Neural substrates of decision-making.

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Broche-Pérez, Y; Herrera Jiménez, L F; Omar-Martínez, E

2016-06-01

Decision-making is the process of selecting a course of action from among 2 or more alternatives by considering the potential outcomes of selecting each option and estimating its consequences in the short, medium and long term. The prefrontal cortex (PFC) has traditionally been considered the key neural structure in decision-making process. However, new studies support the hypothesis that describes a complex neural network including both cortical and subcortical structures. The aim of this review is to summarise evidence on the anatomical structures underlying the decision-making process, considering new findings that support the existence of a complex neural network that gives rise to this complex neuropsychological process. Current evidence shows that the cortical structures involved in decision-making include the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), and dorsolateral prefrontal cortex (DLPFC). This process is assisted by subcortical structures including the amygdala, thalamus, and cerebellum. Findings to date show that both cortical and subcortical brain regions contribute to the decision-making process. The neural basis of decision-making is a complex neural network of cortico-cortical and cortico-subcortical connections which includes subareas of the PFC, limbic structures, and the cerebellum. Copyright © 2014 Sociedad Española de Neurología. Published by Elsevier España, S.L.U. All rights reserved.

Generation of Regionally Specified Neural Progenitors and Functional Neurons from Human Embryonic Stem Cells under Defined Conditions

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Agnete Kirkeby

2012-06-01

Full Text Available To model human neural-cell-fate specification and to provide cells for regenerative therapies, we have developed a method to generate human neural progenitors and neurons from human embryonic stem cells, which recapitulates human fetal brain development. Through the addition of a small molecule that activates canonical WNT signaling, we induced rapid and efficient dose-dependent specification of regionally defined neural progenitors ranging from telencephalic forebrain to posterior hindbrain fates. Ten days after initiation of differentiation, the progenitors could be transplanted to the adult rat striatum, where they formed neuron-rich and tumor-free grafts with maintained regional specification. Cells patterned toward a ventral midbrain (VM identity generated a high proportion of authentic dopaminergic neurons after transplantation. The dopamine neurons showed morphology, projection pattern, and protein expression identical to that of human fetal VM cells grafted in parallel. VM-patterned but not forebrain-patterned neurons released dopamine and reversed motor deficits in an animal model of Parkinson's disease.

Bioimpedance Harmonic Analysis as a Diagnostic Tool to Assess Regional Circulation and Neural Activity

International Nuclear Information System (INIS)

Mudraya, I S; Revenko, S V; Khodyreva, L A; Markosyan, T G; Dudareva, A A; Ibragimov, A R; Romich, V V; Kirpatovsky, V I

2013-01-01

The novel technique based on harmonic analysis of bioimpedance microvariations with original hard- and software complex incorporating a high-resolution impedance converter was used to assess the neural activity and circulation in human urinary bladder and penis in patients with pelvic pain, erectile dysfunction, and overactive bladder. The therapeutic effects of shock wave therapy and Botulinum toxin detrusor injections were evaluated quantitatively according to the spectral peaks at low 0.1 Hz frequency (M for Mayer wave), respiratory (R) and cardiac (C) rhythms with their harmonics. Enhanced baseline regional neural activity identified according to M and R peaks was found to be presumably sympathetic in pelvic pain patients, and parasympathetic – in patients with overactive bladder. Total pulsatile activity and pulsatile resonances found in the bladder as well as in the penile spectrum characterised regional circulation and vascular tone. The abnormal spectral parameters characteristic of the patients with genitourinary diseases shifted to the norm in the cases of efficient therapy. Bioimpedance harmonic analysis seems to be a potent tool to assess regional peculiarities of circulatory and autonomic nervous activity in the course of patient treatment.

Bioimpedance Harmonic Analysis as a Diagnostic Tool to Assess Regional Circulation and Neural Activity

Science.gov (United States)

Mudraya, I. S.; Revenko, S. V.; Khodyreva, L. A.; Markosyan, T. G.; Dudareva, A. A.; Ibragimov, A. R.; Romich, V. V.; Kirpatovsky, V. I.

2013-04-01

The novel technique based on harmonic analysis of bioimpedance microvariations with original hard- and software complex incorporating a high-resolution impedance converter was used to assess the neural activity and circulation in human urinary bladder and penis in patients with pelvic pain, erectile dysfunction, and overactive bladder. The therapeutic effects of shock wave therapy and Botulinum toxin detrusor injections were evaluated quantitatively according to the spectral peaks at low 0.1 Hz frequency (M for Mayer wave), respiratory (R) and cardiac (C) rhythms with their harmonics. Enhanced baseline regional neural activity identified according to M and R peaks was found to be presumably sympathetic in pelvic pain patients, and parasympathetic - in patients with overactive bladder. Total pulsatile activity and pulsatile resonances found in the bladder as well as in the penile spectrum characterised regional circulation and vascular tone. The abnormal spectral parameters characteristic of the patients with genitourinary diseases shifted to the norm in the cases of efficient therapy. Bioimpedance harmonic analysis seems to be a potent tool to assess regional peculiarities of circulatory and autonomic nervous activity in the course of patient treatment.

Discrete Neural Signatures of Basic Emotions.

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Saarimäki, Heini; Gotsopoulos, Athanasios; Jääskeläinen, Iiro P; Lampinen, Jouko; Vuilleumier, Patrik; Hari, Riitta; Sams, Mikko; Nummenmaa, Lauri

2016-06-01

Categorical models of emotions posit neurally and physiologically distinct human basic emotions. We tested this assumption by using multivariate pattern analysis (MVPA) to classify brain activity patterns of 6 basic emotions (disgust, fear, happiness, sadness, anger, and surprise) in 3 experiments. Emotions were induced with short movies or mental imagery during functional magnetic resonance imaging. MVPA accurately classified emotions induced by both methods, and the classification generalized from one induction condition to another and across individuals. Brain regions contributing most to the classification accuracy included medial and inferior lateral prefrontal cortices, frontal pole, precentral and postcentral gyri, precuneus, and posterior cingulate cortex. Thus, specific neural signatures across these regions hold representations of different emotional states in multimodal fashion, independently of how the emotions are induced. Similarity of subjective experiences between emotions was associated with similarity of neural patterns for the same emotions, suggesting a direct link between activity in these brain regions and the subjective emotional experience. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Normalization as a canonical neural computation

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Carandini, Matteo; Heeger, David J.

2012-01-01

There is increasing evidence that the brain relies on a set of canonical neural computations, repeating them across brain regions and modalities to apply similar operations to different problems. A promising candidate for such a computation is normalization, in which the responses of neurons are divided by a common factor that typically includes the summed activity of a pool of neurons. Normalization was developed to explain responses in the primary visual cortex and is now thought to operate throughout the visual system, and in many other sensory modalities and brain regions. Normalization may underlie operations such as the representation of odours, the modulatory effects of visual attention, the encoding of value and the integration of multisensory information. Its presence in such a diversity of neural systems in multiple species, from invertebrates to mammals, suggests that it serves as a canonical neural computation. PMID:22108672

REGION OF NON-INTEREST BASED DIGITAL IMAGE WATERMARKING USING NEURAL NETWORKS

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Bibi Isac

2011-11-01

Full Text Available Copyrights protection of digital data become inevitable in current world. Digital watermarks have been recently proposed as secured scheme for copyright protection, authentication, source tracking, and broadcast monitoring of video, audio, text data and digital images. In this paper a method to embed a watermark in region of non-interest (RONI and a method for adaptive calculation of strength factor using neural network are proposed. The embedding and extraction processes are carried out in the transform domain by using Discrete Wavelet Transform (DWT. Finally, the algorithm robustness is tested against noise addition attacks and geometric distortion attacks. The results authenticate that the proposed watermarking algorithm does not degrade the quality of cover image as the watermark is inserted only in region of non-interest and is resistive to attacks.

A training rule which guarantees finite-region stability for a class of closed-loop neural-network control systems.

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Kuntanapreeda, S; Fullmer, R R

1996-01-01

A training method for a class of neural network controllers is presented which guarantees closed-loop system stability. The controllers are assumed to be nonlinear, feedforward, sampled-data, full-state regulators implemented as single hidden-layer neural networks. The controlled systems must be locally hermitian and observable. Stability of the closed-loop system is demonstrated by determining a Lyapunov function, which can be used to identify a finite stability region about the regulator point.

Molecular regionalization of the developing amphioxus neural tube challenges major partitions of the vertebrate brain.

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Albuixech-Crespo, Beatriz; López-Blanch, Laura; Burguera, Demian; Maeso, Ignacio; Sánchez-Arrones, Luisa; Moreno-Bravo, Juan Antonio; Somorjai, Ildiko; Pascual-Anaya, Juan; Puelles, Eduardo; Bovolenta, Paola; Garcia-Fernàndez, Jordi; Puelles, Luis; Irimia, Manuel; Ferran, José Luis

2017-04-01

All vertebrate brains develop following a common Bauplan defined by anteroposterior (AP) and dorsoventral (DV) subdivisions, characterized by largely conserved differential expression of gene markers. However, it is still unclear how this Bauplan originated during evolution. We studied the relative expression of 48 genes with key roles in vertebrate neural patterning in a representative amphioxus embryonic stage. Unlike nonchordates, amphioxus develops its central nervous system (CNS) from a neural plate that is homologous to that of vertebrates, allowing direct topological comparisons. The resulting genoarchitectonic model revealed that the amphioxus incipient neural tube is unexpectedly complex, consisting of several AP and DV molecular partitions. Strikingly, comparison with vertebrates indicates that the vertebrate thalamus, pretectum, and midbrain domains jointly correspond to a single amphioxus region, which we termed Di-Mesencephalic primordium (DiMes). This suggests that these domains have a common developmental and evolutionary origin, as supported by functional experiments manipulating secondary organizers in zebrafish and mice.

Neural activity in the reward-related brain regions predicts implicit self-esteem: A novel validity test of psychological measures using neuroimaging.

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Izuma, Keise; Kennedy, Kate; Fitzjohn, Alexander; Sedikides, Constantine; Shibata, Kazuhisa

2018-03-01

Self-esteem, arguably the most important attitudes an individual possesses, has been a premier research topic in psychology for more than a century. Following a surge of interest in implicit attitude measures in the 90s, researchers have tried to assess self-esteem implicitly to circumvent the influence of biases inherent in explicit measures. However, the validity of implicit self-esteem measures remains elusive. Critical tests are often inconclusive, as the validity of such measures is examined in the backdrop of imperfect behavioral measures. To overcome this serious limitation, we tested the neural validity of the most widely used implicit self-esteem measure, the implicit association test (IAT). Given the conceptualization of self-esteem as attitude toward the self, and neuroscience findings that the reward-related brain regions represent an individual's attitude or preference for an object when viewing its image, individual differences in implicit self-esteem should be associated with neural signals in the reward-related regions during passive-viewing of self-face (the most obvious representation of the self). Using multi-voxel pattern analysis (MVPA) on functional MRI (fMRI) data, we demonstrate that the neural signals in the reward-related regions were robustly associated with implicit (but not explicit) self-esteem, thus providing unique evidence for the neural validity of the self-esteem IAT. In addition, both implicit and explicit self-esteem were related, although differently, to neural signals in regions involved in self-processing. Our finding highlights the utility of neuroscience methods in addressing fundamental psychological questions and providing unique insights into important psychological constructs. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

The neural basis of loss aversion in decision-making under risk.

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Tom, Sabrina M; Fox, Craig R; Trepel, Christopher; Poldrack, Russell A

2007-01-26

People typically exhibit greater sensitivity to losses than to equivalent gains when making decisions. We investigated neural correlates of loss aversion while individuals decided whether to accept or reject gambles that offered a 50/50 chance of gaining or losing money. A broad set of areas (including midbrain dopaminergic regions and their targets) showed increasing activity as potential gains increased. Potential losses were represented by decreasing activity in several of these same gain-sensitive areas. Finally, individual differences in behavioral loss aversion were predicted by a measure of neural loss aversion in several regions, including the ventral striatum and prefrontal cortex.

Region stability analysis and tracking control of memristive recurrent neural network.

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Bao, Gang; Zeng, Zhigang; Shen, Yanjun

2018-02-01

Memristor is firstly postulated by Leon Chua and realized by Hewlett-Packard (HP) laboratory. Research results show that memristor can be used to simulate the synapses of neurons. This paper presents a class of recurrent neural network with HP memristors. Firstly, it shows that memristive recurrent neural network has more compound dynamics than the traditional recurrent neural network by simulations. Then it derives that n dimensional memristive recurrent neural network is composed of [Formula: see text] sub neural networks which do not have a common equilibrium point. By designing the tracking controller, it can make memristive neural network being convergent to the desired sub neural network. At last, two numerical examples are given to verify the validity of our result. Copyright © 2017 Elsevier Ltd. All rights reserved.

Brain Region-Dependent Rejection of Neural Precursor Cell Transplants

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Nina Fainstein

2018-04-01

Full Text Available The concept of CNS as an immune-privileged site has been challenged by the occurrence of immune surveillance and allogeneic graft rejection in the brain. Here we examined whether the immune response to allogeneic neural grafts is determined by the site of implantation in the CNS. Dramatic regional differences were observed between immune responses to allogeneic neural precursor/stem cell (NPC grafts in the striatum vs. the hippocampus. Striatal grafts were heavily infiltrated with IBA-1+ microglia/macrophages and CD3+ T cells and completely rejected. In contrast, hippocampal grafts exhibited milder IBA-1+ cell infiltration, were not penetrated efficiently by CD3+ cells, and survived efficiently for at least 2 months. To evaluate whether the hippocampal protective effect is universal, astrocytes were then transplanted. Allogeneic astrocyte grafts elicited a vigorous rejection process from the hippocampus. CD200, a major immune-inhibitory signal, plays an important role in protecting grafts from rejection. Indeed, CD200 knock out NPC grafts were rejected more efficiently than wild type NPCs from the striatum. However, lack of CD200 expression did not elicit NPC graft rejection from the hippocampus. In conclusion, the hippocampus has partial immune-privilege properties that are restricted to NPCs and are CD200-independent. The unique hippocampal milieu may be protective for allogeneic NPC grafts, through host-graft interactions enabling sustained immune-regulatory properties of transplanted NPCs. These findings have implications for providing adequate immunosuppression in clinical translation of cell therapy.

Evolvable synthetic neural system

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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.

Adaptive Steganalysis Based on Selection Region and Combined Convolutional Neural Networks

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Donghui Hu

2017-01-01

Full Text Available Digital image steganalysis is the art of detecting the presence of information hiding in carrier images. When detecting recently developed adaptive image steganography methods, state-of-art steganalysis methods cannot achieve satisfactory detection accuracy, because the adaptive steganography methods can adaptively embed information into regions with rich textures via the guidance of distortion function and thus make the effective steganalysis features hard to be extracted. Inspired by the promising success which convolutional neural network (CNN has achieved in the fields of digital image analysis, increasing researchers are devoted to designing CNN based steganalysis methods. But as for detecting adaptive steganography methods, the results achieved by CNN based methods are still far from expected. In this paper, we propose a hybrid approach by designing a region selection method and a new CNN framework. In order to make the CNN focus on the regions with complex textures, we design a region selection method by finding a region with the maximal sum of the embedding probabilities. To evolve more diverse and effective steganalysis features, we design a new CNN framework consisting of three separate subnets with independent structure and configuration parameters and then merge and split the three subnets repeatedly. Experimental results indicate that our approach can lead to performance improvement in detecting adaptive steganography.

Tectonic modeling of Konya-Beysehir Region (Turkey using cellular neural networks

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D. Aydogan

2007-06-01

Full Text Available In this paper, to separate regional-residual anomaly maps and to detect borders of buried geological bodies, we applied the Cellular Neural Network (CNN approach to gravity and magnetic anomaly maps. CNN is a stochastic image processing technique, based optimization of templates, which imply relationships of neighborhood pixels in 2-Dimensional (2D potential anomalies. Here, CNN performance in geophysics, tested by various synthetic examples and the results are compared to classical methods such as boundary analysis and second vertical derivatives. After we obtained satisfactory results in synthetic models, we applied CNN to Bouguer anomaly map of Konya-Beysehir Region, which has complex tectonic structure with various fault combinations. We evaluated CNN outputs and 2D/3D models, which are constructed using forward and inversion methods. Then we presented a new tectonic structure of Konya-Beysehir Region. We have denoted (F1, F2, …, F7 and (Konya1, Konya2 faults according to our evaluations of CNN outputs. Thus, we have concluded that CNN is a compromising stochastic image processing technique in geophysics.

Restriction of neural precursor ability to respond to Nurr1 by early regional specification.

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Chiara Soldati

Full Text Available During neural development, spatially regulated expression of specific transcription factors is crucial for central nervous system (CNS regionalization, generation of neural precursors (NPs and subsequent differentiation of specific cell types within defined regions. A critical role in dopaminergic differentiation in the midbrain (MB has been assigned to the transcription factor Nurr1. Nurr1 controls the expression of key genes involved in dopamine (DA neurotransmission, e.g. tyrosine hydroxylase (TH and the DA transporter (DAT, and promotes the dopaminergic phenotype in embryonic stem cells. We investigated whether cells derived from different areas of the mouse CNS could be directed to differentiate into dopaminergic neurons in vitro by forced expression of the transcription factor Nurr1. We show that Nurr1 overexpression can promote dopaminergic cell fate specification only in NPs obtained from E13.5 ganglionic eminence (GE and MB, but not in NPs isolated from E13.5 cortex (CTX and spinal cord (SC or from the adult subventricular zone (SVZ. Confirming previous studies, we also show that Nurr1 overexpression can increase the generation of TH-positive neurons in mouse embryonic stem cells. These data show that Nurr1 ability to induce a dopaminergic phenotype becomes restricted during CNS development and is critically dependent on the region of NPs derivation. Our results suggest that the plasticity of NPs and their ability to activate a dopaminergic differentiation program in response to Nurr1 is regulated during early stages of neurogenesis, possibly through mechanisms controlling CNS regionalization.

Regional cerebral glucose metabolic changes in oculopalatal myoclonus: implication for neural pathways, underlying the disorder

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Cho, Sang Soo; Moon, So Young; Kim, Ji Soo; Kim, Sang Eun [College of Medicine, Seoul National University, Seoul (Korea, Republic of)

2004-07-01

Palatal myoclonus (PM) is characterized by rhythmic involuntary jerky movements of the soft palate of the throat. When associated with eye movements, it is called oculopalatal myoclonus (OPM). Ordinary PM is characterized by hypertrophic olivary degeneration, a trans-synaptic degeneration following loss of neuronal input to the inferior olivary nucleus due to an interruption of the Guillain-Mollaret triangle usually by a hemorrhage. However, the neural pathways underlying the disorder are uncertain. In an attempt to understand the pathologic neural pathways, we examined the metabolic correlates of this tremulous condition. Brain FDG PET scans were acquired in 8 patients with OPM (age, 49.9{+-}4.6 y: all males: 7 with pontine hemorrhage, 1 with diffuse brainstem infarction) and age-matched 50 healthy males (age, 50.7{+-} 9.0) and the regional glucose metabolism compared using SPM99. For group analysis, the hemispheres containing lesions were assigned to the right side of the brain. Patients with OPM had significant hypometabolism in the ipsilateral (to the lesion) brainstem and superior temporal and parahippocampal gyri (P < 0.05 corrected, k = 100). By contrast, there was significant hypermetabolism in the contralateral middle and inferior temporal gyri, thalamus, middle frontal gyrus and precuneus (P < 0.05 corrected, k=l00). Our data demonstrate the distinct metabolic changes between several ipsilateral and contralateral brain regions (hypometabolism vs. hypermetabolism) in patients with OPM. This may provide clues for understanding the neural pathways underlying the disorder.

Regional cerebral glucose metabolic changes in oculopalatal myoclonus: implication for neural pathways, underlying the disorder

International Nuclear Information System (INIS)

Cho, Sang Soo; Moon, So Young; Kim, Ji Soo; Kim, Sang Eun

2004-01-01

Palatal myoclonus (PM) is characterized by rhythmic involuntary jerky movements of the soft palate of the throat. When associated with eye movements, it is called oculopalatal myoclonus (OPM). Ordinary PM is characterized by hypertrophic olivary degeneration, a trans-synaptic degeneration following loss of neuronal input to the inferior olivary nucleus due to an interruption of the Guillain-Mollaret triangle usually by a hemorrhage. However, the neural pathways underlying the disorder are uncertain. In an attempt to understand the pathologic neural pathways, we examined the metabolic correlates of this tremulous condition. Brain FDG PET scans were acquired in 8 patients with OPM (age, 49.9±4.6 y: all males: 7 with pontine hemorrhage, 1 with diffuse brainstem infarction) and age-matched 50 healthy males (age, 50.7± 9.0) and the regional glucose metabolism compared using SPM99. For group analysis, the hemispheres containing lesions were assigned to the right side of the brain. Patients with OPM had significant hypometabolism in the ipsilateral (to the lesion) brainstem and superior temporal and parahippocampal gyri (P < 0.05 corrected, k = 100). By contrast, there was significant hypermetabolism in the contralateral middle and inferior temporal gyri, thalamus, middle frontal gyrus and precuneus (P < 0.05 corrected, k=l00). Our data demonstrate the distinct metabolic changes between several ipsilateral and contralateral brain regions (hypometabolism vs. hypermetabolism) in patients with OPM. This may provide clues for understanding the neural pathways underlying the disorder

Self-Organizing Maps Neural Networks Applied to the Classification of Ethanol Samples According to the Region of Commercialization

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Aline Regina Walkoff

2017-10-01

Full Text Available Physical-chemical analysis data were collected, from 998 ethanol samples of automotive ethanol commercialized in the northern, midwestern and eastern regions of the state of Paraná. The data presented self-organizing maps (SOM neural networks, which classified them according to those regions. The self-organizing maps best configuration had a 45 x 45 topology and 5000 training epochs, with a final learning rate of 6.7x10-4, a final neighborhood relationship of 3x10-2 and a mean quantization error of 2x10-2. This neural network provided a topological map depicting three separated groups, each one corresponding to samples of a same region of commercialization. Four maps of weights, one for each parameter, were presented. The network established the pH was the most important variable for classification and electrical conductivity the least one. The self-organizing maps application allowed the segmentation of alcohol samples, therefore identifying them according to the region of commercialization. DOI: http://dx.doi.org/10.17807/orbital.v9i4.982

Neural mechanisms of social dominance

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Watanabe, Noriya; Yamamoto, Miyuki

2015-01-01

In a group setting, individuals' perceptions of their own level of dominance or of the dominance level of others, and the ability to adequately control their behavior based on these perceptions are crucial for living within a social environment. Recent advances in neural imaging and molecular technology have enabled researchers to investigate the neural substrates that support the perception of social dominance and the formation of a social hierarchy in humans. At the systems' level, recent studies showed that dominance perception is represented in broad brain regions which include the amygdala, hippocampus, striatum, and various cortical networks such as the prefrontal, and parietal cortices. Additionally, neurotransmitter systems such as the dopaminergic and serotonergic systems, modulate and are modulated by the formation of the social hierarchy in a group. While these monoamine systems have a wide distribution and multiple functions, it was recently found that the Neuropeptide B/W contributes to the perception of dominance and is present in neurons that have a limited projection primarily to the amygdala. The present review discusses the specific roles of these neural regions and neurotransmitter systems in the perception of dominance and in hierarchy formation. PMID:26136644

Neural mechanisms of social dominance

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Noriya eWatanabe

2015-06-01

Full Text Available In a group setting, individuals’ perceptions of their own level of dominance or of the dominance level of others, and the ability to adequately control their behavior based on these perceptions are crucial for living within a social environment. Recent advances in neural imaging and molecular technology have enabled researchers to investigate the neural substrates that support the perception of social dominance and the formation of a social hierarchy in humans. At the systems’ level, recent studies showed that dominance perception is represented in broad brain regions which include the amygdala, hippocampus, striatum, and various cortical networks such as the prefrontal, and parietal cortices. Additionally, neurotransmitter systems such as the dopaminergic and serotonergic systems, modulate and are modulated by the formation of the social hierarchy in a group. While these monoamine systems have a wide distribution and multiple functions, it was recently found that the Neuropeptide B/W contributes to the perception of dominance and is present in neurons that have a limited projection primarily to the amygdala. The present review discusses the specific roles of these neural regions and neurotransmitter systems in the perception of dominance and in hierarchy formation.

Coding of level of ambiguity within neural systems mediating choice.

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Lopez-Paniagua, Dan; Seger, Carol A

2013-01-01

Data from previous neuroimaging studies exploring neural activity associated with uncertainty suggest varying levels of activation associated with changing degrees of uncertainty in neural regions that mediate choice behavior. The present study used a novel task that parametrically controlled the amount of information hidden from the subject; levels of uncertainty ranged from full ambiguity (no information about probability of winning) through multiple levels of partial ambiguity, to a condition of risk only (zero ambiguity with full knowledge of the probability of winning). A parametric analysis compared a linear model in which weighting increased as a function of level of ambiguity, and an inverted-U quadratic models in which partial ambiguity conditions were weighted most heavily. Overall we found that risk and all levels of ambiguity recruited a common "fronto-parietal-striatal" network including regions within the dorsolateral prefrontal cortex, intraparietal sulcus, and dorsal striatum. Activation was greatest across these regions and additional anterior and superior prefrontal regions for the quadratic function which most heavily weighs trials with partial ambiguity. These results suggest that the neural regions involved in decision processes do not merely track the absolute degree ambiguity or type of uncertainty (risk vs. ambiguity). Instead, recruitment of prefrontal regions may result from greater degree of difficulty in conditions of partial ambiguity: when information regarding reward probabilities important for decision making is hidden or not easily obtained the subject must engage in a search for tractable information. Additionally, this study identified regions of activity related to the valuation of potential gains associated with stimuli or options (including the orbitofrontal and medial prefrontal cortices and dorsal striatum) and related to winning (including orbitofrontal cortex and ventral striatum).

Extracting Neural Oscillation Signatures of Laser-Induced Nociception in Pain-Related Regions in Rats

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Xuezhu Li

2017-10-01

Full Text Available Previous studies have shown that multiple brain regions are involved in pain perception and pain-related neural processes by forming a functionally connected pain network. It is still unclear how these pain-related brain areas actively work together to generate the experience of pain. To get a better insight into the pain network, we implanted electrodes in four pain-related areas of rats including the anterior cingulate cortex (ACC, orbitofrontal cortex (OFC, primary somatosensory cortex (S1 and periaqueductal gray (PAG. We analyzed the pattern of local field potential (LFP oscillations under noxious laser stimulations and innoxious laser stimulations. A high-dimensional feature matrix was built based on the LFP characters for both experimental conditions. Generalized linear models (GLMs were trained to classify recorded LFPs under noxious vs. innoxious condition. We found a general power decrease in α and β bands and power increase in γ band in the recorded areas under noxious condition. After noxious laser stimulation, there was a consistent change in LFP power and correlation in all four brain areas among all 13 rats. With GLM classifiers, noxious laser trials were distinguished from innoxious laser trials with high accuracy (86% using high-dimensional LFP features. This work provides a basis for further research to examine which aspects (e.g., sensory, motor or affective processes of noxious stimulation should drive distinct neural activity across the pain network.

Influence of neural adaptation on dynamics and equilibrium state of neural activities in a ring neural network

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Takiyama, Ken

2017-12-01

How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.

Sex differences in the neural basis of emotional memories.

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Canli, Turhan; Desmond, John E; Zhao, Zuo; Gabrieli, John D E

2002-08-06

Psychological studies have found better memory in women than men for emotional events, but the neural basis for this difference is unknown. We used event-related functional MRI to assess whether sex differences in memory for emotional stimuli is associated with activation of different neural systems in men and women. Brain activation in 12 men and 12 women was recorded while they rated their experience of emotional arousal in response to neutral and emotionally negative pictures. In a recognition memory test 3 weeks after scanning, highly emotional pictures were remembered best, and remembered better by women than by men. Men and women activated different neural circuits to encode stimuli effectively into memory even when the analysis was restricted to pictures rated equally arousing by both groups. Men activated significantly more structures than women in a network that included the right amygdala, whereas women activated significantly fewer structures in a network that included the left amygdala. Women had significantly more brain regions where activation correlated with both ongoing evaluation of emotional experience and with subsequent memory for the most emotionally arousing pictures. Greater overlap in brain regions sensitive to current emotion and contributing to subsequent memory may be a neural mechanism for emotions to enhance memory more powerfully in women than in men.

Neural overlap in processing music and speech

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Peretz, Isabelle; Vuvan, Dominique; Lagrois, Marie-Élaine; Armony, Jorge L.

2015-01-01

Neural overlap in processing music and speech, as measured by the co-activation of brain regions in neuroimaging studies, may suggest that parts of the neural circuitries established for language may have been recycled during evolution for musicality, or vice versa that musicality served as a springboard for language emergence. Such a perspective has important implications for several topics of general interest besides evolutionary origins. For instance, neural overlap is an important premise for the possibility of music training to influence language acquisition and literacy. However, neural overlap in processing music and speech does not entail sharing neural circuitries. Neural separability between music and speech may occur in overlapping brain regions. In this paper, we review the evidence and outline the issues faced in interpreting such neural data, and argue that converging evidence from several methodologies is needed before neural overlap is taken as evidence of sharing. PMID:25646513

Neural Activations of Guided Imagery and Music in Negative Emotional Processing: A Functional MRI Study.

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Lee, Sang Eun; Han, Yeji; Park, HyunWook

2016-01-01

The Bonny Method of Guided Imagery and Music uses music and imagery to access and explore personal emotions associated with episodic memories. Understanding the neural mechanism of guided imagery and music (GIM) as combined stimuli for emotional processing informs clinical application. We performed functional magnetic resonance imaging (fMRI) to demonstrate neural mechanisms of GIM for negative emotional processing when personal episodic memory is recalled and re-experienced through GIM processes. Twenty-four healthy volunteers participated in the study, which used classical music and verbal instruction stimuli to evoke negative emotions. To analyze the neural mechanism, activated regions associated with negative emotional and episodic memory processing were extracted by conducting volume analyses for the contrast between GIM and guided imagery (GI) or music (M). The GIM stimuli showed increased activation over the M-only stimuli in five neural regions associated with negative emotional and episodic memory processing, including the left amygdala, left anterior cingulate gyrus, left insula, bilateral culmen, and left angular gyrus (AG). Compared with GI alone, GIM showed increased activation in three regions associated with episodic memory processing in the emotional context, including the right posterior cingulate gyrus, bilateral parahippocampal gyrus, and AG. No neural regions related to negative emotional and episodic memory processing showed more activation for M and GI than for GIM. As a combined multimodal stimulus, GIM may increase neural activations related to negative emotions and episodic memory processing. Findings suggest a neural basis for GIM with personal episodic memories affecting cortical and subcortical structures and functions. © the American Music Therapy Association 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Fluid region segmentation in OCT images based on convolution neural network

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Liu, Dong; Liu, Xiaoming; Fu, Tianyu; Yang, Zhou

2017-07-01

In the retinal image, characteristics of fluid have great significance for diagnosis in eye disease. In the clinical, the segmentation of fluid is usually conducted manually, but is time-consuming and the accuracy is highly depend on the expert's experience. In this paper, we proposed a segmentation method based on convolution neural network (CNN) for segmenting the fluid from fundus image. The B-scans of OCT are segmented into layers, and patches from specific region with annotation are used for training. After the data set being divided into training set and test set, network training is performed and a good segmentation result is obtained, which has a significant advantage over traditional methods such as threshold method.

Soy sauce classification by geographic region and fermentation based on artificial neural network and genetic algorithm.

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Xu, Libin; Li, Yang; Xu, Ning; Hu, Yong; Wang, Chao; He, Jianjun; Cao, Yueze; Chen, Shigui; Li, Dongsheng

2014-12-24

This work demonstrated the possibility of using artificial neural networks to classify soy sauce from China. The aroma profiles of different soy sauce samples were differentiated using headspace solid-phase microextraction. The soy sauce samples were analyzed by gas chromatography-mass spectrometry, and 22 and 15 volatile aroma compounds were selected for sensitivity analysis to classify the samples by fermentation and geographic region, respectively. The 15 selected samples can be classified by fermentation and geographic region with a prediction success rate of 100%. Furans and phenols represented the variables with the greatest contribution in classifying soy sauce samples by fermentation and geographic region, respectively.

Neural overlap in processing music and speech.

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Peretz, Isabelle; Vuvan, Dominique; Lagrois, Marie-Élaine; Armony, Jorge L

2015-03-19

Neural overlap in processing music and speech, as measured by the co-activation of brain regions in neuroimaging studies, may suggest that parts of the neural circuitries established for language may have been recycled during evolution for musicality, or vice versa that musicality served as a springboard for language emergence. Such a perspective has important implications for several topics of general interest besides evolutionary origins. For instance, neural overlap is an important premise for the possibility of music training to influence language acquisition and literacy. However, neural overlap in processing music and speech does not entail sharing neural circuitries. Neural separability between music and speech may occur in overlapping brain regions. In this paper, we review the evidence and outline the issues faced in interpreting such neural data, and argue that converging evidence from several methodologies is needed before neural overlap is taken as evidence of sharing. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Abnormal regional spontaneous neural activity in visual pathway in retinal detachment patients: a resting-state functional MRI study

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Huang X

2017-11-01

Full Text Available Xin Huang,1,2,* Dan Li,3,* Hai-Jun Li,3 Yu-Lin Zhong,1 Shelby Freeberg,4 Jing Bao,1 Xian-Jun Zeng,3 Yi Shao1 1Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Jiangxi Province Clinical Ophthalmology Institute, Nanchang, Jiangxi, People’s Republic of China; 2Department of Ophthalmology, Eye Center, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, Hubei, People’s Republic of China; 3Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China; 4Department of Ophthalmology, University of Florida, Gainesville, FL, USA *These authors contributed equally to this work Objective: The aim of the study was to investigate changes of brain neural homogeneity in retinal detachment (RD patients using the regional homogeneity (ReHo method to understand their relationships with clinical features. Materials and methods: A total of 30 patients with RD (16 men and 14 women, and 30 healthy controls (HCs (16 men and 14 women closely matched in age and sex were recruited. Resting-state functional magnetic resonance imaging scans were performed for all subjects. The ReHo method was used to investigate the brain regional neural homogeneity. Patients with RD were distinguished from HCs by receiver operating characteristic curve. The relationships between the mean ReHo signal values in many brain regions and clinical features in RD patients were calculated by Pearson correlation analysis. Results: Compared with HCs, RD patients had significantly decreased ReHo values in the right occipital lobe, right superior temporal gyrus, bilateral cuneus and left middle frontal gyrus. Moreover, we found that the mean ReHo signal of the bilateral cuneus showed positive relationships with the duration of the RD (r=0.392, P=0.032. Conclusion: The RD patients showed brain neural homogeneity dysfunction in the visual pathway, which may underline the pathological mechanism

Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network.

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Diniz, Pedro Henrique Bandeira; Valente, Thales Levi Azevedo; Diniz, João Otávio Bandeira; Silva, Aristófanes Corrêa; Gattass, Marcelo; Ventura, Nina; Muniz, Bernardo Carvalho; Gasparetto, Emerson Leandro

2018-04-19

White matter lesions are non-static brain lesions that have a prevalence rate up to 98% in the elderly population. Because they may be associated with several brain diseases, it is important that they are detected as soon as possible. Magnetic Resonance Imaging (MRI) provides three-dimensional data with the possibility to detect and emphasize contrast differences in soft tissues, providing rich information about the human soft tissue anatomy. However, the amount of data provided for these images is far too much for manual analysis/interpretation, representing a difficult and time-consuming task for specialists. This work presents a computational methodology capable of detecting regions of white matter lesions of the brain in MRI of FLAIR modality. The techniques highlighted in this methodology are SLIC0 clustering for candidate segmentation and convolutional neural networks for candidate classification. The methodology proposed here consists of four steps: (1) images acquisition, (2) images preprocessing, (3) candidates segmentation and (4) candidates classification. The methodology was applied on 91 magnetic resonance images provided by DASA, and achieved an accuracy of 98.73%, specificity of 98.77% and sensitivity of 78.79% with 0.005 of false positives, without any false positives reduction technique, in detection of white matter lesion regions. It is demonstrated the feasibility of the analysis of brain MRI using SLIC0 and convolutional neural network techniques to achieve success in detection of white matter lesions regions. Copyright © 2018. Published by Elsevier B.V.

Neural control of vascular reactions: impact of emotion and attention.

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Okon-Singer, Hadas; Mehnert, Jan; Hoyer, Jana; Hellrung, Lydia; Schaare, Herma Lina; Dukart, Juergen; Villringer, Arno

2014-03-19

This study investigated the neural regions involved in blood pressure reactions to negative stimuli and their possible modulation by attention. Twenty-four healthy human subjects (11 females; age = 24.75 ± 2.49 years) participated in an affective perceptual load task that manipulated attention to negative/neutral distractor pictures. fMRI data were collected simultaneously with continuous recording of peripheral arterial blood pressure. A parametric modulation analysis examined the impact of attention and emotion on the relation between neural activation and blood pressure reactivity during the task. When attention was available for processing the distractor pictures, negative pictures resulted in behavioral interference, neural activation in brain regions previously related to emotion, a transient decrease of blood pressure, and a positive correlation between blood pressure response and activation in a network including prefrontal and parietal regions, the amygdala, caudate, and mid-brain. These effects were modulated by attention; behavioral and neural responses to highly negative distractor pictures (compared with neutral pictures) were smaller or diminished, as was the negative blood pressure response when the central task involved high perceptual load. Furthermore, comparing high and low load revealed enhanced activation in frontoparietal regions implicated in attention control. Our results fit theories emphasizing the role of attention in the control of behavioral and neural reactions to irrelevant emotional distracting information. Our findings furthermore extend the function of attention to the control of autonomous reactions associated with negative emotions by showing altered blood pressure reactions to emotional stimuli, the latter being of potential clinical relevance.

On structure-exploiting trust-region regularized nonlinear least squares algorithms for neural-network learning.

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Mizutani, Eiji; Demmel, James W

2003-01-01

This paper briefly introduces our numerical linear algebra approaches for solving structured nonlinear least squares problems arising from 'multiple-output' neural-network (NN) models. Our algorithms feature trust-region regularization, and exploit sparsity of either the 'block-angular' residual Jacobian matrix or the 'block-arrow' Gauss-Newton Hessian (or Fisher information matrix in statistical sense) depending on problem scale so as to render a large class of NN-learning algorithms 'efficient' in both memory and operation costs. Using a relatively large real-world nonlinear regression application, we shall explain algorithmic strengths and weaknesses, analyzing simulation results obtained by both direct and iterative trust-region algorithms with two distinct NN models: 'multilayer perceptrons' (MLP) and 'complementary mixtures of MLP-experts' (or neuro-fuzzy modular networks).

Learning from neural control.

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Wang, Cong; Hill, David J

2006-01-01

One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.

Neural dissociations in attitude strength: Distinct regions of cingulate cortex track ambivalence and certainty.

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Luttrell, Andrew; Stillman, Paul E; Hasinski, Adam E; Cunningham, William A

2016-04-01

People's behaviors are often guided by valenced responses to objects in the environment. Beyond positive and negative evaluations, attitudes research has documented the importance of attitude strength--qualities of an attitude that enhance or attenuate its impact and durability. Although neuroscience research has extensively investigated valence, little work exists on other related variables like metacognitive judgments about one's attitudes. It remains unclear, then, whether the various indicators of attitude strength represent a single underlying neural process or whether they reflect independent processes. To examine this, we used functional MRI (fMRI) to identify the neural correlates of attitude strength. Specifically, we focus on ambivalence and certainty, which represent metacognitive judgments that people can make about their evaluations. Although often correlated, prior neuroscience research suggests that these 2 attributes may have distinct neural underpinnings. We investigate this by having participants make evaluative judgments of visually presented words while undergoing fMRI. After scanning, participants rated the degree of ambivalence and certainty they felt regarding their attitudes toward each word. We found that these 2 judgments corresponded to distinct brain regions' activity during the process of evaluation. Ambivalence corresponded to activation in anterior cingulate cortex, dorsomedial prefrontal cortex, and posterior cingulate cortex. Certainty, however, corresponded to activation in unique areas of the precuneus/posterior cingulate cortex. These results support a model treating ambivalence and certainty as distinct, though related, attitude strength variables, and we discuss implications for both attitudes and neuroscience research. (c) 2016 APA, all rights reserved).

Shared beliefs enhance shared feelings: religious/irreligious identifications modulate empathic neural responses.

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Huang, Siyuan; Han, Shihui

2014-01-01

Recent neuroimaging research has revealed stronger empathic neural responses to same-race compared to other-race individuals. Is the in-group favouritism in empathic neural responses specific to race identification or a more general effect of social identification-including those based on religious/irreligious beliefs? The present study investigated whether and how intergroup relationships based on religious/irreligious identifications modulate empathic neural responses to others' pain expressions. We recorded event-related brain potentials from Chinese Christian and atheist participants while they perceived pain or neutral expressions of Chinese faces that were marked as being Christians or atheists. We found that both Christian and atheist participants showed stronger neural activity to pain (versus neutral) expressions at 132-168 ms and 200-320 ms over the frontal region to those with the same (versus different) religious/irreligious beliefs. The in-group favouritism in empathic neural responses was also evident in a later time window (412-612 ms) over the central/parietal regions in Christian but not in atheist participants. Our results indicate that the intergroup relationship based on shared beliefs, either religious or irreligious, can lead to in-group favouritism in empathy for others' suffering.

Neural Based Orthogonal Data Fitting The EXIN Neural Networks

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Cirrincione, Giansalvo

2008-01-01

Written by three leaders in the field of neural based algorithms, Neural Based Orthogonal Data Fitting proposes several neural networks, all endowed with a complete theory which not only explains their behavior, but also compares them with the existing neural and traditional algorithms. The algorithms are studied from different points of view, including: as a differential geometry problem, as a dynamic problem, as a stochastic problem, and as a numerical problem. All algorithms have also been analyzed on real time problems (large dimensional data matrices) and have shown accurate solutions. Wh

The neural basis of testable and non-testable beliefs.

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Jonathon R Howlett

Full Text Available Beliefs about the state of the world are an important influence on both normal behavior and psychopathology. However, understanding of the neural basis of belief processing remains incomplete, and several aspects of belief processing have only recently been explored. Specifically, different types of beliefs may involve fundamentally different inferential processes and thus recruit distinct brain regions. Additionally, neural processing of truth and falsity may differ from processing of certainty and uncertainty. The purpose of this study was to investigate the neural underpinnings of assessment of testable and non-testable propositions in terms of truth or falsity and the level of certainty in a belief. Functional magnetic resonance imaging (fMRI was used to study 14 adults while they rated propositions as true or false and also rated the level of certainty in their judgments. Each proposition was classified as testable or non-testable. Testable propositions activated the DLPFC and posterior cingulate cortex, while non-testable statements activated areas including inferior frontal gyrus, superior temporal gyrus, and an anterior region of the superior frontal gyrus. No areas were more active when a proposition was accepted, while the dorsal anterior cingulate was activated when a proposition was rejected. Regardless of whether a proposition was testable or not, certainty that the proposition was true or false activated a common network of regions including the medial prefrontal cortex, caudate, posterior cingulate, and a region of middle temporal gyrus near the temporo-parietal junction. Certainty in the truth or falsity of a non-testable proposition (a strong belief without empirical evidence activated the insula. The results suggest that different brain regions contribute to the assessment of propositions based on the type of content, while a common network may mediate the influence of beliefs on motivation and behavior based on the level of

Robust Adaptive Neural Control of Morphing Aircraft with Prescribed Performance

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Zhonghua Wu

2017-01-01

Full Text Available This study proposes a low-computational composite adaptive neural control scheme for the longitudinal dynamics of a swept-back wing aircraft subject to parameter uncertainties. To efficiently release the constraint often existing in conventional neural designs, whose closed-loop stability analysis always necessitates that neural networks (NNs be confined in the active regions, a smooth switching function is presented to conquer this issue. By integrating minimal learning parameter (MLP technique, prescribed performance control, and a kind of smooth switching strategy into back-stepping design, a new composite switching adaptive neural prescribed performance control scheme is proposed and a new type of adaptive laws is constructed for the altitude subsystem. Compared with previous neural control scheme for flight vehicle, the remarkable feature is that the proposed controller not only achieves the prescribed performance including transient and steady property but also addresses the constraint on NN. Two comparative simulations are presented to verify the effectiveness of the proposed controller.

