function has not been demonstrated. In this study, we investigated the effect of blueberry supplementation on regional brain activation in older adults at risk for dementia. In a randomized, double-blind, placebo-controlled trial we performed pre- and post-intervention functional magnetic resonance imaging during a working memory (WM) task to assess the effect of blueberry supplementation on blood oxygen level-dependent (BOLD) signal in older adults with mild cognitive impairment, a risk condition for dementia. Following daily supplementation for 16 weeks, blueberry-treated participants exhibited increased BOLD activation in the left pre-central gyrus, left middle frontal gyrus, and left inferior parietal lobe during WM load conditions (corrected P < 0.01). There was no clear indication of WM enhancement associated with blueberry supplementation. Diet records indicated no between-group difference in anthocyanin consumption external to the intervention. These data demonstrate, for the first time, enhanced neural response during WM challenge in blueberry-treated older adults with cognitive decline and are consistent with prior trials showing neurocognitive benefit with blueberry supplementation in this at-risk population.

Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization

PubMed Central

Huang, Daizheng; Wu, Zhihui

2017-01-01

Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods. PMID:28222194

Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization.

PubMed

Huang, Daizheng; Wu, Zhihui

2017-01-01

Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods.

Improving and accelerating the differentiation and functional maturation of human stem cell-derived neurons: role of extracellular calcium and GABA.

PubMed

Kemp, Paul J; Rushton, David J; Yarova, Polina L; Schnell, Christian; Geater, Charlene; Hancock, Jane M; Wieland, Annalena; Hughes, Alis; Badder, Luned; Cope, Emma; Riccardi, Daniela; Randall, Andrew D; Brown, Jonathan T; Allen, Nicholas D; Telezhkin, Vsevolod

2016-11-15

Neurons differentiated from pluripotent stem cells using established neural culture conditions often exhibit functional deficits. Recently, we have developed enhanced media which both synchronize the neurogenesis of pluripotent stem cell-derived neural progenitors and accelerate their functional maturation; together these media are termed SynaptoJuice. This pair of media are pro-synaptogenic and generate authentic, mature synaptic networks of connected forebrain neurons from a variety of induced pluripotent and embryonic stem cell lines. Such enhanced rate and extent of synchronized maturation of pluripotent stem cell-derived neural progenitor cells generates neurons which are characterized by a relatively hyperpolarized resting membrane potential, higher spontaneous and induced action potential activity, enhanced synaptic activity, more complete development of a mature inhibitory GABA A receptor phenotype and faster production of electrical network activity when compared to standard differentiation media. This entire process - from pre-patterned neural progenitor to active neuron - takes 3 weeks or less, making it an ideal platform for drug discovery and disease modelling in the fields of human neurodegenerative and neuropsychiatric disorders, such as Huntington's disease, Parkinson's disease, Alzheimer's disease and Schizophrenia. © 2016 The Authors. The Journal of Physiology © 2016 The Physiological Society.

Switched-Observer-Based Adaptive Neural Control of MIMO Switched Nonlinear Systems With Unknown Control Gains.

PubMed

Long, Lijun; Zhao, Jun

2017-07-01

In this paper, the problem of adaptive neural output-feedback control is addressed for a class of multi-input multioutput (MIMO) switched uncertain nonlinear systems with unknown control gains. Neural networks (NNs) are used to approximate unknown nonlinear functions. In order to avoid the conservativeness caused by adoption of a common observer for all subsystems, an MIMO NN switched observer is designed to estimate unmeasurable states. A new switched observer-based adaptive neural control technique for the problem studied is then provided by exploiting the classical average dwell time (ADT) method and the backstepping method and the Nussbaum gain technique. It effectively handles the obstacle about the coexistence of multiple Nussbaum-type function terms, and improves the classical ADT method, since the exponential decline property of Lyapunov functions for individual subsystems is no longer satisfied. It is shown that the technique proposed is able to guarantee semiglobal uniformly ultimately boundedness of all the signals in the closed-loop system under a class of switching signals with ADT, and the tracking errors converge to a small neighborhood of the origin. The effectiveness of the approach proposed is illustrated by its application to a two inverted pendulum system.

Diminished neural responses predict enhanced intrinsic motivation and sensitivity to external incentive.

PubMed

Marsden, Karen E; Ma, Wei Ji; Deci, Edward L; Ryan, Richard M; Chiu, Pearl H

2015-06-01

The duration and quality of human performance depend on both intrinsic motivation and external incentives. However, little is known about the neuroscientific basis of this interplay between internal and external motivators. Here, we used functional magnetic resonance imaging to examine the neural substrates of intrinsic motivation, operationalized as the free-choice time spent on a task when this was not required, and tested the neural and behavioral effects of external reward on intrinsic motivation. We found that increased duration of free-choice time was predicted by generally diminished neural responses in regions associated with cognitive and affective regulation. By comparison, the possibility of additional reward improved task accuracy, and specifically increased neural and behavioral responses following errors. Those individuals with the smallest neural responses associated with intrinsic motivation exhibited the greatest error-related neural enhancement under the external contingency of possible reward. Together, these data suggest that human performance is guided by a "tonic" and "phasic" relationship between the neural substrates of intrinsic motivation (tonic) and the impact of external incentives (phasic).

The neural substrates of improved phonological processing following successful treatment in a case of phonological alexia and agraphia

PubMed Central

DeMarco, Andrew T.; Wilson, Stephen M.; Rising, Kindle; Rapcsak, Steven Z.; Beeson, Pélagie M.

2018-01-01

Deficits in phonology are among the most common and persistent impairments in aphasia after left hemisphere stroke, and can have significant functional consequences for spoken and written language. While many individuals make considerable gains in response to treatment, the neural substrates supporting these improvements are poorly understood. To address this issue, we used BOLD fMRI to measure regional brain activation in an individual during pseudoword reading before and after treatment targeting phonological skills. After the first phase of treatment, significant improvement in pseudoword reading was associated with greater activation in residual regions of the left dorsal language network, as well as bilateral regions that support attention and cognitive effort outside of canonical language areas. Following a second treatment phase, behavioral gains were maintained, while brain activation returned to pre-treatment levels. In addition to revealing the neural support for improved phonological skills in the face of damage to critical brain regions, this case demonstrated that behavioral advances may ultimately be maintained without the need to sustain a marked increase in cognitive effort. PMID:29350575

Transdiagnostic dimensions of anxiety: Neural mechanisms, executive functions, and new directions.

PubMed

Sharp, Paul B; Miller, Gregory A; Heller, Wendy

2015-11-01

Converging neuroscientific and psychological evidence points to several transdiagnostic factors that cut across DSM-defined disorders, which both affect and are affected by executive dysfunction. Two of these factors, anxious apprehension and anxious arousal, have helped bridge the gap between psychological and neurobiological models of anxiety. The present integration of diverse findings advances an understanding of the relationships between these transdiagnostic anxiety dimensions, their interactions with each other and executive function, and their neural mechanisms. Additionally, a discussion is provided concerning how these constructs fit within the Research Domain Criteria (RDoC) matrix developed by the National Institutes of Mental Health and how they relate to other anxiety constructs studied with different methods and at other units of analysis. Suggestions for future research are offered, including how to (1) improve measurement and delineation of these constructs, (2) use new neuroimaging methods and theoretical approaches of how the brain functions to build neural mechanistic models of these constructs, and (3) advance understanding of the relationships of these constructs to diverse emotional phenomena and executive functions. Copyright © 2015 Elsevier B.V. All rights reserved.

Alpha oscillations and their impairment in affective and post-traumatic stress disorders.

PubMed

Eidelman-Rothman, Moranne; Levy, Jonathan; Feldman, Ruth

2016-09-01

Affective and anxiety disorders are debilitating conditions characterized by impairments in cognitive and social functioning. Elucidating their neural underpinnings may assist in improving diagnosis and developing targeted interventions. Neural oscillations are fundamental for brain functioning. Specifically, oscillations in the alpha frequency range (alpha rhythms) are prevalent in the awake, conscious brain and play an important role in supporting perceptual, cognitive, and social processes. We review studies utilizing various alpha power measurements to assess abnormalities in brain functioning in affective and anxiety disorders as well as obsessive compulsive and post-traumatic stress disorders. Despite some inconsistencies, studies demonstrate associations between aberrant alpha patterns and these disorders both in response to specific cognitive and emotional tasks and during a resting state. We conclude by discussing methodological considerations and future directions, and underscore the need for much further research on the role of alpha functionality in social contexts. As social dysfunction accompanies most psychiatric conditions, research on alpha's involvement in social processes may provide a unique window into the neural mechanisms underlying these disorders. Copyright © 2016 Elsevier Ltd. All rights reserved.

Correlation of the CT Compatible Stereotaxic Craniotomy with MRI Scans of the Patients for Removing Cranial Lesions Located Eloquent Areas and Deep Sites of Brain.

PubMed

Gulsen, Salih

2015-03-15

The first goal in neurosurgery is to protect neural function as long as it is possible. Moreover, while protecting the neural function, a neurosurgeon should extract the maximum amount of tumoral tissue from the tumour region of the brain. So neurosurgery and technological advancement go hand in hand to realize this goal. Using of CT compatible stereotaxy for removing a cranial tumour is to be commended as a cornerstone of these technological advancements. Following CT compatible stereotaxic system applications in neurosurgery, different techniques have taken place in neurosurgical practice. These techniques are magnetic resonance imaging (MRI), MRI compatible stereotaxis, frameless stereotaxy, volumetric stereotaxy, functional MRI, diffusion tensor (DT) imaging techniques (tractography of the white matter), intraoperative MRI and neuronavigation systems. However, to use all of this equipment having these technologies would be impossible because of economic reasons. However, when we correlated this technique with MRI scans of the patients with CT compatible stereotaxy scans, it is possible to provide gross total resection and protect and improve patients' neural functions.

Improving land resource evaluation using fuzzy neural network ensembles

USGS Publications Warehouse

Xue, Yue-Ju; HU, Y.-M.; Liu, S.-G.; YANG, J.-F.; CHEN, Q.-C.; BAO, S.-T.

2007-01-01

Land evaluation factors often contain continuous-, discrete- and nominal-valued attributes. In traditional land evaluation, these different attributes are usually graded into categorical indexes by land resource experts, and the evaluation results rely heavily on experts' experiences. In order to overcome the shortcoming, we presented a fuzzy neural network ensemble method that did not require grading the evaluation factors into categorical indexes and could evaluate land resources by using the three kinds of attribute values directly. A fuzzy back propagation neural network (BPNN), a fuzzy radial basis function neural network (RBFNN), a fuzzy BPNN ensemble, and a fuzzy RBFNN ensemble were used to evaluate the land resources in Guangdong Province. The evaluation results by using the fuzzy BPNN ensemble and the fuzzy RBFNN ensemble were much better than those by using the single fuzzy BPNN and the single fuzzy RBFNN, and the error rate of the single fuzzy RBFNN or fuzzy RBFNN ensemble was lower than that of the single fuzzy BPNN or fuzzy BPNN ensemble, respectively. By using the fuzzy neural network ensembles, the validity of land resource evaluation was improved and reliance on land evaluators' experiences was considerably reduced. ?? 2007 Soil Science Society of China.

Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions

PubMed Central

Testolin, Alberto; Zorzi, Marco

2016-01-01

Connectionist models can be characterized within the more general framework of probabilistic graphical models, which allow to efficiently describe complex statistical distributions involving a large number of interacting variables. This integration allows building more realistic computational models of cognitive functions, which more faithfully reflect the underlying neural mechanisms at the same time providing a useful bridge to higher-level descriptions in terms of Bayesian computations. Here we discuss a powerful class of graphical models that can be implemented as stochastic, generative neural networks. These models overcome many limitations associated with classic connectionist models, for example by exploiting unsupervised learning in hierarchical architectures (deep networks) and by taking into account top-down, predictive processing supported by feedback loops. We review some recent cognitive models based on generative networks, and we point out promising research directions to investigate neuropsychological disorders within this approach. Though further efforts are required in order to fill the gap between structured Bayesian models and more realistic, biophysical models of neuronal dynamics, we argue that generative neural networks have the potential to bridge these levels of analysis, thereby improving our understanding of the neural bases of cognition and of pathologies caused by brain damage. PMID:27468262

Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions.

PubMed

Testolin, Alberto; Zorzi, Marco

2016-01-01

Connectionist models can be characterized within the more general framework of probabilistic graphical models, which allow to efficiently describe complex statistical distributions involving a large number of interacting variables. This integration allows building more realistic computational models of cognitive functions, which more faithfully reflect the underlying neural mechanisms at the same time providing a useful bridge to higher-level descriptions in terms of Bayesian computations. Here we discuss a powerful class of graphical models that can be implemented as stochastic, generative neural networks. These models overcome many limitations associated with classic connectionist models, for example by exploiting unsupervised learning in hierarchical architectures (deep networks) and by taking into account top-down, predictive processing supported by feedback loops. We review some recent cognitive models based on generative networks, and we point out promising research directions to investigate neuropsychological disorders within this approach. Though further efforts are required in order to fill the gap between structured Bayesian models and more realistic, biophysical models of neuronal dynamics, we argue that generative neural networks have the potential to bridge these levels of analysis, thereby improving our understanding of the neural bases of cognition and of pathologies caused by brain damage.

Global exponential stability of bidirectional associative memory neural networks with distributed delays

NASA Astrophysics Data System (ADS)

Song, Qiankun; Cao, Jinde

2007-05-01

A bidirectional associative memory neural network model with distributed delays is considered. By constructing a new Lyapunov functional, employing the homeomorphism theory, M-matrix theory and the inequality (a[greater-or-equal, slanted]0,bk[greater-or-equal, slanted]0,qk>0 with , and r>1), a sufficient condition is obtained to ensure the existence, uniqueness and global exponential stability of the equilibrium point for the model. Moreover, the exponential converging velocity index is estimated, which depends on the delay kernel functions and the system parameters. The results generalize and improve the earlier publications, and remove the usual assumption that the activation functions are bounded . Two numerical examples are given to show the effectiveness of the obtained results.

Pleiotrophin promotes functional recovery after neural transplantation in rats.

PubMed

Hida, Hideki; Masuda, Tadashi; Sato, Toyohiro; Kim, Tae-Sun; Misumi, Sachiyo; Nishino, Hitoo

2007-01-22

Pleiotrophin promotes survival of dopaminergic neurons in vitro. To investigate whether pleiotrophin promotes survival of grafted dopaminergic neurons in vivo, donor cells from ventral mesencephalon were treated with pleiotrophin (100 ng/ml) during cell preparation and grafted into striatum of hemi-Parkinson model rats. Functional recovery in methamphetamine-induced rotations was improved, and more tyrosine hydroxylase-positive cells survived in the striatum in the pleiotrophin-treated group. Pleiotrophin addition to cells just before transplantation also resulted in better functional recovery; however, no caspase-3 activation was seen during cell preparation. Interestingly, the effect of pleiotrophin on the survival was additive to that of glial-cell line-derived neutropic factor. These results revealed that pleiotrophin had effects on donor cells in neural transplantation in vivo.

Neural Correlates of Antidepressant Treatment Response in Adolescents with Major Depressive Disorder

PubMed Central

Klimes-Dougan, Bonnie; Vu, Dung Pham; Westlund Schreiner, Melinda; Mueller, Bryon A.; Eberly, Lynn E.; Camchong, Jazmin; Westervelt, Ana; Lim, Kelvin O.

2016-01-01

Abstract Objective: The neural changes underlying response to antidepressant treatment in adolescents are unknown. Identification of neural change correlates of treatment response could (1) aid in understanding mechanisms of depression and its treatment and (2) serve as target biomarkers for future research. Method: Using functional magnetic resonance imaging, we examined changes in brain activation and functional connectivity in 13 unmedicated adolescents with major depressive disorder (MDD) before and after receiving treatment with a selective serotonin reuptake inhibitor medication for 8 weeks. Specifically, we examined brain activation during a negative emotion task and resting-state functional connectivity (RSFC), focusing on the amygdala to capture networks relevant to negative emotion. We conducted whole-brain analyses to identify how symptom improvement was related to change in brain activation during a negative emotion task or amygdala RSFC. Results: After treatment, clinical improvement was associated with decreased task activation in rostral and subgenual anterior cingulate cortex and increased activation in bilateral insula, bilateral middle frontal cortices, right parahippocampus, and left cerebellum. Analysis of change in amygdala RSFC showed that treatment response was associated with increased amygdala RSFC with right frontal cortex, but decreased amygdala RSFC with right precuneus and right posterior cingulate cortex. Conclusion: The findings represent a foothold for advancing understanding of pathophysiology of MDD in adolescents by revealing the critical neural circuitry changes that underlie a positive response to a standard treatment. Although preliminary, the present study provides a research platform for future work needed to confirm these biomarkers at a larger scale before using them in future target engagement studies of novel treatments. PMID:27159204

Effects of leukemia inhibitory factor and basic fibroblast growth factor on free radicals and endogenous stem cell proliferation in a mouse model of cerebral infarction.

PubMed

Huang, Weihui; Li, Yadan; Lin, Yufeng; Ye, Xue; Zang, Dawei

2012-07-05

The present study established a mouse model of cerebral infarction by middle cerebral artery occlusion, and monitored the effect of 25 μg/kg leukemia inhibitory factor and (or) basic fibroblast growth factor administration 2 hours after model establishment. Results showed that following administration, the number of endogenous neural stem cells in the infarct area significantly increased, malondialdehyde content in brain tissue homogenates significantly decreased, nitric oxide content, glutathione peroxidase and superoxide dismutase activity significantly elevated, and mouse motor function significantly improved as confirmed by the rotarod and bar grab tests. In particular, the effect of leukemia inhibitory factor in combination with basic fibroblast growth factor was the most significant. Results indicate that leukemia inhibitory factor and basic fibroblast growth factor can improve the microenvironment after cerebral infarction by altering free radical levels, improving the quantity of endogenous neural stem cells, and promoting neurological function of mice with cerebral infarction.

Design of cognitive engine for cognitive radio based on the rough sets and radial basis function neural network

NASA Astrophysics Data System (ADS)

Yang, Yanchao; Jiang, Hong; Liu, Congbin; Lan, Zhongli

2013-03-01

Cognitive radio (CR) is an intelligent wireless communication system which can dynamically adjust the parameters to improve system performance depending on the environmental change and quality of service. The core technology for CR is the design of cognitive engine, which introduces reasoning and learning methods in the field of artificial intelligence, to achieve the perception, adaptation and learning capability. Considering the dynamical wireless environment and demands, this paper proposes a design of cognitive engine based on the rough sets (RS) and radial basis function neural network (RBF_NN). The method uses experienced knowledge and environment information processed by RS module to train the RBF_NN, and then the learning model is used to reconfigure communication parameters to allocate resources rationally and improve system performance. After training learning model, the performance is evaluated according to two benchmark functions. The simulation results demonstrate the effectiveness of the model and the proposed cognitive engine can effectively achieve the goal of learning and reconfiguration in cognitive radio.

Unsupervised texture image segmentation by improved neural network ART2

NASA Technical Reports Server (NTRS)

Wang, Zhiling; Labini, G. Sylos; Mugnuolo, R.; Desario, Marco

1994-01-01

We here propose a segmentation algorithm of texture image for a computer vision system on a space robot. An improved adaptive resonance theory (ART2) for analog input patterns is adapted to classify the image based on a set of texture image features extracted by a fast spatial gray level dependence method (SGLDM). The nonlinear thresholding functions in input layer of the neural network have been constructed by two parts: firstly, to reduce the effects of image noises on the features, a set of sigmoid functions is chosen depending on the types of the feature; secondly, to enhance the contrast of the features, we adopt fuzzy mapping functions. The cluster number in output layer can be increased by an autogrowing mechanism constantly when a new pattern happens. Experimental results and original or segmented pictures are shown, including the comparison between this approach and K-means algorithm. The system written in C language is performed on a SUN-4/330 sparc-station with an image board IT-150 and a CCD camera.

Using Neural Networks to Improve the Performance of Radiative Transfer Modeling Used for Geometry Dependent Surface Lambertian-Equivalent Reflectivity Calculations

NASA Technical Reports Server (NTRS)

Fasnacht, Zachary; Qin, Wenhan; Haffner, David P.; Loyola, Diego; Joiner, Joanna; Krotkov, Nickolay; Vasilkov, Alexander; Spurr, Robert

2017-01-01

Surface Lambertian-equivalent reflectivity (LER) is important for trace gas retrievals in the direct calculation of cloud fractions and indirect calculation of the air mass factor. Current trace gas retrievals use climatological surface LER's. Surface properties that impact the bidirectional reflectance distribution function (BRDF) as well as varying satellite viewing geometry can be important for retrieval of trace gases. Geometry Dependent LER (GLER) captures these effects with its calculation of sun normalized radiances (I/F) and can be used in current LER algorithms (Vasilkov et al. 2016). Pixel by pixel radiative transfer calculations are computationally expensive for large datasets. Modern satellite missions such as the Tropospheric Monitoring Instrument (TROPOMI) produce very large datasets as they take measurements at much higher spatial and spectral resolutions. Look up table (LUT) interpolation improves the speed of radiative transfer calculations but complexity increases for non-linear functions. Neural networks perform fast calculations and can accurately predict both non-linear and linear functions with little effort.

Feedback associated with expectation for larger-reward improves visuospatial working memory performances in children with ADHD.

PubMed

Hammer, Rubi; Tennekoon, Michael; Cooke, Gillian E; Gayda, Jessica; Stein, Mark A; Booth, James R

2015-08-01

We tested the interactive effect of feedback and reward on visuospatial working memory in children with ADHD. Seventeen boys with ADHD and 17 Normal Control (NC) boys underwent functional magnetic resonance imaging (fMRI) while performing four visuospatial 2-back tasks that required monitoring the spatial location of letters presented on a display. Tasks varied in reward size (large; small) and feedback availability (no-feedback; feedback). While the performance of NC boys was high in all conditions, boys with ADHD exhibited higher performance (similar to those of NC boys) only when they received feedback associated with large-reward. Performance pattern in both groups was mirrored by neural activity in an executive function neural network comprised of few distinct frontal brain regions. Specifically, neural activity in the left and right middle frontal gyri of boys with ADHD became normal-like only when feedback was available, mainly when feedback was associated with large-reward. When feedback was associated with small-reward, or when large-reward was expected but feedback was not available, boys with ADHD exhibited altered neural activity in the medial orbitofrontal cortex and anterior insula. This suggests that contextual support normalizes activity in executive brain regions in children with ADHD, which results in improved working memory. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Quantitative Analysis of Human Pluripotency and Neural Specification by In-Depth (Phospho)Proteomic Profiling.

PubMed

Singec, Ilyas; Crain, Andrew M; Hou, Junjie; Tobe, Brian T D; Talantova, Maria; Winquist, Alicia A; Doctor, Kutbuddin S; Choy, Jennifer; Huang, Xiayu; La Monaca, Esther; Horn, David M; Wolf, Dieter A; Lipton, Stuart A; Gutierrez, Gustavo J; Brill, Laurence M; Snyder, Evan Y

2016-09-13

Controlled differentiation of human embryonic stem cells (hESCs) can be utilized for precise analysis of cell type identities during early development. We established a highly efficient neural induction strategy and an improved analytical platform, and determined proteomic and phosphoproteomic profiles of hESCs and their specified multipotent neural stem cell derivatives (hNSCs). This quantitative dataset (nearly 13,000 proteins and 60,000 phosphorylation sites) provides unique molecular insights into pluripotency and neural lineage entry. Systems-level comparative analysis of proteins (e.g., transcription factors, epigenetic regulators, kinase families), phosphorylation sites, and numerous biological pathways allowed the identification of distinct signatures in pluripotent and multipotent cells. Furthermore, as predicted by the dataset, we functionally validated an autocrine/paracrine mechanism by demonstrating that the secreted protein midkine is a regulator of neural specification. This resource is freely available to the scientific community, including a searchable website, PluriProt. Published by Elsevier Inc.

Alterations in brain activation during cognitive empathy are related to social functioning in schizophrenia.

PubMed

Smith, Matthew J; Schroeder, Matthew P; Abram, Samantha V; Goldman, Morris B; Parrish, Todd B; Wang, Xue; Derntl, Birgit; Habel, Ute; Decety, Jean; Reilly, James L; Csernansky, John G; Breiter, Hans C

2015-01-01

Impaired cognitive empathy (ie, understanding the emotional experiences of others) is associated with poor social functioning in schizophrenia. However, it is unclear whether the neural activity underlying cognitive empathy relates to social functioning. This study examined the neural activation supporting cognitive empathy performance and whether empathy-related activation during correctly performed trials was associated with self-reported cognitive empathy and measures of social functioning. Thirty schizophrenia outpatients and 24 controls completed a cognitive empathy paradigm during functional magnetic resonance imaging. Neural activity corresponding to correct judgments about the expected emotional expression in a social interaction was compared in schizophrenia subjects relative to control subjects. Participants also completed a self-report measure of empathy and 2 social functioning measures (social competence and social attainment). Schizophrenia subjects demonstrated significantly lower accuracy in task performance and were characterized by hypoactivation in empathy-related frontal, temporal, and parietal regions as well as hyperactivation in occipital regions compared with control subjects during accurate cognitive empathy trials. A cluster with peak activation in the supplementary motor area (SMA) extending to the anterior midcingulate cortex (aMCC) correlated with social competence and social attainment in schizophrenia subjects but not controls. These results suggest that neural correlates of cognitive empathy may be promising targets for interventions aiming to improve social functioning and that brain activation in the SMA/aMCC region could be used as a biomarker for monitoring treatment response. © The Author 2014. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Immature visual neural system in children reflected by contrast sensitivity with adaptive optics correction

PubMed Central

Liu, Rong; Zhou, Jiawei; Zhao, Haoxin; Dai, Yun; Zhang, Yudong; Tang, Yong; Zhou, Yifeng

2014-01-01

This study aimed to explore the neural development status of the visual system of children (around 8 years old) using contrast sensitivity. We achieved this by eliminating the influence of higher order aberrations (HOAs) with adaptive optics correction. We measured HOAs, modulation transfer functions (MTFs) and contrast sensitivity functions (CSFs) of six children and five adults with both corrected and uncorrected HOAs. We found that when HOAs were corrected, children and adults both showed improvements in MTF and CSF. However, the CSF of children was still lower than the adult level, indicating the difference in contrast sensitivity between groups cannot be explained by differences in optical factors. Further study showed that the difference between the groups also could not be explained by differences in non-visual factors. With these results we concluded that the neural systems underlying vision in children of around 8 years old are still immature in contrast sensitivity. PMID:24732728

Learning a trajectory using adjoint functions and teacher forcing

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad B.; Barhen, Jacob

1992-01-01

A new methodology for faster supervised temporal learning in nonlinear neural networks is presented which builds upon the concept of adjoint operators to allow fast computation of the gradients of an error functional with respect to all parameters of the neural architecture, and exploits the concept of teacher forcing to incorporate information on the desired output into the activation dynamics. The importance of the initial or final time conditions for the adjoint equations is discussed. A new algorithm is presented in which the adjoint equations are solved simultaneously (i.e., forward in time) with the activation dynamics of the neural network. We also indicate how teacher forcing can be modulated in time as learning proceeds. The results obtained show that the learning time is reduced by one to two orders of magnitude with respect to previously published results, while trajectory tracking is significantly improved. The proposed methodology makes hardware implementation of temporal learning attractive for real-time applications.

Neural Patterns of Reorganization after Intensive Robot-Assisted Virtual Reality Therapy and Repetitive Task Practice in Patients with Chronic Stroke

PubMed Central

Saleh, Soha; Fluet, Gerard; Qiu, Qinyin; Merians, Alma; Adamovich, Sergei V.; Tunik, Eugene

2017-01-01

Several approaches to rehabilitation of the hand following a stroke have emerged over the last two decades. These treatments, including repetitive task practice (RTP), robotically assisted rehabilitation and virtual rehabilitation activities, produce improvements in hand function but have yet to reinstate function to pre-stroke levels—which likely depends on developing the therapies to impact cortical reorganization in a manner that favors or supports recovery. Understanding cortical reorganization that underlies the above interventions is therefore critical to inform how such therapies can be utilized and improved and is the focus of the current investigation. Specifically, we compare neural reorganization elicited in stroke patients participating in two interventions: a hybrid of robot-assisted virtual reality (RAVR) rehabilitation training and a program of RTP training. Ten chronic stroke subjects participated in eight 3-h sessions of RAVR therapy. Another group of nine stroke subjects participated in eight sessions of matched RTP therapy. Functional magnetic resonance imaging (fMRI) data were acquired during paretic hand movement, before and after training. We compared the difference between groups and sessions (before and after training) in terms of BOLD intensity, laterality index of activation in sensorimotor areas, and the effective connectivity between ipsilesional motor cortex (iMC), contralesional motor cortex, ipsilesional primary somatosensory cortex (iS1), ipsilesional ventral premotor area (iPMv), and ipsilesional supplementary motor area. Last, we analyzed the relationship between changes in fMRI data and functional improvement measured by the Jebsen Taylor Hand Function Test (JTHFT), in an attempt to identify how neurophysiological changes are related to motor improvement. Subjects in both groups demonstrated motor recovery after training, but fMRI data revealed RAVR-specific changes in neural reorganization patterns. First, BOLD signal in multiple regions of interest was reduced and re-lateralized to the ipsilesional side. Second, these changes correlated with improvement in JTHFT scores. Our findings suggest that RAVR training may lead to different neurophysiological changes when compared with traditional therapy. This effect may be attributed to the influence that augmented visual and haptic feedback during RAVR training exerts over higher-order somatosensory and visuomotor areas. PMID:28928708

High definition-transcranial direct current stimulation changes older adults' subjective sleep and corresponding resting-state functional connectivity.

PubMed

Sheng, Jing; Xie, Chao; Fan, Dong-Qiong; Lei, Xu; Yu, Jing

2018-07-01

With advanced age, older adults show functional deterioration in sleep. Transcranial direct current stimulation (tDCS), a noninvasive brain stimulation, modulates individuals' behavioral performance in various cognitive domains. However, the modulation effect and neural mechanisms of tDCS on sleep, especially for the elderly population are not clear. Here, we aimed to investigate whether high-definition transcranial direct current stimulation (HD-tDCS) could modulate community-dwelling older adults' subjective sleep and whether these potential improvements are associated with the large-scale brain activity alterations recorded by functional magnetic resonance imaging. Thirty-one older adults were randomly allocated to the HD-tDCS group and the control group. HD-tDCS was applied for 25 min at 1.5 mA per day for two weeks. The anode electrode was placed over the left dorsolateral prefrontal cortex, surrounded by 4 cathodes at 7 cm radius. All participants completed sleep neuropsychological assessments and fMRI scans individually before and after intervention. Behaviorally, we observed a HD-tDCS-induced enhancement of older adults' sleep duration. On the aspect of the corresponding neural alterations, we observed that HD-tDCS decreased the functional connectivity between the default mode network (DMN) and subcortical network. More importantly, the decoupling connectivity of the DMN-subcortical network was correlated with the improvements of subjective sleep in the HD-tDCS group. Our findings add novel behavioral and neural evidences about tDCS-induced sleep improvement in community-dwelling older adults. With further development, tDCS may be used as an alternative treatment for sleep disorders and alleviate the dysfunction of brain networks induced by aging. Copyright © 2018 Elsevier B.V. All rights reserved.

Signature neural networks: definition and application to multidimensional sorting problems.

PubMed

Latorre, Roberto; de Borja Rodriguez, Francisco; Varona, Pablo

2011-01-01

In this paper we present a self-organizing neural network paradigm that is able to discriminate information locally using a strategy for information coding and processing inspired in recent findings in living neural systems. The proposed neural network uses: 1) neural signatures to identify each unit in the network; 2) local discrimination of input information during the processing; and 3) a multicoding mechanism for information propagation regarding the who and the what of the information. The local discrimination implies a distinct processing as a function of the neural signature recognition and a local transient memory. In the context of artificial neural networks none of these mechanisms has been analyzed in detail, and our goal is to demonstrate that they can be used to efficiently solve some specific problems. To illustrate the proposed paradigm, we apply it to the problem of multidimensional sorting, which can take advantage of the local information discrimination. In particular, we compare the results of this new approach with traditional methods to solve jigsaw puzzles and we analyze the situations where the new paradigm improves the performance.

Estimation of Muscle Force Based on Neural Drive in a Hemispheric Stroke Survivor.

PubMed

Dai, Chenyun; Zheng, Yang; Hu, Xiaogang

2018-01-01

Robotic assistant-based therapy holds great promise to improve the functional recovery of stroke survivors. Numerous neural-machine interface techniques have been used to decode the intended movement to control robotic systems for rehabilitation therapies. In this case report, we tested the feasibility of estimating finger extensor muscle forces of a stroke survivor, based on the decoded descending neural drive through population motoneuron discharge timings. Motoneuron discharge events were obtained by decomposing high-density surface electromyogram (sEMG) signals of the finger extensor muscle. The neural drive was extracted from the normalized frequency of the composite discharge of the motoneuron pool. The neural-drive-based estimation was also compared with the classic myoelectric-based estimation. Our results showed that the neural-drive-based approach can better predict the force output, quantified by lower estimation errors and higher correlations with the muscle force, compared with the myoelectric-based estimation. Our findings suggest that the neural-drive-based approach can potentially be used as a more robust interface signal for robotic therapies during the stroke rehabilitation.

Therapeutic physical exercise in neural injury: friend or foe?

PubMed

Park, Kanghui; Lee, Seunghoon; Hong, Yunkyung; Park, Sookyoung; Choi, Jeonghyun; Chang, Kyu-Tae; Kim, Joo-Heon; Hong, Yonggeun

2015-12-01

[Purpose] The intensity of therapeutic physical exercise is complex and sometimes controversial in patients with neural injuries. This review assessed whether therapeutic physical exercise is beneficial according to the intensity of the physical exercise. [Methods] The authors identified clinically or scientifically relevant articles from PubMed that met the inclusion criteria. [Results] Exercise training can improve body strength and lead to the physiological adaptation of skeletal muscles and the nervous system after neural injuries. Furthermore, neurophysiological and neuropathological studies show differences in the beneficial effects of forced therapeutic exercise in patients with severe or mild neural injuries. Forced exercise alters the distribution of muscle fiber types in patients with neural injuries. Based on several animal studies, forced exercise may promote functional recovery following cerebral ischemia via signaling molecules in ischemic brain regions. [Conclusions] This review describes several types of therapeutic forced exercise and the controversy regarding the therapeutic effects in experimental animals versus humans with neural injuries. This review also provides a therapeutic strategy for physical therapists that grades the intensity of forced exercise according to the level of neural injury.

Nanofibers implant functionalized by neural growth factor as a strategy to innervate a bioengineered tooth.

PubMed

Eap, Sandy; Bécavin, Thibault; Keller, Laetitia; Kökten, Tunay; Fioretti, Florence; Weickert, Jean-Luc; Deveaux, Etienne; Benkirane-Jessel, Nadia; Kuchler-Bopp, Sabine

2014-03-01

Current strategies for jaw reconstruction require multiple procedures, to repair the bone defect, to offer sufficient support, and to place the tooth implant. The entire procedure can be painful and time-consuming, and the desired functional repair can be achieved only when both steps are successful. The ability to engineer combined tooth and bone constructs, which would grow in a coordinated fashion with the surrounding tissues, could potentially improve the clinical outcomes and also reduce patient suffering. A unique nanofibrous and active implant for bone-tooth unit regeneration and also the innervation of this bioengineered tooth are demonstrated. A nanofibrous polycaprolactone membrane is functionalized with neural growth factor, along with dental germ, and tooth innervation follows. Such innervation allows complete functionality and tissue homeostasis of the tooth, such as dentinal sensitivity, odontoblast function, masticatory forces, and blood flow. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Improved Stability and Stabilization Results for Stochastic Synchronization of Continuous-Time Semi-Markovian Jump Neural Networks With Time-Varying Delay.

PubMed

Wei, Yanling; Park, Ju H; Karimi, Hamid Reza; Tian, Yu-Chu; Jung, Hoyoul; Yanling Wei; Park, Ju H; Karimi, Hamid Reza; Yu-Chu Tian; Hoyoul Jung; Tian, Yu-Chu; Wei, Yanling; Jung, Hoyoul; Karimi, Hamid Reza; Park, Ju H

2018-06-01

Continuous-time semi-Markovian jump neural networks (semi-MJNNs) are those MJNNs whose transition rates are not constant but depend on the random sojourn time. Addressing stochastic synchronization of semi-MJNNs with time-varying delay, an improved stochastic stability criterion is derived in this paper to guarantee stochastic synchronization of the response systems with the drive systems. This is achieved through constructing a semi-Markovian Lyapunov-Krasovskii functional together as well as making use of a novel integral inequality and the characteristics of cumulative distribution functions. Then, with a linearization procedure, controller synthesis is carried out for stochastic synchronization of the drive-response systems. The desired state-feedback controller gains can be determined by solving a linear matrix inequality-based optimization problem. Simulation studies are carried out to demonstrate the effectiveness and less conservatism of the presented approach.

New modalities of brain stimulation for stroke rehabilitation

PubMed Central

Lucas, T. H.; Carey, J. R.; Fetz, E. E.

2014-01-01

Stroke is a leading cause of disability, and the number of stroke survivors continues to rise. Traditional neurorehabilitation strategies aimed at restoring function to weakened limbs provide only modest benefit. New brain stimulation techniques designed to augment traditional neurorehabilitation hold promise for reducing the burden of stroke-related disability. Investigators discovered that repetitive transcranial magnetic stimulation (rTMS), trans-cranial direct current stimulation (tDCS), and epidural cortical stimulation (ECS) can enhance neural plasticity in the motor cortex post-stroke. Improved outcomes may be obtained with activity-dependent stimulation, in which brain stimulation is contingent on neural or muscular activity during normal behavior. We review the evidence for improved motor function in stroke patients treated with rTMS, tDCS, and ECS and discuss the mediating physiological mechanisms. We compare these techniques to activity-dependent stimulation, discuss the advantages of this newer strategy for stroke rehabilitation, and suggest future applications for activity-dependent brain stimulation. PMID:23192336

Forced and voluntary exercises equally improve spatial learning and memory and hippocampal BDNF levels.

PubMed

Alomari, Mahmoud A; Khabour, Omar F; Alzoubi, Karem H; Alzubi, Mohammad A

2013-06-15

Multiple evidence suggest the importance of exercise for cognitive and brain functions. Few studies however, compared the behavioral and neural adaptations to force versus voluntary exercise training. Therefore, spatial learning and memory formation and brain-derived neurotrophic factor (BDNF) were examined in Wister male rats after 6 weeks of either daily forced swimming, voluntary running exercises, or sedentary. Learning capabilities and short, 5-hour, and long term memories improved (p<0.05) similarly in the exercise groups, without changes (p>0.05) in the sedentary. Likewise, both exercises resulted in increased (p<0.05) hippocampal BDNF level. The results suggest that forced and voluntary exercises can similarly enhance cognitive- and brain-related tasks, seemingly vie the BDNF pathway. These data further confirm the health benefits of exercise and advocate both exercise modalities to enhance behavioral and neural functions. Copyright © 2013 Elsevier B.V. All rights reserved.

Improved GART neural network model for pattern classification and rule extraction with application to power systems.

PubMed

Yap, Keem Siah; Lim, Chee Peng; Au, Mau Teng

2011-12-01

Generalized adaptive resonance theory (GART) is a neural network model that is capable of online learning and is effective in tackling pattern classification tasks. In this paper, we propose an improved GART model (IGART), and demonstrate its applicability to power systems. IGART enhances the dynamics of GART in several aspects, which include the use of the Laplacian likelihood function, a new vigilance function, a new match-tracking mechanism, an ordering algorithm for determining the sequence of training data, and a rule extraction capability to elicit if-then rules from the network. To assess the effectiveness of IGART and to compare its performances with those from other methods, three datasets that are related to power systems are employed. The experimental results demonstrate the usefulness of IGART with the rule extraction capability in undertaking classification problems in power systems engineering.

Diabetes and Stem Cell Function

PubMed Central

Fujimaki, Shin; Wakabayashi, Tamami; Takemasa, Tohru; Asashima, Makoto; Kuwabara, Tomoko

2015-01-01

Diabetes mellitus is one of the most common serious metabolic diseases that results in hyperglycemia due to defects of insulin secretion or insulin action or both. The present review focuses on the alterations to the diabetic neuronal tissues and skeletal muscle, including stem cells in both tissues, and the preventive effects of physical activity on diabetes. Diabetes is associated with various nervous disorders, such as cognitive deficits, depression, and Alzheimer's disease, and that may be caused by neural stem cell dysfunction. Additionally, diabetes induces skeletal muscle atrophy, the impairment of energy metabolism, and muscle weakness. Similar to neural stem cells, the proliferation and differentiation are attenuated in skeletal muscle stem cells, termed satellite cells. However, physical activity is very useful for preventing the diabetic alteration to the neuronal tissues and skeletal muscle. Physical activity improves neurogenic capacity of neural stem cells and the proliferative and differentiative abilities of satellite cells. The present review proposes physical activity as a useful measure for the patients in diabetes to improve the physiological functions and to maintain their quality of life. It further discusses the use of stem cell-based approaches in the context of diabetes treatment. PMID:26075247

An integrative approach for analyzing hundreds of neurons in task performing mice using wide-field calcium imaging.

PubMed

Mohammed, Ali I; Gritton, Howard J; Tseng, Hua-an; Bucklin, Mark E; Yao, Zhaojie; Han, Xue

2016-02-08

Advances in neurotechnology have been integral to the investigation of neural circuit function in systems neuroscience. Recent improvements in high performance fluorescent sensors and scientific CMOS cameras enables optical imaging of neural networks at a much larger scale. While exciting technical advances demonstrate the potential of this technique, further improvement in data acquisition and analysis, especially those that allow effective processing of increasingly larger datasets, would greatly promote the application of optical imaging in systems neuroscience. Here we demonstrate the ability of wide-field imaging to capture the concurrent dynamic activity from hundreds to thousands of neurons over millimeters of brain tissue in behaving mice. This system allows the visualization of morphological details at a higher spatial resolution than has been previously achieved using similar functional imaging modalities. To analyze the expansive data sets, we developed software to facilitate rapid downstream data processing. Using this system, we show that a large fraction of anatomically distinct hippocampal neurons respond to discrete environmental stimuli associated with classical conditioning, and that the observed temporal dynamics of transient calcium signals are sufficient for exploring certain spatiotemporal features of large neural networks.

Improved axonal regeneration of transected spinal cord mediated by multichannel collagen conduits functionalized with neurotrophin-3 gene.

PubMed

Yao, L; Daly, W; Newland, B; Yao, S; Wang, W; Chen, B K K; Madigan, N; Windebank, A; Pandit, A

2013-12-01

Functionalized biomaterial scaffolds targeted at improving axonal regeneration by enhancing guided axonal growth provide a promising approach for the repair of spinal cord injury. Collagen neural conduits provide structural guidance for neural tissue regeneration, and in this study it is shown that these conduits can also act as a reservoir for sustained gene delivery. Either a G-luciferase marker gene or a neurotrophin-3-encoding gene, complexed to a non-viral, cyclized, PEGylated transfection vector, was loaded within a multichannel collagen conduit. The complexed genes were then released in a controlled fashion using a dual release system both in vitro and in vivo. For evaluation of their biological performance, the loaded conduits were implanted into the completely transected rat thoracic spinal cord (T8-T10). Aligned axon regeneration through the channels of conduits was observed one month post-surgery. The conduits delivering neurotrophin-3 polyplexes resulted in significantly increased neurotrophin-3 levels in the surrounding tissue and a statistically higher number of regenerated axons versus the control conduits (P<0.05). This study suggests that collagen neural conduits delivering a highly effective non-viral therapeutic gene may hold promise for repair of the injured spinal cord.

Implementation study of an analog spiking neural network for assisting cardiac delay prediction in a cardiac resynchronization therapy device.

PubMed

Sun, Qing; Schwartz, François; Michel, Jacques; Herve, Yannick; Dalmolin, Renzo

2011-06-01

In this paper, we aim at developing an analog spiking neural network (SNN) for reinforcing the performance of conventional cardiac resynchronization therapy (CRT) devices (also called biventricular pacemakers). Targeting an alternative analog solution in 0.13- μm CMOS technology, this paper proposes an approach to improve cardiac delay predictions in every cardiac period in order to assist the CRT device to provide real-time optimal heartbeats. The primary analog SNN architecture is proposed and its implementation is studied to fulfill the requirement of very low energy consumption. By using the Hebbian learning and reinforcement learning algorithms, the intended adaptive CRT device works with different functional modes. The simulations of both learning algorithms have been carried out, and they were shown to demonstrate the global functionalities. To improve the realism of the system, we introduce various heart behavior models (with constant/variable heart rates) that allow pathologic simulations with/without noise on the signals of the input sensors. The simulations of the global system (pacemaker models coupled with heart models) have been investigated and used to validate the analog spiking neural network implementation.

Impact of task-related changes in heart rate on estimation of hemodynamic response and model fit.

PubMed

Hillenbrand, Sarah F; Ivry, Richard B; Schlerf, John E

2016-05-15

The blood oxygen level dependent (BOLD) signal, as measured using functional magnetic resonance imaging (fMRI), is widely used as a proxy for changes in neural activity in the brain. Physiological variables such as heart rate (HR) and respiratory variation (RV) affect the BOLD signal in a way that may interfere with the estimation and detection of true task-related neural activity. This interference is of particular concern when these variables themselves show task-related modulations. We first establish that a simple movement task reliably induces a change in HR but not RV. In group data, the effect of HR on the BOLD response was larger and more widespread throughout the brain than were the effects of RV or phase regressors. The inclusion of HR regressors, but not RV or phase regressors, had a small but reliable effect on the estimated hemodynamic response function (HRF) in M1 and the cerebellum. We next asked whether the inclusion of a nested set of physiological regressors combining phase, RV, and HR significantly improved the model fit in individual participants' data sets. There was a significant improvement from HR correction in M1 for the greatest number of participants, followed by RV and phase correction. These improvements were more modest in the cerebellum. These results indicate that accounting for task-related modulation of physiological variables can improve the detection and estimation of true neural effects of interest. Copyright © 2016 Elsevier Inc. All rights reserved.

Neurobiology of cognitive remediation therapy for schizophrenia: a systematic review.

PubMed

Thorsen, Anders Lillevik; Johansson, Kyrre; Løberg, Else-Marie

2014-01-01

Cognitive impairment is an important aspect of schizophrenia, where cognitive remediation therapy (CRT) is a promising treatment for improving cognitive functioning. While neurobiological dysfunction in schizophrenia has been the target of much research, the neural substrate of cognitive remediation and recovery has not been thoroughly examined. The aim of the present article is to systematically review the evidence for neural changes after CRT for schizophrenia. The reviewed studies indicate that CRT affects several brain regions and circuits, including prefrontal, parietal, and limbic areas, both in terms of activity and structure. Changes in prefrontal areas are the most reported finding, fitting to previous evidence of dysfunction in this region. Two limitations of the current research are the few studies and the lack of knowledge on the mechanisms underlying neural and cognitive changes after treatment. Despite these limitations, the current evidence suggests that CRT is associated with both neurobiological and cognitive improvement. The evidence from these findings may shed light on both the neural substrate of cognitive impairment in schizophrenia, and how better treatment can be developed and applied.

Disambiguating brain functional connectivity.

PubMed

Duff, Eugene P; Makin, Tamar; Cottaar, Michiel; Smith, Stephen M; Woolrich, Mark W

2018-06-01

Functional connectivity (FC) analyses of correlations of neural activity are used extensively in neuroimaging and electrophysiology to gain insights into neural interactions. However, analyses assessing changes in correlation fail to distinguish effects produced by sources as different as changes in neural signal amplitudes or noise levels. This ambiguity substantially diminishes the value of FC for inferring system properties and clinical states. Network modelling approaches may avoid ambiguities, but require specific assumptions. We present an enhancement to FC analysis with improved specificity of inferences, minimal assumptions and no reduction in flexibility. The Additive Signal Change (ASC) approach characterizes FC changes into certain prevalent classes of signal change that involve the input of additional signal to existing activity. With FMRI data, the approach reveals a rich diversity of signal changes underlying measured changes in FC, suggesting that it could clarify our current understanding of FC changes in many contexts. The ASC method can also be used to disambiguate other measures of dependency, such as regression and coherence, providing a flexible tool for the analysis of neural data. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Human high intelligence is involved in spectral redshift of biophotonic activities in the brain

PubMed Central

Wang, Niting; Li, Zehua; Xiao, Fangyan; Dai, Jiapei

2016-01-01

Human beings hold higher intelligence than other animals on Earth; however, it is still unclear which brain properties might explain the underlying mechanisms. The brain is a major energy-consuming organ compared with other organs. Neural signal communications and information processing in neural circuits play an important role in the realization of various neural functions, whereas improvement in cognitive function is driven by the need for more effective communication that requires less energy. Combining the ultraweak biophoton imaging system (UBIS) with the biophoton spectral analysis device (BSAD), we found that glutamate-induced biophotonic activities and transmission in the brain, which has recently been demonstrated as a novel neural signal communication mechanism, present a spectral redshift from animals (in order of bullfrog, mouse, chicken, pig, and monkey) to humans, even up to a near-infrared wavelength (∼865 nm) in the human brain. This brain property may be a key biophysical basis for explaining high intelligence in humans because biophoton spectral redshift could be a more economical and effective measure of biophotonic signal communications and information processing in the human brain. PMID:27432962

Combinations of stroke neurorehabilitation to facilitate motor recovery: perspectives on Hebbian plasticity and homeostatic metaplasticity

PubMed Central

Takeuchi, Naoyuki; Izumi, Shin-Ichi

2015-01-01

Motor recovery after stroke involves developing new neural connections, acquiring new functions, and compensating for impairments. These processes are related to neural plasticity. Various novel stroke rehabilitation techniques based on basic science and clinical studies of neural plasticity have been developed to aid motor recovery. Current research aims to determine whether using combinations of these techniques can synergistically improve motor recovery. When different stroke neurorehabilitation therapies are combined, the timing of each therapeutic program must be considered to enable optimal neural plasticity. Synchronizing stroke rehabilitation with voluntary neural and/or muscle activity can lead to motor recovery by targeting Hebbian plasticity. This reinforces the neural connections between paretic muscles and the residual motor area. Homeostatic metaplasticity, which stabilizes the activity of neurons and neural circuits, can either augment or reduce the synergic effect depending on the timing of combination therapy and types of neurorehabilitation that are used. Moreover, the possibility that the threshold and degree of induced plasticity can be altered after stroke should be noted. This review focuses on the mechanisms underlying combinations of neurorehabilitation approaches and their future clinical applications. We suggest therapeutic approaches for cortical reorganization and maximal functional gain in patients with stroke, based on the processes of Hebbian plasticity and homeostatic metaplasticity. Few of the possible combinations of stroke neurorehabilitation have been tested experimentally; therefore, further studies are required to determine the appropriate combination for motor recovery. PMID:26157374

Neural predictors of individual differences in response to math tutoring in primary-grade school children

PubMed Central

Supekar, Kaustubh; Swigart, Anna G.; Tenison, Caitlin; Jolles, Dietsje D.; Rosenberg-Lee, Miriam; Fuchs, Lynn; Menon, Vinod

2013-01-01

Now, more than ever, the ability to acquire mathematical skills efficiently is critical for academic and professional success, yet little is known about the behavioral and neural mechanisms that drive some children to acquire these skills faster than others. Here we investigate the behavioral and neural predictors of individual differences in arithmetic skill acquisition in response to 8-wk of one-to-one math tutoring. Twenty-four children in grade 3 (ages 8–9 y), a critical period for acquisition of basic mathematical skills, underwent structural and resting-state functional MRI scans pretutoring. A significant shift in arithmetic problem-solving strategies from counting to fact retrieval was observed with tutoring. Notably, the speed and accuracy of arithmetic problem solving increased with tutoring, with some children improving significantly more than others. Next, we examined whether pretutoring behavioral and brain measures could predict individual differences in arithmetic performance improvements with tutoring. No behavioral measures, including intelligence quotient, working memory, or mathematical abilities, predicted performance improvements. In contrast, pretutoring hippocampal volume predicted performance improvements. Furthermore, pretutoring intrinsic functional connectivity of the hippocampus with dorsolateral and ventrolateral prefrontal cortices and the basal ganglia also predicted performance improvements. Our findings provide evidence that individual differences in morphometry and connectivity of brain regions associated with learning and memory, and not regions typically involved in arithmetic processing, are strong predictors of responsiveness to math tutoring in children. More generally, our study suggests that quantitative measures of brain structure and intrinsic brain organization can provide a more sensitive marker of skill acquisition than behavioral measures. PMID:23630286

Neural predictors of individual differences in response to math tutoring in primary-grade school children.

PubMed

Supekar, Kaustubh; Swigart, Anna G; Tenison, Caitlin; Jolles, Dietsje D; Rosenberg-Lee, Miriam; Fuchs, Lynn; Menon, Vinod

2013-05-14

Now, more than ever, the ability to acquire mathematical skills efficiently is critical for academic and professional success, yet little is known about the behavioral and neural mechanisms that drive some children to acquire these skills faster than others. Here we investigate the behavioral and neural predictors of individual differences in arithmetic skill acquisition in response to 8-wk of one-to-one math tutoring. Twenty-four children in grade 3 (ages 8-9 y), a critical period for acquisition of basic mathematical skills, underwent structural and resting-state functional MRI scans pretutoring. A significant shift in arithmetic problem-solving strategies from counting to fact retrieval was observed with tutoring. Notably, the speed and accuracy of arithmetic problem solving increased with tutoring, with some children improving significantly more than others. Next, we examined whether pretutoring behavioral and brain measures could predict individual differences in arithmetic performance improvements with tutoring. No behavioral measures, including intelligence quotient, working memory, or mathematical abilities, predicted performance improvements. In contrast, pretutoring hippocampal volume predicted performance improvements. Furthermore, pretutoring intrinsic functional connectivity of the hippocampus with dorsolateral and ventrolateral prefrontal cortices and the basal ganglia also predicted performance improvements. Our findings provide evidence that individual differences in morphometry and connectivity of brain regions associated with learning and memory, and not regions typically involved in arithmetic processing, are strong predictors of responsiveness to math tutoring in children. More generally, our study suggests that quantitative measures of brain structure and intrinsic brain organization can provide a more sensitive marker of skill acquisition than behavioral measures.

Neural reorganization accompanying upper limb motor rehabilitation from stroke with virtual reality-based gesture therapy.

PubMed

Orihuela-Espina, Felipe; Fernández del Castillo, Isabel; Palafox, Lorena; Pasaye, Erick; Sánchez-Villavicencio, Israel; Leder, Ronald; Franco, Jorge Hernández; Sucar, Luis Enrique

2013-01-01

Gesture Therapy is an upper limb virtual reality rehabilitation-based therapy for stroke survivors. It promotes motor rehabilitation by challenging patients with simple computer games representative of daily activities for self-support. This therapy has demonstrated clinical value, but the underlying functional neural reorganization changes associated with this therapy that are responsible for the behavioral improvements are not yet known. We sought to quantify the occurrence of neural reorganization strategies that underlie motor improvements as they occur during the practice of Gesture Therapy and to identify those strategies linked to a better prognosis. Functional magnetic resonance imaging (fMRI) neuroscans were longitudinally collected at 4 time points during Gesture Therapy administration to 8 patients. Behavioral improvements were monitored using the Fugl-Meyer scale and Motricity Index. Activation loci were anatomically labelled and translated to reorganization strategies. Strategies are quantified by counting the number of active clusters in brain regions tied to them. All patients demonstrated significant behavioral improvements (P < .05). Contralesional activation of the unaffected motor cortex, cerebellar recruitment, and compensatory prefrontal cortex activation were the most prominent strategies evoked. A strong and significant correlation between motor dexterity upon commencing therapy and total recruited activity was found (r2 = 0.80; P < .05), and overall brain activity during therapy was inversely related to normalized behavioral improvements (r2 = 0.64; P < .05). Prefrontal cortex and cerebellar activity are the driving forces of the recovery associated with Gesture Therapy. The relation between behavioral and brain changes suggests that those with stronger impairment benefit the most from this paradigm.

Abnormal cardiovascular response to exercise in hypertension: contribution of neural factors.

PubMed

Mitchell, Jere H

2017-06-01

During both dynamic (e.g., endurance) and static (e.g., strength) exercise there are exaggerated cardiovascular responses in hypertension. This includes greater increases in blood pressure, heart rate, and efferent sympathetic nerve activity than in normal controls. Two of the known neural factors that contribute to this abnormal cardiovascular response are the exercise pressor reflex (EPR) and functional sympatholysis. The EPR originates in contracting skeletal muscle and reflexly increases sympathetic efferent nerve activity to the heart and blood vessels as well as decreases parasympathetic efferent nerve activity to the heart. These changes in autonomic nerve activity cause an increase in blood pressure, heart rate, left ventricular contractility, and vasoconstriction in the arterial tree. However, arterial vessels in the contracting skeletal muscle have a markedly diminished vasoconstrictor response. The markedly diminished vasoconstriction in contracting skeletal muscle has been termed functional sympatholysis. It has been shown in hypertension that there is an enhanced EPR, including both its mechanoreflex and metaboreflex components, and an impaired functional sympatholysis. These conditions set up a positive feedback or vicious cycle situation that causes a progressively greater decrease in the blood flow to the exercising muscle. Thus these two neural mechanisms contribute significantly to the abnormal cardiovascular response to exercise in hypertension. In addition, exercise training in hypertension decreases the enhanced EPR, including both mechanoreflex and metaboreflex function, and improves the impaired functional sympatholysis. These two changes, caused by exercise training, improve the muscle blood flow to exercising muscle and cause a more normal cardiovascular response to exercise in hypertension. Copyright © 2017 the American Physiological Society.

Neural correlates of training and transfer effects in working memory in older adults.

PubMed

Heinzel, Stephan; Lorenz, Robert C; Pelz, Patricia; Heinz, Andreas; Walter, Henrik; Kathmann, Norbert; Rapp, Michael A; Stelzel, Christine

2016-07-01

As indicated by previous research, aging is associated with a decline in working memory (WM) functioning, related to alterations in fronto-parietal neural activations. At the same time, previous studies showed that WM training in older adults may improve the performance in the trained task (training effect), and more importantly, also in untrained WM tasks (transfer effects). However, neural correlates of these transfer effects that would improve understanding of its underlying mechanisms, have not been shown in older participants as yet. In this study, we investigated blood-oxygen-level-dependent (BOLD) signal changes during n-back performance and an untrained delayed recognition (Sternberg) task following 12sessions (45min each) of adaptive n-back training in older adults. The Sternberg task used in this study allowed to test for neural training effects independent of specific task affordances of the trained task and to separate maintenance from updating processes. Thirty-two healthy older participants (60-75years) were assigned either to an n-back training or a no-contact control group. Before (t1) and after (t2) training/waiting period, both the n-back task and the Sternberg task were conducted while BOLD signal was measured using functional Magnetic Resonance Imaging (fMRI) in all participants. In addition, neuropsychological tests were performed outside the scanner. WM performance improved with training and behavioral transfer to tests measuring executive functions, processing speed, and fluid intelligence was found. In the training group, BOLD signal in the right lateral middle frontal gyrus/caudal superior frontal sulcus (Brodmann area, BA 6/8) decreased in both the trained n-back and the updating condition of the untrained Sternberg task at t2, compared to the control group. fMRI findings indicate a training-related increase in processing efficiency of WM networks, potentially related to the process of WM updating. Performance gains in untrained tasks suggest that transfer to other cognitive tasks remains possible in aging. Copyright © 2016 Elsevier Inc. All rights reserved.

Co-ordinated structural and functional covariance in the adolescent brain underlies face processing performance

PubMed Central

Joel Shaw, Daniel; Mareček, Radek; Grosbras, Marie-Helene; Leonard, Gabriel; Bruce Pike, G.

2016-01-01

Our ability to process complex social cues presented by faces improves during adolescence. Using multivariate analyses of neuroimaging data collected longitudinally from a sample of 38 adolescents (17 males) when they were 10, 11.5, 13 and 15 years old, we tested the possibility that there exists parallel variations in the structural and functional development of neural systems supporting face processing. By combining measures of task-related functional connectivity and brain morphology, we reveal that both the structural covariance and functional connectivity among ‘distal’ nodes of the face-processing network engaged by ambiguous faces increase during this age range. Furthermore, we show that the trajectory of increasing functional connectivity between the distal nodes occurs in tandem with the development of their structural covariance. This demonstrates a tight coupling between functional and structural maturation within the face-processing network. Finally, we demonstrate that increased functional connectivity is associated with age-related improvements of face-processing performance, particularly in females. We suggest that our findings reflect greater integration among distal elements of the neural systems supporting the processing of facial expressions. This, in turn, might facilitate an enhanced extraction of social information from faces during a time when greater importance is placed on social interactions. PMID:26772669

Microfluidic systems for stem cell-based neural tissue engineering.

PubMed

Karimi, Mahdi; Bahrami, Sajad; Mirshekari, Hamed; Basri, Seyed Masoud Moosavi; Nik, Amirala Bakhshian; Aref, Amir R; Akbari, Mohsen; Hamblin, Michael R

2016-07-05

Neural tissue engineering aims at developing novel approaches for the treatment of diseases of the nervous system, by providing a permissive environment for the growth and differentiation of neural cells. Three-dimensional (3D) cell culture systems provide a closer biomimetic environment, and promote better cell differentiation and improved cell function, than could be achieved by conventional two-dimensional (2D) culture systems. With the recent advances in the discovery and introduction of different types of stem cells for tissue engineering, microfluidic platforms have provided an improved microenvironment for the 3D-culture of stem cells. Microfluidic systems can provide more precise control over the spatiotemporal distribution of chemical and physical cues at the cellular level compared to traditional systems. Various microsystems have been designed and fabricated for the purpose of neural tissue engineering. Enhanced neural migration and differentiation, and monitoring of these processes, as well as understanding the behavior of stem cells and their microenvironment have been obtained through application of different microfluidic-based stem cell culture and tissue engineering techniques. As the technology advances it may be possible to construct a "brain-on-a-chip". In this review, we describe the basics of stem cells and tissue engineering as well as microfluidics-based tissue engineering approaches. We review recent testing of various microfluidic approaches for stem cell-based neural tissue engineering.

Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation

PubMed Central

Krucoff, Max O.; Rahimpour, Shervin; Slutzky, Marc W.; Edgerton, V. Reggie; Turner, Dennis A.

2016-01-01

After an initial period of recovery, human neurological injury has long been thought to be static. In order to improve quality of life for those suffering from stroke, spinal cord injury, or traumatic brain injury, researchers have been working to restore the nervous system and reduce neurological deficits through a number of mechanisms. For example, neurobiologists have been identifying and manipulating components of the intra- and extracellular milieu to alter the regenerative potential of neurons, neuro-engineers have been producing brain-machine and neural interfaces that circumvent lesions to restore functionality, and neurorehabilitation experts have been developing new ways to revitalize the nervous system even in chronic disease. While each of these areas holds promise, their individual paths to clinical relevance remain difficult. Nonetheless, these methods are now able to synergistically enhance recovery of native motor function to levels which were previously believed to be impossible. Furthermore, such recovery can even persist after training, and for the first time there is evidence of functional axonal regrowth and rewiring in the central nervous system of animal models. To attain this type of regeneration, rehabilitation paradigms that pair cortically-based intent with activation of affected circuits and positive neurofeedback appear to be required—a phenomenon which raises new and far reaching questions about the underlying relationship between conscious action and neural repair. For this reason, we argue that multi-modal therapy will be necessary to facilitate a truly robust recovery, and that the success of investigational microscopic techniques may depend on their integration into macroscopic frameworks that include task-based neurorehabilitation. We further identify critical components of future neural repair strategies and explore the most updated knowledge, progress, and challenges in the fields of cellular neuronal repair, neural interfacing, and neurorehabilitation, all with the goal of better understanding neurological injury and how to improve recovery. PMID:28082858

Longitudinal functional brain imaging study in early course schizophrenia before and after cognitive enhancement therapy.

PubMed

Keshavan, Matcheri S; Eack, Shaun M; Prasad, Konasale M; Haller, Chiara S; Cho, Raymond Y

2017-05-01

Schizophrenia is characterized by impaired -social and non social cognition both of which lead to functional deficits. These deficits may benefit from cognitive remediation, but the neural underpinnings of such improvements have not been clearly delineated. We conducted a functional magnetic resonance (fMRI) study in early course schizophrenia patients randomly assigned to cognitive enhancement therapy (CET) or enriched supportive therapy (EST) and treated for two years. Imaging data over three time points including fMRI blood oxygen level dependent (BOLD) data were acquired during performance of a cognitive control paradigm, the Preparing to Overcome Prepotency (POP) task, and functional connectivity data, were analyzed. During the two years of treatment, CET patients showed a continual increase in BOLD activity in the right dorsolateral prefrontal cortex (DLPFC), whereas EST patients tended to show no change in prefrontal brain function throughout treatment. Increases in right DLPFC activity were modestly associated with improved neurocognition (β = .14, p = .041), but not social cognition. Functional connectivity analyses showed reduced connectivity between the DLPFC and the anterior cingulate cortex (ACC) in CET compared to EST over the two years of treatment, which was associated with neurocognitive improvement. These findings suggest that CET leads to enhanced neural activity in brain regions mediating cognitive control and increased efficiency in prefrontal circuits; such changes may be related to the observed therapeutic effects of CET on neurocognitive function. Copyright © 2017. Published by Elsevier Inc.

Targeted Knockdown of Bone Morphogenetic Protein Signaling within Neural Progenitors Protects the Brain and Improves Motor Function following Postnatal Hypoxia-Ischemia

PubMed Central

Dettman, Robert W.; Birch, Derin; Fernando, Augusta; Kessler, John A.; Dizon, Maria L.V.

2018-01-01

Hypoxic-ischemic injury (HI) to the neonatal human brain results in myelin loss that, in some children, can manifest as cerebral palsy. Previously, we had found that neuronal overexpression of the bone morphogenic protein (BMP) inhibitor noggin during development increased oligodendroglia and improved motor function in an experimental model of HI utilizing unilateral common carotid artery ligation followed by hypoxia. As BMPs are known to negatively regulate oligodendroglial fate specification of neural stem cells and alter differentiation of committed oligodendroglia, BMP signaling is likely an important mechanism leading to myelin loss. Here, we showed that BMP signaling is upregulated within oligodendroglia of the neonatal brain. We tested the hypothesis that inhibition of BMP signaling specifically within neural progenitor cells (NPCs) is sufficient to protect oligodendroglia. We conditionally deleted the BMP receptor 2 subtype (BMPR2) in NG2-expressing cells after HI. We found that BMPR2 deletion globally protects the brain as assessed by MRI and protects motor function as assessed by digital gait analysis, and that conditional deletion of BMPR2 maintains oligodendrocyte marker expression by immunofluorescence and Western blot and prevents loss of oligodendroglia. Finally, BMPR2 deletion after HI results in an increase in noncompacted myelin. Thus, our data indicate that inhibition of BMP signaling specifically in NPCs may be a tractable strategy to protect the newborn brain from HI. PMID:29324456

A theoretical framework for understanding neuromuscular response to lower extremity joint injury.

PubMed

Pietrosimone, Brian G; McLeod, Michelle M; Lepley, Adam S

2012-01-01

Neuromuscular alterations are common following lower extremity joint injury and often lead to decreased function and disability. These neuromuscular alterations manifest in inhibition or abnormal facilitation of the uninjured musculature surrounding an injured joint. Unfortunately, these neural alterations are poorly understood, which may affect clinical recognition and treatment of these injuries. Understanding how these neural alterations affect physical function may be important for proper clinical management of lower extremity joint injuries. Pertinent articles focusing on neuromuscular consequences and treatment of knee and ankle injuries were collected from peer-reviewed sources available on the Web of Science and Medline databases from 1975 through 2010. A theoretical model to illustrate potential relationships between neural alterations and clinical impairments was constructed from the current literature. Lower extremity joint injury affects upstream cortical and spinal reflexive excitability pathways as well as downstream muscle function and overall physical performance. Treatment targeting the central nervous system provides an alternate means of treating joint injury that may be effective for patients with neuromuscular alterations. Disability is common following joint injury. There is mounting evidence that alterations in the central nervous system may relate to clinical changes in biomechanics that may predispose patients to further injury, and novel clinical interventions that target neural alterations may improve therapeutic outcomes.

A Theoretical Framework for Understanding Neuromuscular Response to Lower Extremity Joint Injury

PubMed Central

Pietrosimone, Brian G.; McLeod, Michelle M.; Lepley, Adam S.

2012-01-01

Background: Neuromuscular alterations are common following lower extremity joint injury and often lead to decreased function and disability. These neuromuscular alterations manifest in inhibition or abnormal facilitation of the uninjured musculature surrounding an injured joint. Unfortunately, these neural alterations are poorly understood, which may affect clinical recognition and treatment of these injuries. Understanding how these neural alterations affect physical function may be important for proper clinical management of lower extremity joint injuries. Methods: Pertinent articles focusing on neuromuscular consequences and treatment of knee and ankle injuries were collected from peer-reviewed sources available on the Web of Science and Medline databases from 1975 through 2010. A theoretical model to illustrate potential relationships between neural alterations and clinical impairments was constructed from the current literature. Results: Lower extremity joint injury affects upstream cortical and spinal reflexive excitability pathways as well as downstream muscle function and overall physical performance. Treatment targeting the central nervous system provides an alternate means of treating joint injury that may be effective for patients with neuromuscular alterations. Conclusions: Disability is common following joint injury. There is mounting evidence that alterations in the central nervous system may relate to clinical changes in biomechanics that may predispose patients to further injury, and novel clinical interventions that target neural alterations may improve therapeutic outcomes. PMID:23016066

[Functional magnetic resonance imaging in psychiatry and psychotherapy].

PubMed

Derntl, B; Habel, U; Schneider, F

2010-01-01

technical improvements, functional magnetic resonance imaging (fMRI) has become the most popular and versatile imaging method in psychiatric research. The scope of this manuscript is to briefly introduce the basics of MR physics, the blood oxygenation level-dependent (BOLD) contrast as well as the principles of MR study design and functional data analysis. The presentation of exemplary studies on emotion recognition and empathy in schizophrenia patients will highlight the importance of MR methods in psychiatry. Finally, we will demonstrate insights into new developments that will further boost MR techniques in clinical research and will help to gain more insight into dysfunctional neural networks underlying cognitive and emotional deficits in psychiatric patients. Moreover, some techniques such as neurofeedback seem promising for evaluation of therapy effects on a behavioral and neural level.

Positive effect of acute mild exercise on executive function via arousal-related prefrontal activations: an fNIRS study.

PubMed

Byun, Kyeongho; Hyodo, Kazuki; Suwabe, Kazuya; Ochi, Genta; Sakairi, Yosuke; Kato, Morimasa; Dan, Ippeita; Soya, Hideaki

2014-09-01

Despite the practical implication of mild exercise, little is known about its influence on executive function and its neural substrates. To address these issues, the present study examined the effect of an acute bout of mild exercise on executive function and attempted to identify potential neural substrates using non-invasive functional near-infrared spectroscopy (fNIRS). Twenty-five young individuals performed a color-word matching Stroop task (CWST) and a two-dimensional scale to measure changes of psychological mood states both before and after a 10-minute exercise session on a cycle ergometer at light intensity (30% v(·)o2peak) and, for the control session, without exercise. Cortical hemodynamic changes in the prefrontal area were monitored with fNIRS during the CWST in both sessions. The acute bout of mild exercise led to improved Stroop performance, which was positively correlated with increased arousal levels. It also evoked cortical activations regarding Stroop interference on the left dorsolateral prefrontal cortex and frontopolar area. These activations significantly corresponded with both improved cognitive performance and increased arousal levels. Concurrently, this study provides empirical evidence that an acute bout of mild exercise improves executive function mediated by the exercise-induced arousal system, which intensifies cortical activation in task-related prefrontal sub-regions. Copyright © 2014 Elsevier Inc. All rights reserved.

Differential patterns of functional and structural plasticity within and between inferior frontal gyri support training-induced improvements in inhibitory control proficiency.

PubMed

Chavan, Camille F; Mouthon, Michael; Draganski, Bogdan; van der Zwaag, Wietske; Spierer, Lucas

2015-07-01

Ample evidence indicates that inhibitory control (IC), a key executive component referring to the ability to suppress cognitive or motor processes, relies on a right-lateralized fronto-basal brain network. However, whether and how IC can be improved with training and the underlying neuroplastic mechanisms remains largely unresolved. We used functional and structural magnetic resonance imaging to measure the effects of 2 weeks of training with a Go/NoGo task specifically designed to improve frontal top-down IC mechanisms. The training-induced behavioral improvements were accompanied by a decrease in neural activity to inhibition trials within the right pars opercularis and triangularis, and in the left pars orbitalis of the inferior frontal gyri. Analyses of changes in brain anatomy induced by the IC training revealed increases in grey matter volume in the right pars orbitalis and modulations of white matter microstructure in the right pars triangularis. The task-specificity of the effects of training was confirmed by an absence of change in neural activity to a control working memory task. Our combined anatomical and functional findings indicate that differential patterns of functional and structural plasticity between and within inferior frontal gyri enhanced the speed of top-down inhibition processes and in turn IC proficiency. The results suggest that training-based interventions might help overcoming the anatomic and functional deficits of inferior frontal gyri manifesting in inhibition-related clinical conditions. More generally, we demonstrate how multimodal neuroimaging investigations of training-induced neuroplasticity enable revealing novel anatomo-functional dissociations within frontal executive brain networks. © 2015 Wiley Periodicals, Inc.

Effects of transcranial direct current stimulation (tDCS) on behaviour and electrophysiology of language production.

PubMed

Wirth, Miranka; Rahman, Rasha Abdel; Kuenecke, Janina; Koenig, Thomas; Horn, Helge; Sommer, Werner; Dierks, Thomas

2011-12-01

Excitatory anodal transcranial direct current stimulation (A-tDCS) over the left dorsal prefrontal cortex (DPFC) has been shown to improve language production. The present study examined neurophysiological underpinnings of this effect. In a single-blinded within-subject design, we traced effects of A-tDCS compared to sham stimulation over the left DPFC using electrophysiological and behavioural correlates during overt picture naming. Online effects were examined during A-tDCS by employing the semantic interference (SI-)Effect - a marker that denotes the functional integrity of the language system. The behavioural SI-Effect was found to be reduced, whereas the electrophysiological SI-Effect was enhanced over left compared to right temporal scalp-electrode sites. This modulation is suggested to reflect a superior tuning of neural responses within language-related generators. After -(offline) effects of A-tDCS were detected in the delta frequency band, a marker of neural inhibition. After A-tDCS there was a reduction in delta activity during picture naming and the resting state, interpreted to indicate neural disinhibition. Together, these findings demonstrate electrophysiological modulations induced by A-tDCS of the left DPFC. They suggest that A-tDCS is capable of enhancing neural processes during and after application. The present functional and oscillatory neural markers could detect positive effects of prefrontal A-tDCS, which could be of use in the neuro-rehabilitation of frontal language functions. Copyright © 2011 Elsevier Ltd. All rights reserved.

Modified neural networks for rapid recovery of tokamak plasma parameters for real time control

NASA Astrophysics Data System (ADS)

Sengupta, A.; Ranjan, P.

2002-07-01

Two modified neural network techniques are used for the identification of the equilibrium plasma parameters of the Superconducting Steady State Tokamak I from external magnetic measurements. This is expected to ultimately assist in a real time plasma control. As different from the conventional network structure where a single network with the optimum number of processing elements calculates the outputs, a multinetwork system connected in parallel does the calculations here in one of the methods. This network is called the double neural network. The accuracy of the recovered parameters is clearly more than the conventional network. The other type of neural network used here is based on the statistical function parametrization combined with a neural network. The principal component transformation removes linear dependences from the measurements and a dimensional reduction process reduces the dimensionality of the input space. This reduced and transformed input set, rather than the entire set, is fed into the neural network input. This is known as the principal component transformation-based neural network. The accuracy of the recovered parameters in the latter type of modified network is found to be a further improvement over the accuracy of the double neural network. This result differs from that obtained in an earlier work where the double neural network showed better performance. The conventional network and the function parametrization methods have also been used for comparison. The conventional network has been used for an optimization of the set of magnetic diagnostics. The effective set of sensors, as assessed by this network, are compared with the principal component based network. Fault tolerance of the neural networks has been tested. The double neural network showed the maximum resistance to faults in the diagnostics, while the principal component based network performed poorly. Finally the processing times of the methods have been compared. The double network and the principal component network involve the minimum computation time, although the conventional network also performs well enough to be used in real time.

Neuroenhancement of Memory for Children with Autism by a Mind–Body Exercise

PubMed Central

Chan, Agnes S.; Han, Yvonne M. Y.; Sze, Sophia L.; Lau, Eliza M.

2015-01-01

The memory deficits found in individuals with autism spectrum disorder (ASD) may be caused by the lack of an effective strategy to aid memory. The executive control of memory processing is mediated largely by the timely coupling between frontal and posterior brain regions. The present study aimed to explore the potential effect of a Chinese mind–body exercise, namely Nei Gong, for enhancing learning and memory in children with ASD, and the possible neural basis of the improvement. Sixty-six children with ASD were randomly assigned to groups receiving Nei Gong training (NGT), progressive muscle relaxation (PMR) training, or no training for 1 month. Before and after training, the participants were tested individually on a computerized visual memory task while EEG signals were acquired during the memory encoding phase. Children in the NGT group demonstrated significantly enhanced memory performance and more effective use of a memory strategy, which was not observed in the other two groups. Furthermore, the improved memory after NGT was consistent with findings of elevated EEG theta coherence between frontal and posterior brain regions, a measure of functional coupling. The scalp EEG signals were localized by the standardized low resolution brain electromagnetic tomography method and found to originate from a neural network that promotes effective memory processing, including the prefrontal cortex, the parietal cortex, and the medial and inferior temporal cortex. This alteration in neural processing was not found in children receiving PMR or in those who received no training. The present findings suggest that the mind–body exercise program may have the potential effect on modulating neural functional connectivity underlying memory processing and hence enhance memory functions in individuals with autism. PMID:26696946

Freedom of Thought and Mental Integrity: The Moral Requirements for Any Neural Prosthesis

PubMed Central

Lavazza, Andrea

2018-01-01

There are many kinds of neural prostheses available or being researched today. In most cases they are intended to cure or improve the condition of patients affected by some cerebral deficiency. In other cases, their goal is to provide new means to maintain or improve an individual's normal performance. In all these circumstances, one of the possible risks is that of violating the privacy of brain contents (which partly coincide with mental contents) or of depriving individuals of full control over their thoughts (mental states), as the latter are at least partly detectable by new prosthetic technologies. Given the (ethical) premise that the absolute privacy and integrity of the most relevant part of one's brain data is (one of) the most valuable and inviolable human right(s), I argue that a (technical) principle should guide the design and regulation of new neural prostheses. The premise is justified by the fact that whatever the coercion, the threat or the violence undergone, the person can generally preserve a “private repository” of thought in which to defend her convictions and identity, her dignity, and autonomy. Without it, the person may end up in a state of complete subjection to other individuals. The following functional principle is that neural prostheses should be technically designed and built so as to prevent such outcomes. They should: (a) incorporate systems that can find and signal the unauthorized detection, alteration, and diffusion of brain data and brain functioning; (b) be able to stop any unauthorized detection, alteration, and diffusion of brain data. This should not only regard individual devices, but act as a general (technical) operating principle shared by all interconnected systems that deal with decoding brain activity and brain functioning. PMID:29515355

Neuroenhancement of Memory for Children with Autism by a Mind-Body Exercise.

PubMed

Chan, Agnes S; Han, Yvonne M Y; Sze, Sophia L; Lau, Eliza M

2015-01-01

The memory deficits found in individuals with autism spectrum disorder (ASD) may be caused by the lack of an effective strategy to aid memory. The executive control of memory processing is mediated largely by the timely coupling between frontal and posterior brain regions. The present study aimed to explore the potential effect of a Chinese mind-body exercise, namely Nei Gong, for enhancing learning and memory in children with ASD, and the possible neural basis of the improvement. Sixty-six children with ASD were randomly assigned to groups receiving Nei Gong training (NGT), progressive muscle relaxation (PMR) training, or no training for 1 month. Before and after training, the participants were tested individually on a computerized visual memory task while EEG signals were acquired during the memory encoding phase. Children in the NGT group demonstrated significantly enhanced memory performance and more effective use of a memory strategy, which was not observed in the other two groups. Furthermore, the improved memory after NGT was consistent with findings of elevated EEG theta coherence between frontal and posterior brain regions, a measure of functional coupling. The scalp EEG signals were localized by the standardized low resolution brain electromagnetic tomography method and found to originate from a neural network that promotes effective memory processing, including the prefrontal cortex, the parietal cortex, and the medial and inferior temporal cortex. This alteration in neural processing was not found in children receiving PMR or in those who received no training. The present findings suggest that the mind-body exercise program may have the potential effect on modulating neural functional connectivity underlying memory processing and hence enhance memory functions in individuals with autism.

The role of neural precursor cells and self assembling peptides in nerve regeneration

PubMed Central

2013-01-01

Objective Cranial nerve injury involves loss of central neural cells in the brain stem and surrounding support matrix, leading to severe functional impairment. Therapeutically targeting cellular replacement and enhancing structural support may promote neural regeneration. We examined the combinatorial effect of neural precursor cells (NPC) and self assembling peptide (SAP) administration on nerve regeneration. Methods Nerve injury was induced by clip compression of the rodent spinal cord. SAPs were injected immediately into the injured cord and NPCs at 2 weeks post-injury. Behavioral analysis was done weekly and rats were sacrificed at 11 weeks post injury. LFB-H&E staining was done on cord tissue to assess cavitation volume. Motor evoked potentials (MEP) were measured at week 11 to assess nerve conduction and Kaplan meier curves were created to compare survival estimates. Results NPCs and SAPs were distributed both caudal and rostral to the injury site. Behavioral analysis showed that SAP + NPC transplantation significantly improved locomotor score p <0.03) and enhanced survival (log rank test, p = 0.008) compared to control. SAP + NPC treatment also improved nerve conduction velocity (p = 0.008) but did not affect cavitation volume (p = 0.73). Conclusion Combinatorial NPC and SAP injection into injured nerve tissue may enhance neural repair and regeneration. PMID:24351041

The role of neural precursor cells and self assembling peptides in nerve regeneration.

PubMed

Zhao, Xiao; Yao, Gordon S; Liu, Yang; Wang, Jian; Satkunendrarajah, Kajana; Fehlings, Michael

2013-12-19

Cranial nerve injury involves loss of central neural cells in the brain stem and surrounding support matrix, leading to severe functional impairment. Therapeutically targeting cellular replacement and enhancing structural support may promote neural regeneration. We examined the combinatorial effect of neural precursor cells (NPC) and self assembling peptide (SAP) administration on nerve regeneration. Nerve injury was induced by clip compression of the rodent spinal cord. SAPs were injected immediately into the injured cord and NPCs at 2 weeks post-injury. Behavioral analysis was done weekly and rats were sacrificed at 11 weeks post injury. LFB-H&E staining was done on cord tissue to assess cavitation volume. Motor evoked potentials (MEP) were measured at week 11 to assess nerve conduction and Kaplan Meier curves were created to compare survival estimates. NPCs and SAPs were distributed both caudal and rostral to the injury site. Behavioral analysis showed that SAP + NPC transplantation significantly improved locomotor score p <0.03) and enhanced survival (log rank test, p = 0.008) compared to control. SAP + NPC treatment also improved nerve conduction velocity (p = 0.008) but did not affect cavitation volume (p = 0.73). Combinatorial NPC and SAP injection into injured nerve tissue may enhance neural repair and regeneration.

Altered neural correlates of affective processing after internet-delivered cognitive behavior therapy for social anxiety disorder.

PubMed

Månsson, Kristoffer N T; Carlbring, Per; Frick, Andreas; Engman, Jonas; Olsson, Carl-Johan; Bodlund, Owe; Furmark, Tomas; Andersson, Gerhard

2013-12-30

Randomized controlled trials have yielded promising results for internet-delivered cognitive behavior therapy (iCBT) for patients with social anxiety disorder (SAD). The present study investigated anxiety-related neural changes after iCBT for SAD. The amygdala is a critical hub in the neural fear network, receptive to change using emotion regulation strategies and a putative target for iCBT. Twenty-two subjects were included in pre- and post-treatment functional magnetic resonance imaging at 3T assessing neural changes during an affective face processing task. Treatment outcome was assessed using social anxiety self-reports and the Clinical Global Impression-Improvement (CGI-I) scale. ICBT yielded better outcome than ABM (66% vs. 25% CGI-I responders). A significant differential activation of the left amygdala was found with relatively decreased reactivity after iCBT. Changes in the amygdala were related to a behavioral measure of social anxiety. Functional connectivity analysis in the iCBT group showed that the amygdala attenuation was associated with increased activity in the medial orbitofrontal cortex and decreased activity in the right ventrolateral and dorsolateral (dlPFC) cortices. Treatment-induced neural changes with iCBT were consistent with previously reported studies on regular CBT and emotion regulation in general. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Neural network method for lossless two-conductor transmission line equations based on the IELM algorithm

NASA Astrophysics Data System (ADS)

Yang, Yunlei; Hou, Muzhou; Luo, Jianshu; Liu, Taohua

2018-06-01

With the increasing demands for vast amounts of data and high-speed signal transmission, the use of multi-conductor transmission lines is becoming more common. The impact of transmission lines on signal transmission is thus a key issue affecting the performance of high-speed digital systems. To solve the problem of lossless two-conductor transmission line equations (LTTLEs), a neural network model and algorithm are explored in this paper. By selecting the product of two triangular basis functions as the activation function of hidden layer neurons, we can guarantee the separation of time, space, and phase orthogonality. By adding the initial condition to the neural network, an improved extreme learning machine (IELM) algorithm for solving the network weight is obtained. This is different to the traditional method for converting the initial condition into the iterative constraint condition. Calculation software for solving the LTTLEs based on the IELM algorithm is developed. Numerical experiments show that the results are consistent with those of the traditional method. The proposed neural network algorithm can find the terminal voltage of the transmission line and also the voltage of any observation point. It is possible to calculate the value at any given point by using the neural network model to solve the transmission line equation.

Simulation electromagnetic scattering on bodies through integral equation and neural networks methods

NASA Astrophysics Data System (ADS)

Lvovich, I. Ya; Preobrazhenskiy, A. P.; Choporov, O. N.

2018-05-01

The paper deals with the issue of electromagnetic scattering on a perfectly conducting diffractive body of a complex shape. Performance calculation of the body scattering is carried out through the integral equation method. Fredholm equation of the second time was used for calculating electric current density. While solving the integral equation through the moments method, the authors have properly described the core singularity. The authors determined piecewise constant functions as basic functions. The chosen equation was solved through the moments method. Within the Kirchhoff integral approach it is possible to define the scattered electromagnetic field, in some way related to obtained electrical currents. The observation angles sector belongs to the area of the front hemisphere of the diffractive body. To improve characteristics of the diffractive body, the authors used a neural network. All the neurons contained a logsigmoid activation function and weighted sums as discriminant functions. The paper presents the matrix of weighting factors of the connectionist model, as well as the results of the optimized dimensions of the diffractive body. The paper also presents some basic steps in calculation technique of the diffractive bodies, based on the combination of integral equation and neural networks methods.

Brain-Inspired Constructive Learning Algorithms with Evolutionally Additive Nonlinear Neurons

NASA Astrophysics Data System (ADS)

Fang, Le-Heng; Lin, Wei; Luo, Qiang

In this article, inspired partially by the physiological evidence of brain’s growth and development, we developed a new type of constructive learning algorithm with evolutionally additive nonlinear neurons. The new algorithms have remarkable ability in effective regression and accurate classification. In particular, the algorithms are able to sustain a certain reduction of the loss function when the dynamics of the trained network are bogged down in the vicinity of the local minima. The algorithm augments the neural network by adding only a few connections as well as neurons whose activation functions are nonlinear, nonmonotonic, and self-adapted to the dynamics of the loss functions. Indeed, we analytically demonstrate the reduction dynamics of the algorithm for different problems, and further modify the algorithms so as to obtain an improved generalization capability for the augmented neural networks. Finally, through comparing with the classical algorithm and architecture for neural network construction, we show that our constructive learning algorithms as well as their modified versions have better performances, such as faster training speed and smaller network size, on several representative benchmark datasets including the MNIST dataset for handwriting digits.

The Effects of Leptin Replacement on Neural Plasticity

PubMed Central

Paz-Filho, Gilberto J.

2016-01-01

Leptin, an adipokine synthesized and secreted mainly by the adipose tissue, has multiple effects on the regulation of food intake, energy expenditure, and metabolism. Its recently-approved analogue, metreleptin, has been evaluated in clinical trials for the treatment of patients with leptin deficiency due to mutations in the leptin gene, lipodystrophy syndromes, and hypothalamic amenorrhea. In such patients, leptin replacement therapy has led to changes in brain structure and function in intra- and extrahypothalamic areas, including the hippocampus. Furthermore, in one of those patients, improvements in neurocognitive development have been observed. In addition to this evidence linking leptin to neural plasticity and function, observational studies evaluating leptin-sufficient humans have also demonstrated direct correlation between blood leptin levels and brain volume and inverse associations between circulating leptin and risk for the development of dementia. This review summarizes the evidence in the literature on the role of leptin in neural plasticity (in leptin-deficient and in leptin-sufficient individuals) and its effects on synaptic activity, glutamate receptor trafficking, neuronal morphology, neuronal development and survival, and microglial function. PMID:26881138

The Effects of Leptin Replacement on Neural Plasticity.

PubMed

Paz-Filho, Gilberto J

2016-01-01

Leptin, an adipokine synthesized and secreted mainly by the adipose tissue, has multiple effects on the regulation of food intake, energy expenditure, and metabolism. Its recently-approved analogue, metreleptin, has been evaluated in clinical trials for the treatment of patients with leptin deficiency due to mutations in the leptin gene, lipodystrophy syndromes, and hypothalamic amenorrhea. In such patients, leptin replacement therapy has led to changes in brain structure and function in intra- and extrahypothalamic areas, including the hippocampus. Furthermore, in one of those patients, improvements in neurocognitive development have been observed. In addition to this evidence linking leptin to neural plasticity and function, observational studies evaluating leptin-sufficient humans have also demonstrated direct correlation between blood leptin levels and brain volume and inverse associations between circulating leptin and risk for the development of dementia. This review summarizes the evidence in the literature on the role of leptin in neural plasticity (in leptin-deficient and in leptin-sufficient individuals) and its effects on synaptic activity, glutamate receptor trafficking, neuronal morphology, neuronal development and survival, and microglial function.

Neural reactivation links unconscious thought to decision-making performance.

PubMed

Creswell, John David; Bursley, James K; Satpute, Ajay B

2013-12-01

Brief periods of unconscious thought (UT) have been shown to improve decision making compared with making an immediate decision (ID). We reveal a neural mechanism for UT in decision making using blood oxygen level-dependent (BOLD) functional magnetic resonance imaging. Participants (N = 33) encoded information on a set of consumer products (e.g. 48 attributes describing four different cars), and we manipulated whether participants (i) consciously thought about this information (conscious thought), (ii) completed a difficult 2-back working memory task (UT) or (iii) made an immediate decision about the consumer products (ID) in a within-subjects blocked design. To differentiate UT neural activity from 2-back working memory neural activity, participants completed an independent 2-back task and this neural activity was subtracted from neural activity occurring during the UT 2-back task. Consistent with a neural reactivation account, we found that the same regions activated during the encoding of complex decision information (right dorsolateral prefrontal cortex and left intermediate visual cortex) continued to be activated during a subsequent 2-min UT period. Moreover, neural reactivation in these regions was predictive of subsequent behavioral decision-making performance after the UT period. These results provide initial evidence for post-encoding unconscious neural reactivation in facilitating decision making.

Neural reactivation links unconscious thought to decision-making performance

PubMed Central

Bursley, James K.; Satpute, Ajay B.

2013-01-01

Brief periods of unconscious thought (UT) have been shown to improve decision making compared with making an immediate decision (ID). We reveal a neural mechanism for UT in decision making using blood oxygen level-dependent (BOLD) functional magnetic resonance imaging. Participants (N = 33) encoded information on a set of consumer products (e.g. 48 attributes describing four different cars), and we manipulated whether participants (i) consciously thought about this information (conscious thought), (ii) completed a difficult 2-back working memory task (UT) or (iii) made an immediate decision about the consumer products (ID) in a within-subjects blocked design. To differentiate UT neural activity from 2-back working memory neural activity, participants completed an independent 2-back task and this neural activity was subtracted from neural activity occurring during the UT 2-back task. Consistent with a neural reactivation account, we found that the same regions activated during the encoding of complex decision information (right dorsolateral prefrontal cortex and left intermediate visual cortex) continued to be activated during a subsequent 2-min UT period. Moreover, neural reactivation in these regions was predictive of subsequent behavioral decision-making performance after the UT period. These results provide initial evidence for post-encoding unconscious neural reactivation in facilitating decision making. PMID:23314012

Nanoscale laminin coating modulates cortical scarring response around implanted silicon microelectrode arrays

NASA Astrophysics Data System (ADS)

He, Wei; McConnell, George C.; Bellamkonda, Ravi V.

2006-12-01

Neural electrodes could significantly enhance the quality of life for patients with sensory and/or motor deficits as well as improve our understanding of brain functions. However, long-term electrical connectivity between neural tissue and recording sites is compromised by the development of astroglial scar around the recording probes. In this study we investigate the effect of a nanoscale laminin (LN) coating on Si-based neural probes on chronic cortical tissue reaction in a rat model. Tissue reaction was evaluated after 1 day, 1 week, and 4 weeks post-implant for coated and uncoated probes using immunohistochemical techniques to evaluate activated microglia/macrophages (ED-1), astrocytes (GFAP) and neurons (NeuN). The coating did not have an observable effect on neuronal density or proximity to the electrode surface. However, the response of microglia/macrophages and astrocytes was altered by the coating. One day post-implant, we observed an ~60% increase in ED-1 expression near LN-coated probe sites compared with control uncoated probe sites. Four weeks post-implant, we observed an ~20% reduction in ED-1 expression along with an ~50% reduction in GFAP expression at coated relative to uncoated probe sites. These results suggest that LN has a stimulatory effect on early microglia activation, accelerating the phagocytic function of these cells. This hypothesis is further supported by the increased mRNA expression of several pro-inflammatory cytokines (TNF-α, IL-1 and IL-6) in cultured microglia on LN-bound Si substrates. LN immunostaining of coated probes immediately after insertion and retrieval demonstrates that the coating integrity is not compromised by the shear force during insertion. We speculate, based on these encouraging results, that LN coating of Si neural probes could potentially improve chronic neural recordings through dispersion of the astroglial scar.

Enteric nervous system abnormalities are present in human necrotizing enterocolitis: potential neurotransplantation therapy

PubMed Central

2013-01-01

Introduction Intestinal dysmotility following human necrotizing enterocolitis suggests that the enteric nervous system is injured during the disease. We examined human intestinal specimens to characterize the enteric nervous system injury that occurs in necrotizing enterocolitis, and then used an animal model of experimental necrotizing enterocolitis to determine whether transplantation of neural stem cells can protect the enteric nervous system from injury. Methods Human intestinal specimens resected from patients with necrotizing enterocolitis (n = 18), from control patients with bowel atresia (n = 8), and from necrotizing enterocolitis and control patients undergoing stoma closure several months later (n = 14 and n = 6 respectively) were subjected to histologic examination, immunohistochemistry, and real-time reverse-transcription polymerase chain reaction to examine the myenteric plexus structure and neurotransmitter expression. In addition, experimental necrotizing enterocolitis was induced in newborn rat pups and neurotransplantation was performed by administration of fluorescently labeled neural stem cells, with subsequent visualization of transplanted cells and determination of intestinal integrity and intestinal motility. Results There was significant enteric nervous system damage with increased enteric nervous system apoptosis, and decreased neuronal nitric oxide synthase expression in myenteric ganglia from human intestine resected for necrotizing enterocolitis compared with control intestine. Structural and functional abnormalities persisted months later at the time of stoma closure. Similar abnormalities were identified in rat pups exposed to experimental necrotizing enterocolitis. Pups receiving neural stem cell transplantation had improved enteric nervous system and intestinal integrity, differentiation of transplanted neural stem cells into functional neurons, significantly improved intestinal transit, and significantly decreased mortality compared with control pups. Conclusions Significant injury to the enteric nervous system occurs in both human and experimental necrotizing enterocolitis. Neural stem cell transplantation may represent a novel future therapy for patients with necrotizing enterocolitis. PMID:24423414

Computerised working memory based cognitive remediation therapy does not affect Reading the Mind in the Eyes test performance or neural activity during a Facial Emotion Recognition test in psychosis.

PubMed

Mothersill, David; Dillon, Rachael; Hargreaves, April; Castorina, Marco; Furey, Emilia; Fagan, Andrew J; Meaney, James F; Fitzmaurice, Brian; Hallahan, Brian; McDonald, Colm; Wykes, Til; Corvin, Aiden; Robertson, Ian H; Donohoe, Gary

2018-05-27

Working memory based cognitive remediation therapy (CT) for psychosis has recently been associated with broad improvements in performance on untrained tasks measuring working memory, episodic memory and IQ, and changes in associated brain regions. However, it is unclear if these improvements transfer to the domain of social cognition and neural activity related to performance on social cognitive tasks. We examined performance on the Reading the Mind in the Eyes test (Eyes test) in a large sample of participants with psychosis who underwent working memory based CT (N = 43) compared to a Control Group of participants with psychosis (N = 35). In a subset of this sample, we used functional magnetic resonance imaging (fMRI) to examine changes in neural activity during a facial emotion recognition task in participants who underwent CT (N = 15) compared to a Control Group (N = 15). No significant effects of CT were observed on Eyes test performance or on neural activity during facial emotion recognition, either at p<0.05 family-wise error, or at a p<0.001 uncorrected threshold, within a priori social cognitive regions of interest. This study suggests that working memory based CT does not significantly impact an aspect of social cognition which was measured behaviourally and neurally. It provides further evidence that deficits in the ability to decode mental state from facial expressions are dissociable from working memory deficits, and suggests that future CT programs should target social cognition in addition to working memory for the purposes of further enhancing social function. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

External branch of the superior laryngeal nerve monitoring during thyroid and parathyroid surgery: International Neural Monitoring Study Group standards guideline statement.

PubMed

Barczyński, Marcin; Randolph, Gregory W; Cernea, Claudio R; Dralle, Henning; Dionigi, Gianlorenzo; Alesina, Piero F; Mihai, Radu; Finck, Camille; Lombardi, Davide; Hartl, Dana M; Miyauchi, Akira; Serpell, Jonathan; Snyder, Samuel; Volpi, Erivelto; Woodson, Gayle; Kraimps, Jean Louis; Hisham, Abdullah N

2013-09-01

Intraoperative neural monitoring (IONM) during thyroid surgery has gained widespread acceptance as an adjunct to the gold standard of visual identification of the recurrent laryngeal nerve (RLN). Contrary to routine dissection of the RLN, most surgeons tend to avoid rather than routinely expose and identify the external branch of the superior laryngeal nerve (EBSLN) during thyroidectomy or parathyroidectomy. IONM has the potential to be utilized for identification of the EBSLN and functional assessment of its integrity; therefore, IONM might contribute to voice preservation following thyroidectomy or parathyroidectomy. We reviewed the literature and the cumulative experience of the multidisciplinary International Neural Monitoring Study Group (INMSG) with IONM of the EBSLN. A systematic search of the MEDLINE database (from 1950 to the present) with predefined search terms (EBSLN, superior laryngeal nerve, stimulation, neuromonitoring, identification) was undertaken and supplemented by personal communication between members of the INMSG to identify relevant publications in the field. The hypothesis explored in this review is that the use of a standardized approach to the functional preservation of the EBSLN can be facilitated by application of IONM resulting in improved preservation of voice following thyroidectomy or parathyroidectomy. These guidelines are intended to improve the practice of neural monitoring of the EBSLN during thyroidectomy or parathyroidectomy and to optimize clinical utility of this technique based on available evidence and consensus of experts. 5 Copyright © 2013 The American Laryngological, Rhinological and Otological Society, Inc.

From free energy to expected energy: Improving energy-based value function approximation in reinforcement learning.

PubMed

Elfwing, Stefan; Uchibe, Eiji; Doya, Kenji

2016-12-01

Free-energy based reinforcement learning (FERL) was proposed for learning in high-dimensional state and action spaces. However, the FERL method does only really work well with binary, or close to binary, state input, where the number of active states is fewer than the number of non-active states. In the FERL method, the value function is approximated by the negative free energy of a restricted Boltzmann machine (RBM). In our earlier study, we demonstrated that the performance and the robustness of the FERL method can be improved by scaling the free energy by a constant that is related to the size of network. In this study, we propose that RBM function approximation can be further improved by approximating the value function by the negative expected energy (EERL), instead of the negative free energy, as well as being able to handle continuous state input. We validate our proposed method by demonstrating that EERL: (1) outperforms FERL, as well as standard neural network and linear function approximation, for three versions of a gridworld task with high-dimensional image state input; (2) achieves new state-of-the-art results in stochastic SZ-Tetris in both model-free and model-based learning settings; and (3) significantly outperforms FERL and standard neural network function approximation for a robot navigation task with raw and noisy RGB images as state input and a large number of actions. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Improved wavelet packet classification algorithm for vibrational intrusions in distributed fiber-optic monitoring systems

NASA Astrophysics Data System (ADS)

Wang, Bingjie; Pi, Shaohua; Sun, Qi; Jia, Bo

2015-05-01

An improved classification algorithm that considers multiscale wavelet packet Shannon entropy is proposed. Decomposition coefficients at all levels are obtained to build the initial Shannon entropy feature vector. After subtracting the Shannon entropy map of the background signal, components of the strongest discriminating power in the initial feature vector are picked out to rebuild the Shannon entropy feature vector, which is transferred to radial basis function (RBF) neural network for classification. Four types of man-made vibrational intrusion signals are recorded based on a modified Sagnac interferometer. The performance of the improved classification algorithm has been evaluated by the classification experiments via RBF neural network under different diffusion coefficients. An 85% classification accuracy rate is achieved, which is higher than the other common algorithms. The classification results show that this improved classification algorithm can be used to classify vibrational intrusion signals in an automatic real-time monitoring system.

What We Know About the Brain Structure-Function Relationship.

PubMed

Batista-García-Ramó, Karla; Fernández-Verdecia, Caridad Ivette

2018-04-18

How the human brain works is still a question, as is its implication with brain architecture: the non-trivial structure–function relationship. The main hypothesis is that the anatomic architecture conditions, but does not determine, the neural network dynamic. The functional connectivity cannot be explained only considering the anatomical substrate. This involves complex and controversial aspects of the neuroscience field and that the methods and methodologies to obtain structural and functional connectivity are not always rigorously applied. The goal of the present article is to discuss about the progress made to elucidate the structure–function relationship of the Central Nervous System, particularly at the brain level, based on results from human and animal studies. The current novel systems and neuroimaging techniques with high resolutive physio-structural capacity have brought about the development of an integral framework of different structural and morphometric tools such as image processing, computational modeling and graph theory. Different laboratories have contributed with in vivo, in vitro and computational/mathematical models to study the intrinsic neural activity patterns based on anatomical connections. We conclude that multi-modal techniques of neuroimaging are required such as an improvement on methodologies for obtaining structural and functional connectivity. Even though simulations of the intrinsic neural activity based on anatomical connectivity can reproduce much of the observed patterns of empirical functional connectivity, future models should be multifactorial to elucidate multi-scale relationships and to infer disorder mechanisms.

Complement peptide C3a stimulates neural plasticity after experimental brain ischaemia.

PubMed

Stokowska, Anna; Atkins, Alison L; Morán, Javier; Pekny, Tulen; Bulmer, Linda; Pascoe, Michaela C; Barnum, Scott R; Wetsel, Rick A; Nilsson, Jonas A; Dragunow, Mike; Pekna, Marcela

2017-02-01

Ischaemic stroke induces endogenous repair processes that include proliferation and differentiation of neural stem cells and extensive rewiring of the remaining neural connections, yet about 50% of stroke survivors live with severe long-term disability. There is an unmet need for drug therapies to improve recovery by promoting brain plasticity in the subacute to chronic phase after ischaemic stroke. We previously showed that complement-derived peptide C3a regulates neural progenitor cell migration and differentiation in vitro and that C3a receptor signalling stimulates neurogenesis in unchallenged adult mice. To determine the role of C3a-C3a receptor signalling in ischaemia-induced neural plasticity, we subjected C3a receptor-deficient mice, GFAP-C3a transgenic mice expressing biologically active C3a in the central nervous system, and their respective wild-type controls to photothrombotic stroke. We found that C3a overexpression increased, whereas C3a receptor deficiency decreased post-stroke expression of GAP43 (P < 0.01), a marker of axonal sprouting and plasticity, in the peri-infarct cortex. To verify the translational potential of these findings, we used a pharmacological approach. Daily intranasal treatment of wild-type mice with C3a beginning 7 days after stroke induction robustly increased synaptic density (P < 0.01) and expression of GAP43 in peri-infarct cortex (P < 0.05). Importantly, the C3a treatment led to faster and more complete recovery of forepaw motor function (P < 0.05). We conclude that C3a-C3a receptor signalling stimulates post-ischaemic neural plasticity and intranasal treatment with C3a receptor agonists is an attractive approach to improve functional recovery after ischaemic brain injury. © The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

High-frequency stimulation of the subthalamic nucleus restores neural and behavioral functions during reaction time task in a rat model of Parkinson's disease.

PubMed

Li, Xiang-Hong; Wang, Jin-Yan; Gao, Ge; Chang, Jing-Yu; Woodward, Donald J; Luo, Fei

2010-05-15

Deep brain stimulation (DBS) has been used in the clinic to treat Parkinson's disease (PD) and other neuropsychiatric disorders. Our previous work has shown that DBS in the subthalamic nucleus (STN) can improve major motor deficits, and induce a variety of neural responses in rats with unilateral dopamine (DA) lesions. In the present study, we examined the effect of STN DBS on reaction time (RT) performance and parallel changes in neural activity in the cortico-basal ganglia regions of partially bilateral DA- lesioned rats. We recorded neural activity with a multiple-channel single-unit electrode system in the primary motor cortex (MI), the STN, and the substantia nigra pars reticulata (SNr) during RT test. RT performance was severely impaired following bilateral injection of 6-OHDA into the dorsolateral part of the striatum. In parallel with such behavioral impairments, the number of responsive neurons to different behavioral events was remarkably decreased after DA lesion. Bilateral STN DBS improved RT performance in 6-OHDA lesioned rats, and restored operational behavior-related neural responses in cortico-basal ganglia regions. These behavioral and electrophysiological effects of DBS lasted nearly an hour after DBS termination. These results demonstrate that a partial DA lesion-induced impairment of RT performance is associated with changes in neural activity in the cortico-basal ganglia circuit. Furthermore, STN DBS can reverse changes in behavior and neural activity caused by partial DA depletion. The observed long-lasting beneficial effect of STN DBS suggests the involvement of the mechanism of neural plasticity in modulating cortico-basal ganglia circuits. (c) 2009 Wiley-Liss, Inc.

Electromyography function, disability degree, and pain in leprosy patients undergoing neural mobilization treatment.

PubMed

Véras, Larissa Sales Téles; Vale, Rodrigo Gomes de Souza; Mello, Danielli Braga de; Castro, José Adail Fonseca de; Lima, Vicente; Trott, Alexis; Dantas, Estélio Henrique Martin

2012-02-01

This study aimed to evaluate the effect of the neural mobilization technique on electromyography function, disability degree, and pain in patients with leprosy. A sample of 56 individuals with leprosy was randomized into an experimental group, composed of 29 individuals undergoing treatment with neural mobilization, and a control group of 27 individuals who underwent conventional treatment. In both groups, the lesions in the lower limbs were treated. In the treatment with neural mobilization, the procedure used was mobilization of the lumbosacral roots and sciatic nerve biased to the peroneal nerve that innervates the anterior tibial muscle, which was evaluated in the electromyography. Analysis of the electromyography function showed a significant increase (p<0.05) in the experimental group in both the right (Δ%=22.1, p=0.013) and the left anterior tibial muscles (Δ%=27.7, p=0.009), compared with the control group pre- and post-test. Analysis of the strength both in the movement of horizontal extension (Δ%right=11.7, p=0.003/Δ%left=27.4, p=0.002) and in the movement of back flexion (Δ%right=31.1; p=0.000/Δ%left=34.7, p=0.000) showed a significant increase (p<0.05) in both the right and the left segments when comparing the experimental group pre- and post-test. The experimental group showed a significant reduction (p=0.000) in pain perception and disability degree when the pre- and post-test were compared and when compared with the control group in the post-test. Leprosy patients undergoing the technique of neural mobilization had an improvement in electromyography function and muscle strength, reducing disability degree and pain.

Brain Structure-Function Couplings: Year 2 Accomplishments and Programmatic Plans

DTIC Science & Technology

2013-06-01

performance through individual-specific neurotechnologies and enhance Soldier protection technologies to minimize neural injury. The long-term vision of this...envision pathways that enable our basic science accomplishments to foster development of revolutionary Soldier neurotechnologies and Soldier protection...improve Soldier-system performance with Soldier-specific neurotechnologies . We expect mid-term impact with models linking structure and function that can

Spatial attention improves the quality of population codes in human visual cortex.

PubMed

Saproo, Sameer; Serences, John T

2010-08-01

Selective attention enables sensory input from behaviorally relevant stimuli to be processed in greater detail, so that these stimuli can more accurately influence thoughts, actions, and future goals. Attention has been shown to modulate the spiking activity of single feature-selective neurons that encode basic stimulus properties (color, orientation, etc.). However, the combined output from many such neurons is required to form stable representations of relevant objects and little empirical work has formally investigated the relationship between attentional modulations on population responses and improvements in encoding precision. Here, we used functional MRI and voxel-based feature tuning functions to show that spatial attention induces a multiplicative scaling in orientation-selective population response profiles in early visual cortex. In turn, this multiplicative scaling correlates with an improvement in encoding precision, as evidenced by a concurrent increase in the mutual information between population responses and the orientation of attended stimuli. These data therefore demonstrate how multiplicative scaling of neural responses provides at least one mechanism by which spatial attention may improve the encoding precision of population codes. Increased encoding precision in early visual areas may then enhance the speed and accuracy of perceptual decisions computed by higher-order neural mechanisms.

Ultrasoft microwire neural electrodes improve chronic tissue integration

PubMed Central

Du, Zhanhong Jeff; Kolarcik, Christi L.; Kozai, Takashi D.Y.; Luebben, Silvia D.; Sapp, Shawn A.; Zheng, Xin Sally; Nabity, James A.; Cui, X. Tracy

2017-01-01

Chronically implanted neural multi-electrode arrays (MEA) are an essential technology for recording electrical signals from neurons and/or modulating neural activity through stimulation. However, current MEAs, regardless of the type, elicit an inflammatory response that ultimately leads to device failure. Traditionally, rigid materials like tungsten and silicon have been employed to interface with the relatively soft neural tissue. The large stiffness mismatch is thought to exacerbate the inflammatory response. In order to minimize the disparity between the device and the brain, we fabricated novel ultrasoft electrodes consisting of elastomers and conducting polymers with mechanical properties much more similar to those of brain tissue than previous neural implants. In this study, these ultrasoft microelectrodes were inserted and released using a stainless steel shuttle with polyethyleneglycol (PEG) glue. The implanted microwires showed functionality in acute neural stimulation. When implanted for 1 or 8 weeks, the novel soft implants demonstrated significantly reduced inflammatory tissue response at week 8 compared to tungsten wires of similar dimension and surface chemistry. Furthermore, a higher degree of cell body distortion was found next to the tungsten implants compared to the polymer implants. Our results support the use of these novel ultrasoft electrodes for long term neural implants. PMID:28185910

Scaffolds for peripheral nerve repair and reconstruction.

PubMed

Yi, Sheng; Xu, Lai; Gu, Xiaosong

2018-06-02

Trauma-associated peripheral nerve defect is a widespread clinical problem. Autologous nerve grafting, the current gold standard technique for the treatment of peripheral nerve injury, has many internal disadvantages. Emerging studies showed that tissue engineered nerve graft is an effective substitute to autologous nerves. Tissue engineered nerve graft is generally composed of neural scaffolds and incorporating cells and molecules. A variety of biomaterials have been used to construct neural scaffolds, the main component of tissue engineered nerve graft. Synthetic polymers (e.g. silicone, polyglycolic acid, and poly(lactic-co-glycolic acid)) and natural materials (e.g. chitosan, silk fibroin, and extracellular matrix components) are commonly used along or together to build neural scaffolds. Many other materials, including the extracellular matrix, glass fabrics, ceramics, and metallic materials, have also been used to construct neural scaffolds. These biomaterials are fabricated to create specific structures and surface features. Seeding supporting cells and/or incorporating neurotrophic factors to neural scaffolds further improve restoration effects. Preliminary studies demonstrate that clinical applications of these neural scaffolds achieve satisfactory functional recovery. Therefore, tissue engineered nerve graft provides a good alternative to autologous nerve graft and represents a promising frontier in neural tissue engineering. Copyright © 2018 Elsevier Inc. All rights reserved.