Neuron cell positioning on polystyrene in culture by silver-negative ion implantation and region control of neural outgrowth

International Nuclear Information System (INIS)

Tsuji, Hiroshi; Sato, Hiroko; Baba, Takahiro; Ikemura, Shin'ichi; Gotoh, Yasuhito; Ishikawa, Junzo

2000-01-01

A new method to control the position of neuron cell attachment and extension region of neural outgrowth has been developed by using a pattering ion implantation with silver-negative ions into polystyrene dishes. This technique offers a promising method to form an artificially designed neural network in cell culture in vitro. Silver-negative ions were implanted into non-treated polystyrene dishes (NTPS) at conditions of 20 keV and 3x10 15 ions/cm 2 through a pattering mask, which had as many as 67 slits of 60 μm in width and 4 mm in length with a spacing of 60 μm. For cell culture in vitro, nerve cells of PC-12h (rat adrenal phechromocytoma) were used because they respond to a nerve growth factor (NGF). In the first 2 days in culture without NGF, we observed a selective cell attachment only to the ion-implanted region in patterning Ag - implanted polystyrene sample (p-Ag/NTPS). In another 2 days in culture with NGF, the nerve cells expanded neurites only over the ion-implanted region. For collagen-coated p-Ag/NTPS sample of which collagen was coated after the ion implantation (Collagen/p-Ag/NTPS), most nerve cells were also attached on the ion-implanted region. However, neurites expanded in both ion-implanted and unimplanted regions. The contact angle of NTPS decreased after the ion implantation from 86 deg. to 74 deg. . The region selectivity of neuron attachment and neurite extension is considered to be due to contact angle lowering by the ion implantation as radiation effect on the surface

Spatially Nonlinear Interdependence of Alpha-Oscillatory Neural Networks under Chan Meditation

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Pei-Chen Lo

2013-01-01

Full Text Available This paper reports the results of our investigation of the effects of Chan meditation on brain electrophysiological behaviors from the viewpoint of spatially nonlinear interdependence among regional neural networks. Particular emphasis is laid on the alpha-dominated EEG (electroencephalograph. Continuous-time wavelet transform was adopted to detect the epochs containing substantial alpha activities. Nonlinear interdependence quantified by similarity index S(X∣Y, the influence of source signal Y on sink signal X, was applied to the nonlinear dynamical model in phase space reconstructed from multichannel EEG. Experimental group involved ten experienced Chan-Meditation practitioners, while control group included ten healthy subjects within the same age range, yet, without any meditation experience. Nonlinear interdependence among various cortical regions was explored for five local neural-network regions, frontal, posterior, right-temporal, left-temporal, and central regions. In the experimental group, the inter-regional interaction was evaluated for the brain dynamics under three different stages, at rest (stage R, pre-meditation background recording, in Chan meditation (stage M, and the unique Chakra-focusing practice (stage C. Experimental group exhibits stronger interactions among various local neural networks at stages M and C compared with those at stage R. The intergroup comparison demonstrates that Chan-meditation brain possesses better cortical inter-regional interactions than the resting brain of control group.

Multimodal neural correlates of cognitive control in the Human Connectome Project.

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Lerman-Sinkoff, Dov B; Sui, Jing; Rachakonda, Srinivas; Kandala, Sridhar; Calhoun, Vince D; Barch, Deanna M

2017-12-01

Cognitive control is a construct that refers to the set of functions that enable decision-making and task performance through the representation of task states, goals, and rules. The neural correlates of cognitive control have been studied in humans using a wide variety of neuroimaging modalities, including structural MRI, resting-state fMRI, and task-based fMRI. The results from each of these modalities independently have implicated the involvement of a number of brain regions in cognitive control, including dorsal prefrontal cortex, and frontal parietal and cingulo-opercular brain networks. However, it is not clear how the results from a single modality relate to results in other modalities. Recent developments in multimodal image analysis methods provide an avenue for answering such questions and could yield more integrated models of the neural correlates of cognitive control. In this study, we used multiset canonical correlation analysis with joint independent component analysis (mCCA + jICA) to identify multimodal patterns of variation related to cognitive control. We used two independent cohorts of participants from the Human Connectome Project, each of which had data from four imaging modalities. We replicated the findings from the first cohort in the second cohort using both independent and predictive analyses. The independent analyses identified a component in each cohort that was highly similar to the other and significantly correlated with cognitive control performance. The replication by prediction analyses identified two independent components that were significantly correlated with cognitive control performance in the first cohort and significantly predictive of performance in the second cohort. These components identified positive relationships across the modalities in neural regions related to both dynamic and stable aspects of task control, including regions in both the frontal-parietal and cingulo-opercular networks, as well as regions

Enhanced peripheral visual processing in congenitally deaf humans is supported by multiple brain regions, including primary auditory cortex

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Gregory D. Scott

2014-03-01

Full Text Available Brain reorganization associated with altered sensory experience clarifies the critical role of neuroplasticity in development. An example is enhanced peripheral visual processing associated with congenital deafness, but the neural systems supporting this have not been fully characterized. A gap in our understanding of deafness-enhanced peripheral vision is the contribution of primary auditory cortex. Previous studies of auditory cortex that use anatomical normalization across participants were limited by inter-subject variability of Heschl’s gyrus. In addition to reorganized auditory cortex (cross-modal plasticity, a second gap in our understanding is the contribution of altered modality-specific cortices (visual intramodal plasticity in this case, as well as supramodal and multisensory cortices, especially when target detection is required across contrasts. Here we address these gaps by comparing fMRI signal change for peripheral versus perifoveal visual stimulation (11-15° vs. 2°-7° in congenitally deaf and hearing participants in a blocked experimental design with two analytical approaches: a Heschl’s gyrus region of interest analysis and a whole brain analysis. Our results using individually-defined primary auditory cortex (Heschl’s gyrus indicate that fMRI signal change for more peripheral stimuli was greater than perifoveal in deaf but not in hearing participants. Whole-brain analyses revealed differences between deaf and hearing participants for peripheral versus perifoveal visual processing in extrastriate visual cortex including primary auditory cortex, MT+/V5, superior-temporal auditory and multisensory and/or supramodal regions, such as posterior parietal cortex, frontal eye fields, anterior cingulate, and supplementary eye fields. Overall, these data demonstrate the contribution of neuroplasticity in multiple systems including primary auditory cortex, supramodal and multisensory regions, to altered visual processing in

A Study on Regional Frequency Analysis using Artificial Neural Network - the Sumjin River Basin

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Jeong, C.; Ahn, J.; Ahn, H.; Heo, J. H.

2017-12-01

Regional frequency analysis means to make up for shortcomings in the at-site frequency analysis which is about a lack of sample size through the regional concept. Regional rainfall quantile depends on the identification of hydrologically homogeneous regions, hence the regional classification based on hydrological homogeneous assumption is very important. For regional clustering about rainfall, multidimensional variables and factors related geographical features and meteorological figure are considered such as mean annual precipitation, number of days with precipitation in a year and average maximum daily precipitation in a month. Self-Organizing Feature Map method which is one of the artificial neural network algorithm in the unsupervised learning techniques solves N-dimensional and nonlinear problems and be shown results simply as a data visualization technique. In this study, for the Sumjin river basin in South Korea, cluster analysis was performed based on SOM method using high-dimensional geographical features and meteorological factor as input data. then, for the results, in order to evaluate the homogeneity of regions, the L-moment based discordancy and heterogeneity measures were used. Rainfall quantiles were estimated as the index flood method which is one of regional rainfall frequency analysis. Clustering analysis using SOM method and the consequential variation in rainfall quantile were analyzed. This research was supported by a grant(2017-MPSS31-001) from Supporting Technology Development Program for Disaster Management funded by Ministry of Public Safety and Security(MPSS) of the Korean government.

Ear Detection under Uncontrolled Conditions with Multiple Scale Faster Region-Based Convolutional Neural Networks

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Yi Zhang

2017-04-01

Full Text Available Ear detection is an important step in ear recognition approaches. Most existing ear detection techniques are based on manually designing features or shallow learning algorithms. However, researchers found that the pose variation, occlusion, and imaging conditions provide a great challenge to the traditional ear detection methods under uncontrolled conditions. This paper proposes an efficient technique involving Multiple Scale Faster Region-based Convolutional Neural Networks (Faster R-CNN to detect ears from 2D profile images in natural images automatically. Firstly, three regions of different scales are detected to infer the information about the ear location context within the image. Then an ear region filtering approach is proposed to extract the correct ear region and eliminate the false positives automatically. In an experiment with a test set of 200 web images (with variable photographic conditions, 98% of ears were accurately detected. Experiments were likewise conducted on the Collection J2 of University of Notre Dame Biometrics Database (UND-J2 and University of Beira Interior Ear dataset (UBEAR, which contain large occlusion, scale, and pose variations. Detection rates of 100% and 98.22%, respectively, demonstrate the effectiveness of the proposed approach.

A Pontine Region is a Neural Correlate of the Human Affective Processing Network

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Tatia M.C. Lee

2015-11-01

Full Text Available The in vivo neural activity of the pons during the perception of affective stimuli has not been studied despite the strong implications of its role in affective processing. To examine the activity of the pons during the viewing of affective stimuli, and to verify its functional and structural connectivity with other affective neural correlates, a multimodal magnetic resonance imaging methodology was employed in this study. We observed the in vivo activity of the pons when viewing affective stimuli. Furthermore, small-world connectivity indicated that the functional connectivity (FC between the pons and the cortico-limbic affective regions was meaningful, with the coefficient λ being positively associated with self-reported emotional reactivity. The FC between the pons and the cortico-limbic-striatal areas was related to self-reported negative affect. Corroborating this finding was the observation that the tract passing through the pons and the left hippocampus was negatively related to self-reported positive affect and positively correlated with emotional reactivity. Our findings support the framework that the pons works conjunctively with the distributed cortico-limbic-striatal systems in shaping individuals' affective states and reactivity. Our work paves the path for future research on the contribution of the pons to the precipitation and maintenance of affective disorders.

Exploring the neural correlates of visual creativity

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Liew, Sook-Lei; Dandekar, Francesco

2013-01-01

Although creativity has been called the most important of all human resources, its neural basis is still unclear. In the current study, we used fMRI to measure neural activity in participants solving a visuospatial creativity problem that involves divergent thinking and has been considered a canonical right hemisphere task. As hypothesized, both the visual creativity task and the control task as compared to rest activated a variety of areas including the posterior parietal cortex bilaterally and motor regions, which are known to be involved in visuospatial rotation of objects. However, directly comparing the two tasks indicated that the creative task more strongly activated left hemisphere regions including the posterior parietal cortex, the premotor cortex, dorsolateral prefrontal cortex (DLPFC) and the medial PFC. These results demonstrate that even in a task that is specialized to the right hemisphere, robust parallel activity in the left hemisphere supports creative processing. Furthermore, the results support the notion that higher motor planning may be a general component of creative improvisation and that such goal-directed planning of novel solutions may be organized top-down by the left DLPFC and by working memory processing in the medial prefrontal cortex. PMID:22349801

A neural mass model of interconnected regions simulates rhythm propagation observed via TMS-EEG.

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Cona, F; Zavaglia, M; Massimini, M; Rosanova, M; Ursino, M

2011-08-01

Knowledge of cortical rhythms represents an important aspect of modern neuroscience, to understand how the brain realizes its functions. Recent data suggest that different regions in the brain may exhibit distinct electroencephalogram (EEG) rhythms when perturbed by Transcranial Magnetic Stimulation (TMS) and that these rhythms can change due to the connectivity among regions. In this context, in silico simulations may help the validation of these hypotheses that would be difficult to be verified in vivo. Neural mass models can be very useful to simulate specific aspects of electrical brain activity and, above all, to analyze and identify the overall frequency content of EEG in a cortical region of interest (ROI). In this work we implemented a model of connectivity among cortical regions to fit the impulse responses in three ROIs recorded during a series of TMS/EEG experiments performed in five subjects and using three different impulse intensities. In particular we investigated Brodmann Area (BA) 19 (occipital lobe), BA 7 (parietal lobe) and BA 6 (frontal lobe). Results show that the model can reproduce the natural rhythms of the three regions quite well, acting on a few internal parameters. Moreover, the model can explain most rhythm changes induced by stimulation of another region, and inter-subject variability, by estimating just a few long-range connectivity parameters among ROIs. Copyright © 2011 Elsevier Inc. All rights reserved.

Neural correlates of eating disorders: translational potential

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McAdams CJ

2015-09-01

Full Text Available Carrie J McAdams,1,2 Whitney Smith1 1University of Texas at Southwestern Medical Center, 2Department of Psychiatry, Texas Health Presbyterian Hospital of Dallas, Dallas, TX, USA Abstract: Eating disorders are complex and serious psychiatric illnesses whose etiology includes psychological, biological, and social factors. Treatment of eating disorders is challenging as there are few evidence-based treatments and limited understanding of the mechanisms that result in sustained recovery. In the last 20 years, we have begun to identify neural pathways that are altered in eating disorders. Consideration of how these pathways may contribute to an eating disorder can provide an understanding of expected responses to treatments. Eating disorder behaviors include restrictive eating, compulsive overeating, and purging behaviors after eating. Eating disorders are associated with changes in many neural systems. In this targeted review, we focus on three cognitive processes associated with neurocircuitry differences in subjects with eating disorders such as reward, decision-making, and social behavior. We briefly examine how each of these systems function in healthy people, using Neurosynth meta-analysis to identify key regions commonly implicated in these circuits. We review the evidence for disruptions of these regions and systems in eating disorders. Finally, we describe psychiatric and psychological treatments that are likely to function by impacting these regions. Keywords: anorexia nervosa, bulimia nervosa, social cognition, reward processing, decision-making

Neural network approach to the prediction of seismic events based on low-frequency signal monitoring of the Kuril-Kamchatka and Japanese regions

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Irina Popova

2013-08-01

Full Text Available Very-low-frequency/ low-frequency (VLF/LF sub-ionospheric radiowave monitoring has been widely used in recent years to analyze earthquake preparatory processes. The connection between earthquakes with M ≥5.5 and nighttime disturbances of signal amplitude and phase has been established. Thus, it is possible to use nighttime anomalies of VLF/LF signals as earthquake precursors. Here, we propose a method for estimation of the VLF/LF signal sensitivity to seismic processes using a neural network approach. We apply the error back-propagation technique based on a three-level perceptron to predict a seismic event. The back-propagation technique involves two main stages to solve the problem; namely, network training, and recognition (the prediction itself. To train a neural network, we first create a so-called ‘training set’. The ‘teacher’ specifies the correspondence between the chosen input and the output data. In the present case, a representative database includes both the LF data received over three years of monitoring at the station in Petropavlovsk-Kamchatsky (2005-2007, and the seismicity parameters of the Kuril-Kamchatka and Japanese regions. At the first stage, the neural network established the relationship between the characteristic features of the LF signal (the mean and dispersion of a phase and an amplitude at nighttime for a few days before a seismic event and the corresponding level of correlation with a seismic event, or the absence of a seismic event. For the second stage, the trained neural network was applied to predict seismic events from the LF data using twelve time intervals in 2004, 2005, 2006 and 2007. The results of the prediction are discussed.

Neural predictors of sensorimotor adaptation rate and savings.

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Cassady, Kaitlin; Ruitenberg, Marit; Koppelmans, Vincent; Reuter-Lorenz, Patricia; De Dios, Yiri; Gadd, Nichole; Wood, Scott; Riascos Castenada, Roy; Kofman, Igor; Bloomberg, Jacob; Mulavara, Ajitkumar; Seidler, Rachael

2018-04-01

In this study, we investigate whether individual variability in the rate of visuomotor adaptation and multiday savings is associated with differences in regional gray matter volume and resting-state functional connectivity. Thirty-four participants performed a manual adaptation task during two separate test sessions, on average 9 days apart. Functional connectivity strength between sensorimotor, dorsal cingulate, and temporoparietal regions of the brain was found to predict the rate of learning during the early phase of the adaptation task. In contrast, default mode network connectivity strength was found to predict both the rate of learning during the late adaptation phase and savings. As for structural predictors, greater gray matter volume in temporoparietal and occipital regions predicted faster early learning, whereas greater gray matter volume in superior posterior regions of the cerebellum predicted faster late learning. These findings suggest that the offline neural predictors of early adaptation may facilitate the cognitive aspects of sensorimotor adaptation, supported by the involvement of temporoparietal and cingulate networks. The offline neural predictors of late adaptation and savings, including the default mode network and the cerebellum, likely support the storage and modification of newly acquired sensorimotor representations. © 2017 Wiley Periodicals, Inc.

Comparative Analysis of Neural Network Training Methods in Real-time Radiotherapy

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Nouri S.

2017-03-01

Full Text Available Background: The motions of body and tumor in some regions such as chest during radiotherapy treatments are one of the major concerns protecting normal tissues against high doses. By using real-time radiotherapy technique, it is possible to increase the accuracy of delivered dose to the tumor region by means of tracing markers on the body of patients. Objective: This study evaluates the accuracy of some artificial intelligence methods including neural network and those of combination with genetic algorithm as well as particle swarm optimization (PSO estimating tumor positions in real-time radiotherapy. Method: One hundred recorded signals of three external markers were used as input data. The signals from 3 markers thorough 10 breathing cycles of a patient treated via a cyber-knife for a lung tumor were used as data input. Then, neural network method and its combination with genetic or PSO algorithms were applied determining the tumor locations using MATLAB© software program. Results: The accuracies were obtained 0.8%, 12% and 14% in neural network, genetic and particle swarm optimization algorithms, respectively. Conclusion: The internal target volume (ITV should be determined based on the applied neural network algorithm on training steps.

The fiber-optic imaging and manipulation of neural activity during animal behavior.

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Miyamoto, Daisuke; Murayama, Masanori

2016-02-01

Recent progress with optogenetic probes for imaging and manipulating neural activity has further increased the relevance of fiber-optic systems for neural circuitry research. Optical fibers, which bi-directionally transmit light between separate sites (even at a distance of several meters), can be used for either optical imaging or manipulating neural activity relevant to behavioral circuitry mechanisms. The method's flexibility and the specifications of the light structure are well suited for following the behavior of freely moving animals. Furthermore, thin optical fibers allow researchers to monitor neural activity from not only the cortical surface but also deep brain regions, including the hippocampus and amygdala. Such regions are difficult to target with two-photon microscopes. Optogenetic manipulation of neural activity with an optical fiber has the advantage of being selective for both cell-types and projections as compared to conventional electrophysiological brain tissue stimulation. It is difficult to extract any data regarding changes in neural activity solely from a fiber-optic manipulation device; however, the readout of data is made possible by combining manipulation with electrophysiological recording, or the simultaneous application of optical imaging and manipulation using a bundle-fiber. The present review introduces recent progress in fiber-optic imaging and manipulation methods, while also discussing fiber-optic system designs that are suitable for a given experimental protocol. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

The Neural Cell Adhesion Molecule NCAM2/OCAM/RNCAM, a Close Relative to NCAM

DEFF Research Database (Denmark)

Kulahin, Nikolaj; Walmod, Peter

2008-01-01

molecule (NCAM) is a well characterized, ubiquitously expressed CAM that is highly expressed in the nervous system. In addition to mediating cell adhesion, NCAM participates in a multitude of cellular events, including survival, migration, and differentiation of cells, outgrowth of neurites, and formation......Cell adhesion molecules (CAMs) constitute a large class of plasma membrane-anchored proteins that mediate attachment between neighboring cells and between cells and the surrounding extracellular matrix (ECM). However, CAMs are more than simple mediators of cell adhesion. The neural cell adhesion...... and plasticity of synapses. NCAM shares an overall sequence identity of approximately 44% with the neural cell adhesion molecule 2 (NCAM2), a protein also known as olfactory cell adhesion molecule (OCAM) and Rb-8 neural cell adhesion molecule (RNCAM), and the region-for-region sequence homology between the two...

Regionally-specified second trimester fetal neural stem cells reveals differential neurogenic programming.

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Yiping Fan

Full Text Available Neural stem/progenitor cells (NSC have the potential for treatment of a wide range of neurological diseases such as Parkinson Disease and multiple sclerosis. Currently, NSC have been isolated only from hippocampus and subventricular zone (SVZ of the adult brain. It is not known whether NSC can be found in all parts of the developing mid-trimester central nervous system (CNS when the brain undergoes massive transformation and growth. Multipotent NSC from the mid-trimester cerebra, thalamus, SVZ, hippocampus, thalamus, cerebellum, brain stem and spinal cord can be derived and propagated as clonal neurospheres with increasing frequencies with increasing gestations. These NSC can undergo multi-lineage differentiation both in vitro and in vivo, and engraft in a developmental murine model. Regionally-derived NSC are phenotypically distinct, with hippocampal NSC having a significantly higher neurogenic potential (53.6% over other sources (range of 0%-27.5%, p<0.004. Whole genome expression analysis showed differential gene expression between these regionally-derived NSC, which involved the Notch, epidermal growth factor as well as interleukin pathways. We have shown the presence of phenotypically-distinct regionally-derived NSC from the mid-trimester CNS, which may reflect the ontological differences occurring within the CNS. Aside from informing on the role of such cells during fetal growth, they may be useful for different cellular therapy applications.

The neural circuits that generate tics in Tourette's syndrome.

Science.gov (United States)

Wang, Zhishun; Maia, Tiago V; Marsh, Rachel; Colibazzi, Tiziano; Gerber, Andrew; Peterson, Bradley S

2011-12-01

The purpose of this study was to examine neural activity and connectivity within cortico-striato-thalamo-cortical circuits and to reveal circuit-based neural mechanisms that govern tic generation in Tourette's syndrome. Functional magnetic resonance imaging data were acquired from 13 individuals with Tourette's syndrome and 21 healthy comparison subjects during spontaneous or simulated tics. Independent component analysis with hierarchical partner matching was used to isolate neural activity within functionally distinct regions of cortico-striato-thalamo-cortical circuits. Granger causality was used to investigate causal interactions among these regions. The Tourette's syndrome group exhibited stronger neural activity and interregional causality than healthy comparison subjects throughout all portions of the motor pathway, including the sensorimotor cortex, putamen, pallidum, and substantia nigra. Activity in these areas correlated positively with the severity of tic symptoms. Activity within the Tourette's syndrome group was stronger during spontaneous tics than during voluntary tics in the somatosensory and posterior parietal cortices, putamen, and amygdala/hippocampus complex, suggesting that activity in these regions may represent features of the premonitory urges that generate spontaneous tic behaviors. In contrast, activity was weaker in the Tourette's syndrome group than in the healthy comparison group within portions of cortico-striato-thalamo-cortical circuits that exert top-down control over motor pathways (the caudate and anterior cingulate cortex), and progressively less activity in these regions accompanied more severe tic symptoms, suggesting that faulty activity in these circuits may result in their failure to control tic behaviors or the premonitory urges that generate them. Our findings, taken together, suggest that tics are caused by the combined effects of excessive activity in motor pathways and reduced activation in control portions of cortico

Neural correlates of HIV risk feelings.

Science.gov (United States)

Häcker, Frank E K; Schmälzle, Ralf; Renner, Britta; Schupp, Harald T

2015-04-01

Field studies on HIV risk perception suggest that people rely on impressions they have about the safety of their partner. The present fMRI study investigated the neural correlates of the intuitive perception of risk. First, during an implicit condition, participants viewed a series of unacquainted persons and performed a task unrelated to HIV risk. In the following explicit condition, participants evaluated the HIV risk for each presented person. Contrasting responses for high and low HIV risk revealed that risky stimuli evoked enhanced activity in the anterior insula and medial prefrontal regions, which are involved in salience processing and frequently activated by threatening and negative affect-related stimuli. Importantly, neural regions responding to explicit HIV risk judgments were also enhanced in the implicit condition, suggesting a neural mechanism for intuitive impressions of riskiness. Overall, these findings suggest the saliency network as neural correlate for the intuitive sensing of risk. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Temperature prediction model of asphalt pavement in cold regions based on an improved BP neural network

International Nuclear Information System (INIS)

Xu, Bo; Dan, Han-Cheng; Li, Liang

2017-01-01

Highlights: • Pavement temperature prediction model is presented with improved BP neural network. • Dynamic and static methods are presented to predict pavement temperature. • Pavement temperature can be excellently predicted in next 3 h. - Abstract: Ice cover on pavement threatens traffic safety, and pavement temperature is the main factor used to determine whether the wet pavement is icy or not. In this paper, a temperature prediction model of the pavement in winter is established by introducing an improved Back Propagation (BP) neural network model. Before the application of the BP neural network model, many efforts were made to eliminate chaos and determine the regularity of temperature on the pavement surface (e.g., analyze the regularity of diurnal and monthly variations of pavement temperature). New dynamic and static prediction methods are presented by improving the algorithms to intelligently overcome the prediction inaccuracy at the change point of daily temperature. Furthermore, some scenarios have been compared for different dates and road sections to verify the reliability of the prediction model. According to the analysis results, the daily pavement temperatures can be accurately predicted for the next 3 h from the time of prediction by combining the dynamic and static prediction methods. The presented method in this paper can provide technical references for temperature prediction of the pavement and the development of an early-warning system for icy pavements in cold regions.

Metabolic neural mapping in neonatal rats

International Nuclear Information System (INIS)

DiRocco, R.J.; Hall, W.G.

1981-01-01

Functional neural mapping by 14 C-deoxyglucose autoradiography in adult rats has shown that increases in neural metabolic rate that are coupled to increased neurophysiological activity are more evident in axon terminals and dendrites than neuron cell bodies. Regions containing architectonically well-defined concentrations of terminals and dendrites (neuropil) have high metabolic rates when the neuropil is physiologically active. In neonatal rats, however, we find that regions containing well-defined groupings of neuron cell bodies have high metabolic rates in 14 C-deoxyglucose autoradiograms. The striking difference between the morphological appearance of 14 C-deoxyglucose autoradiograms obtained from neonatal and adult rats is probably related to developmental changes in morphometric features of differentiating neurons, as well as associated changes in type and locus of neural work performed

Maternal neural responses to infant cries and faces: relationships with substance use

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Nicole eLandi

2011-06-01

Full Text Available Substance abuse in pregnant and recently postpartum women is a major public health concern because of effects on the infant and on the ability of the adult to care for the infant. In addition to the negative health effects of teratogenic substances on fetal development, substance use can contribute to difficulties associated with the social and behavioral aspects of parenting. Neural circuits associated with parenting behavior overlap with circuits involved in addiction (e.g., frontal, striatal and limbic systems and thus may be co-opted for the craving/reward cycle associated with substance use and abuse and be less available for parenting. The current study investigates the degree to which neural circuits associated with parenting are disrupted in mothers who are substance-using. Specifically, we used functional magnetic resonance imaging to examine the neural response to emotional infant cues (faces and cries in substance-using compared to non-using mothers. In response to both faces (of varying emotional valence and cries (of varying distress levels, substance-using mothers evidenced reduced neural activation in regions that have been previously implicated in reward and motivation as well as regions involved in cognitive control. Specifically, in response to faces, substance users showed reduced activation in prefrontal regions, including the dorsolateral and ventromedial prefrontal cortex, as well as visual processing (occipital lobes and limbic regions (parahippocampus and amygdala. Similarly, in response to infant cries substance-using mothers showed reduced activation relative to non-using mothers in prefrontal regions, auditory sensory processing regions, insula and limbic regions (parahippocampus and amygdala. These findings suggest that infant stimuli may be less salient for substance-using mothers, and such reduced saliency may impair developing infant-caregiver attachment and the ability of mothers to respond appropriately to their

Neural plasticity and its initiating conditions in tinnitus.

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Roberts, L E

2018-03-01

Deafferentation caused by cochlear pathology (which can be hidden from the audiogram) activates forms of neural plasticity in auditory pathways, generating tinnitus and its associated conditions including hyperacusis. This article discusses tinnitus mechanisms and suggests how these mechanisms may relate to those involved in normal auditory information processing. Research findings from animal models of tinnitus and from electromagnetic imaging of tinnitus patients are reviewed which pertain to the role of deafferentation and neural plasticity in tinnitus and hyperacusis. Auditory neurons compensate for deafferentation by increasing their input/output functions (gain) at multiple levels of the auditory system. Forms of homeostatic plasticity are believed to be responsible for this neural change, which increases the spontaneous and driven activity of neurons in central auditory structures in animals expressing behavioral evidence of tinnitus. Another tinnitus correlate, increased neural synchrony among the affected neurons, is forged by spike-timing-dependent neural plasticity in auditory pathways. Slow oscillations generated by bursting thalamic neurons verified in tinnitus animals appear to modulate neural plasticity in the cortex, integrating tinnitus neural activity with information in brain regions supporting memory, emotion, and consciousness which exhibit increased metabolic activity in tinnitus patients. The latter process may be induced by transient auditory events in normal processing but it persists in tinnitus, driven by phantom signals from the auditory pathway. Several tinnitus therapies attempt to suppress tinnitus through plasticity, but repeated sessions will likely be needed to prevent tinnitus activity from returning owing to deafferentation as its initiating condition.

Chondroitin sulfate effects on neural stem cell differentiation.

Science.gov (United States)

Canning, David R; Brelsford, Natalie R; Lovett, Neil W

2016-01-01

We have investigated the role chondroitin sulfate has on cell interactions during neural plate formation in the early chick embryo. Using tissue culture isolates from the prospective neural plate, we have measured neural gene expression profiles associated with neural stem cell differentiation. Removal of chondroitin sulfate from stage 4 neural plate tissue leads to altered associations of N-cadherin-positive neural progenitors and causes changes in the normal sequence of neural marker gene expression. Absence of chondroitin sulfate in the neural plate leads to reduced Sox2 expression and is accompanied by an increase in the expression of anterior markers of neural regionalization. Results obtained in this study suggest that the presence of chondroitin sulfate in the anterior chick embryo is instrumental in maintaining cells in the neural precursor state.

High Frequency Deep Brain Stimulation and Neural Rhythms in Parkinson's Disease.

Science.gov (United States)

Blumenfeld, Zack; Brontë-Stewart, Helen

2015-12-01

High frequency (HF) deep brain stimulation (DBS) is an established therapy for the treatment of Parkinson's disease (PD). It effectively treats the cardinal motor signs of PD, including tremor, bradykinesia, and rigidity. The most common neural target is the subthalamic nucleus, located within the basal ganglia, the region most acutely affected by PD pathology. Using chronically-implanted DBS electrodes, researchers have been able to record underlying neural rhythms from several nodes in the PD network as well as perturb it using DBS to measure the ensuing neural and behavioral effects, both acutely and over time. In this review, we provide an overview of the PD neural network, focusing on the pathophysiological signals that have been recorded from PD patients as well as the mechanisms underlying the therapeutic benefits of HF DBS. We then discuss evidence for the relationship between specific neural oscillations and symptoms of PD, including the aberrant relationships potentially underlying functional connectivity in PD as well as the use of different frequencies of stimulation to more specifically target certain symptoms. Finally, we briefly describe several current areas of investigation and how the ability to record neural data in ecologically-valid settings may allow researchers to explore the relationship between brain and behavior in an unprecedented manner, culminating in the future automation of neurostimulation therapy for the treatment of a variety of neuropsychiatric diseases.

A dynamic neural field model of temporal order judgments.

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Hecht, Lauren N; Spencer, John P; Vecera, Shaun P

2015-12-01

Temporal ordering of events is biased, or influenced, by perceptual organization-figure-ground organization-and by spatial attention. For example, within a region assigned figural status or at an attended location, onset events are processed earlier (Lester, Hecht, & Vecera, 2009; Shore, Spence, & Klein, 2001), and offset events are processed for longer durations (Hecht & Vecera, 2011; Rolke, Ulrich, & Bausenhart, 2006). Here, we present an extension of a dynamic field model of change detection (Johnson, Spencer, Luck, & Schöner, 2009; Johnson, Spencer, & Schöner, 2009) that accounts for both the onset and offset performance for figural and attended regions. The model posits that neural populations processing the figure are more active, resulting in a peak of activation that quickly builds toward a detection threshold when the onset of a target is presented. This same enhanced activation for some neural populations is maintained when a present target is removed, creating delays in the perception of the target's offset. We discuss the broader implications of this model, including insights regarding how neural activation can be generated in response to the disappearance of information. (c) 2015 APA, all rights reserved).

Investigating the Influence of Biological Sex on the Behavioral and Neural Basis of Face Recognition.

Science.gov (United States)

Scherf, K Suzanne; Elbich, Daniel B; Motta-Mena, Natalie V

2017-01-01

There is interest in understanding the influence of biological factors, like sex, on the organization of brain function. We investigated the influence of biological sex on the behavioral and neural basis of face recognition in healthy, young adults. In behavior, there were no sex differences on the male Cambridge Face Memory Test (CFMT)+ or the female CFMT+ (that we created) and no own-gender bias (OGB) in either group. We evaluated the functional topography of ventral stream organization by measuring the magnitude and functional neural size of 16 individually defined face-, two object-, and two place-related regions bilaterally. There were no sex differences in any of these measures of neural function in any of the regions of interest (ROIs) or in group level comparisons. These findings reveal that men and women have similar category-selective topographic organization in the ventral visual pathway. Next, in a separate task, we measured activation within the 16 face-processing ROIs specifically during recognition of target male and female faces. There were no sex differences in the magnitude of the neural responses in any face-processing region. Furthermore, there was no OGB in the neural responses of either the male or female participants. Our findings suggest that face recognition behavior, including the OGB, is not inherently sexually dimorphic. Face recognition is an essential skill for navigating human social interactions, which is reflected equally in the behavior and neural architecture of men and women.

Investigating the Influence of Biological Sex on the Behavioral and Neural Basis of Face Recognition

Science.gov (United States)

2017-01-01

Abstract There is interest in understanding the influence of biological factors, like sex, on the organization of brain function. We investigated the influence of biological sex on the behavioral and neural basis of face recognition in healthy, young adults. In behavior, there were no sex differences on the male Cambridge Face Memory Test (CFMT)+ or the female CFMT+ (that we created) and no own-gender bias (OGB) in either group. We evaluated the functional topography of ventral stream organization by measuring the magnitude and functional neural size of 16 individually defined face-, two object-, and two place-related regions bilaterally. There were no sex differences in any of these measures of neural function in any of the regions of interest (ROIs) or in group level comparisons. These findings reveal that men and women have similar category-selective topographic organization in the ventral visual pathway. Next, in a separate task, we measured activation within the 16 face-processing ROIs specifically during recognition of target male and female faces. There were no sex differences in the magnitude of the neural responses in any face-processing region. Furthermore, there was no OGB in the neural responses of either the male or female participants. Our findings suggest that face recognition behavior, including the OGB, is not inherently sexually dimorphic. Face recognition is an essential skill for navigating human social interactions, which is reflected equally in the behavior and neural architecture of men and women. PMID:28497111

Typology of nonlinear activity waves in a layered neural continuum.

Science.gov (United States)

Koch, Paul; Leisman, Gerry

2006-04-01

Neural tissue, a medium containing electro-chemical energy, can amplify small increments in cellular activity. The growing disturbance, measured as the fraction of active cells, manifests as propagating waves. In a layered geometry with a time delay in synaptic signals between the layers, the delay is instrumental in determining the amplified wavelengths. The growth of the waves is limited by the finite number of neural cells in a given region of the continuum. As wave growth saturates, the resulting activity patterns in space and time show a variety of forms, ranging from regular monochromatic waves to highly irregular mixtures of different spatial frequencies. The type of wave configuration is determined by a number of parameters, including alertness and synaptic conditioning as well as delay. For all cases studied, using numerical solution of the nonlinear Wilson-Cowan (1973) equations, there is an interval in delay in which the wave mixing occurs. As delay increases through this interval, during a series of consecutive waves propagating through a continuum region, the activity within that region changes from a single-frequency to a multiple-frequency pattern and back again. The diverse spatio-temporal patterns give a more concrete form to several metaphors advanced over the years to attempt an explanation of cognitive phenomena: Activity waves embody the "holographic memory" (Pribram, 1991); wave mixing provides a plausible cause of the competition called "neural Darwinism" (Edelman, 1988); finally the consecutive generation of growing neural waves can explain the discontinuousness of "psychological time" (Stroud, 1955).

Activity in part of the neural correlates of consciousness reflects integration.

Science.gov (United States)

Eriksson, Johan

2017-10-01

Integration is commonly viewed as a key process for generating conscious experiences. Accordingly, there should be increased activity within the neural correlates of consciousness when demands on integration increase. We used fMRI and "informational masking" to isolate the neural correlates of consciousness and measured how the associated brain activity changed as a function of required integration. Integration was manipulated by comparing the experience of hearing simple reoccurring tones to hearing harmonic tone triplets. The neural correlates of auditory consciousness included superior temporal gyrus, lateral and medial frontal regions, cerebellum, and also parietal cortex. Critically, only activity in left parietal cortex increased significantly as a function of increasing demands on integration. We conclude that integration can explain part of the neural activity associated with the generation conscious experiences, but that much of associated brain activity apparently reflects other processes. Copyright © 2017 Elsevier Inc. All rights reserved.

The neural basis of task switching changes with skill acquisition

Directory of Open Access Journals (Sweden)

Koji eJimura

2014-05-01

Full Text Available Learning novel skills involves reorganization and optimization of cognitive processing involving a broad network of brain regions. Previous work has shown asymmetric costs of switching to a well-trained task versus a poorly-trained task, but the neural basis of these differential switch costs is unclear. The current study examined the neural signature of task switching in the context of acquisition of new skill. Human participants alternated randomly between a novel visual task (mirror-reversed word reading and a highly practiced one (plain word reading, allowing the isolation of task switching and skill set maintenance. Two scan sessions were separated by two weeks, with behavioral training on the mirror reading task in between the two sessions. Broad cortical regions, including bilateral prefrontal, parietal, and extrastriate cortices, showed decreased activity associated with learning of the mirror reading skill. In contrast, learning to switch to the novel skill was associated with decreased activity in a focal subcortical region in the dorsal striatum. Switching to the highly practiced task was associated with a non-overlapping set of regions, suggesting substantial differences in the neural substrates of switching as a function of task skill. Searchlight multivariate pattern analysis also revealed that learning was associated with decreased pattern information for mirror versus plain reading tasks in fronto-parietal regions. Inferior frontal junction and posterior parietal cortex showed a joint effect of univariate activation and pattern information. These results suggest distinct learning mechanisms task performance and executive control as a function of learning.

Neural regions supporting lexical processing of objects and actions: A case series analysis

Directory of Open Access Journals (Sweden)

Bonnie L Breining

2014-04-01

Full Text Available Introduction. Linking semantic representations to lexical items is an important cognitive process for both producing and comprehending language. Past research has suggested that the bilateral anterior temporal lobes are critical for this process (e.g. Patterson, Nestor, & Rogers, 2007. However, the majority of studies focused on object concepts alone, ignoring actions. The few that considered actions suggest that the temporal poles are not critical for their processing (e.g. Kemmerer et al., 2010. In this case series, we investigated the neural substrates of linking object and action concepts to lexical labels by correlating the volume of defined regions of interest with behavioral performance on picture-word verification and picture naming tasks of individuals with primary progressive aphasia (PPA. PPA is a neurodegenerative condition with heterogeneous neuropathological causes, characterized by increasing language deficits for at least two years in the face of relatively intact cognitive function in other domains (Gorno-Tempini et al., 2011. This population displays appropriate heterogeneity of performance and focal atrophy for investigating the neural substrates involved in lexical semantic processing of objects and actions. Method. Twenty-one individuals with PPA participated in behavioral assessment within six months of high resolution anatomical MRI scans. Behavioral assessments consisted of four tasks: picture-word verification and picture naming of objects and actions. Performance on these assessments was correlated with brain volume measured using atlas-based analysis in twenty regions of interest that are commonly atrophied in PPA and implicated in language processing. Results. Impaired performance for all four tasks significantly correlated with atrophy in the right superior temporal pole, left anterior middle temporal gyrus, and left fusiform gyrus. No regions were identified in which volume correlated with performance for both

Neural Networks: Implementations and Applications

OpenAIRE

Vonk, E.; Veelenturf, L.P.J.; Jain, L.C.

1996-01-01

Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas

The neural organization of perception in chess experts.

Science.gov (United States)

Krawczyk, Daniel C; Boggan, Amy L; McClelland, M Michelle; Bartlett, James C

2011-07-20

The human visual system responds to expertise, and it has been suggested that regions that process faces also process other objects of expertise including chess boards by experts. We tested whether chess and face processing overlap in brain activity using fMRI. Chess experts and novices exhibited face selective areas, but these regions showed no selectivity to chess configurations relative to other stimuli. We next compared neural responses to chess and to scrambled chess displays to isolate areas relevant to expertise. Areas within the posterior cingulate, orbitofrontal cortex, and right temporal cortex were active in this comparison in experts over novices. We also compared chess and face responses within the posterior cingulate and found this area responsive to chess only in experts. These findings indicate that the configurations in chess are not strongly processed by face-selective regions that are selective for faces in individuals who have expertise in both domains. Further, the area most consistently involved in chess did not show overlap with faces. Overall, these results suggest that expert visual processing may be similar at the level of recognition, but need not show the same neural correlates. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

A biologically inspired neural model for visual and proprioceptive integration including sensory training.

Science.gov (United States)

Saidi, Maryam; Towhidkhah, Farzad; Gharibzadeh, Shahriar; Lari, Abdolaziz Azizi

2013-12-01

Humans perceive the surrounding world by integration of information through different sensory modalities. Earlier models of multisensory integration rely mainly on traditional Bayesian and causal Bayesian inferences for single causal (source) and two causal (for two senses such as visual and auditory systems), respectively. In this paper a new recurrent neural model is presented for integration of visual and proprioceptive information. This model is based on population coding which is able to mimic multisensory integration of neural centers in the human brain. The simulation results agree with those achieved by casual Bayesian inference. The model can also simulate the sensory training process of visual and proprioceptive information in human. Training process in multisensory integration is a point with less attention in the literature before. The effect of proprioceptive training on multisensory perception was investigated through a set of experiments in our previous study. The current study, evaluates the effect of both modalities, i.e., visual and proprioceptive training and compares them with each other through a set of new experiments. In these experiments, the subject was asked to move his/her hand in a circle and estimate its position. The experiments were performed on eight subjects with proprioception training and eight subjects with visual training. Results of the experiments show three important points: (1) visual learning rate is significantly more than that of proprioception; (2) means of visual and proprioceptive errors are decreased by training but statistical analysis shows that this decrement is significant for proprioceptive error and non-significant for visual error, and (3) visual errors in training phase even in the beginning of it, is much less than errors of the main test stage because in the main test, the subject has to focus on two senses. The results of the experiments in this paper is in agreement with the results of the neural model

Activity patterns of cultured neural networks on micro electrode arrays

NARCIS (Netherlands)

Rutten, Wim; van Pelt, J.