Learning control of inverted pendulum system by neural network driven fuzzy reasoning: The learning function of NN-driven fuzzy reasoning under changes of reasoning environment

NASA Technical Reports Server (NTRS)

Hayashi, Isao; Nomura, Hiroyoshi; Wakami, Noboru

1991-01-01

Whereas conventional fuzzy reasonings are associated with tuning problems, which are lack of membership functions and inference rule designs, a neural network driven fuzzy reasoning (NDF) capable of determining membership functions by neural network is formulated. In the antecedent parts of the neural network driven fuzzy reasoning, the optimum membership function is determined by a neural network, while in the consequent parts, an amount of control for each rule is determined by other plural neural networks. By introducing an algorithm of neural network driven fuzzy reasoning, inference rules for making a pendulum stand up from its lowest suspended point are determined for verifying the usefulness of the algorithm.

A preliminary study of the effects of working memory training on brain function.

PubMed

Stevens, Michael C; Gaynor, Alexandra; Bessette, Katie L; Pearlson, Godfrey D

2016-06-01

Working memory (WM) training improves WM ability in Attention-Deficit/Hyperactivity Disorder (ADHD), but its efficacy for non-cognitive ADHD impairments ADHD has been sharply debated. The purpose of this preliminary study was to characterize WM training-related changes in ADHD brain function and see if they were linked to clinical improvement. We examined 18 adolescents diagnosed with DSM-IV Combined-subtype ADHD before and after 25 sessions of WM training using a frequently employed approach (Cogmed™) using a nonverbal Sternberg WM fMRI task, neuropsychological tests, and participant- and parent-reports of ADHD symptom severity and associated functional impairment. Whole brain SPM8 analyses identified ADHD activation deficits compared to 18 non-ADHD control participants, then tested whether impaired ADHD frontoparietal brain activation would increase following WM training. Post hoc tests examined the relationships between neural changes and neurocognitive or clinical improvements. As predicted, WM training increased WM performance, ADHD clinical functioning, and WM-related ADHD brain activity in several frontal, parietal and temporal lobe regions. Increased left inferior frontal sulcus region activity was seen in all Encoding, Maintenance, and Retrieval Sternberg task phases. ADHD symptom severity improvements were most often positively correlated with activation gains in brain regions known to be engaged for WM-related executive processing; improvement of different symptom types had different neural correlates. The responsiveness of both amodal WM frontoparietal circuits and executive process-specific WM brain regions was altered by WM training. The latter might represent a promising, relatively unexplored treatment target for researchers seeking to optimize clinical response in ongoing ADHD WM training development efforts.

Individual Differences in Sentence Comprehension: A Functional Magnetic Resonance Imaging Investigation of Syntactic and Lexical Processing Demands

PubMed Central

Prat, Chantel S.; Keller, Timothy A.; Just, Marcel Adam

2008-01-01

Language comprehension is neurally underpinned by a network of collaborating cortical processing centers; individual differences in comprehension must be related to some set of this network’s properties. This study investigated the neural bases of individual differences during sentence comprehension by examining the network’s response to two variations in processing demands: reading sentences containing words of high versus low lexical frequency and having simpler versus more complex syntax. In a functional magnetic resonance imaging study, readers who were independently identified as having high or low working memory capacity for language exhibited three differentiating properties of their language network, namely, neural efficiency, adaptability, and synchronization. First, greater efficiency (defined as a reduction in activation associated with improved performance) was manifested as less activation in the bilateral middle frontal and right lingual gyri in high-capacity readers. Second, increased adaptability was indexed by larger lexical frequency effects in high-capacity readers across bilateral middle frontal, bilateral inferior occipital, and right temporal regions. Third, greater synchronization was observed in high-capacity readers between left temporal and left inferior frontal, left parietal, and right occipital regions. Synchronization interacted with adaptability, such that functional connectivity remained constant or increased with increasing lexical and syntactic demands in high-capacity readers, whereas low-capacity readers either showed no reliable differentiation or a decrease in functional connectivity with increasing demands. These results are among the first to relate multiple cortical network properties to individual differences in reading capacity and suggest a more general framework for understanding the relation between neural function and individual differences in cognitive performance. PMID:17892384

Individual differences in sentence comprehension: a functional magnetic resonance imaging investigation of syntactic and lexical processing demands.

PubMed

Prat, Chantel S; Keller, Timothy A; Just, Marcel Adam

2007-12-01

Language comprehension is neurally underpinned by a network of collaborating cortical processing centers; individual differences in comprehension must be related to some set of this network's properties. This study investigated the neural bases of individual differences during sentence comprehension by examining the network's response to two variations in processing demands: reading sentences containing words of high versus low lexical frequency and having simpler versus more complex syntax. In a functional magnetic resonance imaging study, readers who were independently identified as having high or low working memory capacity for language exhibited three differentiating properties of their language network, namely, neural efficiency, adaptability, and synchronization. First, greater efficiency (defined as a reduction in activation associated with improved performance) was manifested as less activation in the bilateral middle frontal and right lingual gyri in high-capacity readers. Second, increased adaptability was indexed by larger lexical frequency effects in high-capacity readers across bilateral middle frontal, bilateral inferior occipital, and right temporal regions. Third, greater synchronization was observed in high-capacity readers between left temporal and left inferior frontal, left parietal, and right occipital regions. Synchronization interacted with adaptability, such that functional connectivity remained constant or increased with increasing lexical and syntactic demands in high-capacity readers, whereas low-capacity readers either showed no reliable differentiation or a decrease in functional connectivity with increasing demands. These results are among the first to relate multiple cortical network properties to individual differences in reading capacity and suggest a more general framework for understanding the relation between neural function and individual differences in cognitive performance.

A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints.

PubMed

Liang, X B; Wang, J

2000-01-01

This paper presents a continuous-time recurrent neural-network model for nonlinear optimization with any continuously differentiable objective function and bound constraints. Quadratic optimization with bound constraints is a special problem which can be solved by the recurrent neural network. The proposed recurrent neural network has the following characteristics. 1) It is regular in the sense that any optimum of the objective function with bound constraints is also an equilibrium point of the neural network. If the objective function to be minimized is convex, then the recurrent neural network is complete in the sense that the set of optima of the function with bound constraints coincides with the set of equilibria of the neural network. 2) The recurrent neural network is primal and quasiconvergent in the sense that its trajectory cannot escape from the feasible region and will converge to the set of equilibria of the neural network for any initial point in the feasible bound region. 3) The recurrent neural network has an attractivity property in the sense that its trajectory will eventually converge to the feasible region for any initial states even at outside of the bounded feasible region. 4) For minimizing any strictly convex quadratic objective function subject to bound constraints, the recurrent neural network is globally exponentially stable for almost any positive network parameters. Simulation results are given to demonstrate the convergence and performance of the proposed recurrent neural network for nonlinear optimization with bound constraints.

Chaotic simulated annealing by a neural network with a variable delay: design and application.

PubMed

Chen, Shyan-Shiou

2011-10-01

In this paper, we have three goals: the first is to delineate the advantages of a variably delayed system, the second is to find a more intuitive Lyapunov function for a delayed neural network, and the third is to design a delayed neural network for a quadratic cost function. For delayed neural networks, most researchers construct a Lyapunov function based on the linear matrix inequality (LMI) approach. However, that approach is not intuitive. We provide a alternative candidate Lyapunov function for a delayed neural network. On the other hand, if we are first given a quadratic cost function, we can construct a delayed neural network by suitably dividing the second-order term into two parts: a self-feedback connection weight and a delayed connection weight. To demonstrate the advantage of a variably delayed neural network, we propose a transiently chaotic neural network with variable delay and show numerically that the model should possess a better searching ability than Chen-Aihara's model, Wang's model, and Zhao's model. We discuss both the chaotic and the convergent phases. During the chaotic phase, we simply present bifurcation diagrams for a single neuron with a constant delay and with a variable delay. We show that the variably delayed model possesses the stochastic property and chaotic wandering. During the convergent phase, we not only provide a novel Lyapunov function for neural networks with a delay (the Lyapunov function is independent of the LMI approach) but also establish a correlation between the Lyapunov function for a delayed neural network and an objective function for the traveling salesman problem. © 2011 IEEE

Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring

NASA Technical Reports Server (NTRS)

Peck, Charles C.; Dhawan, Atam P.; Meyer, Claudia M.

1991-01-01

A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the space shuttle main engine (SSME), the functional relationship between measured parameters is unknown and complex. Furthermore, the number of possible input parameters is quite large. Many approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms they were employed to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.

Strategies influence neural activity for feedback learning across child and adolescent development.

PubMed

Peters, Sabine; Koolschijn, P Cédric M P; Crone, Eveline A; Van Duijvenvoorde, Anna C K; Raijmakers, Maartje E J

2014-09-01

Learning from feedback is an important aspect of executive functioning that shows profound improvements during childhood and adolescence. This is accompanied by neural changes in the feedback-learning network, which includes pre-supplementary motor area (pre- SMA)/anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), superior parietal cortex (SPC), and the basal ganglia. However, there can be considerable differences within age ranges in performance that are ascribed to differences in strategy use. This is problematic for traditional approaches of analyzing developmental data, in which age groups are assumed to be homogenous in strategy use. In this study, we used latent variable models to investigate if underlying strategy groups could be detected for a feedback-learning task and whether there were differences in neural activation patterns between strategies. In a sample of 268 participants between ages 8 to 25 years, we observed four underlying strategy groups, which were cut across age groups and varied in the optimality of executive functioning. These strategy groups also differed in neural activity during learning; especially the most optimal performing group showed more activity in DLPFC, SPC and pre-SMA/ACC compared to the other groups. However, age differences remained an important contributor to neural activation, even when correcting for strategy. These findings contribute to the debate of age versus performance predictors of neural development, and highlight the importance of studying individual differences in strategy use when studying development. Copyright © 2014 Elsevier Ltd. All rights reserved.

Processing of social and monetary rewards in the human striatum.

PubMed

Izuma, Keise; Saito, Daisuke N; Sadato, Norihiro

2008-04-24

Despite an increasing focus on the neural basis of human decision making in neuroscience, relatively little attention has been paid to decision making in social settings. Moreover, although human social decision making has been explored in a social psychology context, few neural explanations for the observed findings have been considered. To bridge this gap and improve models of human social decision making, we investigated whether acquiring a good reputation, which is an important incentive in human social behaviors, activates the same reward circuitry as monetary rewards. In total, 19 subjects participated in functional magnetic resonance imaging (fMRI) experiments involving monetary and social rewards. The acquisition of one's good reputation robustly activated reward-related brain areas, notably the striatum, and these overlapped with the areas activated by monetary rewards. Our findings support the idea of a "common neural currency" for rewards and represent an important first step toward a neural explanation for complex human social behaviors.

Intelligent control and adaptive systems; Proceedings of the Meeting, Philadelphia, PA, Nov. 7, 8, 1989

NASA Technical Reports Server (NTRS)

Rodriguez, Guillermo (Editor)

1990-01-01

Various papers on intelligent control and adaptive systems are presented. Individual topics addressed include: control architecture for a Mars walking vehicle, representation for error detection and recovery in robot task plans, real-time operating system for robots, execution monitoring of a mobile robot system, statistical mechanics models for motion and force planning, global kinematics for manipulator planning and control, exploration of unknown mechanical assemblies through manipulation, low-level representations for robot vision, harmonic functions for robot path construction, simulation of dual behavior of an autonomous system. Also discussed are: control framework for hand-arm coordination, neural network approach to multivehicle navigation, electronic neural networks for global optimization, neural network for L1 norm linear regression, planning for assembly with robot hands, neural networks in dynamical systems, control design with iterative learning, improved fuzzy process control of spacecraft autonomous rendezvous using a genetic algorithm.

The artist emerges: visual art learning alters neural structure and function.

PubMed

Schlegel, Alexander; Alexander, Prescott; Fogelson, Sergey V; Li, Xueting; Lu, Zhengang; Kohler, Peter J; Riley, Enrico; Tse, Peter U; Meng, Ming

2015-01-15

How does the brain mediate visual artistic creativity? Here we studied behavioral and neural changes in drawing and painting students compared to students who did not study art. We investigated three aspects of cognition vital to many visual artists: creative cognition, perception, and perception-to-action. We found that the art students became more creative via the reorganization of prefrontal white matter but did not find any significant changes in perceptual ability or related neural activity in the art students relative to the control group. Moreover, the art students improved in their ability to sketch human figures from observation, and multivariate patterns of cortical and cerebellar activity evoked by this drawing task became increasingly separable between art and non-art students. Our findings suggest that the emergence of visual artistic skills is supported by plasticity in neural pathways that enable creative cognition and mediate perceptuomotor integration. Copyright © 2014 Elsevier Inc. All rights reserved.

Motor neuron activation in peripheral nerves using infrared neural stimulation

NASA Astrophysics Data System (ADS)

Peterson, E. J.; Tyler, D. J.

2014-02-01

Objective. Localized activation of peripheral axons may improve selectivity of peripheral nerve interfaces. Infrared neural stimulation (INS) employs localized delivery to activate neural tissue. This study investigated INS to determine whether localized delivery limited functionality in larger mammalian nerves. Approach. The rabbit sciatic nerve was stimulated extraneurally with 1875 nm wavelength infrared light, electrical stimulation, or a combination of both. Infrared-sensitive regions (ISR) of the nerve surface and electromyogram (EMG) recruitment of the Medial Gastrocnemius, Lateral Gastrocnemius, Soleus, and Tibialis Anterior were the primary output measures. Stimulation applied included infrared-only, electrical-only, and combined infrared and electrical. Main results. 81% of nerves tested were sensitive to INS, with 1.7 ± 0.5 ISR detected per nerve. INS was selective to a single muscle within 81% of identified ISR. Activation energy threshold did not change significantly with stimulus power, but motor activation decreased significantly when radiant power was decreased. Maximum INS levels typically recruited up to 2-9% of any muscle. Combined infrared and electrical stimulation differed significantly from electrical recruitment in 7% of cases. Significance. The observed selectivity of INS indicates that it may be useful in augmenting rehabilitation, but significant challenges remain in increasing sensitivity and response magnitude to improve the functionality of INS.

Motor Neuron Activation in Peripheral Nerves Using Infrared Neural Stimulation

PubMed Central

Peterson, EJ; Tyler, DJ

2014-01-01

Objective Localized activation of peripheral axons may improve selectivity of peripheral nerve interfaces. Infrared neural stimulation (INS) employs localized delivery to activate neural tissue. This study investigated INS to determine whether localized delivery limited functionality in larger mammalian nerves. Approach The rabbit sciatic nerve was stimulated extraneurally with 1875 nm-wavelength infrared light, electrical stimulation, or a combination of both. Infrared-sensitive regions (ISR) of the nerve surface and electromyogram (EMG) recruitment of the Medial Gastrocnemius, Lateral Gastrocnemius, Soleus, and Tibialis Anterior were the primary output measures. Stimulation applied included infrared-only, electrical-only, and combined infrared and electrical. Main results 81% of nerves tested were sensitive to INS, with 1.7± 0.5 ISR detected per nerve. INS was selective to a single muscle within 81% of identified ISR. Activation energy threshold did not change significantly with stimulus power, but motor activation decreased significantly when radiant power was decreased. Maximum INS levels typically recruited up to 2–9% of any muscle. Combined infrared and electrical stimulation differed significantly from electrical recruitment in 7% of cases. Significance The observed selectivity of INS indicates it may be useful in augmenting rehabilitation, but significant challenges remain in increasing sensitivity and response magnitude to improve the functionality of INS. PMID:24310923

Fetal brain extracellular matrix boosts neuronal network formation in 3D bioengineered model of cortical brain tissue.

PubMed

Sood, Disha; Chwalek, Karolina; Stuntz, Emily; Pouli, Dimitra; Du, Chuang; Tang-Schomer, Min; Georgakoudi, Irene; Black, Lauren D; Kaplan, David L

2016-01-01

The extracellular matrix (ECM) constituting up to 20% of the organ volume is a significant component of the brain due to its instructive role in the compartmentalization of functional microdomains in every brain structure. The composition, quantity and structure of ECM changes dramatically during the development of an organism greatly contributing to the remarkably sophisticated architecture and function of the brain. Since fetal brain is highly plastic, we hypothesize that the fetal brain ECM may contain cues promoting neural growth and differentiation, highly desired in regenerative medicine. Thus, we studied the effect of brain-derived fetal and adult ECM complemented with matricellular proteins on cortical neurons using in vitro 3D bioengineered model of cortical brain tissue. The tested parameters included neuronal network density, cell viability, calcium signaling and electrophysiology. Both, adult and fetal brain ECM as well as matricellular proteins significantly improved neural network formation as compared to single component, collagen I matrix. Additionally, the brain ECM improved cell viability and lowered glutamate release. The fetal brain ECM induced superior neural network formation, calcium signaling and spontaneous spiking activity over adult brain ECM. This study highlights the difference in the neuroinductive properties of fetal and adult brain ECM and suggests that delineating the basis for this divergence may have implications for regenerative medicine.

A new optimized GA-RBF neural network algorithm.

PubMed

Jia, Weikuan; Zhao, Dean; Shen, Tian; Su, Chunyang; Hu, Chanli; Zhao, Yuyan

2014-01-01

When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid.

Neural correlates of heart-focused interoception: a functional magnetic resonance imaging meta-analysis

PubMed Central

2016-01-01

Interoception is the ability to perceive one's internal body state including visceral sensations. Heart-focused interoception has received particular attention, in part due to a readily available task for behavioural assessment, but also due to accumulating evidence for a significant role in emotional experience, decision-making and clinical disorders such as anxiety and depression. Improved understanding of the underlying neural correlates is important to promote development of anatomical-functional models and suitable intervention strategies. In the present meta-analysis, nine studies reporting neural activity associated with interoceptive attentiveness (i.e. focused attention to a particular interoceptive signal for a given time interval) to one's heartbeat were submitted to a multilevel kernel density analysis. The findings corroborated an extended network associated with heart-focused interoceptive attentiveness including the posterior right and left insula, right claustrum, precentral gyrus and medial frontal gyrus. Right-hemispheric dominance emphasizes non-verbal information processing with the posterior insula presumably serving as the major gateway for cardioception. Prefrontal neural activity may reflect both top-down attention deployment and processing of feed-forward cardioceptive information, possibly orchestrated via the claustrum. This article is part of the themed issue ‘Interoception beyond homeostasis: affect, cognition and mental health’. PMID:28080975

Therapeutic value of nerve growth factor in promoting neural stem cell survival and differentiation and protecting against neuronal hearing loss.

PubMed

Han, Zhao; Wang, Cong-Pin; Cong, Ning; Gu, Yu-Yan; Ma, Rui; Chi, Fang-Lu

2017-04-01

Nerve growth factor (NGF) is a neurotrophic factor that modulates survival and differentiation of neural stem cells (NSCs). We investigated the function of NGF in promoting growth and neuronal differentiation of NSCs isolated from mouse cochlear tissue, as well as its protective properties against gentamicin (GMC) ototoxicity. NSCs were isolated from the cochlea of mice and cultured in vitro. Effect of NGF on survival, neurosphere formation, and differentiation of the NSCs, as well as neurite outgrowth and neural excitability in the subsequent in vitro neuronal network, was examined. Mechanotransduction capacity of intact cochlea and auditory brainstem response (ABR) threshold in mice were also measured following GMC treatment to evaluate protection using NGF against GMC-induced neuronal hearing loss. NGF improved survival, neurosphere formation, and neuronal differentiation of mouse cochlear NSCs in vitro, as well as promoted neurite outgrowth and neural excitability in the NSC-differentiated neuronal culture. In addition, NGF protected mechanotransduction capacity and restored ABR threshold in gentamicin ototoxicity mouse model. Our study supports a potential therapeutic value of NGF in promoting proliferation and differentiation of NSCs into functional neurons in vitro, supporting its protective role in the treatment of neuronal hearing loss.

Oxytocin attenuates neural reactivity to masked threat cues from the eyes.

PubMed

Kanat, Manuela; Heinrichs, Markus; Schwarzwald, Ralf; Domes, Gregor

2015-01-01

The neuropeptide oxytocin has recently been shown to modulate covert attention shifts to emotional face cues and to improve discrimination of masked facial emotions. These results suggest that oxytocin modulates facial emotion processing at early perceptual stages prior to full evaluation of the emotional expression. Here, we used functional magnetic resonance imaging to examine whether oxytocin alters neural responses to backwardly masked angry and happy faces while controlling for attention to the eye vs the mouth region. Intranasal oxytocin administration reduced amygdala reactivity to masked emotions when attending to salient facial features, ie, the eyes of angry faces and the mouth of happy faces. In addition, oxytocin decreased neural responses within the fusiform gyrus and brain stem areas, as well as functional coupling between the amygdala and the fusiform gyrus specifically for threat cues from the eyes. Effects of oxytocin on brain activity were not attributable to differences in behavioral performance, as oxytocin had no impact on mere emotion detection. Our results suggest that oxytocin attenuates neural correlates of early arousal by threat signals from the eye region. As reduced threat sensitivity may increase the likelihood of engaging in social interactions, our findings may have important implications for clinical states of social anxiety.

Promises of formal and informal musical activities in advancing neurocognitive development throughout childhood.

PubMed

Putkinen, Vesa; Tervaniemi, Mari; Saarikivi, Katri; Huotilainen, Minna

2015-03-01

Adult musicians show superior neural sound discrimination when compared to nonmusicians. However, it is unclear whether these group differences reflect the effects of experience or preexisting neural enhancement in individuals who seek out musical training. Tracking how brain function matures over time in musically trained and nontrained children can shed light on this issue. Here, we review our recent longitudinal event-related potential (ERP) studies that examine how formal musical training and less formal musical activities influence the maturation of brain responses related to sound discrimination and auditory attention. These studies found that musically trained school-aged children and preschool-aged children attending a musical playschool show more rapid maturation of neural sound discrimination than their control peers. Importantly, we found no evidence for pretraining group differences. In a related cross-sectional study, we found ERP and behavioral evidence for improved executive functions and control over auditory novelty processing in musically trained school-aged children and adolescents. Taken together, these studies provide evidence for the causal role of formal musical training and less formal musical activities in shaping the development of important neural auditory skills and suggest transfer effects with domain-general implications. © 2015 New York Academy of Sciences.

Finite time convergent learning law for continuous neural networks.

PubMed

Chairez, Isaac

2014-02-01

This paper addresses the design of a discontinuous finite time convergent learning law for neural networks with continuous dynamics. The neural network was used here to obtain a non-parametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties was the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on discontinuous algorithms was used to adjust the weights of the neural network. The adaptive algorithm was derived by means of a non-standard Lyapunov function that is lower semi-continuous and differentiable in almost the whole space. A compensator term was included in the identifier to reject some specific perturbations using a nonlinear robust algorithm. Two numerical examples demonstrated the improvements achieved by the learning algorithm introduced in this paper compared to classical schemes with continuous learning methods. The first one dealt with a benchmark problem used in the paper to explain how the discontinuous learning law works. The second one used the methane production model to show the benefits in engineering applications of the learning law proposed in this paper. Copyright © 2013 Elsevier Ltd. All rights reserved.

Prevention of the neurocristopathy Treacher Collins syndrome through inhibition of p53 function.

PubMed

Jones, Natalie C; Lynn, Megan L; Gaudenz, Karin; Sakai, Daisuke; Aoto, Kazushi; Rey, Jean-Phillipe; Glynn, Earl F; Ellington, Lacey; Du, Chunying; Dixon, Jill; Dixon, Michael J; Trainor, Paul A

2008-02-01

Treacher Collins syndrome (TCS) is a congenital disorder of craniofacial development arising from mutations in TCOF1, which encodes the nucleolar phosphoprotein Treacle. Haploinsufficiency of Tcof1 perturbs mature ribosome biogenesis, resulting in stabilization of p53 and the cyclin G1-mediated cell-cycle arrest that underpins the specificity of neuroepithelial apoptosis and neural crest cell hypoplasia characteristic of TCS. Here we show that inhibition of p53 prevents cyclin G1-driven apoptotic elimination of neural crest cells while rescuing the craniofacial abnormalities associated with mutations in Tcof1 and extending life span. These improvements, however, occur independently of the effects on ribosome biogenesis; thus suggesting that it is p53-dependent neuroepithelial apoptosis that is the primary mechanism underlying the pathogenesis of TCS. Our work further implies that neuroepithelial and neural crest cells are particularly sensitive to cellular stress during embryogenesis and that suppression of p53 function provides an attractive avenue for possible clinical prevention of TCS craniofacial birth defects and possibly those of other neurocristopathies.

An ultra low-power CMOS automatic action potential detector.

PubMed

Gosselin, Benoit; Sawan, Mohamad

2009-08-01

We present a low-power complementary metal-oxide semiconductor (CMOS) analog integrated biopotential detector intended for neural recording in wireless multichannel implants. The proposed detector can achieve accurate automatic discrimination of action potential (APs) from the background activity by means of an energy-based preprocessor and a linear delay element. This strategy improves detected waveforms integrity and prompts for better performance in neural prostheses. The delay element is implemented with a low-power continuous-time filter using a ninth-order equiripple allpass transfer function. All circuit building blocks use subthreshold OTAs employing dedicated circuit techniques for achieving ultra low-power and high dynamic range. The proposed circuit function in the submicrowatt range as the implemented CMOS 0.18- microm chip dissipates 780 nW, and it features a size of 0.07 mm(2). So it is suitable for massive integration in a multichannel device with modest overhead. The fabricated detector succeeds to automatically detect APs from underlying background activity. Testbench validation results obtained with synthetic neural waveforms are presented.

Regional application of multi-layer artificial neural networks in 3-D ionosphere tomography

NASA Astrophysics Data System (ADS)

Ghaffari Razin, Mir Reza; Voosoghi, Behzad

2016-08-01

Tomography is a very cost-effective method to study physical properties of the ionosphere. In this paper, residual minimization training neural network (RMTNN) is used in voxel-based tomography to reconstruct of 3-D ionosphere electron density with high spatial resolution. For numerical experiments, observations collected at 37 GPS stations from Iranian permanent GPS network (IPGN) are used. A smoothed TEC approach was used for absolute STEC recovery. To improve the vertical resolution, empirical orthogonal functions (EOFs) obtained from international reference ionosphere 2012 (IRI-2012) used as object function in training neural network. Ionosonde observations is used for validate reliability of the proposed method. Minimum relative error for RMTNN is 1.64% and maximum relative error is 15.61%. Also root mean square error (RMSE) of 0.17 × 1011 (electrons/m3) is computed for RMTNN which is less than RMSE of IRI2012. The results show that RMTNN has higher accuracy and compiles speed than other ionosphere reconstruction methods.

Music listening after stroke: beneficial effects and potential neural mechanisms.

PubMed

Särkämö, Teppo; Soto, David

2012-04-01

Music is an enjoyable leisure activity that also engages many emotional, cognitive, and motor processes in the brain. Here, we will first review previous literature on the emotional and cognitive effects of music listening in healthy persons and various clinical groups. Then we will present findings about the short- and long-term effects of music listening on the recovery of cognitive function in stroke patients and the underlying neural mechanisms of these music effects. First, our results indicate that listening to pleasant music can have a short-term facilitating effect on visual awareness in patients with visual neglect, which is associated with functional coupling between emotional and attentional brain regions. Second, daily music listening can improve auditory and verbal memory, focused attention, and mood as well as induce structural gray matter changes in the early poststroke stage. The psychological and neural mechanisms potentially underlying the rehabilitating effect of music after stroke are discussed. © 2012 New York Academy of Sciences.

Training feed-forward neural networks with gain constraints

PubMed

Hartman

2000-04-01

Inaccurate input-output gains (partial derivatives of outputs with respect to inputs) are common in neural network models when input variables are correlated or when data are incomplete or inaccurate. Accurate gains are essential for optimization, control, and other purposes. We develop and explore a method for training feedforward neural networks subject to inequality or equality-bound constraints on the gains of the learned mapping. Gain constraints are implemented as penalty terms added to the objective function, and training is done using gradient descent. Adaptive and robust procedures are devised for balancing the relative strengths of the various terms in the objective function, which is essential when the constraints are inconsistent with the data. The approach has the virtue that the model domain of validity can be extended via extrapolation training, which can dramatically improve generalization. The algorithm is demonstrated here on artificial and real-world problems with very good results and has been advantageously applied to dozens of models currently in commercial use.

Generation of diverse neural cell types through direct conversion

PubMed Central

Petersen, Gayle F; Strappe, Padraig M

2016-01-01

A characteristic of neurological disorders is the loss of critical populations of cells that the body is unable to replace, thus there has been much interest in identifying methods of generating clinically relevant numbers of cells to replace those that have been damaged or lost. The process of neural direct conversion, in which cells of one lineage are converted into cells of a neural lineage without first inducing pluripotency, shows great potential, with evidence of the generation of a range of functional neural cell types both in vitro and in vivo, through viral and non-viral delivery of exogenous factors, as well as chemical induction methods. Induced neural cells have been proposed as an attractive alternative to neural cells derived from embryonic or induced pluripotent stem cells, with prospective roles in the investigation of neurological disorders, including neurodegenerative disease modelling, drug screening, and cellular replacement for regenerative medicine applications, however further investigations into improving the efficacy and safety of these methods need to be performed before neural direct conversion becomes a clinically viable option. In this review, we describe the generation of diverse neural cell types via direct conversion of somatic cells, with comparison against stem cell-based approaches, as well as discussion of their potential research and clinical applications. PMID:26981169

Current directions in non-invasive low intensity electric brain stimulation for depressive disorder.

PubMed

Schutter, Dennis J L G; Sack, Alexander T

2014-01-01

Non-invasive stimulation of the human brain to improve depressive symptoms is increasingly finding its way in clinical settings as a viable form of somatic treatment. Following successful modulation of neural excitability with subsequent antidepressant effects, neural polarization by administrating weak direct currents to the scalp has gained renewed interest. A new wave of basic and clinical studies seems to underscore the potential therapeutic value of direct current stimulation in the treatment of depression. Issues concerning the lack of mechanistic insights into the workings of modifying brain function through neural polarization and how this process translates to its antidepressant properties calls for additional research. The range of its clinical applicability has yet to be established.

Acute aerobic exercise increases cortical activity during working memory: a functional MRI study in female college students.

PubMed

Li, Lin; Men, Wei-Wei; Chang, Yu-Kai; Fan, Ming-Xia; Ji, Liu; Wei, Gao-Xia

2014-01-01

There is increasing evidence that acute aerobic exercise is associated with improved cognitive function. However, neural correlates of its cognitive plasticity remain largely unknown. The present study examined the effect of a session of acute aerobic exercise on working memory task-evoked brain activity as well as task performance. A within-subjects design with a counterbalanced order was employed. Fifteen young female participants (M = 19.56, SD = 0.81) were scanned using functional magnetic resonance imaging while performing a working memory task, the N-back task, both following an acute exercise session with 20 minutes of moderate intensity and a control rest session. Although an acute session of exercise did not improve behavioral performance, we observed that it had a significant impact on brain activity during the 2-back condition of the N-back task. Specifically, acute exercise induced increased brain activation in the right middle prefrontal gyrus, the right lingual gyrus, and the left fusiform gyrus as well as deactivations in the anterior cingulate cortexes, the left inferior frontal gyrus, and the right paracentral lobule. Despite the lack of an effect on behavioral measures, significant changes after acute exercise with activation of the prefrontal and occipital cortexes and deactivation of the anterior cingulate cortexes and left frontal hemisphere reflect the improvement of executive control processes, indicating that acute exercise could benefit working memory at a macro-neural level. In addition to its effects on reversing recent obesity and disease trends, our results provide substantial evidence highlighting the importance of promoting physical activity across the lifespan to prevent or reverse cognitive and neural decline.

SortNet: learning to rank by a neural preference function.

PubMed

Rigutini, Leonardo; Papini, Tiziano; Maggini, Marco; Scarselli, Franco

2011-09-01

Relevance ranking consists in sorting a set of objects with respect to a given criterion. However, in personalized retrieval systems, the relevance criteria may usually vary among different users and may not be predefined. In this case, ranking algorithms that adapt their behavior from users' feedbacks must be devised. Two main approaches are proposed in the literature for learning to rank: the use of a scoring function, learned by examples, that evaluates a feature-based representation of each object yielding an absolute relevance score, a pairwise approach, where a preference function is learned to determine the object that has to be ranked first in a given pair. In this paper, we present a preference learning method for learning to rank. A neural network, the comparative neural network (CmpNN), is trained from examples to approximate the comparison function for a pair of objects. The CmpNN adopts a particular architecture designed to implement the symmetries naturally present in a preference function. The learned preference function can be embedded as the comparator into a classical sorting algorithm to provide a global ranking of a set of objects. To improve the ranking performances, an active-learning procedure is devised, that aims at selecting the most informative patterns in the training set. The proposed algorithm is evaluated on the LETOR dataset showing promising performances in comparison with other state-of-the-art algorithms.

Concurrent neuromechanical and functional gains following upper-extremity power training post-stroke

PubMed Central

2013-01-01

Background Repetitive task practice is argued to drive neural plasticity following stroke. However, current evidence reveals that hemiparetic weakness impairs the capacity to perform, and practice, movements appropriately. Here we investigated how power training (i.e., high-intensity, dynamic resistance training) affects recovery of upper-extremity motor function post-stroke. We hypothesized that power training, as a component of upper-extremity rehabilitation, would promote greater functional gains than functional task practice without deleterious consequences. Method Nineteen chronic hemiparetic individuals were studied using a crossover design. All participants received both functional task practice (FTP) and HYBRID (combined FTP and power training) in random order. Blinded evaluations performed at baseline, following each intervention block and 6-months post-intervention included: Wolf Motor Function Test (WMFT-FAS, Primary Outcome), upper-extremity Fugl-Meyer Motor Assessment, Ashworth Scale, and Functional Independence Measure. Neuromechanical function was evaluated using isometric and dynamic joint torques and concurrent agonist EMG. Biceps stretch reflex responses were evaluated using passive elbow stretches ranging from 60 to 180º/s and determining: EMG onset position threshold, burst duration, burst intensity and passive torque at each speed. Results Primary outcome: Improvements in WMFT-FAS were significantly greater following HYBRID vs. FTP (p = .049), regardless of treatment order. These functional improvements were retained 6-months post-intervention (p = .03). Secondary outcomes: A greater proportion of participants achieved minimally important differences (MID) following HYBRID vs. FTP (p = .03). MIDs were retained 6-months post-intervention. Ashworth scores were unchanged (p > .05). Increased maximal isometric joint torque, agonist EMG and peak power were significantly greater following HYBRID vs. FTP (p < .05) and effects were retained 6-months post-intervention (p’s < .05). EMG position threshold and burst duration were significantly reduced at fast speeds (≥120º/s) (p’s < 0.05) and passive torque was reduced post-washout (p < .05) following HYBRID. Conclusions Functional and neuromechanical gains were greater following HYBRID vs. FPT. Improved stretch reflex modulation and increased neuromuscular activation indicate potent neural adaptations. Importantly, no deleterious consequences, including exacerbation of spasticity or musculoskeletal complaints, were associated with HYBRID. These results contribute to an evolving body of contemporary evidence regarding the efficacy of high-intensity training in neurorehabilitation and the physiological mechanisms that mediate neural recovery. PMID:23336711

Concurrent neuromechanical and functional gains following upper-extremity power training post-stroke.

PubMed

Patten, Carolynn; Condliffe, Elizabeth G; Dairaghi, Christine A; Lum, Peter S

2013-01-21

Repetitive task practice is argued to drive neural plasticity following stroke. However, current evidence reveals that hemiparetic weakness impairs the capacity to perform, and practice, movements appropriately. Here we investigated how power training (i.e., high-intensity, dynamic resistance training) affects recovery of upper-extremity motor function post-stroke. We hypothesized that power training, as a component of upper-extremity rehabilitation, would promote greater functional gains than functional task practice without deleterious consequences. Nineteen chronic hemiparetic individuals were studied using a crossover design. All participants received both functional task practice (FTP) and HYBRID (combined FTP and power training) in random order. Blinded evaluations performed at baseline, following each intervention block and 6-months post-intervention included: Wolf Motor Function Test (WMFT-FAS, Primary Outcome), upper-extremity Fugl-Meyer Motor Assessment, Ashworth Scale, and Functional Independence Measure. Neuromechanical function was evaluated using isometric and dynamic joint torques and concurrent agonist EMG. Biceps stretch reflex responses were evaluated using passive elbow stretches ranging from 60 to 180º/s and determining: EMG onset position threshold, burst duration, burst intensity and passive torque at each speed. Improvements in WMFT-FAS were significantly greater following HYBRID vs. FTP (p = .049), regardless of treatment order. These functional improvements were retained 6-months post-intervention (p = .03). A greater proportion of participants achieved minimally important differences (MID) following HYBRID vs. FTP (p = .03). MIDs were retained 6-months post-intervention. Ashworth scores were unchanged (p > .05). Increased maximal isometric joint torque, agonist EMG and peak power were significantly greater following HYBRID vs. FTP (p < .05) and effects were retained 6-months post-intervention (p's < .05). EMG position threshold and burst duration were significantly reduced at fast speeds (≥120º/s) (p's < 0.05) and passive torque was reduced post-washout (p < .05) following HYBRID. Functional and neuromechanical gains were greater following HYBRID vs. FPT. Improved stretch reflex modulation and increased neuromuscular activation indicate potent neural adaptations. Importantly, no deleterious consequences, including exacerbation of spasticity or musculoskeletal complaints, were associated with HYBRID. These results contribute to an evolving body of contemporary evidence regarding the efficacy of high-intensity training in neurorehabilitation and the physiological mechanisms that mediate neural recovery.

Co-ordinated structural and functional covariance in the adolescent brain underlies face processing performance.

PubMed

Shaw, Daniel Joel; Mareček, Radek; Grosbras, Marie-Helene; Leonard, Gabriel; Pike, G Bruce; Paus, Tomáš

2016-04-01

Our ability to process complex social cues presented by faces improves during adolescence. Using multivariate analyses of neuroimaging data collected longitudinally from a sample of 38 adolescents (17 males) when they were 10, 11.5, 13 and 15 years old, we tested the possibility that there exists parallel variations in the structural and functional development of neural systems supporting face processing. By combining measures of task-related functional connectivity and brain morphology, we reveal that both the structural covariance and functional connectivity among 'distal' nodes of the face-processing network engaged by ambiguous faces increase during this age range. Furthermore, we show that the trajectory of increasing functional connectivity between the distal nodes occurs in tandem with the development of their structural covariance. This demonstrates a tight coupling between functional and structural maturation within the face-processing network. Finally, we demonstrate that increased functional connectivity is associated with age-related improvements of face-processing performance, particularly in females. We suggest that our findings reflect greater integration among distal elements of the neural systems supporting the processing of facial expressions. This, in turn, might facilitate an enhanced extraction of social information from faces during a time when greater importance is placed on social interactions. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Real time algorithms for sharp wave ripple detection.

PubMed

Sethi, Ankit; Kemere, Caleb

2014-01-01

Neural activity during sharp wave ripples (SWR), short bursts of co-ordinated oscillatory activity in the CA1 region of the rodent hippocampus, is implicated in a variety of memory functions from consolidation to recall. Detection of these events in an algorithmic framework, has thus far relied on simple thresholding techniques with heuristically derived parameters. This study is an investigation into testing and improving the current methods for detection of SWR events in neural recordings. We propose and profile methods to reduce latency in ripple detection. Proposed algorithms are tested on simulated ripple data. The findings show that simple realtime algorithms can improve upon existing power thresholding methods and can detect ripple activity with latencies in the range of 10-20 ms.

[Measurement and performance analysis of functional neural network].

PubMed

Li, Shan; Liu, Xinyu; Chen, Yan; Wan, Hong

2018-04-01

The measurement of network is one of the important researches in resolving neuronal population information processing mechanism using complex network theory. For the quantitative measurement problem of functional neural network, the relation between the measure indexes, i.e. the clustering coefficient, the global efficiency, the characteristic path length and the transitivity, and the network topology was analyzed. Then, the spike-based functional neural network was established and the simulation results showed that the measured network could represent the original neural connections among neurons. On the basis of the former work, the coding of functional neural network in nidopallium caudolaterale (NCL) about pigeon's motion behaviors was studied. We found that the NCL functional neural network effectively encoded the motion behaviors of the pigeon, and there were significant differences in four indexes among the left-turning, the forward and the right-turning. Overall, the establishment method of spike-based functional neural network is available and it is an effective tool to parse the brain information processing mechanism.

Neuro-fuzzy decoding of sensory information from ensembles of simultaneously recorded dorsal root ganglion neurons for functional electrical stimulation applications

NASA Astrophysics Data System (ADS)

Rigosa, J.; Weber, D. J.; Prochazka, A.; Stein, R. B.; Micera, S.

2011-08-01

Functional electrical stimulation (FES) is used to improve motor function after injury to the central nervous system. Some FES systems use artificial sensors to switch between finite control states. To optimize FES control of the complex behavior of the musculo-skeletal system in activities of daily life, it is highly desirable to implement feedback control. In theory, sensory neural signals could provide the required control signals. Recent studies have demonstrated the feasibility of deriving limb-state estimates from the firing rates of primary afferent neurons recorded in dorsal root ganglia (DRG). These studies used multiple linear regression (MLR) methods to generate estimates of limb position and velocity based on a weighted sum of firing rates in an ensemble of simultaneously recorded DRG neurons. The aim of this study was to test whether the use of a neuro-fuzzy (NF) algorithm (the generalized dynamic fuzzy neural networks (GD-FNN)) could improve the performance, robustness and ability to generalize from training to test sets compared to the MLR technique. NF and MLR decoding methods were applied to ensemble DRG recordings obtained during passive and active limb movements in anesthetized and freely moving cats. The GD-FNN model provided more accurate estimates of limb state and generalized better to novel movement patterns. Future efforts will focus on implementing these neural recording and decoding methods in real time to provide closed-loop control of FES using the information extracted from sensory neurons.

Effects of Fluoxetine on Neural Functional Prognosis after Ischemic Stroke: A Randomized Controlled Study in China.

PubMed

He, Yi-Tao; Tang, Bing-Shan; Cai, Zhi-Li; Zeng, Si-Ling; Jiang, Xin; Guo, Yi

2016-04-01

We investigated the effects of fluoxetine on the short-term and long-term neural functional prognoses after ischemic stroke. In this prospective randomized controlled single-blind clinical study in China, eligible patients afflicted with ischemic stroke were randomized into control and treatment groups. Patients in the treatment group received fluoxetine in addition to the basic therapies in the control group over a period of 90 days. The follow-up period was 180 days. We evaluated the effects of fluoxetine on the National Institutes of Health Stroke Scale (NIHSS) score and Barthel Index (BI) score after ischemic stroke through single- and multiple-factor analysis. The mean NIHSS score on day 180 after treatment was significantly lower in the treatment group than in the control group (P = .009). The mean BI scores on days 90 and 180 were significantly higher in the treatment group (P = .026) than in the control group (P = .011). The improvements in the NIHSS and BI scores on days 90 and 180 compared with baseline in the treatment group were all significantly greater than that in the control group (P = .033, P = .013, P = .013, P = .019, respectively). Treatment with fluoxetine was an independent factor affecting the NIHSS and BI scores on day 180 after treatment. Treatment with fluoxetine for 90 days after ischemic stroke can improve the long-term neural functional outcomes. Copyright © 2016 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Insect Responses to Linearly Polarized Reflections: Orphan Behaviors Without Neural Circuits.

PubMed

Heinloth, Tanja; Uhlhorn, Juliane; Wernet, Mathias F

2018-01-01

The e-vector orientation of linearly polarized light represents an important visual stimulus for many insects. Especially the detection of polarized skylight by many navigating insect species is known to improve their orientation skills. While great progress has been made towards describing both the anatomy and function of neural circuit elements mediating behaviors related to navigation, relatively little is known about how insects perceive non-celestial polarized light stimuli, like reflections off water, leaves, or shiny body surfaces. Work on different species suggests that these behaviors are not mediated by the "Dorsal Rim Area" (DRA), a specialized region in the dorsal periphery of the adult compound eye, where ommatidia contain highly polarization-sensitive photoreceptor cells whose receptive fields point towards the sky. So far, only few cases of polarization-sensitive photoreceptors have been described in the ventral periphery of the insect retina. Furthermore, both the structure and function of those neural circuits connecting to these photoreceptor inputs remain largely uncharacterized. Here we review the known data on non-celestial polarization vision from different insect species (dragonflies, butterflies, beetles, bugs and flies) and present three well-characterized examples for functionally specialized non-DRA detectors from different insects that seem perfectly suited for mediating such behaviors. Finally, using recent advances from circuit dissection in Drosophila melanogaster , we discuss what types of potential candidate neurons could be involved in forming the underlying neural circuitry mediating non-celestial polarization vision.

Chinese Herbal Medicine Xueshuantong Enhances Cerebral Blood Flow and Improves Neural Functions in Alzheimer's Disease Mice.

PubMed

Huang, Yangmei; Guo, Baihong; Shi, Bihua; Gao, Qingtao; Zhou, Qiang

2018-01-01

Reduced cerebral blood flow in Alzheimer's disease (AD) may occur in early AD, which contributes to the pathogenesis and/or pathological progression of AD. Reversing this deficit may have therapeutic potential. Certain traditional Chinese herbal medicines (e.g., Saponin and its major component Xueshuantong [XST]) increase blood flow in humans, but whether they could be effective in treating AD patients has not been tested. We found that systemic XST injection elevated cerebral blood flow in APP/PS1 transgenic mice using two-photon time-lapse imaging in the same microvessels before and after injection. Subchronic XST treatment led to improved spatial learning and memory and motor performance in the APP/PS1 mice, suggesting improved neural plasticity and functions. Two-photon time lapse imaging of the same plaques revealed a reduction in plaque size after XST treatment. In addition, western blots experiments showed that XST treatment led to reduced processing of amyloid-β protein precursor (AβPP) and enhanced clearance of amyloid-β (Aβ) without altering the total level of AβPP. We also found increased synapse density in the immediate vicinity of amyloid plaques, suggesting enhanced synaptic function. We conclude that targeting cerebral blood flow can be an effective strategy in treating AD.

Neural mechanisms of attentional control in mindfulness meditation

PubMed Central

Malinowski, Peter

2013-01-01

The scientific interest in meditation and mindfulness practice has recently seen an unprecedented surge. After an initial phase of presenting beneficial effects of mindfulness practice in various domains, research is now seeking to unravel the underlying psychological and neurophysiological mechanisms. Advances in understanding these processes are required for improving and fine-tuning mindfulness-based interventions that target specific conditions such as eating disorders or attention deficit hyperactivity disorders. This review presents a theoretical framework that emphasizes the central role of attentional control mechanisms in the development of mindfulness skills. It discusses the phenomenological level of experience during meditation, the different attentional functions that are involved, and relates these to the brain networks that subserve these functions. On the basis of currently available empirical evidence specific processes as to how attention exerts its positive influence are considered and it is concluded that meditation practice appears to positively impact attentional functions by improving resource allocation processes. As a result, attentional resources are allocated more fully during early processing phases which subsequently enhance further processing. Neural changes resulting from a pure form of mindfulness practice that is central to most mindfulness programs are considered from the perspective that they constitute a useful reference point for future research. Furthermore, possible interrelations between the improvement of attentional control and emotion regulation skills are discussed. PMID:23382709

Neural mechanisms of attentional control in mindfulness meditation.

PubMed

Malinowski, Peter

2013-01-01

The scientific interest in meditation and mindfulness practice has recently seen an unprecedented surge. After an initial phase of presenting beneficial effects of mindfulness practice in various domains, research is now seeking to unravel the underlying psychological and neurophysiological mechanisms. Advances in understanding these processes are required for improving and fine-tuning mindfulness-based interventions that target specific conditions such as eating disorders or attention deficit hyperactivity disorders. This review presents a theoretical framework that emphasizes the central role of attentional control mechanisms in the development of mindfulness skills. It discusses the phenomenological level of experience during meditation, the different attentional functions that are involved, and relates these to the brain networks that subserve these functions. On the basis of currently available empirical evidence specific processes as to how attention exerts its positive influence are considered and it is concluded that meditation practice appears to positively impact attentional functions by improving resource allocation processes. As a result, attentional resources are allocated more fully during early processing phases which subsequently enhance further processing. Neural changes resulting from a pure form of mindfulness practice that is central to most mindfulness programs are considered from the perspective that they constitute a useful reference point for future research. Furthermore, possible interrelations between the improvement of attentional control and emotion regulation skills are discussed.

Epidural anaesthesia and analgesia - effects on surgical stress responses and implications for postoperative nutrition.

PubMed

Holte, K; Kehlet, H

2002-06-01

Surgical injury leads to an endocrine-metabolic and inflammatory response with protein catabolism, increased cardiovascular demands, impaired pulmonary function and paralytic ileus, the most important release mechanisms being afferent neural stimuli and inflammatory mediators. Epidural local anaesthetic blockade of afferent stimuli reduces endocrine metabolic responses, and improve postoperative catabolism. Furthermore, dynamic pain relief is achieved with improved pulmonary function and a pronounced reduction of postoperative ileus, thereby providing optimal conditions for improved mobilization and oral nutrition, and preservation of body composition and muscle function. Studies integrating continuous epidural local anaesthetics with enforced early nutrition and mobilization uniformly suggest an improved recovery, decreased hospital stay and convalescence. Epidural local anaesthetics should be included in a multi-modal rehabilitation programme after major surgical procedures in order to facilitate oral nutrition, improve recovery and reduce morbidity.

Prentice Award Lecture 2011: Removing the Brakes on Plasticity in the Amblyopic Brain

PubMed Central

Levi, Dennis M.

2012-01-01

Experience-dependent plasticity is closely linked with the development of sensory function. Beyond this sensitive period, developmental plasticity is actively limited; however, new studies provide growing evidence for plasticity in the adult visual system. The amblyopic visual system is an excellent model for examining the “brakes” that limit recovery of function beyond the critical period. While amblyopia can often be reversed when treated early, conventional treatment is generally not undertaken in older children and adults. However new clinical and experimental studies in both animals and humans provide evidence for neural plasticity beyond the critical period. The results suggest that perceptual learning and video game play may be effective in improving a range of visual performance measures and importantly the improvements may transfer to better visual acuity and stereopsis. These findings, along with the results of new clinical trials, suggest that it might be time to re-consider our notions about neural plasticity in amblyopia. PMID:22581119

The involvement of audio-motor coupling in the music-supported therapy applied to stroke patients.

PubMed

Rodriguez-Fornells, Antoni; Rojo, Nuria; Amengual, Julià L; Ripollés, Pablo; Altenmüller, Eckart; Münte, Thomas F

2012-04-01

Music-supported therapy (MST) has been developed recently to improve the use of the affected upper extremity after stroke. MST uses musical instruments, an electronic piano and an electronic drum set emitting piano sounds, to retrain fine and gross movements of the paretic upper extremity. In this paper, we first describe the rationale underlying MST, and we review the previous studies conducted on acute and chronic stroke patients using this new neurorehabilitation approach. Second, we address the neural mechanisms involved in the motor movement improvements observed in acute and chronic stroke patients. Third, we provide some recent studies on the involvement of auditory-motor coupling in the MST in chronic stroke patients using functional neuroimaging. Finally, these ideas are discussed and focused on understanding the dynamics involved in the neural circuit underlying audio-motor coupling and how functional connectivity could help to explain the neuroplastic changes observed after therapy in stroke patients. © 2012 New York Academy of Sciences.

In-Vivo Characterization of Glassy Carbon Micro-Electrode Arrays for Neural Applications and Histological Analysis of the Brain Tissue

NASA Astrophysics Data System (ADS)

Vomero, Maria

The aim of this work is to fabricate and characterize glassy carbon Microelectrode Arrays (MEAs) for sensing and stimulating neural activity, and conduct histological analysis of the brain tissue after the implant to determine long-term performance. Neural applications often require robust electrical and electrochemical response over a long period of time, and for those applications we propose to replace the commonly used noble metals like platinum, gold and iridium with glassy carbon. We submit that such material has the potential to improve the performances of traditional neural prostheses, thanks to better charge transfer capabilities and higher electrochemical stability. Great interest and attention is given in this work, in particular, to the investigation of tissue response after several weeks of implants in rodents' brain motor cortex and the associated materials degradation. As part of this work, a new set of devices for Electrocorticography (ECoG) has been designed and fabricated to improve durability and quality of the previous generation of devices, designed and manufactured by the same research group in 2014. In-vivo long-term impedance measurements and brain activity recordings were performed to test the functionality of the neural devices. In-vitro electrical characterization of the carbon electrodes, as well as the study of the adhesion mechanisms between glassy carbon and different substrates is also part of the research described in this book.

Interfacing to the brain’s motor decisions

PubMed Central

2017-01-01

It has been long known that neural activity, recorded with electrophysiological methods, contains rich information about a subject’s motor intentions, sensory experiences, allocation of attention, action planning, and even abstract thoughts. All these functions have been the subject of neurophysiological investigations, with the goal of understanding how neuronal activity represents behavioral parameters, sensory inputs, and cognitive functions. The field of brain-machine interfaces (BMIs) strives for a somewhat different goal: it endeavors to extract information from neural modulations to create a communication link between the brain and external devices. Although many remarkable successes have been already achieved in the BMI field, questions remain regarding the possibility of decoding high-order neural representations, such as decision making. Could BMIs be employed to decode the neural representations of decisions underlying goal-directed actions? In this review we lay out a framework that describes the computations underlying goal-directed actions as a multistep process performed by multiple cortical and subcortical areas. We then discuss how BMIs could connect to different decision-making steps and decode the neural processing ongoing before movements are initiated. Such decision-making BMIs could operate as a system with prediction that offers many advantages, such as shorter reaction time, better error processing, and improved unsupervised learning. To present the current state of the art, we review several recent BMIs incorporating decision-making components. PMID:28003406

Rapid generation of OPC-like cells from human pluripotent stem cells for treating spinal cord injury.

PubMed

Kim, Dae-Sung; Jung, Se Jung; Lee, Jae Souk; Lim, Bo Young; Kim, Hyun Ah; Yoo, Jeong-Eun; Kim, Dong-Wook; Leem, Joong Woo

2017-07-28

Remyelination via the transplantation of oligodendrocyte precursor cells (OPCs) has been considered as a strategy to improve the locomotor deficits caused by traumatic spinal cord injury (SCI). To date, enormous efforts have been made to derive OPCs from human pluripotent stem cells (hPSCs), and significant progress in the transplantation of such cells in SCI animal models has been reported. The current methods generally require a long period of time (>2 months) to obtain transplantable OPCs, which hampers their clinical utility for patients with SCI. Here we demonstrate a rapid and efficient method to differentiate hPSCs into neural progenitors that retain the features of OPCs (referred to as OPC-like cells). We used cell sorting to select A2B5-positive cells from hPSC-derived neural rosettes and cultured the selected cells in the presence of signaling cues, including sonic hedgehog, PDGF and insulin-like growth factor-1. This method robustly generated neural cells positive for platelet-derived growth factor receptor-α (PDGFRα) and NG2 (~90%) after 4 weeks of differentiation. Behavioral tests revealed that the transplantation of the OPC-like cells into the spinal cords of rats with contusive SCI at the thoracic level significantly improved hindlimb locomotor function. Electrophysiological assessment revealed enhanced neural conduction through the injury site. Histological examination showed increased numbers of axon with myelination at the injury site and graft-derived myelin formation with no evidence of tumor formation. Our method provides a cell source from hPSCs that has the potential to recover motor function following SCI.

A pilot study investigating changes in neural processing after mindfulness training in elite athletes.

PubMed

Haase, Lori; May, April C; Falahpour, Maryam; Isakovic, Sara; Simmons, Alan N; Hickman, Steven D; Liu, Thomas T; Paulus, Martin P

2015-01-01

The ability to pay close attention to the present moment can be a crucial factor for performing well in a competitive situation. Training mindfulness is one approach to potentially improve elite athletes' ability to focus their attention on the present moment. However, virtually nothing is known about whether these types of interventions alter neural systems that are important for optimal performance. This pilot study examined whether an intervention aimed at improving mindfulness [Mindful Performance Enhancement, Awareness and Knowledge (mPEAK)] changes neural activation patterns during an interoceptive challenge. Participants completed a task involving anticipation and experience of loaded breathing during functional magnetic resonance imaging recording. There were five main results following mPEAK training: (1) elite athletes self-reported higher levels of interoceptive awareness and mindfulness and lower levels of alexithymia; (2) greater insula and anterior cingulate cortex (ACC) activation during anticipation and post-breathing load conditions; (3) increased ACC activation during the anticipation condition was associated with increased scores on the describing subscale of the Five Facet Mindfulness Questionnaire; (4) increased insula activation during the post-load condition was associated with decreases in the Toronto Alexithymia Scale identifying feelings subscale; (5) decreased resting state functional connectivity between the PCC and the right medial frontal cortex and the ACC. Taken together, this pilot study suggests that mPEAK training may lead to increased attention to bodily signals and greater neural processing during the anticipation and recovery from interoceptive perturbations. This association between attention to and processing of interoceptive afferents may result in greater adaptation during stressful situations in elite athletes.

Rapid generation of OPC-like cells from human pluripotent stem cells for treating spinal cord injury

PubMed Central

Kim, Dae-Sung; Jung, Se Jung; Lee, Jae Souk; Lim, Bo Young; Kim, Hyun Ah; Yoo, Jeong-Eun; Kim, Dong-Wook; Leem, Joong Woo

2017-01-01

Remyelination via the transplantation of oligodendrocyte precursor cells (OPCs) has been considered as a strategy to improve the locomotor deficits caused by traumatic spinal cord injury (SCI). To date, enormous efforts have been made to derive OPCs from human pluripotent stem cells (hPSCs), and significant progress in the transplantation of such cells in SCI animal models has been reported. The current methods generally require a long period of time (>2 months) to obtain transplantable OPCs, which hampers their clinical utility for patients with SCI. Here we demonstrate a rapid and efficient method to differentiate hPSCs into neural progenitors that retain the features of OPCs (referred to as OPC-like cells). We used cell sorting to select A2B5-positive cells from hPSC-derived neural rosettes and cultured the selected cells in the presence of signaling cues, including sonic hedgehog, PDGF and insulin-like growth factor-1. This method robustly generated neural cells positive for platelet-derived growth factor receptor-α (PDGFRα) and NG2 (~90%) after 4 weeks of differentiation. Behavioral tests revealed that the transplantation of the OPC-like cells into the spinal cords of rats with contusive SCI at the thoracic level significantly improved hindlimb locomotor function. Electrophysiological assessment revealed enhanced neural conduction through the injury site. Histological examination showed increased numbers of axon with myelination at the injury site and graft-derived myelin formation with no evidence of tumor formation. Our method provides a cell source from hPSCs that has the potential to recover motor function following SCI. PMID:28751784

Neural correlates of working memory training in HIV patients: study protocol for a randomized controlled trial.

PubMed

Chang, L; Løhaugen, G C; Douet, V; Miller, E N; Skranes, J; Ernst, T

2016-02-02

Potent combined antiretroviral therapy decreased the incidence and severity of HIV-associated neurocognitive disorders (HAND); however, no specific effective pharmacotherapy exists for HAND. Patients with HIV commonly have deficits in working memory and attention, which may negatively impact many other cognitive domains, leading to HAND. Since HAND may lead to loss of independence in activities of daily living and negative emotional well-being, and incur a high economic burden, effective treatments for HAND are urgently needed. This study aims to determine whether adaptive working memory training might improve cognitive functions and neural network efficiency and possibly decrease neuroinflammation. This study also aims to assess whether subjects with the LMX1A-rs4657412 TT(AA) genotype show greater training effects from working memory training than TC(AG) or CC(GG)-carriers. 60 HIV-infected and 60 seronegative control participants will be randomized to a double-blind active-controlled study, using adaptive versus non-adaptive Cogmed Working Memory Training® (CWMT), 20-25 sessions over 5-8 weeks. Each subject will be assessed with near- and far-transfer cognitive tasks, self-reported mood and executive function questionnaires, and blood-oxygenation level-dependent functional MRI during working memory (n-back) and visual attention (ball tracking) tasks, at baseline, 1-month, and 6-months after CWMT. Furthermore, genotyping for LMX1A-rs4657412 will be performed to identify whether subjects with the TT(AA)-genotype show greater gain or neural efficiency after CWMT than those with other genotypes. Lastly, cerebrospinal fluid will be obtained before and after CWMT to explore changes in levels of inflammatory proteins (cytokines and chemokines) and monoamines. Improving working memory in HIV patients, using CWMT, might slow the progression or delay the onset of HAND. Observation of decreased brain activation or normalized neural networks, using fMRI, after CWMT would lead to a better understanding of how neural networks are modulated by CWMT. Moreover, validating the greater training gain in subjects with the LMX1A-TT(AA) genotype could lead to a personalized approach for future working memory training studies. Demonstrating and understanding the neural correlates of the efficacy of CWMT in HIV patients could lead to a safe adjunctive therapy for HAND, and possibly other brain disorders. ClinicalTrial.gov, NCT02602418.

Cognitive deficits caused by prefrontal cortical and hippocampal neural disinhibition.

PubMed

Bast, Tobias; Pezze, Marie; McGarrity, Stephanie

2017-10-01

We review recent evidence concerning the significance of inhibitory GABA transmission and of neural disinhibition, that is, deficient GABA transmission, within the prefrontal cortex and the hippocampus, for clinically relevant cognitive functions. Both regions support important cognitive functions, including attention and memory, and their dysfunction has been implicated in cognitive deficits characterizing neuropsychiatric disorders. GABAergic inhibition shapes cortico-hippocampal neural activity, and, recently, prefrontal and hippocampal neural disinhibition has emerged as a pathophysiological feature of major neuropsychiatric disorders, especially schizophrenia and age-related cognitive decline. Regional neural disinhibition, disrupting spatio-temporal control of neural activity and causing aberrant drive of projections, may disrupt processing within the disinhibited region and efferent regions. Recent studies in rats showed that prefrontal and hippocampal neural disinhibition (by local GABA antagonist microinfusion) dysregulates burst firing, which has been associated with important aspects of neural information processing. Using translational tests of clinically relevant cognitive functions, these studies showed that prefrontal and hippocampal neural disinhibition disrupts regional cognitive functions (including prefrontal attention and hippocampal memory function). Moreover, hippocampal neural disinhibition disrupted attentional performance, which does not require the hippocampus but requires prefrontal-striatal circuits modulated by the hippocampus. However, some prefrontal and hippocampal functions (including inhibitory response control) are spared by regional disinhibition. We consider conceptual implications of these findings, regarding the distinct relationships of distinct cognitive functions to prefrontal and hippocampal GABA tone and neural activity. Moreover, the findings support the proposition that prefrontal and hippocampal neural disinhibition contributes to clinically relevant cognitive deficits, and we consider pharmacological strategies for ameliorating cognitive deficits by rebalancing disinhibition-induced aberrant neural activity. Linked Articles This article is part of a themed section on Pharmacology of Cognition: a Panacea for Neuropsychiatric Disease? To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v174.19/issuetoc. © 2017 The British Pharmacological Society.

Mechanisms of Long Non-Coding RNAs in the Assembly and Plasticity of Neural Circuitry.

PubMed

Wang, Andi; Wang, Junbao; Liu, Ying; Zhou, Yan

2017-01-01

The mechanisms underlying development processes and functional dynamics of neural circuits are far from understood. Long non-coding RNAs (lncRNAs) have emerged as essential players in defining identities of neural cells, and in modulating neural activities. In this review, we summarized latest advances concerning roles and mechanisms of lncRNAs in assembly, maintenance and plasticity of neural circuitry, as well as lncRNAs' implications in neurological disorders. We also discussed technical advances and challenges in studying functions and mechanisms of lncRNAs in neural circuitry. Finally, we proposed that lncRNA studies would advance our understanding on how neural circuits develop and function in physiology and disease conditions.

Accelerating Learning By Neural Networks

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad; Barhen, Jacob

1992-01-01

Electronic neural networks made to learn faster by use of terminal teacher forcing. Method of supervised learning involves addition of teacher forcing functions to excitations fed as inputs to output neurons. Initially, teacher forcing functions are strong enough to force outputs to desired values; subsequently, these functions decay with time. When learning successfully completed, terminal teacher forcing vanishes, and dynamics or neural network become equivalent to those of conventional neural network. Simulated neural network with terminal teacher forcing learned to produce close approximation of circular trajectory in 400 iterations.

Neural Markers and Rehabilitation of Executive Functioning in Veterans with TBI and PTSD

DTIC Science & Technology

2015-10-01

with reduced irritability/impulsivity and improved social/occupational functioning. Functional magnetic resonance imaging ( fMRI ) will be used to...transport Veterans five minutes away to the Dr. Belger’s lab at UNC Hospital where EEG and fMRI will be conducted and then transport Veterans back to... fMRI was arranged through the UNC-Chapel Hill School of Medicine at month 9. Participants may either take a shuttle from the research laboratory to

Long-Term Experience of Chinese Calligraphic Handwriting Is Associated with Better Executive Functions and Stronger Resting-State Functional Connectivity in Related Brain Regions

PubMed Central

He, Yong; Gao, Yang; Zhang, Cuiping; Chen, Chuansheng; Bi, Suyu; Yang, Pin; Wang, Yiwen; Wang, Wenjing

2017-01-01

Chinese calligraphic handwriting (CCH) is a traditional art form that requires high levels of concentration and motor control. Previous research has linked short-term training in CCH to improvements in attention and memory. Little is known about the potential impacts of long-term CCH practice on a broader array of executive functions and their potential neural substrates. In this cross-sectional study, we recruited 36 practitioners with at least 5 years of CCH experience and 50 control subjects with no more than one month of CCH practice and investigated their differences in the three components of executive functions (i.e., shifting, updating, and inhibition). Valid resting-state fMRI data were collected from 31 CCH and 40 control participants. Compared with the controls, CCH individuals showed better updating (as measured by the Corsi Block Test) and inhibition (as measured by the Stroop Word-Color Test), but the two groups did not differ in shifting (as measured by a cue-target task). The CCH group showed stronger resting-state functional connectivity (RSFC) than the control group in brain areas involved in updating and inhibition. These results suggested that long-term CCH training may be associated with improvements in specific aspects of executive functions and strengthened neural networks in related brain regions. PMID:28129407

Long-Term Experience of Chinese Calligraphic Handwriting Is Associated with Better Executive Functions and Stronger Resting-State Functional Connectivity in Related Brain Regions.

PubMed

Chen, Wen; He, Yong; Gao, Yang; Zhang, Cuiping; Chen, Chuansheng; Bi, Suyu; Yang, Pin; Wang, Yiwen; Wang, Wenjing

2017-01-01

Chinese calligraphic handwriting (CCH) is a traditional art form that requires high levels of concentration and motor control. Previous research has linked short-term training in CCH to improvements in attention and memory. Little is known about the potential impacts of long-term CCH practice on a broader array of executive functions and their potential neural substrates. In this cross-sectional study, we recruited 36 practitioners with at least 5 years of CCH experience and 50 control subjects with no more than one month of CCH practice and investigated their differences in the three components of executive functions (i.e., shifting, updating, and inhibition). Valid resting-state fMRI data were collected from 31 CCH and 40 control participants. Compared with the controls, CCH individuals showed better updating (as measured by the Corsi Block Test) and inhibition (as measured by the Stroop Word-Color Test), but the two groups did not differ in shifting (as measured by a cue-target task). The CCH group showed stronger resting-state functional connectivity (RSFC) than the control group in brain areas involved in updating and inhibition. These results suggested that long-term CCH training may be associated with improvements in specific aspects of executive functions and strengthened neural networks in related brain regions.

Three-Dimensional-Bioprinted Dopamine-Based Matrix for Promoting Neural Regeneration.

PubMed

Zhou, Xuan; Cui, Haitao; Nowicki, Margaret; Miao, Shida; Lee, Se-Jun; Masood, Fahed; Harris, Brent T; Zhang, Lijie Grace

2018-03-14

Central nerve repair and regeneration remain challenging problems worldwide, largely because of the extremely weak inherent regenerative capacity and accompanying fibrosis of native nerves. Inadequate solutions to the unmet needs for clinical therapeutics encourage the development of novel strategies to promote nerve regeneration. Recently, 3D bioprinting techniques, as one of a set of valuable tissue engineering technologies, have shown great promise toward fabricating complex and customizable artificial tissue scaffolds. Gelatin methacrylate (GelMA) possesses excellent biocompatible and biodegradable properties because it contains many arginine-glycine-aspartic acids (RGD) and matrix metalloproteinase sequences. Dopamine (DA), as an essential neurotransmitter, has proven effective in regulating neuronal development and enhancing neurite outgrowth. In this study, GelMA-DA neural scaffolds with hierarchical structures were 3D-fabricated using our custom-designed stereolithography-based printer. DA was functionalized on GelMA to synthesize a biocompatible printable ink (GelMA-DA) for improving neural differentiation. Additionally, neural stem cells (NSCs) were employed as the primary cell source for these scaffolds because of their ability to terminally differentiate into a variety of cell types including neurons, astrocytes, and oligodendrocytes. The resultant GelMA-DA scaffolds exhibited a highly porous and interconnected 3D environment, which is favorable for supporting NSC growth. Confocal microscopy analysis of neural differentiation demonstrated that a distinct neural network was formed on the GelMA-DA scaffolds. In particular, the most significant improvements were the enhanced neuron gene expression of TUJ1 and MAP2. Overall, our results demonstrated that 3D-printed customizable GelMA-DA scaffolds have a positive role in promoting neural differentiation, which is promising for advancing nerve repair and regeneration in the future.

A new BP Fourier algorithm and its application in English teaching evaluation

NASA Astrophysics Data System (ADS)

Pei, Xuehui; Pei, Guixin

2017-08-01

BP neural network algorithm has wide adaptability and accuracy when used in complicated system evaluation, but its calculation defects such as slow convergence have limited its practical application. The paper tries to speed up the calculation convergence of BP neural network algorithm with Fourier basis functions and presents a new BP Fourier algorithm for complicated system evaluation. First, shortages and working principle of BP algorithm are analyzed for subsequent targeted improvement; Second, the presented BP Fourier algorithm adopts Fourier basis functions to simplify calculation structure, designs new calculation transfer function between input and output layers, and conducts theoretical analysis to prove the efficiency of the presented algorithm; Finally, the presented algorithm is used in evaluating university English teaching and the application results shows that the presented BP Fourier algorithm has better performance in calculation efficiency and evaluation accuracy and can be used in evaluating complicated system practically.

From disability to ability: comprehensive rehabilitation providing a holistic functional improvement in a child with neglected neural tube defect.

PubMed

Mishra, Kriti; Siddharth, V

2017-09-25

Neural Tube defects are one of the most common congenital disorders, presenting in a paediatric rehabilitation set-up. With its wide spectrum of clinical presentation and possible complications, the condition can significantly impact an individual's functional capacity and quality of life. The condition also affects the family of the child leaving them with a lifelong impairment to cope up with. Through this 16-year-old child, we shed light on the effects of providing rehabilitation, even at a later stage and its benefits. We also get a glimpse of difficulties in availing rehabilitation services in developing countries and the need to reach out many more neglected children like him with good functional abilities. © BMJ Publishing Group Ltd (unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Combining Upper Limb Robotic Rehabilitation with Other Therapeutic Approaches after Stroke: Current Status, Rationale, and Challenges

PubMed Central

Grosmaire, Anne Gaëlle; Battini, Elena

2017-01-01

A better understanding of the neural substrates that underlie motor recovery after stroke has led to the development of innovative rehabilitation strategies and tools that incorporate key elements of motor skill relearning, that is, intensive motor training involving goal-oriented repeated movements. Robotic devices for the upper limb are increasingly used in rehabilitation. Studies have demonstrated the effectiveness of these devices in reducing motor impairments, but less so for the improvement of upper limb function. Other studies have begun to investigate the benefits of combined approaches that target muscle function (functional electrical stimulation and botulinum toxin injections), modulate neural activity (noninvasive brain stimulation), and enhance motivation (virtual reality) in an attempt to potentialize the benefits of robot-mediated training. The aim of this paper is to overview the current status of such combined treatments and to analyze the rationale behind them. PMID:29057269

A synthetic neural cell adhesion molecule mimetic peptide promotes synaptogenesis, enhances presynaptic function, and facilitates memory consolidation.

PubMed

Cambon, Karine; Hansen, Stine M; Venero, Cesar; Herrero, A Isabel; Skibo, Galina; Berezin, Vladimir; Bock, Elisabeth; Sandi, Carmen

2004-04-28

The neural cell adhesion molecule (NCAM) plays a critical role in development and plasticity of the nervous system and is involved in the mechanisms of learning and memory. Here, we show that intracerebroventricular administration of the FG loop (FGL), a synthetic 15 amino acid peptide corresponding to the binding site of NCAM for the fibroblast growth factor receptor 1 (FGFR1), immediately after training rats in fear conditioning or water maze learning, induced a long-lasting improvement of memory. In primary cultures of hippocampal neurons, FGL enhanced the presynaptic function through activation of FGFR1 and promoted synapse formation. These results provide the first evidence for a memory-facilitating effect resulting from a treatment that mimics NCAM function. They suggest that increased efficacy of synaptic transmission and formation of new synapses probably mediate the cognition-enhancing properties displayed by the peptide.

Neural Basis of Reinforcement Learning and Decision Making

PubMed Central

Lee, Daeyeol; Seo, Hyojung; Jung, Min Whan

2012-01-01

Reinforcement learning is an adaptive process in which an animal utilizes its previous experience to improve the outcomes of future choices. Computational theories of reinforcement learning play a central role in the newly emerging areas of neuroeconomics and decision neuroscience. In this framework, actions are chosen according to their value functions, which describe how much future reward is expected from each action. Value functions can be adjusted not only through reward and penalty, but also by the animal’s knowledge of its current environment. Studies have revealed that a large proportion of the brain is involved in representing and updating value functions and using them to choose an action. However, how the nature of a behavioral task affects the neural mechanisms of reinforcement learning remains incompletely understood. Future studies should uncover the principles by which different computational elements of reinforcement learning are dynamically coordinated across the entire brain. PMID:22462543

A dynamical systems approach for estimating phase interactions between rhythms of different frequencies from experimental data.

PubMed

Onojima, Takayuki; Goto, Takahiro; Mizuhara, Hiroaki; Aoyagi, Toshio

2018-01-01

Synchronization of neural oscillations as a mechanism of brain function is attracting increasing attention. Neural oscillation is a rhythmic neural activity that can be easily observed by noninvasive electroencephalography (EEG). Neural oscillations show the same frequency and cross-frequency synchronization for various cognitive and perceptual functions. However, it is unclear how this neural synchronization is achieved by a dynamical system. If neural oscillations are weakly coupled oscillators, the dynamics of neural synchronization can be described theoretically using a phase oscillator model. We propose an estimation method to identify the phase oscillator model from real data of cross-frequency synchronized activities. The proposed method can estimate the coupling function governing the properties of synchronization. Furthermore, we examine the reliability of the proposed method using time-series data obtained from numerical simulation and an electronic circuit experiment, and show that our method can estimate the coupling function correctly. Finally, we estimate the coupling function between EEG oscillation and the speech sound envelope, and discuss the validity of these results.

A Decline in Response Variability Improves Neural Signal Detection during Auditory Task Performance.

PubMed

von Trapp, Gardiner; Buran, Bradley N; Sen, Kamal; Semple, Malcolm N; Sanes, Dan H

2016-10-26

The detection of a sensory stimulus arises from a significant change in neural activity, but a sensory neuron's response is rarely identical to successive presentations of the same stimulus. Large trial-to-trial variability would limit the central nervous system's ability to reliably detect a stimulus, presumably affecting perceptual performance. However, if response variability were to decrease while firing rate remained constant, then neural sensitivity could improve. Here, we asked whether engagement in an auditory detection task can modulate response variability, thereby increasing neural sensitivity. We recorded telemetrically from the core auditory cortex of gerbils, both while they engaged in an amplitude-modulation detection task and while they sat quietly listening to the identical stimuli. Using a signal detection theory framework, we found that neural sensitivity was improved during task performance, and this improvement was closely associated with a decrease in response variability. Moreover, units with the greatest change in response variability had absolute neural thresholds most closely aligned with simultaneously measured perceptual thresholds. Our findings suggest that the limitations imposed by response variability diminish during task performance, thereby improving the sensitivity of neural encoding and potentially leading to better perceptual sensitivity. The detection of a sensory stimulus arises from a significant change in neural activity. However, trial-to-trial variability of the neural response may limit perceptual performance. If the neural response to a stimulus is quite variable, then the response on a given trial could be confused with the pattern of neural activity generated when the stimulus is absent. Therefore, a neural mechanism that served to reduce response variability would allow for better stimulus detection. By recording from the cortex of freely moving animals engaged in an auditory detection task, we found that variability of the neural response becomes smaller during task performance, thereby improving neural detection thresholds. Copyright © 2016 the authors 0270-6474/16/3611097-10$15.00/0.

A Decline in Response Variability Improves Neural Signal Detection during Auditory Task Performance

PubMed Central

Buran, Bradley N.; Sen, Kamal; Semple, Malcolm N.; Sanes, Dan H.

2016-01-01

The detection of a sensory stimulus arises from a significant change in neural activity, but a sensory neuron's response is rarely identical to successive presentations of the same stimulus. Large trial-to-trial variability would limit the central nervous system's ability to reliably detect a stimulus, presumably affecting perceptual performance. However, if response variability were to decrease while firing rate remained constant, then neural sensitivity could improve. Here, we asked whether engagement in an auditory detection task can modulate response variability, thereby increasing neural sensitivity. We recorded telemetrically from the core auditory cortex of gerbils, both while they engaged in an amplitude-modulation detection task and while they sat quietly listening to the identical stimuli. Using a signal detection theory framework, we found that neural sensitivity was improved during task performance, and this improvement was closely associated with a decrease in response variability. Moreover, units with the greatest change in response variability had absolute neural thresholds most closely aligned with simultaneously measured perceptual thresholds. Our findings suggest that the limitations imposed by response variability diminish during task performance, thereby improving the sensitivity of neural encoding and potentially leading to better perceptual sensitivity. SIGNIFICANCE STATEMENT The detection of a sensory stimulus arises from a significant change in neural activity. However, trial-to-trial variability of the neural response may limit perceptual performance. If the neural response to a stimulus is quite variable, then the response on a given trial could be confused with the pattern of neural activity generated when the stimulus is absent. Therefore, a neural mechanism that served to reduce response variability would allow for better stimulus detection. By recording from the cortex of freely moving animals engaged in an auditory detection task, we found that variability of the neural response becomes smaller during task performance, thereby improving neural detection thresholds. PMID:27798189

Resting-State Functional Connectivity-Based Biomarkers and Functional MRI-Based Neurofeedback for Psychiatric Disorders: A Challenge for Developing Theranostic Biomarkers.

PubMed

Yamada, Takashi; Hashimoto, Ryu-Ichiro; Yahata, Noriaki; Ichikawa, Naho; Yoshihara, Yujiro; Okamoto, Yasumasa; Kato, Nobumasa; Takahashi, Hidehiko; Kawato, Mitsuo

2017-10-01

Psychiatric research has been hampered by an explanatory gap between psychiatric symptoms and their neural underpinnings, which has resulted in poor treatment outcomes. This situation has prompted us to shift from symptom-based diagnosis to data-driven diagnosis, aiming to redefine psychiatric disorders as disorders of neural circuitry. Promising candidates for data-driven diagnosis include resting-state functional connectivity MRI (rs-fcMRI)-based biomarkers. Although biomarkers have been developed with the aim of diagnosing patients and predicting the efficacy of therapy, the focus has shifted to the identification of biomarkers that represent therapeutic targets, which would allow for more personalized treatment approaches. This type of biomarker (i.e., "theranostic biomarker") is expected to elucidate the disease mechanism of psychiatric conditions and to offer an individualized neural circuit-based therapeutic target based on the neural cause of a condition. To this end, researchers have developed rs-fcMRI-based biomarkers and investigated a causal relationship between potential biomarkers and disease-specific behavior using functional MRI (fMRI)-based neurofeedback on functional connectivity. In this review, we introduce a recent approach for creating a theranostic biomarker, which consists mainly of 2 parts: (1) developing an rs-fcMRI-based biomarker that can predict diagnosis and/or symptoms with high accuracy, and (2) the introduction of a proof-of-concept study investigating the relationship between normalizing the biomarker and symptom changes using fMRI-based neurofeedback. In parallel with the introduction of recent studies, we review rs-fcMRI-based biomarker and fMRI-based neurofeedback, focusing on the technological improvements and limitations associated with clinical use. © The Author 2017. Published by Oxford University Press on behalf of CINP.

Resting-State Functional Connectivity-Based Biomarkers and Functional MRI-Based Neurofeedback for Psychiatric Disorders: A Challenge for Developing Theranostic Biomarkers

PubMed Central

Yamada, Takashi; Hashimoto, Ryu-ichiro; Yahata, Noriaki; Ichikawa, Naho; Yoshihara, Yujiro; Okamoto, Yasumasa; Kato, Nobumasa; Takahashi, Hidehiko

2017-01-01

Abstract Psychiatric research has been hampered by an explanatory gap between psychiatric symptoms and their neural underpinnings, which has resulted in poor treatment outcomes. This situation has prompted us to shift from symptom-based diagnosis to data-driven diagnosis, aiming to redefine psychiatric disorders as disorders of neural circuitry. Promising candidates for data-driven diagnosis include resting-state functional connectivity MRI (rs-fcMRI)-based biomarkers. Although biomarkers have been developed with the aim of diagnosing patients and predicting the efficacy of therapy, the focus has shifted to the identification of biomarkers that represent therapeutic targets, which would allow for more personalized treatment approaches. This type of biomarker (i.e., “theranostic biomarker”) is expected to elucidate the disease mechanism of psychiatric conditions and to offer an individualized neural circuit-based therapeutic target based on the neural cause of a condition. To this end, researchers have developed rs-fcMRI-based biomarkers and investigated a causal relationship between potential biomarkers and disease-specific behavior using functional MRI (fMRI)-based neurofeedback on functional connectivity. In this review, we introduce a recent approach for creating a theranostic biomarker, which consists mainly of 2 parts: (1) developing an rs-fcMRI-based biomarker that can predict diagnosis and/or symptoms with high accuracy, and (2) the introduction of a proof-of-concept study investigating the relationship between normalizing the biomarker and symptom changes using fMRI-based neurofeedback. In parallel with the introduction of recent studies, we review rs-fcMRI-based biomarker and fMRI-based neurofeedback, focusing on the technological improvements and limitations associated with clinical use. PMID:28977523

Resting-state Functional Magnetic Resonance Imaging Analysis of Brain Functional Activity in Rats with Ischemic Stroke Treated by Electro-acupuncture.

PubMed

Liang, Shengxiang; Lin, Yunjiao; Lin, Bingbing; Li, Jianhong; Liu, Weilin; Chen, Lidian; Zhao, Shujun; Tao, Jing

2017-09-01

To evaluate whether electro-acupuncture (EA) treatment at acupoints of Zusanli (ST 36) and Quchi (LI 11) could reduce motor impairments and enhance brain functional recovery in rats with ischemic stroke. A rat model of middle cerebral artery occlusion (MCAO) was established. EA at ST 36 and LI 11was started at 24 hours (MCAO + EA group) after ischemic stroke. The nontreatment (MCAO) and sham-operated control (SC) groups were included as controls. The neurologic deficits of all groups were assessed by Zea Longa scores and the modified neurologic severity scores on 24 hours and 8 days after MCAO. To further investigate the effect of EA on infract volume and brain function, magnetic resonance imaging was used to estimate the brain lesion and brain neural activities of each group at 8 days after ischemic stroke. Within 1 week after EA treatment, the neurologic deficits were significantly alleviated, and the cerebral infarctions were improved, including visual cortex, motor cortex, striatum, dorsal thalamus, and hippocampus. Furthermore, whole brain neural activities of auditory cortex, lateral nucleus group of dorsal thalamus, hippocampus, motor cortex, orbital cortex, sensory cortex, and striatum were decreased in MCAO group, whereas that of brain neural activities were increased after EA treatment, suggesting these brain regions are in accordance with the brain structure analysis. EA at ST 36 and LI 11 could enhance the neural activity of motor function-related brain regions, including motor cortex, dorsal thalamus, and striatum in rats, which is a potential treatment for ischemia stroke. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Enhanced Thalamic Functional Connectivity with No fMRI Responses to Affected Forelimb Stimulation in Stroke-Recovered Rats.

PubMed

Shim, Woo H; Suh, Ji-Yeon; Kim, Jeong K; Jeong, Jaeseung; Kim, Young R

2016-01-01

Neurological recovery after stroke has been extensively investigated to provide better understanding of neurobiological mechanism, therapy, and patient management. Recent advances in neuroimaging techniques, particularly functional MRI (fMRI), have widely contributed to unravel the relationship between the altered neural function and stroke-affected brain areas. As results of previous investigations, the plastic reorganization and/or gradual restoration of the hemodynamic fMRI responses to neural stimuli have been suggested as relevant mechanisms underlying the stroke recovery process. However, divergent study results and modality-dependent outcomes have clouded the proper interpretation of variable fMRI signals. Here, we performed both evoked and resting state fMRI (rs-fMRI) to clarify the link between the fMRI phenotypes and post-stroke functional recovery. The experiments were designed to examine the altered neural activity within the contra-lesional hemisphere and other undamaged brain regions using rat models with large unilateral stroke, which despite the severe injury, exhibited nearly full recovery at ∼6 months after stroke. Surprisingly, both blood oxygenation level-dependent and blood volume-weighted (CBVw) fMRI activities elicited by electrical stimulation of the stroke-affected forelimb were completely absent, failing to reveal the neural origin of the behavioral recovery. In contrast, the functional connectivity maps showed highly robust rs-fMRI activity concentrated in the contra-lesional ventromedial nucleus of thalamus (VM). The negative finding in the stimuli-induced fMRI study using the popular rat middle cerebral artery model denotes weak association between the fMRI hemodynamic responses and neurological improvement. The results strongly caution the indiscreet interpretation of stroke-affected fMRI signals and demonstrate rs-fMRI as a complementary tool for efficiently characterizing stroke recovery.

Ultrasoft microwire neural electrodes improve chronic tissue integration.

PubMed

Du, Zhanhong Jeff; Kolarcik, Christi L; Kozai, Takashi D Y; Luebben, Silvia D; Sapp, Shawn A; Zheng, Xin Sally; Nabity, James A; Cui, X Tracy

2017-04-15

Chronically implanted neural multi-electrode arrays (MEA) are an essential technology for recording electrical signals from neurons and/or modulating neural activity through stimulation. However, current MEAs, regardless of the type, elicit an inflammatory response that ultimately leads to device failure. Traditionally, rigid materials like tungsten and silicon have been employed to interface with the relatively soft neural tissue. The large stiffness mismatch is thought to exacerbate the inflammatory response. In order to minimize the disparity between the device and the brain, we fabricated novel ultrasoft electrodes consisting of elastomers and conducting polymers with mechanical properties much more similar to those of brain tissue than previous neural implants. In this study, these ultrasoft microelectrodes were inserted and released using a stainless steel shuttle with polyethyleneglycol (PEG) glue. The implanted microwires showed functionality in acute neural stimulation. When implanted for 1 or 8weeks, the novel soft implants demonstrated significantly reduced inflammatory tissue response at week 8 compared to tungsten wires of similar dimension and surface chemistry. Furthermore, a higher degree of cell body distortion was found next to the tungsten implants compared to the polymer implants. Our results support the use of these novel ultrasoft electrodes for long term neural implants. One critical challenge to the translation of neural recording/stimulation electrode technology to clinically viable devices for brain computer interface (BCI) or deep brain stimulation (DBS) applications is the chronic degradation of device performance due to the inflammatory tissue reaction. While many hypothesize that soft and flexible devices elicit reduced inflammatory tissue responses, there has yet to be a rigorous comparison between soft and stiff implants. We have developed an ultra-soft microelectrode with Young's modulus lower than 1MPa, closely mimicking the brain tissue modulus. Here, we present a rigorous histological comparison of this novel ultrasoft electrode and conventional stiff electrode with the same size, shape and surface chemistry, implanted in rat brains for 1-week and 8-weeks. Significant improvement was observed for ultrasoft electrodes, including inflammatory tissue reaction, electrode-tissue integration as well as mechanical disturbance to nearby neurons. A full spectrum of new techniques were developed in this study, from insertion shuttle to in situ sectioning of the microelectrode to automated cell shape analysis, all of which should contribute new methods to the field. Finally, we showed the electrical functionality of the ultrasoft electrode, demonstrating the potential of flexible neural implant devices for future research and clinical use. Copyright © 2017. Published by Elsevier Ltd.

Fast response and high sensitivity to microsaccades in a cascading-adaptation neural network with short-term synaptic depression

NASA Astrophysics Data System (ADS)

Yuan, Wu-Jie; Zhou, Jian-Fang; Zhou, Changsong

2016-04-01

Microsaccades are very small eye movements during fixation. Experimentally, they have been found to play an important role in visual information processing. However, neural responses induced by microsaccades are not yet well understood and are rarely studied theoretically. Here we propose a network model with a cascading adaptation including both retinal adaptation and short-term depression (STD) at thalamocortical synapses. In the neural network model, we compare the microsaccade-induced neural responses in the presence of STD and those without STD. It is found that the cascading with STD can give rise to faster and sharper responses to microsaccades. Moreover, STD can enhance response effectiveness and sensitivity to microsaccadic spatiotemporal changes, suggesting improved detection of small eye movements (or moving visual objects). We also explore the mechanism of the response properties in the model. Our studies strongly indicate that STD plays an important role in neural responses to microsaccades. Our model considers simultaneously retinal adaptation and STD at thalamocortical synapses in the study of microsaccade-induced neural activity, and may be useful for further investigation of the functional roles of microsaccades in visual information processing.

Physical exercise induces expression of CD31 and facilitates neural function recovery in rats with focal cerebral infarction.

PubMed

Hu, Xiquan; Zheng, Haiqing; Yan, Tiebin; Pan, Sanqiang; Fang, Jie; Jiang, Ruishu; Ma, Shangfeng

2010-05-01

The present study was aimed at examining the role of physical exercise in the improvement of damaged neural function and the induction of angiogenesis. An infarction model was induced by ligating the left middle cerebral artery occlusion (MCAO) in a total of 66 adult Sprague-Dawley rats that were further randomly divided into three groups: the physical exercise group (n=30), which was given running wheel exercise every day after MCAO, the control group (n=30) and sham-operated group (n=6), which were fed in standard cages without any special training exercise. The rats were killed on the third, seventh and fourteenth days and the neurological severity scores were examined for evaluating the neural function. And the neogenetic microvessels around the peri-infarction region were checked with the specific marker CD31. Although neogenetic microvessels in the peri-infarction region were observed in both control group and physical exercise group, which showed the highest signal on the seventh day after ischemia, the number of CD31 positive cells significantly increased in physical exercise group in comparison with those in control group on the seventh and fourteenth days after ischemia (p<0.01). Moreover, the neurological severity scores in the physical exercise group showed more quick declination as compared to those in control group from the seventh day after ischemic. Our results suggested that physical exercise plays an important role in the recovery of damaged neural function and induction of angiogenesis after cerebral infarction in rats.

FAST INdiCATE Trial protocol. Clinical efficacy of functional strength training for upper limb motor recovery early after stroke: neural correlates and prognostic indicators.

PubMed

Pomeroy, Valerie M; Ward, Nick S; Johansen-Berg, Heidi; van Vliet, Paulette; Burridge, Jane; Hunter, Susan M; Lemon, Roger N; Rothwell, John; Weir, Christopher J; Wing, Alan; Walker, Andrew A; Kennedy, Niamh; Barton, Garry; Greenwood, Richard J; McConnachie, Alex

2014-02-01

Functional strength training in addition to conventional physical therapy could enhance upper limb recovery early after stroke more than movement performance therapy plus conventional physical therapy. To determine (a) the relative clinical efficacy of conventional physical therapy combined with functional strength training and conventional physical therapy combined with movement performance therapy for upper limb recovery; (b) the neural correlates of response to conventional physical therapy combined with functional strength training and conventional physical therapy combined with movement performance therapy; (c) whether any one or combination of baseline measures predict motor improvement in response to conventional physical therapy combined with functional strength training or conventional physical therapy combined with movement performance therapy. Randomized, controlled, observer-blind trial. The sample will consist of 288 participants with upper limb paresis resulting from a stroke that occurred within the previous 60 days. All will be allocated to conventional physical therapy combined with functional strength training or conventional physical therapy combined with movement performance therapy. Functional strength training and movement performance therapy will be undertaken for up to 1·5 h/day, five-days/week for six-weeks. Measurements will be undertaken before randomization, six-weeks thereafter, and six-months after stroke. Primary efficacy outcome will be the Action Research Arm Test. Explanatory measurements will include voxel-wise estimates of brain activity during hand movement, brain white matter integrity (fractional anisotropy), and brain-muscle connectivity (e.g. latency of motor evoked potentials). The primary clinical efficacy analysis will compare treatment groups using a multilevel normal linear model adjusting for stratification variables and for which therapist administered the treatment. Effect of conventional physical therapy combined with functional strength training versus conventional physical therapy combined with movement performance therapy will be summarized using the adjusted mean difference and 95% confidence interval. To identify the neural correlates of improvement in both groups, we will investigate associations between change from baseline in clinical outcomes and each explanatory measure. To identify baseline measurements that independently predict motor improvement, we will develop a multiple regression model. © 2013 The Authors. International Journal of Stroke published by John Wiley & Sons Ltd on behalf of World Stroke Organization.

A preliminary study of the effects of working memory training on brain function in Attention-Deficit/Hyperactivity Disorder

PubMed Central

Stevens, Michael C.; Gaynor, Alexandra; Bessette, Katie L.; Pearlson, Godfrey D.

2015-01-01

Working memory (WM) training improves WM ability in Attention-Deficit/Hyperactivity Disorder (ADHD), but its efficacy for non-cognitive ADHD impairments ADHD has been sharply debated. The purpose of this preliminary study was to characterize WM training-related changes in ADHD brain function and see if they were linked to clinical improvement. We examined 18 adolescents diagnosed with DSM-IV Combined-subtype ADHD before and after 25 sessions of WM training using a frequently employed approach (CogmedTM) using a nonverbal Sternberg WM fMRI task, neuropsychological tests, and participant- and parent-reports of ADHD symptom severity and associated functional impairment. Whole brain SPM8 analyses identified ADHD activation deficits compared to 18 non-ADHD control participants, then tested whether impaired ADHD frontoparietal brain activation would increase following WM training. Post hoc tests examined the relationships between neural changes and neurocognitive or clinical improvements. As predicted, WM training increased WM performance, ADHD clinical functioning, and WM-related ADHD brain activity in several frontal, parietal and temporal lobe regions. Increased left inferior frontal sulcus region activity was seen in all Encoding, Maintenance, and Retrieval Sternberg task phases. ADHD symptom severity improvements were most often positively correlated with activation gains in brain regions known to be engaged for WM-related executive processing; improvement of different symptom types had different neural correlates. The responsiveness of both amodal WM frontoparietal circuits and executive process-specific WM brain regions was altered by WM training. The latter might represent a promising, relatively unexplored treatment target for researchers seeking to optimize clinical response in ongoing ADHD WM training development efforts. PMID:26138580

Neural Stem Cells Expressing bFGF Reduce Brain Damage and Restore Sensorimotor Function after Neonatal Hypoxia-Ischemia.

PubMed

Ye, Qingsong; Wu, Yanqing; Wu, Jiamin; Zou, Shuang; Al-Zaazaai, Ali Ahmed; Zhang, Hongyu; Shi, Hongxue; Xie, Ling; Liu, Yanlong; Xu, Ke; He, Huacheng; Zhang, Fabiao; Ji, Yiming; He, Yan; Xiao, Jian

2018-01-01

Neonatal hypoxia-ischemia (HI) causes severe brain damage and significantly increases neonatal morbidity and mortality. Increasing evidences have verified that stem cell-based therapy has the potential to rescue the ischemic tissue and restore function via secreting growth factors after HI. Here, we had investigated whether intranasal neural stem cells (NSCs) treatment improves the recovery of neonatal HI, and NSCs overexpressing basic fibroblast growth factor (bFGF) has a better therapeutic effect for recovery than NSCs treatment only. We performed permanent occlusion of the right common carotid artery in 9-day old ICR mice as animal model of neonatal hypoxia-ischemia. At 3 days post-HI, NSC, NSC-GFP, NSC-bFGF and vehicle were delivered intranasally. To determine the effect of intranasal NSC, NSC-GFP and NSC-bFGF treatment on recovery after HI, we analyzed brain damage, sensor-motor function and cell differentiation. It was observed that intranasal NSC, NSC-GFP and NSC-bFGF treatment decreased gray and white matter loss area in comparison with vehicle-treated mouse. NSC, NSC-GFP and NSC-bFGF treatment also significantly improved sensor motor function in cylinder rearing test and adhesive removal test, however, NSC-bFGF-treatment was more effective than NSC-treatment in the improvement of somatosensory function. Furthermore, compared with NSC and NSC-GFP, NSC-bFGF treatment group appeared to differentiate into more neurons. Taken together, intranasal administration of NSCs is a promising therapy for treatment of neonatal HI, but NSCs overexpressing bFGF promotes the survival and differentiation of NSCs, and consequently achieves a better therapeutic effect in improving recovery after neonatal HI. © 2018 The Author(s). Published by S. Karger AG, Basel.

Predicting the Naturalistic Course of Major Depressive Disorder Using Clinical and Multimodal Neuroimaging Information: A Multivariate Pattern Recognition Study.

PubMed

Schmaal, Lianne; Marquand, Andre F; Rhebergen, Didi; van Tol, Marie-José; Ruhé, Henricus G; van der Wee, Nic J A; Veltman, Dick J; Penninx, Brenda W J H

2015-08-15

A chronic course of major depressive disorder (MDD) is associated with profound alterations in brain volumes and emotional and cognitive processing. However, no neurobiological markers have been identified that prospectively predict MDD course trajectories. This study evaluated the prognostic value of different neuroimaging modalities, clinical characteristics, and their combination to classify MDD course trajectories. One hundred eighteen MDD patients underwent structural and functional magnetic resonance imaging (MRI) (emotional facial expressions and executive functioning) and were clinically followed-up at 2 years. Three MDD trajectories (chronic n = 23, gradual improving n = 36, and fast remission n = 59) were identified based on Life Chart Interview measuring the presence of symptoms each month. Gaussian process classifiers were employed to evaluate prognostic value of neuroimaging data and clinical characteristics (including baseline severity, duration, and comorbidity). Chronic patients could be discriminated from patients with more favorable trajectories from neural responses to various emotional faces (up to 73% accuracy) but not from structural MRI and functional MRI related to executive functioning. Chronic patients could also be discriminated from remitted patients based on clinical characteristics (accuracy 69%) but not when age differences between the groups were taken into account. Combining different task contrasts or data sources increased prediction accuracies in some but not all cases. Our findings provide evidence that the prediction of naturalistic course of depression over 2 years is improved by considering neuroimaging data especially derived from neural responses to emotional facial expressions. Neural responses to emotional salient faces more accurately predicted outcome than clinical data. Copyright © 2015 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Neural Networks and other Techniques for Fault Identification and Isolation of Aircraft Systems

NASA Technical Reports Server (NTRS)

Innocenti, M.; Napolitano, M.

2003-01-01

Fault identification, isolation, and accomodation have become critical issues in the overall performance of advanced aircraft systems. Neural Networks have shown to be a very attractive alternative to classic adaptation methods for identification and control of non-linear dynamic systems. The purpose of this paper is to show the improvements in neural network applications achievable through the use of learning algorithms more efficient than the classic Back-Propagation, and through the implementation of the neural schemes in parallel hardware. The results of the analysis of a scheme for Sensor Failure, Detection, Identification and Accommodation (SFDIA) using experimental flight data of a research aircraft model are presented. Conventional approaches to the problem are based on observers and Kalman Filters while more recent methods are based on neural approximators. The work described in this paper is based on the use of neural networks (NNs) as on-line learning non-linear approximators. The performances of two different neural architectures were compared. The first architecture is based on a Multi Layer Perceptron (MLP) NN trained with the Extended Back Propagation algorithm (EBPA). The second architecture is based on a Radial Basis Function (RBF) NN trained with the Extended-MRAN (EMRAN) algorithms. In addition, alternative methods for communications links fault detection and accomodation are presented, relative to multiple unmanned aircraft applications.

Neural responses to kindness and malevolence differ in illness and recovery in women with anorexia nervosa.

PubMed

McAdams, Carrie J; Lohrenz, Terry; Montague, P Read

2015-12-01

In anorexia nervosa, problems with social relationships contribute to illness, and improvements in social support are associated with recovery. Using the multiround trust game and 3T MRI, we compare neural responses in a social relationship in three groups of women: women with anorexia nervosa, women in long-term weight recovery from anorexia nervosa, and healthy comparison women. Surrogate markers related to social signals in the game were computed each round to assess whether the relationship was improving (benevolence) or deteriorating (malevolence) for each subject. Compared with healthy women, neural responses to benevolence were diminished in the precuneus and right angular gyrus in both currently-ill and weight-recovered subjects with anorexia, but neural responses to malevolence differed in the left fusiform only in currently-ill subjects. Next, using a whole-brain regression, we identified an office assessment, the positive personalizing bias, that was inversely correlated with neural activity in the occipital lobe, the precuneus and posterior cingulate, the bilateral temporoparietal junctions, and dorsal anterior cingulate, during benevolence for all groups of subjects. The positive personalizing bias is a self-report measure that assesses the degree with which a person attributes positive experiences to other people. These data suggest that problems in perceiving kindness may be a consistent trait related to the development of anorexia nervosa, whereas recognizing malevolence may be related to recovery. Future work on social brain function, in both healthy and psychiatric populations, should consider positive personalizing biases as a possible marker of neural differences related to kindness perception. © 2015 Wiley Periodicals, Inc.

Neural Responses to Kindness and Malevolence Differ in Illness and Recovery in Women With Anorexia Nervosa

PubMed Central

McAdams, Carrie J.; Lohrenz, Terry; Montague, P. Read

2015-01-01

In anorexia nervosa, problems with social relationships contribute to illness, and improvements in social support are associated with recovery. Using the multiround trust game and 3T MRI, we compare neural responses in a social relationship in three groups of women: women with anorexia nervosa, women in long-term weight recovery from anorexia nervosa, and healthy comparison women. Surrogate markers related to social signals in the game were computed each round to assess whether the relationship was improving (benevolence) or deteriorating (malevolence) for each subject. Compared with healthy women, neural responses to benevolence were diminished in the precuneus and right angular gyrus in both currently-ill and weight-recovered subjects with anorexia, but neural responses to malevolence differed in the left fusiform only in currently-ill subjects. Next, using a whole-brain regression, we identified an office assessment, the positive personalizing bias, that was inversely correlated with neural activity in the occipital lobe, the precuneus and posterior cingulate, the bilateral temporoparietal junctions, and dorsal anterior cingulate, during benevolence for all groups of subjects. The positive personalizing bias is a self-report measure that assesses the degree with which a person attributes positive experiences to other people. These data suggest that problems in perceiving kindness may be a consistent trait related to the development of anorexia nervosa, whereas recognizing malevolence may be related to recovery. Future work on social brain function, in both healthy and psychiatric populations, should consider positive personalizing biases as a possible marker of neural differences related to kindness perception. PMID:26416161

Extending unified-theory-of-reinforcement neural networks to steady-state operant behavior.

PubMed

Calvin, Olivia L; McDowell, J J

2016-06-01

The unified theory of reinforcement has been used to develop models of behavior over the last 20 years (Donahoe et al., 1993). Previous research has focused on the theory's concordance with the respondent behavior of humans and animals. In this experiment, neural networks were developed from the theory to extend the unified theory of reinforcement to operant behavior on single-alternative variable-interval schedules. This area of operant research was selected because previously developed neural networks could be applied to it without significant alteration. Previous research with humans and animals indicates that the pattern of their steady-state behavior is hyperbolic when plotted against the obtained rate of reinforcement (Herrnstein, 1970). A genetic algorithm was used in the first part of the experiment to determine parameter values for the neural networks, because values that were used in previous research did not result in a hyperbolic pattern of behavior. After finding these parameters, hyperbolic and other similar functions were fitted to the behavior produced by the neural networks. The form of the neural network's behavior was best described by an exponentiated hyperbola (McDowell, 1986; McLean and White, 1983; Wearden, 1981), which was derived from the generalized matching law (Baum, 1974). In post-hoc analyses the addition of a baseline rate of behavior significantly improved the fit of the exponentiated hyperbola and removed systematic residuals. The form of this function was consistent with human and animal behavior, but the estimated parameter values were not. Copyright © 2016 Elsevier B.V. All rights reserved.

Prediction of recovery of motor function after stroke.

PubMed

Stinear, Cathy

2010-12-01

Stroke is a leading cause of disability. The ability to live independently after stroke depends largely on the reduction of motor impairment and the recovery of motor function. Accurate prediction of motor recovery assists rehabilitation planning and supports realistic goal setting by clinicians and patients. Initial impairment is negatively related to degree of recovery, but inter-individual variability makes accurate prediction difficult. Neuroimaging and neurophysiological assessments can be used to measure the extent of stroke damage to the motor system and predict subsequent recovery of function, but these techniques are not yet used routinely. The use of motor impairment scores and neuroimaging has been refined by two recent studies in which these investigations were used at multiple time points early after stroke. Voluntary finger extension and shoulder abduction within 5 days of stroke predicted subsequent recovery of upper-limb function. Diffusion-weighted imaging within 7 days detected the effects of stroke on caudal motor pathways and was predictive of lasting motor impairment. Thus, investigations done soon after stroke had good prognostic value. The potential prognostic value of cortical activation and neural plasticity has been explored for the first time by two recent studies. Functional MRI detected a pattern of cortical activation at the acute stage that was related to subsequent reduction in motor impairment. Transcranial magnetic stimulation enabled measurement of neural plasticity in the primary motor cortex, which was related to subsequent disability. These studies open interesting new lines of enquiry. WHERE NEXT?: The accuracy of prediction might be increased by taking into account the motor system's capacity for functional reorganisation in response to therapy, in addition to the extent of stroke-related damage. Improved prognostic accuracy could also be gained by combining simple tests of motor impairment with neuroimaging, genotyping, and neurophysiological assessment of neural plasticity. The development of algorithms to guide the sequential combinations of these assessments could also further increase accuracy, in addition to improving rehabilitation planning and outcomes. Copyright © 2010 Elsevier Ltd. All rights reserved.

Gold Nanoparticles for Neural Prosthetics Devices

PubMed Central

Zhang, Huanan; Shih, Jimmy; Zhu, Jian; Kotov, Nicholas A.

2012-01-01

Treatments of neurological diseases and the realization of brain-computer interfaces require ultrasmall electrodes which are “invisible” to resident immune cells. Functional electrodes smaller than 50μm are impossible to produce with traditional materials due to high interfacial impedance at the characteristic frequency of neural activity and insufficient charge storage capacity. The problem can be resolved by using gold nanoparticle nanocomposites. Careful comparison indicates that layer-by-layer assembled films from Au NPs provide more than threefold improvement in interfacial impedance and one order of magnitude increase in charge storage capacity. Prototypes of microelectrodes could be made using traditional photolithography. Integration of unique nanocomposite materials with microfabrication techniques opens the door for practical realization of the ultrasmall implantable electrodes. Further improvement of electrical properties is expected when using special shapes of gold nanoparticles. PMID:22734673

Major Upgrades to the AIRS Version-6 Water Vapor Profile Methodology

NASA Technical Reports Server (NTRS)

Susskind, Joel; Blaisdell, John; Iredell, Lena; Lee, Jae N.

2015-01-01

Additional changes in Version-6.19 include all previous updates made to the q(p) retrieval since Version-6: Modified Neural-Net q0(p) guess above the tropopause Linearly tapers the neural net guess to match climatology at 70 mb, not at the top of the atmosphereChanged the 11 trapezoid q(p) perturbation functions used in Version-6 so as to match the 24 functions used in T(p) retrieval step. These modifications resulted in improved water vapor profiles in Version-6.19 compared to Version-6.Version-6.19 is tested for all of August 2013 and August 2014, as well for select other days. Before finalized and operational in 2016, the V-6.19 can be acquired upon request for limited time intervals.

Improving quantitative structure-activity relationship models using Artificial Neural Networks trained with dropout.

PubMed

Mendenhall, Jeffrey; Meiler, Jens

2016-02-01

Dropout is an Artificial Neural Network (ANN) training technique that has been shown to improve ANN performance across canonical machine learning (ML) datasets. Quantitative Structure Activity Relationship (QSAR) datasets used to relate chemical structure to biological activity in Ligand-Based Computer-Aided Drug Discovery pose unique challenges for ML techniques, such as heavily biased dataset composition, and relatively large number of descriptors relative to the number of actives. To test the hypothesis that dropout also improves QSAR ANNs, we conduct a benchmark on nine large QSAR datasets. Use of dropout improved both enrichment false positive rate and log-scaled area under the receiver-operating characteristic curve (logAUC) by 22-46 % over conventional ANN implementations. Optimal dropout rates are found to be a function of the signal-to-noise ratio of the descriptor set, and relatively independent of the dataset. Dropout ANNs with 2D and 3D autocorrelation descriptors outperform conventional ANNs as well as optimized fingerprint similarity search methods.

Improving Quantitative Structure-Activity Relationship Models using Artificial Neural Networks Trained with Dropout

PubMed Central

Mendenhall, Jeffrey; Meiler, Jens

2016-01-01

Dropout is an Artificial Neural Network (ANN) training technique that has been shown to improve ANN performance across canonical machine learning (ML) datasets. Quantitative Structure Activity Relationship (QSAR) datasets used to relate chemical structure to biological activity in Ligand-Based Computer-Aided Drug Discovery (LB-CADD) pose unique challenges for ML techniques, such as heavily biased dataset composition, and relatively large number of descriptors relative to the number of actives. To test the hypothesis that dropout also improves QSAR ANNs, we conduct a benchmark on nine large QSAR datasets. Use of dropout improved both Enrichment false positive rate (FPR) and log-scaled area under the receiver-operating characteristic curve (logAUC) by 22–46% over conventional ANN implementations. Optimal dropout rates are found to be a function of the signal-to-noise ratio of the descriptor set, and relatively independent of the dataset. Dropout ANNs with 2D and 3D autocorrelation descriptors outperform conventional ANNs as well as optimized fingerprint similarity search methods. PMID:26830599

Effects of High-Rate Pulse Trains on Electrode Discrimination in Cochlear Implant Users

PubMed Central

Runge-Samuelson, Christina L.

2009-01-01

Overcoming issues related to abnormally high neural synchrony in response to electrical stimulation is one aspect in improving hearing with a cochlear implant. Desynchronization of electrical stimuli have shown benefits in neural encoding of electrical signals and improvements in psychophysical tasks. In the present study, 10 participants with either CII or HiRes 90k Advanced Bionics devices were tested for the effects of desynchronizing constant-amplitude high-rate (5,000 Hz) pulse trains on electrode discrimination of sinusoidal stimuli (1,000 Hz). When averaged across the sinusoidal dynamic range, overall improvements in electrode discrimination with high-rate pulses were found for 8 of 10 participants. This effect was significant for the group (p = .003). Nonmonotonic patterns of electrode discrimination as a function of sinusoidal stimulation level were observed. By providing additional spectral channels, it is possible that clinical implementation of constant-amplitude high-rate pulse trains in a signal processing strategy may improve performance with the device. PMID:19447763

From motor cortex to visual cortex: the application of noninvasive brain stimulation to amblyopia.

PubMed

Thompson, Benjamin; Mansouri, Behzad; Koski, Lisa; Hess, Robert F

2012-04-01

Noninvasive brain stimulation is a technique for inducing changes in the excitability of discrete neural populations in the human brain. A current model of the underlying pathological processes contributing to the loss of motor function after stroke has motivated a number of research groups to investigate the potential therapeutic application of brain stimulation to stroke rehabilitation. The loss of motor function is modeled as resulting from a combination of reduced excitability in the lesioned motor cortex and an increased inhibitory drive from the nonlesioned hemisphere over the lesioned hemisphere. This combination of impaired neural function and pathological suppression resonates with current views on the cause of the visual impairment in amblyopia. Here, we discuss how the rationale for using noninvasive brain stimulation in stroke rehabilitation can be applied to amblyopia, review a proof-of-principle study demonstrating that brain stimulation can temporarily improve amblyopic eye function, and propose future research avenues. Copyright © 2010 Wiley Periodicals, Inc.

Existence and global exponential stability of periodic solution to BAM neural networks with periodic coefficients and continuously distributed delays

NASA Astrophysics Data System (ADS)

Zhou, distributed delays [rapid communication] T.; Chen, A.; Zhou, Y.