2001-01-01

A hybrid neuro-electronic interface is a cell-cultured micro electrode array, acting as a neural information transducer for stimulation and/or recording of neural activity in the brain or the spinal cord (ventral motor region or dorsal sensory region). It consists of an array of micro electrodes on

The neural correlates of reciprocity are sensitive to prior experience of reciprocity.

Science.gov (United States)

Cáceda, Ricardo; Prendes-Alvarez, Stefania; Hsu, Jung-Jiin; Tripathi, Shanti P; Kilts, Clint D; James, G Andrew

2017-08-14

Reciprocity is central to human relationships and is strongly influenced by multiple factors including the nature of social exchanges and their attendant emotional reactions. Despite recent advances in the field, the neural processes involved in this modulation of reciprocal behavior by ongoing social interaction are poorly understood. We hypothesized that activity within a discrete set of neural networks including a putative moral cognitive neural network is associated with reciprocity behavior. Nineteen healthy adults underwent functional magnetic resonance imaging scanning while playing the trustee role in the Trust Game. Personality traits and moral development were assessed. Independent component analysis was used to identify task-related functional brain networks and assess their relationship to behavior. The saliency network (insula and anterior cingulate) was positively correlated with reciprocity behavior. A consistent array of brain regions supports the engagement of emotional, self-referential and planning processes during social reciprocity behavior. Published by Elsevier B.V.

Computerized detection of multiple sclerosis candidate regions based on a level set method using an artificial neural network

International Nuclear Information System (INIS)

Kuwazuru, Junpei; Magome, Taiki; Arimura, Hidetaka; Yamashita, Yasuo; Oki, Masafumi; Toyofuku, Fukai; Kakeda, Shingo; Yamamoto, Daisuke

2010-01-01

Yamamoto et al. developed the system for computer-aided detection of multiple sclerosis (MS) candidate regions. In a level set method in their proposed method, they employed the constant threshold value for the edge indicator function related to a speed function of the level set method. However, it would be appropriate to adjust the threshold value to each MS candidate region, because the edge magnitudes in MS candidates differ from each other. Our purpose of this study was to develop a computerized detection of MS candidate regions in MR images based on a level set method using an artificial neural network (ANN). To adjust the threshold value for the edge indicator function in the level set method to each true positive (TP) and false positive (FP) region, we constructed the ANN. The ANN could provide the suitable threshold value for each candidate region in the proposed level set method so that TP regions can be segmented and FP regions can be removed. Our proposed method detected MS regions at a sensitivity of 82.1% with 0.204 FPs per slice and similarity index of MS candidate regions was 0.717 on average. (author)

NeuroMEMS: Neural Probe Microtechnologies

Directory of Open Access Journals (Sweden)

Sam Musallam

2008-10-01

Full Text Available 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 ultralong 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.

Decreased neural precursor cell pool in NADPH oxidase 2-deficiency: From mouse brain to neural differentiation of patient derived iPSC

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Zeynab Nayernia

2017-10-01

Full Text Available There is emerging evidence for the involvement of reactive oxygen species (ROS in the regulation of stem cells and cellular differentiation. Absence of the ROS-generating NADPH oxidase NOX2 in chronic granulomatous disease (CGD patients, predominantly manifests as immune deficiency, but has also been associated with decreased cognition. Here, we investigate the role of NOX enzymes in neuronal homeostasis in adult mouse brain and in neural cells derived from human induced pluripotent stem cells (iPSC. High levels of NOX2 were found in mouse adult neurogenic regions. In NOX2-deficient mice, neurogenic regions showed diminished redox modifications, as well as decrease in neuroprecursor numbers and in expression of genes involved in neural differentiation including NES, BDNF and OTX2. iPSC from healthy subjects and patients with CGD were used to study the role of NOX2 in human in vitro neuronal development. Expression of NOX2 was low in undifferentiated iPSC, upregulated upon neural induction, and disappeared during neuronal differentiation. In human neurospheres, NOX2 protein and ROS generation were polarized within the inner cell layer of rosette structures. NOX2 deficiency in CGD-iPSCs resulted in an abnormal neural induction in vitro, as revealed by a reduced expression of neuroprogenitor markers (NES, BDNF, OTX2, NRSF/REST, and a decreased generation of mature neurons. Vector-mediated NOX2 expression in NOX2-deficient iPSCs rescued neurogenesis. Taken together, our study provides novel evidence for a regulatory role of NOX2 during early stages of neurogenesis in mouse and human.

Rhesus monkey neural stem cell transplantation promotes neural regeneration in rats with hippocampal lesions

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Li-juan Ye

2016-01-01

Full Text Available Rhesus monkey neural stem cells are capable of differentiating into neurons and glial cells. Therefore, neural stem cell transplantation can be used to promote functional recovery of the nervous system. Rhesus monkey neural stem cells (1 × 105 cells/μL were injected into bilateral hippocampi of rats with hippocampal lesions. Confocal laser scanning microscopy demonstrated that green fluorescent protein-labeled transplanted cells survived and grew well. Transplanted cells were detected at the lesion site, but also in the nerve fiber-rich region of the cerebral cortex and corpus callosum. Some transplanted cells differentiated into neurons and glial cells clustering along the ventricular wall, and integrated into the recipient brain. Behavioral tests revealed that spatial learning and memory ability improved, indicating that rhesus monkey neural stem cells noticeably improve spatial learning and memory abilities in rats with hippocampal lesions.

Neural reactivation links unconscious thought to decision-making performance.

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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.

A regional neural network model for predicting mean daily river water temperature

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Wagner, Tyler; DeWeber, Jefferson Tyrell

2014-01-01

Water temperature is a fundamental property of river habitat and often a key aspect of river resource management, but measurements to characterize thermal regimes are not available for most streams and rivers. As such, we developed an artificial neural network (ANN) ensemble model to predict mean daily water temperature in 197,402 individual stream reaches during the warm season (May–October) throughout the native range of brook trout Salvelinus fontinalis in the eastern U.S. We compared four models with different groups of predictors to determine how well water temperature could be predicted by climatic, landform, and land cover attributes, and used the median prediction from an ensemble of 100 ANNs as our final prediction for each model. The final model included air temperature, landform attributes and forested land cover and predicted mean daily water temperatures with moderate accuracy as determined by root mean squared error (RMSE) at 886 training sites with data from 1980 to 2009 (RMSE = 1.91 °C). Based on validation at 96 sites (RMSE = 1.82) and separately for data from 2010 (RMSE = 1.93), a year with relatively warmer conditions, the model was able to generalize to new stream reaches and years. The most important predictors were mean daily air temperature, prior 7 day mean air temperature, and network catchment area according to sensitivity analyses. Forest land cover at both riparian and catchment extents had relatively weak but clear negative effects. Predicted daily water temperature averaged for the month of July matched expected spatial trends with cooler temperatures in headwaters and at higher elevations and latitudes. Our ANN ensemble is unique in predicting daily temperatures throughout a large region, while other regional efforts have predicted at relatively coarse time steps. The model may prove a useful tool for predicting water temperatures in sampled and unsampled rivers under current conditions and future projections of climate

Research progress on neural mechanisms of primary insomnia by MRI

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Man WANG

2018-04-01

Full Text Available In recent years, more and more researches focused on the neural mechanism of primary insomnia (PI, especially with the development and application of MRI, and researches of brain structure and function related with primary insomnia were more and more in-depth. According to the hyperarousal hypothesis, there are abnormal structure, function and metabolism under certain brain regions of the cortex and subcortex of primary insomnia patients, including amygdala, hippocampus, cingulate gyrus, insular lobe, frontal lobe and parietal lobe. This paper reviewed the research progress of neural mechanisms of primary insomnia by using MRI. DOI: 10.3969/j.issn.1672-6731.2018.03.003

The Functional Role of Neural Oscillations in Non-Verbal Emotional Communication.

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Symons, Ashley E; El-Deredy, Wael; Schwartze, Michael; Kotz, Sonja A

2016-01-01

Effective interpersonal communication depends on the ability to perceive and interpret nonverbal emotional expressions from multiple sensory modalities. Current theoretical models propose that visual and auditory emotion perception involves a network of brain regions including the primary sensory cortices, the superior temporal sulcus (STS), and orbitofrontal cortex (OFC). However, relatively little is known about how the dynamic interplay between these regions gives rise to the perception of emotions. In recent years, there has been increasing recognition of the importance of neural oscillations in mediating neural communication within and between functional neural networks. Here we review studies investigating changes in oscillatory activity during the perception of visual, auditory, and audiovisual emotional expressions, and aim to characterize the functional role of neural oscillations in nonverbal emotion perception. Findings from the reviewed literature suggest that theta band oscillations most consistently differentiate between emotional and neutral expressions. While early theta synchronization appears to reflect the initial encoding of emotionally salient sensory information, later fronto-central theta synchronization may reflect the further integration of sensory information with internal representations. Additionally, gamma synchronization reflects facilitated sensory binding of emotional expressions within regions such as the OFC, STS, and, potentially, the amygdala. However, the evidence is more ambiguous when it comes to the role of oscillations within the alpha and beta frequencies, which vary as a function of modality (or modalities), presence or absence of predictive information, and attentional or task demands. Thus, the synchronization of neural oscillations within specific frequency bands mediates the rapid detection, integration, and evaluation of emotional expressions. Moreover, the functional coupling of oscillatory activity across multiples

Neural Markers in Pediatric Bipolar Disorder and Familial Risk for Bipolar Disorder.

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Wiggins, Jillian Lee; Brotman, Melissa A; Adleman, Nancy E; Kim, Pilyoung; Wambach, Caroline G; Reynolds, Richard C; Chen, Gang; Towbin, Kenneth; Pine, Daniel S; Leibenluft, Ellen

2017-01-01

Bipolar disorder (BD) is highly heritable. Neuroimaging studies comparing unaffected youth at high familial risk for BD (i.e., those with a first-degree relative with the disorder; termed "high-risk" [HR]) to "low-risk" (LR) youth (i.e., those without a first-degree relative with BD) and to patients with BD may help identify potential brain-based markers associated with risk (i.e., regions where HR+BD≠LR), resilience (HR≠BD+LR), or illness (BD≠HR+LR). During functional magnetic resonance imaging (fMRI), 99 youths (i.e., adolescents and young adults) aged 9.8 to 24.8 years (36 BD, 22 HR, 41 LR) performed a task probing face emotion labeling, previously shown to be impaired behaviorally in youth with BD and HR youth. We found three patterns of results. Candidate risk endophenotypes (i.e., where BD and HR shared deficits) included dysfunction in higher-order face processing regions (e.g., middle temporal gyrus, dorsolateral prefrontal cortex). Candidate resilience markers and disorder sequelae (where HR and BD, respectively, show unique alterations relative to the other two groups) included different patterns of neural responses across other regions mediating face processing (e.g., fusiform), executive function (e.g., inferior frontal gyrus), and social cognition (e.g., default network, superior temporal sulcus, temporo-parietal junction). If replicated in longitudinal studies and with additional populations, neural patterns suggesting risk endophenotypes could be used to identify individuals at risk for BD who may benefit from prevention measures. Moreover, information about risk and resilience markers could be used to develop novel treatments that recruit neural markers of resilience and attenuate neural patterns associated with risk. Clinical trial registration information-Studies of Brain Function and Course of Illness in Pediatric Bipolar Disorder and Child and Adolescent Bipolar Disorder Brain Imaging and Treatment Study; http://clinicaltrials.gov/; NCT

Birth prevalence of neural tube defects and orofacial clefts in India: a systematic review and meta-analysis.

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Komal Preet Allagh

Full Text Available In the last two decades, India has witnessed a substantial decrease in infant mortality attributed to infectious disease and malnutrition. However, the mortality attributed to birth defects remains constant. Studies on the prevalence of birth defects such as neural tube defects and orofacial clefts in India have reported inconsistent results. Therefore, we conducted a systematic review of observational studies to document the birth prevalence of neural tube defects and orofacial clefts.A comprehensive literature search for observational studies was conducted in MEDLINE and EMBASE databases using key MeSH terms (neural tube defects OR cleft lip OR cleft palate AND Prevalence AND India. Two reviewers independently reviewed the retrieved studies, and studies satisfying the eligibility were included. The quality of included studies was assessed using selected criteria from STROBE statement.The overall pooled birth prevalence (random effect of neural tube defects in India is 4.5 per 1000 total births (95% CI 4.2 to 4.9. The overall pooled birth prevalence (random effect of orofacial clefts is 1.3 per 1000 total births (95% CI 1.1 to 1.5. Subgroup analyses were performed by region, time period, consanguinity, and gender of newborn.The overall prevalence of neural tube defects from India is high compared to other regions of the world, while that of orofacial clefts is similar to other countries. The majority of studies included in the review were hospital based. The quality of these studies ranged from low to moderate. Further well-designed, high quality community-based observational studies are needed to accurately estimate the burden of neural tube defects and orofacial clefts in India.

Resting-state hemodynamics are spatiotemporally coupled to synchronized and symmetric neural activity in excitatory neurons

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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 (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 correlates of socioeconomic status in the developing human brain.

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Noble, Kimberly G; Houston, Suzanne M; Kan, Eric; Sowell, Elizabeth R

2012-07-01

Socioeconomic disparities in childhood are associated with remarkable differences in cognitive and socio-emotional development during a time when dramatic changes are occurring in the brain. Yet, the neurobiological pathways through which socioeconomic status (SES) shapes development remain poorly understood. Behavioral evidence suggests that language, memory, social-emotional processing, and cognitive control exhibit relatively large differences across SES. Here we investigated whether volumetric differences could be observed across SES in several neural regions that support these skills. In a sample of 60 socioeconomically diverse children, highly significant SES differences in regional brain volume were observed in the hippocampus and the amygdala. In addition, SES × age interactions were observed in the left superior temporal gyrus and left inferior frontal gyrus, suggesting increasing SES differences with age in these regions. These results were not explained by differences in gender, race or IQ. Likely mechanisms include differences in the home linguistic environment and exposure to stress, which may serve as targets for intervention at a time of high neural plasticity. © 2012 Blackwell Publishing Ltd.

Non-Linear State Estimation Using Pre-Trained Neural Networks

DEFF Research Database (Denmark)

Bayramoglu, Enis; Andersen, Nils Axel; Ravn, Ole

2010-01-01

effecting the transformation. This function is approximated by a neural network using offline training. The training is based on monte carlo sampling. A way to obtain parametric distributions of flexible shape to be used easily with these networks is also presented. The method can also be used to improve...... other parametric methods around regions with strong non-linearities by including them inside the network....

The Neural Circuits that Generate Tics in Gilles de la Tourette Syndrome

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Wang, Zhishun; Maia, Tiago V.; Marsh, Rachel; Colibazzi, Tiziano; Gerber, Andrew; Peterson, Bradley S.

2014-01-01

Objective To study neural activity and connectivity within cortico-striato-thalamo-cortical circuits and to reveal circuit-based neural mechanisms that govern tic generation in Tourette syndrome. Method We acquired fMRI data from 13 participants with Tourette syndrome and 21 controls during spontaneous or simulated tics. We used independent component analysis with hierarchical partner matching to isolate neural activity within functionally distinct regions of cortico-striato-thalamo-cortical circuits. We used Granger causality to investigate causal interactions among these regions. Results We found that the Tourette group exhibited stronger neural activity and interregional causality than controls throughout all portions of the motor pathway including sensorimotor cortex, putamen, pallidum, and substania nigra. Activity in these areas correlated positively with the severity of tic symptoms. Activity within the Tourette group was stronger during spontaneous tics than during voluntary tics in somatosensory and posterior parietal cortices, putamen, and amygdala/hippocampus complex, suggesting that activity in these regions may represent features of the premonitory urges that generate spontaneous tic behaviors. In contrast, activity was weaker in the Tourette group than in controls within portions of cortico-striato-thalamo-cortical circuits that exert top-down control over motor pathways (caudate and anterior cingulate cortex), and progressively less activity in these regions accompanied more severe tic symptoms, suggesting that faulty activity in these circuits may fail to control tic behaviors or the premonitory urges that generate them. Conclusions Our findings taken together suggest that tics are caused by the combined effects of excessive activity in motor pathways and reduced activation in control portions of cortico-striato-thalamo-cortical circuits. PMID:21955933

A two-layer recurrent neural network for nonsmooth convex optimization problems.

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Qin, Sitian; Xue, Xiaoping

2015-06-01

In this paper, a two-layer recurrent neural network is proposed to solve the nonsmooth convex optimization problem subject to convex inequality and linear equality constraints. Compared with existing neural network models, the proposed neural network has a low model complexity and avoids penalty parameters. It is proved that from any initial point, the state of the proposed neural network reaches the equality feasible region in finite time and stays there thereafter. Moreover, the state is unique if the initial point lies in the equality feasible region. The equilibrium point set of the proposed neural network is proved to be equivalent to the Karush-Kuhn-Tucker optimality set of the original optimization problem. It is further proved that the equilibrium point of the proposed neural network is stable in the sense of Lyapunov. Moreover, from any initial point, the state is proved to be convergent to an equilibrium point of the proposed neural network. Finally, as applications, the proposed neural network is used to solve nonlinear convex programming with linear constraints and L1 -norm minimization problems.

Forecasting solar proton event with artificial neural network

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Gong, J.; Wang, J.; Xue, B.; Liu, S.; Zou, Z.

Solar proton event (SPE), relatively rare but popular in solar maximum, can bring hazard situation to spacecraft. As a special event, SPE always accompanies flare, which is also called proton flare. To produce such an eruptive event, large amount energy must be accumulated within the active region. So we can investigate the character of the active region and its evolving trend, together with other such as cm radio emission and soft X-ray background to evaluate the potential of SEP in chosen area. In order to summarize the omen of SPEs in the active regions behind the observed parameters, we employed AI technology. Full connecting neural network was chosen to fulfil this job. After constructing the network, we train it with 13 parameters that was able to exhibit the character of active regions and their evolution trend. More than 80 sets of event parameter were defined to teach the neural network to identify whether an active region was potential of SPE. Then we test this model with a data base consisting SPE and non-SPE cases that was not used to train the neural network. The result showed that 75% of the choice by the model was right.

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.

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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.

Cannabis abstinence during treatment and one-year follow-up: relationship to neural activity in men.

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Kober, Hedy; DeVito, Elise E; DeLeone, Cameron M; Carroll, Kathleen M; Potenza, Marc N

2014-09-01

Cannabis is among the most frequently abused substances in the United States. Cognitive control is a contributory factor in the maintenance of substance-use disorders and may relate to treatment response. Therefore, we assessed whether cognitive-control-related neural activity before treatment differs between treatment-seeking cannabis-dependent and healthy individuals and relates to cannabis-abstinence measures during treatment and 1-year follow-up. Cannabis-dependent males (N=20) completed a functional magnetic resonance imaging (fMRI) cognitive-control (Stroop) task before a 12-week randomized controlled trial of cognitive-behavioral therapy and/or contingency management. A healthy-comparison group (N=20) also completed the fMRI task. Cannabis use was assessed by urine toxicology and self-report during treatment, and by self-report across a 1-year follow-up period (N=18). The cannabis-dependent group displayed diminished Stroop-related neural activity relative to the healthy-comparison group in multiple regions, including those strongly implicated in cognitive-control and addiction-related processes (eg, dorsolateral prefrontal cortex and ventral striatum). The groups did not differ significantly in response times (cannabis-dependent, N=12; healthy-comparison, N=14). Within the cannabis-dependent group, greater Stroop-related activity in regions including the dorsal anterior cingulate cortex was associated with less cannabis use during treatment. Greater activity in regions including the ventral striatum was associated with less cannabis use during 1-year posttreatment follow-up. These data suggest that lower cognitive-control-related neural activity in classic 'control' regions (eg, dorsolateral prefrontal cortex and dorsal anterior cingulate) and classic 'salience/reward/learning' regions (eg, ventral striatum) differentiates cannabis-dependent individuals from healthy individuals and relates to less abstinence within-treatment and during long-term follow

Apoptosis in neural crest cells by functional loss of APC tumor suppressor gene

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Hasegawa, Sumitaka; Sato, Tomoyuki; Akazawa, Hiroshi; Okada, Hitoshi; Maeno, Akiteru; Ito, Masaki; Sugitani, Yoshinobu; Shibata, Hiroyuki; Miyazaki, Jun-ichi; Katsuki, Motoya; Yamauchi, Yasutaka; Yamamura, Ken-ichi; Katamine, Shigeru; Noda, Tetsuo

2002-01-01

Apc is a gene associated with familial adenomatous polyposis coli (FAP) and its inactivation is a critical step in colorectal tumor formation. The protein product, adenomatous polyposis coli (APC), acts to down-regulate intracellular levels of β-catenin, a key signal transducer in the Wnt signaling. Conditional targeting of Apc in the neural crest of mice caused massive apoptosis of cephalic and cardiac neural crest cells at about 11.5 days post coitum, resulting in craniofacial and cardiac anomalies at birth. Notably, the apoptotic cells localized in the regions where β-catenin had accumulated. In contrast to its role in colorectal epithelial cells, inactivation of APC leads to dysregulation of β-catenin/Wnt signaling with resultant apoptosis in certain tissues including neural crest cells. PMID:11756652

Tracking performance and global stability guaranteed neural control of uncertain hypersonic flight vehicle

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Tao Teng

2016-02-01

Full Text Available In this article, a global adaptive neural dynamic surface control with predefined tracking performance is developed for a class of hypersonic flight vehicles, whose accurate dynamics is hard to obtain. The control scheme developed in this paper overcomes the limitations of neural approximation region by employing a switching mechanism which incorporates an additional robust controller outside the neural approximation region to pull the transient state variables back when they overstep the neural approximation region, such that globally uniformly ultimately bounded stability can be guaranteed. Especially, the developed global adaptive neural control also improves the tracking performance by introducing an error transformation mechanism, such that both transient and steady-state performance can be shaped according to the predefined bounds. Simulation studies on the hypersonic flight vehicle validate that the designed controller has good velocity modulation and velocity stability performance.

The Neural Border: Induction, Specification and Maturation of the territory that generates Neural Crest cells.

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Pla, Patrick; Monsoro-Burq, Anne H

2018-05-28

The neural crest is induced at the edge between the neural plate and the nonneural ectoderm, in an area called the neural (plate) border, during gastrulation and neurulation. In recent years, many studies have explored how this domain is patterned, and how the neural crest is induced within this territory, that also participates to the prospective dorsal neural tube, the dorsalmost nonneural ectoderm, as well as placode derivatives in the anterior area. This review highlights the tissue interactions, the cell-cell signaling and the molecular mechanisms involved in this dynamic spatiotemporal patterning, resulting in the induction of the premigratory neural crest. Collectively, these studies allow building a complex neural border and early neural crest gene regulatory network, mostly composed by transcriptional regulations but also, more recently, including novel signaling interactions. Copyright © 2018. Published by Elsevier Inc.

Risk factors of neural tube defects: A reality of Batna region in Algeria

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Romyla Bourouba

2018-07-01

Full Text Available Background: Neural tube defects (NTDs are severe birth defects, with genetic and/or environmental risk factors. Aim: The objective of this study was to analyze data on NTDs cases at the Batna Maternity Hospital and to investigate some environmental and two genetic risk factors suspected in the etiology of NTDs. Subjects and methods: This study was conducted on 82 healthy participants and 48 mothers with an NTD child. Peripheral blood samples were collected, in EDTA tubes and frozen at −20 °C until DNA extraction by conventional method. Genetic analysis of methylene tetrahydrofolate reductase C677T polymorphism was determined by real time PCR, while cystathionine-beta-synthase 844 insertion was investigated by traditional PCR. Chi-square analyses were used to evaluate differences in the distribution of data. The odds-ratio was also calculated. A P-value less than 0.05 were significant. Results: The incidence of NTD in Batna region was 1.58 per 1000 births. The rate of NTD was significantly higher in females than males, highest affected NTD newborn’s was observed in mothers aged between 25 and 29 years and the consanguinity among all NTD cases was 30%. Data showed no significant association of NTDs with personal education, obesity, diabetes, but regarding folic acid consumption, about 86% of NTD’s mothers in our region didn’t take pre-conceptional supplementation with this vitamin .Genetic factors results didn't show a significant association of NTDs with specific mutations of the variant C677T MTHFR, and no gene-gene interaction of CBS insertion and C677T polymorphism was found, despite a significant difference in heterozygote frequency of CBS 844ins68 genotype between NTD’s mothers and controls, OR: 2.85(1.18–6.88. Conclusion: NTD represents a real public health problem in Batna, Algeria. Various genetic and/or nutritional factors are implicated, although the mechanism is not clear. We suggest that further research should continue

Distinct Neural Substrates for Maintaining Locations and Spatial Relations in Working Memory

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Kara J Blacker

2016-11-01

Full Text Available Previous work has demonstrated a distinction between maintenance of two types of spatial information in working memory (WM: spatial locations and spatial relations. While a body of work has investigated the neural mechanisms of sensory-based information like spatial locations, little is known about how spatial relations are maintained in WM. In two experiments, we used fMRI to investigate the involvement of early visual cortex in the maintenance of spatial relations in WM. In both experiments, we found less quadrant-specific BOLD activity in visual cortex when a single spatial relation, compared to a single spatial location, was held in WM. Also across both experiments, we found a consistent set of brain regions that were differentially activated during maintenance of locations versus relations. Maintaining a location, compared to a relation, was associated with greater activity in typical spatial WM regions like posterior parietal cortex and prefrontal regions. Whereas maintaining a relation, compared to a location, was associated with greater activity in the parahippocampal gyrus and precuneus/retrosplenial cortex. Further, in Experiment 2 we manipulated WM load and included trials where participants had to maintain three spatial locations or relations. Under this high load condition, the regions sensitive to locations versus relations were somewhat different than under low load. We also identified regions that were sensitive to load specifically for location or relation maintenance, as well as overlapping regions sensitive to load more generally. These results suggest that the neural substrates underlying WM maintenance of spatial locations and relations are distinct from one another and that the neural representations of these distinct types of spatial information change with load.

Emission-line diagnostics of nearby H II regions including interacting binary populations

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Xiao, Lin; Stanway, Elizabeth R.; Eldridge, J. J.

2018-06-01

We present numerical models of the nebular emission from H II regions around young stellar populations over a range of compositions and ages. The synthetic stellar populations include both single stars and interacting binary stars. We compare these models to the observed emission lines of 254 H II regions of 13 nearby spiral galaxies and 21 dwarf galaxies drawn from archival data. The models are created using the combination of the BPASS (Binary Population and Spectral Synthesis) code with the photoionization code CLOUDY to study the differences caused by the inclusion of interacting binary stars in the stellar population. We obtain agreement with the observed emission line ratios from the nearby star-forming regions and discuss the effect of binary-star evolution pathways on the nebular ionization of H II regions. We find that at population ages above 10 Myr, single-star models rapidly decrease in flux and ionization strength, while binary-star models still produce strong flux and high [O III]/H β ratios. Our models can reproduce the metallicity of H II regions from spiral galaxies, but we find higher metallicities than previously estimated for the H II regions from dwarf galaxies. Comparing the equivalent width of H β emission between models and observations, we find that accounting for ionizing photon leakage can affect age estimates for H II regions. When it is included, the typical age derived for H II regions is 5 Myr from single-star models, and up to 10 Myr with binary-star models. This is due to the existence of binary-star evolution pathways, which produce more hot Wolf-Rayet and helium stars at older ages. For future reference, we calculate new BPASS binary maximal starburst lines as a function of metallicity, and for the total model population, and present these in Appendix A.

A direct comparison of appetitive and aversive anticipation: Overlapping and distinct neural activation.

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Sege, Christopher T; Bradley, Margaret M; Weymar, Mathias; Lang, Peter J

2017-05-30

fMRI studies of reward find increased neural activity in ventral striatum and medial prefrontal cortex (mPFC), whereas other regions, including the dorsolateral prefrontal cortex (dlPFC), anterior cingulate cortex (ACC), and anterior insula, are activated when anticipating aversive exposure. Although these data suggest differential activation during anticipation of pleasant or of unpleasant exposure, they also arise in the context of different paradigms (e.g., preparation for reward vs. threat of shock) and participants. To determine overlapping and unique regions active during emotional anticipation, we compared neural activity during anticipation of pleasant or unpleasant exposure in the same participants. Cues signalled the upcoming presentation of erotic/romantic, violent, or everyday pictures while BOLD activity during the 9-s anticipatory period was measured using fMRI. Ventral striatum and a ventral mPFC subregion were activated when anticipating pleasant, but not unpleasant or neutral, pictures, whereas activation in other regions was enhanced when anticipating appetitive or aversive scenes. Copyright © 2017 Elsevier B.V. All rights reserved.

Neural mechanisms of interference control in working memory capacity.

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Bomyea, Jessica; Taylor, Charles T; Spadoni, Andrea D; Simmons, Alan N

2018-02-01

The extent to which one can use cognitive resources to keep information in working memory is known to rely on (1) active maintenance of target representations and (2) downregulation of interference from irrelevant representations. Neurobiologically, the global capacity of working memory is thought to depend on the prefrontal and parietal cortices; however, the neural mechanisms involved in controlling interference specifically in working memory capacity tasks remain understudied. In this study, 22 healthy participants completed a modified complex working memory capacity task (Reading Span) with trials of varying levels of interference control demands while undergoing functional MRI. Neural activity associated with interference control demands was examined separately during encoding and recall phases of the task. Results suggested a widespread network of regions in the prefrontal, parietal, and occipital cortices, and the cingulate and cerebellum associated with encoding, and parietal and occipital regions associated with recall. Results align with prior findings emphasizing the importance of frontoparietal circuits for working memory performance, including the role of the inferior frontal gyrus, cingulate, occipital cortex, and cerebellum in regulation of interference demands. © 2017 Wiley Periodicals, Inc.

A Recurrent Neural Network for Nonlinear Fractional Programming

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Quan-Ju Zhang

2012-01-01

Full Text Available This paper presents a novel recurrent time continuous neural network model which performs nonlinear fractional optimization subject to interval constraints on each of the optimization variables. The network is proved to be complete in the sense that the set of optima of the objective function to be minimized with interval constraints coincides with the set of equilibria of the neural network. It is also shown that the network is primal and globally convergent in the sense that its trajectory cannot escape from the feasible region and will converge to an exact optimal solution for any initial point being chosen in the feasible interval region. Simulation results are given to demonstrate further the global convergence and good performance of the proposing neural network for nonlinear fractional programming problems with interval constraints.

Getting the word out: neural correlates of enthusiastic message propagation.

Science.gov (United States)

Falk, Emily B; O'Donnell, Matthew Brook; Lieberman, Matthew D

2012-01-01

What happens in the mind of a person who first hears a potentially exciting idea?We examined the neural precursors of spreading ideas with enthusiasm, and dissected enthusiasm into component processes that can be identified through automated linguistic analysis, gestalt human ratings of combined linguistic and non-verbal cues, and points of convergence/divergence between the two. We combined tools from natural language processing (NLP) with data gathered using fMRI to link the neurocognitive mechanisms that are set in motion during initial exposure to ideas and subsequent behaviors of these message communicators outside of the scanner. Participants' neural activity was recorded as they reviewed ideas for potential television show pilots. Participants' language from video-taped interviews collected post-scan was transcribed and given to an automated linguistic sentiment analysis (SA) classifier, which returned ratings for evaluative language (evaluative vs. descriptive) and valence (positive vs. negative). Separately, human coders rated the enthusiasm with which participants transmitted each idea. More positive sentiment ratings by the automated classifier were associated with activation in neural regions including medial prefrontal cortex; MPFC, precuneus/posterior cingulate cortex; PC/PCC, and medial temporal lobe; MTL. More evaluative, positive, descriptions were associated exclusively with neural activity in temporal-parietal junction (TPJ). Finally, human ratings indicative of more enthusiastic sentiment were associated with activation across these regions (MPFC, PC/PCC, DMPFC, TPJ, and MTL) as well as in ventral striatum (VS), inferior parietal lobule and premotor cortex. Taken together, these data demonstrate novel links between neural activity during initial idea encoding and the enthusiasm with which the ideas are subsequently delivered. This research lays the groundwork to use machine learning and neuroimaging data to study word of mouth communication and

Getting the word out: Neural correlates of enthusiastic message propagation

Directory of Open Access Journals (Sweden)

Emily eFalk

2012-11-01

Full Text Available What happens in the mind of a person who first hears a potentially exciting idea? We examined the neural precursors of spreading ideas with enthusiasm, and dissect enthusiasm into component processes that can be identified through automated linguistic analysis, gestalt human ratings of combined linguistic and non-verbal cues, and points of convergence/divergence between the two. We combined tools from natural language processing with data gathered using fMRI, to link the neurocognitive mechanisms that are set in motion during initial exposure to ideas and subsequent behaviors of these message communicators outside of the scanner. Participants’ neural activity was recorded as they reviewed ideas for potential television show pilots. Participants’ language from video-taped interviews collected post-scan was transcribed and given to an automated linguistic sentiment analysis classifier, which returned ratings for evaluative language (evaluative vs. descriptive and valence (positive vs. negative. Separately, human coders rated the enthusiasm with which participants transmitted each idea. More positive sentiment ratings by the automated classifier were associated with activation in neural regions including medial prefrontal cortex; MPFC, precuneus/posterior cingulate cortex; PC/PCC, and medial temporal lobe; MTL. More evaluative, positive, descriptions were associated exclusively with neural activity in temporal parietal junction (TPJ. Finally, human ratings indicative of more enthusiastic sentiment were associated with activation across these regions (MPFC, PC/PCC, DMPFC, TPJ, MTL as well as in ventral striatum, inferior parietal lobule and premotor cortex. Taken together, these data demonstrate novel links between neural activity during initial idea encoding and the enthusiasm with which the ideas are subsequently delivered. These data also demonstrate the novel use of machine learning tools to link natural language data to neuroimaging data.

Getting the word out: neural correlates of enthusiastic message propagation

Science.gov (United States)

Falk, Emily B.; O'Donnell, Matthew Brook; Lieberman, Matthew D.

2012-01-01

What happens in the mind of a person who first hears a potentially exciting idea?We examined the neural precursors of spreading ideas with enthusiasm, and dissected enthusiasm into component processes that can be identified through automated linguistic analysis, gestalt human ratings of combined linguistic and non-verbal cues, and points of convergence/divergence between the two. We combined tools from natural language processing (NLP) with data gathered using fMRI to link the neurocognitive mechanisms that are set in motion during initial exposure to ideas and subsequent behaviors of these message communicators outside of the scanner. Participants' neural activity was recorded as they reviewed ideas for potential television show pilots. Participants' language from video-taped interviews collected post-scan was transcribed and given to an automated linguistic sentiment analysis (SA) classifier, which returned ratings for evaluative language (evaluative vs. descriptive) and valence (positive vs. negative). Separately, human coders rated the enthusiasm with which participants transmitted each idea. More positive sentiment ratings by the automated classifier were associated with activation in neural regions including medial prefrontal cortex; MPFC, precuneus/posterior cingulate cortex; PC/PCC, and medial temporal lobe; MTL. More evaluative, positive, descriptions were associated exclusively with neural activity in temporal-parietal junction (TPJ). Finally, human ratings indicative of more enthusiastic sentiment were associated with activation across these regions (MPFC, PC/PCC, DMPFC, TPJ, and MTL) as well as in ventral striatum (VS), inferior parietal lobule and premotor cortex. Taken together, these data demonstrate novel links between neural activity during initial idea encoding and the enthusiasm with which the ideas are subsequently delivered. This research lays the groundwork to use machine learning and neuroimaging data to study word of mouth communication and

A regional GNSS-VTEC model over Nigeria using neural networks: A novel approach

Directory of Open Access Journals (Sweden)

Daniel Okoh

2016-01-01

Full Text Available A neural network model of the Global Navigation Satellite System – vertical total electron content (GNSS-VTEC over Nigeria is developed. A new approach that has been utilized in this work is the consideration of the International Reference Ionosphere's (IRI's critical plasma frequency (foF2 parameter as an additional neuron for the network's input layer. The work also explores the effects of using various other input layer neurons like disturbance storm time (DST and sunspot number. All available GNSS data from the Nigerian Permanent GNSS Network (NIGNET were used, and these cover the period from 2011 to 2015, for 14 stations. Asides increasing the learning accuracy of the networks, the inclusion of the IRI's foF2 parameter as an input neuron is ideal for making the networks to learn long-term solar cycle variations. This is important especially for regions, like in this work, where the GNSS data is available for less than the period of a solar cycle. The neural network model developed in this work has been tested for time-varying and spatial performances. The latest 10% of the GNSS observations from each of the stations were used to test the forecasting ability of the networks, while data from 2 of the stations were entirely used for spatial performance testing. The results show that root-mean-squared-errors were generally less than 8.5 TEC units for all modes of testing performed using the optimal network. When compared to other models, the model developed in this work was observed to reduce the prediction errors to about half those of the NeQuick and the IRI model.

Optical neural network system for pose determination of spinning satellites

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Lee, Andrew; Casasent, David

1990-01-01

An optical neural network architecture and algorithm based on a Hopfield optimization network are presented for multitarget tracking. This tracker utilizes a neuron for every possible target track, and a quadratic energy function of neural activities which is minimized using gradient descent neural evolution. The neural net tracker is demonstrated as part of a system for determining position and orientation (pose) of spinning satellites with respect to a robotic spacecraft. The input to the system is time sequence video from a single camera. Novelty detection and filtering are utilized to locate and segment novel regions from the input images. The neural net multitarget tracker determines the correspondences (or tracks) of the novel regions as a function of time, and hence the paths of object (satellite) parts. The path traced out by a given part or region is approximately elliptical in image space, and the position, shape and orientation of the ellipse are functions of the satellite geometry and its pose. Having a geometric model of the satellite, and the elliptical path of a part in image space, the three-dimensional pose of the satellite is determined. Digital simulation results using this algorithm are presented for various satellite poses and lighting conditions.

Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks.

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Bandeira Diniz, João Otávio; Bandeira Diniz, Pedro Henrique; Azevedo Valente, Thales Levi; Corrêa Silva, Aristófanes; de Paiva, Anselmo Cardoso; Gattass, Marcelo

2018-03-01

The processing of medical image is an important tool to assist in minimizing the degree of uncertainty of the specialist, while providing specialists with an additional source of detect and diagnosis information. Breast cancer is the most common type of cancer that affects the female population around the world. It is also the most deadly type of cancer among women. It is the second most common type of cancer among all others. The most common examination to diagnose breast cancer early is mammography. In the last decades, computational techniques have been developed with the purpose of automatically detecting structures that maybe associated with tumors in mammography examination. This work presents a computational methodology to automatically detection of mass regions in mammography by using a convolutional neural network. The materials used in this work is the DDSM database. The method proposed consists of two phases: training phase and test phase. The training phase has 2 main steps: (1) create a model to classify breast tissue into dense and non-dense (2) create a model to classify regions of breast into mass and non-mass. The test phase has 7 step: (1) preprocessing; (2) registration; (3) segmentation; (4) first reduction of false positives; (5) preprocessing of regions segmented; (6) density tissue classification (7) second reduction of false positives where regions will be classified into mass and non-mass. The proposed method achieved 95.6% of accuracy in classify non-dense breasts tissue and 97,72% accuracy in classify dense breasts. To detect regions of mass in non-dense breast, the method achieved a sensitivity value of 91.5%, and specificity value of 90.7%, with 91% accuracy. To detect regions in dense breasts, our method achieved 90.4% of sensitivity and 96.4% of specificity, with accuracy of 94.8%. According to the results achieved by CNN, we demonstrate the feasibility of using convolutional neural networks on medical image processing techniques for

Noun and verb processing in aphasia: Behavioural profiles and neural correlates

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Reem S.W. Alyahya

Full Text Available The behavioural and neural processes underpinning different word classes, particularly nouns and verbs, have been a long-standing area of interest in psycholinguistic, neuropsychology and aphasiology research. This topic has theoretical implications concerning the organisation of the language system, as well as clinical consequences related to the management of patients with language deficits. Research findings, however, have diverged widely, which might, in part, reflect methodological differences, particularly related to controlling the psycholinguistic variations between nouns and verbs. The first aim of this study, therefore, was to develop a set of neuropsychological tests that assessed single-word production and comprehension with a matched set of nouns and verbs. Secondly, the behavioural profiles and neural correlates of noun and verb processing were explored, based on these novel tests, in a relatively large cohort of 48 patients with chronic post-stroke aphasia. A data-driven approach, principal component analysis (PCA, was also used to determine how noun and verb production and comprehension were related to the patients' underlying fundamental language domains. The results revealed no performance differences between noun and verb production and comprehension once matched on multiple psycholinguistic features including, most critically, imageability. Interestingly, the noun-verb differences found in previous studies were replicated in this study once un-matched materials were used. Lesion-symptom mapping revealed overlapping neural correlates of noun and verb processing along left temporal and parietal regions. These findings support the view that the neural representation of noun and verb processing at single-word level are jointly-supported by distributed cortical regions. The PCA generated five fundamental language and cognitive components of aphasia: phonological production, phonological recognition, semantics, fluency, and

Real Time Eye Detector with Cascaded Convolutional Neural Networks

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Bin Li

2018-01-01

Full Text Available An accurate and efficient eye detector is essential for many computer vision applications. In this paper, we present an efficient method to evaluate the eye location from facial images. First, a group of candidate regions with regional extreme points is quickly proposed; then, a set of convolution neural networks (CNNs is adopted to determine the most likely eye region and classify the region as left or right eye; finally, the center of the eye is located with other CNNs. In the experiments using GI4E, BioID, and our datasets, our method attained a detection accuracy which is comparable to existing state-of-the-art methods; meanwhile, our method was faster and adaptable to variations of the images, including external light changes, facial occlusion, and changes in image modality.

The neural correlates of gist-based true and false recognition

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Gutchess, Angela H.; Schacter, Daniel L.

2012-01-01

When information is thematically related to previously studied information, gist-based processes contribute to false recognition. Using functional MRI, we examined the neural correlates of gist-based recognition as a function of increasing numbers of studied exemplars. Sixteen participants incidentally encoded small, medium, and large sets of pictures, and we compared the neural response at recognition using parametric modulation analyses. For hits, regions in middle occipital, middle temporal, and posterior parietal cortex linearly modulated their activity according to the number of related encoded items. For false alarms, visual, parietal, and hippocampal regions were modulated as a function of the encoded set size. The present results are consistent with prior work in that the neural regions supporting veridical memory also contribute to false memory for related information. The results also reveal that these regions respond to the degree of relatedness among similar items, and implicate perceptual and constructive processes in gist-based false memory. PMID:22155331

Neural Parallel Engine: A toolbox for massively parallel neural signal processing.

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Tam, Wing-Kin; Yang, Zhi

2018-05-01

Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. Copyright © 2018 Elsevier B.V. All rights reserved.

Adolescents' emotional competence is associated with parents' neural sensitivity to emotions.

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Telzer, Eva H; Qu, Yang; Goldenberg, Diane; Fuligni, Andrew J; Galván, Adriana; Lieberman, Matthew D

2014-01-01

An essential component of youths' successful development is learning to appropriately respond to emotions, including the ability to recognize, identify, and describe one's feelings. Such emotional competence is thought to arise through the parent-child relationship. Yet, the mechanisms by which parents transmit emotional competence to their children are difficult to measure because they are often implicit, idiosyncratic, and not easily articulated by parents or children. In the current study, we used a multifaceted approach that went beyond self-report measures and examined whether parental neural sensitivity to emotions predicted their child's emotional competence. Twenty-two adolescent-parent dyads completed an fMRI scan during which they labeled the emotional expressions of negatively valenced faces. Results indicate that parents who recruited the amygdala, VLPFC, and brain regions involved in mentalizing (i.e., inferring others' emotional states) had adolescent children with greater emotional competence. These results held after controlling for parents' self-reports of emotional expressivity and adolescents' self-reports of the warmth and support of their parent relationships. In addition, adolescents recruited neural regions involved in mentalizing during affect labeling, which significantly mediated the associated between parental neural sensitivity and adolescents' emotional competence, suggesting that youth are modeling or referencing their parents' emotional profiles, thereby contributing to better emotional competence.