2005-08-01

By using the continuation theorem of coincidence degree theory and Liapunov function, we obtain some sufficient criteria to ensure the existence and global exponential stability of periodic solution to the bidirectional associative memory (BAM) neural networks with periodic coefficients and continuously distributed delays. These results improve and generalize the works of papers [J. Cao, L. Wang, Phys. Rev. E 61 (2000) 1825] and [Z. Liu, A. Chen, J. Cao, L. Huang, IEEE Trans. Circuits Systems I 50 (2003) 1162]. An example is given to illustrate that the criteria are feasible.

Reflectin as a Material for Neural Stem Cell Growth

PubMed Central

2015-01-01

Cephalopods possess remarkable camouflage capabilities, which are enabled by their complex skin structure and sophisticated nervous system. Such unique characteristics have in turn inspired the design of novel functional materials and devices. Within this context, recent studies have focused on investigating the self-assembly, optical, and electrical properties of reflectin, a protein that plays a key role in cephalopod structural coloration. Herein, we report the discovery that reflectin constitutes an effective material for the growth of human neural stem/progenitor cells. Our findings may hold relevance both for understanding cephalopod embryogenesis and for developing improved protein-based bioelectronic devices. PMID:26703760

Predictive Behavior of a Computational Foot/Ankle Model through Artificial Neural Networks.

PubMed

Chande, Ruchi D; Hargraves, Rosalyn Hobson; Ortiz-Robinson, Norma; Wayne, Jennifer S

2017-01-01

Computational models are useful tools to study the biomechanics of human joints. Their predictive performance is heavily dependent on bony anatomy and soft tissue properties. Imaging data provides anatomical requirements while approximate tissue properties are implemented from literature data, when available. We sought to improve the predictive capability of a computational foot/ankle model by optimizing its ligament stiffness inputs using feedforward and radial basis function neural networks. While the former demonstrated better performance than the latter per mean square error, both networks provided reasonable stiffness predictions for implementation into the computational model.

The Technology of Suppressing Harmonics with Complex Neural Network is Applied to Microgrid

NASA Astrophysics Data System (ADS)

Zhang, Jing; Li, Zhan-Ying; Wang, Yan-ping; Li, Yang; Zong, Ke-yong

2018-03-01

According to the traits of harmonics in microgrid, a new CANN controller which combines BP and RBF neural network is proposed to control APF to detect and suppress harmonics. This controller has the function of current prediction. By simulation in Matlab / Simulink, this design can shorten the delay time nearly 0.02s (a power supply current cycle) in comparison with the traditional controller based on ip-iq method. The new controller also has higher compensation accuracy and better dynamic tracking traits, it can greatly suppress the harmonics and improve the power quality.

Using Dual Process Models to Examine Impulsivity Throughout Neural Maturation.

PubMed

Leshem, Rotem

2016-01-01

The multivariate construct of impulsivity is examined through neural systems and connections that comprise the executive functioning system. It is proposed that cognitive and behavioral components of impulsivity can be divided into two distinct groups, mediated by (1) the cognitive control system: deficits in top-down cognitive control processes referred to as action/cognitive impulsivity and (2) the socioemotional system: related to bottom-up affective/motivational processes referred to as affective impulsivity. Examination of impulsivity from a developmental viewpoint can guide future research, potentially enabling the selection of more effective interventions for impulsive individuals, based on the cognitive components requiring improvement.

Improvement of the Hopfield Neural Network by MC-Adaptation Rule

NASA Astrophysics Data System (ADS)

Zhou, Zhen; Zhao, Hong

2006-06-01

We show that the performance of the Hopfield neural networks, especially the quality of the recall and the capacity of the effective storing, can be greatly improved by making use of a recently presented neural network designing method without altering the whole structure of the network. In the improved neural network, a memory pattern is recalled exactly from initial states having a given degree of similarity with the memory pattern, and thus one can avoids to apply the overlap criterion as carried out in the Hopfield neural networks.

Increased functional connectivity within memory networks following memory rehabilitation in multiple sclerosis.

PubMed

Leavitt, Victoria M; Wylie, Glenn R; Girgis, Peter A; DeLuca, John; Chiaravalloti, Nancy D

2014-09-01

Identifying effective behavioral treatments to improve memory in persons with learning and memory impairment is a primary goal for neurorehabilitation researchers. Memory deficits are the most common cognitive symptom in multiple sclerosis (MS), and hold negative professional and personal consequences for people who are often in the prime of their lives when diagnosed. A 10-session behavioral treatment, the modified Story Memory Technique (mSMT), was studied in a randomized, placebo-controlled clinical trial. Behavioral improvements and increased fMRI activation were shown after treatment. Here, connectivity within the neural networks underlying memory function was examined with resting-state functional connectivity (RSFC) in a subset of participants from the clinical trial. We hypothesized that the treatment would result in increased integrity of connections within two primary memory networks of the brain, the hippocampal memory network, and the default network (DN). Seeds were placed in left and right hippocampus, and the posterior cingulate cortex. Increased connectivity was found between left hippocampus and cortical regions specifically involved in memory for visual imagery, as well as among critical hubs of the DN. These results represent the first evidence for efficacy of a behavioral intervention to impact the integrity of neural networks subserving memory functions in persons with MS.

Neurocognitive Development of Relational Reasoning

ERIC Educational Resources Information Center

Crone, Eveline A.; Wendelken, Carter; van Leijenhorst, Linda; Honomichl, Ryan D.; Christoff, Kalina; Bunge, Silvia A.

2009-01-01

Relational reasoning is an essential component of fluid intelligence, and is known to have a protracted developmental trajectory. To date, little is known about the neural changes that underlie improvements in reasoning ability over development. In this event-related functional magnetic resonance imaging (fMRI) study, children aged 8-12 and adults…

Improving the accuracy of Density Functional Theory (DFT) calculation for homolysis bond dissociation energies of Y-NO bond: generalized regression neural network based on grey relational analysis and principal component analysis.

PubMed

Li, Hong Zhi; Tao, Wei; Gao, Ting; Li, Hui; Lu, Ying Hua; Su, Zhong Min

2011-01-01

We propose a generalized regression neural network (GRNN) approach based on grey relational analysis (GRA) and principal component analysis (PCA) (GP-GRNN) to improve the accuracy of density functional theory (DFT) calculation for homolysis bond dissociation energies (BDE) of Y-NO bond. As a demonstration, this combined quantum chemistry calculation with the GP-GRNN approach has been applied to evaluate the homolysis BDE of 92 Y-NO organic molecules. The results show that the ull-descriptor GRNN without GRA and PCA (F-GRNN) and with GRA (G-GRNN) approaches reduce the root-mean-square (RMS) of the calculated homolysis BDE of 92 organic molecules from 5.31 to 0.49 and 0.39 kcal mol(-1) for the B3LYP/6-31G (d) calculation. Then the newly developed GP-GRNN approach further reduces the RMS to 0.31 kcal mol(-1). Thus, the GP-GRNN correction on top of B3LYP/6-31G (d) can improve the accuracy of calculating the homolysis BDE in quantum chemistry and can predict homolysis BDE which cannot be obtained experimentally.

Impact of one HF-rTMS session on fine motor function in right-handed healthy female subjects: a comparison of stimulation over the left versus the right dorsolateral prefrontal cortex.

PubMed

Baeken, C; Schrijvers, D L; Sabbe, B G C; Vanderhasselt, M A; De Raedt, R

2012-01-01

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive tool to investigate neural conduction in motor processes. Most rTMS research has been conducted by targeting the primary motor cortex. Several studies have also found increased psychomotor speed after rTMS of the dorsolateral prefrontal cortex (DLPFC). However, these studies were mainly performed in psychiatric patients, only targeting the left DLPFC, and often without sham control. Moreover, psychomotor speed is mostly measured based on tasks that also require higher executive functions. Here, we examined the lateralized effect of one sham-controlled high-frequency rTMS session applied to the left or right DLPFC on fine motor function in 36 healthy right-handed females, using the Fitts' paradigm. We found a significant improvement in psychomotor speed only after actively stimulating the right DLPFC. Our results support the assumption of a right prefrontal neural network implicated in visuomotor behavior and performance processes, and that the improvement in psychomotor speed is not a secondary effect of decreased mood. Copyright © 2012 S. Karger AG, Basel.

Neuroplastic changes in patients with schizophrenia undergoing cognitive remediation: triple-blind trial

PubMed Central

Ramsay, Ian S.; Nienow, Tasha M.; Marggraf, Matthew P.; MacDonald, Angus W.

2017-01-01

Background Patients with schizophrenia have shown cognitive improvements following cognitive remediation, but the neuroplastic changes that support these processes are not fully understood. Aims To use a triple-blind, placebo-controlled trial to examine neural activation before and after cognitive remediation or a computer skills training (CST) placebo (trial registration: NCT00995553)). Method Twenty-seven participants underwent functional magnetic resonance imaging before and after being randomised to either cognitive remediation intervention or CST. Participants completed two variants of the N-back task during scanning and were assessed on measures of cognition, functional capacity, community functioning and symptoms. Results We observed a group × time interaction in the left prefrontal cortex, wherein the cognitive remediation group showed increased activation. These changes correlated with improved task accuracy within the cognitive remediation group, whereas there was no relationship between changes in activation in untrained cognitive measures. Significant changes were not observed in other hypothesised areas for the cognitive remediation group. Conclusions We replicated the finding that cognitive remediation increases left lateral prefrontal activation during a working memory task in patients with schizophrenia, suggesting this may be an important neural target for these types of interventions. PMID:28153927

Neuroplastic changes in patients with schizophrenia undergoing cognitive remediation: triple-blind trial.

PubMed

Ramsay, Ian S; Nienow, Tasha M; Marggraf, Matthew P; MacDonald, Angus W

2017-03-01

Background Patients with schizophrenia have shown cognitive improvements following cognitive remediation, but the neuroplastic changes that support these processes are not fully understood. Aims To use a triple-blind, placebo-controlled trial to examine neural activation before and after cognitive remediation or a computer skills training (CST) placebo (trial registration: NCT00995553)). Method Twenty-seven participants underwent functional magnetic resonance imaging before and after being randomised to either cognitive remediation intervention or CST. Participants completed two variants of the N-back task during scanning and were assessed on measures of cognition, functional capacity, community functioning and symptoms. Results We observed a group × time interaction in the left prefrontal cortex, wherein the cognitive remediation group showed increased activation. These changes correlated with improved task accuracy within the cognitive remediation group, whereas there was no relationship between changes in activation in untrained cognitive measures. Significant changes were not observed in other hypothesised areas for the cognitive remediation group. Conclusions We replicated the finding that cognitive remediation increases left lateral prefrontal activation during a working memory task in patients with schizophrenia, suggesting this may be an important neural target for these types of interventions. © The Royal College of Psychiatrists 2017.

A new class of methods for functional connectivity estimation

NASA Astrophysics Data System (ADS)

Lin, Wutu

Measuring functional connectivity from neural recordings is important in understanding processing in cortical networks. The covariance-based methods are the current golden standard for functional connectivity estimation. However, the link between the pair-wise correlations and the physiological connections inside the neural network is unclear. Therefore, the power of inferring physiological basis from functional connectivity estimation is limited. To build a stronger tie and better understand the relationship between functional connectivity and physiological neural network, we need (1) a realistic model to simulate different types of neural recordings with known ground truth for benchmarking; (2) a new functional connectivity method that produce estimations closely reflecting the physiological basis. In this thesis, (1) I tune a spiking neural network model to match with human sleep EEG data, (2) introduce a new class of methods for estimating connectivity from different kinds of neural signals and provide theory proof for its superiority, (3) apply it to simulated fMRI data as an application.

Low-energy extracorporeal shock wave therapy for promotion of vascular endothelial growth factor expression and angiogenesis and improvement of locomotor and sensory functions after spinal cord injury.

PubMed

Yahata, Kenichiro; Kanno, Haruo; Ozawa, Hiroshi; Yamaya, Seiji; Tateda, Satoshi; Ito, Kenta; Shimokawa, Hiroaki; Itoi, Eiji

2016-12-01

OBJECTIVE Extracorporeal shock wave therapy (ESWT) is widely used to treat various human diseases. Low-energy ESWT increases expression of vascular endothelial growth factor (VEGF) in cultured endothelial cells. The VEGF stimulates not only endothelial cells to promote angiogenesis but also neural cells to induce neuroprotective effects. A previous study by these authors demonstrated that low-energy ESWT promoted expression of VEGF in damaged neural tissue and improved locomotor function after spinal cord injury (SCI). However, the neuroprotective mechanisms in the injured spinal cord produced by low-energy ESWT are still unknown. In the present study, the authors investigated the cell specificity of VEGF expression in injured spinal cords and angiogenesis induced by low-energy ESWT. They also examined the neuroprotective effects of low-energy ESWT on cell death, axonal damage, and white matter sparing as well as the therapeutic effect for improvement of sensory function following SCI. METHODS Adult female Sprague-Dawley rats were divided into the SCI group (SCI only) and SCI-SW group (low-energy ESWT applied after SCI). Thoracic SCI was produced using a New York University Impactor. Low-energy ESWT was applied to the injured spinal cord 3 times a week for 3 weeks after SCI. Locomotor function was evaluated using the Basso, Beattie, and Bresnahan open-field locomotor score for 42 days after SCI. Mechanical and thermal allodynia in the hindpaw were evaluated for 42 days. Double staining for VEGF and various cell-type markers (NeuN, GFAP, and Olig2) was performed at Day 7; TUNEL staining was also performed at Day 7. Immunohistochemical staining for CD31, α-SMA, and 5-HT was performed on spinal cord sections taken 42 days after SCI. Luxol fast blue staining was performed at Day 42. RESULTS Low-energy ESWT significantly improved not only locomotion but also mechanical and thermal allodynia following SCI. In the double staining, expression of VEGF was observed in NeuN-, GFAP-, and Olig2-labeled cells. Low-energy ESWT significantly promoted CD31 and α-SMA expressions in the injured spinal cords. In addition, low-energy ESWT significantly reduced the TUNEL-positive cells in the injured spinal cords. Furthermore, the immunodensity of 5-HT-positive axons was significantly higher in the animals treated by low-energy ESWT. The areas of spared white matter were obviously larger in the SCI-SW group than in the SCI group, as indicated by Luxol fast blue staining. CONCLUSIONS The results of this study suggested that low-energy ESWT promotes VEGF expression in various neural cells and enhances angiogenesis in damaged neural tissue after SCI. Furthermore, the neuroprotective effect of VEGF induced by low-energy ESWT can suppress cell death and axonal damage and consequently improve locomotor and sensory functions after SCI. Thus, low-energy ESWT can be a novel therapeutic strategy for treatment of SCI.

Functional Magnetic Resonance Imaging Evaluation of Auricular Percutaneous Electrical Neural Field Stimulation for Fibromyalgia: Protocol for a Feasibility Study.

PubMed

Gebre, Melat; Woodbury, Anna; Napadow, Vitaly; Krishnamurthy, Venkatagiri; Krishnamurthy, Lisa C; Sniecinski, Roman; Crosson, Bruce

2018-02-06

Fibromyalgia is a chronic pain state that includes widespread musculoskeletal pain, fatigue, psychiatric symptoms, cognitive and sleep disturbances, and multiple somatic symptoms. Current therapies are often insufficient or come with significant risks, and while there is an increasing demand for non-pharmacologic and especially non-opioid pain management such as that offered through complementary and alternative medicine therapies, there is currently insufficient evidence to recommend these therapies. Percutaneous electrical neural stimulation (PENS) is an evidence-based treatment option for pain conditions that involves electrical current stimulation through needles inserted into the skin. Percutaneous electrical neural field stimulation (PENFS) of the auricle is similar to PENS, but instead of targeting a single neurovascular bundle, PENFS stimulates the entire ear, covering all auricular branches of the cranial nerves, including the vagus nerve. The neural mechanisms of PENFS for fibromyalgia symptom relief are unknown. We hypothesize that PENFS treatment will decrease functional brain connectivity between the default mode network (DMN) and right posterior insula in fibromyalgia patients. We expect that the decrease in functional connectivity between the DMN and insula will correlate with patient-reported analgesic improvements as indicated by the Defense and Veterans Pain Rating Scale (DVPRS) and will be anti-correlated with patient-reported analgesic medication consumption. Exploratory analyses will be performed for further hypothesis generation. A total of 20 adults from the Atlanta Veterans Affairs Medical Center diagnosed with fibromyalgia will be randomized into 2 groups: 10 subjects to a control (standard therapy) group and 10 subjects to a PENFS treatment group. The pragmatic, standard therapy group will include pharmacologic treatments such as anticonvulsants, non-steroidal anti-inflammatory drugs, topical agents and physical therapy individualized to patient comorbidities and preferences, prescribed by a pain management practitioner. The PENFS group will include the above therapies in addition to the PENFS treatments. The PENFS subject group will have the Neuro-Stim System placed on the ear for 5 days then removed and replaced once per week for 4 weeks. The primary outcome will be resting functional magnetic resonance imaging connectivity between DMN and insula, which will also be correlated with pain relief and functional improvements. This connectivity will be analyzed utilizing functional connectivity magnetic resonance imaging (fcMRI) and will be compared with patient-reported analgesic improvements as indicated by the DVPRS and patient-reported analgesic medication consumption. Pain and function will be further evaluated using Patient-Reported Outcomes Measurement Information System measures and measures describing a person's functional status from Activity and Participation section of the International Classification of Functioning Disability and Health. This trial has been funded by the Veterans Health Administration Program Office. This study attained approval by the Emory University/Veterans Affairs (VA) institutional review board and VA Research & Development committee. Institutional review board expedited approval was granted on 2/7/17 (IRB00092224). The study start date is 6/1/17 and estimated completion date is 5/31/20. The recruitment started in June 2017. This is a feasibility study that is meant to demonstrate the practicality of using fcMRI to study the neural correlates of PENFS outcomes and provide information regarding power calculations in order to design and execute a larger randomized controlled clinical trial to determine the efficacy of PENFS for improving pain and function. ClinicalTrials.gov NCT03008837; https://clinicaltrials.gov/ct2/show/NCT03008837 (Archived by WebCite at http://www.webcitation.org/6wrY3NmaQ). ©Melat Gebre, Anna Woodbury, Vitaly Napadow, Venkatagiri Krishnamurthy, Lisa C. Krishnamurthy, Roman Sniecinski, Bruce Crosson. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 06.02.2018.

Anodal transcranial direct current stimulation transiently improves contrast sensitivity and normalizes visual cortex activation in individuals with amblyopia.

PubMed

Spiegel, Daniel P; Byblow, Winston D; Hess, Robert F; Thompson, Benjamin

2013-10-01

Amblyopia is a neurodevelopmental disorder of vision that is associated with abnormal patterns of neural inhibition within the visual cortex. This disorder is often considered to be untreatable in adulthood because of insufficient visual cortex plasticity. There is increasing evidence that interventions that target inhibitory interactions within the visual cortex, including certain types of noninvasive brain stimulation, can improve visual function in adults with amblyopia. We tested the hypothesis that anodal transcranial direct current stimulation (a-tDCS) would improve visual function in adults with amblyopia by enhancing the neural response to inputs from the amblyopic eye. Thirteen adults with amblyopia participated and contrast sensitivity in the amblyopic and fellow fixing eye was assessed before, during and after a-tDCS or cathodal tDCS (c-tDCS). Five participants also completed a functional magnetic resonance imaging (fMRI) study designed to investigate the effect of a-tDCS on the blood oxygen level-dependent response within the visual cortex to inputs from the amblyopic versus the fellow fixing eye. A subgroup of 8/13 participants showed a transient improvement in amblyopic eye contrast sensitivity for at least 30 minutes after a-tDCS. fMRI measurements indicated that the characteristic cortical response asymmetry in amblyopes, which favors the fellow eye, was reduced by a-tDCS. These preliminary results suggest that a-tDCS deserves further investigation as a potential tool to enhance amblyopia treatment outcomes in adults.

Pattern classification and recognition of invertebrate functional groups using self-organizing neural networks.

PubMed

Zhang, WenJun

2007-07-01

Self-organizing neural networks can be used to mimic non-linear systems. The main objective of this study is to make pattern classification and recognition on sampling information using two self-organizing neural network models. Invertebrate functional groups sampled in the irrigated rice field were classified and recognized using one-dimensional self-organizing map and self-organizing competitive learning neural networks. Comparisons between neural network models, distance (similarity) measures, and number of neurons were conducted. The results showed that self-organizing map and self-organizing competitive learning neural network models were effective in pattern classification and recognition of sampling information. Overall the performance of one-dimensional self-organizing map neural network was better than self-organizing competitive learning neural network. The number of neurons could determine the number of classes in the classification. Different neural network models with various distance (similarity) measures yielded similar classifications. Some differences, dependent upon the specific network structure, would be found. The pattern of an unrecognized functional group was recognized with the self-organizing neural network. A relative consistent classification indicated that the following invertebrate functional groups, terrestrial blood sucker; terrestrial flyer; tourist (nonpredatory species with no known functional role other than as prey in ecosystem); gall former; collector (gather, deposit feeder); predator and parasitoid; leaf miner; idiobiont (acarine ectoparasitoid), were classified into the same group, and the following invertebrate functional groups, external plant feeder; terrestrial crawler, walker, jumper or hunter; neustonic (water surface) swimmer (semi-aquatic), were classified into another group. It was concluded that reliable conclusions could be drawn from comparisons of different neural network models that use different distance (similarity) measures. Results with the larger consistency will be more reliable.

Application of a neural network for reflectance spectrum classification

NASA Astrophysics Data System (ADS)

Yang, Gefei; Gartley, Michael

2017-05-01

Traditional reflectance spectrum classification algorithms are based on comparing spectrum across the electromagnetic spectrum anywhere from the ultra-violet to the thermal infrared regions. These methods analyze reflectance on a pixel by pixel basis. Inspired by high performance that Convolution Neural Networks (CNN) have demonstrated in image classification, we applied a neural network to analyze directional reflectance pattern images. By using the bidirectional reflectance distribution function (BRDF) data, we can reformulate the 4-dimensional into 2 dimensions, namely incident direction × reflected direction × channels. Meanwhile, RIT's micro-DIRSIG model is utilized to simulate additional training samples for improving the robustness of the neural networks training. Unlike traditional classification by using hand-designed feature extraction with a trainable classifier, neural networks create several layers to learn a feature hierarchy from pixels to classifier and all layers are trained jointly. Hence, the our approach of utilizing the angular features are different to traditional methods utilizing spatial features. Although training processing typically has a large computational cost, simple classifiers work well when subsequently using neural network generated features. Currently, most popular neural networks such as VGG, GoogLeNet and AlexNet are trained based on RGB spatial image data. Our approach aims to build a directional reflectance spectrum based neural network to help us to understand from another perspective. At the end of this paper, we compare the difference among several classifiers and analyze the trade-off among neural networks parameters.

Detection of micro gap weld joint by using magneto-optical imaging and Kalman filtering compensated with RBF neural network

NASA Astrophysics Data System (ADS)

Gao, Xiangdong; Chen, Yuquan; You, Deyong; Xiao, Zhenlin; Chen, Xiaohui

2017-02-01

An approach for seam tracking of micro gap weld whose width is less than 0.1 mm based on magneto optical (MO) imaging technique during butt-joint laser welding of steel plates is investigated. Kalman filtering(KF) technology with radial basis function(RBF) neural network for weld detection by an MO sensor was applied to track the weld center position. Because the laser welding system process noises and the MO sensor measurement noises were colored noises, the estimation accuracy of traditional KF for seam tracking was degraded by the system model with extreme nonlinearities and could not be solved by the linear state-space model. Also, the statistics characteristics of noises could not be accurately obtained in actual welding. Thus, a RBF neural network was applied to the KF technique to compensate for the weld tracking errors. The neural network can restrain divergence filter and improve the system robustness. In comparison of traditional KF algorithm, the RBF with KF was not only more effectively in improving the weld tracking accuracy but also reduced noise disturbance. Experimental results showed that magneto optical imaging technique could be applied to detect micro gap weld accurately, which provides a novel approach for micro gap seam tracking.

Correcting wave predictions with artificial neural networks

NASA Astrophysics Data System (ADS)

Makarynskyy, O.; Makarynska, D.

2003-04-01

The predictions of wind waves with different lead times are necessary in a large scope of coastal and open ocean activities. Numerical wave models, which usually provide this information, are based on deterministic equations that do not entirely account for the complexity and uncertainty of the wave generation and dissipation processes. An attempt to improve wave parameters short-term forecasts based on artificial neural networks is reported. In recent years, artificial neural networks have been used in a number of coastal engineering applications due to their ability to approximate the nonlinear mathematical behavior without a priori knowledge of interrelations among the elements within a system. The common multilayer feed-forward networks, with a nonlinear transfer functions in the hidden layers, were developed and employed to forecast the wave characteristics over one hour intervals starting from one up to 24 hours, and to correct these predictions. Three non-overlapping data sets of wave characteristics, both from a buoy, moored roughly 60 miles west of the Aran Islands, west coast of Ireland, were used to train and validate the neural nets involved. The networks were trained with error back propagation algorithm. Time series plots and scatterplots of the wave characteristics as well as tables with statistics show an improvement of the results achieved due to the correction procedure employed.

An Adaptive B-Spline Neural Network and Its Application in Terminal Sliding Mode Control for a Mobile Satcom Antenna Inertially Stabilized Platform.

PubMed

Zhang, Xiaolei; Zhao, Yan; Guo, Kai; Li, Gaoliang; Deng, Nianmao

2017-04-28

The mobile satcom antenna (MSA) enables a moving vehicle to communicate with a geostationary Earth orbit satellite. To realize continuous communication, the MSA should be aligned with the satellite in both sight and polarization all the time. Because of coupling effects, unknown disturbances, sensor noises and unmodeled dynamics existing in the system, the control system should have a strong adaptability. The significant features of terminal sliding mode control method are robustness and finite time convergence, but the robustness is related to the large switching control gain which is determined by uncertain issues and can lead to chattering phenomena. Neural networks can reduce the chattering and approximate nonlinear issues. In this work, a novel B-spline curve-based B-spline neural network (BSNN) is developed. The improved BSNN has the capability of shape changing and self-adaption. In addition, the output of the proposed BSNN is applied to approximate the nonlinear function in the system. The results of simulations and experiments are also compared with those of PID method, non-singularity fast terminal sliding mode (NFTSM) control and radial basis function (RBF) neural network-based NFTSM. It is shown that the proposed method has the best performance, with reliable control precision.

Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks.

PubMed

Hanson, Jack; Yang, Yuedong; Paliwal, Kuldip; Zhou, Yaoqi

2017-03-01

Capturing long-range interactions between structural but not sequence neighbors of proteins is a long-standing challenging problem in bioinformatics. Recently, long short-term memory (LSTM) networks have significantly improved the accuracy of speech and image classification problems by remembering useful past information in long sequential events. Here, we have implemented deep bidirectional LSTM recurrent neural networks in the problem of protein intrinsic disorder prediction. The new method, named SPOT-Disorder, has steadily improved over a similar method using a traditional, window-based neural network (SPINE-D) in all datasets tested without separate training on short and long disordered regions. Independent tests on four other datasets including the datasets from critical assessment of structure prediction (CASP) techniques and >10 000 annotated proteins from MobiDB, confirmed SPOT-Disorder as one of the best methods in disorder prediction. Moreover, initial studies indicate that the method is more accurate in predicting functional sites in disordered regions. These results highlight the usefulness combining LSTM with deep bidirectional recurrent neural networks in capturing non-local, long-range interactions for bioinformatics applications. SPOT-disorder is available as a web server and as a standalone program at: http://sparks-lab.org/server/SPOT-disorder/index.php . j.hanson@griffith.edu.au or yuedong.yang@griffith.edu.au or yaoqi.zhou@griffith.edu.au. Supplementary data is available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Pharmacological Tools to Study the Role of Astrocytes in Neural Network Functions.

PubMed

Peña-Ortega, Fernando; Rivera-Angulo, Ana Julia; Lorea-Hernández, Jonathan Julio

2016-01-01

Despite that astrocytes and microglia do not communicate by electrical impulses, they can efficiently communicate among them, with each other and with neurons, to participate in complex neural functions requiring broad cell-communication and long-lasting regulation of brain function. Glial cells express many receptors in common with neurons; secrete gliotransmitters as well as neurotrophic and neuroinflammatory factors, which allow them to modulate synaptic transmission and neural excitability. All these properties allow glial cells to influence the activity of neuronal networks. Thus, the incorporation of glial cell function into the understanding of nervous system dynamics will provide a more accurate view of brain function. Our current knowledge of glial cell biology is providing us with experimental tools to explore their participation in neural network modulation. In this chapter, we review some of the classical, as well as some recent, pharmacological tools developed for the study of astrocyte's influence in neural function. We also provide some examples of the use of these pharmacological agents to understand the role of astrocytes in neural network function and dysfunction.

Acute administration of ucf-101 ameliorates the locomotor impairments induced by a traumatic spinal cord injury.

PubMed

Reigada, D; Nieto-Díaz, M; Navarro-Ruiz, R; Caballero-López, M J; Del Águila, A; Muñoz-Galdeano, T; Maza, R M

2015-08-06

Secondary death of neural cells plays a key role in the physiopathology and the functional consequences of traumatic spinal cord injury (SCI). Pharmacological manipulation of cell death pathways leading to the preservation of neural cells is acknowledged as a main therapeutic goal in SCI. In the present work, we hypothesize that administration of the neuroprotective cell-permeable compound ucf-101 will reduce neural cell death during the secondary damage of SCI, increasing tissue preservation and reducing the functional deficits. To test this hypothesis, we treated mice with ucf-101 during the first week after a moderate contusive SCI. Our results reveal that ucf-101 administration protects neural cells from the deleterious secondary mechanisms triggered by the trauma, reducing the extension of tissue damage and improving motor function recovery. Our studies also suggest that the effects of ucf-101 may be mediated through the inhibition of HtrA2/OMI and the concomitant increase of inhibitor of apoptosis protein XIAP, as well as the induction of ERK1/2 activation and/or expression. In vitro assays confirm the effects of ucf-101 on both pathways as well as on the reduction of caspase cascade activation and apoptotic cell death in a neuroblastoma cell line. These results suggest that ucf-101 can be a promising therapeutic tool for SCI that deserves more detailed analyses. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

Insect Responses to Linearly Polarized Reflections: Orphan Behaviors Without Neural Circuits

PubMed Central

Heinloth, Tanja; Uhlhorn, Juliane; Wernet, Mathias F.

2018-01-01

The e-vector orientation of linearly polarized light represents an important visual stimulus for many insects. Especially the detection of polarized skylight by many navigating insect species is known to improve their orientation skills. While great progress has been made towards describing both the anatomy and function of neural circuit elements mediating behaviors related to navigation, relatively little is known about how insects perceive non-celestial polarized light stimuli, like reflections off water, leaves, or shiny body surfaces. Work on different species suggests that these behaviors are not mediated by the “Dorsal Rim Area” (DRA), a specialized region in the dorsal periphery of the adult compound eye, where ommatidia contain highly polarization-sensitive photoreceptor cells whose receptive fields point towards the sky. So far, only few cases of polarization-sensitive photoreceptors have been described in the ventral periphery of the insect retina. Furthermore, both the structure and function of those neural circuits connecting to these photoreceptor inputs remain largely uncharacterized. Here we review the known data on non-celestial polarization vision from different insect species (dragonflies, butterflies, beetles, bugs and flies) and present three well-characterized examples for functionally specialized non-DRA detectors from different insects that seem perfectly suited for mediating such behaviors. Finally, using recent advances from circuit dissection in Drosophila melanogaster, we discuss what types of potential candidate neurons could be involved in forming the underlying neural circuitry mediating non-celestial polarization vision. PMID:29615868

Increase in MST activity correlates with visual motion learning: A functional MRI study of perceptual learning

PubMed Central

Larcombe, Stephanie J.; Kennard, Chris

2017-01-01

Abstract Repeated practice of a specific task can improve visual performance, but the neural mechanisms underlying this improvement in performance are not yet well understood. Here we trained healthy participants on a visual motion task daily for 5 days in one visual hemifield. Before and after training, we used functional magnetic resonance imaging (fMRI) to measure the change in neural activity. We also imaged a control group of participants on two occasions who did not receive any task training. While in the MRI scanner, all participants completed the motion task in the trained and untrained visual hemifields separately. Following training, participants improved their ability to discriminate motion direction in the trained hemifield and, to a lesser extent, in the untrained hemifield. The amount of task learning correlated positively with the change in activity in the medial superior temporal (MST) area. MST is the anterior portion of the human motion complex (hMT+). MST changes were localized to the hemisphere contralateral to the region of the visual field, where perceptual training was delivered. Visual areas V2 and V3a showed an increase in activity between the first and second scan in the training group, but this was not correlated with performance. The contralateral anterior hippocampus and bilateral dorsolateral prefrontal cortex (DLPFC) and frontal pole showed changes in neural activity that also correlated with the amount of task learning. These findings emphasize the importance of MST in perceptual learning of a visual motion task. Hum Brain Mapp 39:145–156, 2018. © 2017 Wiley Periodicals, Inc. PMID:28963815

Innovative approaches to the rehabilitation of upper extremity hemiparesis using virtual environments

PubMed Central

MERIANS, A. S.; TUNIK, E.; FLUET, G. G.; QIU, Q.; ADAMOVICH, S. V.

2017-01-01

Aim Upper-extremity interventions for hemiparesis are a challenging aspect of stroke rehabilitation. Purpose of this paper is to report the feasibility of using virtual environments (VEs) in combination with robotics to assist recovery of hand-arm function and to present preliminary data demonstrating the potential of using sensory manipulations in VE to drive activation in targeted neural regions. Methods We trained 8 subjects for 8 three hour sessions using a library of complex VE’s integrated with robots, comparing training arm and hand separately to training arm and hand together. Instrumented gloves and hand exoskeleton were used for hand tracking and haptic effects. Haptic Master robotic arm was used for arm tracking and generating three-dimensional haptic VEs. To investigate the use of manipulations in VE to drive neural activations, we created a “virtual mirror” that subjects used while performing a unimanual task. Cortical activation was measured with functional MRI (fMRI) and transcranial magnetic stimulation. Results Both groups showed improvement in kinematics and measures of real-world function. The group trained using their arm and hand together showed greater improvement. In a stroke subject, fMRI data suggested virtual mirror feedback could activate the sensorimotor cortex contralateral to the reflected hand (ipsilateral to the moving hand) thus recruiting the lesioned hemisphere. Conclusion Gaming simulations interfaced with robotic devices provide a training medium that can modify movement patterns. In addition to showing that our VE therapies can optimize behavioral performance, we show preliminary evidence to support the potential of using specific sensory manipulations to selectively recruit targeted neural circuits. PMID:19158659

Neural interface of mirror therapy in chronic stroke patients: a functional magnetic resonance imaging study.

PubMed

Bhasin, Ashu; Padma Srivastava, M V; Kumaran, Senthil S; Bhatia, Rohit; Mohanty, Sujata

2012-01-01

Recovery in stroke is mediated by neural plasticity. Neuro-restorative therapies improve recovery after stroke by promoting repair and function. Mirror neuron system (MNS) has been studied widely in humans in stroke and phantom sensations. Study subjects included 20 patients with chronic stroke and 10 healthy controls. Patients had clinical disease-severity scores, functional magnetic resonance imaging (fMRI) and diffuse tensor imaging (DTI) at baseline, 8 and at 24 weeks. Block design with alternate baseline and activation cycles was used with a total of 90 whole brain echo planar imaging (EPI) measurements (timed repetition (TR) = 4520 ms, timed echo (TE) = 44 ms, slices = 31, slice thickness = 4 mm, EPI factor 127, matrix = 128 × 128, FOV = 230 mm). Whole brain T1-weighted images were acquired using 3D sequence (MPRage) with 120 contiguous slices of 1.0 mm thickness. The mirror therapy was aimed via laptop system integrated with web camera, mirroring the movement of the unaffected hand. This therapy was administered for 5 days in a week for 60-90 min for 8 weeks. All the patients showed statistical significant improvement in Fugl Meyer and modified Barthel Index (P < 0.05) whereas the change in Medical Research Council (MRC) power grade was not significant post-therapy (8 weeks). There was an increase in the laterality index (LI) of ipsilesional BA 4 and BA 6 at 8 weeks exhibiting recruitment and focusing principles of neural plasticity. Mirror therapy simulated the "action-observation" hypothesis exhibiting recovery in patients with chronic stroke. Therapy induced cortical reorganization was also observed from our study.

Impaired Tuning of Neural Ensembles and the Pathophysiology of Schizophrenia: A Translational and Computational Neuroscience Perspective.

PubMed

Krystal, John H; Anticevic, Alan; Yang, Genevieve J; Dragoi, George; Driesen, Naomi R; Wang, Xiao-Jing; Murray, John D

2017-05-15

The functional optimization of neural ensembles is central to human higher cognitive functions. When the functions through which neural activity is tuned fail to develop or break down, symptoms and cognitive impairments arise. This review considers ways in which disturbances in the balance of excitation and inhibition might develop and be expressed in cortical networks in association with schizophrenia. This presentation is framed within a developmental perspective that begins with disturbances in glutamate synaptic development in utero. It considers developmental correlates and consequences, including compensatory mechanisms that increase intrinsic excitability or reduce inhibitory tone. It also considers the possibility that these homeostatic increases in excitability have potential negative functional and structural consequences. These negative functional consequences of disinhibition may include reduced working memory-related cortical activity associated with the downslope of the "inverted-U" input-output curve, impaired spatial tuning of neural activity and impaired sparse coding of information, and deficits in the temporal tuning of neural activity and its implication for neural codes. The review concludes by considering the functional significance of noisy activity for neural network function. The presentation draws on computational neuroscience and pharmacologic and genetic studies in animals and humans, particularly those involving N-methyl-D-aspartate glutamate receptor antagonists, to illustrate principles of network regulation that give rise to features of neural dysfunction associated with schizophrenia. While this presentation focuses on schizophrenia, the general principles outlined in the review may have broad implications for considering disturbances in the regulation of neural ensembles in psychiatric disorders. Published by Elsevier Inc.

Research on artificial neural network intrusion detection photochemistry based on the improved wavelet analysis and transformation

NASA Astrophysics Data System (ADS)

Li, Hong; Ding, Xue

2017-03-01

This paper combines wavelet analysis and wavelet transform theory with artificial neural network, through the pretreatment on point feature attributes before in intrusion detection, to make them suitable for improvement of wavelet neural network. The whole intrusion classification model gets the better adaptability, self-learning ability, greatly enhances the wavelet neural network for solving the problem of field detection invasion, reduces storage space, contributes to improve the performance of the constructed neural network, and reduces the training time. Finally the results of the KDDCup99 data set simulation experiment shows that, this method reduces the complexity of constructing wavelet neural network, but also ensures the accuracy of the intrusion classification.

Distinct Neural-Functional Effects of Treatments With Selective Serotonin Reuptake Inhibitors, Electroconvulsive Therapy, and Transcranial Magnetic Stimulation and Their Relations to Regional Brain Function in Major Depression: A Meta-analysis.

PubMed

Chau, David T; Fogelman, Phoebe; Nordanskog, Pia; Drevets, Wayne C; Hamilton, J Paul

2017-05-01

Functional neuroimaging studies have examined the neural substrates of treatments for major depressive disorder (MDD). Low sample size and methodological heterogeneity, however, undermine the generalizability of findings from individual studies. We conducted a meta-analysis to identify reliable neural changes resulting from different modes of treatment for MDD and compared them with each other and with reliable neural functional abnormalities observed in depressed versus control samples. We conducted a meta-analysis of studies reporting changes in brain activity (e.g., as indexed by positron emission tomography) following treatments with selective serotonin reuptake inhibitors (SSRIs), electroconvulsive therapy (ECT), or transcranial magnetic stimulation. Additionally, we examined the statistical reliability of overlap among thresholded meta-analytic SSRI, ECT, and transcranial magnetic stimulation maps as well as a map of abnormal neural function in MDD. Our meta-analysis revealed that 1) SSRIs decrease activity in the anterior insula, 2) ECT decreases activity in central nodes of the default mode network, 3) transcranial magnetic stimulation does not result in reliable neural changes, and 4) regional effects of these modes of treatment do not significantly overlap with each other or with regions showing reliable functional abnormality in MDD. SSRIs and ECT produce neurally distinct effects relative to each other and to the functional abnormalities implicated in depression. These treatments therefore may exert antidepressant effects by diminishing neural functions not implicated in depression but that nonetheless impact mood. We discuss how the distinct neural changes resulting from SSRIs and ECT can account for both treatment effects and side effects from these therapies as well as how to individualize these treatments. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

A dynamical systems approach for estimating phase interactions between rhythms of different frequencies from experimental data

PubMed Central

Goto, Takahiro; Aoyagi, Toshio

2018-01-01

Synchronization of neural oscillations as a mechanism of brain function is attracting increasing attention. Neural oscillation is a rhythmic neural activity that can be easily observed by noninvasive electroencephalography (EEG). Neural oscillations show the same frequency and cross-frequency synchronization for various cognitive and perceptual functions. However, it is unclear how this neural synchronization is achieved by a dynamical system. If neural oscillations are weakly coupled oscillators, the dynamics of neural synchronization can be described theoretically using a phase oscillator model. We propose an estimation method to identify the phase oscillator model from real data of cross-frequency synchronized activities. The proposed method can estimate the coupling function governing the properties of synchronization. Furthermore, we examine the reliability of the proposed method using time-series data obtained from numerical simulation and an electronic circuit experiment, and show that our method can estimate the coupling function correctly. Finally, we estimate the coupling function between EEG oscillation and the speech sound envelope, and discuss the validity of these results. PMID:29337999

Podocalyxin Is a Novel Polysialylated Neural Adhesion Protein with Multiple Roles in Neural Development and Synapse Formation

PubMed Central

Vitureira, Nathalia; Andrés, Rosa; Pérez-Martínez, Esther; Martínez, Albert; Bribián, Ana; Blasi, Juan; Chelliah, Shierley; López-Doménech, Guillermo; De Castro, Fernando; Burgaya, Ferran; McNagny, Kelly; Soriano, Eduardo

2010-01-01

Neural development and plasticity are regulated by neural adhesion proteins, including the polysialylated form of NCAM (PSA-NCAM). Podocalyxin (PC) is a renal PSA-containing protein that has been reported to function as an anti-adhesin in kidney podocytes. Here we show that PC is widely expressed in neurons during neural development. Neural PC interacts with the ERM protein family, and with NHERF1/2 and RhoA/G. Experiments in vitro and phenotypic analyses of podxl-deficient mice indicate that PC is involved in neurite growth, branching and axonal fasciculation, and that PC loss-of-function reduces the number of synapses in the CNS and in the neuromuscular system. We also show that whereas some of the brain PC functions require PSA, others depend on PC per se. Our results show that PC, the second highly sialylated neural adhesion protein, plays multiple roles in neural development. PMID:20706633

Effects and mechanisms of melatonin on neural differentiation of induced pluripotent stem cells.

PubMed

Shu, Tao; Wu, Tao; Pang, Mao; Liu, Chang; Wang, Xuan; Wang, Juan; Liu, Bin; Rong, Limin

2016-06-03

Melatonin, a lipophilic molecule mainly synthesized in the pineal gland, has properties of antioxidation, anti-inflammation, and antiapoptosis to improve neuroprotective functions. Here, we investigate effects and mechanisms of melatonin on neural differentiation of induced pluripotent stem cells (iPSCs). iPSCs were induced into neural stem cells (NSCs), then further differentiated into neurons in medium with or without melatonin, melatonin receptor antagonist (Luzindole) or Phosphatidylinositide 3 kinase (PI3K) inhibitor (LY294002). Melatonin significantly promoted the number of neurospheres and cell viability. In addition, Melatonin markedly up-regulated gene and protein expression of Nestin and MAP2. However, Luzindole or LY294002 attenuated these increase. The expression of pAKT/AKT were increased by Melatonin, while Luzindole or LY294002 declined these melatonin-induced increase. These results suggest that melatonin significantly increased neural differentiation of iPSCs via activating PI3K/AKT signaling pathway through melatonin receptor. Copyright © 2016 Elsevier Inc. All rights reserved.

Chinese Sentence Classification Based on Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Gu, Chengwei; Wu, Ming; Zhang, Chuang

2017-10-01

Sentence classification is one of the significant issues in Natural Language Processing (NLP). Feature extraction is often regarded as the key point for natural language processing. Traditional ways based on machine learning can not take high level features into consideration, such as Naive Bayesian Model. The neural network for sentence classification can make use of contextual information to achieve greater results in sentence classification tasks. In this paper, we focus on classifying Chinese sentences. And the most important is that we post a novel architecture of Convolutional Neural Network (CNN) to apply on Chinese sentence classification. In particular, most of the previous methods often use softmax classifier for prediction, we embed a linear support vector machine to substitute softmax in the deep neural network model, minimizing a margin-based loss to get a better result. And we use tanh as an activation function, instead of ReLU. The CNN model improve the result of Chinese sentence classification tasks. Experimental results on the Chinese news title database validate the effectiveness of our model.

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

PubMed

Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

2016-01-11

Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields

NASA Astrophysics Data System (ADS)

Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

2016-01-01

Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

Design strategies for dynamic closed-loop optogenetic neurocontrol in vivo

NASA Astrophysics Data System (ADS)

Bolus, M. F.; Willats, A. A.; Whitmire, C. J.; Rozell, C. J.; Stanley, G. B.

2018-04-01

Objective. Controlling neural activity enables the possibility of manipulating sensory perception, cognitive processes, and body movement, in addition to providing a powerful framework for functionally disentangling the neural circuits that underlie these complex phenomena. Over the last decade, optogenetic stimulation has become an increasingly important and powerful tool for understanding neural circuit function, owing to the ability to target specific cell types and bidirectionally modulate neural activity. To date, most stimulation has been provided in open-loop or in an on/off closed-loop fashion, where previously-determined stimulation is triggered by an event. Here, we describe and demonstrate a design approach for precise optogenetic control of neuronal firing rate modulation using feedback to guide stimulation continuously. Approach. Using the rodent somatosensory thalamus as an experimental testbed for realizing desired time-varying patterns of firing rate modulation, we utilized a moving average exponential filter to estimate firing rate online from single-unit spiking measured extracellularly. This estimate of instantaneous rate served as feedback for a proportional integral (PI) controller, which was designed during the experiment based on a linear-nonlinear Poisson (LNP) model of the neuronal response to light. Main results. The LNP model fit during the experiment enabled robust closed-loop control, resulting in good tracking of sinusoidal and non-sinusoidal targets, and rejection of unmeasured disturbances. Closed-loop control also enabled manipulation of trial-to-trial variability. Significance. Because neuroscientists are faced with the challenge of dissecting the functions of circuit components, the ability to maintain control of a region of interest in spite of changes in ongoing neural activity will be important for disambiguating function within networks. Closed-loop stimulation strategies are ideal for control that is robust to such changes, and the employment of continuous feedback to adjust stimulation in real-time can improve the quality of data collected using optogenetic manipulation.

Integration of donor mesenchymal stem cell-derived neuron-like cells into host neural network after rat spinal cord transection.

PubMed

Zeng, Xiang; Qiu, Xue-Cheng; Ma, Yuan-Huan; Duan, Jing-Jing; Chen, Yuan-Feng; Gu, Huai-Yu; Wang, Jun-Mei; Ling, Eng-Ang; Wu, Jin-Lang; Wu, Wutian; Zeng, Yuan-Shan

2015-06-01

Functional deficits following spinal cord injury (SCI) primarily attribute to loss of neural connectivity. We therefore tested if novel tissue engineering approaches could enable neural network repair that facilitates functional recovery after spinal cord transection (SCT). Rat bone marrow-derived mesenchymal stem cells (MSCs), genetically engineered to overexpress TrkC, receptor of neurotrophin-3 (NT-3), were pre-differentiated into cells carrying neuronal features via co-culture with NT-3 overproducing Schwann cells in 3-dimensional gelatin sponge (GS) scaffold for 14 days in vitro. Intra-GS formation of MSC assemblies emulating neural network (MSC-GS) were verified morphologically via electron microscopy (EM) and functionally by whole-cell patch clamp recording of spontaneous post-synaptic currents. The differentiated MSCs still partially maintained prototypic property with the expression of some mesodermal cytokines. MSC-GS or GS was then grafted acutely into a 2 mm-wide transection gap in the T9-T10 spinal cord segments of adult rats. Eight weeks later, hindlimb function of the MSC-GS-treated SCT rats was significantly improved relative to controls receiving the GS or lesion only as indicated by BBB score. The MSC-GS transplantation also significantly recovered cortical motor evoked potential (CMEP). Histologically, MSC-derived neuron-like cells maintained their synapse-like structures in vivo; they additionally formed similar connections with host neurites (i.e., mostly serotonergic fibers plus a few corticospinal axons; validated by double-labeled immuno-EM). Moreover, motor cortex electrical stimulation triggered c-fos expression in the grafted and lumbar spinal cord cells of the treated rats only. Our data suggest that MSC-derived neuron-like cells resulting from NT-3-TrkC-induced differentiation can partially integrate into transected spinal cord and this strategy should be further investigated for reconstructing disrupted neural circuits. Copyright © 2015 Elsevier Ltd. All rights reserved.

Prediction of silicon oxynitride plasma etching using a generalized regression neural network

NASA Astrophysics Data System (ADS)

Kim, Byungwhan; Lee, Byung Teak

2005-08-01

A prediction model of silicon oxynitride (SiON) etching was constructed using a neural network. Model prediction performance was improved by means of genetic algorithm. The etching was conducted in a C2F6 inductively coupled plasma. A 24 full factorial experiment was employed to systematically characterize parameter effects on SiON etching. The process parameters include radio frequency source power, bias power, pressure, and C2F6 flow rate. To test the appropriateness of the trained model, additional 16 experiments were conducted. For comparison, four types of statistical regression models were built. Compared to the best regression model, the optimized neural network model demonstrated an improvement of about 52%. The optimized model was used to infer etch mechanisms as a function of parameters. The pressure effect was noticeably large only as relatively large ion bombardment was maintained in the process chamber. Ion-bombardment-activated polymer deposition played the most significant role in interpreting the complex effect of bias power or C2F6 flow rate. Moreover, [CF2] was expected to be the predominant precursor to polymer deposition.

Prevention of the neurocristopathy Treacher Collins syndrome through inhibition of p53 function

PubMed Central

Jones, Natalie C; Lynn, Megan L; Gaudenz, Karin; Sakai, Daisuke; Aoto, Kazushi; Rey, Jean-Phillipe; Glynn, Earl F; Ellington, Lacey; Du, Chunying; Dixon, Jill; Dixon, Michael J; Trainor, Paul A

2010-01-01

Treacher Collins syndrome (TCS) is a congenital disorder of craniofacial development arising from mutations in TCOF1, which encodes the nucleolar phosphoprotein Treacle. Haploinsufficiency of Tcof1 perturbs mature ribosome biogenesis, resulting in stabilization of p53 and the cyclin G1–mediated cell-cycle arrest that underpins the specificity of neuroepithelial apoptosis and neural crest cell hypoplasia characteristic of TCS. Here we show that inhibition of p53 prevents cyclin G1–driven apoptotic elimination of neural crest cells while rescuing the craniofacial abnormalities associated with mutations in Tcof1 and extending life span. These improvements, however, occur independently of the effects on ribosome biogenesis; thus suggesting that it is p53-dependent neuroepithelial apoptosis that is the primary mechanism underlying the pathogenesis of TCS. Our work further implies that neuroepithelial and neural crest cells are particularly sensitive to cellular stress during embryogenesis and that suppression of p53 function provides an attractive avenue for possible clinical prevention of TCS craniofacial birth defects and possibly those of other neurocristopathies. PMID:18246078

Striatum-medial prefrontal cortex connectivity predicts developmental changes in reinforcement learning.

PubMed

van den Bos, Wouter; Cohen, Michael X; Kahnt, Thorsten; Crone, Eveline A

2012-06-01

During development, children improve in learning from feedback to adapt their behavior. However, it is still unclear which neural mechanisms might underlie these developmental changes. In the current study, we used a reinforcement learning model to investigate neurodevelopmental changes in the representation and processing of learning signals. Sixty-seven healthy volunteers between ages 8 and 22 (children: 8-11 years, adolescents: 13-16 years, and adults: 18-22 years) performed a probabilistic learning task while in a magnetic resonance imaging scanner. The behavioral data demonstrated age differences in learning parameters with a stronger impact of negative feedback on expected value in children. Imaging data revealed that the neural representation of prediction errors was similar across age groups, but functional connectivity between the ventral striatum and the medial prefrontal cortex changed as a function of age. Furthermore, the connectivity strength predicted the tendency to alter expectations after receiving negative feedback. These findings suggest that the underlying mechanisms of developmental changes in learning are not related to differences in the neural representation of learning signals per se but rather in how learning signals are used to guide behavior and expectations.

3D in vitro modeling of the central nervous system

PubMed Central

Hopkins, Amy M.; DeSimone, Elise; Chwalek, Karolina; Kaplan, David L.

2015-01-01

There are currently more than 600 diseases characterized as affecting the central nervous system (CNS) which inflict neural damage. Unfortunately, few of these conditions have effective treatments available. Although significant efforts have been put into developing new therapeutics, drugs which were promising in the developmental phase have high attrition rates in late stage clinical trials. These failures could be circumvented if current 2D in vitro and in vivo models were improved. 3D, tissue-engineered in vitro systems can address this need and enhance clinical translation through two approaches: (1) bottom-up, and (2) top-down (developmental/regenerative) strategies to reproduce the structure and function of human tissues. Critical challenges remain including biomaterials capable of matching the mechanical properties and extracellular matrix (ECM) composition of neural tissues, compartmentalized scaffolds that support heterogeneous tissue architectures reflective of brain organization and structure, and robust functional assays for in vitro tissue validation. The unique design parameters defined by the complex physiology of the CNS for construction and validation of 3D in vitro neural systems are reviewed here. PMID:25461688

The effect of cervical traction combined with neural mobilization on pain and disability in cervical radiculopathy. A case report.

PubMed

Savva, Christos; Giakas, Giannis

2013-10-01

Cervical radiculopathy is the result of cervical nerve root pathology that may lead to chronic pain and disability. Although manual therapy interventions including cervical traction and neural mobilization have been advocated to decrease pain and disability caused by cervical radiculopathy, their analgesic effect has been questioned due to the low quality of research evidence. The purpose of this paper is to present the effect of cervical traction combined with neural mobilization on pain and disability in a patient experiencing cervical radiculopathy. A 52-year-old woman presented with a 2 month history of neurological cervico-brachial pain and whose presentation was consistent with cervical radiculopathy. Cervical traction and a slider neural mobilization of the medial nerve were applied simultaneously to reduce the patient's pain and disability measured at baseline and at 2 and 4 weeks using the Numeric Pain Rating Scale, the Neck Disability Index and the Patient-Specific Functional Scale. Improvements in all outcome measures were noted over a period of four weeks. Scores in all outcome measures revealed that the patient's pain had almost disappeared and that she was able to perform her household chores and job tasks without difficulties and limitations. In conclusion, the findings of this study support that the application of cervical traction combined with neural mobilization can produce significant improvements in terms of pain and disability in cervical radiculopathy. Copyright © 2012 Elsevier Ltd. All rights reserved.

A neural model of valuation and information virality

PubMed Central

Baek, Elisa C.; O’Donnell, Matthew Brook; Kim, Hyun Suk; Cappella, Joseph N.

2017-01-01

Information sharing is an integral part of human interaction that serves to build social relationships and affects attitudes and behaviors in individuals and large groups. We present a unifying neurocognitive framework of mechanisms underlying information sharing at scale (virality). We argue that expectations regarding self-related and social consequences of sharing (e.g., in the form of potential for self-enhancement or social approval) are integrated into a domain-general value signal that encodes the value of sharing a piece of information. This value signal translates into population-level virality. In two studies (n = 41 and 39 participants), we tested these hypotheses using functional neuroimaging. Neural activity in response to 80 New York Times articles was observed in theory-driven regions of interest associated with value, self, and social cognitions. This activity then was linked to objectively logged population-level data encompassing n = 117,611 internet shares of the articles. In both studies, activity in neural regions associated with self-related and social cognition was indirectly related to population-level sharing through increased neural activation in the brain's value system. Neural activity further predicted population-level outcomes over and above the variance explained by article characteristics and commonly used self-report measures of sharing intentions. This parsimonious framework may help advance theory, improve predictive models, and inform new approaches to effective intervention. More broadly, these data shed light on the core functions of sharing—to express ourselves in positive ways and to strengthen our social bonds. PMID:28242678

Resting-state hemodynamics are spatiotemporally coupled to synchronized and symmetric neural activity in excitatory neurons.