Adolescents’ emotional competence is associated with parents’ neural sensitivity to emotions

Directory of Open Access Journals (Sweden)

Eva H Telzer

2014-07-01

Full Text Available An essential component of youths’ successful development is learning to appropriately respond to emotions, including the ability to recognize, identify, and describe one’s feelings. Such emotional competence is thought to arise through the parent-child relationship. Yet, the mechanisms by which parents transmit emotional competence to their children are difficult to measure because they are often implicit, idiosyncratic, and not easily articulated by parents or children. In the current study, we used a multifaceted approach that went beyond self-report measures and examined whether parental neural sensitivity to emotions predicted their child’s emotional competence. Twenty-two adolescent-parent dyads completed an fMRI scan during which they labeled the emotional expressions of negatively valenced faces. Results indicate that parents who recruited the amygdala, VLPFC, and brain regions involved in mentalizing (i.e., inferring others’ emotional states had adolescent children with greater emotional competence. These results held after controlling for parents’ self-reports of emotional expressivity and adolescents’ self-reports of the warmth and support of their parent relationships. In addition, adolescents recruited neural regions involved in mentalizing during affect labeling, which significantly mediated the associated between parental neural sensitivity and adolescents’ emotional competence, suggesting that youth are modeling or referencing their parents’ emotional profiles, thereby contributing to better emotional competence.

Neural codes of seeing architectural styles

OpenAIRE

Choo, Heeyoung; Nasar, Jack L.; Nikrahei, Bardia; Walther, Dirk B.

2017-01-01

Images of iconic buildings, such as the CN Tower, instantly transport us to specific places, such as Toronto. Despite the substantial impact of architectural design on people′s visual experience of built environments, we know little about its neural representation in the human brain. In the present study, we have found patterns of neural activity associated with specific architectural styles in several high-level visual brain regions, but not in primary visual cortex (V1). This finding sugges...

The neural sociometer: brain mechanisms underlying state self-esteem.

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Eisenberger, Naomi I; Inagaki, Tristen K; Muscatell, Keely A; Byrne Haltom, Kate E; Leary, Mark R

2011-11-01

On the basis of the importance of social connection for survival, humans may have evolved a "sociometer"-a mechanism that translates perceptions of rejection or acceptance into state self-esteem. Here, we explored the neural underpinnings of the sociometer by examining whether neural regions responsive to rejection or acceptance were associated with state self-esteem. Participants underwent fMRI while viewing feedback words ("interesting," "boring") ostensibly chosen by another individual (confederate) to describe the participant's previously recorded interview. Participants rated their state self-esteem in response to each feedback word. Results demonstrated that greater activity in rejection-related neural regions (dorsal ACC, anterior insula) and mentalizing regions was associated with lower-state self-esteem. Additionally, participants whose self-esteem decreased from prescan to postscan versus those whose self-esteem did not showed greater medial prefrontal cortical activity, previously associated with self-referential processing, in response to negative feedback. Together, the results inform our understanding of the origin and nature of our feelings about ourselves.

Body mass is positively associated with neural response to sweet taste, but not alcohol, among drinkers.

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Gardiner, Casey K; YorkWilliams, Sophie L; Bryan, Angela D; Hutchison, Kent E

2017-07-28

Obesity is a large and growing public health concern, presenting enormous economic and health costs to individuals and society. A burgeoning literature demonstrates that overweight and obese individuals display different neural processing of rewarding stimuli, including caloric substances, as compared to healthy weight individuals. However, much extant research on the neurobiology of obesity has focused on addiction models, without highlighting potentially separable neural underpinnings of caloric intake versus substance use. The present research explores these differences by examining neural response to alcoholic beverages and a sweet non-alcoholic beverage, among a sample of individuals with varying weight status and patterns of alcohol use and misuse. Participants received tastes of a sweet beverage (litchi juice) and alcoholic beverages during fMRI scanning. When controlling for alcohol use, elevated weight status was associated with increased activation in response to sweet taste in regions including the cingulate cortex, hippocampus, precuneus, and fusiform gyrus. However, weight status was not associated with neural response to alcoholic beverages. Copyright © 2017 Elsevier B.V. All rights reserved.

FMRI Study of Neural Responses to Implicit Infant Emotion in Anorexia Nervosa

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Jenni Leppanen

2017-05-01

Full Text Available Difficulties in social–emotional processing have been proposed to play an important role in the development and maintenance of anorexia nervosa (AN. Few studies, thus far, have investigated neural processes that underlie these difficulties, including processing emotional facial expressions. However, the majority of these studies have investigated neural responses to adult emotional display, which may be confounded by elevated sensitivity to social rank and threat in AN. Therefore, the aim of this study was to investigate the neural processes underlying implicit processing of positively and negatively valenced infant emotional display in AN. Twenty-one adult women with AN and twenty-six healthy comparison (HC women were presented with images of positively valenced, negatively valenced, and neutral infant faces during a fMRI scan. Significant differences between the groups in positive > neutral and negative > neutral contrasts were investigated in a priori regions of interest, including the bilateral amygdala, insula, and lateral prefrontal cortex (PFC. The findings revealed that the AN participants showed relatively increased recruitment while the HC participants showed relatively reduced recruitment of the bilateral amygdala and the right dorsolateral PFC in the positive > neutral contrast. In the negative > neutral contrast, the AN group showed relatively increased recruitment of the left posterior insula while the HC groups showed relatively reduced recruitment of this region. These findings suggest that people with AN may engage in implicit prefrontal down-regulation of elevated limbic reactivity to positively social–emotional stimuli.

Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining.

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Tang, Tianyu; Zhou, Shilin; Deng, Zhipeng; Zou, Huanxin; Lei, Lin

2017-02-10

Detecting vehicles in aerial imagery plays an important role in a wide range of applications. The current vehicle detection methods are mostly based on sliding-window search and handcrafted or shallow-learning-based features, having limited description capability and heavy computational costs. Recently, due to the powerful feature representations, region convolutional neural networks (CNN) based detection methods have achieved state-of-the-art performance in computer vision, especially Faster R-CNN. However, directly using it for vehicle detection in aerial images has many limitations: (1) region proposal network (RPN) in Faster R-CNN has poor performance for accurately locating small-sized vehicles, due to the relatively coarse feature maps; and (2) the classifier after RPN cannot distinguish vehicles and complex backgrounds well. In this study, an improved detection method based on Faster R-CNN is proposed in order to accomplish the two challenges mentioned above. Firstly, to improve the recall, we employ a hyper region proposal network (HRPN) to extract vehicle-like targets with a combination of hierarchical feature maps. Then, we replace the classifier after RPN by a cascade of boosted classifiers to verify the candidate regions, aiming at reducing false detection by negative example mining. We evaluate our method on the Munich vehicle dataset and the collected vehicle dataset, with improvements in accuracy and robustness compared to existing methods.

Isolating Discriminant Neural Activity in the Presence of Eye Movements and Concurrent Task Demands

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Jon Touryan

2017-07-01

Full Text Available A growing number of studies use the combination of eye-tracking and electroencephalographic (EEG measures to explore the neural processes that underlie visual perception. In these studies, fixation-related potentials (FRPs are commonly used to quantify early and late stages of visual processing that follow the onset of each fixation. However, FRPs reflect a mixture of bottom-up (sensory-driven and top-down (goal-directed processes, in addition to eye movement artifacts and unrelated neural activity. At present there is little consensus on how to separate this evoked response into its constituent elements. In this study we sought to isolate the neural sources of target detection in the presence of eye movements and over a range of concurrent task demands. Here, participants were asked to identify visual targets (Ts amongst a grid of distractor stimuli (Ls, while simultaneously performing an auditory N-back task. To identify the discriminant activity, we used independent components analysis (ICA for the separation of EEG into neural and non-neural sources. We then further separated the neural sources, using a modified measure-projection approach, into six regions of interest (ROIs: occipital, fusiform, temporal, parietal, cingulate, and frontal cortices. Using activity from these ROIs, we identified target from non-target fixations in all participants at a level similar to other state-of-the-art classification techniques. Importantly, we isolated the time course and spectral features of this discriminant activity in each ROI. In addition, we were able to quantify the effect of cognitive load on both fixation-locked potential and classification performance across regions. Together, our results show the utility of a measure-projection approach for separating task-relevant neural activity into meaningful ROIs within more complex contexts that include eye movements.

Recycling signals in the neural crest

OpenAIRE

Taneyhill, Lisa A.; Bronner-Fraser, Marianne E.

2006-01-01

Vertebrate neural crest cells are multipotent and differentiate into structures that include cartilage and the bones of the face, as well as much of the peripheral nervous system. Understanding how different model vertebrates utilize signaling pathways reiteratively during various stages of neural crest formation and differentiation lends insight into human disorders associated with the neural crest.

Recycling signals in the neural crest.

Science.gov (United States)

Taneyhill, Lisa A; Bronner-Fraser, Marianne

2005-01-01

Vertebrate neural crest cells are multipotent and differentiate into structures that include cartilage and the bones of the face, as well as much of the peripheral nervous system. Understanding how different model vertebrates utilize signaling pathways reiteratively during various stages of neural crest formation and differentiation lends insight into human disorders associated with the neural crest.

Functional neural networks underlying response inhibition in adolescents and adults.

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Stevens, Michael C; Kiehl, Kent A; Pearlson, Godfrey D; Calhoun, Vince D

2007-07-19

This study provides the first description of neural network dynamics associated with response inhibition in healthy adolescents and adults. Functional and effective connectivity analyses of whole brain hemodynamic activity elicited during performance of a Go/No-Go task were used to identify functionally integrated neural networks and characterize their causal interactions. Three response inhibition circuits formed a hierarchical, inter-dependent system wherein thalamic modulation of input to premotor cortex by fronto-striatal regions led to response suppression. Adolescents differed from adults in the degree of network engagement, regional fronto-striatal-thalamic connectivity, and network dynamics. We identify and characterize several age-related differences in the function of neural circuits that are associated with behavioral performance changes across adolescent development.

Exploring the spatio-temporal neural basis of face learning

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Yang, Ying; Xu, Yang; Jew, Carol A.; Pyles, John A.; Kass, Robert E.; Tarr, Michael J.

2017-01-01

Humans are experts at face individuation. Although previous work has identified a network of face-sensitive regions and some of the temporal signatures of face processing, as yet, we do not have a clear understanding of how such face-sensitive regions support learning at different time points. To study the joint spatio-temporal neural basis of face learning, we trained subjects to categorize two groups of novel faces and recorded their neural responses using magnetoencephalography (MEG) throughout learning. A regression analysis of neural responses in face-sensitive regions against behavioral learning curves revealed significant correlations with learning in the majority of the face-sensitive regions in the face network, mostly between 150–250 ms, but also after 300 ms. However, the effect was smaller in nonventral regions (within the superior temporal areas and prefrontal cortex) than that in the ventral regions (within the inferior occipital gyri (IOG), midfusiform gyri (mFUS) and anterior temporal lobes). A multivariate discriminant analysis also revealed that IOG and mFUS, which showed strong correlation effects with learning, exhibited significant discriminability between the two face categories at different time points both between 150–250 ms and after 300 ms. In contrast, the nonventral face-sensitive regions, where correlation effects with learning were smaller, did exhibit some significant discriminability, but mainly after 300 ms. In sum, our findings indicate that early and recurring temporal components arising from ventral face-sensitive regions are critically involved in learning new faces. PMID:28570739

The effects of gratitude expression on neural activity.

Science.gov (United States)

Kini, Prathik; Wong, Joel; McInnis, Sydney; Gabana, Nicole; Brown, Joshua W

2016-03-01

Gratitude is a common aspect of social interaction, yet relatively little is known about the neural bases of gratitude expression, nor how gratitude expression may lead to longer-term effects on brain activity. To address these twin issues, we recruited subjects who coincidentally were entering psychotherapy for depression and/or anxiety. One group participated in a gratitude writing intervention, which required them to write letters expressing gratitude. The therapy-as-usual control group did not perform a writing intervention. After three months, subjects performed a "Pay It Forward" task in the fMRI scanner. In the task, subjects were repeatedly endowed with a monetary gift and then asked to pass it on to a charitable cause to the extent they felt grateful for the gift. Operationalizing gratitude as monetary gifts allowed us to engage the subjects and quantify the gratitude expression for subsequent analyses. We measured brain activity and found regions where activity correlated with self-reported gratitude experience during the task, even including related constructs such as guilt motivation and desire to help as statistical controls. These were mostly distinct from brain regions activated by empathy or theory of mind. Also, our between groups cross-sectional study found that a simple gratitude writing intervention was associated with significantly greater and lasting neural sensitivity to gratitude - subjects who participated in gratitude letter writing showed both behavioral increases in gratitude and significantly greater neural modulation by gratitude in the medial prefrontal cortex three months later. Copyright © 2015 Elsevier Inc. All rights reserved.

Neural network for nonsmooth pseudoconvex optimization with general convex constraints.

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Bian, Wei; Ma, Litao; Qin, Sitian; Xue, Xiaoping

2018-05-01

In this paper, a one-layer recurrent neural network is proposed for solving a class of nonsmooth, pseudoconvex optimization problems with general convex constraints. Based on the smoothing method, we construct a new regularization function, which does not depend on any information of the feasible region. Thanks to the special structure of the regularization function, we prove the global existence, uniqueness and "slow solution" character of the state of the proposed neural network. Moreover, the state solution of the proposed network is proved to be convergent to the feasible region in finite time and to the optimal solution set of the related optimization problem subsequently. In particular, the convergence of the state to an exact optimal solution is also considered in this paper. Numerical examples with simulation results are given to show the efficiency and good characteristics of the proposed network. In addition, some preliminary theoretical analysis and application of the proposed network for a wider class of dynamic portfolio optimization are included. Copyright © 2018 Elsevier Ltd. All rights reserved.

Robust Adaptive Neural Control of Morphing Aircraft with Prescribed Performance

OpenAIRE

Wu, Zhonghua; Lu, Jingchao; Shi, Jingping; Liu, Yang; Zhou, Qing

2017-01-01

This study proposes a low-computational composite adaptive neural control scheme for the longitudinal dynamics of a swept-back wing aircraft subject to parameter uncertainties. To efficiently release the constraint often existing in conventional neural designs, whose closed-loop stability analysis always necessitates that neural networks (NNs) be confined in the active regions, a smooth switching function is presented to conquer this issue. By integrating minimal learning parameter (MLP) tech...

77 FR 62535 - Hydro Aluminum North America, Inc., Midwest Region, Including On-Site Leased Workers From...

Science.gov (United States)

2012-10-15

... Aluminum North America, Inc., Midwest Region, Including On- Site Leased Workers From Employment Group, Aerotek, and Manpower, Kalamazoo, Michigan; Hydro Aluminum North America, Inc., Midwest Region, Including... Aluminum North America, Inc., Kalamazoo, Michigan. The subject worker group includes on-site leased workers...

Neural crest contributions to the lamprey head

Science.gov (United States)

McCauley, David W.; Bronner-Fraser, Marianne

2003-01-01

The neural crest is a vertebrate-specific cell population that contributes to the facial skeleton and other derivatives. We have performed focal DiI injection into the cranial neural tube of the developing lamprey in order to follow the migratory pathways of discrete groups of cells from origin to destination and to compare neural crest migratory pathways in a basal vertebrate to those of gnathostomes. The results show that the general pathways of cranial neural crest migration are conserved throughout the vertebrates, with cells migrating in streams analogous to the mandibular and hyoid streams. Caudal branchial neural crest cells migrate ventrally as a sheet of cells from the hindbrain and super-pharyngeal region of the neural tube and form a cylinder surrounding a core of mesoderm in each pharyngeal arch, similar to that seen in zebrafish and axolotl. In addition to these similarities, we also uncovered important differences. Migration into the presumptive caudal branchial arches of the lamprey involves both rostral and caudal movements of neural crest cells that have not been described in gnathostomes, suggesting that barriers that constrain rostrocaudal movement of cranial neural crest cells may have arisen after the agnathan/gnathostome split. Accordingly, neural crest cells from a single axial level contributed to multiple arches and there was extensive mixing between populations. There was no apparent filling of neural crest derivatives in a ventral-to-dorsal order, as has been observed in higher vertebrates, nor did we find evidence of a neural crest contribution to cranial sensory ganglia. These results suggest that migratory constraints and additional neural crest derivatives arose later in gnathostome evolution.

Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS

Directory of Open Access Journals (Sweden)

Christopher Bergmeir

2012-01-01

Full Text Available Neural networks are important standard machine learning procedures for classification and regression. We describe the R package RSNNS that provides a convenient interface to the popular Stuttgart Neural Network Simulator SNNS. The main features are (a encapsulation of the relevant SNNS parts in a C++ class, for sequential and parallel usage of different networks, (b accessibility of all of the SNNSalgorithmic functionality from R using a low-level interface, and (c a high-level interface for convenient, R-style usage of many standard neural network procedures. The package also includes functions for visualization and analysis of the models and the training procedures, as well as functions for data input/output from/to the original SNNSfile formats.

Neural networks at the Tevatron

International Nuclear Information System (INIS)

Badgett, W.; Burkett, K.; Campbell, M.K.; Wu, D.Y.; Bianchin, S.; DeNardi, M.; Pauletta, G.; Santi, L.; Caner, A.; Denby, B.; Haggerty, H.; Lindsey, C.S.; Wainer, N.; Dall'Agata, M.; Johns, K.; Dickson, M.; Stanco, L.; Wyss, J.L.

1992-10-01

This paper summarizes neural network applications at the Fermilab Tevatron, including the first online hardware application in high energy physics (muon tracking): the CDF and DO neural network triggers; offline quark/gluon discrimination at CDF; ND a new tool for top to multijets recognition at CDF

Neural Computations in a Dynamical System with Multiple Time Scales.

Science.gov (United States)

Mi, Yuanyuan; Lin, Xiaohan; Wu, Si

2016-01-01

Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions.

A Neural Marker for Social Bias Toward In-group Accents.

Science.gov (United States)

Bestelmeyer, Patricia E G; Belin, Pascal; Ladd, D Robert

2015-10-01

Accents provide information about the speaker's geographical, socio-economic, and ethnic background. Research in applied psychology and sociolinguistics suggests that we generally prefer our own accent to other varieties of our native language and attribute more positive traits to it. Despite the widespread influence of accents on social interactions, educational and work settings the neural underpinnings of this social bias toward our own accent and, what may drive this bias, are unexplored. We measured brain activity while participants from two different geographical backgrounds listened passively to 3 English accent types embedded in an adaptation design. Cerebral activity in several regions, including bilateral amygdalae, revealed a significant interaction between the participants' own accent and the accent they listened to: while repetition of own accents elicited an enhanced neural response, repetition of the other group's accent resulted in reduced responses classically associated with adaptation. Our findings suggest that increased social relevance of, or greater emotional sensitivity to in-group accents, may underlie the own-accent bias. Our results provide a neural marker for the bias associated with accents, and show, for the first time, that the neural response to speech is partly shaped by the geographical background of the listener. © The Author 2014. Published by Oxford University Press.

Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene.

Science.gov (United States)

Li, Jun; Mei, Xue; Prokhorov, Danil; Tao, Dacheng

2017-03-01

Hierarchical neural networks have been shown to be effective in learning representative image features and recognizing object classes. However, most existing networks combine the low/middle level cues for classification without accounting for any spatial structures. For applications such as understanding a scene, how the visual cues are spatially distributed in an image becomes essential for successful analysis. This paper extends the framework of deep neural networks by accounting for the structural cues in the visual signals. In particular, two kinds of neural networks have been proposed. First, we develop a multitask deep convolutional network, which simultaneously detects the presence of the target and the geometric attributes (location and orientation) of the target with respect to the region of interest. Second, a recurrent neuron layer is adopted for structured visual detection. The recurrent neurons can deal with the spatial distribution of visible cues belonging to an object whose shape or structure is difficult to explicitly define. Both the networks are demonstrated by the practical task of detecting lane boundaries in traffic scenes. The multitask convolutional neural network provides auxiliary geometric information to help the subsequent modeling of the given lane structures. The recurrent neural network automatically detects lane boundaries, including those areas containing no marks, without any explicit prior knowledge or secondary modeling.

Sex differences in neural responses to stress and alcohol context cues.

Science.gov (United States)

Seo, Dongju; Jia, Zhiru; Lacadie, Cheryl M; Tsou, Kristen A; Bergquist, Keri; Sinha, Rajita

2011-11-01

Stress and alcohol context cues are each associated with alcohol-related behaviors, yet neural responses underlying these processes remain unclear. This study investigated the neural correlates of stress and alcohol context cue experiences and examined sex differences in these responses. Using functional magnetic resonance imaging, brain responses were examined while 43 right-handed, socially drinking, healthy individuals (23 females) engaged in brief guided imagery of personalized stress, alcohol-cue, and neutral-relaxing scenarios. Stress and alcohol-cue exposure increased activity in the cortico-limbic-striatal circuit (P left anterior insula, striatum, and visuomotor regions (parietal and occipital lobe, and cerebellum). Activity in the left dorsal striatum increased during stress, while bilateral ventral striatum activity was evident during alcohol-cue exposure. Men displayed greater stress-related activations in the mPFC, rostral ACC, posterior insula, amygdala, and hippocampus than women, whereas women showed greater alcohol-cue-related activity in the superior and middle frontal gyrus (SFG/MFG) than men. Stress-induced anxiety was positively associated with activity in emotion-modulation regions, including the medial OFC, ventromedial PFC, left superior-mPFC, and rostral ACC in men, but in women with activation in the SFG/MFG, regions involved in cognitive processing. Alcohol craving was significantly associated with the striatum (encompassing dorsal, and ventral) in men, supporting its involvement in alcohol "urge" in healthy men. These results indicate sex differences in neural processing of stress and alcohol-cue experiences and have implications for sex-specific vulnerabilities to stress- and alcohol-related psychiatric disorders. Copyright © 2010 Wiley-Liss, Inc.

Multivariate Cross-Classification: Applying machine learning techniques to characterize abstraction in neural representations

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Jonas eKaplan

2015-03-01

Full Text Available Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC, and review several domains where it has recently made an impact. MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching. It has been used to test for similarity between neural patterns evoked by perception and those generated from memory. Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands. We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application.

Neural bases of congenital amusia in tonal language speakers.

Science.gov (United States)

Zhang, Caicai; Peng, Gang; Shao, Jing; Wang, William S-Y

2017-03-01

Congenital amusia is a lifelong neurodevelopmental disorder of fine-grained pitch processing. In this fMRI study, we examined the neural bases of congenial amusia in speakers of a tonal language - Cantonese. Previous studies on non-tonal language speakers suggest that the neural deficits of congenital amusia lie in the music-selective neural circuitry in the right inferior frontal gyrus (IFG). However, it is unclear whether this finding can generalize to congenital amusics in tonal languages. Tonal language experience has been reported to shape the neural processing of pitch, which raises the question of how tonal language experience affects the neural bases of congenital amusia. To investigate this question, we examined the neural circuitries sub-serving the processing of relative pitch interval in pitch-matched Cantonese level tone and musical stimuli in 11 Cantonese-speaking amusics and 11 musically intact controls. Cantonese-speaking amusics exhibited abnormal brain activities in a widely distributed neural network during the processing of lexical tone and musical stimuli. Whereas the controls exhibited significant activation in the right superior temporal gyrus (STG) in the lexical tone condition and in the cerebellum regardless of the lexical tone and music conditions, no activation was found in the amusics in those regions, which likely reflects a dysfunctional neural mechanism of relative pitch processing in the amusics. Furthermore, the amusics showed abnormally strong activation of the right middle frontal gyrus and precuneus when the pitch stimuli were repeated, which presumably reflect deficits of attending to repeated pitch stimuli or encoding them into working memory. No significant group difference was found in the right IFG in either the whole-brain analysis or region-of-interest analysis. These findings imply that the neural deficits in tonal language speakers might differ from those in non-tonal language speakers, and overlap partly with the

Practical neural network recipies in C++

CERN Document Server

Masters

2014-01-01

This text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming experience to select a neural network model appropriate to solving a particular problem, and to produce a working program implementing that network. The book provides guidance along the entire problem-solving path, including designing the training set, preprocessing variables, training and validating the network, and evaluating its performance. Though the book is not intended as a general course in neural networks, no background in neural works is assum

Statistical modelling of neural networks in γ-spectrometry applications

International Nuclear Information System (INIS)

Vigneron, V.; Martinez, J.M.; Morel, J.; Lepy, M.C.

1995-01-01

Layered Neural Networks, which are a class of models based on neural computation, are applied to the measurement of uranium enrichment, i.e. the isotope ratio 235 U/( 235 U + 236 U + 238 U). The usual method consider a limited number of Γ-ray and X-ray peaks, and require previously calibrated instrumentation for each sample. But, in practice, the source-detector ensemble geometry conditions are critically different, thus a means of improving the above convention methods is to reduce the region of interest: this is possible by focusing on the K α X region where the three elementary components are present. Real data are used to study the performance of neural networks. Training is done with a Maximum Likelihood method to measure uranium 235 U and 238 U quantities in infinitely thick samples. (authors). 18 refs., 6 figs., 3 tabs

Racial bias in neural empathic responses to pain.

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Luis Sebastian Contreras-Huerta

Full Text Available Recent studies have shown that perceiving the pain of others activates brain regions in the observer associated with both somatosensory and affective-motivational aspects of pain, principally involving regions of the anterior cingulate and anterior insula cortex. The degree of these empathic neural responses is modulated by racial bias, such that stronger neural activation is elicited by observing pain in people of the same racial group compared with people of another racial group. The aim of the present study was to examine whether a more general social group category, other than race, could similarly modulate neural empathic responses and perhaps account for the apparent racial bias reported in previous studies. Using a minimal group paradigm, we assigned participants to one of two mixed-race teams. We use the term race to refer to the Chinese or Caucasian appearance of faces and whether the ethnic group represented was the same or different from the appearance of the participant' own face. Using fMRI, we measured neural empathic responses as participants observed members of their own group or other group, and members of their own race or other race, receiving either painful or non-painful touch. Participants showed clear group biases, with no significant effect of race, on behavioral measures of implicit (affective priming and explicit group identification. Neural responses to observed pain in the anterior cingulate cortex, insula cortex, and somatosensory areas showed significantly greater activation when observing pain in own-race compared with other-race individuals, with no significant effect of minimal groups. These results suggest that racial bias in neural empathic responses is not influenced by minimal forms of group categorization, despite the clear association participants showed with in-group more than out-group members. We suggest that race may be an automatic and unconscious mechanism that drives the initial neural responses to

Racial Bias in Neural Empathic Responses to Pain

Science.gov (United States)

Contreras-Huerta, Luis Sebastian; Baker, Katharine S.; Reynolds, Katherine J.; Batalha, Luisa; Cunnington, Ross

2013-01-01

Recent studies have shown that perceiving the pain of others activates brain regions in the observer associated with both somatosensory and affective-motivational aspects of pain, principally involving regions of the anterior cingulate and anterior insula cortex. The degree of these empathic neural responses is modulated by racial bias, such that stronger neural activation is elicited by observing pain in people of the same racial group compared with people of another racial group. The aim of the present study was to examine whether a more general social group category, other than race, could similarly modulate neural empathic responses and perhaps account for the apparent racial bias reported in previous studies. Using a minimal group paradigm, we assigned participants to one of two mixed-race teams. We use the term race to refer to the Chinese or Caucasian appearance of faces and whether the ethnic group represented was the same or different from the appearance of the participant' own face. Using fMRI, we measured neural empathic responses as participants observed members of their own group or other group, and members of their own race or other race, receiving either painful or non-painful touch. Participants showed clear group biases, with no significant effect of race, on behavioral measures of implicit (affective priming) and explicit group identification. Neural responses to observed pain in the anterior cingulate cortex, insula cortex, and somatosensory areas showed significantly greater activation when observing pain in own-race compared with other-race individuals, with no significant effect of minimal groups. These results suggest that racial bias in neural empathic responses is not influenced by minimal forms of group categorization, despite the clear association participants showed with in-group more than out-group members. We suggest that race may be an automatic and unconscious mechanism that drives the initial neural responses to observed pain in

Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks

OpenAIRE

Laine, Samuli; Karras, Tero; Aila, Timo; Herva, Antti; Saito, Shunsuke; Yu, Ronald; Li, Hao; Lehtinen, Jaakko

2016-01-01

We present a real-time deep learning framework for video-based facial performance capture -- the dense 3D tracking of an actor's face given a monocular video. Our pipeline begins with accurately capturing a subject using a high-end production facial capture pipeline based on multi-view stereo tracking and artist-enhanced animations. With 5-10 minutes of captured footage, we train a convolutional neural network to produce high-quality output, including self-occluded regions, from a monocular v...

Neural Response to Biological Motion in Healthy Adults Varies as a Function of Autistic-Like Traits

Directory of Open Access Journals (Sweden)

Meghan H. Puglia

2017-07-01

Full Text Available Perception of biological motion is an important social cognitive ability that has been mapped to specialized brain regions. Perceptual deficits and neural differences during biological motion perception have previously been associated with autism, a disorder classified by social and communication difficulties and repetitive and restricted interests and behaviors. However, the traits associated with autism are not limited to diagnostic categories, but are normally distributed within the general population and show the same patterns of heritability across the continuum. In the current study, we investigate whether self-reported autistic-like traits in healthy adults are associated with variable neural response during passive viewing of biological motion displays. Results show that more autistic-like traits, particularly those associated with the communication domain, are associated with increased neural response in key regions involved in social cognitive processes, including prefrontal and left temporal cortices. This distinct pattern of activation might reflect differential neurodevelopmental processes for individuals with varying autistic-like traits, and highlights the importance of considering the full trait continuum in future work.

Signal Processing and Neural Network Simulator

Science.gov (United States)

Tebbe, Dennis L.; Billhartz, Thomas J.; Doner, John R.; Kraft, Timothy T.

1995-04-01

The signal processing and neural network simulator (SPANNS) is a digital signal processing simulator with the capability to invoke neural networks into signal processing chains. This is a generic tool which will greatly facilitate the design and simulation of systems with embedded neural networks. The SPANNS is based on the Signal Processing WorkSystemTM (SPWTM), a commercial-off-the-shelf signal processing simulator. SPW provides a block diagram approach to constructing signal processing simulations. Neural network paradigms implemented in the SPANNS include Backpropagation, Kohonen Feature Map, Outstar, Fully Recurrent, Adaptive Resonance Theory 1, 2, & 3, and Brain State in a Box. The SPANNS was developed by integrating SAIC's Industrial Strength Neural Networks (ISNN) Software into SPW.

Neural correlates of own- and other-race face recognition in children: a functional near-infrared spectroscopy study.

Science.gov (United States)

Ding, Xiao Pan; Fu, Genyue; Lee, Kang

2014-01-15

The present study used the functional Near-infrared Spectroscopy (fNIRS) methodology to investigate the neural correlates of elementary school children's own- and other-race face processing. An old-new paradigm was used to assess children's recognition ability of own- and other-race faces. FNIRS data revealed that other-race faces elicited significantly greater [oxy-Hb] changes than own-race faces in the right middle frontal gyrus and inferior frontal gyrus regions (BA9) and the left cuneus (BA18). With increased age, the [oxy-Hb] activity differences between own- and other-race faces, or the neural other-race effect (NORE), underwent significant changes in these two cortical areas: at younger ages, the neural response to the other-race faces was modestly greater than that to the own-race faces, but with increased age, the neural response to the own-race faces became increasingly greater than that to the other-race faces. Moreover, these areas had strong regional functional connectivity with a swath of the cortical regions in terms of the neural other-race effect that also changed with increased age. We also found significant and positive correlations between the behavioral other-race effect (reaction time) and the neural other-race effect in the right middle frontal gyrus and inferior frontal gyrus regions (BA9). These results taken together suggest that children, like adults, devote different amounts of neural resources to processing own- and other-race faces, but the size and direction of the neural other-race effect and associated functional regional connectivity change with increased age. © 2013.

Empathy and aversion: the neural signature of mentalizing in Tourette syndrome.

Science.gov (United States)

Eddy, C M; Cavanna, A E; Hansen, P C

2017-02-01

Previous studies suggest that adults with Tourette syndrome (TS) can respond unconventionally on tasks involving social cognition. We therefore hypothesized that these patients would exhibit different neural responses to healthy controls in response to emotionally salient expressions of human eyes. Twenty-five adults with TS and 25 matched healthy controls were scanned using fMRI during the standard version of the Reading the Mind in the Eyes Task which requires mental state judgements, and a novel comparison version requiring judgements about age. During prompted mental state recognition, greater activity was apparent in TS within left orbitofrontal cortex, posterior cingulate, right amygdala and right temporo-parietal junction (TPJ), while reduced activity was apparent in regions including left inferior parietal cortex. Age judgement elicited greater activity in TS within precuneus, medial prefrontal and temporal regions involved in mentalizing. The interaction between group and task revealed differential activity in areas including right inferior frontal gyrus. Task-related activity in the TPJ covaried with global ratings of the urge to tic. While recognizing mental states, adults with TS exhibit greater activity than controls in brain areas involved in the processing of negative emotion, in addition to reduced activity in regions associated with the attribution of agency. In addition, increased recruitment of areas involved in mental state reasoning is apparent in these patients when mentalizing is not a task requirement. Our findings highlight differential neural reactivity in response to emotive social cues in TS, which may interact with tic expression.

Exploring neural dysfunction in 'clinical high risk' for psychosis: a quantitative review of fMRI studies.

Science.gov (United States)

Dutt, Anirban; Tseng, Huai-Hsuan; Fonville, Leon; Drakesmith, Mark; Su, Liang; Evans, John; Zammit, Stanley; Jones, Derek; Lewis, Glyn; David, Anthony S

2015-02-01

Individuals at clinical high risk (CHR) of developing psychosis present with widespread functional abnormalities in the brain. Cognitive deficits, including working memory (WM) problems, as commonly elicited by n-back tasks, are observed in CHR individuals. However, functional MRI (fMRI) studies, comprising a heterogeneous cluster of general and social cognition paradigms, have not necessarily demonstrated consistent and conclusive results in this population. Hence, a comprehensive review of fMRI studies, spanning almost one decade, was carried out to observe for general trends with respect to brain regions and cognitive systems most likely to be dysfunctional in CHR individuals. 32 studies were included for this review, out of which 22 met the criteria for quantitative analysis using activation likelihood estimation (ALE). Task related contrast activations were firstly analysed by comparing CHR and healthy control participants in the total pooled sample, followed by a comparison of general cognitive function studies (excluding social cognition paradigms), and finally by only looking at n-back working memory task based studies. Findings from the ALE implicated four key dysfunctional and distinct neural regions in the CHR group, namely the right inferior parietal lobule (rIPL), the left medial frontal gyrus (lmFG), the left superior temporal gyrus (lSTG) and the right fronto-polar cortex (rFPC) of the superior frontal gyrus (SFG). Narrowing down to relatively few significant dysfunctional neural regions is a step forward in reducing the apparent ambiguity of overall findings, which would help to target specific neural regions and pathways of interest for future research in CHR populations. Copyright © 2014. Published by Elsevier Ltd.

International Conference on Artificial Neural Networks (ICANN)

CERN Document Server

Mladenov, Valeri; Kasabov, Nikola; Artificial Neural Networks : Methods and Applications in Bio-/Neuroinformatics

2015-01-01

The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new al...

Neural crest stem cell multipotency requires Foxd3 to maintain neural potential and repress mesenchymal fates.

Science.gov (United States)

Mundell, Nathan A; Labosky, Patricia A

2011-02-01

Neural crest (NC) progenitors generate a wide array of cell types, yet molecules controlling NC multipotency and self-renewal and factors mediating cell-intrinsic distinctions between multipotent versus fate-restricted progenitors are poorly understood. Our earlier work demonstrated that Foxd3 is required for maintenance of NC progenitors in the embryo. Here, we show that Foxd3 mediates a fate restriction choice for multipotent NC progenitors with loss of Foxd3 biasing NC toward a mesenchymal fate. Neural derivatives of NC were lost in Foxd3 mutant mouse embryos, whereas abnormally fated NC-derived vascular smooth muscle cells were ectopically located in the aorta. Cranial NC defects were associated with precocious differentiation towards osteoblast and chondrocyte cell fates, and individual mutant NC from different anteroposterior regions underwent fate changes, losing neural and increasing myofibroblast potential. Our results demonstrate that neural potential can be separated from NC multipotency by the action of a single gene, and establish novel parallels between NC and other progenitor populations that depend on this functionally conserved stem cell protein to regulate self-renewal and multipotency.

Using neural networks to describe tracer correlations

Directory of Open Access Journals (Sweden)

D. J. Lary

2004-01-01

Full Text Available Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and methane volume mixing ratio (v.m.r.. In this study a neural network using Quickprop learning and one hidden layer with eight nodes was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9995. Such an accurate representation of tracer-tracer correlations allows more use to be made of long-term datasets to constrain chemical models. Such as the dataset from the Halogen Occultation Experiment (HALOE which has continuously observed CH4  (but not N2O from 1991 till the present. The neural network Fortran code used is available for download.

Mapping face categorization in the human ventral occipitotemporal cortex with direct neural intracranial recordings.

Science.gov (United States)

Rossion, Bruno; Jacques, Corentin; Jonas, Jacques

2018-02-26

The neural basis of face categorization has been widely investigated with functional magnetic resonance imaging (fMRI), identifying a set of face-selective local regions in the ventral occipitotemporal cortex (VOTC). However, indirect recording of neural activity with fMRI is associated with large fluctuations of signal across regions, often underestimating face-selective responses in the anterior VOTC. While direct recording of neural activity with subdural grids of electrodes (electrocorticography, ECoG) or depth electrodes (stereotactic electroencephalography, SEEG) offers a unique opportunity to fill this gap in knowledge, these studies rather reveal widely distributed face-selective responses. Moreover, intracranial recordings are complicated by interindividual variability in neuroanatomy, ambiguity in definition, and quantification of responses of interest, as well as limited access to sulci with ECoG. Here, we propose to combine SEEG in large samples of individuals with fast periodic visual stimulation to objectively define, quantify, and characterize face categorization across the whole VOTC. This approach reconciles the wide distribution of neural face categorization responses with their (right) hemispheric and regional specialization, and reveals several face-selective regions in anterior VOTC sulci. We outline the challenges of this research program to understand the neural basis of face categorization and high-level visual recognition in general. © 2018 New York Academy of Sciences.

A One-Layer Recurrent Neural Network for Constrained Complex-Variable Convex Optimization.

Science.gov (United States)

Qin, Sitian; Feng, Jiqiang; Song, Jiahui; Wen, Xingnan; Xu, Chen

2018-03-01

In this paper, based on calculus and penalty method, a one-layer recurrent neural network is proposed for solving constrained complex-variable convex optimization. It is proved that for any initial point from a given domain, the state of the proposed neural network reaches the feasible region in finite time and converges to an optimal solution of the constrained complex-variable convex optimization finally. In contrast to existing neural networks for complex-variable convex optimization, the proposed neural network has a lower model complexity and better convergence. Some numerical examples and application are presented to substantiate the effectiveness of the proposed neural network.

Neural correlates of apathy in patients with neurodegenerative disorders, acquired brain injury, and psychiatric disorders.

Science.gov (United States)

Kos, Claire; van Tol, Marie-José; Marsman, Jan-Bernard C; Knegtering, Henderikus; Aleman, André

2016-10-01

Apathy can be described as a loss of goal-directed purposeful behavior and is common in a variety of neurological and psychiatric disorders. Although previous studies investigated associations between abnormal brain functioning and apathy, it is unclear whether the neural basis of apathy is similar across different pathological conditions. The purpose of this systematic review was to provide an extensive overview of the neuroimaging literature on apathy including studies of various patient populations, and evaluate whether the current state of affairs suggest disorder specific or shared neural correlates of apathy. Results suggest that abnormalities within fronto-striatal circuits are most consistently associated with apathy across the different pathological conditions. Of note, abnormalities within the inferior parietal cortex were also linked to apathy, a region previously not included in neuroanatomical models of apathy. The variance in brain regions implicated in apathy may suggest that different routes towards apathy are possible. Future research should investigate possible alterations in different processes underlying goal-directed behavior, ranging from intention and goal-selection to action planning and execution. Copyright © 2016. Published by Elsevier Ltd.

Theory of mind in schizophrenia: exploring neural mechanisms of belief attribution.

Science.gov (United States)

Lee, Junghee; Quintana, Javier; Nori, Poorang; Green, Michael F

2011-01-01

Although previous behavioral studies have shown that schizophrenia patients have impaired theory of mind (ToM), the neural mechanisms associated with this impairment are poorly understood. This study aimed to identify the neural mechanisms of ToM in schizophrenia, using functional magnetic resonance imaging (fMRI) with a belief attribution task. In the scanner, 12 schizophrenia patients and 13 healthy control subjects performed the belief attribution task with three conditions: a false belief condition, a false photograph condition, and a simple reading condition. For the false belief versus simple reading conditions, schizophrenia patients showed reduced neural activation in areas including the temporoparietal junction (TPJ) and medial prefrontal cortex (MPFC) compared with controls. Further, during the false belief versus false photograph conditions, we observed increased activations in the TPJ and the MPFC in healthy controls, but not in schizophrenia patients. For the false photograph versus simple reading condition, both groups showed comparable neural activations. Schizophrenia patients showed reduced task-related activation in the TPJ and the MPFC during the false belief condition compared with controls, but not for the false photograph condition. This pattern suggests that reduced activation in these regions is associated with, and specific to, impaired ToM in schizophrenia.

Differences in the neural correlates of frontal lobe tests.

Science.gov (United States)

Matsuoka, Teruyuki; Kato, Yuka; Imai, Ayu; Fujimoto, Hiroshi; Shibata, Keisuke; Nakamura, Kaeko; Yamada, Kei; Narumoto, Jin

2018-01-01

The Executive Interview (EXIT25), the executive clock-drawing task (CLOX1), and the Frontal Assessment Battery (FAB) are used to assess executive function at the bedside. These tests assess distinct psychometric properties. The aim of this study was to examine differences in the neural correlates of the EXIT25, CLOX1, and FAB based on magnetic resonance imaging. Fifty-eight subjects (30 with Alzheimer's disease, 10 with mild cognitive impairment, and 18 healthy controls) participated in this study. Multiple regression analyses were performed to examine the brain regions correlated with the EXIT25, CLOX1, and FAB scores. Age, gender, and years of education were included as covariates. Statistical thresholds were set to uncorrected P-values of 0.001 at the voxel level and 0.05 at the cluster level. The EXIT25 score correlated inversely with the regional grey matter volume in the left lateral frontal lobe (Brodmann areas 6, 9, 44, and 45). The CLOX1 score correlated positively with the regional grey matter volume in the right orbitofrontal cortex (Brodmann area 11) and the left supramarginal gyrus (Brodmann area 40). The FAB score correlated positively with the regional grey matter volume in the right precentral gyrus (Brodmann area 6). The left lateral frontal lobe (Brodmann area 9) and the right lateral frontal lobe (Brodmann area 46) were identified as common brain regions that showed association with EXIT25, CLOX1, and FAB based only a voxel-level threshold. The results of this study suggest that the EXIT25, CLOX1, and FAB may be associated with the distinct neural correlates of the frontal cortex. © 2018 Japanese Psychogeriatric Society.