PubMed

Ma, Ying; Shaik, Mohammed A; Kozberg, Mariel G; Kim, Sharon H; Portes, Jacob P; Timerman, Dmitriy; Hillman, Elizabeth M C

2016-12-27

Brain hemodynamics serve as a proxy for neural activity in a range of noninvasive neuroimaging techniques including functional magnetic resonance imaging (fMRI). In resting-state fMRI, hemodynamic fluctuations have been found to exhibit patterns of bilateral synchrony, with correlated regions inferred to have functional connectivity. However, the relationship between resting-state hemodynamics and underlying neural activity has not been well established, making the neural underpinnings of functional connectivity networks unclear. In this study, neural activity and hemodynamics were recorded simultaneously over the bilateral cortex of awake and anesthetized Thy1-GCaMP mice using wide-field optical mapping. Neural activity was visualized via selective expression of the calcium-sensitive fluorophore GCaMP in layer 2/3 and 5 excitatory neurons. Characteristic patterns of resting-state hemodynamics were accompanied by more rapidly changing bilateral patterns of resting-state neural activity. Spatiotemporal hemodynamics could be modeled by convolving this neural activity with hemodynamic response functions derived through both deconvolution and gamma-variate fitting. Simultaneous imaging and electrophysiology confirmed that Thy1-GCaMP signals are well-predicted by multiunit activity. Neurovascular coupling between resting-state neural activity and hemodynamics was robust and fast in awake animals, whereas coupling in urethane-anesthetized animals was slower, and in some cases included lower-frequency (<0.04 Hz) hemodynamic fluctuations that were not well-predicted by local Thy1-GCaMP recordings. These results support that resting-state hemodynamics in the awake and anesthetized brain are coupled to underlying patterns of excitatory neural activity. The patterns of bilaterally-symmetric spontaneous neural activity revealed by wide-field Thy1-GCaMP imaging may depict the neural foundation of functional connectivity networks detected in resting-state fMRI.

Resting-state hemodynamics are spatiotemporally coupled to synchronized and symmetric neural activity in excitatory neurons

PubMed Central

Ma, Ying; Shaik, Mohammed A.; Kozberg, Mariel G.; Portes, Jacob P.; Timerman, Dmitriy

2016-01-01

Brain hemodynamics serve as a proxy for neural activity in a range of noninvasive neuroimaging techniques including functional magnetic resonance imaging (fMRI). In resting-state fMRI, hemodynamic fluctuations have been found to exhibit patterns of bilateral synchrony, with correlated regions inferred to have functional connectivity. However, the relationship between resting-state hemodynamics and underlying neural activity has not been well established, making the neural underpinnings of functional connectivity networks unclear. In this study, neural activity and hemodynamics were recorded simultaneously over the bilateral cortex of awake and anesthetized Thy1-GCaMP mice using wide-field optical mapping. Neural activity was visualized via selective expression of the calcium-sensitive fluorophore GCaMP in layer 2/3 and 5 excitatory neurons. Characteristic patterns of resting-state hemodynamics were accompanied by more rapidly changing bilateral patterns of resting-state neural activity. Spatiotemporal hemodynamics could be modeled by convolving this neural activity with hemodynamic response functions derived through both deconvolution and gamma-variate fitting. Simultaneous imaging and electrophysiology confirmed that Thy1-GCaMP signals are well-predicted by multiunit activity. Neurovascular coupling between resting-state neural activity and hemodynamics was robust and fast in awake animals, whereas coupling in urethane-anesthetized animals was slower, and in some cases included lower-frequency (<0.04 Hz) hemodynamic fluctuations that were not well-predicted by local Thy1-GCaMP recordings. These results support that resting-state hemodynamics in the awake and anesthetized brain are coupled to underlying patterns of excitatory neural activity. The patterns of bilaterally-symmetric spontaneous neural activity revealed by wide-field Thy1-GCaMP imaging may depict the neural foundation of functional connectivity networks detected in resting-state fMRI. PMID:27974609

Neural Network Technique for Continous Transition from Ocean to Coastal Retrackers

NASA Astrophysics Data System (ADS)

Hazrina Idris, Nurul; Deng, Xiaoli; Hawani Idris, Nurul

2017-04-01

This paper presents the development of neural network for continuous transition of altimeter sea surface heights when switching from ocean to coastal waveform retrackers. In attempting to produce precise coastal sea level anomaly (SLA) via retracking waveforms, issue arose when employing multiple retracking algorithms (i.e. MLE-4, sub-waveform and threshold). The existence of relative offset between those retrackers creates 'jump' in the retracked SLA profiles. In this study, the offset between retrackers is minimized using multi-layer feed forward neural network technique. The technique reduces the offset values by modelling the complicated functions of those retracked SLAs. The technique is tested over the region of the Great Barrier Reef (GBR), Australia. The validation with Townsville and Bundaberg tide gauges shows that the threshold retracker achieves temporal correlations (r) of 0.84 and 0.75, respectively, and root mean square (RMS) error is 16 cm for both stations, indicating that the retracker produces more accurate SLAs than those of two retrackers. Meanwhile, values of r (RMS error) for MLE-4 is only 0.79 (18 cm) and 0.71 (16 cm), respectively, and for sub-waveform is 0.82 (16 cm) and 0.67 (16 cm), respectively. Therefore, with the neural network, retracked SLAs from MLE-4 and sub-waveform are aligned to those of the threshold retracker. The performance of neural network is compared with the normal procedure of offset removal, which is based on the mean of SLA differences (mean method). The performance is assessed by computing the standard deviation of difference (STD) between the SLAs above a referenced ellipsoid and the geoidal height, and the improvement of percentage (IMP). The results indicate that the neural network provides improvement in SLA precision in all 12 cases, while the mean method provides improvement in 10 out of 12 cases and deterioration is seen in two cases. In terms of STD and IMP, neural network reduces the offset better than those of the mean method. The IMPs with neural network reaches up to 67% for Jason-1 and 73% for Jason-2, meanwhile with mean method the IMPs only reaches up to 28% and 46%, respectively. In conclusion, the neural network technique is efficient to reduce the offset among retrackers by handling the linear and nonlinear relationship between retrackers, thus providing seamless transition from the open ocean to the coast, and vice versa. Studies in currently on-going are to consider other geophysical parameters, such as significant wave height that might be related to the variation of the offset, in the neural network.

Neural networks for function approximation in nonlinear control

NASA Technical Reports Server (NTRS)

Linse, Dennis J.; Stengel, Robert F.

1990-01-01

Two neural network architectures are compared with a classical spline interpolation technique for the approximation of functions useful in a nonlinear control system. A standard back-propagation feedforward neural network and a cerebellar model articulation controller (CMAC) neural network are presented, and their results are compared with a B-spline interpolation procedure that is updated using recursive least-squares parameter identification. Each method is able to accurately represent a one-dimensional test function. Tradeoffs between size requirements, speed of operation, and speed of learning indicate that neural networks may be practical for identification and adaptation in a nonlinear control environment.

RIP140/PGC-1α axis involved in vitamin A-induced neural differentiation by increasing mitochondrial function.

PubMed

Mu, Qing; Yu, Weidong; Zheng, Shuying; Shi, Hongxia; Li, Mei; Sun, Jie; Wang, Di; Hou, Xiaoli; Liu, Ling; Wang, Xinjuan; Zhao, Zhuran; Liang, Rong; Zhang, Xue; Dong, Wei; Zeng, Chaomei; Guo, Jingzhu

2018-03-07

Vitamin A deficiency and mitochondrial dysfunction are both associated with neural differentiation-related disorders, such as Alzheimer's disease (AD) and Down syndrome (DS). The mechanism of vitamin A-induced neural differentiation and the notion that vitamin A can regulate the morphology and function of mitochondria in its induction of neural differentiation through the RIP140/PGC-1α axis are unclear. The aim of this study was to investigate the roles and underlying mechanisms of RIP140/PGC-1α axis in vitamin A-induced neural differentiation. Human neuroblastoma cells (SH-SY5Y) were used as a model of neural stem cells, which were incubated with DMSO, 9-cis-retinoic acid (9-cis-RA), 13-cis-retinoic acid (13-cis-RA) and all-trans-retinoic acid (at-RA). Neural differentiation of SH-SY5Y was evaluated by Sandquist calculation, combined with immunofluorescence and real-time polymerase chain reaction (PCR) of neural markers. Mitochondrial function was estimated by ultrastructure assay using transmission electron microscopy (TEM) combined with the expression of PGC-1α and NEMGs using real-time PCR. The participation of the RA signaling pathway was demonstrated by adding RA receptor antagonists. Vitamin A derivatives are able to regulate mitochondrial morphology and function, and furthermore to induce neural differentiation through the RA signaling pathway. The RIP140/PGC-1α axis is involved in the regulation of mitochondrial function in vitamin A derivative-induced neural differentiation.

Brain and language: evidence for neural multifunctionality.

PubMed

Cahana-Amitay, Dalia; Albert, Martin L

2014-01-01

This review paper presents converging evidence from studies of brain damage and longitudinal studies of language in aging which supports the following thesis: the neural basis of language can best be understood by the concept of neural multifunctionality. In this paper the term "neural multifunctionality" refers to incorporation of nonlinguistic functions into language models of the intact brain, reflecting a multifunctional perspective whereby a constant and dynamic interaction exists among neural networks subserving cognitive, affective, and praxic functions with neural networks specialized for lexical retrieval, sentence comprehension, and discourse processing, giving rise to language as we know it. By way of example, we consider effects of executive system functions on aspects of semantic processing among persons with and without aphasia, as well as the interaction of executive and language functions among older adults. We conclude by indicating how this multifunctional view of brain-language relations extends to the realm of language recovery from aphasia, where evidence of the influence of nonlinguistic factors on the reshaping of neural circuitry for aphasia rehabilitation is clearly emerging.

Vortioxetine reduces BOLD signal during performance of the N-back working memory task: a randomised neuroimaging trial in remitted depressed patients and healthy controls.

PubMed

Smith, J; Browning, M; Conen, S; Smallman, R; Buchbjerg, J; Larsen, K G; Olsen, C K; Christensen, S R; Dawson, G R; Deakin, J F; Hawkins, P; Morris, R; Goodwin, G; Harmer, C J

2018-05-01

Cognitive dysfunction is common in depression during both acute episodes and remission. Vortioxetine is a novel multimodal antidepressant that has improved cognitive function including executive function in depressed patients in randomised placebo-controlled clinical trials. However, it is unclear whether vortioxetine is able to target directly the neural circuitry implicated in the cognitive deficits in depression. Remitted depressed (n=48) and healthy volunteers (n=48) were randomised to receive 14 days treatment with 20 mg vortioxetine or placebo in a double-blind design. The effects of treatment on functional magnetic resonance imaging responses during an N-back working memory task were assessed at baseline and at the end of treatment. Neuropsychological measures of executive function, speed and information processing, attention and learning and memory were examined with the Trail Making Test (TMT), Rey Auditory Learning Test and Digit Symbol Substitution Test before and after treatment; subjective cognitive function was assessed using the Perceived Deficits Questionnaire (PDQ). Compared with placebo, vortioxetine reduced activation in the right dorsolateral prefrontal cortex and left hippocampus during the N-back task compared with placebo. Vortioxetine also increased TMT-A performance and self-reported cognitive function on the PDQ. These effects were seen across both subject groups. Vortioxetine modulates neural responses across a circuit subserving working memory in a direction opposite to the changes described in depression, when performance is maintained. This study provides evidence that vortioxetine has direct effects on the neural circuitry supporting cognitive function that can be dissociated from its effects on the mood symptoms of depression.

Cognitive training and Bacopa monnieri: Evidence for a combined intervention to alleviate age associated cognitive decline.

PubMed

McPhee, Grace M; Downey, Luke A; Noble, Anthony; Stough, Con

2016-10-01

As the elderly population grows the impact of age associated cognitive decline as well as neurodegenerative diseases such as Alzheimer's disease and dementia will increase. Ageing is associated with consistent impairments in cognitive processes (e.g., processing speed, memory, executive function and learning) important for work, well-being, life satisfaction and overall participation in society. Recently, there has been increased effort to conduct research examining methods to improve cognitive function in older citizens. Cognitive training has been shown to improve performance in some cognitive domains; including memory, processing speed, executive function and attention in older adults. These cognitive changes are thought to be related to improvements in brain connectivity and neural circuitry. Bacopa monnieri has also been shown to improve specific domains of cognition, sensitive to age associated cognitive decline (particularly processing speed and memory). These Bacopa monnieri dependent improvements may be due to the increase in specific neuro-molecular mechanisms implicated in the enhancement of neural connections in the brain (i.e. synaptogenesis). In particular, a number of animal studies have shown Bacopa monnieri consumption upregulates calcium dependent kinases in the synapse and post-synaptic cell, crucial for strengthening and growing connections between neurons. These effects have been shown to occur in areas important for cognitive processes, such as the hippocampus. As Bacopa monnieri has shown neuro-molecular mechanisms that encourage synaptogenesis, while cognitive training enhances brain connectivity, Bacopa monnieri supplementation could theoretically enhance and strengthen synaptic changes acquired through cognitive training. Therefore, the current paper hypothesises that the combination of these two interventions could improve cognitive outcomes, over and above the effects of administrating these interventions independently, as an effective treatment to ameliorate age associated cognitive decline. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predicting human protein function with multi-task deep neural networks.

PubMed

Fa, Rui; Cozzetto, Domenico; Wan, Cen; Jones, David T

2018-01-01

Machine learning methods for protein function prediction are urgently needed, especially now that a substantial fraction of known sequences remains unannotated despite the extensive use of functional assignments based on sequence similarity. One major bottleneck supervised learning faces in protein function prediction is the structured, multi-label nature of the problem, because biological roles are represented by lists of terms from hierarchically organised controlled vocabularies such as the Gene Ontology. In this work, we build on recent developments in the area of deep learning and investigate the usefulness of multi-task deep neural networks (MTDNN), which consist of upstream shared layers upon which are stacked in parallel as many independent modules (additional hidden layers with their own output units) as the number of output GO terms (the tasks). MTDNN learns individual tasks partially using shared representations and partially from task-specific characteristics. When no close homologues with experimentally validated functions can be identified, MTDNN gives more accurate predictions than baseline methods based on annotation frequencies in public databases or homology transfers. More importantly, the results show that MTDNN binary classification accuracy is higher than alternative machine learning-based methods that do not exploit commonalities and differences among prediction tasks. Interestingly, compared with a single-task predictor, the performance improvement is not linearly correlated with the number of tasks in MTDNN, but medium size models provide more improvement in our case. One of advantages of MTDNN is that given a set of features, there is no requirement for MTDNN to have a bootstrap feature selection procedure as what traditional machine learning algorithms do. Overall, the results indicate that the proposed MTDNN algorithm improves the performance of protein function prediction. On the other hand, there is still large room for deep learning techniques to further enhance prediction ability.

Training Data Requirement for a Neural Network to Predict Aerodynamic Coefficients

NASA Technical Reports Server (NTRS)

Korsmeyer, David (Technical Monitor); Rajkumar, T.; Bardina, Jorge

2003-01-01

Basic aerodynamic coefficients are modeled as functions of angle of attack, speed brake deflection angle, Mach number, and side slip angle. Most of the aerodynamic parameters can be well-fitted using polynomial functions. We previously demonstrated that a neural network is a fast, reliable way of predicting aerodynamic coefficients. We encountered few under fitted and/or over fitted results during prediction. The training data for the neural network are derived from wind tunnel test measurements and numerical simulations. The basic questions that arise are: how many training data points are required to produce an efficient neural network prediction, and which type of transfer functions should be used between the input-hidden layer and hidden-output layer. In this paper, a comparative study of the efficiency of neural network prediction based on different transfer functions and training dataset sizes is presented. The results of the neural network prediction reflect the sensitivity of the architecture, transfer functions, and training dataset size.

Perceptual Learning Selectively Refines Orientation Representations in Early Visual Cortex

PubMed Central

Jehee, Janneke F.M.; Ling, Sam; Swisher, Jascha D.; van Bergen, Ruben S.; Tong, Frank

2013-01-01

Although practice has long been known to improve perceptual performance, the neural basis of this improvement in humans remains unclear. Using fMRI in conjunction with a novel signal detection-based analysis, we show that extensive practice selectively enhances the neural representation of trained orientations in the human visual cortex. Twelve observers practiced discriminating small changes in the orientation of a laterally presented grating over 20 or more daily one-hour training sessions. Training on average led to a two-fold improvement in discrimination sensitivity, specific to the trained orientation and the trained location, with minimal improvement found for untrained orthogonal orientations or for orientations presented in the untrained hemifield. We measured the strength of orientation-selective responses in individual voxels in early visual areas (V1–V4) using signal detection measures, both pre- and post-training. Although the overall amplitude of the BOLD response was no greater after training, practice nonetheless specifically enhanced the neural representation of the trained orientation at the trained location. This training-specific enhancement of orientation-selective responses was observed in the primary visual cortex (V1) as well as higher extrastriate visual areas V2–V4, and moreover, reliably predicted individual differences in the behavioral effects of perceptual learning. These results demonstrate that extensive training can lead to targeted functional reorganization of the human visual cortex, refining the cortical representation of behaviorally relevant information. PMID:23175828

Perceptual learning selectively refines orientation representations in early visual cortex.

PubMed

Jehee, Janneke F M; Ling, Sam; Swisher, Jascha D; van Bergen, Ruben S; Tong, Frank

2012-11-21

Although practice has long been known to improve perceptual performance, the neural basis of this improvement in humans remains unclear. Using fMRI in conjunction with a novel signal detection-based analysis, we show that extensive practice selectively enhances the neural representation of trained orientations in the human visual cortex. Twelve observers practiced discriminating small changes in the orientation of a laterally presented grating over 20 or more daily 1 h training sessions. Training on average led to a twofold improvement in discrimination sensitivity, specific to the trained orientation and the trained location, with minimal improvement found for untrained orthogonal orientations or for orientations presented in the untrained hemifield. We measured the strength of orientation-selective responses in individual voxels in early visual areas (V1-V4) using signal detection measures, both before and after training. Although the overall amplitude of the BOLD response was no greater after training, practice nonetheless specifically enhanced the neural representation of the trained orientation at the trained location. This training-specific enhancement of orientation-selective responses was observed in the primary visual cortex (V1) as well as higher extrastriate visual areas V2-V4, and moreover, reliably predicted individual differences in the behavioral effects of perceptual learning. These results demonstrate that extensive training can lead to targeted functional reorganization of the human visual cortex, refining the cortical representation of behaviorally relevant information.

The Puzzle of Visual Development: Behavior and Neural Limits.

PubMed

Kiorpes, Lynne

2016-11-09

The development of visual function takes place over many months or years in primate infants. Visual sensitivity is very poor near birth and improves over different times courses for different visual functions. The neural mechanisms that underlie these processes are not well understood despite many decades of research. The puzzle arises because research into the factors that limit visual function in infants has found surprisingly mature neural organization and adult-like receptive field properties in very young infants. The high degree of visual plasticity that has been documented during the sensitive period in young children and animals leaves the brain vulnerable to abnormal visual experience. Abnormal visual experience during the sensitive period can lead to amblyopia, a developmental disorder of vision affecting ∼3% of children. This review provides a historical perspective on research into visual development and the disorder amblyopia. The mismatch between the status of the primary visual cortex and visual behavior, both during visual development and in amblyopia, is discussed, and several potential resolutions are considered. It seems likely that extrastriate visual areas further along the visual pathways may set important limits on visual function and show greater vulnerability to abnormal visual experience. Analyses based on multiunit, population activity may provide useful representations of the information being fed forward from primary visual cortex to extrastriate processing areas and to the motor output. Copyright © 2016 the authors 0270-6474/16/3611384-10$15.00/0.

Quantum computing: a prime modality in neurosurgery's future.

PubMed

Lee, Brian; Liu, Charles Y; Apuzzo, Michael L J

2012-11-01

With each significant development in the field of neurosurgery, our dependence on computers, small and large, has continuously increased. From something as mundane as bipolar cautery to sophisticated intraoperative navigation with real-time magnetic resonance imaging-assisted surgical guidance, both technologies, however simple or complex, require computational processing power to function. The next frontier for neurosurgery involves developing a greater understanding of the brain and furthering our capabilities as surgeons to directly affect brain circuitry and function. This has come in the form of implantable devices that can electronically and nondestructively influence the cortex and nuclei with the purpose of restoring neuronal function and improving quality of life. We are now transitioning from devices that are turned on and left alone, such as vagus nerve stimulators and deep brain stimulators, to "smart" devices that can listen and react to the body as the situation may dictate. The development of quantum computers and their potential to be thousands, if not millions, of times faster than current "classical" computers, will significantly affect the neurosciences, especially the field of neurorehabilitation and neuromodulation. Quantum computers may advance our understanding of the neural code and, in turn, better develop and program implantable neural devices. When quantum computers reach the point where we can actually implant such devices in patients, the possibilities of what can be done to interface and restore neural function will be limitless. Copyright © 2012 Elsevier Inc. All rights reserved.

Auditory Cortical Plasticity Drives Training-Induced Cognitive Changes in Schizophrenia

PubMed Central

Dale, Corby L.; Brown, Ethan G.; Fisher, Melissa; Herman, Alexander B.; Dowling, Anne F.; Hinkley, Leighton B.; Subramaniam, Karuna; Nagarajan, Srikantan S.; Vinogradov, Sophia

2016-01-01

Schizophrenia is characterized by dysfunction in basic auditory processing, as well as higher-order operations of verbal learning and executive functions. We investigated whether targeted cognitive training of auditory processing improves neural responses to speech stimuli, and how these changes relate to higher-order cognitive functions. Patients with schizophrenia performed an auditory syllable identification task during magnetoencephalography before and after 50 hours of either targeted cognitive training or a computer games control. Healthy comparison subjects were assessed at baseline and after a 10 week no-contact interval. Prior to training, patients (N = 34) showed reduced M100 response in primary auditory cortex relative to healthy participants (N = 13). At reassessment, only the targeted cognitive training patient group (N = 18) exhibited increased M100 responses. Additionally, this group showed increased induced high gamma band activity within left dorsolateral prefrontal cortex immediately after stimulus presentation, and later in bilateral temporal cortices. Training-related changes in neural activity correlated with changes in executive function scores but not verbal learning and memory. These data suggest that computerized cognitive training that targets auditory and verbal learning operations enhances both sensory responses in auditory cortex as well as engagement of prefrontal regions, as indexed during an auditory processing task with low demands on working memory. This neural circuit enhancement is in turn associated with better executive function but not verbal memory. PMID:26152668

Neuromodulation of the neural circuits controlling the lower urinary tract

PubMed Central

Gad, Parag N.; Roy, Roland R.; Zhong, Hui; Gerasimenko, Yury P.; Taccola, Giuliano; Edgerton, V. Reggie

2017-01-01

The inability to control timely bladder emptying is one of the most serious challenges among the many functional deficits that occur after a spinal cord injury. We previously demonstrated that electrodes placed epidurally on the dorsum of the spinal cord can be used in animals and humans to recover postural and locomotor function after complete paralysis and can be used to enable voiding in spinal rats. In the present study, we examined the neuromodulation of lower urinary tract function associated with acute epidural spinal cord stimulation, locomotion, and peripheral nerve stimulation in adult rats. Herein we demonstrate that electrically evoked potentials in the hindlimb muscles and external urethral sphincter are modulated uniquely when the rat is stepping bipedally and not voiding, immediately pre-voiding, or when voiding. We also show that spinal cord stimulation can effectively neuromodulate the lower urinary tract via frequency-dependent stimulation patterns and that neural peripheral nerve stimulation can activate the external urethral sphincter both directly and via relays in the spinal cord. The data demonstrate that the sensorimotor networks controlling bladder and locomotion are highly integrated neurophysiologically and behaviorally and demonstrate how these two functions are modulated by sensory input from the tibial and pudental nerves. A more detailed understanding of the high level of interaction between these networks could lead to the integration of multiple neurophysiological strategies to improve bladder function. These data suggest that the development of strategies to improve bladder function should simultaneously engage these highly integrated networks in an activity-dependent manner. PMID:27381425

A Squeezed Artificial Neural Network for the Symbolic Network Reliability Functions of Binary-State Networks.

PubMed

Yeh, Wei-Chang

Network reliability is an important index to the provision of useful information for decision support in the modern world. There is always a need to calculate symbolic network reliability functions (SNRFs) due to dynamic and rapid changes in network parameters. In this brief, the proposed squeezed artificial neural network (SqANN) approach uses the Monte Carlo simulation to estimate the corresponding reliability of a given designed matrix from the Box-Behnken design, and then the Taguchi method is implemented to find the appropriate number of neurons and activation functions of the hidden layer and the output layer in ANN to evaluate SNRFs. According to the experimental results of the benchmark networks, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN-based approach with at least 16.6% improvement in the median absolute deviation in the cost of extra 2 s on average for all experiments.Network reliability is an important index to the provision of useful information for decision support in the modern world. There is always a need to calculate symbolic network reliability functions (SNRFs) due to dynamic and rapid changes in network parameters. In this brief, the proposed squeezed artificial neural network (SqANN) approach uses the Monte Carlo simulation to estimate the corresponding reliability of a given designed matrix from the Box-Behnken design, and then the Taguchi method is implemented to find the appropriate number of neurons and activation functions of the hidden layer and the output layer in ANN to evaluate SNRFs. According to the experimental results of the benchmark networks, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN-based approach with at least 16.6% improvement in the median absolute deviation in the cost of extra 2 s on average for all experiments.

Inferring Functional Neural Connectivity with Phase Synchronization Analysis: A Review of Methodology

PubMed Central

Sun, Junfeng; Li, Zhijun; Tong, Shanbao

2012-01-01

Functional neural connectivity is drawing increasing attention in neuroscience research. To infer functional connectivity from observed neural signals, various methods have been proposed. Among them, phase synchronization analysis is an important and effective one which examines the relationship of instantaneous phase between neural signals but neglecting the influence of their amplitudes. In this paper, we review the advances in methodologies of phase synchronization analysis. In particular, we discuss the definitions of instantaneous phase, the indexes of phase synchronization and their significance test, the issues that may affect the detection of phase synchronization and the extensions of phase synchronization analysis. In practice, phase synchronization analysis may be affected by observational noise, insufficient samples of the signals, volume conduction, and reference in recording neural signals. We make comments and suggestions on these issues so as to better apply phase synchronization analysis to inferring functional connectivity from neural signals. PMID:22577470

Neural Cross-Frequency Coupling: Connecting Architectures, Mechanisms, and Functions.

PubMed

Hyafil, Alexandre; Giraud, Anne-Lise; Fontolan, Lorenzo; Gutkin, Boris

2015-11-01

Neural oscillations are ubiquitously observed in the mammalian brain, but it has proven difficult to tie oscillatory patterns to specific cognitive operations. Notably, the coupling between neural oscillations at different timescales has recently received much attention, both from experimentalists and theoreticians. We review the mechanisms underlying various forms of this cross-frequency coupling. We show that different types of neural oscillators and cross-frequency interactions yield distinct signatures in neural dynamics. Finally, we associate these mechanisms with several putative functions of cross-frequency coupling, including neural representations of multiple environmental items, communication over distant areas, internal clocking of neural processes, and modulation of neural processing based on temporal predictions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Simultaneous functional photoacoustic microscopy and electrocorticography reveal the impact of rtPA on dynamic neurovascular functions after cerebral ischemia.

PubMed

Bandla, Aishwarya; Liao, Lun-De; Chan, Su Jing; Ling, Ji Min; Liu, Yu-Hang; Shih, Yen-Yu Ian; Pan, Han-Chi; Wong, Peter Tsun-Hon; Lai, Hsin-Yi; King, Nicolas Kon Kam; Chen, You-Yin; Ng, Wai Hoe; Thakor, Nitish V

2018-06-01

The advance of thrombolytic therapy has been hampered by the lack of optimization of the therapy during the hyperacute phase of focal ischemia. Here, we investigate neurovascular dynamics using a custom-designed hybrid electrocorticography (ECoG)-functional photoacoustic microscopy (fPAM) imaging system during the hyperacute phase (first 6 h) of photothrombotic ischemia (PTI) in male Wistar rats following recombinant tissue plasminogen activator (rtPA)-mediated thrombolysis. We reported, for the first time, the changes in neural activity and cerebral hemodynamic responses following rtPA infusion at different time points post PTI. Interestingly, very early administration of rtPA (< 1 h post PTI) resulted in only partial recovery of neurovascular dynamics (specifically , neural activity recovered to 71 ± 3.5% of baseline and hemodynamics to only 52 ± 2.6% of baseline) and late administration of rtPA (> 4 h post PTI) resulted in the deterioration of neurovascular function. A therapeutic window between 1 and 3 h post PTI was found to improve recovery of neurovascular function (i.e. significant restoration of neural activity to 93 ± 4.2% of baseline and hemodynamics to 81 ± 2.1% of baseline, respectively). The novel combination of fPAM and ECoG enables direct mapping of neurovascular dynamics and serves as a platform to evaluate potential interventions for stroke.

Neural electrode resilience against dielectric damage may be improved by use of highly doped silicon as a conductive material.

PubMed

Caldwell, Ryan; Sharma, Rohit; Takmakov, Pavel; Street, Matthew G; Solzbacher, Florian; Tathireddy, Prashant; Rieth, Loren

2018-01-01

Dielectric damage occurring in vivo to neural electrodes, leading to conductive material exposure and impedance reduction over time, limits the functional lifetime and clinical viability of neuroprosthetics. We used silicon micromachined Utah Electrode Arrays (UEAs) with iridium oxide (IrO x ) tip metallization and parylene C dielectric encapsulation to understand the factors affecting device resilience and drive improvements. In vitro impedance measurements and finite element analyses were conducted to evaluate how exposed surface area of silicon and IrO x affect UEA properties. Through an aggressive in vitro reactive accelerated aging (RAA) protocol, in vivo parylene degradation was simulated on UEAs to explore agreement with our models. Electrochemical properties of silicon and other common electrode materials were compared to help inform material choice in future neural electrode designs. Exposure of silicon on UEAs was found to primarily affect impedance at frequencies >1kHz, while characteristics at 1 kHz and below were largely unchanged. Post-RAA impedance reduction of UEAs was mitigated in cases where dielectric damage was more likely to expose silicon instead of IrO x . Silicon was found to have a per-area electrochemical impedance >10×higher than many common electrode materials regardless of doping level and resistivity, making it best suited for use as a low-shunting conductor. Non-semiconductor electrode materials commonly used in neural electrode design are more susceptible to shunting neural interface signals through dielectric defects, compared to highly doped silicon. Strategic use of silicon and similar materials may increase neural electrode robustness against encapsulation failures. Copyright © 2017 Elsevier B.V. All rights reserved.

Early changes in emotional processing as a marker of clinical response to SSRI treatment in depression.

PubMed

Godlewska, B R; Browning, M; Norbury, R; Cowen, P J; Harmer, C J

2016-11-22

Antidepressant treatment reduces behavioural and neural markers of negative emotional bias early in treatment and has been proposed as a mechanism of antidepressant drug action. Here, we provide a critical test of this hypothesis by assessing whether neural markers of early emotional processing changes predict later clinical response in depression. Thirty-five unmedicated patients with major depression took the selective serotonin re-uptake inhibitor (SSRI), escitalopram (10 mg), over 6 weeks, and were classified as responders (22 patients) versus non-responders (13 patients), based on at least a 50% reduction in symptoms by the end of treatment. The neural response to fearful and happy emotional facial expressions was assessed before and after 7 days of treatment using functional magnetic resonance imaging. Changes in the neural response to these facial cues after 7 days of escitalopram were compared in patients as a function of later clinical response. A sample of healthy controls was also assessed. At baseline, depressed patients showed greater activation to fear versus happy faces than controls in the insula and dorsal anterior cingulate. Depressed patients who went on to respond to the SSRI had a greater reduction in neural activity to fearful versus happy facial expressions after just 7 days of escitalopram across a network of regions including the anterior cingulate, insula, amygdala and thalamus. Mediation analysis confirmed that the direct effect of neural change on symptom response was not mediated by initial changes in depressive symptoms. These results support the hypothesis that early changes in emotional processing with antidepressant treatment are the basis of later clinical improvement. As such, early correction of negative bias may be a key mechanism of antidepressant drug action and a potentially useful predictor of therapeutic response.

A Rapid Aerodynamic Design Procedure Based on Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Rai, Man Mohan

2001-01-01

An aerodynamic design procedure that uses neural networks to model the functional behavior of the objective function in design space has been developed. This method incorporates several improvements to an earlier method that employed a strategy called parameter-based partitioning of the design space in order to reduce the computational costs associated with design optimization. As with the earlier method, the current method uses a sequence of response surfaces to traverse the design space in search of the optimal solution. The new method yields significant reductions in computational costs by using composite response surfaces with better generalization capabilities and by exploiting synergies between the optimization method and the simulation codes used to generate the training data. These reductions in design optimization costs are demonstrated for a turbine airfoil design study where a generic shape is evolved into an optimal airfoil.

Neural reorganization underlies improvement in stroke-induced motor dysfunction by music-supported therapy.

PubMed

Altenmüller, E; Marco-Pallares, J; Münte, T F; Schneider, S

2009-07-01

Motor impairments are common after stroke, but efficacious therapies for these dysfunctions are scarce. By extending an earlier study on the effects of music-supported therapy, behavioral indices of motor function as well as electrophysiological measures were obtained before and after a series of therapy sessions to assess whether this new treatment leads to neural reorganization and motor recovery in patients after stroke. The study group comprised 32 stroke patients in a large rehabilitation hospital; they had moderately impaired motor function and no previous musical experience. Over a period of 3 weeks, these patients received 15 sessions of music-supported therapy using a manualized step-by-step approach. For comparison 30 additional patients received standard rehabilitation procedures. Fine as well as gross motor skills were trained by using either a MIDI-piano or electronic drum pads programmed to emit piano tones. Motor functions were assessed by an extensive test battery. In addition, we studied event-related desynchronization/synchronization and coherences from all 62 patients performing self-paced movements of the index finger (MIDI-piano) and of the whole arm (drum pads). Results showed that music-supported therapy yielded significant improvement in fine as well as gross motor skills with respect to speed, precision, and smoothness of movements. Neurophysiological data showed a more pronounced event-related desynchronization before movement onset and a more pronounced coherence in the music-supported therapy group in the post-training assessment, whereas almost no differences were observed in the control group. Thus we see that music-supported therapy leads to marked improvements of motor function after stroke and that these are accompanied by electrophysiological changes indicative of a better cortical connectivity and improved activation of the motor cortex.

Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia*

PubMed Central

Kim, Sung-Phil; Simeral, John D; Hochberg, Leigh R; Donoghue, John P; Black, Michael J

2010-01-01

Computer-mediated connections between human motor cortical neurons and assistive devices promise to improve or restore lost function in people with paralysis. Recently, a pilot clinical study of an intracortical neural interface system demonstrated that a tetraplegic human was able to obtain continuous two-dimensional control of a computer cursor using neural activity recorded from his motor cortex. This control, however, was not sufficiently accurate for reliable use in many common computer control tasks. Here, we studied several central design choices for such a system including the kinematic representation for cursor movement, the decoding method that translates neuronal ensemble spiking activity into a control signal and the cursor control task used during training for optimizing the parameters of the decoding method. In two tetraplegic participants, we found that controlling a cursor's velocity resulted in more accurate closed-loop control than controlling its position directly and that cursor velocity control was achieved more rapidly than position control. Control quality was further improved over conventional linear filters by using a probabilistic method, the Kalman filter, to decode human motor cortical activity. Performance assessment based on standard metrics used for the evaluation of a wide range of pointing devices demonstrated significantly improved cursor control with velocity rather than position decoding. PMID:19015583

Barrier Function-Based Neural Adaptive Control With Locally Weighted Learning and Finite Neuron Self-Growing Strategy.

PubMed

Jia, Zi-Jun; Song, Yong-Duan

2017-06-01

This paper presents a new approach to construct neural adaptive control for uncertain nonaffine systems. By integrating locally weighted learning with barrier Lyapunov function (BLF), a novel control design method is presented to systematically address the two critical issues in neural network (NN) control field: one is how to fulfill the compact set precondition for NN approximation, and the other is how to use varying rather than a fixed NN structure to improve the functionality of NN control. A BLF is exploited to ensure the NN inputs to remain bounded during the entire system operation. To account for system nonlinearities, a neuron self-growing strategy is proposed to guide the process for adding new neurons to the system, resulting in a self-adjustable NN structure for better learning capabilities. It is shown that the number of neurons needed to accomplish the control task is finite, and better performance can be obtained with less number of neurons as compared with traditional methods. The salient feature of the proposed method also lies in the continuity of the control action everywhere. Furthermore, the resulting control action is smooth almost everywhere except for a few time instants at which new neurons are added. Numerical example illustrates the effectiveness of the proposed approach.

Ventral striatal hyperconnectivity during rewarded interference control in adolescents with ADHD.

PubMed

Ma, Ili; van Holstein, Mieke; Mies, Gabry W; Mennes, Maarten; Buitelaar, Jan; Cools, Roshan; Cillessen, Antonius H N; Krebs, Ruth M; Scheres, Anouk

2016-09-01

Attention-deficit/hyperactivity disorder (ADHD) is characterized by cognitive deficits (e.g., interference control) and altered reward processing. Cognitive control is influenced by incentive motivation and according to current theoretical models, ADHD is associated with abnormal interactions between incentive motivation and cognitive control. However, the neural mechanisms by which reward modulates cognitive control in individuals with ADHD are unknown. We used event-related functional resonance imaging (fMRI) to study neural responses during a rewarded Stroop color-word task in adolescents (14-17 years) with ADHD (n = 25; 19 boys) and healthy controls (n = 33; 22 boys). Adolescents with ADHD showed increased reward signaling within the superior frontal gyrus and ventral striatum (VS) relative to controls. Importantly, functional connectivity analyses revealed a hyperconnectivity between VS and motor control regions in the ADHD group, as a function of reward-cognitive control integration. Connectivity was associated with performance improvement in controls but not in the ADHD group, suggesting inefficient connectivity. Adolescents with ADHD show increased neural sensitivity to rewards and its interactions with interference control in VS and motor regions, respectively. The findings support theoretical models of altered reward-cognitive control integration in individuals with ADHD. Copyright © 2016 Elsevier Ltd. All rights reserved.

Neural Conflict–Control Mechanisms Improve Memory for Target Stimuli

PubMed Central

Krebs, Ruth M.; Boehler, Carsten N.; De Belder, Maya; Egner, Tobias

2015-01-01

According to conflict-monitoring models, conflict serves as an internal signal for reinforcing top-down attention to task-relevant information. While evidence based on measures of ongoing task performance supports this idea, implications for long-term consequences, that is, memory, have not been tested yet. Here, we evaluated the prediction that conflict-triggered attentional enhancement of target-stimulus processing should be associated with superior subsequent memory for those stimuli. By combining functional magnetic resonance imaging (fMRI) with a novel variant of a face-word Stroop task that employed trial-unique face stimuli as targets, we were able to assess subsequent (incidental) memory for target faces as a function of whether a given face had previously been accompanied by congruent, neutral, or incongruent (conflicting) distracters. In line with our predictions, incongruent distracters not only induced behavioral conflict, but also gave rise to enhanced memory for target faces. Moreover, conflict-triggered neural activity in prefrontal and parietal regions was predictive of subsequent retrieval success, and displayed conflict-enhanced functional coupling with medial-temporal lobe regions. These data provide support for the proposal that conflict evokes enhanced top-down attention to task-relevant stimuli, thereby promoting their encoding into long-term memory. Our findings thus delineate the neural mechanisms of a novel link between cognitive control and memory. PMID:24108799

Cardiovascular Development and the Colonizing Cardiac Neural Crest Lineage

PubMed Central

Snider, Paige; Olaopa, Michael; Firulli, Anthony B.; Conway, Simon J.

2007-01-01

Although it is well established that transgenic manipulation of mammalian neural crest-related gene expression and microsurgical removal of premigratory chicken and Xenopus embryonic cardiac neural crest progenitors results in a wide spectrum of both structural and functional congenital heart defects, the actual functional mechanism of the cardiac neural crest cells within the heart is poorly understood. Neural crest cell migration and appropriate colonization of the pharyngeal arches and outflow tract septum is thought to be highly dependent on genes that regulate cell-autonomous polarized movement (i.e., gap junctions, cadherins, and noncanonical Wnt1 pathway regulators). Once the migratory cardiac neural crest subpopulation finally reaches the heart, they have traditionally been thought to participate in septation of the common outflow tract into separate aortic and pulmonary arteries. However, several studies have suggested these colonizing neural crest cells may also play additional unexpected roles during cardiovascular development and may even contribute to a crest-derived stem cell population. Studies in both mice and chick suggest they can also enter the heart from the venous inflow as well as the usual arterial outflow region, and may contribute to the adult semilunar and atrioventricular valves as well as part of the cardiac conduction system. Furthermore, although they are not usually thought to give rise to the cardiomyocyte lineage, neural crest cells in the zebrafish (Danio rerio) can contribute to the myocardium and may have different functions in a species-dependent context. Intriguingly, both ablation of chick and Xenopus premigratory neural crest cells, and a transgenic deletion of mouse neural crest cell migration or disruption of the normal mammalian neural crest gene expression profiles, disrupts ventral myocardial function and/or cardiomyocyte proliferation. Combined, this suggests that either the cardiac neural crest secrete factor/s that regulate myocardial proliferation, can signal to the epicardium to subsequently secrete a growth factor/s, or may even contribute directly to the heart. Although there are species differences between mouse, chick, and Xenopus during cardiac neural crest cell morphogenesis, recent data suggest mouse and chick are more similar to each other than to the zebrafish neural crest cell lineage. Several groups have used the genetically defined Pax3 (splotch) mutant mice model to address the role of the cardiac neural crest lineage. Here we review the current literature, the neural crest-related role of the Pax3 transcription factor, and discuss potential function/s of cardiac neural crest-derived cells during cardiovascular developmental remodeling. PMID:17619792

Neural markers of errors as endophenotypes in neuropsychiatric disorders

PubMed Central

Manoach, Dara S.; Agam, Yigal

2013-01-01

Learning from errors is fundamental to adaptive human behavior. It requires detecting errors, evaluating what went wrong, and adjusting behavior accordingly. These dynamic adjustments are at the heart of behavioral flexibility and accumulating evidence suggests that deficient error processing contributes to maladaptively rigid and repetitive behavior in a range of neuropsychiatric disorders. Neuroimaging and electrophysiological studies reveal highly reliable neural markers of error processing. In this review, we evaluate the evidence that abnormalities in these neural markers can serve as sensitive endophenotypes of neuropsychiatric disorders. We describe the behavioral and neural hallmarks of error processing, their mediation by common genetic polymorphisms, and impairments in schizophrenia, obsessive-compulsive disorder, and autism spectrum disorders. We conclude that neural markers of errors meet several important criteria as endophenotypes including heritability, established neuroanatomical and neurochemical substrates, association with neuropsychiatric disorders, presence in syndromally-unaffected family members, and evidence of genetic mediation. Understanding the mechanisms of error processing deficits in neuropsychiatric disorders may provide novel neural and behavioral targets for treatment and sensitive surrogate markers of treatment response. Treating error processing deficits may improve functional outcome since error signals provide crucial information for flexible adaptation to changing environments. Given the dearth of effective interventions for cognitive deficits in neuropsychiatric disorders, this represents a potentially promising approach. PMID:23882201

Neural markers of errors as endophenotypes in neuropsychiatric disorders.

PubMed

Manoach, Dara S; Agam, Yigal

2013-01-01

Learning from errors is fundamental to adaptive human behavior. It requires detecting errors, evaluating what went wrong, and adjusting behavior accordingly. These dynamic adjustments are at the heart of behavioral flexibility and accumulating evidence suggests that deficient error processing contributes to maladaptively rigid and repetitive behavior in a range of neuropsychiatric disorders. Neuroimaging and electrophysiological studies reveal highly reliable neural markers of error processing. In this review, we evaluate the evidence that abnormalities in these neural markers can serve as sensitive endophenotypes of neuropsychiatric disorders. We describe the behavioral and neural hallmarks of error processing, their mediation by common genetic polymorphisms, and impairments in schizophrenia, obsessive-compulsive disorder, and autism spectrum disorders. We conclude that neural markers of errors meet several important criteria as endophenotypes including heritability, established neuroanatomical and neurochemical substrates, association with neuropsychiatric disorders, presence in syndromally-unaffected family members, and evidence of genetic mediation. Understanding the mechanisms of error processing deficits in neuropsychiatric disorders may provide novel neural and behavioral targets for treatment and sensitive surrogate markers of treatment response. Treating error processing deficits may improve functional outcome since error signals provide crucial information for flexible adaptation to changing environments. Given the dearth of effective interventions for cognitive deficits in neuropsychiatric disorders, this represents a potentially promising approach.

Efficient self-organizing multilayer neural network for nonlinear system modeling.

PubMed

Han, Hong-Gui; Wang, Li-Dan; Qiao, Jun-Fei

2013-07-01

It has been shown extensively that the dynamic behaviors of a neural system are strongly influenced by the network architecture and learning process. To establish an artificial neural network (ANN) with self-organizing architecture and suitable learning algorithm for nonlinear system modeling, an automatic axon-neural network (AANN) is investigated in the following respects. First, the network architecture is constructed automatically to change both the number of hidden neurons and topologies of the neural network during the training process. The approach introduced in adaptive connecting-and-pruning algorithm (ACP) is a type of mixed mode operation, which is equivalent to pruning or adding the connecting of the neurons, as well as inserting some required neurons directly. Secondly, the weights are adjusted, using a feedforward computation (FC) to obtain the information for the gradient during learning computation. Unlike most of the previous studies, AANN is able to self-organize the architecture and weights, and to improve the network performances. Also, the proposed AANN has been tested on a number of benchmark problems, ranging from nonlinear function approximating to nonlinear systems modeling. The experimental results show that AANN can have better performances than that of some existing neural networks. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Transplantation of oligodendrocyte precursors and sonic hedgehog results in improved function and white matter sparing in the spinal cords of adult rats after contusion.

PubMed

Bambakidis, Nicholas C; Miller, Robert H

2004-01-01

A substantial cause of neurological disability in spinal cord injury is oligodendrocyte death leading to demyelination and axonal degeneration. Rescuing oligodendrocytes and preserving myelin is expected to result in significant improvement in functional outcome after spinal cord injury. Although previous investigators have used cellular transplantation of xenografted pluripotent embryonic stem cells and observed improved functional outcome, these transplants have required steroid administration and only a minority of these cells develop into oligodendrocytes. The objective of the present study was to determine whether allografts of oligodendrocyte precursors transplanted into an area of incomplete spinal cord contusion would improve behavioral and electrophysiological measures of spinal cord function. Additional treatment incorporated the use of the glycoprotein molecule Sonic hedgehog (Shh), which has been shown to play a critical role in oligodendroglial development and induce proliferation of endogenous neural precursors after spinal cord injury. Laboratory study. Moderate spinal cord contusion injury was produced in 39 adult rats at T9-T10. Ten animals died during the course of the study. Nine rats served as contusion controls (Group 1). Six rats were treated with oligodendrocyte precursor transplantation 5 days after injury (Group 2). The transplanted cells were isolated from newborn rat pups using immunopanning techniques. Another eight rats received an injection of recombinant Shh along with the oligodendrocyte precursors (Group 3), while six more rats were treated with Shh alone (Group 4). Eight additional rats received only T9 laminectomies to serve as noninjured controls (Group 0). Animals were followed for 28 days. After an initial complete hindlimb paralysis, rats of all groups receiving a contusive injury recovered substantial function within 1 week. By 28 days, rats in Groups 2 and 3 scored 4.7 and 5.8 points better on the Basso, Beattie, Bresnahan (BBB) open field locomotor score than rats in group 1 (Groups 2 and 3=18.2 and 19.4 points, respectively, after 28 days vs. Group 1=13.6 points; p=.015). Rats in Group 4 scored no better than those in Group 1 (BBB=16.4). Motor evoked potential (MEP) recordings revealed a strong trend towards significant improvement in latency measurements in all treatment groups compared with controls at 28 days, although three animals in Group 1 and two animals in Group 3 were not recordable. Histological examination demonstrated significantly more spared white matter in the same groups that correlated with the improvements in BBB scores and MEP latencies. Immunohistochemical analysis showed the survival, proliferation and migration of the transplanted cells, as well as the induction of proliferating endogenous neural precursor cells in animals treated with Shh. These findings suggest that the transplantation of oligodendrocyte precursors may improve axonal conduction and spinal cord function in the injured spinal cord. The benefits seem more pronounced with the addition of Shh, and the addition of Shh alone results in the proliferation of an endogenous population of neural precursor cells.

Combining neural networks and genetic algorithms for hydrological flow forecasting

NASA Astrophysics Data System (ADS)

Neruda, Roman; Srejber, Jan; Neruda, Martin; Pascenko, Petr

2010-05-01

We present a neural network approach to rainfall-runoff modeling for small size river basins based on several time series of hourly measured data. Different neural networks are considered for short time runoff predictions (from one to six hours lead time) based on runoff and rainfall data observed in previous time steps. Correlation analysis shows that runoff data, short time rainfall history, and aggregated API values are the most significant data for the prediction. Neural models of multilayer perceptron and radial basis function networks with different numbers of units are used and compared with more traditional linear time series predictors. Out of possible 48 hours of relevant history of all the input variables, the most important ones are selected by means of input filters created by a genetic algorithm. The genetic algorithm works with population of binary encoded vectors defining input selection patterns. Standard genetic operators of two-point crossover, random bit-flipping mutation, and tournament selection were used. The evaluation of objective function of each individual consists of several rounds of building and testing a particular neural network model. The whole procedure is rather computational exacting (taking hours to days on a desktop PC), thus a high-performance mainframe computer has been used for our experiments. Results based on two years worth data from the Ploucnice river in Northern Bohemia suggest that main problems connected with this approach to modeling are ovetraining that can lead to poor generalization, and relatively small number of extreme events which makes it difficult for a model to predict the amplitude of the event. Thus, experiments with both absolute and relative runoff predictions were carried out. In general it can be concluded that the neural models show about 5 per cent improvement in terms of efficiency coefficient over liner models. Multilayer perceptrons with one hidden layer trained by back propagation algorithm and predicting relative runoff show the best behavior so far. Utilizing the genetically evolved input filter improves the performance of yet another 5 per cent. In the future we would like to continue with experiments in on-line prediction using real-time data from Smeda River with 6 hours lead time forecast. Following the operational reality we will focus on classification of the runoffs into flood alert levels, and reformulation of the time series prediction task as a classification problem. The main goal of all this work is to improve flood warning system operated by the Czech Hydrometeorological Institute.

Slits Affect the Timely Migration of Neural Crest Cells via Robo Receptor

PubMed Central

Giovannone, Dion; Reyes, Michelle; Reyes, Rachel; Correa, Lisa; Martinez, Darwin; Ra, Hannah; Gomez, Gustavo; Kaiser, Josh; Ma, Le; Stein, Mary-Pat; de Bellard, Maria Elena

2013-01-01

SUMMARY Background Neural crest cells emerge by delamination from the dorsal neural tube and give rise to various components of the peripheral nervous system in vertebrate embryos. These cells change from non-motile into highly motile cells migrating to distant areas before further differentiation. Mechanisms controlling delamination and subsequent migration of neural crest cells are not fully understood. Slit2, a chemorepellant for axonal guidance that repels and stimulates motility of trunk neural crest cells away from the gut has recently been suggested to be a tumor suppressor molecule. The goal of this study was to further investigate the role of Slit2 in trunk neural crest cell migration by constitutive expression in neural crest cells. Results We found that Slit gain-of-function significantly impaired neural crest cell migration while Slit loss-of-function favored migration. In addition, we observed that the distribution of key cytoskeletal markers was disrupted in both gain and loss of function instances. Conclusions These findings suggest that Slit molecules might be involved in the processes that allow neural crest cells to begin migration and transitioning to a mesenchymal type. PMID:22689303

Satisfiability of logic programming based on radial basis function neural networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged

2014-07-10

In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We appliedmore » the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.« less

A limit-cycle self-organizing map architecture for stable arm control.

PubMed

Huang, Di-Wei; Gentili, Rodolphe J; Katz, Garrett E; Reggia, James A

2017-01-01

Inspired by the oscillatory nature of cerebral cortex activity, we recently proposed and studied self-organizing maps (SOMs) based on limit cycle neural activity in an attempt to improve the information efficiency and robustness of conventional single-node, single-pattern representations. Here we explore for the first time the use of limit cycle SOMs to build a neural architecture that controls a robotic arm by solving inverse kinematics in reach-and-hold tasks. This multi-map architecture integrates open-loop and closed-loop controls that learn to self-organize oscillatory neural representations and to harness non-fixed-point neural activity even for fixed-point arm reaching tasks. We show through computer simulations that our architecture generalizes well, achieves accurate, fast, and smooth arm movements, and is robust in the face of arm perturbations, map damage, and variations of internal timing parameters controlling the flow of activity. A robotic implementation is evaluated successfully without further training, demonstrating for the first time that limit cycle maps can control a physical robot arm. We conclude that architectures based on limit cycle maps can be organized to function effectively as neural controllers. Copyright © 2016 Elsevier Ltd. All rights reserved.

Sentiment analysis: a comparison of deep learning neural network algorithm with SVM and naϊve Bayes for Indonesian text

NASA Astrophysics Data System (ADS)

Calvin Frans Mariel, Wahyu; Mariyah, Siti; Pramana, Setia

2018-03-01

Deep learning is a new era of machine learning techniques that essentially imitate the structure and function of the human brain. It is a development of deeper Artificial Neural Network (ANN) that uses more than one hidden layer. Deep Learning Neural Network has a great ability on recognizing patterns from various data types such as picture, audio, text, and many more. In this paper, the authors tries to measure that algorithm’s ability by applying it into the text classification. The classification task herein is done by considering the content of sentiment in a text which is also called as sentiment analysis. By using several combinations of text preprocessing and feature extraction techniques, we aim to compare the precise modelling results of Deep Learning Neural Network with the other two commonly used algorithms, the Naϊve Bayes and Support Vector Machine (SVM). This algorithm comparison uses Indonesian text data with balanced and unbalanced sentiment composition. Based on the experimental simulation, Deep Learning Neural Network clearly outperforms the Naϊve Bayes and SVM and offers a better F-1 Score while for the best feature extraction technique which improves that modelling result is Bigram.

Functional and structural neural alterations in Internet gaming disorder: A systematic review and meta-analysis.

PubMed

Yao, Yuan-Wei; Liu, Lu; Ma, Shan-Shan; Shi, Xin-Hui; Zhou, Nan; Zhang, Jin-Tao; Potenza, Marc N

2017-12-01

This meta-analytic study aimed to identify the common and specific neural alterations in Internet gaming disorder (IGD) across different domains and modalities. Two separate meta-analyses for functional neural activation and gray-matter volume were conducted. Sub-meta-analyses for the domains of reward, cold-executive, and hot-executive functions were also performed, respectively. IGD subjects, compared with healthy controls, showed: (1) hyperactivation in the anterior and posterior cingulate cortices, caudate, posterior inferior frontal gyrus (IFG), which were mainly associated with studies measuring reward and cold-executive functions; and, (2) hypoactivation in the anterior IFG in relation to hot-executive function, the posterior insula, somatomotor and somatosensory cortices in relation to reward function. Furthermore, IGD subjects showed reduced gray-matter volume in the anterior cingulate, orbitofrontal, dorsolateral prefrontal, and premotor cortices. These findings suggest that IGD is associated with both functional and structural neural alterations in fronto-striatal and fronto-cingulate regions. Moreover, multi-domain assessments capture different aspects of neural alterations in IGD, which may be helpful for developing effective interventions targeting specific functions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Brain and Language: Evidence for Neural Multifunctionality

PubMed Central

Cahana-Amitay, Dalia; Albert, Martin L.