Modelling the continuous cooling transformation diagram of engineering steels using neural networks. Part I. Phase regions

Energy Technology Data Exchange (ETDEWEB)

Wolk, P.J. van der; Wang, J. [Delft Univ. of Technology (Netherlands); Sietsma, J.; Zwaag, S. van der [Delft Univ. of Technology, Lab. for Materials Science (Netherlands)

2002-12-01

A neural network model for the calculation of the phase regions of the continuous cooling transformation (CCT) diagram of engineering steels has been developed. The model is based on experimental CCT diagrams of 459 low-alloy steels, and calculates the CCT diagram as a function of composition and austenitisation temperature. In considering the composition, 9 alloying elements are taken into account. The model reproduces the original diagrams rather accurately, with deviations that are not larger than the average experimental inaccuracy of the experimental diagrams. Therefore, it can be considered an adequate alternative to the experimental determination of the CCT diagram of a certain steel within the composition range used. The effects of alloying elements can be quantified, either individually or in combination, with the model. Nonlinear composition dependencies are observed. (orig.)

Complex-Valued Neural Networks

CERN Document Server

Hirose, Akira

2012-01-01

This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published in 2006, which lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields. In the second edition the recent trends in CVNNs research are included, resulting in e.g. almost a doubled number of references. The parametron invented in 1954 is also referred to with discussion on analogy and disparity. Also various additional arguments on the advantages of the complex-valued neural networks enhancing the difference to real-valued neural networks are given in various sections. The book is useful for those beginning their studies, for instance, in adaptive signal processing for highly functional sensing and imaging, control in unknown and changing environment, robotics inspired by human neural systems, and brain-like information processing, as well as interdisciplina...

Histamine H3 receptor density is negatively correlated with neural activity related to working memory in humans.

Science.gov (United States)

Ito, Takehito; Kimura, Yasuyuki; Seki, Chie; Ichise, Masanori; Yokokawa, Keita; Kawamura, Kazunori; Takahashi, Hidehiko; Higuchi, Makoto; Zhang, Ming-Rong; Suhara, Tetsuya; Yamada, Makiko

2018-06-14

The histamine H 3 receptor is regarded as a drug target for cognitive impairments in psychiatric disorders. H 3 receptors are expressed in neocortical areas, including the prefrontal cortex, the key region of cognitive functions such as working memory. However, the role of prefrontal H 3 receptors in working memory has not yet been clarified. Therefore, using functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) techniques, we aimed to investigate the association between the neural activity of working memory and the density of H 3 receptors in the prefrontal cortex. Ten healthy volunteers underwent both fMRI and PET scans. The N-back task was used to assess the neural activities related to working memory. H 3 receptor density was measured with the selective PET radioligand [ 11 C] TASP457. The neural activity of the right dorsolateral prefrontal cortex during the performance of the N-back task was negatively correlated with the density of H 3 receptors in this region. Higher neural activity of working memory was associated with lower H 3 receptor density in the right dorsolateral prefrontal cortex. This finding elucidates the role of H 3 receptors in working memory and indicates the potential of H 3 receptors as a therapeutic target for the cognitive impairments associated with neuropsychiatric disorders.

Systematic review of the neural basis of social cognition in patients with mood disorders.

Science.gov (United States)

Cusi, Andrée M; Nazarov, Anthony; Holshausen, Katherine; Macqueen, Glenda M; McKinnon, Margaret C

2012-05-01

This review integrates neuroimaging studies of 2 domains of social cognition--emotion comprehension and theory of mind (ToM)--in patients with major depressive disorder and bipolar disorder. The influence of key clinical and method variables on patterns of neural activation during social cognitive processing is also examined. Studies were identified using PsycINFO and PubMed (January 1967 to May 2011). The search terms were "fMRI," "emotion comprehension," "emotion perception," "affect comprehension," "affect perception," "facial expression," "prosody," "theory of mind," "mentalizing" and "empathy" in combination with "major depressive disorder," "bipolar disorder," "major depression," "unipolar depression," "clinical depression" and "mania." Taken together, neuroimaging studies of social cognition in patients with mood disorders reveal enhanced activation in limbic and emotion-related structures and attenuated activity within frontal regions associated with emotion regulation and higher cognitive functions. These results reveal an overall lack of inhibition by higher-order cognitive structures on limbic and emotion-related structures during social cognitive processing in patients with mood disorders. Critically, key variables, including illness burden, symptom severity, comorbidity, medication status and cognitive load may moderate this pattern of neural activation. Studies that did not include control tasks or a comparator group were included in this review. Further work is needed to examine the contribution of key moderator variables and to further elucidate the neural networks underlying altered social cognition in patients with mood disorders. The neural networks under lying higher-order social cognitive processes, including empathy, remain unexplored in patients with mood disorders.

Neural markers of loss aversion in resting-state brain activity.

Science.gov (United States)

Canessa, Nicola; Crespi, Chiara; Baud-Bovy, Gabriel; Dodich, Alessandra; Falini, Andrea; Antonellis, Giulia; Cappa, Stefano F

2017-02-01

Neural responses in striatal, limbic and somatosensory brain regions track individual differences in loss aversion, i.e. the higher sensitivity to potential losses compared with equivalent gains in decision-making under risk. The engagement of structures involved in the processing of aversive stimuli and experiences raises a further question, i.e. whether the tendency to avoid losses rather than acquire gains represents a transient fearful overreaction elicited by choice-related information, or rather a stable component of one's own preference function, reflecting a specific pattern of neural activity. We tested the latter hypothesis by assessing in 57 healthy human subjects whether the relationship between behavioral and neural loss aversion holds at rest, i.e. when the BOLD signal is collected during 5minutes of cross-fixation in the absence of an explicit task. Within the resting-state networks highlighted by a spatial group Independent Component Analysis (gICA), we found a significant correlation between strength of activity and behavioral loss aversion in the left ventral striatum and right posterior insula/supramarginal gyrus, i.e. the very same regions displaying a pattern of neural loss aversion during explicit choices. Cross-study analyses confirmed that this correlation holds when voxels identified by gICA are used as regions of interest in task-related activity and vice versa. These results suggest that the individual degree of (neural) loss aversion represents a stable dimension of decision-making, which reflects in specific metrics of intrinsic brain activity at rest possibly modulating cortical excitability at choice. Copyright © 2016 Elsevier Inc. All rights reserved.

The positional identity of iPSC-derived neural progenitor cells along the anterior-posterior axis is controlled in a dosage-dependent manner by bFGF and EGF

DEFF Research Database (Denmark)

Zhou, Shuling; Ochalek, Anna; Szczesna, Karolina

2016-01-01

Neural rosettes derived from human induced pluripotent stem cells (iPSCs) have been claimed to be a highly robust in vitro cellular model for biomedical application. They are able to propagate in vitro in the presence of mitogens, including basic fibroblast growth factor (bFGF) and epidermal growth...... factor (EGF). However, these two mitogens are also involved in anterior-posterior patterning in a gradient dependent manner along the neural tube axis. Here, we compared the regional identity of neural rosette cells and specific neural subtypes of their progeny propagated with low and high concentrations...... of the neural rosettes, resulting in subsequent cholinergic neuron differentiation. Thus, our results indicate that different concentrations of bFGF and EGF supplemented during propagation of neural rosettes are involved in altering the identity of the resultant neural cells....

ANT Advanced Neural Tool

Energy Technology Data Exchange (ETDEWEB)

Labrador, I.; Carrasco, R.; Martinez, L.

1996-07-01

This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs.

ANT Advanced Neural Tool

International Nuclear Information System (INIS)

Labrador, I.; Carrasco, R.; Martinez, L.

1996-01-01

This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs

Rodent Zic Genes in Neural Network Wiring.

Science.gov (United States)

Herrera, Eloísa

2018-01-01

The formation of the nervous system is a multistep process that yields a mature brain. Failure in any of the steps of this process may cause brain malfunction. In the early stages of embryonic development, neural progenitors quickly proliferate and then, at a specific moment, differentiate into neurons or glia. Once they become postmitotic neurons, they migrate to their final destinations and begin to extend their axons to connect with other neurons, sometimes located in quite distant regions, to establish different neural circuits. During the last decade, it has become evident that Zic genes, in addition to playing important roles in early development (e.g., gastrulation and neural tube closure), are involved in different processes of late brain development, such as neuronal migration, axon guidance, and refinement of axon terminals. ZIC proteins are therefore essential for the proper wiring and connectivity of the brain. In this chapter, we review our current knowledge of the role of Zic genes in the late stages of neural circuit formation.

Neural activity in the posterior superior temporal region during eye contact perception correlates with autistic traits.

Science.gov (United States)

Hasegawa, Naoya; Kitamura, Hideaki; Murakami, Hiroatsu; Kameyama, Shigeki; Sasagawa, Mutsuo; Egawa, Jun; Endo, Taro; Someya, Toshiyuki

2013-08-09

The present study investigated the relationship between neural activity associated with gaze processing and autistic traits in typically developed subjects using magnetoencephalography. Autistic traits in 24 typically developed college students with normal intelligence were assessed using the Autism Spectrum Quotient (AQ). The Minimum Current Estimates method was applied to estimate the cortical sources of magnetic responses to gaze stimuli. These stimuli consisted of apparent motion of the eyes, displaying direct or averted gaze motion. Results revealed gaze-related brain activations in the 150-250 ms time window in the right posterior superior temporal sulcus (pSTS), and in the 150-450 ms time window in medial prefrontal regions. In addition, the mean amplitude in the 150-250 ms time window in the right pSTS region was modulated by gaze direction, and its activity in response to direct gaze stimuli correlated with AQ score. pSTS activation in response to direct gaze is thought to be related to higher-order social processes. Thus, these results suggest that brain activity linking eye contact and social signals is associated with autistic traits in a typical population. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Podocalyxin Is a Novel Polysialylated Neural Adhesion Protein with Multiple Roles in Neural Development and Synapse Formation

Science.gov (United States)

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

Neural correlates underlying musical semantic memory.

Science.gov (United States)

Groussard, M; Viader, F; Landeau, B; Desgranges, B; Eustache, F; Platel, H

2009-07-01

Numerous functional imaging studies have examined the neural basis of semantic memory mainly using verbal and visuospatial materials. Musical material also allows an original way to explore semantic memory processes. We used PET imaging to determine the neural substrates that underlie musical semantic memory using different tasks and stimuli. The results of three PET studies revealed a greater involvement of the anterior part of the temporal lobe. Concerning clinical observations and our neuroimaging data, the musical lexicon (and most widely musical semantic memory) appears to be sustained by a temporo-prefrontal cerebral network involving right and left cerebral regions.

Fuzzy logic and neural networks basic concepts & application

CERN Document Server

Alavala, Chennakesava R

2008-01-01

About the Book: The primary purpose of this book is to provide the student with a comprehensive knowledge of basic concepts of fuzzy logic and neural networks. The hybridization of fuzzy logic and neural networks is also included. No previous knowledge of fuzzy logic and neural networks is required. Fuzzy logic and neural networks have been discussed in detail through illustrative examples, methods and generic applications. Extensive and carefully selected references is an invaluable resource for further study of fuzzy logic and neural networks. Each chapter is followed by a question bank

Neural activity, neural connectivity, and the processing of emotionally valenced information in older adults: links with life satisfaction.

Science.gov (United States)

Waldinger, Robert J; Kensinger, Elizabeth A; Schulz, Marc S

2011-09-01

This study examines whether differences in late-life well-being are linked to how older adults encode emotionally valenced information. Using fMRI with 39 older adults varying in life satisfaction, we examined how viewing positive and negative images would affect activation and connectivity of an emotion-processing network. Participants engaged most regions within this network more robustly for positive than for negative images, but within the PFC this effect was moderated by life satisfaction, with individuals higher in satisfaction showing lower levels of activity during the processing of positive images. Participants high in satisfaction showed stronger correlations among network regions-particularly between the amygdala and other emotion processing regions-when viewing positive, as compared with negative, images. Participants low in satisfaction showed no valence effect. Findings suggest that late-life satisfaction is linked with how emotion-processing regions are engaged and connected during processing of valenced information. This first demonstration of a link between neural recruitment and late-life well-being suggests that differences in neural network activation and connectivity may account for the preferential encoding of positive information seen in some older adults.

Neural Computations in a Dynamical System with Multiple Time Scales

Directory of Open Access Journals (Sweden)

Yuanyuan Mi

2016-09-01

Full Text Available Neural systems display rich short-term dynamics at various levels, e.g., spike-frequencyadaptation (SFA at single neurons, and short-term facilitation (STF and depression (STDat neuronal synapses. These dynamical features typically covers a broad range of time scalesand exhibit large diversity in different brain regions. It remains unclear what the computationalbenefit for the brain to have such variability in short-term dynamics is. In this study, we proposethat the brain can exploit such dynamical features to implement multiple seemingly contradictorycomputations in a single neural circuit. To demonstrate this idea, we use continuous attractorneural network (CANN as a working model and include STF, SFA and STD with increasing timeconstants in their dynamics. Three computational tasks are considered, which are persistent activity,adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, andhence cannot be implemented by a single dynamical feature or any combination with similar timeconstants. However, with properly coordinated STF, SFA and STD, we show that the network isable to implement the three computational tasks concurrently. We hope this study will shed lighton the understanding of how the brain orchestrates its rich dynamics at various levels to realizediverse cognitive functions.

Experiencing Past and Future Personal Events: Functional Neuroimaging Evidence on the Neural Bases of Mental Time Travel

Science.gov (United States)

Botzung, Anne; Denkova, Ekaterina; Manning, Lilianne

2008-01-01

Functional MRI was used in healthy subjects to investigate the existence of common neural structures supporting re-experiencing the past and pre-experiencing the future. Past and future events evocation appears to involve highly similar patterns of brain activation including, in particular, the medial prefrontal cortex, posterior regions and the…

Transcriptional profiling of adult neural stem-like cells from the human brain.

Directory of Open Access Journals (Sweden)

Cecilie Jonsgar Sandberg

Full Text Available There is a great potential for the development of new cell replacement strategies based on adult human neural stem-like cells. However, little is known about the hierarchy of cells and the unique molecular properties of stem- and progenitor cells of the nervous system. Stem cells from the adult human brain can be propagated and expanded in vitro as free floating neurospheres that are capable of self-renewal and differentiation into all three cell types of the central nervous system. Here we report the first global gene expression study of adult human neural stem-like cells originating from five human subventricular zone biopsies (mean age 42, range 33-60. Compared to adult human brain tissue, we identified 1,189 genes that were significantly up- and down-regulated in adult human neural stem-like cells (1% false discovery rate. We found that adult human neural stem-like cells express stem cell markers and have reduced levels of markers that are typical of the mature cells in the nervous system. We report that the genes being highly expressed in adult human neural stem-like cells are associated with developmental processes and the extracellular region of the cell. The calcium signaling pathway and neuroactive ligand-receptor interactions are enriched among the most differentially regulated genes between adult human neural stem-like cells and adult human brain tissue. We confirmed the expression of 10 of the most up-regulated genes in adult human neural stem-like cells in an additional sample set that included adult human neural stem-like cells (n = 6, foetal human neural stem cells (n = 1 and human brain tissues (n = 12. The NGFR, SLITRK6 and KCNS3 receptors were further investigated by immunofluorescence and shown to be heterogeneously expressed in spheres. These receptors could potentially serve as new markers for the identification and characterisation of neural stem- and progenitor cells or as targets for manipulation of cellular

Neural networks and statistical learning

CERN Document Server

Du, Ke-Lin

2014-01-01

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...

Generating regionalized neuronal cells from pluripotency, a step-by-step protocol

Directory of Open Access Journals (Sweden)

Agnete eKirkeby

2013-01-01

Full Text Available Human pluripotent stem cells possess the potential to generate cells for regenerative therapies in patients with neurodegenerative diseases, and constitute an excellent cell source for studying human neural development and disease modeling. Protocols for neural differentiation of human pluripotent stem cells have undergone significant progress during recent years, allowing for rapid and synchronized neural conversion. Differentiation procedures can further be combined with accurate and efficient positional patterning to yield regionalized neural progenitors and subtype-specific neurons corresponding to different parts of the developing human brain. Here, we present a step-by-step protocol for neuralization and regionalization of human pluripotent cells for transplantation studies or in vitro analysis.

Up-regulation of neural indicators of empathic concern in an offender population.

Science.gov (United States)

Arbuckle, Nathan L; Shane, Matthew S

2017-08-01

Empathic concern has traditionally been conceived of as a spontaneous reaction to others experiencing pain or distress. As such, the potential role of more deliberate control over empathic responses has frequently been overlooked. The present fMRI study evaluated the role of such deliberate control in empathic concern by examining the extent to which a sample of offenders recruited through probation/parole could voluntarily modulate their neural activity to another person in pain. Offenders were asked to either passively view pictures of other people in painful or non-painful situations, or to actively modulate their level of concern for the person in pain. During passive viewing of painful versus non-painful pictures, offenders showed minimal neural activity in regions previously linked to empathy for pain (e.g., dorsal anterior cingulate cortex and bilateral insula). However, when instructed to try to increase their concern for the person in pain, offenders demonstrated significant increases within these regions. These findings are consistent with recent theories of empathy as motivational in nature, and suggest that limitations in empathic concern may include a motivational component.

Neural evidence that human emotions share core affective properties.

Science.gov (United States)

Wilson-Mendenhall, Christine D; Barrett, Lisa Feldman; Barsalou, Lawrence W

2013-06-01

Research on the "emotional brain" remains centered around the idea that emotions like fear, happiness, and sadness result from specialized and distinct neural circuitry. Accumulating behavioral and physiological evidence suggests, instead, that emotions are grounded in core affect--a person's fluctuating level of pleasant or unpleasant arousal. A neuroimaging study revealed that participants' subjective ratings of valence (i.e., pleasure/displeasure) and of arousal evoked by various fear, happiness, and sadness experiences correlated with neural activity in specific brain regions (orbitofrontal cortex and amygdala, respectively). We observed these correlations across diverse instances within each emotion category, as well as across instances from all three categories. Consistent with a psychological construction approach to emotion, the results suggest that neural circuitry realizes more basic processes across discrete emotions. The implicated brain regions regulate the body to deal with the world, producing the affective changes at the core of emotions and many other psychological phenomena.

Application of neural network to CT

International Nuclear Information System (INIS)

Ma, Xiao-Feng; Takeda, Tatsuoki

1999-01-01

This paper presents a new method for two-dimensional image reconstruction by using a multilayer neural network. Multilayer neural networks are extensively investigated and practically applied to solution of various problems such as inverse problems or time series prediction problems. From learning an input-output mapping from a set of examples, neural networks can be regarded as synthesizing an approximation of multidimensional function (that is, solving the problem of hypersurface reconstruction, including smoothing and interpolation). From this viewpoint, neural networks are well suited to the solution of CT image reconstruction. Though a conventionally used object function of a neural network is composed of a sum of squared errors of the output data, we can define an object function composed of a sum of residue of an integral equation. By employing an appropriate line integral for this integral equation, we can construct a neural network that can be used for CT. We applied this method to some model problems and obtained satisfactory results. As it is not necessary to discretized the integral equation using this reconstruction method, therefore it is application to the problem of complicated geometrical shapes is also feasible. Moreover, in neural networks, interpolation is performed quite smoothly, as a result, inverse mapping can be achieved smoothly even in case of including experimental and numerical errors, However, use of conventional back propagation technique for optimization leads to an expensive computation cost. To overcome this drawback, 2nd order optimization methods or parallel computing will be applied in future. (J.P.N.)

An ArcGIS approach to include tectonic structures in point data regionalization.

Science.gov (United States)

Darsow, Andreas; Schafmeister, Maria-Theresia; Hofmann, Thilo

2009-01-01

Point data derived from drilling logs must often be regionalized. However, aquifers may show discontinuous surface structures, such as the offset of an aquitard caused by tectonic faults. One main challenge has been to incorporate these structures into the regionalization process of point data. We combined ordinary kriging and inverse distance weighted (IDW) interpolation to account for neotectonic structures in the regionalization process. The study area chosen to test this approach is the largest porous aquifer in Austria. It consists of three basins formed by neotectonic events and delimited by steep faults with a vertical offset of the aquitard up to 70 m within very short distances. First, ordinary kriging was used to incorporate the characteristic spatial variability of the aquitard location by means of a variogram. The tectonic faults could be included into the regionalization process by using breaklines with buffer zones. All data points inside the buffer were deleted. Last, IDW was performed, resulting in an aquitard map representing the discontinuous surface structures. This approach enables one to account for such surfaces using the standard software package ArcGIS; therefore, it could be adopted in many practical applications.

Therapeutic physical exercise in neural injury: friend or foe?

Science.gov (United States)

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.

Emerging trends in neuro engineering and neural computation

CERN Document Server

Lee, Kendall; Garmestani, Hamid; Lim, Chee

2017-01-01

This book focuses on neuro-engineering and neural computing, a multi-disciplinary field of research attracting considerable attention from engineers, neuroscientists, microbiologists and material scientists. It explores a range of topics concerning the design and development of innovative neural and brain interfacing technologies, as well as novel information acquisition and processing algorithms to make sense of the acquired data. The book also highlights emerging trends and advances regarding the applications of neuro-engineering in real-world scenarios, such as neural prostheses, diagnosis of neural degenerative diseases, deep brain stimulation, biosensors, real neural network-inspired artificial neural networks (ANNs) and the predictive modeling of information flows in neuronal networks. The book is broadly divided into three main sections including: current trends in technological developments, neural computation techniques to make sense of the neural behavioral data, and application of these technologie...

The Neural Basis of Vocal Pitch Imitation in Humans.

Science.gov (United States)

Belyk, Michel; Pfordresher, Peter Q; Liotti, Mario; Brown, Steven

2016-04-01

Vocal imitation is a phenotype that is unique to humans among all primate species, and so an understanding of its neural basis is critical in explaining the emergence of both speech and song in human evolution. Two principal neural models of vocal imitation have emerged from a consideration of nonhuman animals. One hypothesis suggests that putative mirror neurons in the inferior frontal gyrus pars opercularis of Broca's area may be important for imitation. An alternative hypothesis derived from the study of songbirds suggests that the corticostriate motor pathway performs sensorimotor processes that are specific to vocal imitation. Using fMRI with a sparse event-related sampling design, we investigated the neural basis of vocal imitation in humans by comparing imitative vocal production of pitch sequences with both nonimitative vocal production and pitch discrimination. The strongest difference between these tasks was found in the putamen bilaterally, providing a striking parallel to the role of the analogous region in songbirds. Other areas preferentially activated during imitation included the orofacial motor cortex, Rolandic operculum, and SMA, which together outline the corticostriate motor loop. No differences were seen in the inferior frontal gyrus. The corticostriate system thus appears to be the central pathway for vocal imitation in humans, as predicted from an analogy with songbirds.

Neural correlates of long-term intense romantic love.

Science.gov (United States)

Acevedo, Bianca P; Aron, Arthur; Fisher, Helen E; Brown, Lucy L

2012-02-01

The present study examined the neural correlates of long-term intense romantic love using functional magnetic resonance imaging (fMRI). Ten women and 7 men married an average of 21.4 years underwent fMRI while viewing facial images of their partner. Control images included a highly familiar acquaintance; a close, long-term friend; and a low-familiar person. Effects specific to the intensely loved, long-term partner were found in: (i) areas of the dopamine-rich reward and basal ganglia system, such as the ventral tegmental area (VTA) and dorsal striatum, consistent with results from early-stage romantic love studies; and (ii) several regions implicated in maternal attachment, such as the globus pallidus (GP), substantia nigra, Raphe nucleus, thalamus, insular cortex, anterior cingulate and posterior cingulate. Correlations of neural activity in regions of interest with widely used questionnaires showed: (i) VTA and caudate responses correlated with romantic love scores and inclusion of other in the self; (ii) GP responses correlated with friendship-based love scores; (iii) hypothalamus and posterior hippocampus responses correlated with sexual frequency; and (iv) caudate, septum/fornix, posterior cingulate and posterior hippocampus responses correlated with obsession. Overall, results suggest that for some individuals the reward-value associated with a long-term partner may be sustained, similar to new love, but also involves brain systems implicated in attachment and pair-bonding.

Neural correlates of long-term intense romantic love

Science.gov (United States)

Aron, Arthur; Fisher, Helen E.; Brown, Lucy L.

2012-01-01

The present study examined the neural correlates of long-term intense romantic love using functional magnetic resonance imaging (fMRI). Ten women and 7 men married an average of 21.4 years underwent fMRI while viewing facial images of their partner. Control images included a highly familiar acquaintance; a close, long-term friend; and a low-familiar person. Effects specific to the intensely loved, long-term partner were found in: (i) areas of the dopamine-rich reward and basal ganglia system, such as the ventral tegmental area (VTA) and dorsal striatum, consistent with results from early-stage romantic love studies; and (ii) several regions implicated in maternal attachment, such as the globus pallidus (GP), substantia nigra, Raphe nucleus, thalamus, insular cortex, anterior cingulate and posterior cingulate. Correlations of neural activity in regions of interest with widely used questionnaires showed: (i) VTA and caudate responses correlated with romantic love scores and inclusion of other in the self; (ii) GP responses correlated with friendship-based love scores; (iii) hypothalamus and posterior hippocampus responses correlated with sexual frequency; and (iv) caudate, septum/fornix, posterior cingulate and posterior hippocampus responses correlated with obsession. Overall, results suggest that for some individuals the reward-value associated with a long-term partner may be sustained, similar to new love, but also involves brain systems implicated in attachment and pair-bonding. PMID:21208991

22nd Italian Workshop on Neural Nets

CERN Document Server

Bassis, Simone; Esposito, Anna; Morabito, Francesco

2013-01-01

This volume collects a selection of contributions which has been presented at the 22nd Italian Workshop on Neural Networks, the yearly meeting of the Italian Society for Neural Networks (SIREN). The conference was held in Italy, Vietri sul Mare (Salerno), during May 17-19, 2012. The annual meeting of SIREN is sponsored by International Neural Network Society (INNS), European Neural Network Society (ENNS) and IEEE Computational Intelligence Society (CIS). The book – as well as the workshop-  is organized in three main components, two special sessions and a group of regular sessions featuring different aspects and point of views of artificial neural networks and natural intelligence, also including applications of present compelling interest.

Neural Correlates of Direct and Indirect Suppression of Autobiographical Memories.

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Noreen, Saima; O'Connor, Akira R; MacLeod, Malcolm D

2016-01-01

Research indicates that there are two possible mechanisms by which particular target memories can be intentionally forgotten. Direct suppression, which involves the suppression of the unwanted memory directly, and is dependent on a fronto-hippocampal modulatory process, and, memory substitution, which includes directing one's attention to an alternative memory in order to prevent the unwanted memory from coming to mind, and involves engaging the caudal prefrontal cortex (cPFC) and the mid-ventrolateral prefrontal cortex (VLPFC) regions. Research to date, however, has investigated the neural basis of memory suppression of relatively simple information. The aim of the current study was to use fMRI to identify the neural mechanisms associated with the suppression of autobiographical memories. In the present study, 22 participants generated memories in response to a series of cue words. In a second session, participants learnt these cue-memory pairings, and were subsequently presented with a cue word and asked either to recall (think) or to suppress (no-think) the associated memory, or to think of an alternative memory in order to suppress the original memory (memory-substitution). Our findings demonstrated successful forgetting effects in the no-think and memory substitution conditions. Although we found no activation in the dorsolateral prefrontal cortex, there was reduced hippocampal activation during direct suppression. In the memory substitution condition, however, we failed to find increased activation in the cPFC and VLPFC regions. Our findings suggest that the suppression of autobiographical memories may rely on different neural mechanisms to those established for other types of material in memory.

Neural Correlates of Direct and Indirect Suppression of Autobiographical Memories

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Saima eNoreen

2016-03-01

Full Text Available Research indicates that there are two possible mechanisms by which particular target memories can be intentionally forgotten. Direct suppression, which involves the suppression of the unwanted memory directly, and is dependent on a fronto-hippocampal modulatory process, and, memory substitution, which includes directing one's attention to an alternative memory in order to prevent the unwanted memory from coming to mind, and involves engaging the caudal prefrontal cortex (cPFC and the mid-ventrolateral prefrontal cortex (VLPFC regions. Research to date, however, has investigated the neural basis of memory suppression of relatively simple information. The aim of the current study was to use fMRI to identify the neural mechanisms associated with the suppression of autobiographical memories. In the present study, 22 participants generated memories in response to a series of cue words. In a second session, participants learnt these cue-memory pairings, and were subsequently presented with a cue word and asked either to recall (think or to suppress (no-think the associated memory, or to think of an alternative memory in order to suppress the original memory (memory-substitution. Our findings demonstrated successful forgetting effects in the no-think and memory substitution conditions. Although we found no activation in the dorsolateral prefrontal cortex there was reduced hippocampal activation during direct suppression. In the memory substitution condition, however, we failed to find increased activation in the cPFC and VLPFC regions. Our findings suggest that the suppression of autobiographical memories may rely on different neural mechanisms to those established for other types of material in memory.

Neural representations of emotion are organized around abstract event features.

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Skerry, Amy E; Saxe, Rebecca

2015-08-03

Research on emotion attribution has tended to focus on the perception of overt expressions of at most five or six basic emotions. However, our ability to identify others' emotional states is not limited to perception of these canonical expressions. Instead, we make fine-grained inferences about what others feel based on the situations they encounter, relying on knowledge of the eliciting conditions for different emotions. In the present research, we provide convergent behavioral and neural evidence concerning the representations underlying these concepts. First, we find that patterns of activity in mentalizing regions contain information about subtle emotional distinctions conveyed through verbal descriptions of eliciting situations. Second, we identify a space of abstract situation features that well captures the emotion discriminations subjects make behaviorally and show that this feature space outperforms competing models in capturing the similarity space of neural patterns in these regions. Together, the data suggest that our knowledge of others' emotions is abstract and high dimensional, that brain regions selective for mental state reasoning support relatively subtle distinctions between emotion concepts, and that the neural representations in these regions are not reducible to more primitive affective dimensions such as valence and arousal. Copyright © 2015 Elsevier Ltd. All rights reserved.

Inverting radiometric measurements with a neural network

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Measure, Edward M.; Yee, Young P.; Balding, Jeff M.; Watkins, Wendell R.

1992-02-01

A neural network scheme for retrieving remotely sensed vertical temperature profiles was applied to observed ground based radiometer measurements. The neural network used microwave radiance measurements and surface measurements of temperature and pressure as inputs. Because the microwave radiometer is capable of measuring 4 oxygen channels at 5 different elevation angles (9, 15, 25, 40, and 90 degs), 20 microwave measurements are potentially available. Because these measurements have considerable redundancy, a neural network was experimented with, accepting as inputs microwave measurements taken at 53.88 GHz, 40 deg; 57.45 GHz, 40 deg; and 57.45, 90 deg. The primary test site was located at White Sands Missile Range (WSMR), NM. Results are compared with measurements made simultaneously with balloon borne radiosonde instruments and with radiometric temperature retrievals made using more conventional retrieval algorithms. The neural network was trained using a Widrow-Hoff delta rule procedure. Functions of date to include season dependence in the retrieval process and functions of time to include diurnal effects were used as inputs to the neural network.

Neural networks with discontinuous/impact activations

CERN Document Server

Akhmet, Marat

2014-01-01

This book presents as its main subject new models in mathematical neuroscience. A wide range of neural networks models with discontinuities are discussed, including impulsive differential equations, differential equations with piecewise constant arguments, and models of mixed type. These models involve discontinuities, which are natural because huge velocities and short distances are usually observed in devices modeling the networks. A discussion of the models, appropriate for the proposed applications, is also provided. This book also: Explores questions related to the biological underpinning for models of neural networks\\ Considers neural networks modeling using differential equations with impulsive and piecewise constant argument discontinuities Provides all necessary mathematical basics for application to the theory of neural networks Neural Networks with Discontinuous/Impact Activations is an ideal book for researchers and professionals in the field of engineering mathematics that have an interest in app...

Maximum entropy methods for extracting the learned features of deep neural networks.

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Finnegan, Alex; Song, Jun S

2017-10-01

New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

A one-layer recurrent neural network for constrained nonconvex optimization.

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Li, Guocheng; Yan, Zheng; Wang, Jun

2015-01-01

In this paper, a one-layer recurrent neural network is proposed for solving nonconvex optimization problems subject to general inequality constraints, designed based on an exact penalty function method. It is proved herein that any neuron state of the proposed neural network is convergent to the feasible region in finite time and stays there thereafter, provided that the penalty parameter is sufficiently large. The lower bounds of the penalty parameter and convergence time are also estimated. In addition, any neural state of the proposed neural network is convergent to its equilibrium point set which satisfies the Karush-Kuhn-Tucker conditions of the optimization problem. Moreover, the equilibrium point set is equivalent to the optimal solution to the nonconvex optimization problem if the objective function and constraints satisfy given conditions. Four numerical examples are provided to illustrate the performances of the proposed neural network.

Common and distinct neural mechanisms of attentional switching and response conflict.

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Kim, Chobok; Johnson, Nathan F; Gold, Brian T

2012-08-21

The human capacities for overcoming prepotent actions and flexibly switching between tasks represent cornerstones of cognitive control. Functional neuroimaging has implicated a diverse set of brain regions contributing to each of these cognitive control processes. However, the extent to which attentional switching and response conflict draw on shared or distinct neural mechanisms remains unclear. The current study examined the neural correlates of response conflict and attentional switching using event-related functional magnetic resonance imaging (fMRI) and a fully randomized 2×2 design. We manipulated an arrow-word version of the Stroop task to measure conflict and switching in the context of a single task decision, in response to a common set of stimuli. Under these common conditions, both behavioral and imaging data showed significant main effects of conflict and switching but no interaction. However, conjunction analyses identified frontal regions involved in both switching and response conflict, including the dorsal anterior cingulate cortex (dACC) and left inferior frontal junction. In addition, connectivity analyses demonstrated task-dependent functional connectivity patterns between dACC and inferior temporal cortex for attentional switching and between dACC and posterior parietal cortex for response conflict. These results suggest that the brain makes use of shared frontal regions, but can dynamically modulate the connectivity patterns of some of those regions, to deal with attentional switching and response conflict. Copyright © 2012 Elsevier B.V. All rights reserved.

Artificial Neural Network Analysis of Xinhui Pericarpium Citri ...

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Methods: Artificial neural networks (ANN) models, including general regression neural network (GRNN) and multi-layer ... N-hexane (HPLC grade) was purchased from. Fisher Scientific. ..... Simultaneous Quantification of Seven Flavonoids in.

Sex differences of gray matter morphology in cortico-limbic-striatal neural system in major depressive disorder.

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Kong, Lingtao; Chen, Kaiyuan; Womer, Fay; Jiang, Wenyan; Luo, Xingguang; Driesen, Naomi; Liu, Jie; Blumberg, Hilary; Tang, Yanqing; Xu, Ke; Wang, Fei

2013-06-01

Sex differences are observed in both epidemiological and clinical aspects of major depressive disorder (MDD). The cortico-limbic-striatal neural system, including the prefrontal cortex, amygdala, hippocampus, and striatum, have shown sexually dimorphic morphological features and have been implicated in the dysfunctional regulation of mood and emotion in MDD. In this study, we utilized a whole-brain, voxel-based approach to examine sex differences in the regional distribution of gray matter (GM) morphological abnormalities in medication-naïve participants with MDD. Participants included 29 medication-naïve individuals with MDD (16 females and 13 males) and 33 healthy controls (HC) (17 females and 16 males). Gray matter morphology of the cortico-limbic-striatal neural system was examined using voxel-based morphometry analyzes of high-resolution structural magnetic resonance imaging scans. The main effect of diagnosis and interaction effect of diagnosis by sex on GM morphology were statistically significant (p sex-related patterns of abnormalities within the cortico-limbic-strial neural system, such as predominant prefrontal-limbic abnormalities in MDD females vs. predominant prefrontal-striatal abnormalities in MDD males, suggest differences in neural circuitry that may mediate sex differences in the clinical presentation of MDD and potential targets for sex-differentiated treatment of the disorder. Copyright © 2013 Elsevier Ltd. All rights reserved.

Segmentation of Drosophila Heart in Optical Coherence Microscopy Images Using Convolutional Neural Networks

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Duan, Lian; Qin, Xi; He, Yuanhao; Sang, Xialin; Pan, Jinda; Xu, Tao; Men, Jing; Tanzi, Rudolph E.; Li, Airong; Ma, Yutao; Zhou, Chao

2018-01-01

Convolutional neural networks are powerful tools for image segmentation and classification. Here, we use this method to identify and mark the heart region of Drosophila at different developmental stages in the cross-sectional images acquired by a custom optical coherence microscopy (OCM) system. With our well-trained convolutional neural network model, the heart regions through multiple heartbeat cycles can be marked with an intersection over union (IOU) of ~86%. Various morphological and dyn...

Anger under control: neural correlates of frustration as a function of trait aggression.

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Christina M Pawliczek

Full Text Available Antisocial behavior and aggression are prominent symptoms in several psychiatric disorders including antisocial personality disorder. An established precursor to aggression is a frustrating event, which can elicit anger or exasperation, thereby prompting aggressive responses. While some studies have investigated the neural correlates of frustration and aggression, examination of their relation to trait aggression in healthy populations are rare. Based on a screening of 550 males, we formed two extreme groups, one including individuals reporting high (n=21 and one reporting low (n=18 trait aggression. Using functional magnetic resonance imaging (fMRI at 3T, all participants were put through a frustration task comprising unsolvable anagrams of German nouns. Despite similar behavioral performance, males with high trait aggression reported higher ratings of negative affect and anger after the frustration task. Moreover, they showed relatively decreased activation in the frontal brain regions and the dorsal anterior cingulate cortex (dACC as well as relatively less amygdala activation in response to frustration. Our findings indicate distinct frontal and limbic processing mechanisms following frustration modulated by trait aggression. In response to a frustrating event, HA individuals show some of the personality characteristics and neural processing patterns observed in abnormally aggressive populations. Highlighting the impact of aggressive traits on the behavioral and neural responses to frustration in non-psychiatric extreme groups can facilitate further characterization of neural dysfunctions underlying psychiatric disorders that involve abnormal frustration processing and aggression.

Mammographic Image Analysis of Breast Using Neural Network

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Lesa MAMBWE

2015-07-01

Full Text Available This paper discusses the various stages of detecting tumours of the breast mammogram images. A Neural Network algorithm is applied for obtaining the complete classification of the tumour into normal or abnormal. The most important procedure or technique for obtaining the classification is the feature extraction, by extracting a few of discriminative features, first-order statistical intensities and gradients. The Image Pre-processing technique is essential prior to Image Segmentation in order to obtain accurate segmentation. Thus mass detection can be carried out. The processes involved in achieving the three techniques mentioned above include global equalization transformation, denoising, binarization, breast orientation determination and the pectoral muscle suppression. The presented feature difference matrices could be created by five features extracted from a suspicious region of interest (ROI. Grey Level Co-occurrence Matrix (GLCM aids the obtaining of statistical features such as correlation, energy, entropy and homogeneity. The other statistical to features to obtain are area, moment, variance, entropy, standard deviation and moment. The Neural network technique yields results of abnormal mammograms.

Optical-Correlator Neural Network Based On Neocognitron

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Chao, Tien-Hsin; Stoner, William W.

1994-01-01

Multichannel optical correlator implements shift-invariant, high-discrimination pattern-recognizing neural network based on paradigm of neocognitron. Selected as basic building block of this neural network because invariance under shifts is inherent advantage of Fourier optics included in optical correlators in general. Neocognitron is conceptual electronic neural-network model for recognition of visual patterns. Multilayer processing achieved by iteratively feeding back output of feature correlator to input spatial light modulator and updating Fourier filters. Neural network trained by use of characteristic features extracted from target images. Multichannel implementation enables parallel processing of large number of selected features.

Statistical downscaling of precipitation using long short-term memory recurrent neural networks

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Misra, Saptarshi; Sarkar, Sudeshna; Mitra, Pabitra

2017-11-01

Hydrological impacts of global climate change on regional scale are generally assessed by downscaling large-scale climatic variables, simulated by General Circulation Models (GCMs), to regional, small-scale hydrometeorological variables like precipitation, temperature, etc. In this study, we propose a new statistical downscaling model based on Recurrent Neural Network with Long Short-Term Memory which captures the spatio-temporal dependencies in local rainfall. The previous studies have used several other methods such as linear regression, quantile regression, kernel regression, beta regression, and artificial neural networks. Deep neural networks and recurrent neural networks have been shown to be highly promising in modeling complex and highly non-linear relationships between input and output variables in different domains and hence we investigated their performance in the task of statistical downscaling. We have tested this model on two datasets—one on precipitation in Mahanadi basin in India and the second on precipitation in Campbell River basin in Canada. Our autoencoder coupled long short-term memory recurrent neural network model performs the best compared to other existing methods on both the datasets with respect to temporal cross-correlation, mean squared error, and capturing the extremes.

Concurrent OCT imaging of stimulus evoked retinal neural activation and hemodynamic responses

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Son, Taeyoon; Wang, Benquan; Lu, Yiming; Chen, Yanjun; Cao, Dingcai; Yao, Xincheng

2017-02-01

It is well established that major retinal diseases involve distortions of the retinal neural physiology and blood vascular structures. However, the details of distortions in retinal neurovascular coupling associated with major eye diseases are not well understood. In this study, a multi-modal optical coherence tomography (OCT) imaging system was developed to enable concurrent imaging of retinal neural activity and vascular hemodynamics. Flicker light stimulation was applied to mouse retinas to evoke retinal neural responses and hemodynamic changes. The OCT images were acquired continuously during the pre-stimulation, light-stimulation, and post-stimulation phases. Stimulus-evoked intrinsic optical signals (IOSs) and hemodynamic changes were observed over time in blood-free and blood regions, respectively. Rapid IOSs change occurred almost immediately after stimulation. Both positive and negative signals were observed in adjacent retinal areas. The hemodynamic changes showed time delays after stimulation. The signal magnitudes induced by light stimulation were observed in blood regions and did not show significant changes in blood-free regions. These differences may arise from different mechanisms in blood vessels and neural tissues in response to light stimulation. These characteristics agreed well with our previous observations in mouse retinas. Further development of the multimodal OCT may provide a new imaging method for studying how retinal structures and metabolic and neural functions are affected by age-related macular degeneration (AMD), glaucoma, diabetic retinopathy (DR), and other diseases, which promises novel noninvasive biomarkers for early disease detection and reliable treatment evaluations of eye diseases.

A fully implantable rodent neural stimulator

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Perry, D. W. J.; Grayden, D. B.; Shepherd, R. K.; Fallon, J. B.

2012-02-01

The ability to electrically stimulate neural and other excitable tissues in behaving experimental animals is invaluable for both the development of neural prostheses and basic neurological research. We developed a fully implantable neural stimulator that is able to deliver two channels of intra-cochlear electrical stimulation in the rat. It is powered via a novel omni-directional inductive link and includes an on-board microcontroller with integrated radio link, programmable current sources and switching circuitry to generate charge-balanced biphasic stimulation. We tested the implant in vivo and were able to elicit both neural and behavioural responses. The implants continued to function for up to five months in vivo. While targeted to cochlear stimulation, with appropriate electrode arrays the stimulator is well suited to stimulating other neurons within the peripheral or central nervous systems. Moreover, it includes significant on-board data acquisition and processing capabilities, which could potentially make it a useful platform for telemetry applications, where there is a need to chronically monitor physiological variables in unrestrained animals.

Neural representations of the self and the mother for Chinese individuals.

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Gaowa Wuyun

Full Text Available An important question in social neuroscience is the similarities and differences in the neural representations between the self and close others. Most studies examining this topic have identified the medial prefrontal cortex (MPFC region as the primary area involved in this process. However, several studies have reported conflicting data, making further investigation of this topic very important. In this functional magnetic resonance imaging (fMRI study, we investigated the brain activity in the anterior cingulate cortex (ACC when Chinese participants passively listened to their self-name (SN, their mother's name (MN, and unknown names (UN. The results showed that compared with UN recognition, SN perception was associated with a robust activation in a widely distributed bilateral network, including the cortical midline structure (the MPFC and ACC, the inferior frontal gyrus, and the middle temporal gyrus. The SN invoked the bilateral superior temporal gyrus in contrast to the MN; the MN recognition provoked a stronger activation in the central and posterior brain regions in contrast to the SN recognition. The SN and MN caused an activation of overlapping areas, namely, the ACC, MPFC, and superior frontal gyrus. These results suggest that Chinese individuals utilize certain common brain region in processing both the SN and the MN. The present findings provide evidence for the neural basis of the self and close others for Chinese individuals.

Decentralized neural control application to robotics

CERN Document Server

Garcia-Hernandez, Ramon; Sanchez, Edgar N; Alanis, Alma y; Ruz-Hernandez, Jose A

2017-01-01

This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors. This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF). The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold. The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network. The third control scheme applies a decentralized neural i...

Continuity and change in children's longitudinal neural responses to numbers.

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Emerson, Robert W; Cantlon, Jessica F

2015-03-01

Human children possess the ability to approximate numerical quantity nonverbally from a young age. Over the course of early childhood, children develop increasingly precise representations of numerical values, including a symbolic number system that allows them to conceive of numerical information as Arabic numerals or number words. Functional brain imaging studies of adults report that activity in bilateral regions of the intraparietal sulcus (IPS) represents a key neural correlate of numerical cognition. Developmental neuroimaging studies indicate that the right IPS develops its number-related neural response profile more rapidly than the left IPS during early childhood. One prediction that can be derived from previous findings is that there is longitudinal continuity in the number-related neural responses of the right IPS over development while the development of the left IPS depends on the acquisition of numerical skills. We tested this hypothesis using fMRI in a longitudinal design with children ages 4 to 9. We found that neural responses in the right IPS are correlated over a 1-2-year period in young children whereas left IPS responses change systematically as a function of children's numerical discrimination acuity. The data are consistent with the hypothesis that functional properties of the right IPS in numerical processing are stable over early childhood whereas the functions of the left IPS are dynamically modulated by the development of numerical skills. © 2014 John Wiley & Sons Ltd.

Predictable information in neural signals during resting state is reduced in autism spectrum disorder.

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Brodski-Guerniero, Alla; Naumer, Marcus J; Moliadze, Vera; Chan, Jason; Althen, Heike; Ferreira-Santos, Fernando; Lizier, Joseph T; Schlitt, Sabine; Kitzerow, Janina; Schütz, Magdalena; Langer, Anne; Kaiser, Jochen; Freitag, Christine M; Wibral, Michael

2018-04-04

The neurophysiological underpinnings of the nonsocial symptoms of autism spectrum disorder (ASD) which include sensory and perceptual atypicalities remain poorly understood. Well-known accounts of less dominant top-down influences and more dominant bottom-up processes compete to explain these characteristics. These accounts have been recently embedded in the popular framework of predictive coding theory. To differentiate between competing accounts, we studied altered information dynamics in ASD by quantifying predictable information in neural signals. Predictable information in neural signals measures the amount of stored information that is used for the next time step of a neural process. Thus, predictable information limits the (prior) information which might be available for other brain areas, for example, to build predictions for upcoming sensory information. We studied predictable information in neural signals based on resting-state magnetoencephalography (MEG) recordings of 19 ASD patients and 19 neurotypical controls aged between 14 and 27 years. Using whole-brain beamformer source analysis, we found reduced predictable information in ASD patients across the whole brain, but in particular in posterior regions of the default mode network. In these regions, epoch-by-epoch predictable information was positively correlated with source power in the alpha and beta frequency range as well as autocorrelation decay time. Predictable information in precuneus and cerebellum was negatively associated with nonsocial symptom severity, indicating a relevance of the analysis of predictable information for clinical research in ASD. Our findings are compatible with the assumption that use or precision of prior knowledge is reduced in ASD patients. © 2018 Wiley Periodicals, Inc.

Altered Synchronizations among Neural Networks in Geriatric Depression.

Science.gov (United States)

Wang, Lihong; Chou, Ying-Hui; Potter, Guy G; Steffens, David C

2015-01-01

Although major depression has been considered as a manifestation of discoordinated activity between affective and cognitive neural networks, only a few studies have examined the relationships among neural networks directly. Because of the known disconnection theory, geriatric depression could be a useful model in studying the interactions among different networks. In the present study, using independent component analysis to identify intrinsically connected neural networks, we investigated the alterations in synchronizations among neural networks in geriatric depression to better understand the underlying neural mechanisms. Resting-state fMRI data was collected from thirty-two patients with geriatric depression and thirty-two age-matched never-depressed controls. We compared the resting-state activities between the two groups in the default-mode, central executive, attention, salience, and affective networks as well as correlations among these networks. The depression group showed stronger activity than the controls in an affective network, specifically within the orbitofrontal region. However, unlike the never-depressed controls, geriatric depression group lacked synchronized/antisynchronized activity between the affective network and the other networks. Those depressed patients with lower executive function has greater synchronization between the salience network with the executive and affective networks. Our results demonstrate the effectiveness of the between-network analyses in examining neural models for geriatric depression.

Neural system for updating object working memory from different sources: sensory stimuli or long-term memory.

Science.gov (United States)

Roth, Jennifer K; Courtney, Susan M

2007-11-15

Working memory (WM) is the active maintenance of currently relevant information so that it is available for use. A crucial component of WM is the ability to update the contents when new information becomes more relevant than previously maintained information. New information can come from different sources, including from sensory stimuli (SS) or from long-term memory (LTM). Updating WM may involve a single neural system regardless of source, distinct systems for each source, or a common network with additional regions involved specifically in sensory or LTM processes. The current series of experiments indicates that a single fronto-parietal network (including supplementary motor area, parietal, left inferior frontal junction, middle frontal gyrus) is active in updating WM regardless of the source of information. Bilateral cuneus was more active during updating WM from LTM than updating from SS, but the activity in this region was attributable to recalling information from LTM regardless of whether that information was to be entered into WM for future use or not. No regions were found to be more active during updating from SS than updating from LTM. Functional connectivity analysis revealed that different regions within this common update network were differentially more correlated with visual processing regions when participants updated from SS, and more correlated with LTM processing regions when participants updated from the contents of LTM. These results suggest that a single neural mechanism is responsible for controlling the contents of WM regardless of whether that information originates from a sensory stimulus or from LTM. This network of regions involved in updating WM interacts with the rest of the brain differently depending on the source of newly relevant information.

Automated bony region identification using artificial neural networks: reliability and validation measurements

International Nuclear Information System (INIS)

Gassman, Esther E.; Kallemeyn, Nicole A.; DeVries, Nicole A.; Shivanna, Kiran H.; Powell, Stephanie M.; Magnotta, Vincent A.; Ramme, Austin J.; Adams, Brian D.; Grosland, Nicole M.

2008-01-01

The objective was to develop tools for automating the identification of bony structures, to assess the reliability of this technique against manual raters, and to validate the resulting regions of interest against physical surface scans obtained from the same specimen. Artificial intelligence-based algorithms have been used for image segmentation, specifically artificial neural networks (ANNs). For this study, an ANN was created and trained to identify the phalanges of the human hand. The relative overlap between the ANN and a manual tracer was 0.87, 0.82, and 0.76, for the proximal, middle, and distal index phalanx bones respectively. Compared with the physical surface scans, the ANN-generated surface representations differed on average by 0.35 mm, 0.29 mm, and 0.40 mm for the proximal, middle, and distal phalanges respectively. Furthermore, the ANN proved to segment the structures in less than one-tenth of the time required by a manual rater. The ANN has proven to be a reliable and valid means of segmenting the phalanx bones from CT images. Employing automated methods such as the ANN for segmentation, eliminates the likelihood of rater drift and inter-rater variability. Automated methods also decrease the amount of time and manual effort required to extract the data of interest, thereby making the feasibility of patient-specific modeling a reality. (orig.)

Automated bony region identification using artificial neural networks: reliability and validation measurements

Energy Technology Data Exchange (ETDEWEB)

Gassman, Esther E.; Kallemeyn, Nicole A.; DeVries, Nicole A.; Shivanna, Kiran H. [The University of Iowa, Department of Biomedical Engineering, Seamans Center for the Engineering Arts and Sciences, Iowa City, IA (United States); The University of Iowa, Center for Computer-Aided Design, Iowa City, IA (United States); Powell, Stephanie M. [The University of Iowa, Department of Biomedical Engineering, Seamans Center for the Engineering Arts and Sciences, Iowa City, IA (United States); University of Iowa Hospitals and Clinics, The University of Iowa, Department of Radiology, Iowa City, IA (United States); Magnotta, Vincent A. [The University of Iowa, Department of Biomedical Engineering, Seamans Center for the Engineering Arts and Sciences, Iowa City, IA (United States); The University of Iowa, Center for Computer-Aided Design, Iowa City, IA (United States); University of Iowa Hospitals and Clinics, The University of Iowa, Department of Radiology, Iowa City, IA (United States); Ramme, Austin J. [University of Iowa Hospitals and Clinics, The University of Iowa, Department of Radiology, Iowa City, IA (United States); Adams, Brian D. [The University of Iowa, Department of Biomedical Engineering, Seamans Center for the Engineering Arts and Sciences, Iowa City, IA (United States); University of Iowa Hospitals and Clinics, The University of Iowa, Department of Orthopaedics and Rehabilitation, Iowa City, IA (United States); Grosland, Nicole M. [The University of Iowa, Department of Biomedical Engineering, Seamans Center for the Engineering Arts and Sciences, Iowa City, IA (United States); University of Iowa Hospitals and Clinics, The University of Iowa, Department of Orthopaedics and Rehabilitation, Iowa City, IA (United States); The University of Iowa, Center for Computer-Aided Design, Iowa City, IA (United States)

2008-04-15

The objective was to develop tools for automating the identification of bony structures, to assess the reliability of this technique against manual raters, and to validate the resulting regions of interest against physical surface scans obtained from the same specimen. Artificial intelligence-based algorithms have been used for image segmentation, specifically artificial neural networks (ANNs). For this study, an ANN was created and trained to identify the phalanges of the human hand. The relative overlap between the ANN and a manual tracer was 0.87, 0.82, and 0.76, for the proximal, middle, and distal index phalanx bones respectively. Compared with the physical surface scans, the ANN-generated surface representations differed on average by 0.35 mm, 0.29 mm, and 0.40 mm for the proximal, middle, and distal phalanges respectively. Furthermore, the ANN proved to segment the structures in less than one-tenth of the time required by a manual rater. The ANN has proven to be a reliable and valid means of segmenting the phalanx bones from CT images. Employing automated methods such as the ANN for segmentation, eliminates the likelihood of rater drift and inter-rater variability. Automated methods also decrease the amount of time and manual effort required to extract the data of interest, thereby making the feasibility of patient-specific modeling a reality. (orig.)

Inductive differentiation of two neural lineages reconstituted in a microculture system from Xenopus early gastrula cells.

Science.gov (United States)

Mitani, S; Okamoto, H

1991-05-01

Neural induction of ectoderm cells has been reconstituted and examined in a microculture system derived from dissociated early gastrula cells of Xenopus laevis. We have used monoclonal antibodies as specific markers to monitor cellular differentiation from three distinct ectoderm lineages in culture (N1 for CNS neurons from neural tube, Me1 for melanophores from neural crest and E3 for skin epidermal cells from epidermal lineages). CNS neurons and melanophores differentiate when deep layer cells of the ventral ectoderm (VE, prospective epidermis region; 150 cells/culture) and an appropriate region of the marginal zone (MZ, prospective mesoderm region; 5-150 cells/culture) are co-cultured, but not in cultures of either cell type on their own; VE cells cultured alone yield epidermal cells as we have previously reported. The extent of inductive neural differentiation in the co-culture system strongly depends on the origin and number of MZ cells initially added to culture wells. The potency to induce CNS neurons is highest for dorsal MZ cells and sharply decreases as more ventrally located cells are used. The same dorsoventral distribution of potency is seen in the ability of MZ cells to inhibit epidermal differentiation. In contrast, the ability of MZ cells to induce melanophores shows the reverse polarity, ventral to dorsal. These data indicate that separate developmental mechanisms are used for the induction of neural tube and neural crest lineages. Co-differentiation of CNS neurons or melanophores with epidermal cells can be obtained in a single well of co-cultures of VE cells (150) and a wide range of numbers of MZ cells (5 to 100). Further, reproducible differentiation of both neural lineages requires intimate association between cells from the two gastrula regions; virtually no differentiation is obtained when cells from the VE and MZ are separated in a culture well. These results indicate that the inducing signals from MZ cells for both neural tube and neural

Dynamic training algorithm for dynamic neural networks

International Nuclear Information System (INIS)

Tan, Y.; Van Cauwenberghe, A.; Liu, Z.

1996-01-01

The widely used backpropagation algorithm for training neural networks based on the gradient descent has a significant drawback of slow convergence. A Gauss-Newton method based recursive least squares (RLS) type algorithm with dynamic error backpropagation is presented to speed-up the learning procedure of neural networks with local recurrent terms. Finally, simulation examples concerning the applications of the RLS type algorithm to identification of nonlinear processes using a local recurrent neural network are also included in this paper

Probing many-body localization with neural networks

Science.gov (United States)

Schindler, Frank; Regnault, Nicolas; Neupert, Titus

2017-06-01

We show that a simple artificial neural network trained on entanglement spectra of individual states of a many-body quantum system can be used to determine the transition between a many-body localized and a thermalizing regime. Specifically, we study the Heisenberg spin-1/2 chain in a random external field. We employ a multilayer perceptron with a single hidden layer, which is trained on labeled entanglement spectra pertaining to the fully localized and fully thermal regimes. We then apply this network to classify spectra belonging to states in the transition region. For training, we use a cost function that contains, in addition to the usual error and regularization parts, a term that favors a confident classification of the transition region states. The resulting phase diagram is in good agreement with the one obtained by more conventional methods and can be computed for small systems. In particular, the neural network outperforms conventional methods in classifying individual eigenstates pertaining to a single disorder realization. It allows us to map out the structure of these eigenstates across the transition with spatial resolution. Furthermore, we analyze the network operation using the dreaming technique to show that the neural network correctly learns by itself the power-law structure of the entanglement spectra in the many-body localized regime.

Visual motion imagery neurofeedback based on the hMT+/V5 complex: evidence for a feedback-specific neural circuit involving neocortical and cerebellar regions

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Banca, Paula; Sousa, Teresa; Catarina Duarte, Isabel; Castelo-Branco, Miguel

2015-12-01

Objective. Current approaches in neurofeedback/brain-computer interface research often focus on identifying, on a subject-by-subject basis, the neural regions that are best suited for self-driven modulation. It is known that the hMT+/V5 complex, an early visual cortical region, is recruited during explicit and implicit motion imagery, in addition to real motion perception. This study tests the feasibility of training healthy volunteers to regulate the level of activation in their hMT+/V5 complex using real-time fMRI neurofeedback and visual motion imagery strategies. Approach. We functionally localized the hMT+/V5 complex to further use as a target region for neurofeedback. An uniform strategy based on motion imagery was used to guide subjects to neuromodulate hMT+/V5. Main results. We found that 15/20 participants achieved successful neurofeedback. This modulation led to the recruitment of a specific network as further assessed by psychophysiological interaction analysis. This specific circuit, including hMT+/V5, putative V6 and medial cerebellum was activated for successful neurofeedback runs. The putamen and anterior insula were recruited for both successful and non-successful runs. Significance. Our findings indicate that hMT+/V5 is a region that can be modulated by focused imagery and that a specific cortico-cerebellar circuit is recruited during visual motion imagery leading to successful neurofeedback. These findings contribute to the debate on the relative potential of extrinsic (sensory) versus intrinsic (default-mode) brain regions in the clinical application of neurofeedback paradigms. This novel circuit might be a good target for future neurofeedback approaches that aim, for example, the training of focused attention in disorders such as ADHD.

Neural mechanisms of mindfulness and meditation: Evidence from neuroimaging studies

Institute of Scientific and Technical Information of China (English)

William; R; Marchand

2014-01-01

Mindfulness is the dispassionate,moment-by-moment awareness of sensations,emotions and thoughts.Mindfulness-based interventions are being increasingly used for stress,psychological well being,coping with chronic illness as well as adjunctive treatments for psychiatric disorders.However,the neural mechanisms associated with mindfulness have not been well characterized.Recent functional and structural neuroimaging studies are beginning to provide insights into neural processes associated with the practice of mindfulness.A review of this literature revealed compelling evidence that mindfulness impacts the function of the medial cortex and associated default mode network as well as insula and amygdala.Additionally,mindfulness practice appears to effect lateral frontal regions and basal ganglia,at least in some cases.Structural imaging studies are consistent with these findings and also indicate changes in the hippocampus.While many questions remain unanswered,the current literature provides evidence of brain regions and networks relevant for understanding neural processes associated with mindfulness.

Solar wind/local interstellar medium interaction including charge exchange with neural hydrogen

Science.gov (United States)

Pauls, H. Louis; Zank, Gary P.

1995-01-01

We present results from a hydrodynamic model of the interaction of the solar wind with the local interstellar medium (LISM), self-consistently taking into account the effects of charge exchange between the plasma component and the interstellar neutrals. The simulation is fully time dependent, and is carried out in two or three dimensions, depending on whether the helio-latitudinal dependence of the solar wind speed and number density (both giving rise to three dimensional effects) are included. As a first approximation it is assumed that the neutral component of the flow can be described by a single, isotropic fluid. Clearly, this is not the actual situation, since charge exchange with the supersonic solar wind plasma in the region of the nose results in a 'second' neutral fluid propagating in the opposite direction as that of the LISM neutrals.

Supervised Machine Learning for Regionalization of Environmental Data: Distribution of Uranium in Groundwater in Ukraine

Science.gov (United States)

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.

Changes in the interaction of resting-state neural networks from adolescence to adulthood.

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Stevens, Michael C; Pearlson, Godfrey D; Calhoun, Vince D

2009-08-01

This study examined how the mutual interactions of functionally integrated neural networks during resting-state fMRI differed between adolescence and adulthood. Independent component analysis (ICA) was used to identify functionally connected neural networks in 100 healthy participants aged 12-30 years. Hemodynamic timecourses that represented integrated neural network activity were analyzed with tools that quantified system "causal density" estimates, which indexed the proportion of significant Granger causality relationships among system nodes. Mutual influences among networks decreased with age, likely reflecting stronger within-network connectivity and more efficient between-network influences with greater development. Supplemental tests showed that this normative age-related reduction in causal density was accompanied by fewer significant connections to and from each network, regional increases in the strength of functional integration within networks, and age-related reductions in the strength of numerous specific system interactions. The latter included paths between lateral prefrontal-parietal circuits and "default mode" networks. These results contribute to an emerging understanding that activity in widely distributed networks thought to underlie complex cognition influences activity in other networks. (c) 2009 Wiley-Liss, Inc.

Neural Oscillations and Synchrony in Brain Dysfunction and Neuropsychiatric Disorders: It's About Time.

Science.gov (United States)

Mathalon, Daniel H; Sohal, Vikaas S

2015-08-01

Neural oscillations are rhythmic fluctuations over time in the activity or excitability of single neurons, local neuronal populations or "assemblies," and/or multiple regionally distributed neuronal assemblies. Synchronized oscillations among large numbers of neurons are evident in electrocorticographic, electroencephalographic, magnetoencephalographic, and local field potential recordings and are generally understood to depend on inhibition that paces assemblies of excitatory neurons to produce alternating temporal windows of reduced and increased excitability. Synchronization of neural oscillations is supported by the extensive networks of local and long-range feedforward and feedback bidirectional connections between neurons. Here, we review some of the major methods and measures used to characterize neural oscillations, with a focus on gamma oscillations. Distinctions are drawn between stimulus-independent oscillations recorded during resting states or intervals between task events, stimulus-induced oscillations that are time locked but not phase locked to stimuli, and stimulus-evoked oscillations that are both time and phase locked to stimuli. Synchrony of oscillations between recording sites, and between the amplitudes and phases of oscillations of different frequencies (cross-frequency coupling), is described and illustrated. Molecular mechanisms underlying gamma oscillations are also reviewed. Ultimately, understanding the temporal organization of neuronal network activity, including interactions between neural oscillations, is critical for elucidating brain dysfunction in neuropsychiatric disorders.

A neural measure of behavioral engagement: task-residual low-frequency blood oxygenation level-dependent activity in the precuneus.

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Zhang, Sheng; Li, Chiang-Shan Ray

2010-01-15

Brain imaging has provided a useful tool to examine the neural processes underlying human cognition. A critical question is whether and how task engagement influences the observed regional brain activations. Here we highlighted this issue and derived a neural measure of task engagement from the task-residual low-frequency blood oxygenation level-dependent (BOLD) activity in the precuneus. Using independent component analysis, we identified brain regions in the default circuit - including the precuneus and medial prefrontal cortex (mPFC) - showing greater activation during resting as compared to task residuals in 33 individuals. Time series correlations with the posterior cingulate cortex as the seed region showed that connectivity with the precuneus was significantly stronger during resting as compared to task residuals. We hypothesized that if the task-residual BOLD activity in the precuneus reflects engagement, it should account for a certain amount of variance in task-related regional brain activation. In an additional experiment of 59 individuals performing a stop signal task, we observed that the fractional amplitude of low-frequency fluctuation (fALFF) of the precuneus but not the mPFC accounted for approximately 10% of the variance in prefrontal activation related to attentional monitoring and response inhibition. Taken together, these results suggest that task-residual fALFF in the precuneus may be a potential indicator of task engagement. This measurement may serve as a useful covariate in identifying motivation-independent neural processes that underlie the pathogenesis of a psychiatric or neurological condition.

Review of the Neural Oscillations Underlying Meditation

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Darrin J. Lee

2018-03-01

Full Text Available Objective: Meditation is one type of mental training that has been shown to produce many cognitive benefits. Meditation practice is associated with improvement in concentration and reduction of stress, depression, and anxiety symptoms. Furthermore, different forms of meditation training are now being used as interventions for a variety of psychological and somatic illnesses. These benefits are thought to occur as a result of neurophysiologic changes. The most commonly studied specific meditation practices are focused attention (FA, open-monitoring (OM, as well as transcendental meditation (TM, and loving-kindness (LK meditation. In this review, we compare the neural oscillatory patterns during these forms of meditation.Method: We performed a systematic review of neural oscillations during FA, OM, TM, and LK meditation practices, comparing meditators to meditation-naïve adults.Results: FA, OM, TM, and LK meditation are associated with global increases in oscillatory activity in meditators compared to meditation-naïve adults, with larger changes occurring as the length of meditation training increases. While FA and OM are related to increases in anterior theta activity, only FA is associated with changes in posterior theta oscillations. Alpha activity increases in posterior brain regions during both FA and OM. In anterior regions, FA shows a bilateral increase in alpha power, while OM shows a decrease only in left-sided power. Gamma activity in these meditation practices is similar in frontal regions, but increases are variable in parietal and occipital regions.Conclusions: The current literature suggests distinct differences in neural oscillatory activity among FA, OM, TM, and LK meditation practices. Further characterizing these oscillatory changes may better elucidate the cognitive and therapeutic effects of specific meditation practices, and potentially lead to the development of novel neuromodulation targets to take advantage of their

Young adult smokers' neural response to graphic cigarette warning labels

Directory of Open Access Journals (Sweden)

Adam E. Green

2016-06-01

Conclusions: In this sample of young adult smokers, GWLs promoted neural activation in brain regions involved in cognitive and affective decision-making and memory formation and the effects of GWLs did not differ on branded or plain cigarette packaging. These findings complement other recent neuroimaging GWL studies conducted with older adult smokers and with adolescents by demonstrating similar patterns of neural activation in response to GWLs among young adult smokers.

The neural basis of the bystander effect--the influence of group size on neural activity when witnessing an emergency.

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Hortensius, Ruud; de Gelder, Beatrice

2014-06-01

Naturalistic observation and experimental studies in humans and other primates show that observing an individual in need automatically triggers helping behavior. The aim of the present study is to clarify the neurofunctional basis of social influences on individual helping behavior. We investigate whether when participants witness an emergency, while performing an unrelated color-naming task in an fMRI scanner, the number of bystanders present at the emergency influences neural activity in regions related to action preparation. The results show a decrease in activity with the increase in group size in the left pre- and postcentral gyri and left medial frontal gyrus. In contrast, regions related to visual perception and attention show an increase in activity. These results demonstrate the neural mechanisms of social influence on automatic action preparation that is at the core of helping behavior when witnessing an emergency. Copyright © 2014 Elsevier Inc. All rights reserved.

A one-layer recurrent neural network for constrained nonsmooth invex optimization.

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Li, Guocheng; Yan, Zheng; Wang, Jun

2014-02-01

Invexity is an important notion in nonconvex optimization. In this paper, a one-layer recurrent neural network is proposed for solving constrained nonsmooth invex optimization problems, designed based on an exact penalty function method. It is proved herein that any state of the proposed neural network is globally convergent to the optimal solution set of constrained invex optimization problems, with a sufficiently large penalty parameter. In addition, any neural state is globally convergent to the unique optimal solution, provided that the objective function and constraint functions are pseudoconvex. Moreover, any neural state is globally convergent to the feasible region in finite time and stays there thereafter. The lower bounds of the penalty parameter and convergence time are also estimated. Two numerical examples are provided to illustrate the performances of the proposed neural network. Copyright © 2013 Elsevier Ltd. All rights reserved.

Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network.

Directory of Open Access Journals (Sweden)

Seung Seog Han

Full Text Available Although there have been reports of the successful diagnosis of skin disorders using deep learning, unrealistically large clinical image datasets are required for artificial intelligence (AI training. We created datasets of standardized nail images using a region-based convolutional neural network (R-CNN trained to distinguish the nail from the background. We used R-CNN to generate training datasets of 49,567 images, which we then used to fine-tune the ResNet-152 and VGG-19 models. The validation datasets comprised 100 and 194 images from Inje University (B1 and B2 datasets, respectively, 125 images from Hallym University (C dataset, and 939 images from Seoul National University (D dataset. The AI (ensemble model; ResNet-152 + VGG-19 + feedforward neural networks results showed test sensitivity/specificity/ area under the curve values of (96.0 / 94.7 / 0.98, (82.7 / 96.7 / 0.95, (92.3 / 79.3 / 0.93, (87.7 / 69.3 / 0.82 for the B1, B2, C, and D datasets. With a combination of the B1 and C datasets, the AI Youden index was significantly (p = 0.01 higher than that of 42 dermatologists doing the same assessment manually. For B1+C and B2+ D dataset combinations, almost none of the dermatologists performed as well as the AI. By training with a dataset comprising 49,567 images, we achieved a diagnostic accuracy for onychomycosis using deep learning that was superior to that of most of the dermatologists who participated in this study.

Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network.

Science.gov (United States)

Han, Seung Seog; Park, Gyeong Hun; Lim, Woohyung; Kim, Myoung Shin; Na, Jung Im; Park, Ilwoo; Chang, Sung Eun

2018-01-01

Although there have been reports of the successful diagnosis of skin disorders using deep learning, unrealistically large clinical image datasets are required for artificial intelligence (AI) training. We created datasets of standardized nail images using a region-based convolutional neural network (R-CNN) trained to distinguish the nail from the background. We used R-CNN to generate training datasets of 49,567 images, which we then used to fine-tune the ResNet-152 and VGG-19 models. The validation datasets comprised 100 and 194 images from Inje University (B1 and B2 datasets, respectively), 125 images from Hallym University (C dataset), and 939 images from Seoul National University (D dataset). The AI (ensemble model; ResNet-152 + VGG-19 + feedforward neural networks) results showed test sensitivity/specificity/ area under the curve values of (96.0 / 94.7 / 0.98), (82.7 / 96.7 / 0.95), (92.3 / 79.3 / 0.93), (87.7 / 69.3 / 0.82) for the B1, B2, C, and D datasets. With a combination of the B1 and C datasets, the AI Youden index was significantly (p = 0.01) higher than that of 42 dermatologists doing the same assessment manually. For B1+C and B2+ D dataset combinations, almost none of the dermatologists performed as well as the AI. By training with a dataset comprising 49,567 images, we achieved a diagnostic accuracy for onychomycosis using deep learning that was superior to that of most of the dermatologists who participated in this study.

The mouse that roared: neural mechanisms of social hierarchy.

Science.gov (United States)

Wang, Fei; Kessels, Helmut W; Hu, Hailan

2014-11-01

Hierarchical social status greatly influences behavior and health. Human and animal studies have begun to identify the brain regions that are activated during the formation of social hierarchies. They point towards the prefrontal cortex (PFC) as a central regulator, with brain areas upstream of the PFC conveying information about social status, and downstream brain regions executing dominance behavior. This review summarizes our current knowledge on the neural circuits that control social status. We discuss how the neural mechanisms for various types of dominance behavior can be studied in laboratory rodents by selective manipulation of neuronal activity or synaptic plasticity. These studies may help in finding the cause of social stress-related mental and physical health problems. Copyright © 2014 Elsevier Ltd. All rights reserved.

Using Artificial Neural Networks to Determine Significant Factors Affecting the Pricing of WPT Effluent for Industrial Uses in Isfahan

Directory of Open Access Journals (Sweden)

Masoud Mirmohamadsaseghi

2017-03-01

Full Text Available The evidence indicates increasing trend of use of municipal wastewater treatment effluent as an alternative source of water both in developed and developing countries. Proper pricing of this unconventional water is one of the most effective economic tools to encourage optimum use of fresh water resources. In this study, artificial neural network is employed to identify and assess the factors affecting effluent tariffs supplied to local industries in Isfahan region. Given the wide variety of factors involved in the ultimate value of wastewater traement plant effluent, an assortment of relevant factors  has been considered in this study; the factors include the population served by the treatment plant, volume of effluent produced, maintenance, repair and replacement. costs of operating plants, topography, different water uses in the region, industrial wastewater collection fees, unit cost of pipe and fittings, and the volumes of water supplied from springs and aqueducts  in the region. Neural network modeling is used as a tool to determine the significance of each factor for pricing effluent. Based on the available data and the neural network models, the effects of different model architectures with different intermediate layers and numbers of nodes in each layer on the price of wastewater were investigated to develop aand adopt a final neural network model. Results indicate that the proposed neural network model enjoys a high potential and has been well capable of determining the weights of the parameter affecting in pricing effluent. Based on the the results of this study, the factors with the greatest role in effluent pricing are unit cost of pipe and fittings, industrial use of water, and the costs of plant maintentance, repair and replacement.

Neural Correlates of Social Influence Among Cannabis Users.

Science.gov (United States)

Gilman, Jodi M

2017-06-01

Although peer influence is an important factor in the initiation and maintenance of cannabis use, few studies have investigated the neural correlates of peer influence among cannabis users. The current review summarizes research on the neuroscience of social influence in cannabis users, with the goal of highlighting gaps in the literature and the need for future research. Brain regions underlying peer influence may function differently in cannabis users. Compared to non-using controls, regions of the brain underlying reward, such as the striatum, show greater connectivity with frontal regions, and also show hyperactivity when participants are presented with peer information. Other subcortical regions, such as the insula, show hypoactivation during social exclusion in cannabis users, indicating that neural responses to peer interactions may be altered in cannabis users. Although neuroscience is increasingly being used to study social behavior, few studies have specifically focused on cannabis use, and therefore it is difficult to draw conclusions about social mechanisms that may differentiate cannabis users and controls. This area of research may be a promising avenue in which to explore a critical factor underlying cannabis use and addiction.

Environmental radioactivity in the North Atlantic region. The Faroe Islands and Greenland included. 1983

International Nuclear Information System (INIS)

Aarkrog, A.; Boelskifte, S.; Buch, E.; Christensen, G.C.; Dahlgaard, H.; Hallstadius, L.; Hansen, H.; Holm, E.; Mattsson, S.; Meide, A.

1984-12-01

Measurements of fallout radioactivity in the North Atlantic region including the Faroe Islands and Greenland are reported. Strontium-90 and cesium-137 was determined in samples of precipitation, sea water, vegetation, various foodstuffs (including milk in the Faroes) and drinking water. Estimates are given of the mean contents of 90 Sr and 137 Cs in human diet in the Faroes and Greenland in 1983. Results from samplings of surface sea water and seaweed in the Norwegian and Greenland Seas and along the Norwegian and Greenland west coasts are reported. Beside radiocesium and 90 Sr some of these samples have also been anlysed for tritium,plutonium and americium. Finally technetium-99 data on seaweed samples collected in the North Atlantic region since the beginning of the sixties are presented. (author)

Environmental radioactivity in the North Atlantic region. The Faroe Islands and Greenland included. 1984

International Nuclear Information System (INIS)

Aarkrog, A.; Boelskifte, S.; Buch, E.; Christensen, G.C.; Dahlgaard, H.; Hallstadius, L.; Hansen, H.; Holm, E.

1985-12-01

Measurements of fallout radioactivity in the North Atlantic region including the Faroe Islands and Greenland are reported. Strontium-90 and cesium-137 was determined in samples of precipitation, sea water, vegetation, various foodstuffs (including milk in the Faroes) and drinking water. Estimates are given of the mean contents of 90 Sr and 137 Cs in human diet in the Faroes and Greenland in 1984. Results from samplings of surface sea water and seaweed in the Norwegian and Greenland Seas and along the Norwegian and Greenland west coasts are reported. Beside radiocesium and 90 Sr some of these samples have also been anlysed for tritium, polonium, plutonium and americium. Finally technetium-99 data on seaweed samples collected in the North Atlantic region since the beginning of the sixties are presented. (author)

Environmental radioactivity in the North Atlantic region. The Faroe Islands and Greenland included. 1985

International Nuclear Information System (INIS)

Aarkrog, A.; Boelskifte, S.; Dahlgaard, H.; Hansen, H.; Buch, E.; Christensen, G.C.; Hallstadius, L.; Rioseco, J.; Holm, E.

1987-06-01

Measurements of fallout radioactivity in the North Atlantic region including the Faroe Islands and Greenland are reported. Strontium-90 and cesium-137 was determined in samples of precipitation, sea water vegetation, various foodstuffs (including milk in the Faroes) and drinking water. Estimates are given of the mean contents of 90 Sr a 137 Cs in human diet in the Faroes and Greenland in 1985. Results from samplings of surface sea water and seaweed in the English Channel, the Fram Strait and along the Norwegian and Greenland coast are reported. Beside radiocesium and 90 Sr some of these samples have also been analysed for tritium, plutonium and americium. Finally technetium-99 data on seaweed and sea water samples collected in the North Atlantic region are presented. 14 refs. (author)

Forecasting Flare Activity Using Deep Convolutional Neural Networks

Science.gov (United States)

Hernandez, T.

2017-12-01

Current operational flare forecasting relies on human morphological analysis of active regions and the persistence of solar flare activity through time (i.e. that the Sun will continue to do what it is doing right now: flaring or remaining calm). In this talk we present the results of applying deep Convolutional Neural Networks (CNNs) to the problem of solar flare forecasting. CNNs operate by training a set of tunable spatial filters that, in combination with neural layer interconnectivity, allow CNNs to automatically identify significant spatial structures predictive for classification and regression problems. We will start by discussing the applicability and success rate of the approach, the advantages it has over non-automated forecasts, and how mining our trained neural network provides a fresh look into the mechanisms behind magnetic energy storage and release.

Adrenergic innervation of the developing chick heart: neural crest ablations to produce sympathetically aneural hearts

International Nuclear Information System (INIS)

Kirby, M.; Stewart, D.

1984-01-01

Ablation of various regions of premigratory trunk neural crest which gives rise to the sympathetic trunks was used to remove sympathetic cardiac innervation. Neuronal uptake of [ 3 H]-norepinephrine was used as an index of neuronal development in the chick atrium. Following ablation of neural crest over somites 10-15 or 15-20, uptake was significantly decreased in the atrium at 16 and 17 days of development. Ablation of neural crest over somites 5-10 and 20-25 caused no decrease in [ 3 H]-norepinephrine uptake. Removal of neural crest over somites 5-25 or 10-20 caused approximately equal depletions of [ 3 H]-norepinephrine uptake in the atrium. Cardiac norepinephrine concentration was significantly depressed following ablation of neural crest over somites 5-25 but not over somites 10-20. Light-microscopic and histofluorescent preparations confirmed the absence of sympathetic trunks in the region of the normal origin of the sympathetic cardiac nerves following neural crest ablation over somites 10-20. The neural tube and dorsal root ganglia were damaged in the area of the neural-crest ablation; however, all of these structures were normal cranial and caudal to the lesioned area. Development of most of the embryos as well as the morphology of all of the hearts was normal following the lesion. These results indicate that it is possible to produce sympathetically aneural hearts by neural-crest ablation; however, sympathetic cardiac nerves account for an insignificant amount of cardiac norepinephrine

Application of a neural network for reflectance spectrum classification

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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.

Neural speech recognition: continuous phoneme decoding using spatiotemporal representations of human cortical activity

Science.gov (United States)

Moses, David A.; Mesgarani, Nima; Leonard, Matthew K.; Chang, Edward F.

2016-10-01

Objective. The superior temporal gyrus (STG) and neighboring brain regions play a key role in human language processing. Previous studies have attempted to reconstruct speech information from brain activity in the STG, but few of them incorporate the probabilistic framework and engineering methodology used in modern speech recognition systems. In this work, we describe the initial efforts toward the design of a neural speech recognition (NSR) system that performs continuous phoneme recognition on English stimuli with arbitrary vocabulary sizes using the high gamma band power of local field potentials in the STG and neighboring cortical areas obtained via electrocorticography. Approach. The system implements a Viterbi decoder that incorporates phoneme likelihood estimates from a linear discriminant analysis model and transition probabilities from an n-gram phonemic language model. Grid searches were used in an attempt to determine optimal parameterizations of the feature vectors and Viterbi decoder. Main results. The performance of the system was significantly improved by using spatiotemporal representations of the neural activity (as opposed to purely spatial representations) and by including language modeling and Viterbi decoding in the NSR system. Significance. These results emphasize the importance of modeling the temporal dynamics of neural responses when analyzing their variations with respect to varying stimuli and demonstrate that speech recognition techniques can be successfully leveraged when decoding speech from neural signals. Guided by the results detailed in this work, further development of the NSR system could have applications in the fields of automatic speech recognition and neural prosthetics.

Neural dichotomy of word concreteness: a view from functional neuroimaging.

Science.gov (United States)

Kumar, Uttam

2016-02-01

Our perception about the representation and processing of concrete and abstract concepts is based on the fact that concrete words are highly imagined and remembered faster than abstract words. In order to explain the processing differences between abstract and concrete concepts, various theories have been proposed, yet there is no unanimous consensus about its neural implication. The present study investigated the processing of concrete and abstract words during an orthography judgment task (implicit semantic processing) using functional magnetic resonance imaging to validate the involvement of the neural regions. Relative to non-words, both abstract and concrete words show activation in the regions of bilateral hemisphere previously associated with semantic processing. The common areas (conjunction analyses) observed for abstract and concrete words are bilateral inferior frontal gyrus (BA 44/45), left superior parietal (BA 7), left fusiform gyrus and bilateral middle occipital. The additional areas for abstract words were noticed in bilateral superior temporal and bilateral middle temporal region, whereas no distinct region was noticed for concrete words. This suggests that words with abstract concepts recruit additional language regions in the brain.

Medical image segmentation by means of constraint satisfaction neural network

International Nuclear Information System (INIS)

Chen, C.T.; Tsao, C.K.; Lin, W.C.

1990-01-01

This paper applies the concept of constraint satisfaction neural network (CSNN) to the problem of medical image segmentation. Constraint satisfaction (or constraint propagation), the procedure to achieve global consistency through local computation, is an important paradigm in artificial intelligence. CSNN can be viewed as a three-dimensional neural network, with the two-dimensional image matrix as its base, augmented by various constraint labels for each pixel. These constraint labels can be interpreted as the connections and the topology of the neural network. Through parallel and iterative processes, the CSNN will approach a solution that satisfies the given constraints thus providing segmented regions with global consistency

The neural basis of unconditional love.

Science.gov (United States)

Beauregard, Mario; Courtemanche, Jérôme; Paquette, Vincent; St-Pierre, Evelyne Landry

2009-05-15

Functional neuroimaging studies have shown that romantic love and maternal love are mediated by regions specific to each, as well as overlapping regions in the brain's reward system. Nothing is known yet regarding the neural underpinnings of unconditional love. The main goal of this functional magnetic resonance imaging study was to identify the brain regions supporting this form of love. Participants were scanned during a control condition and an experimental condition. In the control condition, participants were instructed to simply look at a series of pictures depicting individuals with intellectual disabilities. In the experimental condition, participants were instructed to feel unconditional love towards the individuals depicted in a series of similar pictures. Significant loci of activation were found, in the experimental condition compared with the control condition, in the middle insula, superior parietal lobule, right periaqueductal gray, right globus pallidus (medial), right caudate nucleus (dorsal head), left ventral tegmental area and left rostro-dorsal anterior cingulate cortex. These results suggest that unconditional love is mediated by a distinct neural network relative to that mediating other emotions. This network contains cerebral structures known to be involved in romantic love or maternal love. Some of these structures represent key components of the brain's reward system.

Robust adaptive fuzzy neural tracking control for a class of unknown ...

Indian Academy of Sciences (India)

In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is proposed. The proposed AFNC is comprised of a fuzzy neural controller and a robust controller. The fuzzy neural controller including a fuzzy neural network identifier (FNNI) is the principal controller. The FNNI is used for ...

Brain noise is task dependent and region specific.

Science.gov (United States)

Misić, Bratislav; Mills, Travis; Taylor, Margot J; McIntosh, Anthony R

2010-11-01

The emerging organization of anatomical and functional connections during human brain development is thought to facilitate global integration of information. Recent empirical and computational studies have shown that this enhanced capacity for information processing enables a diversified dynamic repertoire that manifests in neural activity as irregularity and noise. However, transient functional networks unfold over multiple time, scales and the embedding of a particular region depends not only on development, but also on the manner in which sensory and cognitive systems are engaged. Here we show that noise is a facet of neural activity that is also sensitive to the task context and is highly region specific. Children (6-16 yr) and adults (20-41 yr) performed a one-back face recognition task with inverted and upright faces. Neuromagnetic activity was estimated at several hundred sources in the brain by applying a beamforming technique to the magnetoencephalogram (MEG). During development, neural activity became more variable across the whole brain, with most robust increases in medial parietal regions, such as the precuneus and posterior cingulate cortex. For young children and adults, activity evoked by upright faces was more variable and noisy compared with inverted faces, and this effect was reliable only in the right fusiform gyrus. These results are consistent with the notion that upright faces engender a variety of integrative neural computations, such as the relations among facial features and their holistic constitution. This study shows that transient changes in functional integration modulated by task demand are evident in the variability of regional neural activity.

A loop-based neural architecture for structured behavior encoding and decoding.