2014-01-01

This review paper presents converging evidence from studies of brain damage and longitudinal studies of language in aging which supports the following thesis: the neural basis of language can best be understood by the concept of neural multifunctionality. In this paper the term “neural multifunctionality” refers to incorporation of nonlinguistic functions into language models of the intact brain, reflecting a multifunctional perspective whereby a constant and dynamic interaction exists among neural networks subserving cognitive, affective, and praxic functions with neural networks specialized for lexical retrieval, sentence comprehension, and discourse processing, giving rise to language as we know it. By way of example, we consider effects of executive system functions on aspects of semantic processing among persons with and without aphasia, as well as the interaction of executive and language functions among older adults. We conclude by indicating how this multifunctional view of brain-language relations extends to the realm of language recovery from aphasia, where evidence of the influence of nonlinguistic factors on the reshaping of neural circuitry for aphasia rehabilitation is clearly emerging. PMID:25009368

Markov chain-incorporated and synthetic data-supported conditional artificial neural network models for forecasting monthly precipitation in arid regions

NASA Astrophysics Data System (ADS)

Aksoy, Hafzullah; Dahamsheh, Ahmad

2018-07-01

For forecasting monthly precipitation in an arid region, the feed forward back-propagation, radial basis function and generalized regression artificial neural networks (ANNs) are used in this study. The ANN models are improved after incorporation of a Markov chain-based algorithm (MC-ANNs) with which the percentage of dry months is forecasted perfectly, thus generation of any non-physical negative precipitation is eliminated. Due to the fact that recorded precipitation time series are usually shorter than the length needed for a proper calibration of ANN models, synthetic monthly precipitation data are generated by Thomas-Fiering model to further improve the performance of forecasting. For case studies from Jordan, it is seen that only a slightly better performance is achieved with the use of MC and synthetic data. A conditional statement is, therefore, established and imbedded into the ANN models after the incorporation of MC and support of synthetic data, to substantially improve the ability of the models for forecasting monthly precipitation in arid regions.

Electron beam lithographic modeling assisted by artificial intelligence technology

NASA Astrophysics Data System (ADS)

Nakayamada, Noriaki; Nishimura, Rieko; Miura, Satoru; Nomura, Haruyuki; Kamikubo, Takashi

2017-07-01

We propose a new concept of tuning a point-spread function (a "kernel" function) in the modeling of electron beam lithography using the machine learning scheme. Normally in the work of artificial intelligence, the researchers focus on the output results from a neural network, such as success ratio in image recognition or improved production yield, etc. In this work, we put more focus on the weights connecting the nodes in a convolutional neural network, which are naturally the fractions of a point-spread function, and take out those weighted fractions after learning to be utilized as a tuned kernel. Proof-of-concept of the kernel tuning has been demonstrated using the examples of proximity effect correction with 2-layer network, and charging effect correction with 3-layer network. This type of new tuning method can be beneficial to give researchers more insights to come up with a better model, yet it might be too early to be deployed to production to give better critical dimension (CD) and positional accuracy almost instantly.

Altered Connectivity and Action Model Formation in Autism Is Autism

PubMed Central

Mostofsky, Stewart H.; Ewen, Joshua B.

2014-01-01

Internal action models refer to sensory-motor programs that form the brain basis for a wide range of skilled behavior and for understanding others’ actions. Development of these action models, particularly those reliant on visual cues from the external world, depends on connectivity between distant brain regions. Studies of children with autism reveal anomalous patterns of motor learning and impaired execution of skilled motor gestures. These findings robustly correlate with measures of social and communicative function, suggesting that anomalous action model formation may contribute to impaired development of social and communicative (as well as motor) capacity in autism. Examination of the pattern of behavioral findings, as well as convergent data from neuroimaging techniques, further suggests that autism-associated action model formation may be related to abnormalities in neural connectivity, particularly decreased function of long-range connections. This line of study can lead to important advances in understanding the neural basis of autism and, more critically, can be used to guide effective therapies targeted at improving social, communicative, and motor function. PMID:21467306

Improved spatial accuracy of functional maps in the rat olfactory bulb using supervised machine learning approach.

PubMed

Murphy, Matthew C; Poplawsky, Alexander J; Vazquez, Alberto L; Chan, Kevin C; Kim, Seong-Gi; Fukuda, Mitsuhiro

2016-08-15

Functional MRI (fMRI) is a popular and important tool for noninvasive mapping of neural activity. As fMRI measures the hemodynamic response, the resulting activation maps do not perfectly reflect the underlying neural activity. The purpose of this work was to design a data-driven model to improve the spatial accuracy of fMRI maps in the rat olfactory bulb. This system is an ideal choice for this investigation since the bulb circuit is well characterized, allowing for an accurate definition of activity patterns in order to train the model. We generated models for both cerebral blood volume weighted (CBVw) and blood oxygen level dependent (BOLD) fMRI data. The results indicate that the spatial accuracy of the activation maps is either significantly improved or at worst not significantly different when using the learned models compared to a conventional general linear model approach, particularly for BOLD images and activity patterns involving deep layers of the bulb. Furthermore, the activation maps computed by CBVw and BOLD data show increased agreement when using the learned models, lending more confidence to their accuracy. The models presented here could have an immediate impact on studies of the olfactory bulb, but perhaps more importantly, demonstrate the potential for similar flexible, data-driven models to improve the quality of activation maps calculated using fMRI data. Copyright © 2016 Elsevier Inc. All rights reserved.

Neural Summation in the Hawkmoth Visual System Extends the Limits of Vision in Dim Light.

PubMed

Stöckl, Anna Lisa; O'Carroll, David Charles; Warrant, Eric James

2016-03-21

Most of the world's animals are active in dim light and depend on good vision for the tasks of daily life. Many have evolved visual adaptations that permit a performance superior to that of manmade imaging devices [1]. In insects, a major model visual system, nocturnal species show impressive visual abilities ranging from flight control [2, 3], to color discrimination [4, 5], to navigation using visual landmarks [6-8] or dim celestial compass cues [9, 10]. In addition to optical adaptations that improve their sensitivity in dim light [11], neural summation of light in space and time-which enhances the coarser and slower features of the scene at the expense of noisier finer and faster features-has been suggested to improve sensitivity in theoretical [12-14], anatomical [15-17], and behavioral [18-20] studies. How these summation strategies function neurally is, however, presently unknown. Here, we quantified spatial and temporal summation in the motion vision pathway of a nocturnal hawkmoth. We show that spatial and temporal summation combine supralinearly to substantially increase contrast sensitivity and visual information rate over four decades of light intensity, enabling hawkmoths to see at light levels 100 times dimmer than without summation. Our results reveal how visual motion is calculated neurally in dim light and how spatial and temporal summation improve sensitivity while simultaneously maximizing spatial and temporal resolution, thus extending models of insect motion vision derived predominantly from diurnal flies. Moreover, the summation strategies we have revealed may benefit manmade vision systems optimized for variable light levels [21]. Copyright © 2016 Elsevier Ltd. All rights reserved.

Neural stem cells encapsulated in a functionalized self-assembling peptide hydrogel for brain tissue engineering.

PubMed

Cheng, Tzu-Yun; Chen, Ming-Hong; Chang, Wen-Han; Huang, Ming-Yuan; Wang, Tzu-Wei

2013-03-01

Brain injury is almost irreparable due to the poor regenerative capability of neural tissue. Nowadays, new therapeutic strategies have been focused on stem cell therapy and supplying an appropriate three dimensional (3D) matrix for the repair of injured brain tissue. In this study, we specifically linked laminin-derived IKVAV motif on the C-terminal to enrich self-assembling peptide RADA(16) as a functional peptide-based scaffold. Our purpose is providing a functional self-assembling peptide 3D hydrogel with encapsulated neural stem cells to enhance the reconstruction of the injured brain. The physiochemical properties reported that RADA(16)-IKVAV can self-assemble into nanofibrous morphology with bilayer β-sheet structure and become gelationed hydrogel with mechanical stiffness similar to brain tissue. The in vitro results showed that the extended IKVAV sequence can serve as a signal or guiding cue to direct the encapsulated neural stem cells (NSCs) adhesion and then towards neuronal differentiation. Animal study was conducted in a rat brain surgery model to demonstrate the damage in cerebral neocortex/neopallium loss. The results showed that the injected peptide solution immediately in situ formed the 3D hydrogel filling up the cavity and bridging the gaps. The histological analyses revealed the RADA(16)-IKVAV self-assembling peptide hydrogel not only enhanced survival of encapsulated NSCs but also reduced the formation of glial astrocytes. The peptide hydrogel with IKVAV extended motifs also showed the support of encapsulated NSCs in neuronal differentiation and the improvement in brain tissue regeneration after 6 weeks post-transplantation. Copyright © 2012 Elsevier Ltd. All rights reserved.

Increase in MST activity correlates with visual motion learning: A functional MRI study of perceptual learning.

PubMed

Larcombe, Stephanie J; Kennard, Chris; Bridge, Holly

2018-01-01

Repeated practice of a specific task can improve visual performance, but the neural mechanisms underlying this improvement in performance are not yet well understood. Here we trained healthy participants on a visual motion task daily for 5 days in one visual hemifield. Before and after training, we used functional magnetic resonance imaging (fMRI) to measure the change in neural activity. We also imaged a control group of participants on two occasions who did not receive any task training. While in the MRI scanner, all participants completed the motion task in the trained and untrained visual hemifields separately. Following training, participants improved their ability to discriminate motion direction in the trained hemifield and, to a lesser extent, in the untrained hemifield. The amount of task learning correlated positively with the change in activity in the medial superior temporal (MST) area. MST is the anterior portion of the human motion complex (hMT+). MST changes were localized to the hemisphere contralateral to the region of the visual field, where perceptual training was delivered. Visual areas V2 and V3a showed an increase in activity between the first and second scan in the training group, but this was not correlated with performance. The contralateral anterior hippocampus and bilateral dorsolateral prefrontal cortex (DLPFC) and frontal pole showed changes in neural activity that also correlated with the amount of task learning. These findings emphasize the importance of MST in perceptual learning of a visual motion task. Hum Brain Mapp 39:145-156, 2018. © 2017 Wiley Periodicals, Inc. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Puzzle Pieces: Neural Structure and Function in Prader-Willi Syndrome

PubMed Central

Manning, Katherine E.; Holland, Anthony J.

2015-01-01

Prader-Willi syndrome (PWS) is a neurodevelopmental disorder of genomic imprinting, presenting with a behavioural phenotype encompassing hyperphagia, intellectual disability, social and behavioural difficulties, and propensity to psychiatric illness. Research has tended to focus on the cognitive and behavioural investigation of these features, and, with the exception of eating behaviour, the neural physiology is currently less well understood. A systematic review was undertaken to explore findings relating to neural structure and function in PWS, using search terms designed to encompass all published articles concerning both in vivo and post-mortem studies of neural structure and function in PWS. This supported the general paucity of research in this area, with many articles reporting case studies and qualitative descriptions or focusing solely on the overeating behaviour, although a number of systematic investigations were also identified. Research to date implicates a combination of subcortical and higher order structures in PWS, including those involved in processing reward, motivation, affect and higher order cognitive functions, with both anatomical and functional investigations indicating abnormalities. It appears likely that PWS involves aberrant activity across distributed neural networks. The characterisation of neural structure and function warrants both replication and further systematic study. PMID:28943631

Puzzle Pieces: Neural Structure and Function in Prader-Willi Syndrome.

PubMed

Manning, Katherine E; Holland, Anthony J

2015-12-17

Prader-Willi syndrome (PWS) is a neurodevelopmental disorder of genomic imprinting, presenting with a behavioural phenotype encompassing hyperphagia, intellectual disability, social and behavioural difficulties, and propensity to psychiatric illness. Research has tended to focus on the cognitive and behavioural investigation of these features, and, with the exception of eating behaviour, the neural physiology is currently less well understood. A systematic review was undertaken to explore findings relating to neural structure and function in PWS, using search terms designed to encompass all published articles concerning both in vivo and post-mortem studies of neural structure and function in PWS. This supported the general paucity of research in this area, with many articles reporting case studies and qualitative descriptions or focusing solely on the overeating behaviour, although a number of systematic investigations were also identified. Research to date implicates a combination of subcortical and higher order structures in PWS, including those involved in processing reward, motivation, affect and higher order cognitive functions, with both anatomical and functional investigations indicating abnormalities. It appears likely that PWS involves aberrant activity across distributed neural networks. The characterisation of neural structure and function warrants both replication and further systematic study.

Process modeling and parameter optimization using radial basis function neural network and genetic algorithm for laser welding of dissimilar materials

NASA Astrophysics Data System (ADS)

Ai, Yuewei; Shao, Xinyu; Jiang, Ping; Li, Peigen; Liu, Yang; Yue, Chen

2015-11-01

The welded joints of dissimilar materials have been widely used in automotive, ship and space industries. The joint quality is often evaluated by weld seam geometry, microstructures and mechanical properties. To obtain the desired weld seam geometry and improve the quality of welded joints, this paper proposes a process modeling and parameter optimization method to obtain the weld seam with minimum width and desired depth of penetration for laser butt welding of dissimilar materials. During the process, Taguchi experiments are conducted on the laser welding of the low carbon steel (Q235) and stainless steel (SUS301L-HT). The experimental results are used to develop the radial basis function neural network model, and the process parameters are optimized by genetic algorithm. The proposed method is validated by a confirmation experiment. Simultaneously, the microstructures and mechanical properties of the weld seam generated from optimal process parameters are further studied by optical microscopy and tensile strength test. Compared with the unoptimized weld seam, the welding defects are eliminated in the optimized weld seam and the mechanical properties are improved. The results show that the proposed method is effective and reliable for improving the quality of welded joints in practical production.

Improved expression of halorhodopsin for light-induced silencing of neuronal activity

PubMed Central

Zhao, Shengli; Cunha, Catarina; Zhang, Feng; Liu, Qun; Gloss, Bernd; Deisseroth, Karl; Augustine, George J.; Feng, Guoping

2011-01-01

The ability to control and manipulate neuronal activity within an intact mammalian brain is of key importance for mapping functional connectivity and for dissecting the neural circuitry underlying behaviors. We have previously generated transgenic mice that express channelrhodopsin-2 for light-induced activation of neurons and mapping of neural circuits. Here we describe transgenic mice that express halorhodopsin (NpHR), a light-driven chloride pump, that can be used to silence neuronal activity via light. Using the Thy-1 promoter to target NpHR expression to neurons, we found that neurons in these mice expressed high levels of NpHR-YFP and that illumination of cortical pyramidal neurons expressing NpHR-YFP led to rapid, reversible photoinhibition of action potential firing in these cells. However, NpHR-YFP expression led to the formation of numerous intracellular blebs, which may disrupt neuronal function. Labeling of various subcellular markers indicated that the blebs arise from retention of NpHR-YFP in the endoplasmic reticulum. By improving the signal peptide sequence and adding an ER export signal to NpHR-YFP, we eliminated the formation of blebs and dramatically increased the membrane expression of NpHR-YFP. Thus, the improved version of NpHR should serve as an excellent tool for neuronal silencing in vitro and in vivo. PMID:18931914

Weight-elimination neural networks applied to coronary surgery mortality prediction.

PubMed

Ennett, Colleen M; Frize, Monique

2003-06-01

The objective was to assess the effectiveness of the weight-elimination cost function in improving classification performance of artificial neural networks (ANNs) and to observe how changing the a priori distribution of the training set affects network performance. Backpropagation feedforward ANNs with and without weight-elimination estimated mortality for coronary artery surgery patients. The ANNs were trained and tested on cases with 32 input variables describing the patient's medical history; the output variable was in-hospital mortality (mortality rates: training 3.7%, test 3.8%). Artificial training sets with mortality rates of 20%, 50%, and 80% were created to observe the impact of training with a higher-than-normal prevalence. When the results were averaged, weight-elimination networks achieved higher sensitivity rates than those without weight-elimination. Networks trained on higher-than-normal prevalence achieved higher sensitivity rates at the cost of lower specificity and correct classification. The weight-elimination cost function can improve the classification performance when the network is trained with a higher-than-normal prevalence. A network trained with a moderately high artificial mortality rate (artificial mortality rate of 20%) can improve the sensitivity of the model without significantly affecting other aspects of the model's performance. The ANN mortality model achieved comparable performance as additive and statistical models for coronary surgery mortality estimation in the literature.

Intelligent complementary sliding-mode control for LUSMS-based X-Y-theta motion control stage.

PubMed

Lin, Faa-Jeng; Chen, Syuan-Yi; Shyu, Kuo-Kai; Liu, Yen-Hung

2010-07-01

An intelligent complementary sliding-mode control (ICSMC) system using a recurrent wavelet-based Elman neural network (RWENN) estimator is proposed in this study to control the mover position of a linear ultrasonic motors (LUSMs)-based X-Y-theta motion control stage for the tracking of various contours. By the addition of a complementary generalized error transformation, the complementary sliding-mode control (CSMC) can efficiently reduce the guaranteed ultimate bound of the tracking error by half compared with the slidingmode control (SMC) while using the saturation function. To estimate a lumped uncertainty on-line and replace the hitting control of the CSMC directly, the RWENN estimator is adopted in the proposed ICSMC system. In the RWENN, each hidden neuron employs a different wavelet function as an activation function to improve both the convergent precision and the convergent time compared with the conventional Elman neural network (ENN). The estimation laws of the RWENN are derived using the Lyapunov stability theorem to train the network parameters on-line. A robust compensator is also proposed to confront the uncertainties including approximation error, optimal parameter vectors, and higher-order terms in Taylor series. Finally, some experimental results of various contours tracking show that the tracking performance of the ICSMC system is significantly improved compared with the SMC and CSMC systems.

Motor and mental training in older people: Transfer, interference, and associated functional neural responses.

PubMed

Boraxbekk, C J; Hagkvist, Filip; Lindner, Philip

2016-08-01

Learning new motor skills may become more difficult with advanced age. In the present study, we randomized 56 older individuals, including 30 women (mean age 70.6 years), to 6 weeks of motor training, mental (motor imagery) training, or a combination of motor and mental training of a finger tapping sequence. Performance improvements and post-training functional magnetic resonance imaging (fMRI) were used to investigate performance gains and associated underlying neural processes. Motor-only training and a combination of motor and mental training improved performance in the trained task more than mental-only training. The fMRI data showed that motor training was associated with a representation in the premotor cortex and mental training with a representation in the secondary visual cortex. Combining motor and mental training resulted in both premotor and visual cortex representations. During fMRI scanning, reduced performance was observed in the combined motor and mental training group, possibly indicating interference between the two training methods. We concluded that motor and motor imagery training in older individuals is associated with different functional brain responses. Furthermore, adding mental training to motor training did not result in additional performance gains compared to motor-only training and combining training methods may result in interference between representations, reducing performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

Titania nanotube arrays as potential interfaces for neurological prostheses

NASA Astrophysics Data System (ADS)

Sorkin, Jonathan Andrew

Neural prostheses can make a dramatic improvement for those suffering from visual and auditory, cognitive, and motor control disabilities, allowing them regained functionality by the use of stimulating or recording electrical signaling. However, the longevity of these devices is limited due to the neural tissue response to the implanted device. In response to the implant penetrating the blood brain barrier and causing trauma to the tissue, the body forms a to scar to isolate the implant in order to protect the nearby tissue. The scar tissue is a result of reactive gliosis and produces an insulated sheath, encapsulating the implant. The glial sheath limits the stimulating or recording capabilities of the implant, reducing its effectiveness over the long term. A favorable interaction with this tissue would be the direct adhesion of neurons onto the contacts of the implant, and the prevention of glial encapsulation. With direct neuronal adhesion the effectiveness and longevity of the device would be significantly improved. Titania nanotube arrays, fabricated using electrochemical anodization, provide a conductive architecture capable of altering cellular response. This work focuses on the fabrication of different titania nanotube array architectures to determine how their structures and properties influence the response of neural tissue, modeled using the C17.2 murine neural stem cell subclone, and if glial encapsulation can be reduced while neuronal adhesion is promoted.

Impact of Linearity and Write Noise of Analog Resistive Memory Devices in a Neural Algorithm Accelerator

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jacobs-Gedrim, Robin B.; Agarwal, Sapan; Knisely, Kathrine E.

Resistive memory (ReRAM) shows promise for use as an analog synapse element in energy-efficient neural network algorithm accelerators. A particularly important application is the training of neural networks, as this is the most computationally-intensive procedure in using a neural algorithm. However, training a network with analog ReRAM synapses can significantly reduce the accuracy at the algorithm level. In order to assess this degradation, analog properties of ReRAM devices were measured and hand-written digit recognition accuracy was modeled for the training using backpropagation. Bipolar filamentary devices utilizing three material systems were measured and compared: one oxygen vacancy system, Ta-TaO x, andmore » two conducting metallization systems, Cu-SiO 2, and Ag/chalcogenide. Analog properties and conductance ranges of the devices are optimized by measuring the response to varying voltage pulse characteristics. Key analog device properties which degrade the accuracy are update linearity and write noise. Write noise may improve as a function of device manufacturing maturity, but write nonlinearity appears relatively consistent among the different device material systems and is found to be the most significant factor affecting accuracy. As a result, this suggests that new materials and/or fundamentally different resistive switching mechanisms may be required to improve device linearity and achieve higher algorithm training accuracy.« less

Impact of Linearity and Write Noise of Analog Resistive Memory Devices in a Neural Algorithm Accelerator

DOE PAGES

Jacobs-Gedrim, Robin B.; Agarwal, Sapan; Knisely, Kathrine E.; ...

2017-12-01

Resistive memory (ReRAM) shows promise for use as an analog synapse element in energy-efficient neural network algorithm accelerators. A particularly important application is the training of neural networks, as this is the most computationally-intensive procedure in using a neural algorithm. However, training a network with analog ReRAM synapses can significantly reduce the accuracy at the algorithm level. In order to assess this degradation, analog properties of ReRAM devices were measured and hand-written digit recognition accuracy was modeled for the training using backpropagation. Bipolar filamentary devices utilizing three material systems were measured and compared: one oxygen vacancy system, Ta-TaO x, andmore » two conducting metallization systems, Cu-SiO 2, and Ag/chalcogenide. Analog properties and conductance ranges of the devices are optimized by measuring the response to varying voltage pulse characteristics. Key analog device properties which degrade the accuracy are update linearity and write noise. Write noise may improve as a function of device manufacturing maturity, but write nonlinearity appears relatively consistent among the different device material systems and is found to be the most significant factor affecting accuracy. As a result, this suggests that new materials and/or fundamentally different resistive switching mechanisms may be required to improve device linearity and achieve higher algorithm training accuracy.« less

Prefrontal consolidation supports the attainment of fear memory accuracy

PubMed Central

Vieira, Philip A.; Lovelace, Jonathan W.; Corches, Alex; Rashid, Asim J.; Josselyn, Sheena A.

2014-01-01

The neural mechanisms underlying the attainment of fear memory accuracy for appropriate discriminative responses to aversive and nonaversive stimuli are unclear. Considerable evidence indicates that coactivator of transcription and histone acetyltransferase cAMP response element binding protein (CREB) binding protein (CBP) is critically required for normal neural function. CBP hypofunction leads to severe psychopathological symptoms in human and cognitive abnormalities in genetic mutant mice with severity dependent on the neural locus and developmental time of the gene inactivation. Here, we showed that an acute hypofunction of CBP in the medial prefrontal cortex (mPFC) results in a disruption of fear memory accuracy in mice. In addition, interruption of CREB function in the mPFC also leads to a deficit in auditory discrimination of fearful stimuli. While mice with deficient CBP/CREB signaling in the mPFC maintain normal responses to aversive stimuli, they exhibit abnormal responses to similar but nonrelevant stimuli when compared to control animals. These data indicate that improvement of fear memory accuracy involves mPFC-dependent suppression of fear responses to nonrelevant stimuli. Evidence from a context discriminatory task and a newly developed task that depends on the ability to distinguish discrete auditory cues indicated that CBP-dependent neural signaling within the mPFC circuitry is an important component of the mechanism for disambiguating the meaning of fear signals with two opposing values: aversive and nonaversive. PMID:25031365

Discrete-time BAM neural networks with variable delays

NASA Astrophysics Data System (ADS)

Liu, Xin-Ge; Tang, Mei-Lan; Martin, Ralph; Liu, Xin-Bi

2007-07-01

This Letter deals with the global exponential stability of discrete-time bidirectional associative memory (BAM) neural networks with variable delays. Using a Lyapunov functional, and linear matrix inequality techniques (LMI), we derive a new delay-dependent exponential stability criterion for BAM neural networks with variable delays. As this criterion has no extra constraints on the variable delay functions, it can be applied to quite general BAM neural networks with a broad range of time delay functions. It is also easy to use in practice. An example is provided to illustrate the theoretical development.

New results for global exponential synchronization in neural networks via functional differential inclusions.

PubMed

Wang, Dongshu; Huang, Lihong; Tang, Longkun

2015-08-01

This paper is concerned with the synchronization dynamical behaviors for a class of delayed neural networks with discontinuous neuron activations. Continuous and discontinuous state feedback controller are designed such that the neural networks model can realize exponential complete synchronization in view of functional differential inclusions theory, Lyapunov functional method and inequality technique. The new proposed results here are very easy to verify and also applicable to neural networks with continuous activations. Finally, some numerical examples show the applicability and effectiveness of our main results.

The neural basis of implicit learning and memory: a review of neuropsychological and neuroimaging research.

PubMed

Reber, Paul J

2013-08-01

Memory systems research has typically described the different types of long-term memory in the brain as either declarative versus non-declarative or implicit versus explicit. These descriptions reflect the difference between declarative, conscious, and explicit memory that is dependent on the medial temporal lobe (MTL) memory system, and all other expressions of learning and memory. The other type of memory is generally defined by an absence: either the lack of dependence on the MTL memory system (nondeclarative) or the lack of conscious awareness of the information acquired (implicit). However, definition by absence is inherently underspecified and leaves open questions of how this type of memory operates, its neural basis, and how it differs from explicit, declarative memory. Drawing on a variety of studies of implicit learning that have attempted to identify the neural correlates of implicit learning using functional neuroimaging and neuropsychology, a theory of implicit memory is presented that describes it as a form of general plasticity within processing networks that adaptively improve function via experience. Under this model, implicit memory will not appear as a single, coherent, alternative memory system but will instead be manifested as a principle of improvement from experience based on widespread mechanisms of cortical plasticity. The implications of this characterization for understanding the role of implicit learning in complex cognitive processes and the effects of interactions between types of memory will be discussed for examples within and outside the psychology laboratory. Copyright © 2013 Elsevier Ltd. All rights reserved.

Cross-hemispheric functional connectivity in the human fetal brain.

PubMed

Thomason, Moriah E; Dassanayake, Maya T; Shen, Stephen; Katkuri, Yashwanth; Alexis, Mitchell; Anderson, Amy L; Yeo, Lami; Mody, Swati; Hernandez-Andrade, Edgar; Hassan, Sonia S; Studholme, Colin; Jeong, Jeong-Won; Romero, Roberto

2013-02-20

Compelling evidence indicates that psychiatric and developmental disorders are generally caused by disruptions in the functional connectivity (FC) of brain networks. Events occurring during development, and in particular during fetal life, have been implicated in the genesis of such disorders. However, the developmental timetable for the emergence of neural FC during human fetal life is unknown. We present the results of resting-state functional magnetic resonance imaging performed in 25 healthy human fetuses in the second and third trimesters of pregnancy (24 to 38 weeks of gestation). We report the presence of bilateral fetal brain FC and regional and age-related variation in FC. Significant bilateral connectivity was evident in half of the 42 areas tested, and the strength of FC between homologous cortical brain regions increased with advancing gestational age. We also observed medial to lateral gradients in fetal functional brain connectivity. These findings improve understanding of human fetal central nervous system development and provide a basis for examining the role of insults during fetal life in the subsequent development of disorders in neural FC.

Impact of horizontal and vertical localization scales on microwave sounder SAPHIR radiance assimilation

NASA Astrophysics Data System (ADS)

Krishnamoorthy, C.; Balaji, C.

2016-05-01

In the present study, the effect of horizontal and vertical localization scales on the assimilation of direct SAPHIR radiances is studied. An Artificial Neural Network (ANN) has been used as a surrogate for the forward radiative calculations. The training input dataset for ANN consists of vertical layers of atmospheric pressure, temperature, relative humidity and other hydrometeor profiles with 6 channel Brightness Temperatures (BTs) as output. The best neural network architecture has been arrived at, by a neuron independence study. Since vertical localization of radiance data requires weighting functions, a ANN has been trained for this purpose. The radiances were ingested into the NWP using the Ensemble Kalman Filter (EnKF) technique. The horizontal localization has been taken care of, by using a Gaussian localization function centered around the observed coordinates. Similarly, the vertical localization is accomplished by assuming a function which depends on the weighting function of the channel to be assimilated. The effect of both horizontal and vertical localizations has been studied in terms of ensemble spread in the precipitation. Aditionally, improvements in 24 hr forecast from assimilation are also reported.

Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong.

PubMed

Lu, Wei-Zhen; Wang, Wen-Jian; Wang, Xie-Kang; Yan, Sui-Hang; Lam, Joseph C

2004-09-01

The forecasting of air pollutant trends has received much attention in recent years. It is an important and popular topic in environmental science, as concerns have been raised about the health impacts caused by unacceptable ambient air pollutant levels. Of greatest concern are metropolitan cities like Hong Kong. In Hong Kong, respirable suspended particulates (RSP), nitrogen oxides (NOx), and nitrogen dioxide (NO2) are major air pollutants due to the dominant usage of diesel fuel by commercial vehicles and buses. Hence, the study of the influence and the trends relating to these pollutants is extremely significant to the public health and the image of the city. The use of neural network techniques to predict trends relating to air pollutants is regarded as a reliable and cost-effective method for the task of prediction. The works reported here involve developing an improved neural network model that combines both the principal component analysis technique and the radial basis function network and forecasts pollutant tendencies based on a recorded database. Compared with general neural network models, the proposed model features a more simple network architecture, a faster training speed, and a more satisfactory prediction performance. The improved model was evaluated with hourly time series of RSP, NOx and NO2 concentrations monitored at the Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000 and proved to be effective. The model developed is a potential tool for forecasting air quality parameters and is superior to traditional neural network methods.

No Effect of Commercial Cognitive Training on Brain Activity, Choice Behavior, or Cognitive Performance.

PubMed

Kable, Joseph W; Caulfield, M Kathleen; Falcone, Mary; McConnell, Mairead; Bernardo, Leah; Parthasarathi, Trishala; Cooper, Nicole; Ashare, Rebecca; Audrain-McGovern, Janet; Hornik, Robert; Diefenbach, Paul; Lee, Frank J; Lerman, Caryn

2017-08-02

Increased preference for immediate over delayed rewards and for risky over certain rewards has been associated with unhealthy behavioral choices. Motivated by evidence that enhanced cognitive control can shift choice behavior away from immediate and risky rewards, we tested whether training executive cognitive function could influence choice behavior and brain responses. In this randomized controlled trial, 128 young adults (71 male, 57 female) participated in 10 weeks of training with either a commercial web-based cognitive training program or web-based video games that do not specifically target executive function or adapt the level of difficulty throughout training. Pretraining and post-training, participants completed cognitive assessments and functional magnetic resonance imaging during performance of the following validated decision-making tasks: delay discounting (choices between smaller rewards now vs larger rewards in the future) and risk sensitivity (choices between larger riskier rewards vs smaller certain rewards). Contrary to our hypothesis, we found no evidence that cognitive training influences neural activity during decision-making; nor did we find effects of cognitive training on measures of delay discounting or risk sensitivity. Participants in the commercial training condition improved with practice on the specific tasks they performed during training, but participants in both conditions showed similar improvement on standardized cognitive measures over time. Moreover, the degree of improvement was comparable to that observed in individuals who were reassessed without any training whatsoever. Commercial adaptive cognitive training appears to have no benefits in healthy young adults above those of standard video games for measures of brain activity, choice behavior, or cognitive performance. SIGNIFICANCE STATEMENT Engagement of neural regions and circuits important in executive cognitive function can bias behavioral choices away from immediate rewards. Activity in these regions may be enhanced through adaptive cognitive training. Commercial brain training programs claim to improve a broad range of mental processes; however, evidence for transfer beyond trained tasks is mixed. We undertook the first randomized controlled trial of the effects of commercial adaptive cognitive training (Lumosity) on neural activity and decision-making in young adults ( N = 128) compared with an active control (playing on-line video games). We found no evidence for relative benefits of cognitive training with respect to changes in decision-making behavior or brain response, or for cognitive task performance beyond those specifically trained. Copyright © 2017 the authors 0270-6474/17/377390-13$15.00/0.

No Effect of Commercial Cognitive Training on Brain Activity, Choice Behavior, or Cognitive Performance

PubMed Central

Caulfield, M. Kathleen; McConnell, Mairead; Bernardo, Leah; Parthasarathi, Trishala; Cooper, Nicole; Ashare, Rebecca; Audrain-McGovern, Janet; Lee, Frank J.; Lerman, Caryn

2017-01-01

Increased preference for immediate over delayed rewards and for risky over certain rewards has been associated with unhealthy behavioral choices. Motivated by evidence that enhanced cognitive control can shift choice behavior away from immediate and risky rewards, we tested whether training executive cognitive function could influence choice behavior and brain responses. In this randomized controlled trial, 128 young adults (71 male, 57 female) participated in 10 weeks of training with either a commercial web-based cognitive training program or web-based video games that do not specifically target executive function or adapt the level of difficulty throughout training. Pretraining and post-training, participants completed cognitive assessments and functional magnetic resonance imaging during performance of the following validated decision-making tasks: delay discounting (choices between smaller rewards now vs larger rewards in the future) and risk sensitivity (choices between larger riskier rewards vs smaller certain rewards). Contrary to our hypothesis, we found no evidence that cognitive training influences neural activity during decision-making; nor did we find effects of cognitive training on measures of delay discounting or risk sensitivity. Participants in the commercial training condition improved with practice on the specific tasks they performed during training, but participants in both conditions showed similar improvement on standardized cognitive measures over time. Moreover, the degree of improvement was comparable to that observed in individuals who were reassessed without any training whatsoever. Commercial adaptive cognitive training appears to have no benefits in healthy young adults above those of standard video games for measures of brain activity, choice behavior, or cognitive performance. SIGNIFICANCE STATEMENT Engagement of neural regions and circuits important in executive cognitive function can bias behavioral choices away from immediate rewards. Activity in these regions may be enhanced through adaptive cognitive training. Commercial brain training programs claim to improve a broad range of mental processes; however, evidence for transfer beyond trained tasks is mixed. We undertook the first randomized controlled trial of the effects of commercial adaptive cognitive training (Lumosity) on neural activity and decision-making in young adults (N = 128) compared with an active control (playing on-line video games). We found no evidence for relative benefits of cognitive training with respect to changes in decision-making behavior or brain response, or for cognitive task performance beyond those specifically trained. PMID:28694338

Method of gear fault diagnosis based on EEMD and improved Elman neural network

NASA Astrophysics Data System (ADS)

Zhang, Qi; Zhao, Wei; Xiao, Shungen; Song, Mengmeng

2017-05-01

Aiming at crack and wear and so on of gears Fault information is difficult to diagnose usually due to its weak, a gear fault diagnosis method that is based on EEMD and improved Elman neural network fusion is proposed. A number of IMF components are obtained by decomposing denoised all kinds of fault signals with EEMD, and the pseudo IMF components is eliminated by using the correlation coefficient method to obtain the effective IMF component. The energy characteristic value of each effective component is calculated as the input feature quantity of Elman neural network, and the improved Elman neural network is based on standard network by adding a feedback factor. The fault data of normal gear, broken teeth, cracked gear and attrited gear were collected by field collecting. The results were analyzed by the diagnostic method proposed in this paper. The results show that compared with the standard Elman neural network, Improved Elman neural network has the advantages of high diagnostic efficiency.

Neural Correlates of Cross-Cultural Adaptation: How to Improve the Training and Selection for Military Personnel Involved in Cross-Cultural Interactions

DTIC Science & Technology

2015-09-01

of this study was to better understand the culture-general skills and traits needed to conduct successful cross-cultural interactions when lacking...5  2  Study 1: Online Behavioral Study ...12  3  Study 2: Functional Neuroimaging Study .......................................................... 13  3.1

Noradrenergic modulation of neural erotic stimulus perception.

PubMed

Graf, Heiko; Wiegers, Maike; Metzger, Coraline Danielle; Walter, Martin; Grön, Georg; Abler, Birgit

2017-09-01

We recently investigated neuromodulatory effects of the noradrenergic agent reboxetine and the dopamine receptor affine amisulpride in healthy subjects on dynamic erotic stimulus processing. Whereas amisulpride left sexual functions and neural activations unimpaired, we observed detrimental activations under reboxetine within the caudate nucleus corresponding to motivational components of sexual behavior. However, broadly impaired subjective sexual functioning under reboxetine suggested effects on further neural components. We now investigated the same sample under these two agents with static erotic picture stimulation as alternative stimulus presentation mode to potentially observe further neural treatment effects of reboxetine. 19 healthy males were investigated under reboxetine, amisulpride and placebo for 7 days each within a double-blind cross-over design. During fMRI static erotic picture were presented with preceding anticipation periods. Subjective sexual functions were assessed by a self-reported questionnaire. Neural activations were attenuated within the caudate nucleus, putamen, ventral striatum, the pregenual and anterior midcingulate cortex and in the orbitofrontal cortex under reboxetine. Subjective diminished sexual arousal under reboxetine was correlated with attenuated neural reactivity within the posterior insula. Again, amisulpride left neural activations along with subjective sexual functioning unimpaired. Neither reboxetine nor amisulpride altered differential neural activations during anticipation of erotic stimuli. Our results verified detrimental effects of noradrenergic agents on neural motivational but also emotional and autonomic components of sexual behavior. Considering the overlap of neural network alterations with those evoked by serotonergic agents, our results suggest similar neuromodulatory effects of serotonergic and noradrenergic agents on common neural pathways relevant for sexual behavior. Copyright © 2017 Elsevier B.V. and ECNP. All rights reserved.

A comparison between HMLP and HRBF for attitude control.

PubMed

Fortuna, L; Muscato, G; Xibilia, M G

2001-01-01

In this paper the problem of controlling the attitude of a rigid body, such as a Spacecraft, in three-dimensional space is approached by introducing two new control strategies developed in hypercomplex algebra. The proposed approaches are based on two parallel controllers, both derived in quaternion algebra. The first is a feedback controller of the proportional derivative (PD) type, while the second is a feedforward controller, which is implemented either by means of a hypercomplex multilayer perceptron (HMLP) neural network or by means of a hypercomplex radial basis function (HRBF) neural network. Several simulations show the performance of the two approaches. The results are also compared with a classical PD controller and with an adaptive controller, showing the improvements obtained by using neural networks, especially when an external disturbance acts on the rigid body. In particular the HMLP network gave better results when considering trajectories not presented during the learning phase.

Enhanced neural stem cell functions in conductive annealed carbon nanofibrous scaffolds with electrical stimulation.

PubMed

Zhu, Wei; Ye, Tao; Lee, Se-Jun; Cui, Haitao; Miao, Shida; Zhou, Xuan; Shuai, Danmeng; Zhang, Lijie Grace

2017-05-25

Carbon-based nanomaterials have shown great promise in regenerative medicine because of their unique electrical, mechanical, and biological properties; however, it is still difficult to engineer 2D pure carbon nanomaterials into a 3D scaffold while maintaining its structural integrity. In the present study, we developed novel carbon nanofibrous scaffolds by annealing electrospun mats at elevated temperature. The resultant scaffold showed a cohesive structure and excellent mechanical flexibility. The graphitic structure generated by annealing renders superior electrical conductivity to the carbon nanofibrous scaffold. By integrating the conductive scaffold with biphasic electrical stimulation, neural stem cell proliferation was promoted associating with upregulated neuronal gene expression level and increased microtubule-associated protein 2 immunofluorescence, demonstrating an improved neuronal differentiation and maturation. The findings suggest that the integration of the conducting carbon nanofibrous scaffold and electrical stimulation may pave a new avenue for neural tissue regeneration. Copyright © 2017 Elsevier Inc. All rights reserved.

Classification of cardiac patient states using artificial neural networks

PubMed Central

Kannathal, N; Acharya, U Rajendra; Lim, Choo Min; Sadasivan, PK; Krishnan, SM

2003-01-01

Electrocardiogram (ECG) is a nonstationary signal; therefore, the disease indicators may occur at random in the time scale. This may require the patient be kept under observation for long intervals in the intensive care unit of hospitals for accurate diagnosis. The present study examined the classification of the states of patients with certain diseases in the intensive care unit using their ECG and an Artificial Neural Networks (ANN) classification system. The states were classified into normal, abnormal and life threatening. Seven significant features extracted from the ECG were fed as input parameters to the ANN for classification. Three neural network techniques, namely, back propagation, self-organizing maps and radial basis functions, were used for classification of the patient states. The ANN classifier in this case was observed to be correct in approximately 99% of the test cases. This result was further improved by taking 13 features of the ECG as input for the ANN classifier. PMID:19649222

A neural circuit mechanism for regulating vocal variability during song learning in zebra finches.

PubMed

Garst-Orozco, Jonathan; Babadi, Baktash; Ölveczky, Bence P

2014-12-15

Motor skill learning is characterized by improved performance and reduced motor variability. The neural mechanisms that couple skill level and variability, however, are not known. The zebra finch, a songbird, presents a unique opportunity to address this question because production of learned song and induction of vocal variability are instantiated in distinct circuits that converge on a motor cortex analogue controlling vocal output. To probe the interplay between learning and variability, we made intracellular recordings from neurons in this area, characterizing how their inputs from the functionally distinct pathways change throughout song development. We found that inputs that drive stereotyped song-patterns are strengthened and pruned, while inputs that induce variability remain unchanged. A simple network model showed that strengthening and pruning of action-specific connections reduces the sensitivity of motor control circuits to variable input and neural 'noise'. This identifies a simple and general mechanism for learning-related regulation of motor variability.

Neural rotational speed control for wave energy converters

NASA Astrophysics Data System (ADS)

Amundarain, M.; Alberdi, M.; Garrido, A. J.; Garrido, I.

2011-02-01

Among the benefits arising from an increasing use of renewable energy are: enhanced security of energy supply, stimulation of economic growth, job creation and protection of the environment. In this context, this study analyses the performance of an oscillating water column device for wave energy conversion in function of the stalling behaviour in Wells turbines, one of the most widely used turbines in wave energy plants. For this purpose, a model of neural rotational speed control system is presented, simulated and implemented. This scheme is employed to appropriately adapt the speed of the doubly-fed induction generator coupled to the turbine according to the pressure drop entry, so as to avoid the undesired stalling behaviour. It is demonstrated that the proposed neural rotational speed control design adequately matches the desired relationship between the slip of the doubly-fed induction generator and the pressure drop input, improving the power generated by the turbine generator module.

A Hyaluronan-Based Injectable Hydrogel Improves the Survival and Integration of Stem Cell Progeny following Transplantation.

PubMed

Ballios, Brian G; Cooke, Michael J; Donaldson, Laura; Coles, Brenda L K; Morshead, Cindi M; van der Kooy, Derek; Shoichet, Molly S

2015-06-09

The utility of stem cells and their progeny in adult transplantation models has been limited by poor survival and integration. We designed an injectable and bioresorbable hydrogel blend of hyaluronan and methylcellulose (HAMC) and tested it with two cell types in two animal models, thereby gaining an understanding of its general applicability for enhanced cell distribution, survival, integration, and functional repair relative to conventional cell delivery in saline. HAMC improves cell survival and integration of retinal stem cell (RSC)-derived rods in the retina. The pro-survival mechanism of HAMC is ascribed to the interaction of the CD44 receptor with HA. Transient disruption of the retinal outer limiting membrane, combined with HAMC delivery, results in significantly improved rod survival and visual function. HAMC also improves the distribution, viability, and functional repair of neural stem and progenitor cells (NSCs). The HAMC delivery system improves cell transplantation efficacy in two CNS models, suggesting broad applicability. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Beta-Actin Is Required for Proper Mouse Neural Crest Ontogeny

PubMed Central

Tondeleir, Davina; Noelanders, Rivka; Bakkali, Karima; Ampe, Christophe

2014-01-01

The mouse genome consists of six functional actin genes of which the expression patterns are temporally and spatially regulated during development and in the adult organism. Deletion of beta-actin in mouse is lethal during embryonic development, although there is compensatory expression of other actin isoforms. This suggests different isoform specific functions and, more in particular, an important function for beta-actin during early mammalian development. We here report a role for beta-actin during neural crest ontogeny. Although beta-actin null neural crest cells show expression of neural crest markers, less cells delaminate and their migration arrests shortly after. These phenotypes were associated with elevated apoptosis levels in neural crest cells, whereas proliferation levels were unchanged. Specifically the pre-migratory neural crest cells displayed higher levels of apoptosis, suggesting increased apoptosis in the neural tube accounts for the decreased amount of migrating neural crest cells seen in the beta-actin null embryos. These cells additionally displayed a lack of membrane bound N-cadherin and dramatic decrease in cadherin-11 expression which was more pronounced in the pre-migratory neural crest population, potentially indicating linkage between the cadherin-11 expression and apoptosis. By inhibiting ROCK ex vivo, the knockout neural crest cells regained migratory capacity and cadherin-11 expression was upregulated. We conclude that the presence of beta-actin is vital for survival, specifically of pre-migratory neural crest cells, their proper emigration from the neural tube and their subsequent migration. Furthermore, the absence of beta-actin affects cadherin-11 and N-cadherin function, which could partly be alleviated by ROCK inhibition, situating the Rho-ROCK signaling in a feedback loop with cadherin-11. PMID:24409333

Structure-function clustering in multiplex brain networks

NASA Astrophysics Data System (ADS)

Crofts, J. J.; Forrester, M.; O'Dea, R. D.

2016-10-01

A key question in neuroscience is to understand how a rich functional repertoire of brain activity arises within relatively static networks of structurally connected neural populations: elucidating the subtle interactions between evoked “functional connectivity” and the underlying “structural connectivity” has the potential to address this. These structural-functional networks (and neural networks more generally) are more naturally described using a multilayer or multiplex network approach, in favour of standard single-layer network analyses that are more typically applied to such systems. In this letter, we address such issues by exploring important structure-function relations in the Macaque cortical network by modelling it as a duplex network that comprises an anatomical layer, describing the known (macro-scale) network topology of the Macaque monkey, and a functional layer derived from simulated neural activity. We investigate and characterize correlations between structural and functional layers, as system parameters controlling simulated neural activity are varied, by employing recently described multiplex network measures. Moreover, we propose a novel measure of multiplex structure-function clustering which allows us to investigate the emergence of functional connections that are distinct from the underlying cortical structure, and to highlight the dependence of multiplex structure on the neural dynamical regime.

Postconditioning with repeated mild hypoxia protects neonatal hypoxia-ischemic rats against brain damage and promotes rehabilitation of brain function.

PubMed

Deng, Qingqing; Chang, Yanqun; Cheng, Xiaomao; Luo, Xingang; Zhang, Jing; Tang, Xiaoyuan

2018-05-01

Mild hypoxia conditioning induced by repeated episodes of transient ischemia is a clinically applicable method for protecting the brain against injury after hypoxia-ischemic brain damage. To assess the effect of repeated mild hypoxia postconditioning on brain damage and long-term neural functional recovery after hypoxia-ischemic brain damage. Rats received different protocols of repeated mild hypoxia postconditioning. Seven-day-old rats with hypoxia ischemic brain damage (HIBD) from the left carotid ligation procedure plus 2 h hypoxic stress (8% O 2 at 37 °C) were further receiving repeated mild hypoxia intermittently. The gross anatomy, functional analyses, hypoxia inducible factor 1 alpha (HIF-1a) expression, and neuronal apoptosis of the rat brains were subsequently examined. Compared to the HIBD group, rats postconditioned with mild hypoxia had elevated HIF-1a expression, more Nissl-stain positive cells in their brain tissue and their brains functioned better in behavioral analyses. The recovery of the brain function may be directly linked to the inhibitory effect of HIF-1α on neuronal apoptosis. Furthermore, there were significantly less neuronal apoptosis in the hippocampal CA1 region of the rats postconditioned with mild hypoxia, which might also be related to the higher HIF-1a expression and better brain performance. Overall, these results suggested that postconditioning of neonatal rats after HIBD with mild hypoxia increased HIF-1a expression, exerted a neuroprotective effect and promoted neural functional recovery. Repeated mild hypoxia postconditioning protects neonatal rats with HIBD against brain damage and improves neural functional recovery. Our results may have clinical implications for treating infants with HIBD. Copyright © 2018 Elsevier Inc. All rights reserved.

Vortioxetine reduces BOLD signal during performance of the N-back working memory task: a randomised neuroimaging trial in remitted depressed patients and healthy controls

PubMed Central

Smith, J; Browning, M; Conen, S; Smallman, R; Buchbjerg, J; Larsen, K G; Olsen, C K; Christensen, S R; Dawson, G R; Deakin, J F; Hawkins, P; Morris, R; Goodwin, G; Harmer, C J

2018-01-01

Cognitive dysfunction is common in depression during both acute episodes and remission. Vortioxetine is a novel multimodal antidepressant that has improved cognitive function including executive function in depressed patients in randomised placebo-controlled clinical trials. However, it is unclear whether vortioxetine is able to target directly the neural circuitry implicated in the cognitive deficits in depression. Remitted depressed (n=48) and healthy volunteers (n=48) were randomised to receive 14 days treatment with 20 mg vortioxetine or placebo in a double-blind design. The effects of treatment on functional magnetic resonance imaging responses during an N-back working memory task were assessed at baseline and at the end of treatment. Neuropsychological measures of executive function, speed and information processing, attention and learning and memory were examined with the Trail Making Test (TMT), Rey Auditory Learning Test and Digit Symbol Substitution Test before and after treatment; subjective cognitive function was assessed using the Perceived Deficits Questionnaire (PDQ). Compared with placebo, vortioxetine reduced activation in the right dorsolateral prefrontal cortex and left hippocampus during the N-back task compared with placebo. Vortioxetine also increased TMT-A performance and self-reported cognitive function on the PDQ. These effects were seen across both subject groups. Vortioxetine modulates neural responses across a circuit subserving working memory in a direction opposite to the changes described in depression, when performance is maintained. This study provides evidence that vortioxetine has direct effects on the neural circuitry supporting cognitive function that can be dissociated from its effects on the mood symptoms of depression. PMID:28533517

Nuevas tecnicas basadas en redes neuronales para el diseno de filtros de microondas multicapa apantallados

NASA Astrophysics Data System (ADS)

Pascual Garcia, Juan

In this PhD thesis one method of shielded multilayer circuit neural network based analysis has been developed. One of the most successful analysis procedures of these kind of structures is the Integral Equation technique (IE) solved by the Method of Moments (MoM). In order to solve the IE, in the version which uses the media relevant potentials, it is necessary to have a formulation of the Green's functions associated to the mentioned potentials. The main computational burden in the IE resolution lies on the numerical evaluation of the Green's functions. In this work, the circuit analysis has been drastically accelerated thanks to the approximation of the Green's functions by means of neural networks. Once trained, the neural networks substitute the Green's functions in the IE. Two different types of neural networks have been used: the Radial basis function neural networks (RBFNN) and the Chebyshev neural networks. Thanks mainly to two distinct operations the correct approximation of the Green's functions has been possible. On the one hand, a very effective input space division has been developed. On the other hand, the elimination of the singularity makes feasible the approximation of slow variation functions. Two different singularity elimination strategies have been developed. The first one is based on the multiplication by the source-observation points distance (rho). The second one outperforms the first one. It consists of the extraction of two layers of spatial images from the whole summation of images. With regard to the Chebyshev neural networks, the OLS training algorithm has been applied in a novel fashion. This method allows the optimum design in this kind of neural networks. In this way, the performance of these neural networks outperforms greatly the RBFNNs one. In both networks, the time gain reached makes the neural method profitable. The time invested in the input space division and in the neural training is negligible with only few circuit analysis. To show, in a practical way, the ability of the neural based analysis method, two new design procedures have been developed. The first method uses the Genetic Algorithms to optimize an initial filter which does not fulfill the established specifications. A new fitness function, specially well suited to design filters, has been defined in order to assure the correct convergence of the optimization process. This new function measures the fulfillment of the specifications and it also prevents the appearance of the premature convergence problem. The second method is found on the approximation, by means of neural networks, of the relations between the electrical parameters, which defined the circuit response, and the physical dimensions that synthesize the aforementioned parameters. The neural networks trained with these data can be used in the design of many circuits in a given structure. Both methods had been show their ability in the design of practical filters.

AKT signaling displays multifaceted functions in neural crest development.

PubMed

Sittewelle, Méghane; Monsoro-Burq, Anne H

2018-05-31

AKT signaling is an essential intracellular pathway controlling cell homeostasis, cell proliferation and survival, as well as cell migration and differentiation in adults. Alterations impacting the AKT pathway are involved in many pathological conditions in human disease. Similarly, during development, multiple transmembrane molecules, such as FGF receptors, PDGF receptors or integrins, activate AKT to control embryonic cell proliferation, migration, differentiation, and also cell fate decisions. While many studies in mouse embryos have clearly implicated AKT signaling in the differentiation of several neural crest derivatives, information on AKT functions during the earliest steps of neural crest development had remained relatively scarce until recently. However, recent studies on known and novel regulators of AKT signaling demonstrate that this pathway plays critical roles throughout the development of neural crest progenitors. Non-mammalian models such as fish and frog embryos have been instrumental to our understanding of AKT functions in neural crest development, both in neural crest progenitors and in the neighboring tissues. This review combines current knowledge acquired from all these different vertebrate animal models to describe the various roles of AKT signaling related to neural crest development in vivo. We first describe the importance of AKT signaling in patterning the tissues involved in neural crest induction, namely the dorsal mesoderm and the ectoderm. We then focus on AKT signaling functions in neural crest migration and differentiation. Copyright © 2018 Elsevier Inc. All rights reserved.

The Expression and Function of the Achaete-Scute Genes in Tribolium castaneum Reveals Conservation and Variation in Neural Pattern Formation and Cell Fate Specification

NASA Technical Reports Server (NTRS)

Wheeler, Scott R.; Carrico, Michelle L.; Wilson, Beth A.; Brown, Susan J.; Skeath, James B.

2003-01-01

SUMMARY The study of achaete-scute (ac/sc) genes has recently become a paradigm to understand the evolution and development of the arthropod nervous system. We describe the identification and characterization of the ache genes in the coleopteran insect species Tribolium castaneum. We have identified two Tribolium ache genes - achaete-scute homolog (Tc-ASH) a proneural gene and asense (Tc-ase) a neural precursor gene that reside in a gene complex. Focusing on the embryonic central nervous system we fmd that Tc-ASH is expressed in all neural precursors and the proneural clusters from which they segregate. Through RNAi and misexpression studies we show that Tc-ASH is necessary for neural precursor formation in Triboliurn and sufficient for neural precursor formation in Drosophila. Comparison of the function of the Drosophila and Triboliurn proneural ac/sc genes suggests that in the Drosophila lineage these genes have maintained their ancestral function in neural precursor formation and have acquired a new role in the fate specification of individual neural precursors. Furthermore, we find that Tc-use is expressed in all neural precursors suggesting an important and conserved role for asense genes in insect nervous system development. Our analysis of the Triboliurn ache genes indicates significant plasticity in gene number, expression and function, and implicates these modifications in the evolution of arthropod neural development.

The expression and function of the achaete-scute genes in Tribolium castaneum reveals conservation and variation in neural pattern formation and cell fate specification

NASA Technical Reports Server (NTRS)

Wheeler, Scott R.; Carrico, Michelle L.; Wilson, Beth A.; Brown, Susan J.; Skeath, James B.

2003-01-01

The study of achaete-scute (ac/sc) genes has recently become a paradigm to understand the evolution and development of the arthropod nervous system. We describe the identification and characterization of the ac/sc genes in the coleopteran insect species Tribolium castaneum. We have identified two Tribolium ac/sc genes - achaete-scute homolog (Tc-ASH) a proneural gene and asense (Tc-ase) a neural precursor gene that reside in a gene complex. Focusing on the embryonic central nervous system we find that Tc-ASH is expressed in all neural precursors and the proneural clusters from which they segregate. Through RNAi and misexpression studies we show that Tc-ASH is necessary for neural precursor formation in Tribolium and sufficient for neural precursor formation in Drosophila. Comparison of the function of the Drosophila and Tribolium proneural ac/sc genes suggests that in the Drosophila lineage these genes have maintained their ancestral function in neural precursor formation and have acquired a new role in the fate specification of individual neural precursors. Furthermore, we find that Tc-ase is expressed in all neural precursors suggesting an important and conserved role for asense genes in insect nervous system development. Our analysis of the Tribolium ac/sc genes indicates significant plasticity in gene number, expression and function, and implicates these modifications in the evolution of arthropod neural development.

Targeting Neural Synchrony Deficits is Sufficient to Improve Cognition in a Schizophrenia-Related Neurodevelopmental Model

PubMed Central

Lee, Heekyung; Dvorak, Dino; Fenton, André A.