Science.gov (United States)

Gisiger, Thomas; Boukadoum, Mounir

2018-02-01

We present a new type of artificial neural network that generalizes on anatomical and dynamical aspects of the mammal brain. Its main novelty lies in its topological structure which is built as an array of interacting elementary motifs shaped like loops. These loops come in various types and can implement functions such as gating, inhibitory or executive control, or encoding of task elements to name a few. Each loop features two sets of neurons and a control region, linked together by non-recurrent projections. The two neural sets do the bulk of the loop's computations while the control unit specifies the timing and the conditions under which the computations implemented by the loop are to be performed. By functionally linking many such loops together, a neural network is obtained that may perform complex cognitive computations. To demonstrate the potential offered by such a system, we present two neural network simulations. The first illustrates the structure and dynamics of a single loop implementing a simple gating mechanism. The second simulation shows how connecting four loops in series can produce neural activity patterns that are sufficient to pass a simplified delayed-response task. We also show that this network reproduces electrophysiological measurements gathered in various regions of the brain of monkeys performing similar tasks. We also demonstrate connections between this type of neural network and recurrent or long short-term memory network models, and suggest ways to generalize them for future artificial intelligence research. Copyright © 2017 Elsevier Ltd. All rights reserved.

A light and faster regional convolutional neural network for object detection in optical remote sensing images

Science.gov (United States)

Ding, Peng; Zhang, Ye; Deng, Wei-Jian; Jia, Ping; Kuijper, Arjan

2018-07-01

Detection of objects from satellite optical remote sensing images is very important for many commercial and governmental applications. With the development of deep convolutional neural networks (deep CNNs), the field of object detection has seen tremendous advances. Currently, objects in satellite remote sensing images can be detected using deep CNNs. In general, optical remote sensing images contain many dense and small objects, and the use of the original Faster Regional CNN framework does not yield a suitably high precision. Therefore, after careful analysis we adopt dense convoluted networks, a multi-scale representation and various combinations of improvement schemes to enhance the structure of the base VGG16-Net for improving the precision. We propose an approach to reduce the test-time (detection time) and memory requirements. To validate the effectiveness of our approach, we perform experiments using satellite remote sensing image datasets of aircraft and automobiles. The results show that the improved network structure can detect objects in satellite optical remote sensing images more accurately and efficiently.

The Neural Basis of Changing Social Norms through Persuasion.

Science.gov (United States)

Yomogida, Yukihito; Matsumoto, Madoka; Aoki, Ryuta; Sugiura, Ayaka; Phillips, Adam N; Matsumoto, Kenji

2017-11-24

Social norms regulate behavior, and changes in norms have a great impact on society. In most modern societies, norms change through interpersonal communication and persuasive messages found in media. Here, we examined the neural basis of persuasion-induced changes in attitude toward and away from norms using fMRI. We measured brain activity while human participants were exposed to persuasive messages directed toward specific norms. Persuasion directed toward social norms specifically activated a set of brain regions including temporal poles, temporo-parietal junction, and medial prefrontal cortex. Beyond these regions, when successful, persuasion away from an accepted norm specifically recruited the left middle temporal and supramarginal gyri. Furthermore, in combination with data from a separate attitude-rating task, we found that left supramarginal gyrus activity represented participant attitude toward norms and tracked the persuasion-induced attitude changes that were away from agreement.

Neurobiology of pair bonding in fishes; convergence of neural mechanisms across distant vertebrate lineages

KAUST Repository

Nowicki, Jessica; Pratchett, Morgan; Walker, Stefan; Coker, Darren James; O'Connell, Lauren A.

2017-01-01

Pair bonding has independently evolved numerous times among vertebrates. The governing neural mechanisms of pair bonding have only been studied in depth in the mammalian model species, the prairie vole, Microtus ochrogaster. In this species, oxytocin (OT), arginine vasopressin (AVP), dopamine (DA), and opioid (OP) systems play key roles in signaling in the formation and maintenance of pair bonding by targeting specific social and reward-mediating brain regions. By contrast, the neural basis of pair bonding is poorly studied in other vertebrates, and especially those of early origins, limiting our understanding of the evolutionary history of pair bonding regulatory mechanisms. We compared receptor gene expression between pair bonded and solitary individuals across eight socio-functional brain regions. We found that in females, ITR and V1aR receptor expression varied in the lateral septum-like region (the Vv/Vl), while in both sexes D1R, D2R, and MOR expression varied within the mesolimbic reward system, including a striatum-like region (the Vc); mirroring sites of action in M. ochrogaster. This study provides novel insights into the neurobiology of teleost pair bonding. It also reveals high convergence in the neurochemical mechanisms governing pair bonding across actinopterygians and sarcopterygians, by repeatedly co-opting and similarly assembling deep neurochemical and neuroanatomical homologies that originated in ancestral osteithes.

Neurobiology of pair bonding in fishes; convergence of neural mechanisms across distant vertebrate lineages

KAUST Repository

Nowicki, Jessica

2017-11-14

Pair bonding has independently evolved numerous times among vertebrates. The governing neural mechanisms of pair bonding have only been studied in depth in the mammalian model species, the prairie vole, Microtus ochrogaster. In this species, oxytocin (OT), arginine vasopressin (AVP), dopamine (DA), and opioid (OP) systems play key roles in signaling in the formation and maintenance of pair bonding by targeting specific social and reward-mediating brain regions. By contrast, the neural basis of pair bonding is poorly studied in other vertebrates, and especially those of early origins, limiting our understanding of the evolutionary history of pair bonding regulatory mechanisms. We compared receptor gene expression between pair bonded and solitary individuals across eight socio-functional brain regions. We found that in females, ITR and V1aR receptor expression varied in the lateral septum-like region (the Vv/Vl), while in both sexes D1R, D2R, and MOR expression varied within the mesolimbic reward system, including a striatum-like region (the Vc); mirroring sites of action in M. ochrogaster. This study provides novel insights into the neurobiology of teleost pair bonding. It also reveals high convergence in the neurochemical mechanisms governing pair bonding across actinopterygians and sarcopterygians, by repeatedly co-opting and similarly assembling deep neurochemical and neuroanatomical homologies that originated in ancestral osteithes.

Neural activation to monetary reward is associated with amphetamine reward sensitivity.

Science.gov (United States)

Crane, Natania A; Gorka, Stephanie M; Weafer, Jessica; Langenecker, Scott A; de Wit, Harriet; Phan, K Luan

2018-03-14

One known risk factor for drug use and abuse is sensitivity to rewarding effects of drugs. It is not known whether this risk factor extends to sensitivity to non-drug rewards. In this study with healthy young adults, we examined the association between sensitivity to the subjective rewarding effects of amphetamine and a neural indicator of anticipation of monetary reward. We hypothesized that greater euphorigenic response to amphetamine would be associated with greater neural activation to anticipation of monetary reward (Win > Loss). Healthy participants (N = 61) completed four laboratory sessions in which they received d-amphetamine (20 mg) and placebo in alternating order, providing self-report measures of euphoria and stimulation at regular intervals. At a separate visit 1-3 weeks later, participants completed the guessing reward task (GRT) during fMRI in a drug-free state. Participants reporting greater euphoria after amphetamine also exhibited greater neural activation during monetary reward anticipation in mesolimbic reward regions, including the bilateral caudate and putamen. This is the first study to show a relationship between neural correlates of monetary reward and sensitivity to the subjective rewarding effects of amphetamine in humans. These findings support growing evidence that sensitivity to reward in general is a risk factor for drug use and abuse, and suggest that sensitivity of drug-induced euphoria may reflect a general sensitivity to rewards. This may be an index of vulnerability for drug use or abuse.

Neural Networks and Micromechanics

Science.gov (United States)

Kussul, Ernst; Baidyk, Tatiana; Wunsch, Donald C.

The title of the book, "Neural Networks and Micromechanics," seems artificial. However, the scientific and technological developments in recent decades demonstrate a very close connection between the two different areas of neural networks and micromechanics. The purpose of this book is to demonstrate this connection. Some artificial intelligence (AI) methods, including neural networks, could be used to improve automation system performance in manufacturing processes. However, the implementation of these AI methods within industry is rather slow because of the high cost of conducting experiments using conventional manufacturing and AI systems. To lower the cost, we have developed special micromechanical equipment that is similar to conventional mechanical equipment but of much smaller size and therefore of lower cost. This equipment could be used to evaluate different AI methods in an easy and inexpensive way. The proved methods could be transferred to industry through appropriate scaling. In this book, we describe the prototypes of low cost microequipment for manufacturing processes and the implementation of some AI methods to increase precision, such as computer vision systems based on neural networks for microdevice assembly and genetic algorithms for microequipment characterization and the increase of microequipment precision.

Differential neural substrates of working memory and cognitive skill learning in healthy young volunteers

International Nuclear Information System (INIS)

Cho, Sang Soo; Lee, Eun Ju; Yoon, Eun Jin; Kim, Yu Kyeong; Lee, Won Woo; Kim, Sang Eun

2005-01-01

It is known that different neural circuits are involved in working memory and cognitive skill learning that represent explicit and implicit memory functions, respectively. In the present study, we investigated the metabolic correlates of working memory and cognitive skill learning with correlation analysis of FDG PET images. Fourteen right-handed healthy subjects (age, 24 ± 2 yr; 5 males and 9 females) underwent brain FDG PET and neuropsychological testing. Two-back task and weather prediction task were used for the evaluation of working memory and cognitive skill learning, respectively, Correlation between regional glucose metabolism and cognitive task performance was examined using SPM99. A significant positive correlation between 2-back task performance and regional glucose metabolism was found in the prefrontal regions and superior temporal gyri bilaterally. In the first term of weather prediction task the task performance correlated positively with glucose metabolism in the bilateral prefrontal areas, left middle temporal and posterior cingulate gyri, and left thalamus. In the second and third terms of the task, the correlation found in the prefrontal areas, superior temporal and anterior cingulate gyri bilaterally, right insula, left parahippocampal gyrus, and right caudate nucleus. We identified the neural substrates that are related with performance of working memory and cognitive skill learning. These results indicate that brain regions associated with the explicit memory system are recruited in early periods of cognitive skill learning, but additional brain regions including caudate nucleus are involved in late periods of cognitive skill learning

Differential neural substrates of working memory and cognitive skill learning in healthy young volunteers

Energy Technology Data Exchange (ETDEWEB)

Cho, Sang Soo; Lee, Eun Ju; Yoon, Eun Jin; Kim, Yu Kyeong; Lee, Won Woo; Kim, Sang Eun [Seoul National Univ. College of Medicine, Seoul (Korea, Republic of)

2005-07-01

It is known that different neural circuits are involved in working memory and cognitive skill learning that represent explicit and implicit memory functions, respectively. In the present study, we investigated the metabolic correlates of working memory and cognitive skill learning with correlation analysis of FDG PET images. Fourteen right-handed healthy subjects (age, 24 {+-} 2 yr; 5 males and 9 females) underwent brain FDG PET and neuropsychological testing. Two-back task and weather prediction task were used for the evaluation of working memory and cognitive skill learning, respectively, Correlation between regional glucose metabolism and cognitive task performance was examined using SPM99. A significant positive correlation between 2-back task performance and regional glucose metabolism was found in the prefrontal regions and superior temporal gyri bilaterally. In the first term of weather prediction task the task performance correlated positively with glucose metabolism in the bilateral prefrontal areas, left middle temporal and posterior cingulate gyri, and left thalamus. In the second and third terms of the task, the correlation found in the prefrontal areas, superior temporal and anterior cingulate gyri bilaterally, right insula, left parahippocampal gyrus, and right caudate nucleus. We identified the neural substrates that are related with performance of working memory and cognitive skill learning. These results indicate that brain regions associated with the explicit memory system are recruited in early periods of cognitive skill learning, but additional brain regions including caudate nucleus are involved in late periods of cognitive skill learning.

Neural electrical activity and neural network growth.

Science.gov (United States)

Gafarov, F M

2018-05-01

The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

Cultural influences on neural basis of intergroup empathy.

Science.gov (United States)

Cheon, Bobby K; Im, Dong-Mi; Harada, Tokiko; Kim, Ji-Sook; Mathur, Vani A; Scimeca, Jason M; Parrish, Todd B; Park, Hyun Wook; Chiao, Joan Y

2011-07-15

Cultures vary in the extent to which people prefer social hierarchical or egalitarian relations between individuals and groups. Here we examined the effect of cultural variation in preference for social hierarchy on the neural basis of intergroup empathy. Using cross-cultural neuroimaging, we measured neural responses while Korean and American participants observed scenes of racial ingroup and outgroup members in emotional pain. Compared to Caucasian-American participants, Korean participants reported experiencing greater empathy and elicited stronger activity in the left temporo-parietal junction (L-TPJ), a region previously associated with mental state inference, for ingroup compared to outgroup members. Furthermore, preferential reactivity within this region to the pain of ingroup relative to outgroup members was associated with greater preference for social hierarchy and ingroup biases in empathy. Together, these results suggest that cultural variation in preference for social hierarchy leads to cultural variation in ingroup-preferences in empathy, due to increased engagement of brain regions associated with representing and inferring the mental states of others. Copyright © 2011 Elsevier Inc. All rights reserved.

Neural Correlates of Sexual Orientation in Heterosexual, Bisexual, and Homosexual Men

Science.gov (United States)

Safron, Adam; Sylva, David; Klimaj, Victoria; Rosenthal, A. M.; Li, Meng; Walter, Martin; Bailey, J. Michael

2017-01-01

Studies of subjective and genital sexual arousal in monosexual (i.e. heterosexual and homosexual) men have repeatedly found that erotic stimuli depicting men’s preferred sex produce strong responses, whereas erotic stimuli depicting the other sex produce much weaker responses. Inconsistent results have previously been obtained in bisexual men, who have sometimes demonstrated distinctly bisexual responses, but other times demonstrated patterns more similar to those observed in monosexual men. We used fMRI to investigate neural correlates of responses to erotic pictures and videos in heterosexual, bisexual, and homosexual men, ages 25–50. Sixty participants were included in video analyses, and 62 were included in picture analyses. We focused on the ventral striatum (VS), due to its association with incentive motivation. Patterns were consistent with sexual orientation, with heterosexual and homosexual men showing female-favoring and male-favoring responses, respectively. Bisexual men tended to show less differentiation between male and female stimuli. Consistent patterns were observed in the whole brain, including the VS, and also in additional regions such as occipitotemporal, anterior cingulate, and orbitofrontal cortices. This study extends previous findings of gender-specific neural responses in monosexual men, and provides initial evidence for distinct brain activity patterns in bisexual men. PMID:28145518

Age-Related Reversals in Neural Recruitment across Memory Retrieval Phases.

Science.gov (United States)

Ford, Jaclyn H; Kensinger, Elizabeth A

2017-05-17

Over the last several decades, neuroimaging research has identified age-related neural changes that occur during cognitive tasks. These changes are used to help researchers identify functional changes that contribute to age-related impairments in cognitive performance. One commonly reported example of such a change is an age-related decrease in the recruitment of posterior sensory regions coupled with an increased recruitment of prefrontal regions across multiple cognitive tasks. This shift is often described as a compensatory recruitment of prefrontal regions due to age-related sensory-processing deficits in posterior regions. However, age is not only associated with spatial shifts in recruitment, but also with temporal shifts, in which younger and older adults recruit the same neural region at different points in a task trial. The current study examines the possible contribution of temporal modifications in the often-reported posterior-anterior shift. Participants, ages 19-85, took part in a memory retrieval task with a protracted retrieval trial consisting of an initial memory search phase and a subsequent detail elaboration phase. Age-related neural patterns during search replicated prior reports of age-related decreases in posterior recruitment and increases in prefrontal recruitment. However, during the later elaboration phase, the same posterior regions were associated with age-related increases in activation. Further, ROI and functional connectivity results suggest that these posterior regions function similarly during search and elaboration. These results suggest that the often-reported posterior-anterior shift may not reflect the inability of older adults to engage in sensory processing, but rather a change in when they recruit this processing. SIGNIFICANCE STATEMENT The current study provides evidence that the often-reported posterior-anterior shift in aging may not reflect a global sensory-processing deficit, as has often been reported, but rather a

Photon spectrometry utilizing neural networks

International Nuclear Information System (INIS)

Silveira, R.; Benevides, C.; Lima, F.; Vilela, E.

2015-01-01

Having in mind the time spent on the uneventful work of characterization of the radiation beams used in a ionizing radiation metrology laboratory, the Metrology Service of the Centro Regional de Ciencias Nucleares do Nordeste - CRCN-NE verified the applicability of artificial intelligence (artificial neural networks) to perform the spectrometry in photon fields. For this, was developed a multilayer neural network, as an application for the classification of patterns in energy, associated with a thermoluminescent dosimetric system (TLD-700 and TLD-600). A set of dosimeters was initially exposed to various well known medium energies, between 40 keV and 1.2 MeV, coinciding with the beams determined by ISO 4037 standard, for the dose of 10 mSv in the quantity Hp(10), on a chest phantom (ISO slab phantom) with the purpose of generating a set of training data for the neural network. Subsequently, a new set of dosimeters irradiated in unknown energies was presented to the network with the purpose to test the method. The methodology used in this work was suitable for application in the classification of energy beams, having obtained 100% of the classification performed. (authors)

Regional difference of radiosensitivity of neural cells in the fetal brain of mice on day 13 of gestation

International Nuclear Information System (INIS)

Hoshino, Kiyoshi; Kameyama, Yoshiro

1986-01-01

Pregnant Slc: ICR mice were exposed to a single whole-body X-irradiation at a dose of 12.5 R or 25 R on day 13 of gestation. After irradiation, fetuses were obtained from mothers at 1- or 3-hour intervals and coronal histological sections were made. Pyknotic cells were counted in the ventricular zone of brain mantle, hippocampal anlage and olfactory bulb. In the 25 R group, peak incidences of pyknotic cells in brain mantle, hippocampal anlage and olfactory bulb were 13.2 %, 6.9 % and 2.2 %, respectively. In the 12.5 R group, these were 6.0 %, 3.2 % and 1.7 %, respectively. This result indicates that neural cells in the ventricular zone of brain mantle are the most radiosensitive among the cerebral regions examined in day-13 mouse fetuses. (author)

Environmental radioactivity in the North Atlantic region including the Faroe Islands and Greenland

International Nuclear Information System (INIS)

Aarkrog, A.; Chen, Q.J.; Dahlgaard, H.; Hansen, H.; Nielsen, S.P.; Buch, E.; Christensen, G.C.; Holm, E.

1992-01-01

Measurements of fallout radioactivity in the North Atlantic region including the Faroe Islands and Greenland are reported. Strontium-90, cesium-137 and cesium-134 were determined in samples of precipitation, sea water, vegetation, various foodstuffs (including milk in the Faroes), and drinking water. Estimates are given of the mean contents of 90 Sr and 137 Cs in human diet in the Faroes and Greenland in 1988 and 1989. 99 Tc data on marine samples, in particular sea water from the Greenland Sea, are reported. (au) (91 taps., 51 ills., 7 refs.)

Central neural pathways for thermoregulation

Science.gov (United States)

Morrison, Shaun F.; Nakamura, Kazuhiro

2010-01-01

Central neural circuits orchestrate a homeostatic repertoire to maintain body temperature during environmental temperature challenges and to alter body temperature during the inflammatory response. This review summarizes the functional organization of the neural pathways through which cutaneous thermal receptors alter thermoregulatory effectors: the cutaneous circulation for heat loss, the brown adipose tissue, skeletal muscle and heart for thermogenesis and species-dependent mechanisms (sweating, panting and saliva spreading) for evaporative heat loss. These effectors are regulated by parallel but distinct, effector-specific neural pathways that share a common peripheral thermal sensory input. The thermal afferent circuits include cutaneous thermal receptors, spinal dorsal horn neurons and lateral parabrachial nucleus neurons projecting to the preoptic area to influence warm-sensitive, inhibitory output neurons which control thermogenesis-promoting neurons in the dorsomedial hypothalamus that project to premotor neurons in the rostral ventromedial medulla, including the raphe pallidus, that descend to provide the excitation necessary to drive thermogenic thermal effectors. A distinct population of warm-sensitive preoptic neurons controls heat loss through an inhibitory input to raphe pallidus neurons controlling cutaneous vasoconstriction. PMID:21196160

Optimizing Markovian modeling of chaotic systems with recurrent neural networks

International Nuclear Information System (INIS)

Cechin, Adelmo L.; Pechmann, Denise R.; Oliveira, Luiz P.L. de

2008-01-01

In this paper, we propose a methodology for optimizing the modeling of an one-dimensional chaotic time series with a Markov Chain. The model is extracted from a recurrent neural network trained for the attractor reconstructed from the data set. Each state of the obtained Markov Chain is a region of the reconstructed state space where the dynamics is approximated by a specific piecewise linear map, obtained from the network. The Markov Chain represents the dynamics of the time series in its statistical essence. An application to a time series resulted from Lorenz system is included

Neural substrates underlying effort, time, and risk-based decision making in motivated behavior.

Science.gov (United States)

Bailey, Matthew R; Simpson, Eleanor H; Balsam, Peter D

2016-09-01

All mobile organisms rely on adaptive motivated behavior to overcome the challenges of living in an environment in which essential resources may be limited. A variety of influences ranging from an organism's environment, experiential history, and physiological state all influence a cost-benefit analysis which allows motivation to energize behavior and direct it toward specific goals. Here we review the substantial amount of research aimed at discovering the interconnected neural circuits which allow organisms to carry-out the cost-benefit computations which allow them to behave in adaptive ways. We specifically focus on how the brain deals with different types of costs, including effort requirements, delays to reward and payoff riskiness. An examination of this broad literature highlights the importance of the extended neural circuits which enable organisms to make decisions about these different types of costs. This involves Cortical Structures, including the Anterior Cingulate Cortex (ACC), the Orbital Frontal Cortex (OFC), the Infralimbic Cortex (IL), and prelimbic Cortex (PL), as well as the Baso-Lateral Amygdala (BLA), the Nucleus Accumbens (NAcc), the Ventral Pallidal (VP), the Sub Thalamic Nucleus (STN) among others. Some regions are involved in multiple aspects of cost-benefit computations while the involvement of other regions is restricted to information relating to specific types of costs. Copyright © 2016 Elsevier Inc. All rights reserved.

Neural correlates of conventional and harm/welfare-based moral decision-making.

Science.gov (United States)

White, Stuart F; Zhao, Hui; Leong, Kelly Kimiko; Smetana, Judith G; Nucci, Larry P; Blair, R James R

2017-12-01

The degree to which social norms are processed by a unitary system or dissociable systems remains debated. Much research on children's social-cognitive judgments has supported the distinction between "moral" (harm/welfare-based) and "conventional" norms. However, the extent to which these norms are processed by dissociable neural systems remains unclear. To address this issue, 23 healthy participants were scanned with functional magnetic resonance imaging (fMRI) while they rated the wrongness of harm/welfare-based and conventional transgressions and neutral vignettes. Activation significantly greater than the neutral vignette baseline was observed in regions implicated in decision-making regions including rostral/ventral medial frontal, anterior insula and dorsomedial frontal cortices when evaluating both harm/welfare-based and social-conventional transgressions. Greater activation when rating harm/welfare-based relative to social-conventional transgressions was seen through much of ACC and bilateral inferior frontal gyrus. Greater activation was observed in superior temporal gyrus, bilateral middle temporal gyrus, left PCC, and temporal-parietal junction when rating social-conventional transgressions relative to harm/welfare-based transgressions. These data suggest that decisions regarding the wrongness of actions, irrespective of whether they involve care/harm-based or conventional transgressions, recruit regions generally implicated in affect-based decision-making. However, there is neural differentiation between harm/welfare-based and conventional transgressions. This may reflect the particular importance of processing the intent of transgressors of conventional norms and perhaps the greater emotional content or salience of harm/welfare-based transgressions.

Cannabis cue-induced brain activation correlates with drug craving in limbic and visual salience regions: Preliminary results

Science.gov (United States)

Charboneau, Evonne J.; Dietrich, Mary S.; Park, Sohee; Cao, Aize; Watkins, Tristan J; Blackford, Jennifer U; Benningfield, Margaret M.; Martin, Peter R.; Buchowski, Maciej S.; Cowan, Ronald L.

2013-01-01

Craving is a major motivator underlying drug use and relapse but the neural correlates of cannabis craving are not well understood. This study sought to determine whether visual cannabis cues increase cannabis craving and whether cue-induced craving is associated with regional brain activation in cannabis-dependent individuals. Cannabis craving was assessed in 16 cannabis-dependent adult volunteers while they viewed cannabis cues during a functional MRI (fMRI) scan. The Marijuana Craving Questionnaire was administered immediately before and after each of three cannabis cue-exposure fMRI runs. FMRI blood-oxygenation-level-dependent (BOLD) signal intensity was determined in regions activated by cannabis cues to examine the relationship of regional brain activation to cannabis craving. Craving scores increased significantly following exposure to visual cannabis cues. Visual cues activated multiple brain regions, including inferior orbital frontal cortex, posterior cingulate gyrus, parahippocampal gyrus, hippocampus, amygdala, superior temporal pole, and occipital cortex. Craving scores at baseline and at the end of all three runs were significantly correlated with brain activation during the first fMRI run only, in the limbic system (including amygdala and hippocampus) and paralimbic system (superior temporal pole), and visual regions (occipital cortex). Cannabis cues increased craving in cannabis-dependent individuals and this increase was associated with activation in the limbic, paralimbic, and visual systems during the first fMRI run, but not subsequent fMRI runs. These results suggest that these regions may mediate visually cued aspects of drug craving. This study provides preliminary evidence for the neural basis of cue-induced cannabis craving and suggests possible neural targets for interventions targeted at treating cannabis dependence. PMID:24035535

Neural networks: Application to medical imaging

Science.gov (United States)

Clarke, Laurence P.

1994-01-01

The research mission is the development of computer assisted diagnostic (CAD) methods for improved diagnosis of medical images including digital x-ray sensors and tomographic imaging modalities. The CAD algorithms include advanced methods for adaptive nonlinear filters for image noise suppression, hybrid wavelet methods for feature segmentation and enhancement, and high convergence neural networks for feature detection and VLSI implementation of neural networks for real time analysis. Other missions include (1) implementation of CAD methods on hospital based picture archiving computer systems (PACS) and information networks for central and remote diagnosis and (2) collaboration with defense and medical industry, NASA, and federal laboratories in the area of dual use technology conversion from defense or aerospace to medicine.

Social exclusion in middle childhood: rejection events, slow-wave neural activity, and ostracism distress.

Science.gov (United States)

Crowley, Michael J; Wu, Jia; Molfese, Peter J; Mayes, Linda C

2010-01-01

This study examined neural activity with event-related potentials (ERPs) in middle childhood during a computer-simulated ball-toss game, Cyberball. After experiencing fair play initially, children were ultimately excluded by the other players. We focused specifically on “not my turn” events within fair play and rejection events within social exclusion. Dense-array ERPs revealed that rejection events are perceived rapidly. Condition differences (“not my turn” vs. rejection) were evident in a posterior ERP peaking at 420 ms consistent, with a larger P3 effect for rejection events indicating that in middle childhood rejection events are differentiated in <500 ms. Condition differences were evident for slow-wave activity (500-900 ms) in the medial frontal cortical region and the posterior occipital-parietal region, with rejection events more negative frontally and more positive posteriorly. Distress from the rejection experience was associated with a more negative frontal slow wave and a larger late positive slow wave, but only for rejection events. Source modeling with Geosouce software suggested that slow-wave neural activity in cortical regions previously identified in functional imaging studies of ostracism, including subgenual cortex, ventral anterior cingulate cortex, and insula, was greater for rejection events vs. “not my turn” events. © 2010 Psychology Press

Neural synchronization during face-to-face communication.

Science.gov (United States)

Jiang, Jing; Dai, Bohan; Peng, Danling; Zhu, Chaozhe; Liu, Li; Lu, Chunming

2012-11-07

Although the human brain may have evolutionarily adapted to face-to-face communication, other modes of communication, e.g., telephone and e-mail, increasingly dominate our modern daily life. This study examined the neural difference between face-to-face communication and other types of communication by simultaneously measuring two brains using a hyperscanning approach. The results showed a significant increase in the neural synchronization in the left inferior frontal cortex during a face-to-face dialog between partners but none during a back-to-back dialog, a face-to-face monologue, or a back-to-back monologue. Moreover, the neural synchronization between partners during the face-to-face dialog resulted primarily from the direct interactions between the partners, including multimodal sensory information integration and turn-taking behavior. The communicating behavior during the face-to-face dialog could be predicted accurately based on the neural synchronization level. These results suggest that face-to-face communication, particularly dialog, has special neural features that other types of communication do not have and that the neural synchronization between partners may underlie successful face-to-face communication.

Robust adaptive backstepping neural networks control for spacecraft rendezvous and docking with input saturation.

Science.gov (United States)

Xia, Kewei; Huo, Wei

2016-05-01

This paper presents a robust adaptive neural networks control strategy for spacecraft rendezvous and docking with the coupled position and attitude dynamics under input saturation. Backstepping technique is applied to design a relative attitude controller and a relative position controller, respectively. The dynamics uncertainties are approximated by radial basis function neural networks (RBFNNs). A novel switching controller consists of an adaptive neural networks controller dominating in its active region combined with an extra robust controller to avoid invalidation of the RBFNNs destroying stability of the system outside the neural active region. An auxiliary signal is introduced to compensate the input saturation with anti-windup technique, and a command filter is employed to approximate derivative of the virtual control in the backstepping procedure. Globally uniformly ultimately bounded of the relative states is proved via Lyapunov theory. Simulation example demonstrates effectiveness of the proposed control scheme. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Synaptic inputs compete during rapid formation of the calyx of Held: a new model system for neural development.

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Holcomb, Paul S; Hoffpauir, Brian K; Hoyson, Mitchell C; Jackson, Dakota R; Deerinck, Thomas J; Marrs, Glenn S; Dehoff, Marlin; Wu, Jonathan; Ellisman, Mark H; Spirou, George A

2013-08-07

Hallmark features of neural circuit development include early exuberant innervation followed by competition and pruning to mature innervation topography. Several neural systems, including the neuromuscular junction and climbing fiber innervation of Purkinje cells, are models to study neural development in part because they establish a recognizable endpoint of monoinnervation of their targets and because the presynaptic terminals are large and easily monitored. We demonstrate here that calyx of Held (CH) innervation of its target, which forms a key element of auditory brainstem binaural circuitry, exhibits all of these characteristics. To investigate CH development, we made the first application of serial block-face scanning electron microscopy to neural development with fine temporal resolution and thereby accomplished the first time series for 3D ultrastructural analysis of neural circuit formation. This approach revealed a growth spurt of added apposed surface area (ASA)>200 μm2/d centered on a single age at postnatal day 3 in mice and an initial rapid phase of growth and competition that resolved to monoinnervation in two-thirds of cells within 3 d. This rapid growth occurred in parallel with an increase in action potential threshold, which may mediate selection of the strongest input as the winning competitor. ASAs of competing inputs were segregated on the cell body surface. These data suggest mechanisms to select "winning" inputs by regional reinforcement of postsynaptic membrane to mediate size and strength of competing synaptic inputs.

Analysis of Power Laws, Shape Collapses, and Neural Complexity: New Techniques and MATLAB Support via the NCC Toolbox.

Science.gov (United States)

Marshall, Najja; Timme, Nicholas M; Bennett, Nicholas; Ripp, Monica; Lautzenhiser, Edward; Beggs, John M

2016-01-01

Neural systems include interactions that occur across many scales. Two divergent methods for characterizing such interactions have drawn on the physical analysis of critical phenomena and the mathematical study of information. Inferring criticality in neural systems has traditionally rested on fitting power laws to the property distributions of "neural avalanches" (contiguous bursts of activity), but the fractal nature of avalanche shapes has recently emerged as another signature of criticality. On the other hand, neural complexity, an information theoretic measure, has been used to capture the interplay between the functional localization of brain regions and their integration for higher cognitive functions. Unfortunately, treatments of all three methods-power-law fitting, avalanche shape collapse, and neural complexity-have suffered from shortcomings. Empirical data often contain biases that introduce deviations from true power law in the tail and head of the distribution, but deviations in the tail have often been unconsidered; avalanche shape collapse has required manual parameter tuning; and the estimation of neural complexity has relied on small data sets or statistical assumptions for the sake of computational efficiency. In this paper we present technical advancements in the analysis of criticality and complexity in neural systems. We use maximum-likelihood estimation to automatically fit power laws with left and right cutoffs, present the first automated shape collapse algorithm, and describe new techniques to account for large numbers of neural variables and small data sets in the calculation of neural complexity. In order to facilitate future research in criticality and complexity, we have made the software utilized in this analysis freely available online in the MATLAB NCC (Neural Complexity and Criticality) Toolbox.

Neural correlates of proactive and reactive aggression in adolescent twins.

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Yang, Yaling; Joshi, Shantanu H; Jahanshad, Neda; Thompson, Paul M; Baker, Laura A

2017-05-01

Verbal and physical aggression begin early in life and steadily decline thereafter in normal development. As a result, elevated aggressive behavior in adolescence may signal atypical development and greater vulnerability for negative mental and health outcomes. Converging evidence suggests that brain disturbances in regions involved in impulse control, emotional regulation, and sensation seeking may contribute to heightened aggression. However, little is known regarding the neural mechanisms underlying subtypes of aggression (i.e., proactive and reactive aggression) and whether they differ between males and females. Using a sample of 106 14-year-old adolescent twins, this study found that striatal enlargement was associated with both proactive and reactive aggression. We also found that volumetric alterations in several frontal regions including smaller middle frontal and larger orbitofrontal cortex were correlated with higher levels of aggression in adolescent twins. In addition, cortical thickness analysis showed that thickness alterations in many overlapping regions including middle frontal, superior frontal, and anterior cingulate cortex and temporal regions were associated with aggression in adolescent twins. Results support the involvement of fronto-limbic-striatal circuit in the etiology of aggression during adolescence. Aggr. Behav. 43:230-240, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

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

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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).

Neural activity predicts attitude change in cognitive dissonance.

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van Veen, Vincent; Krug, Marie K; Schooler, Jonathan W; Carter, Cameron S

2009-11-01

When our actions conflict with our prior attitudes, we often change our attitudes to be more consistent with our actions. This phenomenon, known as cognitive dissonance, is considered to be one of the most influential theories in psychology. However, the neural basis of this phenomenon is unknown. Using a Solomon four-group design, we scanned participants with functional MRI while they argued that the uncomfortable scanner environment was nevertheless a pleasant experience. We found that cognitive dissonance engaged the dorsal anterior cingulate cortex and anterior insula; furthermore, we found that the activation of these regions tightly predicted participants' subsequent attitude change. These effects were not observed in a control group. Our findings elucidate the neural representation of cognitive dissonance, and support the role of the anterior cingulate cortex in detecting cognitive conflict and the neural prediction of attitude change.

The Burden of Binge and Heavy Drinking on the Brain: Effects on Adolescent and Young Adult Neural Structure and Function

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Anita Cservenka

2017-06-01

Full Text Available Introduction: Adolescence and young adulthood are periods of continued biological and psychosocial maturation. Thus, there may be deleterious effects of consuming large quantities of alcohol on neural development and associated cognition during this time. The purpose of this mini review is to highlight neuroimaging research that has specifically examined the effects of binge and heavy drinking on adolescent and young adult brain structure and function.Methods: We review cross-sectional and longitudinal studies of young binge and heavy drinkers that have examined brain structure (e.g., gray and white matter volume, cortical thickness, white matter microstructure and investigated brain response using functional magnetic resonance imaging (fMRI.Results: Binge and heavy-drinking adolescents and young adults have systematically thinner and lower volume in prefrontal cortex and cerebellar regions, and attenuated white matter development. They also show elevated brain activity in fronto-parietal regions during working memory, verbal learning, and inhibitory control tasks. In response to alcohol cues, relative to controls or light-drinking individuals, binge and heavy drinkers show increased neural response mainly in mesocorticolimbic regions, including the striatum, anterior cingulate cortex (ACC, hippocampus, and amygdala. Mixed findings are present in risky decision-making tasks, which could be due to large variation in task design and analysis.Conclusions: These findings suggest altered neural structure and activity in binge and heavy-drinking youth may be related to the neurotoxic effects of consuming alcohol in large quantities during a highly plastic neurodevelopmental period, which could result in neural reorganization, and increased risk for developing an alcohol use disorder (AUD.

Using imagination to understand the neural basis of episodic memory

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Hassabis, Demis; Kumaran, Dharshan; Maguire, Eleanor A.

2008-01-01

Functional MRI (fMRI) studies investigating the neural basis of episodic memory recall, and the related task of thinking about plausible personal future events, have revealed a consistent network of associated brain regions. Surprisingly little, however, is understood about the contributions individual brain areas make to the overall recollective experience. In order to examine this, we employed a novel fMRI paradigm where subjects had to imagine fictitious experiences. In contrast to future thinking, this results in experiences that are not explicitly temporal in nature or as reliant on self-processing. By using previously imagined fictitious experiences as a comparison for episodic memories, we identified the neural basis of a key process engaged in common, namely scene construction, involving the generation, maintenance and visualisation of complex spatial contexts. This was associated with activations in a distributed network, including hippocampus, parahippocampal gyrus, and retrosplenial cortex. Importantly, we disambiguated these common effects from episodic memory-specific responses in anterior medial prefrontal cortex, posterior cingulate cortex and precuneus. These latter regions may support self-schema and familiarity processes, and contribute to the brain's ability to distinguish real from imaginary memories. We conclude that scene construction constitutes a common process underlying episodic memory and imagination of fictitious experiences, and suggest it may partially account for the similar brain networks implicated in navigation, episodic future thinking, and the default mode. We suggest that further brain regions are co-opted into this core network in a task-specific manner to support functions such as episodic memory that may have additional requirements. PMID:18160644

Neural Networks for Modeling and Control of Particle Accelerators

CERN Document Server

Edelen, A.L.; Chase, B.E.; Edstrom, D.; Milton, S.V.; Stabile, P.

2016-01-01

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.

Adaptive enhanced sampling by force-biasing using neural networks

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Guo, Ashley Z.; Sevgen, Emre; Sidky, Hythem; Whitmer, Jonathan K.; Hubbell, Jeffrey A.; de Pablo, Juan J.

2018-04-01

A machine learning assisted method is presented for molecular simulation of systems with rugged free energy landscapes. The method is general and can be combined with other advanced sampling techniques. In the particular implementation proposed here, it is illustrated in the context of an adaptive biasing force approach where, rather than relying on discrete force estimates, one can resort to a self-regularizing artificial neural network to generate continuous, estimated generalized forces. By doing so, the proposed approach addresses several shortcomings common to adaptive biasing force and other algorithms. Specifically, the neural network enables (1) smooth estimates of generalized forces in sparsely sampled regions, (2) force estimates in previously unexplored regions, and (3) continuous force estimates with which to bias the simulation, as opposed to biases generated at specific points of a discrete grid. The usefulness of the method is illustrated with three different examples, chosen to highlight the wide range of applicability of the underlying concepts. In all three cases, the new method is found to enhance considerably the underlying traditional adaptive biasing force approach. The method is also found to provide improvements over previous implementations of neural network assisted algorithms.

Neural network modeling of emotion

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Levine, Daniel S.

2007-03-01

This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.

Modular representation of layered neural networks.

Science.gov (United States)

Watanabe, Chihiro; Hiramatsu, Kaoru; Kashino, Kunio

2018-01-01

Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood. In this paper, we propose a new method for extracting a global and simplified structure from a layered neural network. Based on network analysis, the proposed method detects communities or clusters of units with similar connection patterns. We show its effectiveness by applying it to three use cases. (1) Network decomposition: it can decompose a trained neural network into multiple small independent networks thus dividing the problem and reducing the computation time. (2) Training assessment: the appropriateness of a trained result with a given hyperparameter or randomly chosen initial parameters can be evaluated by using a modularity index. And (3) data analysis: in practical data it reveals the community structure in the input, hidden, and output layers, which serves as a clue for discovering knowledge from a trained neural network. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Effects of GABAergic Polarity Changes on Episodic Neural Network Activity in Developing Neural Systems

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Wilfredo Blanco

2017-09-01

Full Text Available Early in development, neural systems have primarily excitatory coupling, where even GABAergic synapses are excitatory. Many of these systems exhibit spontaneous episodes of activity that have been characterized through both experimental and computational studies. As development progress the neural system goes through many changes, including synaptic remodeling, intrinsic plasticity in the ion channel expression, and a transformation of GABAergic synapses from excitatory to inhibitory. What effect each of these, and other, changes have on the network behavior is hard to know from experimental studies since they all happen in parallel. One advantage of a computational approach is that one has the ability to study developmental changes in isolation. Here, we examine the effects of GABAergic synapse polarity change on the spontaneous activity of both a mean field and a neural network model that has both glutamatergic and GABAergic coupling, representative of a developing neural network. We find some intuitive behavioral changes as the GABAergic neurons go from excitatory to inhibitory, shared by both models, such as a decrease in the duration of episodes. We also find some paradoxical changes in the activity that are only present in the neural network model. In particular, we find that during early development the inter-episode durations become longer on average, while later in development they become shorter. In addressing this unexpected finding, we uncover a priming effect that is particularly important for a small subset of neurons, called the “intermediate neurons.” We characterize these neurons and demonstrate why they are crucial to episode initiation, and why the paradoxical behavioral change result from priming of these neurons. The study illustrates how even arguably the simplest of developmental changes that occurs in neural systems can present non-intuitive behaviors. It also makes predictions about neural network behavioral changes

Medical Imaging with Neural Networks

International Nuclear Information System (INIS)

Pattichis, C.; Cnstantinides, A.

1994-01-01

The objective of this paper is to provide an overview of the recent developments in the use of artificial neural networks in medical imaging. The areas of medical imaging that are covered include : ultrasound, magnetic resonance, nuclear medicine and radiological (including computerized tomography). (authors)

Neural crest cells: from developmental biology to clinical interventions.

Science.gov (United States)

Noisa, Parinya; Raivio, Taneli

2014-09-01

Neural crest cells are multipotent cells, which are specified in embryonic ectoderm in the border of neural plate and epiderm during early development by interconnection of extrinsic stimuli and intrinsic factors. Neural crest cells are capable of differentiating into various somatic cell types, including melanocytes, craniofacial cartilage and bone, smooth muscle, and peripheral nervous cells, which supports their promise for cell therapy. In this work, we provide a comprehensive review of wide aspects of neural crest cells from their developmental biology to applicability in medical research. We provide a simplified model of neural crest cell development and highlight the key external stimuli and intrinsic regulators that determine the neural crest cell fate. Defects of neural crest cell development leading to several human disorders are also mentioned, with the emphasis of using human induced pluripotent stem cells to model neurocristopathic syndromes. © 2014 Wiley Periodicals, Inc.

The role of automaticity and attention in neural processes underlying empathy for happiness, sadness, and anxiety

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Sylvia A. Morelli

2013-05-01

Full Text Available Although many studies have examined the neural basis of experiencing empathy, relatively little is known about how empathic processes are affected by different attentional conditions. Thus, we examined whether instructions to empathize might amplify responses in empathy-related regions and whether cognitive load would diminish the involvement of these regions. 32 participants completed a functional magnetic resonance imaging session assessing empathic responses to individuals experiencing happy, sad, and anxious events. Stimuli were presented under three conditions: watching naturally, while instructed to empathize, and under cognitive load. Across analyses, we found evidence for a core set of neural regions that support empathic processes (dorsomedial prefrontal cortex, DMPFC; medial prefrontal cortex, MPFC; temporoparietal junction, TPJ; amygdala; ventral anterior insula, AI; septal area, SA. Two key regions – the ventral AI and SA – were consistently active across all attentional conditions, suggesting that they are automatically engaged during empathy. In addition, watching versus empathizing with targets was not markedly different and instead led to similar subjective and neural responses to others’ emotional experiences. In contrast, cognitive load reduced the subjective experience of empathy and diminished neural responses in several regions related to empathy (DMPFC, MPFC, TPJ, amygdala and social cognition. The current results reveal how attention impacts empathic processes and provides insight into how empathy may unfold in everyday interactions.