2014-01-01

Cognitive symptoms are core features of mental disorders but procognitive treatments are limited. We have proposed a “discoordination” hypothesis that cognitive impairment results from aberrant coordination of neural activity. We reported that neonatal ventral hippocampus lesion (NVHL) rats, an established neurodevelopmental model of schizophrenia, have abnormal neural synchrony and cognitive deficits in the active place avoidance task. During stillness, we observed that cortical local field potentials sometimes resembled epileptiform spike-wave discharges with higher prevalence in NVHL rats, indicating abnormal neural synchrony due perhaps to imbalanced excitation–inhibition coupling. Here, within the context of the hypothesis, we investigated whether attenuating abnormal neural synchrony will improve cognition in NVHL rats. We report that: (1) inter-hippocampal synchrony in the theta and beta bands is correlated with active place avoidance performance; (2) the anticonvulsant ethosuximide attenuated the abnormal spike-wave activity, improved cognitive control, and reduced hyperlocomotion; (3) ethosuximide not only normalized the task-associated theta and beta synchrony between the two hippocampi but also increased synchrony between the medial prefrontal cortex and hippocampus above control levels; (4) the antipsychotic olanzapine was less effective at improving cognitive control and normalizing place avoidance-related inter-hippocampal neural synchrony, although it reduced hyperactivity; and (5) olanzapine caused an abnormal pattern of frequency-independent increases in neural synchrony, in both NVHL and control rats. These data suggest that normalizing aberrant neural synchrony can be beneficial and that drugs targeting the pathophysiology of abnormally coordinated neural activities may be a promising theoretical framework and strategy for developing treatments that improve cognition in neurodevelopmental disorders such as schizophrenia. PMID:24592242

Function approximation using combined unsupervised and supervised learning.

PubMed

Andras, Peter

2014-03-01

Function approximation is one of the core tasks that are solved using neural networks in the context of many engineering problems. However, good approximation results need good sampling of the data space, which usually requires exponentially increasing volume of data as the dimensionality of the data increases. At the same time, often the high-dimensional data is arranged around a much lower dimensional manifold. Here we propose the breaking of the function approximation task for high-dimensional data into two steps: (1) the mapping of the high-dimensional data onto a lower dimensional space corresponding to the manifold on which the data resides and (2) the approximation of the function using the mapped lower dimensional data. We use over-complete self-organizing maps (SOMs) for the mapping through unsupervised learning, and single hidden layer neural networks for the function approximation through supervised learning. We also extend the two-step procedure by considering support vector machines and Bayesian SOMs for the determination of the best parameters for the nonlinear neurons in the hidden layer of the neural networks used for the function approximation. We compare the approximation performance of the proposed neural networks using a set of functions and show that indeed the neural networks using combined unsupervised and supervised learning outperform in most cases the neural networks that learn the function approximation using the original high-dimensional data.

Should I stay or should I go? Cadherin function and regulation in the neural crest

PubMed Central

Taneyhill, Lisa A.; Schiffmacher, Andrew T.

2017-01-01

Our increasing comprehension of neural crest cell development has reciprocally advanced our understanding of cadherin expression, regulation, and function. As a transient population of multipotent stem cells that significantly contribute to the vertebrate body plan, neural crest cells undergo a variety of transformative processes and exhibit many cellular behaviors, including epithelial-to-mesenchymal-transition (EMT), motility, collective cell migration, and differentiation. Multiple studies have elucidated regulatory and mechanistic details of specific cadherins during neural crest cell development in a highly contextual manner. Collectively, these results reveal that gradual changes within neural crest cells are accompanied by often times subtle, yet important, alterations in cadherin expression and function. The primary focus of this review is to coalesce recent data on cadherins in neural crest cells, from their specification to their emergence as motile cells soon after EMT, and to highlight the complexities of cadherin expression beyond our current perceptions, including the hypothesis that the neural crest EMT is a transition involving a predominantly singular cadherin switch. Further advancements in genetic approaches and molecular techniques will provide greater opportunities to integrate data from various model systems in order to distinguish unique or overlapping functions of cadherins expressed at any point throughout the ontogeny of the neural crest. PMID:28253541

Neural Systems for Speech and Song in Autism

ERIC Educational Resources Information Center

Lai, Grace; Pantazatos, Spiro P.; Schneider, Harry; Hirsch, Joy

2012-01-01

Despite language disabilities in autism, music abilities are frequently preserved. Paradoxically, brain regions associated with these functions typically overlap, enabling investigation of neural organization supporting speech and song in autism. Neural systems sensitive to speech and song were compared in low-functioning autistic and age-matched…

Deep neural network-based domain adaptation for classification of remote sensing images

NASA Astrophysics Data System (ADS)

Ma, Li; Song, Jiazhen

2017-10-01

We investigate the effectiveness of deep neural network for cross-domain classification of remote sensing images in this paper. In the network, class centroid alignment is utilized as a domain adaptation strategy, making the network able to transfer knowledge from the source domain to target domain on a per-class basis. Since predicted labels of target data should be used to estimate the centroid of each class, we use overall centroid alignment as a coarse domain adaptation method to improve the estimation accuracy. In addition, rectified linear unit is used as the activation function to produce sparse features, which may improve the separation capability. The proposed network can provide both aligned features and an adaptive classifier, as well as obtain label-free classification of target domain data. The experimental results using Hyperion, NCALM, and WorldView-2 remote sensing images demonstrated the effectiveness of the proposed approach.

Sensorless FOC Performance Improved with On-Line Speed and Rotor Resistance Estimator Based on an Artificial Neural Network for an Induction Motor Drive

PubMed Central

Gutierrez-Villalobos, Jose M.; Rodriguez-Resendiz, Juvenal; Rivas-Araiza, Edgar A.; Martínez-Hernández, Moisés A.

2015-01-01

Three-phase induction motor drive requires high accuracy in high performance processes in industrial applications. Field oriented control, which is one of the most employed control schemes for induction motors, bases its function on the electrical parameter estimation coming from the motor. These parameters make an electrical machine driver work improperly, since these electrical parameter values change at low speeds, temperature changes, and especially with load and duty changes. The focus of this paper is the real-time and on-line electrical parameters with a CMAC-ADALINE block added in the standard FOC scheme to improve the IM driver performance and endure the driver and the induction motor lifetime. Two kinds of neural network structures are used; one to estimate rotor speed and the other one to estimate rotor resistance of an induction motor. PMID:26131677

Sensorless FOC Performance Improved with On-Line Speed and Rotor Resistance Estimator Based on an Artificial Neural Network for an Induction Motor Drive.

PubMed

Gutierrez-Villalobos, Jose M; Rodriguez-Resendiz, Juvenal; Rivas-Araiza, Edgar A; Martínez-Hernández, Moisés A

2015-06-29

Three-phase induction motor drive requires high accuracy in high performance processes in industrial applications. Field oriented control, which is one of the most employed control schemes for induction motors, bases its function on the electrical parameter estimation coming from the motor. These parameters make an electrical machine driver work improperly, since these electrical parameter values change at low speeds, temperature changes, and especially with load and duty changes. The focus of this paper is the real-time and on-line electrical parameters with a CMAC-ADALINE block added in the standard FOC scheme to improve the IM driver performance and endure the driver and the induction motor lifetime. Two kinds of neural network structures are used; one to estimate rotor speed and the other one to estimate rotor resistance of an induction motor.

Stability analysis of neural networks with time-varying delay: Enhanced stability criteria and conservatism comparisons

NASA Astrophysics Data System (ADS)

Lin, Wen-Juan; He, Yong; Zhang, Chuan-Ke; Wu, Min

2018-01-01

This paper is concerned with the stability analysis of neural networks with a time-varying delay. To assess system stability accurately, the conservatism reduction of stability criteria has attracted many efforts, among which estimating integral terms as exact as possible is a key issue. At first, this paper develops a new relaxed integral inequality to reduce the estimation gap of popular Wirtinger-based inequality (WBI). Then, for showing the advantages of the proposed inequality over several existing inequalities that also improve the WBI, four stability criteria are derived through different inequalities and the same Lyapunov-Krasovskii functional (LKF), and the conservatism comparison of them is analyzed theoretically. Moreover, an improved criterion is established by combining the proposed inequality and an augmented LKF with delay-product-type terms. Finally, several numerical examples are used to demonstrate the advantages of the proposed method.

Imaging Neural Activity Using Thy1-GCaMP Transgenic mice

PubMed Central

Chen, Qian; Cichon, Joseph; Wang, Wenting; Qiu, Li; Lee, Seok-Jin R.; Campbell, Nolan R.; DeStefino, Nicholas; Goard, Michael J.; Fu, Zhanyan; Yasuda, Ryohei; Looger, Loren L.; Arenkiel, Benjamin R.; Gan, Wen-Biao; Feng, Guoping

2014-01-01

Summary The ability to chronically monitor neuronal activity in the living brain is essential for understanding the organization and function of the nervous system. The genetically encoded green fluorescent protein based calcium sensor GCaMP provides a powerful tool for detecting calcium transients in neuronal somata, processes, and synapses that are triggered by neuronal activities. Here we report the generation and characterization of transgenic mice that express improved GCaMPs in various neuronal subpopulations under the control of the Thy1 promoter. In vitro and in vivo studies show that calcium transients induced by spontaneous and stimulus-evoked neuronal activities can be readily detected at the level of individual cells and synapses in acute brain slices, as well as chronically in awake behaving animals. These GCaMP transgenic mice allow investigation of activity patterns in defined neuronal populations in the living brain, and will greatly facilitate dissecting complex structural and functional relationships of neural networks. PMID:23083733

Addition of visual noise boosts evoked potential-based brain-computer interface.

PubMed

Xie, Jun; Xu, Guanghua; Wang, Jing; Zhang, Sicong; Zhang, Feng; Li, Yeping; Han, Chengcheng; Li, Lili

2014-05-14

Although noise has a proven beneficial role in brain functions, there have not been any attempts on the dedication of stochastic resonance effect in neural engineering applications, especially in researches of brain-computer interfaces (BCIs). In our study, a steady-state motion visual evoked potential (SSMVEP)-based BCI with periodic visual stimulation plus moderate spatiotemporal noise can achieve better offline and online performance due to enhancement of periodic components in brain responses, which was accompanied by suppression of high harmonics. Offline results behaved with a bell-shaped resonance-like functionality and 7-36% online performance improvements can be achieved when identical visual noise was adopted for different stimulation frequencies. Using neural encoding modeling, these phenomena can be explained as noise-induced input-output synchronization in human sensory systems which commonly possess a low-pass property. Our work demonstrated that noise could boost BCIs in addressing human needs.

Neural system modeling and simulation using Hybrid Functional Petri Net.

PubMed

Tang, Yin; Wang, Fei

2012-02-01

The Petri net formalism has been proved to be powerful in biological modeling. It not only boasts of a most intuitive graphical presentation but also combines the methods of classical systems biology with the discrete modeling technique. Hybrid Functional Petri Net (HFPN) was proposed specially for biological system modeling. An array of well-constructed biological models using HFPN yielded very interesting results. In this paper, we propose a method to represent neural system behavior, where biochemistry and electrical chemistry are both included using the Petri net formalism. We built a model for the adrenergic system using HFPN and employed quantitative analysis. Our simulation results match the biological data well, showing that the model is very effective. Predictions made on our model further manifest the modeling power of HFPN and improve the understanding of the adrenergic system. The file of our model and more results with their analysis are available in our supplementary material.

Neural activity during affect labeling predicts expressive writing effects on well-being: GLM and SVM approaches

PubMed Central

Memarian, Negar; Torre, Jared B.; Haltom, Kate E.; Stanton, Annette L.

2017-01-01

Abstract Affect labeling (putting feelings into words) is a form of incidental emotion regulation that could underpin some benefits of expressive writing (i.e. writing about negative experiences). Here, we show that neural responses during affect labeling predicted changes in psychological and physical well-being outcome measures 3 months later. Furthermore, neural activity of specific frontal regions and amygdala predicted those outcomes as a function of expressive writing. Using supervised learning (support vector machines regression), improvements in four measures of psychological and physical health (physical symptoms, depression, anxiety and life satisfaction) after an expressive writing intervention were predicted with an average of 0.85% prediction error [root mean square error (RMSE) %]. The predictions were significantly more accurate with machine learning than with the conventional generalized linear model method (average RMSE: 1.3%). Consistent with affect labeling research, right ventrolateral prefrontal cortex (RVLPFC) and amygdalae were top predictors of improvement in the four outcomes. Moreover, RVLPFC and left amygdala predicted benefits due to expressive writing in satisfaction with life and depression outcome measures, respectively. This study demonstrates the substantial merit of supervised machine learning for real-world outcome prediction in social and affective neuroscience. PMID:28992270

The Principle of the Micro-Electronic Neural Bridge and a Prototype System Design.

PubMed

Huang, Zong-Hao; Wang, Zhi-Gong; Lu, Xiao-Ying; Li, Wen-Yuan; Zhou, Yu-Xuan; Shen, Xiao-Yan; Zhao, Xin-Tai

2016-01-01

The micro-electronic neural bridge (MENB) aims to rebuild lost motor function of paralyzed humans by routing movement-related signals from the brain, around the damage part in the spinal cord, to the external effectors. This study focused on the prototype system design of the MENB, including the principle of the MENB, the neural signal detecting circuit and the functional electrical stimulation (FES) circuit design, and the spike detecting and sorting algorithm. In this study, we developed a novel improved amplitude threshold spike detecting method based on variable forward difference threshold for both training and bridging phase. The discrete wavelet transform (DWT), a new level feature coefficient selection method based on Lilliefors test, and the k-means clustering method based on Mahalanobis distance were used for spike sorting. A real-time online spike detecting and sorting algorithm based on DWT and Euclidean distance was also implemented for the bridging phase. Tested by the data sets available at Caltech, in the training phase, the average sensitivity, specificity, and clustering accuracies are 99.43%, 97.83%, and 95.45%, respectively. Validated by the three-fold cross-validation method, the average sensitivity, specificity, and classification accuracy are 99.43%, 97.70%, and 96.46%, respectively.

[The mechanism and function of hippocampal neural oscillation].

PubMed

Lu, Ning; Xing, Dan-Qin; Sheng, Tao; Lu, Wei

2017-10-25

Neural oscillation is rhythmic or repetitive neural activity in the central nervous system that is usually generated by oscillatory activity of neuronal ensembles, reflecting regular and synchronized activities within these cell populations. According to several oscillatory bands covering frequencies from approximately 0.5 Hz to >100 Hz, neural oscillations are usually classified as delta oscillation (0.5-3 Hz), theta oscillation (4-12 Hz), beta oscillation (12-30 Hz), gamma oscillation (30-100 Hz) and sharp-wave ripples (>100 Hz ripples superimposed on 0.01-3 Hz sharp waves). Neural oscillation in different frequencies can be detected in different brain regions of human and animal during perception, motion and sleep, and plays an essential role in cognition, learning and memory process. In this review, we summarize recent findings on neural oscillations in hippocampus, as well as the mechanism and function of hippocampal theta oscillation, gamma oscillation and sharp-wave ripples. This review may yield new insights into the functions of neural oscillation in general.

Finite-time convergent recurrent neural network with a hard-limiting activation function for constrained optimization with piecewise-linear objective functions.

PubMed

Liu, Qingshan; Wang, Jun

2011-04-01

This paper presents a one-layer recurrent neural network for solving a class of constrained nonsmooth optimization problems with piecewise-linear objective functions. The proposed neural network is guaranteed to be globally convergent in finite time to the optimal solutions under a mild condition on a derived lower bound of a single gain parameter in the model. The number of neurons in the neural network is the same as the number of decision variables of the optimization problem. Compared with existing neural networks for optimization, the proposed neural network has a couple of salient features such as finite-time convergence and a low model complexity. Specific models for two important special cases, namely, linear programming and nonsmooth optimization, are also presented. In addition, applications to the shortest path problem and constrained least absolute deviation problem are discussed with simulation results to demonstrate the effectiveness and characteristics of the proposed neural network.

Chromatin Remodeling BAF (SWI/SNF) Complexes in Neural Development and Disorders

PubMed Central

Sokpor, Godwin; Xie, Yuanbin; Rosenbusch, Joachim; Tuoc, Tran

2017-01-01

The ATP-dependent BRG1/BRM associated factor (BAF) chromatin remodeling complexes are crucial in regulating gene expression by controlling chromatin dynamics. Over the last decade, it has become increasingly clear that during neural development in mammals, distinct ontogenetic stage-specific BAF complexes derived from combinatorial assembly of their subunits are formed in neural progenitors and post-mitotic neural cells. Proper functioning of the BAF complexes plays critical roles in neural development, including the establishment and maintenance of neural fates and functionality. Indeed, recent human exome sequencing and genome-wide association studies have revealed that mutations in BAF complex subunits are linked to neurodevelopmental disorders such as Coffin-Siris syndrome, Nicolaides-Baraitser syndrome, Kleefstra's syndrome spectrum, Hirschsprung's disease, autism spectrum disorder, and schizophrenia. In this review, we focus on the latest insights into the functions of BAF complexes during neural development and the plausible mechanistic basis of how mutations in known BAF subunits are associated with certain neurodevelopmental disorders. PMID:28824374

Chromatin Remodeling BAF (SWI/SNF) Complexes in Neural Development and Disorders.

PubMed

Sokpor, Godwin; Xie, Yuanbin; Rosenbusch, Joachim; Tuoc, Tran

2017-01-01

The ATP-dependent BRG1/BRM associated factor (BAF) chromatin remodeling complexes are crucial in regulating gene expression by controlling chromatin dynamics. Over the last decade, it has become increasingly clear that during neural development in mammals, distinct ontogenetic stage-specific BAF complexes derived from combinatorial assembly of their subunits are formed in neural progenitors and post-mitotic neural cells. Proper functioning of the BAF complexes plays critical roles in neural development, including the establishment and maintenance of neural fates and functionality. Indeed, recent human exome sequencing and genome-wide association studies have revealed that mutations in BAF complex subunits are linked to neurodevelopmental disorders such as Coffin-Siris syndrome, Nicolaides-Baraitser syndrome, Kleefstra's syndrome spectrum, Hirschsprung's disease, autism spectrum disorder, and schizophrenia. In this review, we focus on the latest insights into the functions of BAF complexes during neural development and the plausible mechanistic basis of how mutations in known BAF subunits are associated with certain neurodevelopmental disorders.

Effects of Transcranial Direct Current Stimulation on Neural Networks in Young and Older Adults

PubMed

Martin, Andrew K; Meinzer, Marcus; Lindenberg, Robert; Sieg, Mira M; Nachtigall, Laura; Flöel, Agnes

2017-11-01

Transcranial direct current stimulation (tDCS) may be a viable tool to improve motor and cognitive function in advanced age. However, although a number of studies have demonstrated improved cognitive performance in older adults, other studies have failed to show restorative effects. The neural effects of beneficial stimulation response in both age groups is lacking. In the current study, tDCS was administered during simultaneous fMRI in 42 healthy young and older participants. Semantic word generation and motor speech baseline tasks were used to investigate behavioral and neural effects of uni- and bihemispheric motor cortex tDCS in a three-way, crossover, sham tDCS controlled design. Independent components analysis assessed differences in task-related activity between the two age groups and tDCS effects at the network level. We also explored whether laterality of language network organization was effected by tDCS. Behaviorally, both active tDCS conditions significantly improved semantic word retrieval performance in young and older adults and were comparable between groups and stimulation conditions. Network-level tDCS effects were identified in the ventral and dorsal anterior cingulate networks in the combined sample during semantic fluency and motor speech tasks. In addition, a shift toward enhanced left laterality was identified in the older adults for both active stimulation conditions. Thus, tDCS results in common network-level modulations and behavioral improvements for both age groups, with an additional effect of increasing left laterality in older adults.

Effects of multitasking-training on gray matter structure and resting state neural mechanisms

PubMed Central

Takeuchi, Hikaru; Taki, Yasuyuki; Nouchi, Rui; Hashizume, Hiroshi; Sekiguchi, Atsushi; Kotozaki, Yuka; Nakagawa, Seishu; Miyauchi, Carlos Makoto; Sassa, Yuko; Kawashima, Ryuta

2014-01-01

Multitasking (MT) constitutes engaging in two or more cognitive activities at the same time. MT-training improves performance on untrained MT tasks and alters the functional activity of the brain during MT. However, the effects of MT-training on neural mechanisms beyond MT-related functions are not known. We investigated the effects of 4 weeks of MT-training on regional gray matter volume (rGMV) and functional connectivity during rest (resting-FC) in young human adults. MT-training was associated with increased rGMV in three prefrontal cortical regions (left lateral rostral prefrontal cortex (PFC), dorsolateral PFC (DLPFC), and left inferior frontal junction), the left posterior parietal cortex, and the left temporal and lateral occipital areas as well as decreased resting-FC between the right DLPFC and an anatomical cluster around the ventral anterior cingulate cortex (ACC). Our findings suggest that participation in MT-training is as a whole associated with task-irrelevant plasticity (i.e., neural changes are not limited to certain specific task conditions) in regions and the network that are assumed to play roles in MT as well as diverse higher-order cognitive functions. We could not dissociate the effects of each task component and the diverse cognitive processes involved in MT because of the nature of the study, and these remain to be investigated. Hum Brain Mapp 35:3646–3660, 2014. © 2013 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc. PMID:24343872

Thermoregulation in multiple sclerosis.

PubMed

Davis, Scott L; Wilson, Thad E; White, Andrea T; Frohman, Elliot M

2010-11-01

Multiple sclerosis (MS) is a progressive neurological disorder that disrupts axonal myelin in the central nervous system. Demyelination produces alterations in saltatory conduction, slowed conduction velocity, and a predisposition to conduction block. An estimated 60-80% of MS patients experience temporary worsening of clinical signs and neurological symptoms with heat exposure. Additionally, MS may produce impaired neural control of autonomic and endocrine functions. This review focuses on five main themes regarding the current understanding of thermoregulatory dysfunction in MS: 1) heat sensitivity; 2) central regulation of body temperature; 3) thermoregulatory effector responses; 4) heat-induced fatigue; and 5) countermeasures to improve or maintain function during thermal stress. Heat sensitivity in MS is related to the detrimental effects of increased temperature on action potential propagation in demyelinated axons, resulting in conduction slowing and/or block, which can be quantitatively characterized using precise measurements of ocular movements. MS lesions can also occur in areas of the brain responsible for the control and regulation of body temperature and thermoregulatory effector responses, resulting in impaired neural control of sudomotor pathways or neural-induced changes in eccrine sweat glands, as evidenced by observations of reduced sweating responses in MS patients. Fatigue during thermal stress is common in MS and results in decreased motor function and increased symptomatology likely due to impairments in central conduction. Although not comprehensive, some evidence exists concerning treatments (cooling, precooling, and pharmacological) for the MS patient to preserve function and decrease symptom worsening during heat stress.

Consequences of cancer treatments on adult hippocampal neurogenesis: implications for cognitive function and depressive symptoms

PubMed Central

Pereira Dias, Gisele; Hollywood, Ronan; Bevilaqua, Mário Cesar do Nascimento; da Silveira da Luz, Anna Claudia Domingos; Hindges, Robert; Nardi, Antonio Egidio; Thuret, Sandrine

2014-01-01

The human brain is capable of generating new functional neurons throughout life, a phenomenon known as adult neurogenesis. The generation of new neurons is sustained throughout adulthood due to the proliferation and differentiation of adult neural stem cells. This process in humans is uniquely located in the subgranular zone of the dentate gyrus in the hippocampus. Adult hippocampal neurogenesis (AHN) is thought to play a major role in hippocampus-dependent functions, such as spatial awareness, long-term memory, emotionality, and mood. The overall aim of current treatments for cancer (such as radiotherapy and chemotherapy) is to prevent aberrant cell division of cell populations associated with malignancy. However, the treatments in question are absolutist in nature and hence inhibit all cell division. An unintended consequence of this cessation of cell division is the impairment of adult neural stem cell proliferation and AHN. Patients undergoing treatment for cancerous malignancies often display specific forms of memory deficits, as well as depressive symptoms. This review aims to discuss the effects of cancer treatments on AHN and propose a link between the inhibition of the neurogenetic process in the hippocampus and the advent of the cognitive and mood-based deficits observed in patients and animal models undergoing cancer therapies. Possible evidence for coadjuvant interventions aiming to protect neural cells, and subsequently the mood and cognitive functions they regulate, from the ablative effects of cancer treatment are discussed as potential clinical tools to improve mental health among cancer patients. PMID:24470543

Consequences of cancer treatments on adult hippocampal neurogenesis: implications for cognitive function and depressive symptoms.

PubMed

Pereira Dias, Gisele; Hollywood, Ronan; Bevilaqua, Mário Cesar do Nascimento; da Luz, Anna Claudia Domingos da Silveira; Hindges, Robert; Nardi, Antonio Egidio; Thuret, Sandrine

2014-04-01

The human brain is capable of generating new functional neurons throughout life, a phenomenon known as adult neurogenesis. The generation of new neurons is sustained throughout adulthood due to the proliferation and differentiation of adult neural stem cells. This process in humans is uniquely located in the subgranular zone of the dentate gyrus in the hippocampus. Adult hippocampal neurogenesis (AHN) is thought to play a major role in hippocampus-dependent functions, such as spatial awareness, long-term memory, emotionality, and mood. The overall aim of current treatments for cancer (such as radiotherapy and chemotherapy) is to prevent aberrant cell division of cell populations associated with malignancy. However, the treatments in question are absolutist in nature and hence inhibit all cell division. An unintended consequence of this cessation of cell division is the impairment of adult neural stem cell proliferation and AHN. Patients undergoing treatment for cancerous malignancies often display specific forms of memory deficits, as well as depressive symptoms. This review aims to discuss the effects of cancer treatments on AHN and propose a link between the inhibition of the neurogenetic process in the hippocampus and the advent of the cognitive and mood-based deficits observed in patients and animal models undergoing cancer therapies. Possible evidence for coadjuvant interventions aiming to protect neural cells, and subsequently the mood and cognitive functions they regulate, from the ablative effects of cancer treatment are discussed as potential clinical tools to improve mental health among cancer patients.

Neuromodulation of the neural circuits controlling the lower urinary tract.

PubMed

Gad, Parag N; Roy, Roland R; Zhong, Hui; Gerasimenko, Yury P; Taccola, Giuliano; Edgerton, V Reggie

2016-11-01

The inability to control timely bladder emptying is one of the most serious challenges among the many functional deficits that occur after a spinal cord injury. We previously demonstrated that electrodes placed epidurally on the dorsum of the spinal cord can be used in animals and humans to recover postural and locomotor function after complete paralysis and can be used to enable voiding in spinal rats. In the present study, we examined the neuromodulation of lower urinary tract function associated with acute epidural spinal cord stimulation, locomotion, and peripheral nerve stimulation in adult rats. Herein we demonstrate that electrically evoked potentials in the hindlimb muscles and external urethral sphincter are modulated uniquely when the rat is stepping bipedally and not voiding, immediately pre-voiding, or when voiding. We also show that spinal cord stimulation can effectively neuromodulate the lower urinary tract via frequency-dependent stimulation patterns and that neural peripheral nerve stimulation can activate the external urethral sphincter both directly and via relays in the spinal cord. The data demonstrate that the sensorimotor networks controlling bladder and locomotion are highly integrated neurophysiologically and behaviorally and demonstrate how these two functions are modulated by sensory input from the tibial and pudental nerves. A more detailed understanding of the high level of interaction between these networks could lead to the integration of multiple neurophysiological strategies to improve bladder function. These data suggest that the development of strategies to improve bladder function should simultaneously engage these highly integrated networks in an activity-dependent manner. Copyright © 2016. Published by Elsevier Inc.

Improved Adjoint-Operator Learning For A Neural Network

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad; Barhen, Jacob

1995-01-01

Improved method of adjoint-operator learning reduces amount of computation and associated computational memory needed to make electronic neural network learn temporally varying pattern (e.g., to recognize moving object in image) in real time. Method extension of method described in "Adjoint-Operator Learning for a Neural Network" (NPO-18352).

Relationship between brainstem, cortical and behavioral measures relevant to pitch salience in humans.

PubMed

Krishnan, Ananthanarayan; Bidelman, Gavin M; Smalt, Christopher J; Ananthakrishnan, Saradha; Gandour, Jackson T

2012-10-01

Neural representation of pitch-relevant information at both the brainstem and cortical levels of processing is influenced by language or music experience. However, the functional roles of brainstem and cortical neural mechanisms in the hierarchical network for language processing, and how they drive and maintain experience-dependent reorganization are not known. In an effort to evaluate the possible interplay between these two levels of pitch processing, we introduce a novel electrophysiological approach to evaluate pitch-relevant neural activity at the brainstem and auditory cortex concurrently. Brainstem frequency-following responses and cortical pitch responses were recorded from participants in response to iterated rippled noise stimuli that varied in stimulus periodicity (pitch salience). A control condition using iterated rippled noise devoid of pitch was employed to ensure pitch specificity of the cortical pitch response. Neural data were compared with behavioral pitch discrimination thresholds. Results showed that magnitudes of neural responses increase systematically and that behavioral pitch discrimination improves with increasing stimulus periodicity, indicating more robust encoding for salient pitch. Absence of cortical pitch response in the control condition confirms that the cortical pitch response is specific to pitch. Behavioral pitch discrimination was better predicted by brainstem and cortical responses together as compared to each separately. The close correspondence between neural and behavioral data suggest that neural correlates of pitch salience that emerge in early, preattentive stages of processing in the brainstem may drive and maintain with high fidelity the early cortical representations of pitch. These neural representations together contain adequate information for the development of perceptual pitch salience. Copyright © 2012 Elsevier Ltd. All rights reserved.

Biomimetic hydrogels direct spinal progenitor cell differentiation and promote functional recovery after spinal cord injury.

PubMed

Geissler, Sydney A; Sabin, Alexandra L; Besser, Rachel R; Gooden, Olivia M; Shirk, Bryce D; Nguyen, Quan M; Khaing, Zin Z; Schmidt, Christine E

2018-04-01

Demyelination that results from disease or traumatic injury, such as spinal cord injury (SCI), can have a devastating effect on neural function and recovery. Many researchers are examining treatments to minimize demyelination by improving oligodendrocyte availability in vivo. Transplantation of stem and oligodendrocyte progenitor cells is a promising option, however, trials are plagued by undirected differentiation. Here we introduce a biomaterial that has been optimized to direct the differentiation of neural progenitor cells (NPCs) toward oligodendrocytes as a cell delivery vehicle after SCI. A collagen-based hydrogel was modified to mimic the mechanical properties of the neonatal spinal cord, and components present in the developing extracellular matrix were included to provide appropriate chemical cues to the NPCs to direct their differentiation toward oligodendrocytes. The hydrogel with cells was then transplanted into a unilateral cervical contusion model of SCI to examine the functional recovery with this treatment. Six behavioral tests and histological assessment were performed to examine the in vivo response to this treatment. Our results demonstrate that we can achieve a significant increase in oligodendrocyte differentiation of NPCs compared to standard culture conditions using a three-component biomaterial composed of collagen, hyaluronic acid, and laminin that has mechanical properties matched to those of neonatal neural tissue. Additionally, SCI rats with hydrogel transplants, with and without NPCs, showed functional recovery. Animals transplanted with hydrogels with NPCs showed significantly increased functional recovery over six weeks compared to the media control group. The three-component hydrogel presented here has the potential to provide cues to direct differentiation in vivo to encourage regeneration of the central nervous system.

Some comparisons of complexity in dictionary-based and linear computational models.

PubMed

Gnecco, Giorgio; Kůrková, Věra; Sanguineti, Marcello

2011-03-01

Neural networks provide a more flexible approximation of functions than traditional linear regression. In the latter, one can only adjust the coefficients in linear combinations of fixed sets of functions, such as orthogonal polynomials or Hermite functions, while for neural networks, one may also adjust the parameters of the functions which are being combined. However, some useful properties of linear approximators (such as uniqueness, homogeneity, and continuity of best approximation operators) are not satisfied by neural networks. Moreover, optimization of parameters in neural networks becomes more difficult than in linear regression. Experimental results suggest that these drawbacks of neural networks are offset by substantially lower model complexity, allowing accuracy of approximation even in high-dimensional cases. We give some theoretical results comparing requirements on model complexity for two types of approximators, the traditional linear ones and so called variable-basis types, which include neural networks, radial, and kernel models. We compare upper bounds on worst-case errors in variable-basis approximation with lower bounds on such errors for any linear approximator. Using methods from nonlinear approximation and integral representations tailored to computational units, we describe some cases where neural networks outperform any linear approximator. Copyright © 2010 Elsevier Ltd. All rights reserved.

At the interface: convergence of neural regeneration and neural prostheses for restoration of function.

PubMed

Grill, W M; McDonald, J W; Peckham, P H; Heetderks, W; Kocsis, J; Weinrich, M

2001-01-01

The rapid pace of recent advances in development and application of electrical stimulation of the nervous system and in neural regeneration has created opportunities to combine these two approaches to restoration of function. This paper relates the discussion on this topic from a workshop at the International Functional Electrical Stimulation Society. The goals of this workshop were to discuss the current state of interaction between the fields of neural regeneration and neural prostheses and to identify potential areas of future research that would have the greatest impact on achieving the common goal of restoring function after neurological damage. Identified areas include enhancement of axonal regeneration with applied electric fields, development of hybrid neural interfaces combining synthetic silicon and biologically derived elements, and investigation of the role of patterned neural activity in regulating various neuronal processes and neurorehabilitation. Increased communication and cooperation between the two communities and recognition by each field that the other has something to contribute to their efforts are needed to take advantage of these opportunities. In addition, creative grants combining the two approaches and more flexible funding mechanisms to support the convergence of their perspectives are necessary to achieve common objectives.

Distinct Functional Networks Associated with Improvement of Affective Symptoms and Cognitive Function During Citalopram Treatment in Geriatric Depression

PubMed Central

Diaconescu, Andreea Oliviana; Kramer, Elisse; Hermann, Carol; Ma, Yilong; Dhawan, Vijay; Chaly, Thomas; Eidelberg, David; McIntosh, Anthony Randal; Smith, Gwenn S.

2010-01-01

Variability in the affective and cognitive symptom response to antidepressant treatment has been observed in geriatric depression. The underlying neural circuitry is poorly understood. The current study evaluated the cerebral glucose metabolic effects of citalopram treatment and applied multivariate, functional connectivity analyses to identify brain networks associated with improvements in affective symptoms and cognitive function. Sixteen geriatric depressed patients underwent resting Positron Emission Tomography (PET) studies of cerebral glucose metabolism and assessment of affective symptoms and cognitive function before and after eight weeks of selective serotonin reuptake inhibitor treatment (citalopram). Voxel-wise analyses of the normalized glucose metabolic data showed decreased cerebral metabolism during citalopram treatment in the anterior cingulate gyrus, middle temporal gyrus, precuneus, amygdala, and parahippocampal gyrus. Increased metabolism was observed in the putamen, occipital cortex and cerebellum. Functional connectivity analyses revealed two networks which were uniquely associated with improvement of affective symptoms and cognitive function during treatment. A subcortical-limbic-frontal network was associated with improvement in affect (depression and anxiety), while a medial temporal-parietal-frontal network was associated with improvement in cognition (immediate verbal learning/memory and verbal fluency). The regions that comprise the cognitive network overlap with the regions that are affected in Alzheimer’s dementia. Thus, alterations in specific brain networks associated with improvement of affective symptoms and cognitive function are observed during citalopram treatment in geriatric depression. PMID:20886575

Ultra-low noise miniaturized neural amplifier with hardware averaging.

PubMed

Dweiri, Yazan M; Eggers, Thomas; McCallum, Grant; Durand, Dominique M

2015-08-01

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.

Prediction of Aerodynamic Coefficients using Neural Networks for Sparse Data

NASA Technical Reports Server (NTRS)

Rajkumar, T.; Bardina, Jorge; Clancy, Daniel (Technical Monitor)

2002-01-01

Basic aerodynamic coefficients are modeled as functions of angles of attack and sideslip with vehicle lateral symmetry and compressibility effects. Most of the aerodynamic parameters can be well-fitted using polynomial functions. In this paper a fast, reliable way of predicting aerodynamic coefficients is produced using a neural network. The training data for the neural network is derived from wind tunnel test and numerical simulations. The coefficients of lift, drag, pitching moment are expressed as a function of alpha (angle of attack) and Mach number. The results produced from preliminary neural network analysis are very good.

Multistability of memristive Cohen-Grossberg neural networks with non-monotonic piecewise linear activation functions and time-varying delays.

PubMed

Nie, Xiaobing; Zheng, Wei Xing; Cao, Jinde

2015-11-01

The problem of coexistence and dynamical behaviors of multiple equilibrium points is addressed for a class of memristive Cohen-Grossberg neural networks with non-monotonic piecewise linear activation functions and time-varying delays. By virtue of the fixed point theorem, nonsmooth analysis theory and other analytical tools, some sufficient conditions are established to guarantee that such n-dimensional memristive Cohen-Grossberg neural networks can have 5(n) equilibrium points, among which 3(n) equilibrium points are locally exponentially stable. It is shown that greater storage capacity can be achieved by neural networks with the non-monotonic activation functions introduced herein than the ones with Mexican-hat-type activation function. In addition, unlike most existing multistability results of neural networks with monotonic activation functions, those obtained 3(n) locally stable equilibrium points are located both in saturated regions and unsaturated regions. The theoretical findings are verified by an illustrative example with computer simulations. Copyright © 2015 Elsevier Ltd. All rights reserved.

Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm

PubMed Central

Dura-Bernal, Salvador; Li, Kan; Neymotin, Samuel A.; Francis, Joseph T.; Principe, Jose C.; Lytton, William W.

2016-01-01

Neural stimulation can be used as a tool to elicit natural sensations or behaviors by modulating neural activity. This can be potentially used to mitigate the damage of brain lesions or neural disorders. However, in order to obtain the optimal stimulation sequences, it is necessary to develop neural control methods, for example by constructing an inverse model of the target system. For real brains, this can be very challenging, and often unfeasible, as it requires repeatedly stimulating the neural system to obtain enough probing data, and depends on an unwarranted assumption of stationarity. By contrast, detailed brain simulations may provide an alternative testbed for understanding the interactions between ongoing neural activity and external stimulation. Unlike real brains, the artificial system can be probed extensively and precisely, and detailed output information is readily available. Here we employed a spiking network model of sensorimotor cortex trained to drive a realistic virtual musculoskeletal arm to reach a target. The network was then perturbed, in order to simulate a lesion, by either silencing neurons or removing synaptic connections. All lesions led to significant behvaioral impairments during the reaching task. The remaining cells were then systematically probed with a set of single and multiple-cell stimulations, and results were used to build an inverse model of the neural system. The inverse model was constructed using a kernel adaptive filtering method, and was used to predict the neural stimulation pattern required to recover the pre-lesion neural activity. Applying the derived neurostimulation to the lesioned network improved the reaching behavior performance. This work proposes a novel neurocontrol method, and provides theoretical groundwork on the use biomimetic brain models to develop and evaluate neurocontrollers that restore the function of damaged brain regions and the corresponding motor behaviors. PMID:26903796

Improved Estimation and Interpretation of Correlations in Neural Circuits

PubMed Central

Yatsenko, Dimitri; Josić, Krešimir; Ecker, Alexander S.; Froudarakis, Emmanouil; Cotton, R. James; Tolias, Andreas S.

2015-01-01

Ambitious projects aim to record the activity of ever larger and denser neuronal populations in vivo. Correlations in neural activity measured in such recordings can reveal important aspects of neural circuit organization. However, estimating and interpreting large correlation matrices is statistically challenging. Estimation can be improved by regularization, i.e. by imposing a structure on the estimate. The amount of improvement depends on how closely the assumed structure represents dependencies in the data. Therefore, the selection of the most efficient correlation matrix estimator for a given neural circuit must be determined empirically. Importantly, the identity and structure of the most efficient estimator informs about the types of dominant dependencies governing the system. We sought statistically efficient estimators of neural correlation matrices in recordings from large, dense groups of cortical neurons. Using fast 3D random-access laser scanning microscopy of calcium signals, we recorded the activity of nearly every neuron in volumes 200 μm wide and 100 μm deep (150–350 cells) in mouse visual cortex. We hypothesized that in these densely sampled recordings, the correlation matrix should be best modeled as the combination of a sparse graph of pairwise partial correlations representing local interactions and a low-rank component representing common fluctuations and external inputs. Indeed, in cross-validation tests, the covariance matrix estimator with this structure consistently outperformed other regularized estimators. The sparse component of the estimate defined a graph of interactions. These interactions reflected the physical distances and orientation tuning properties of cells: The density of positive ‘excitatory’ interactions decreased rapidly with geometric distances and with differences in orientation preference whereas negative ‘inhibitory’ interactions were less selective. Because of its superior performance, this ‘sparse+latent’ estimator likely provides a more physiologically relevant representation of the functional connectivity in densely sampled recordings than the sample correlation matrix. PMID:25826696

Accuracy Maximization Analysis for Sensory-Perceptual Tasks: Computational Improvements, Filter Robustness, and Coding Advantages for Scaled Additive Noise

PubMed Central

Burge, Johannes

2017-01-01

Accuracy Maximization Analysis (AMA) is a recently developed Bayesian ideal observer method for task-specific dimensionality reduction. Given a training set of proximal stimuli (e.g. retinal images), a response noise model, and a cost function, AMA returns the filters (i.e. receptive fields) that extract the most useful stimulus features for estimating a user-specified latent variable from those stimuli. Here, we first contribute two technical advances that significantly reduce AMA’s compute time: we derive gradients of cost functions for which two popular estimators are appropriate, and we implement a stochastic gradient descent (AMA-SGD) routine for filter learning. Next, we show how the method can be used to simultaneously probe the impact on neural encoding of natural stimulus variability, the prior over the latent variable, noise power, and the choice of cost function. Then, we examine the geometry of AMA’s unique combination of properties that distinguish it from better-known statistical methods. Using binocular disparity estimation as a concrete test case, we develop insights that have general implications for understanding neural encoding and decoding in a broad class of fundamental sensory-perceptual tasks connected to the energy model. Specifically, we find that non-orthogonal (partially redundant) filters with scaled additive noise tend to outperform orthogonal filters with constant additive noise; non-orthogonal filters and scaled additive noise can interact to sculpt noise-induced stimulus encoding uncertainty to match task-irrelevant stimulus variability. Thus, we show that some properties of neural response thought to be biophysical nuisances can confer coding advantages to neural systems. Finally, we speculate that, if repurposed for the problem of neural systems identification, AMA may be able to overcome a fundamental limitation of standard subunit model estimation. As natural stimuli become more widely used in the study of psychophysical and neurophysiological performance, we expect that task-specific methods for feature learning like AMA will become increasingly important. PMID:28178266

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

DOE PAGES

Ling, Julia; Kurzawski, Andrew; Templeton, Jeremy

2016-10-18

There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS) turbulence models that are informed by and can represent a richer set of turbulence physics. This paper presents a method of using deep neural networks to learn a model for the Reynolds stress anisotropy tensor from high-fidelity simulation data. A novel neural network architecture is proposed which uses a multiplicative layer with an invariant tensor basis to embed Galilean invariance into the predicted anisotropy tensor. It is demonstrated that this neural network architecture provides improved prediction accuracy compared with a generic neural network architecture that does not embed this invariance property.more » Furthermore, the Reynolds stress anisotropy predictions of this invariant neural network are propagated through to the velocity field for two test cases. For both test cases, significant improvement versus baseline RANS linear eddy viscosity and nonlinear eddy viscosity models is demonstrated.« less

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ling, Julia; Kurzawski, Andrew; Templeton, Jeremy

There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS) turbulence models that are informed by and can represent a richer set of turbulence physics. This paper presents a method of using deep neural networks to learn a model for the Reynolds stress anisotropy tensor from high-fidelity simulation data. A novel neural network architecture is proposed which uses a multiplicative layer with an invariant tensor basis to embed Galilean invariance into the predicted anisotropy tensor. It is demonstrated that this neural network architecture provides improved prediction accuracy compared with a generic neural network architecture that does not embed this invariance property.more » Furthermore, the Reynolds stress anisotropy predictions of this invariant neural network are propagated through to the velocity field for two test cases. For both test cases, significant improvement versus baseline RANS linear eddy viscosity and nonlinear eddy viscosity models is demonstrated.« less

System and method for determining stability of a neural system

NASA Technical Reports Server (NTRS)

Curtis, Steven A. (Inventor)

2011-01-01

Disclosed are methods, systems, and computer-readable media for determining stability of a neural system. The method includes tracking a function world line of an N element neural system within at least one behavioral space, determining whether the tracking function world line is approaching a psychological stability surface, and implementing a quantitative solution that corrects instability if the tracked function world line is approaching the psychological stability surface.

Neonatal Brain Hemorrhage (NBH) of Prematurity: Translational Mechanisms of the Vascular-Neural Network

PubMed Central

Lekic, Tim; Klebe, Damon; Poblete, Roy; Krafft, Paul R.; Rolland, William B.; Tang, Jiping; Zhang, John H.

2015-01-01

Neonatal brain hemorrhage (NBH) of prematurity is an unfortunate consequence of preterm birth. Complications result in shunt dependence and long-term structural changes such as post-hemorrhagic hydrocephalus, periventricular leukomalacia, gliosis, and neurological dysfunction. Several animal models are available to study this condition, and many basic mechanisms, etiological factors, and outcome consequences, are becoming understood. NBH is an important clinical condition, of which treatment may potentially circumvent shunt complication, and improve functional recovery (cerebral palsy, and cognitive impairments). This review highlights key pathophysiological findings of the neonatal vascular-neural network in the context of molecular mechanisms targeting the post-hemorrhagic hydrocephalus affecting this vulnerable infant population. PMID:25620100

Supervised Machine Learning for Regionalization of Environmental Data: Distribution of Uranium in Groundwater in Ukraine

NASA Astrophysics Data System (ADS)

Govorov, Michael; Gienko, Gennady; Putrenko, Viktor

2018-05-01

In this paper, several supervised machine learning algorithms were explored to define homogeneous regions of con-centration of uranium in surface waters in Ukraine using multiple environmental parameters. The previous study was focused on finding the primary environmental parameters related to uranium in ground waters using several methods of spatial statistics and unsupervised classification. At this step, we refined the regionalization using Artifi-cial Neural Networks (ANN) techniques including Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Convolutional Neural Network (CNN). The study is focused on building local ANN models which may significantly improve the prediction results of machine learning algorithms by taking into considerations non-stationarity and autocorrelation in spatial data.

Enhancement of intrinsic optical signal recording with split spectrum optical coherence tomography

NASA Astrophysics Data System (ADS)

Thapa, Damber; Wang, Benquan; Lu, Yiming; Son, Taeyoon; Yao, Xincheng

2017-09-01

Functional optical coherence tomography (OCT) of stimulus-evoked intrinsic optical signal (IOS) promises to be a new methodology for high-resolution mapping of retinal neural dysfunctions. However, its practical applications for non-invasive examination of retinal function have been hindered by the low signal-to-noise ratio (SNR) and small magnitude of IOSs. Split spectrum amplitude-decorrelation has been demonstrated to improve the image quality of OCT angiography. In this study, we exploited split spectrum strategy to improve the sensitivity of IOS recording. The full OCT spectrum was split into multiple spectral bands and IOSs from each sub-band were calculated separately and then combined to generate a single IOS image sequence. The algorithm was tested on in vivo images of frog retinas. It significantly improved both IOS magnitude and SNR, which are essential for practical applications of functional IOS imaging.

Rapid Postnatal Expansion of Neural Networks Occurs in an Environment of Altered Neurovascular and Neurometabolic Coupling.

PubMed

Kozberg, Mariel G; Ma, Ying; Shaik, Mohammed A; Kim, Sharon H; Hillman, Elizabeth M C

2016-06-22

In the adult brain, increases in neural activity lead to increases in local blood flow. However, many prior measurements of functional hemodynamics in the neonatal brain, including functional magnetic resonance imaging (fMRI) in human infants, have noted altered and even inverted hemodynamic responses to stimuli. Here, we demonstrate that localized neural activity in early postnatal mice does not evoke blood flow increases as in the adult brain, and elucidate the neural and metabolic correlates of these altered functional hemodynamics as a function of developmental age. Using wide-field GCaMP imaging, the development of neural responses to somatosensory stimulus is visualized over the entire bilaterally exposed cortex. Neural responses are observed to progress from tightly localized, unilateral maps to bilateral responses as interhemispheric connectivity becomes established. Simultaneous hemodynamic imaging confirms that spatiotemporally coupled functional hyperemia is not present during these early stages of postnatal brain development, and develops gradually as cortical connectivity is established. Exploring the consequences of this lack of functional hyperemia, measurements of oxidative metabolism via flavoprotein fluorescence suggest that neural activity depletes local oxygen to below baseline levels at early developmental stages. Analysis of hemoglobin oxygenation dynamics at the same age confirms oxygen depletion for both stimulus-evoked and resting-state neural activity. This state of unmet metabolic demand during neural network development poses new questions about the mechanisms of neurovascular development and its role in both normal and abnormal brain development. These results also provide important insights for the interpretation of fMRI studies of the developing brain. This work demonstrates that the postnatal development of neuronal connectivity is accompanied by development of the mechanisms that regulate local blood flow in response to neural activity. Novel in vivo imaging reveals that, in the developing mouse brain, strong and localized GCaMP neural responses to stimulus fail to evoke local blood flow increases, leading to a state in which oxygen levels become locally depleted. These results demonstrate that the development of cortical connectivity occurs in an environment of altered energy availability that itself may play a role in shaping normal brain development. These findings have important implications for understanding the pathophysiology of abnormal developmental trajectories, and for the interpretation of functional magnetic resonance imaging data acquired in the developing brain. Copyright © 2016 the authors 0270-6474/16/366704-14$15.00/0.

Enhanced neural function in highly aberrated eyes following perceptual learning with adaptive optics.

PubMed

Sabesan, Ramkumar; Barbot, Antoine; Yoon, Geunyoung

2017-03-01

Highly aberrated keratoconic (KC) eyes do not elicit the expected visual advantage from customized optical corrections. This is attributed to the neural insensitivity arising from chronic visual experience with poor retinal image quality, dominated by low spatial frequencies. The goal of this study was to investigate if targeted perceptual learning with adaptive optics (AO) can stimulate neural plasticity in these highly aberrated eyes. The worse eye of 2 KC subjects was trained in a contrast threshold test under AO correction. Prior to training, tumbling 'E' visual acuity and contrast sensitivity at 4, 8, 12, 16, 20, 24 and 28 c/deg were measured in both the trained and untrained eyes of each subject with their routine prescription and with AO correction for a 6mm pupil. The high spatial frequency requiring 50% contrast for detection with AO correction was picked as the training frequency. Subjects were required to train on a contrast detection test with AO correction for 1h for 5 consecutive days. During each training session, threshold contrast measurement at the training frequency with AO was conducted. Pre-training measures were repeated after the 5 training sessions in both eyes (i.e., post-training). After training, contrast sensitivity under AO correction improved on average across spatial frequency by a factor of 1.91 (range: 1.77-2.04) and 1.75 (1.22-2.34) for the two subjects. This improvement in contrast sensitivity transferred to visual acuity with the two subjects improving by 1.5 and 1.3 lines respectively with AO following training. One of the two subjects denoted an interocular transfer of training and an improvement in performance with their routine prescription post-training. This training-induced visual benefit demonstrates the potential of AO as a tool for neural rehabilitation in patients with abnormal corneas. Moreover, it reveals a sufficient degree of neural plasticity in normally developed adults who have a long history of abnormal visual experience due to optical imperfections. Copyright © 2016 Elsevier Ltd. All rights reserved.

Neural conflict-control mechanisms improve memory for target stimuli.

PubMed

Krebs, Ruth M; Boehler, Carsten N; De Belder, Maya; Egner, Tobias

2015-03-01

According to conflict-monitoring models, conflict serves as an internal signal for reinforcing top-down attention to task-relevant information. While evidence based on measures of ongoing task performance supports this idea, implications for long-term consequences, that is, memory, have not been tested yet. Here, we evaluated the prediction that conflict-triggered attentional enhancement of target-stimulus processing should be associated with superior subsequent memory for those stimuli. By combining functional magnetic resonance imaging (fMRI) with a novel variant of a face-word Stroop task that employed trial-unique face stimuli as targets, we were able to assess subsequent (incidental) memory for target faces as a function of whether a given face had previously been accompanied by congruent, neutral, or incongruent (conflicting) distracters. In line with our predictions, incongruent distracters not only induced behavioral conflict, but also gave rise to enhanced memory for target faces. Moreover, conflict-triggered neural activity in prefrontal and parietal regions was predictive of subsequent retrieval success, and displayed conflict-enhanced functional coupling with medial-temporal lobe regions. These data provide support for the proposal that conflict evokes enhanced top-down attention to task-relevant stimuli, thereby promoting their encoding into long-term memory. Our findings thus delineate the neural mechanisms of a novel link between cognitive control and memory. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Effects of music production on cortical plasticity within cognitive rehabilitation of patients with mild traumatic brain injury.

PubMed

Vik, Berit Marie Dykesteen; Skeie, Geir Olve; Vikane, Eirik; Specht, Karsten

2018-01-01

We explored the effects of playing the piano on patients with cognitive impairment after mild traumatic brain injury (mTBI) and, addressed the question if this approach would stimulate neural networks in re-routing neural connections and link up cortical circuits that had been functional inhibited due to disruption of brain tissue. Functional neuroimaging scans (fMRI) and neuropsychological tests were performed pre-post intervention. Three groups participated, one mTBI group (n = 7), two groups of healthy participants, one with music training (n = 11), one baseline group without music (n = 12). The music groups participated in 8 weeks music-supported intervention. The patient group revealed training-related neuroplasticity in the orbitofrontal cortex. fMRI results fit well with outcome from neuropsychological tests with significant enhancement of cognitive performance in the music groups. Ninety per cent of mTBI group returned to work post intervention. Here, for the first time, we demonstrated behavioural improvements and functional brain changes after 8 weeks of playing piano on patients with mTBI having attention, memory and social interaction problems. We present evidence for a causal relationship between musical training and reorganisation of neural networks promoting enhanced cognitive performance. These results add a novel music-supported intervention within rehabilitation of patients with cognitive deficits following mTBI.

EDITORIAL: Focus on the neural interface Focus on the neural interface

NASA Astrophysics Data System (ADS)

Durand, Dominique M.

2009-10-01

The possibility of an effective connection between neural tissue and computers has inspired scientists and engineers to develop new ways of controlling and obtaining information from the nervous system. These applications range from `brain hacking' to neural control of artificial limbs with brain signals. Notwithstanding the significant advances in neural prosthetics in the last few decades and the success of some stimulation devices such as cochlear prosthesis, neurotechnology remains below its potential for restoring neural function in patients with nervous system disorders. One of the reasons for this limited impact can be found at the neural interface and close attention to the integration between electrodes and tissue should improve the possibility of successful outcomes. The neural interfaces research community consists of investigators working in areas such as deep brain stimulation, functional neuromuscular/electrical stimulation, auditory prostheses, cortical prostheses, neuromodulation, microelectrode array technology, brain-computer/machine interfaces. Following the success of previous neuroprostheses and neural interfaces workshops, funding (from NIH) was obtained to establish a biennial conference in the area of neural interfaces. The first Neural Interfaces Conference took place in Cleveland, OH in 2008 and several topics from this conference have been selected for publication in this special section of the Journal of Neural Engineering. Three `perspectives' review the areas of neural regeneration (Corredor and Goldberg), cochlear implants (O'Leary et al) and neural prostheses (Anderson). Seven articles focus on various aspects of neural interfacing. One of the most popular of these areas is the field of brain-computer interfaces. Fraser et al, report on a method to generate robust control with simple signal processing algorithms of signals obtained with electrodes implanted in the brain. One problem with implanted electrode arrays, however, is that they can fail to record reliably neural signals for long periods of time. McConnell et al show that by measuring the impedance of the tissue, one can evaluate the extent of the tissue response to the presence of the electrode. Another problem with the neural interface is the mismatch of the mechanical properties between electrode and tissue. Basinger et al use finite element modeling to analyze this mismatch in retinal prostheses and guide the design of new implantable devices. Electrical stimulation has been the method of choice to activate externally the nervous system. However, Zhang et al show that a novel dual hybrid device integrating electrical and optical stimulation can provide an effective interface for simultaneous recording and stimulation. By interfacing an EMG recording system and a movement detection system, Johnson and Fuglevand develop a model capable of predicting muscle activity during movement that could be important for the development of motor prostheses. Sensory restoration is another unsolved problem in neural prostheses. By developing a novel interface between the dorsal root ganglia and electrodes arrays, Gaunt et al show that it is possible to recruit afferent fibers for sensory substitution. Finally, by interfacing directly with muscles, Jung and colleagues show that stimulation of muscles involved in locomotion following spinal cord damage in rats can provide an effective treatment modality for incomplete spinal cord injury. This series of articles clearly shows that the interface is indeed one of the keys to successful therapeutic neural devices. The next Neural Interfaces Conference will take place in Los Angeles, CA in June 2010 and one can expect to see new developments in neural engineering obtained by focusing on the neural interface.