Neural pathway in the right hemisphere underlies verbal insight problem solving.

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Zhao, Q; Zhou, Z; Xu, H; Fan, W; Han, L

2014-01-03

Verbal insight problem solving means to break mental sets, to select the novel semantic information and to form novel, task-related associations. Although previous studies have identified the brain regions associated with these key processes, the interaction among these regions during insight is still unclear. In the present study, we explored the functional connectivity between the key regions during solving Chinese 'chengyu' riddles by using event-related functional magnetic resonance imaging. Results showed that both insight and noninsight solutions activated the bilateral inferior frontal gyri, middle temporal gyri and hippocampi, and these regions constituted a frontal to temporal to hippocampal neural pathway. Compared with noninsight solution, insight solution had a stronger functional connectivity between the inferior frontal gyrus and middle temporal gyrus in the right hemisphere. Our study reveals the neural pathway of information processing during verbal insight problem solving, and supports the right-hemisphere advantage theory of insight. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

Insights into neural crest development from studies of avian embryos

OpenAIRE

Gandhi, Shashank; Bronner, Marianne E.

2018-01-01

The neural crest is a multipotent and highly migratory cell type that contributes to many of the defining features of vertebrates, including the skeleton of the head and most of the peripheral nervous system. 150 years after the discovery of the neural crest, avian embryos remain one of the most important model organisms for studying neural crest development. In this review, we describe aspects of neural crest induction, migration and axial level differences, highlighting what is known about ...

Age-related difference in the effective neural connectivity associated with probabilistic category learning

International Nuclear Information System (INIS)

Yoon, Eun Jin; Cho, Sang Soo; Kim, Hee Jung; Bang, Seong Ae; Park, Hyun Soo; Kim, Yu Kyeong; Kim, Sang Eun

2007-01-01

Although it is well known that explicit memory is affected by the deleterious changes in brain with aging, but effect of aging in implicit memory such as probabilistic category learning (PCL) is not clear. To identify the effect of aging on the neural interaction for successful PCL, we investigated the neural substrates of PCL and the age-related changes of the neural network between these brain regions. 23 young (age, 252 y; 11 males) and 14 elderly (673 y; 7 males) healthy subjects underwent FDG PET during a resting state and 150-trial weather prediction (WP) task. Correlations between the WP hit rates and regional glucose metabolism were assessed using SPM2 (P diff (37) = 142.47, P<0.005), Systematic comparisons of each path revealed that frontal crosscallosal and the frontal to parahippocampal connection were most responsible for the model differences (P<0.05). For the successful PCL, the elderly recruits the basal ganglia implicit memory system but MTL recruitment differs from the young. The inadequate MTL correlation pattern in the elderly is may be caused by the changes of the neural pathway related with explicit memory. These neural changes can explain the decreased performance of PCL in elderly subjects

The breaking of a delayed ring neural network contributes to stability: The rule and exceptions.

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Khokhlova, T N; Kipnis, M M

2013-12-01

We prove that in our mathematical model the breaking of a delayed ring neural network extends the stability region in the parameters space, if the number of the neurons is sufficiently large. If the number of neurons is small, then a "paradoxical" region exists in the parameters space, wherein the ring neural configuration is stable, while the linear one is unstable. We study the conditions under which the paradoxical region is nonempty. We discuss how our mathematical modeling reflects neurosurgical operations with the severing of particular connections in the brain. Copyright © 2013 Elsevier Ltd. All rights reserved.

The neural and behavioral correlates of social evaluation in childhood

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Michelle Achterberg

2017-04-01

Full Text Available Being accepted or rejected by peers is highly salient for developing social relations in childhood. We investigated the behavioral and neural correlates of social feedback and subsequent aggression in 7–10-year-old children, using the Social Network Aggression Task (SNAT. Participants viewed pictures of peers that gave positive, neutral or negative feedback to the participant’s profile. Next, participants could blast a loud noise towards the peer, as an index of aggression. We included three groups (N = 19, N = 28 and N = 27 and combined the results meta-analytically. Negative social feedback resulted in the most behavioral aggression, with large combined effect-sizes. Whole brain condition effects for each separate sample failed to show robust effects, possibly due to the small samples. Exploratory analyses over the combined test and replication samples confirmed heightened activation in the medial prefrontal cortex (mPFC after negative social feedback. Moreover, meta-analyses of activity in predefined regions of interest showed that negative social feedback resulted in more neural activation in the amygdala, anterior insula and the mPFC/anterior cingulate cortex. Together, the results show that social motivation is already highly salient in middle childhood, and indicate that the SNAT is a valid paradigm for assessing the neural and behavioral correlates of social evaluation in children.

Intranasal oxytocin modulates neural functional connectivity during human social interaction.

Science.gov (United States)

Rilling, James K; Chen, Xiangchuan; Chen, Xu; Haroon, Ebrahim

2018-02-10

Oxytocin (OT) modulates social behavior in primates and many other vertebrate species. Studies in non-primate animals have demonstrated that, in addition to influencing activity within individual brain areas, OT influences functional connectivity across networks of areas involved in social behavior. Previously, we used fMRI to image brain function in human subjects during a dyadic social interaction task following administration of either intranasal oxytocin (INOT) or placebo, and analyzed the data with a standard general linear model. Here, we conduct an extensive re-analysis of these data to explore how OT modulates functional connectivity across a neural network that animal studies implicate in social behavior. OT induced widespread increases in functional connectivity in response to positive social interactions among men and widespread decreases in functional connectivity in response to negative social interactions among women. Nucleus basalis of Meynert, an important regulator of selective attention and motivation with a particularly high density of OT receptors, had the largest number of OT-modulated connections. Regions known to receive mesolimbic dopamine projections such as the nucleus accumbens and lateral septum were also hubs for OT effects on functional connectivity. Our results suggest that the neural mechanism by which OT influences primate social cognition may include changes in patterns of activity across neural networks that regulate social behavior in other animals. © 2018 Wiley Periodicals, Inc.

Thermoelastic steam turbine rotor control based on neural network

Science.gov (United States)

Rzadkowski, Romuald; Dominiczak, Krzysztof; Radulski, Wojciech; Szczepanik, R.

2015-12-01

Considered here are Nonlinear Auto-Regressive neural networks with eXogenous inputs (NARX) as a mathematical model of a steam turbine rotor for controlling steam turbine stress on-line. In order to obtain neural networks that locate critical stress and temperature points in the steam turbine during transient states, an FE rotor model was built. This model was used to train the neural networks on the basis of steam turbine transient operating data. The training included nonlinearity related to steam turbine expansion, heat exchange and rotor material properties during transients. Simultaneous neural networks are algorithms which can be implemented on PLC controllers. This allows for the application neural networks to control steam turbine stress in industrial power plants.

Neural Correlates of Attitude Change Following Positive and Negative Advertisements

Science.gov (United States)

Kato, Junko; Ide, Hiroko; Kabashima, Ikuo; Kadota, Hiroshi; Takano, Kouji; Kansaku, Kenji

2009-01-01

Understanding changes in attitudes towards others is critical to understanding human behaviour. Neuropolitical studies have found that the activation of emotion-related areas in the brain is linked to resilient political preferences, and neuroeconomic research has analysed the neural correlates of social preferences that favour or oppose consideration of intrinsic rewards. This study aims to identify the neural correlates in the prefrontal cortices of changes in political attitudes toward others that are linked to social cognition. Functional magnetic resonance imaging (fMRI) experiments have presented videos from previous electoral campaigns and television commercials for major cola brands and then used the subjects' self-rated affinity toward political candidates as behavioural indicators. After viewing negative campaign videos, subjects showing stronger fMRI activation in the dorsolateral prefrontal cortex lowered their ratings of the candidate they originally supported more than did those with smaller fMRI signal changes in the same region. Subjects showing stronger activation in the medial prefrontal cortex tended to increase their ratings more than did those with less activation. The same regions were not activated by viewing negative advertisements for cola. Correlations between the self-rated values and the neural signal changes underscore the metric representation of observed decisions (i.e., whether to support or not) in the brain. This indicates that neurometric analysis may contribute to the exploration of the neural correlates of daily social behaviour. PMID:19503749

Modelling the perceptual similarity of facial expressions from image statistics and neural responses.

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Sormaz, Mladen; Watson, David M; Smith, William A P; Young, Andrew W; Andrews, Timothy J

2016-04-01

The ability to perceive facial expressions of emotion is essential for effective social communication. We investigated how the perception of facial expression emerges from the image properties that convey this important social signal, and how neural responses in face-selective brain regions might track these properties. To do this, we measured the perceptual similarity between expressions of basic emotions, and investigated how this is reflected in image measures and in the neural response of different face-selective regions. We show that the perceptual similarity of different facial expressions (fear, anger, disgust, sadness, happiness) can be predicted by both surface and feature shape information in the image. Using block design fMRI, we found that the perceptual similarity of expressions could also be predicted from the patterns of neural response in the face-selective posterior superior temporal sulcus (STS), but not in the fusiform face area (FFA). These results show that the perception of facial expression is dependent on the shape and surface properties of the image and on the activity of specific face-selective regions. Copyright © 2016 Elsevier Inc. All rights reserved.

Routes to the past: neural substrates of direct and generative autobiographical memory retrieval.

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Addis, Donna Rose; Knapp, Katie; Roberts, Reece P; Schacter, Daniel L

2012-02-01

Models of autobiographical memory propose two routes to retrieval depending on cue specificity. When available cues are specific and personally-relevant, a memory can be directly accessed. However, when available cues are generic, one must engage a generative retrieval process to produce more specific cues to successfully access a relevant memory. The current study sought to characterize the neural bases of these retrieval processes. During functional magnetic resonance imaging (fMRI), participants were shown personally-relevant cues to elicit direct retrieval, or generic cues (nouns) to elicit generative retrieval. We used spatiotemporal partial least squares to characterize the spatial and temporal characteristics of the networks associated with direct and generative retrieval. Both retrieval tasks engaged regions comprising the autobiographical retrieval network, including hippocampus, and medial prefrontal and parietal cortices. However, some key neural differences emerged. Generative retrieval differentially recruited lateral prefrontal and temporal regions early on during the retrieval process, likely supporting the strategic search operations and initial recovery of generic autobiographical information. However, many regions were activated more strongly during direct versus generative retrieval, even when we time-locked the analysis to the successful recovery of events in both conditions. This result suggests that there may be fundamental differences between memories that are accessed directly and those that are recovered via the iterative search and retrieval process that characterizes generative retrieval. Copyright © 2011 Elsevier Inc. All rights reserved.

Salient regions detection using convolutional neural networks and color volume

Science.gov (United States)

Liu, Guang-Hai; Hou, Yingkun

2018-03-01

Convolutional neural network is an important technique in machine learning, pattern recognition and image processing. In order to reduce the computational burden and extend the classical LeNet-5 model to the field of saliency detection, we propose a simple and novel computing model based on LeNet-5 network. In the proposed model, hue, saturation and intensity are utilized to extract depth cues, and then we integrate depth cues and color volume to saliency detection following the basic structure of the feature integration theory. Experimental results show that the proposed computing model outperforms some existing state-of-the-art methods on MSRA1000 and ECSSD datasets.

Neural patterning of human induced pluripotent stem cells in 3-D cultures for studying biomolecule-directed differential cellular responses.

Science.gov (United States)

Yan, Yuanwei; Bejoy, Julie; Xia, Junfei; Guan, Jingjiao; Zhou, Yi; Li, Yan

2016-09-15

Appropriate neural patterning of human induced pluripotent stem cells (hiPSCs) is critical to generate specific neural cells/tissues and even mini-brains that are physiologically relevant to model neurological diseases. However, the capacity of signaling factors that regulate 3-D neural tissue patterning in vitro and differential responses of the resulting neural populations to various biomolecules have not yet been fully understood. By tuning neural patterning of hiPSCs with small molecules targeting sonic hedgehog (SHH) signaling, this study generated different 3-D neuronal cultures that were mainly comprised of either cortical glutamatergic neurons or motor neurons. Abundant glutamatergic neurons were observed following the treatment with an antagonist of SHH signaling, cyclopamine, while Islet-1 and HB9-expressing motor neurons were enriched by an SHH agonist, purmorphamine. In neurons derived with different neural patterning factors, whole-cell patch clamp recordings showed similar voltage-gated Na(+)/K(+) currents, depolarization-evoked action potentials and spontaneous excitatory post-synaptic currents. Moreover, these different neuronal populations exhibited differential responses to three classes of biomolecules, including (1) matrix metalloproteinase inhibitors that affect extracellular matrix remodeling; (2) N-methyl-d-aspartate that induces general neurotoxicity; and (3) amyloid β (1-42) oligomers that cause neuronal subtype-specific neurotoxicity. This study should advance our understanding of hiPSC self-organization and neural tissue development and provide a transformative approach to establish 3-D models for neurological disease modeling and drug discovery. Appropriate neural patterning of human induced pluripotent stem cells (hiPSCs) is critical to generate specific neural cells, tissues and even mini-brains that are physiologically relevant to model neurological diseases. However, the capability of sonic hedgehog-related small molecules to tune

Neural Stem Cells: Implications for the Conventional Radiotherapy of Central Nervous System Malignancies

International Nuclear Information System (INIS)

Barani, Igor J.; Benedict, Stanley H.; Lin, Peck-Sun

2007-01-01

Advances in basic neuroscience related to neural stem cells and their malignant counterparts are challenging traditional models of central nervous system tumorigenesis and intrinsic brain repair. Neurogenesis persists into adulthood predominantly in two neurogenic centers: subventricular zone and subgranular zone. Subventricular zone is situated adjacent to lateral ventricles and subgranular zone is confined to the dentate gyrus of the hippocampus. Neural stem cells not only self-renew and differentiate along multiple lineages in these regions, but also contribute to intrinsic brain plasticity and repair. Ionizing radiation can depopulate these exquisitely sensitive regions directly or impair in situ neurogenesis by indirect, dose-dependent and inflammation-mediated mechanisms, even at doses <2 Gy. This review discusses the fundamental neural stem cell concepts within the framework of cumulative clinical experience with the treatment of central nervous system malignancies using conventional radiotherapy

Morphological neural networks

Energy Technology Data Exchange (ETDEWEB)

Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)

1996-12-31

The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.

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

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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

Doctor, Teacher, and Stethoscope: Neural Representation of Different Types of Semantic Relations.

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Xu, Yangwen; Wang, Xiaosha; Wang, Xiaoying; Men, Weiwei; Gao, Jia-Hong; Bi, Yanchao

2018-03-28

Concepts can be related in many ways. They can belong to the same taxonomic category (e.g., "doctor" and "teacher," both in the category of people) or be associated with the same event context (e.g., "doctor" and "stethoscope," both associated with medical scenarios). How are these two major types of semantic relations coded in the brain? We constructed stimuli from three taxonomic categories (people, manmade objects, and locations) and three thematic categories (school, medicine, and sports) and investigated the neural representations of these two dimensions using representational similarity analyses in human participants (10 men and nine women). In specific regions of interest, the left anterior temporal lobe (ATL) and the left temporoparietal junction (TPJ), we found that, whereas both areas had significant effects of taxonomic information, the taxonomic relations had stronger effects in the ATL than in the TPJ ("doctor" and "teacher" closer in ATL neural activity), with the reverse being true for thematic relations ("doctor" and "stethoscope" closer in TPJ neural activity). A whole-brain searchlight analysis revealed that widely distributed regions, mainly in the left hemisphere, represented the taxonomic dimension. Interestingly, the significant effects of the thematic relations were only observed after the taxonomic differences were controlled for in the left TPJ, the right superior lateral occipital cortex, and other frontal, temporal, and parietal regions. In summary, taxonomic grouping is a primary organizational dimension across distributed brain regions, with thematic grouping further embedded within such taxonomic structures. SIGNIFICANCE STATEMENT How are concepts organized in the brain? It is well established that concepts belonging to the same taxonomic categories (e.g., "doctor" and "teacher") share neural representations in specific brain regions. How concepts are associated in other manners (e.g., "doctor" and "stethoscope," which are thematically

The Effects of Low-Dose Bisphenol A and Bisphenol F on Neural Differentiation of a Fetal Brain-Derived Neural Progenitor Cell Line.

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Fujiwara, Yuki; Miyazaki, Wataru; Koibuchi, Noriyuki; Katoh, Takahiko

2018-01-01

Environmental chemicals are known to disrupt the endocrine system in humans and to have adverse effects on several organs including the developing brain. Recent studies indicate that exposure to environmental chemicals during gestation can interfere with neuronal differentiation, subsequently affecting normal brain development in newborns. Xenoestrogen, bisphenol A (BPA), which is widely used in plastic products, is one such chemical. Adverse effects of exposure to BPA during pre- and postnatal periods include the disruption of brain function. However, the effect of BPA on neural differentiation remains unclear. In this study, we explored the effects of BPA or bisphenol F (BPF), an alternative compound for BPA, on neural differentiation using ReNcell, a human fetus-derived neural progenitor cell line. Maintenance in growth factor-free medium initiated the differentiation of ReNcell to neuronal cells including neurons, astrocytes, and oligodendrocytes. We exposed the cells to BPA or BPF for 3 days from the period of initiation and performed real-time PCR for neural markers such as β III-tubulin and glial fibrillary acidic protein (GFAP), and Olig2. The β III-tubulin mRNA level decreased in response to BPA, but not BPF, exposure. We also observed that the number of β III-tubulin-positive cells in the BPA-exposed group was less than that of the control group. On the other hand, there were no changes in the MAP2 mRNA level. These results indicate that BPA disrupts neural differentiation in human-derived neural progenitor cells, potentially disrupting brain development.

The Effects of Low-Dose Bisphenol A and Bisphenol F on Neural Differentiation of a Fetal Brain-Derived Neural Progenitor Cell Line

Directory of Open Access Journals (Sweden)

Yuki Fujiwara

2018-02-01

Full Text Available Environmental chemicals are known to disrupt the endocrine system in humans and to have adverse effects on several organs including the developing brain. Recent studies indicate that exposure to environmental chemicals during gestation can interfere with neuronal differentiation, subsequently affecting normal brain development in newborns. Xenoestrogen, bisphenol A (BPA, which is widely used in plastic products, is one such chemical. Adverse effects of exposure to BPA during pre- and postnatal periods include the disruption of brain function. However, the effect of BPA on neural differentiation remains unclear. In this study, we explored the effects of BPA or bisphenol F (BPF, an alternative compound for BPA, on neural differentiation using ReNcell, a human fetus-derived neural progenitor cell line. Maintenance in growth factor-free medium initiated the differentiation of ReNcell to neuronal cells including neurons, astrocytes, and oligodendrocytes. We exposed the cells to BPA or BPF for 3 days from the period of initiation and performed real-time PCR for neural markers such as β III-tubulin and glial fibrillary acidic protein (GFAP, and Olig2. The β III-tubulin mRNA level decreased in response to BPA, but not BPF, exposure. We also observed that the number of β III-tubulin-positive cells in the BPA-exposed group was less than that of the control group. On the other hand, there were no changes in the MAP2 mRNA level. These results indicate that BPA disrupts neural differentiation in human-derived neural progenitor cells, potentially disrupting brain development.

Machine Learning Topological Invariants with Neural Networks

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Zhang, Pengfei; Shen, Huitao; Zhai, Hui

2018-02-01

In this Letter we supervisedly train neural networks to distinguish different topological phases in the context of topological band insulators. After training with Hamiltonians of one-dimensional insulators with chiral symmetry, the neural network can predict their topological winding numbers with nearly 100% accuracy, even for Hamiltonians with larger winding numbers that are not included in the training data. These results show a remarkable success that the neural network can capture the global and nonlinear topological features of quantum phases from local inputs. By opening up the neural network, we confirm that the network does learn the discrete version of the winding number formula. We also make a couple of remarks regarding the role of the symmetry and the opposite effect of regularization techniques when applying machine learning to physical systems.

NEURAL NETWORKS FOR STOCK MARKET OPTION PRICING

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Sergey A. Sannikov

2017-03-01

Full Text Available Introduction: The use of neural networks for non-linear models helps to understand where linear model drawbacks, coused by their specification, reveal themselves. This paper attempts to find this out. The objective of research is to determine the meaning of “option prices calculation using neural networks”. Materials and Methods: We use two kinds of variables: endogenous (variables included in the model of neural network and variables affecting on the model (permanent disturbance. Results: All data are divided into 3 sets: learning, affirming and testing. All selected variables are normalised from 0 to 1. Extreme values of income were shortcut. Discussion and Conclusions: Using the 33-14-1 neural network with direct links we obtained two sets of forecasts. Optimal criteria of strategies in stock markets’ option pricing were developed.

An evolutionarily conserved intronic region controls the spatiotemporal expression of the transcription factor Sox10

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Pavan William J

2008-10-01

Full Text Available Abstract Background A major challenge lies in understanding the complexities of gene regulation. Mutation of the transcription factor SOX10 is associated with several human diseases. The disease phenotypes reflect the function of SOX10 in diverse tissues including the neural crest, central nervous system and otic vesicle. As expected, the SOX10 expression pattern is complex and highly dynamic, but little is known of the underlying mechanisms regulating its spatiotemporal pattern. SOX10 expression is highly conserved between all vertebrates characterised. Results We have combined in vivo testing of DNA fragments in zebrafish and computational comparative genomics to identify the first regulatory regions of the zebrafish sox10 gene. Both approaches converged on the 3' end of the conserved 1st intron as being critical for spatial patterning of sox10 in the embryo. Importantly, we have defined a minimal region crucial for this function. We show that this region contains numerous binding sites for transcription factors known to be essential in early neural crest induction, including Tcf/Lef, Sox and FoxD3. We show that the identity and relative position of these binding sites are conserved between zebrafish and mammals. A further region, partially required for oligodendrocyte expression, lies in the 5' region of the same intron and contains a putative CSL binding site, consistent with a role for Notch signalling in sox10 regulation. Furthermore, we show that β-catenin, Notch signalling and Sox9 can induce ectopic sox10 expression in early embryos, consistent with regulatory roles predicted from our transgenic and computational results. Conclusion We have thus identified two major sites of sox10 regulation in vertebrates and provided evidence supporting a role for at least three factors in driving sox10 expression in neural crest, otic epithelium and oligodendrocyte domains.

Efficient and rapid derivation of primitive neural stem cells and generation of brain subtype neurons from human pluripotent stem cells.

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Yan, Yiping; Shin, Soojung; Jha, Balendu Shekhar; Liu, Qiuyue; Sheng, Jianting; Li, Fuhai; Zhan, Ming; Davis, Janine; Bharti, Kapil; Zeng, Xianmin; Rao, Mahendra; Malik, Nasir; Vemuri, Mohan C

2013-11-01

Human pluripotent stem cells (hPSCs), including human embryonic stem cells and human induced pluripotent stem cells, are unique cell sources for disease modeling, drug discovery screens, and cell therapy applications. The first step in producing neural lineages from hPSCs is the generation of neural stem cells (NSCs). Current methods of NSC derivation involve the time-consuming, labor-intensive steps of an embryoid body generation or coculture with stromal cell lines that result in low-efficiency derivation of NSCs. In this study, we report a highly efficient serum-free pluripotent stem cell neural induction medium that can induce hPSCs into primitive NSCs (pNSCs) in 7 days, obviating the need for time-consuming, laborious embryoid body generation or rosette picking. The pNSCs expressed the neural stem cell markers Pax6, Sox1, Sox2, and Nestin; were negative for Oct4; could be expanded for multiple passages; and could be differentiated into neurons, astrocytes, and oligodendrocytes, in addition to the brain region-specific neuronal subtypes GABAergic, dopaminergic, and motor neurons. Global gene expression of the transcripts of pNSCs was comparable to that of rosette-derived and human fetal-derived NSCs. This work demonstrates an efficient method to generate expandable pNSCs, which can be further differentiated into central nervous system neurons and glia with temporal, spatial, and positional cues of brain regional heterogeneity. This method of pNSC derivation sets the stage for the scalable production of clinically relevant neural cells for cell therapy applications in good manufacturing practice conditions.

Neural Activity Patterns in the Human Brain Reflect Tactile Stickiness Perception

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Kim, Junsuk; Yeon, Jiwon; Ryu, Jaekyun; Park, Jang-Yeon; Chung, Soon-Cheol; Kim, Sung-Phil

2017-01-01

Our previous human fMRI study found brain activations correlated with tactile stickiness perception using the uni-variate general linear model (GLM) (Yeon et al., 2017). Here, we conducted an in-depth investigation on neural correlates of sticky sensations by employing a multivoxel pattern analysis (MVPA) on the same dataset. In particular, we statistically compared multi-variate neural activities in response to the three groups of sticky stimuli: A supra-threshold group including a set of sticky stimuli that evoked vivid sticky perception; an infra-threshold group including another set of sticky stimuli that barely evoked sticky perception; and a sham group including acrylic stimuli with no physically sticky property. Searchlight MVPAs were performed to search for local activity patterns carrying neural information of stickiness perception. Similar to the uni-variate GLM results, significant multi-variate neural activity patterns were identified in postcentral gyrus, subcortical (basal ganglia and thalamus), and insula areas (insula and adjacent areas). Moreover, MVPAs revealed that activity patterns in posterior parietal cortex discriminated the perceptual intensities of stickiness, which was not present in the uni-variate analysis. Next, we applied a principal component analysis (PCA) to the voxel response patterns within identified clusters so as to find low-dimensional neural representations of stickiness intensities. Follow-up clustering analyses clearly showed separate neural grouping configurations between the Supra- and Infra-threshold groups. Interestingly, this neural categorization was in line with the perceptual grouping pattern obtained from the psychophysical data. Our findings thus suggest that different stickiness intensities would elicit distinct neural activity patterns in the human brain and may provide a neural basis for the perception and categorization of tactile stickiness. PMID:28936171

Introduction to neural networks with electric power applications

International Nuclear Information System (INIS)

Wildberger, A.M.; Hickok, K.A.

1990-01-01

This is an introduction to the general field of neural networks with emphasis on prospects for their application in the power industry. It is intended to provide enough background information for its audience to begin to follow technical developments in neural networks and to recognize those which might impact on electric power engineering. Beginning with a brief discussion of natural and artificial neurons, the characteristics of neural networks in general and how they learn, neural networks are compared with other modeling tools such as simulation and expert systems in order to provide guidance in selecting appropriate applications. In the power industry, possible applications include plant control, dispatching, and maintenance scheduling. In particular, neural networks are currently being investigated for enhancements to the Thermal Performance Advisor (TPA) which General Physics Corporation (GP) has developed to improve the efficiency of electric power generation

Neural Networks

International Nuclear Information System (INIS)

Smith, Patrick I.

2003-01-01

Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

Medical Imaging with Neural Networks

Energy Technology Data Exchange (ETDEWEB)

Pattichis, C [Department of Computer Science, University of Cyprus, Kallipoleos 75, P.O.Box 537, Nicosia (Cyprus); Cnstantinides, A [Department of Electrical Engineering, Imperial College of Science, Technology and Medicine, London SW7 2BT (United Kingdom)

1994-12-31

The objective of this paper is to provide an overview of the recent developments in the use of artificial neural networks in medical imaging. The areas of medical imaging that are covered include : ultrasound, magnetic resonance, nuclear medicine and radiological (including computerized tomography). (authors). 61 refs, 4 tabs.

Led into temptation? Rewarding brand logos bias the neural encoding of incidental economic decisions.

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Murawski, Carsten; Harris, Philip G; Bode, Stefan; Domínguez D, Juan F; Egan, Gary F

2012-01-01

Human decision-making is driven by subjective values assigned to alternative choice options. These valuations are based on reward cues. It is unknown, however, whether complex reward cues, such as brand logos, may bias the neural encoding of subjective value in unrelated decisions. In this functional magnetic resonance imaging (fMRI) study, we subliminally presented brand logos preceding intertemporal choices. We demonstrated that priming biased participants' preferences towards more immediate rewards in the subsequent temporal discounting task. This was associated with modulations of the neural encoding of subjective values of choice options in a network of brain regions, including but not restricted to medial prefrontal cortex. Our findings demonstrate the general susceptibility of the human decision making system to apparently incidental contextual information. We conclude that the brain incorporates seemingly unrelated value information that modifies decision making outside the decision-maker's awareness.

Neural substrates underlying balanced time perspective: A combined voxel-based morphometry and resting-state functional connectivity study.

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Guo, Yiqun; Chen, Zhiyi; Feng, Tingyong

2017-08-14

Balanced time perspective (BTP), which is defined as a mental ability to switch flexibly among different time perspectives Zimbardo and Boyd (1999), has been suggested to be a central component of positive psychology Boniwell and Zimbardo (2004). BTP reflects individual's cognitive flexibility towards different time frames, which leads to many positive outcomes, including positive mood, subjective wellbeing, emotional intelligence, fluid intelligence, and executive control. However, the neural basis of BTP is still unclear. To address this question, we quantified individual's deviation from the BTP (DBTP), and investigated the neural substrates of DBTP using both voxel-based morphometry (VBM) and resting-state functional connectivity (RSFC) methods VBM analysis found that DBTP scores were positively correlated with gray matter volume (GMV) in the ventral precuneus. We further found that DBTP scores were negatively associated with RSFCs between the ventral precuneus seed region and medial prefrontal cortex (mPFC), bilateral temporoparietal junction (TPJ), parahippocampa gyrus (PHG), and middle frontal gyrus (MFG). These brain regions found in both VBM and RSFC analyses are commonly considered as core nodes of the default mode network (DMN) that is known to be involved in many functions, including episodic and autobiographical memory, self-related processing, theory of mind, and imagining the future. These functions of the DMN are also essential to individuals with BTP. Taken together, we provide the first evidence for the structural and functional neural basis of BTP, and highlight the crucial role of the DMN in cultivating an individual's BTP. Copyright © 2017 Elsevier B.V. All rights reserved.

Neural basis for dynamic updating of object representation in visual working memory.

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Takahama, Sachiko; Miyauchi, Satoru; Saiki, Jun

2010-02-15

In real world, objects have multiple features and change dynamically. Thus, object representations must satisfy dynamic updating and feature binding. Previous studies have investigated the neural activity of dynamic updating or feature binding alone, but not both simultaneously. We investigated the neural basis of feature-bound object representation in a dynamically updating situation by conducting a multiple object permanence tracking task, which required observers to simultaneously process both the maintenance and dynamic updating of feature-bound objects. Using an event-related design, we separated activities during memory maintenance and change detection. In the search for regions showing selective activation in dynamic updating of feature-bound objects, we identified a network during memory maintenance that was comprised of the inferior precentral sulcus, superior parietal lobule, and middle frontal gyrus. In the change detection period, various prefrontal regions, including the anterior prefrontal cortex, were activated. In updating object representation of dynamically moving objects, the inferior precentral sulcus closely cooperates with a so-called "frontoparietal network", and subregions of the frontoparietal network can be decomposed into those sensitive to spatial updating and feature binding. The anterior prefrontal cortex identifies changes in object representation by comparing memory and perceptual representations rather than maintaining object representations per se, as previously suggested. Copyright 2009 Elsevier Inc. All rights reserved.

A neural network based methodology to predict site-specific spectral acceleration values

Science.gov (United States)

Kamatchi, P.; Rajasankar, J.; Ramana, G. V.; Nagpal, A. K.

2010-12-01

A general neural network based methodology that has the potential to replace the computationally-intensive site-specific seismic analysis of structures is proposed in this paper. The basic framework of the methodology consists of a feed forward back propagation neural network algorithm with one hidden layer to represent the seismic potential of a region and soil amplification effects. The methodology is implemented and verified with parameters corresponding to Delhi city in India. For this purpose, strong ground motions are generated at bedrock level for a chosen site in Delhi due to earthquakes considered to originate from the central seismic gap of the Himalayan belt using necessary geological as well as geotechnical data. Surface level ground motions and corresponding site-specific response spectra are obtained by using a one-dimensional equivalent linear wave propagation model. Spectral acceleration values are considered as a target parameter to verify the performance of the methodology. Numerical studies carried out to validate the proposed methodology show that the errors in predicted spectral acceleration values are within acceptable limits for design purposes. The methodology is general in the sense that it can be applied to other seismically vulnerable regions and also can be updated by including more parameters depending on the state-of-the-art in the subject.

Artificial neural networks in neutron dosimetry

Energy Technology Data Exchange (ETDEWEB)

Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Mercado, G.A.; Perales M, W.A.; Robles R, J.A. [Unidades Academicas de Estudios Nucleares, UAZ, A.P. 336, 98000 Zacatecas (Mexico); Gallego, E.; Lorente, A. [Depto. de Ingenieria Nuclear, Universidad Politecnica de Madrid, (Spain)

2005-07-01

An artificial neural network has been designed to obtain the neutron doses using only the Bonner spheres spectrometer's count rates. Ambient, personal and effective neutron doses were included. 187 neutron spectra were utilized to calculate the Bonner count rates and the neutron doses. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. Re-binned spectra, UTA4 response matrix and fluence-to-dose coefficients were used to calculate the count rates in Bonner spheres spectrometer and the doses. Count rates were used as input and the respective doses were used as output during neural network training. Training and testing was carried out in Mat lab environment. The artificial neural network performance was evaluated using the {chi}{sup 2}- test, where the original and calculated doses were compared. The use of Artificial Neural Networks in neutron dosimetry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

Artificial neural networks in neutron dosimetry

International Nuclear Information System (INIS)

Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Mercado, G.A.; Perales M, W.A.; Robles R, J.A.; Gallego, E.; Lorente, A.

2005-01-01

An artificial neural network has been designed to obtain the neutron doses using only the Bonner spheres spectrometer's count rates. Ambient, personal and effective neutron doses were included. 187 neutron spectra were utilized to calculate the Bonner count rates and the neutron doses. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. Re-binned spectra, UTA4 response matrix and fluence-to-dose coefficients were used to calculate the count rates in Bonner spheres spectrometer and the doses. Count rates were used as input and the respective doses were used as output during neural network training. Training and testing was carried out in Mat lab environment. The artificial neural network performance was evaluated using the χ 2 - test, where the original and calculated doses were compared. The use of Artificial Neural Networks in neutron dosimetry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

The neural basis of learning to spell again: An fMRI study of spelling training in acquired dysgraphia.

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Jeremy Purcell

2015-05-01

1 For all participants we identified brain areas associated with a normalized response for the TRAINING words at the post-training time point. 2 For all participants we identified an up-regulation of the TRAINING response (i.e., the TRAINING neural response was initially low and then increased post-training; whereas in only one participant did we also observe a down-regulation of the training response (i.e., the TRAINING neural response was initially high, but then decreased post-training. 3 Although the areas associated with the normalized TRAINING response were different in each individual, they all include areas typically associated with the spelling system (Purcell et al. 2011, including the right homologues of typically left hemisphere spelling regions. Across the participants, the following areas of normalization were observed: bilateral superior temporal gyrus, inferior frontal gyrus, and the bilateral inferior temporal/fusiform gyrus. Discussion: We found that the predominant BOLD response to training involved an up-regulation of the neural response to spelling the TRAINING items. In addition, we found individual differences in the neurotopography of the normalization response patterns although all were with within brain areas that form a part of the spelling network(Purcell et al. 2011. This work provides evidence regarding one aspect of the multiplicity of neural responses associated with recovery of spelling in individuals with acquired dysgraphia.

Neural mechanisms by which attention modulates the comparison of remembered and perceptual representations.

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Bo-Cheng Kuo

Full Text Available Attention is important for effectively comparing incoming perceptual information with the contents of visual short-term memory (VSTM, such that any differences can be detected. However, how attentional mechanisms operate upon these comparison processes remains largely unknown. Here we investigate the underlying neural mechanisms by which attention modulates the comparisons between VSTM and perceptual representations using functional magnetic resonance imaging (fMRI. Participants performed a cued change detection task. Spatial cues were presented to orient their attention either to the location of an item in VSTM prior to its comparison (retro-cues, or simultaneously (simultaneous-cues with the probe array. A no-cue condition was also included. When attention cannot be effectively deployed in advance (i.e. following the simultaneous-cues, we observed a distributed and extensive activation pattern in the prefrontal and parietal cortices in support of successful change detection. This was not the case when participants can deploy their attention in advance (i.e. following the retro-cues. The region-of-interest analyses confirmed that neural responses for successful change detection versus correct rejection in the visual and parietal regions were significantly different for simultaneous-cues compared to retro-cues. Importantly, we found enhanced functional connectivity between prefrontal and parietal cortices when detecting changes on the simultaneous-cue trials. Moreover, we demonstrated a close relationship between this functional connectivity and d' scores. Together, our findings elucidate the attentional and neural mechanisms by which items held in VSTM are compared with incoming perceptual information.

The Neural Basis of Typewriting: A Functional MRI Study.

Science.gov (United States)

Higashiyama, Yuichi; Takeda, Katsuhiko; Someya, Yoshiaki; Kuroiwa, Yoshiyuki; Tanaka, Fumiaki

2015-01-01

To investigate the neural substrate of typewriting Japanese words and to detect the difference between the neural substrate of typewriting and handwriting, we conducted a functional magnetic resonance imaging (fMRI) study in 16 healthy volunteers. All subjects were skillful touch typists and performed five tasks: a typing task, a writing task, a reading task, and two control tasks. Three brain regions were activated during both the typing and the writing tasks: the left superior parietal lobule, the left supramarginal gyrus, and the left premotor cortex close to Exner's area. Although typing and writing involved common brain regions, direct comparison between the typing and the writing task revealed greater left posteromedial intraparietal cortex activation in the typing task. In addition, activity in the left premotor cortex was more rostral in the typing task than in the writing task. These findings suggest that, although the brain circuits involved in Japanese typewriting are almost the same as those involved in handwriting, there are brain regions that are specific for typewriting.

The Neural Basis of Typewriting: A Functional MRI Study.

Directory of Open Access Journals (Sweden)

Yuichi Higashiyama

Full Text Available To investigate the neural substrate of typewriting Japanese words and to detect the difference between the neural substrate of typewriting and handwriting, we conducted a functional magnetic resonance imaging (fMRI study in 16 healthy volunteers. All subjects were skillful touch typists and performed five tasks: a typing task, a writing task, a reading task, and two control tasks. Three brain regions were activated during both the typing and the writing tasks: the left superior parietal lobule, the left supramarginal gyrus, and the left premotor cortex close to Exner's area. Although typing and writing involved common brain regions, direct comparison between the typing and the writing task revealed greater left posteromedial intraparietal cortex activation in the typing task. In addition, activity in the left premotor cortex was more rostral in the typing task than in the writing task. These findings suggest that, although the brain circuits involved in Japanese typewriting are almost the same as those involved in handwriting, there are brain regions that are specific for typewriting.

Altered topology of neural circuits in congenital prosopagnosia.

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Rosenthal, Gideon; Tanzer, Michal; Simony, Erez; Hasson, Uri; Behrmann, Marlene; Avidan, Galia

2017-08-21

Using a novel, fMRI-based inter-subject functional correlation (ISFC) approach, which isolates stimulus-locked inter-regional correlation patterns, we compared the cortical topology of the neural circuit for face processing in participants with an impairment in face recognition, congenital prosopagnosia (CP), and matched controls. Whereas the anterior temporal lobe served as the major network hub for face processing in controls, this was not the case for the CPs. Instead, this group evinced hyper-connectivity in posterior regions of the visual cortex, mostly associated with the lateral occipital and the inferior temporal cortices. Moreover, the extent of this hyper-connectivity was correlated with the face recognition deficit. These results offer new insights into the perturbed cortical topology in CP, which may serve as the underlying neural basis of the behavioral deficits typical of this disorder. The approach adopted here has the potential to uncover altered topologies in other neurodevelopmental disorders, as well.

Airplane detection in remote sensing images using convolutional neural networks

Science.gov (United States)

Ouyang, Chao; Chen, Zhong; Zhang, Feng; Zhang, Yifei

2018-03-01

Airplane detection in remote sensing images remains a challenging problem and has also been taking a great interest to researchers. In this paper we propose an effective method to detect airplanes in remote sensing images using convolutional neural networks. Deep learning methods show greater advantages than the traditional methods with the rise of deep neural networks in target detection, and we give an explanation why this happens. To improve the performance on detection of airplane, we combine a region proposal algorithm with convolutional neural networks. And in the training phase, we divide the background into multi classes rather than one class, which can reduce false alarms. Our experimental results show that the proposed method is effective and robust in detecting airplane.

Uncovering the neuroanatomical correlates of cognitive, affective and conative theory of mind in paediatric traumatic brain injury: a neural systems perspective.

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Ryan, Nicholas P; Catroppa, Cathy; Beare, Richard; Silk, Timothy J; Hearps, Stephen J; Beauchamp, Miriam H; Yeates, Keith O; Anderson, Vicki A

2017-09-01

Deficits in theory of mind (ToM) are common after neurological insult acquired in the first and second decade of life, however the contribution of large-scale neural networks to ToM deficits in children with brain injury is unclear. Using paediatric traumatic brain injury (TBI) as a model, this study investigated the sub-acute effect of paediatric traumatic brain injury on grey-matter volume of three large-scale, domain-general brain networks (the Default Mode Network, DMN; the Central Executive Network, CEN; and the Salience Network, SN), as well as two domain-specific neural networks implicated in social-affective processes (the Cerebro-Cerebellar Mentalizing Network, CCMN and the Mirror Neuron/Empathy Network, MNEN). We also evaluated prospective structure-function relationships between these large-scale neural networks and cognitive, affective and conative ToM. 3D T1- weighted magnetic resonance imaging sequences were acquired sub-acutely in 137 children [TBI: n = 103; typically developing (TD) children: n = 34]. All children were assessed on measures of ToM at 24-months post-injury. Children with severe TBI showed sub-acute volumetric reductions in the CCMN, SN, MNEN, CEN and DMN, as well as reduced grey-matter volumes of several hub regions of these neural networks. Volumetric reductions in the CCMN and several of its hub regions, including the cerebellum, predicted poorer cognitive ToM. In contrast, poorer affective and conative ToM were predicted by volumetric reductions in the SN and MNEN, respectively. Overall, results suggest that cognitive, affective and conative ToM may be prospectively predicted by individual differences in structure of different neural systems-the CCMN, SN and MNEN, respectively. The prospective relationship between cerebellar volume and cognitive ToM outcomes is a novel finding in our paediatric brain injury sample and suggests that the cerebellum may play a role in the neural networks important for ToM. These findings are

hmmr mediates anterior neural tube closure and morphogenesis in the frog Xenopus.

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Prager, Angela; Hagenlocher, Cathrin; Ott, Tim; Schambony, Alexandra; Feistel, Kerstin

2017-10-01

Development of the central nervous system requires orchestration of morphogenetic processes which drive elevation and apposition of the neural folds and their fusion into a neural tube. The newly formed tube gives rise to the brain in anterior regions and continues to develop into the spinal cord posteriorly. Conspicuous differences between the anterior and posterior neural tube become visible already during neural tube closure (NTC). Planar cell polarity (PCP)-mediated convergent extension (CE) movements are restricted to the posterior neural plate, i.e. hindbrain and spinal cord, where they propagate neural fold apposition. The lack of CE in the anterior neural plate correlates with a much slower mode of neural fold apposition anteriorly. The morphogenetic processes driving anterior NTC have not been addressed in detail. Here, we report a novel role for the breast cancer susceptibility gene and microtubule (MT) binding protein Hmmr (Hyaluronan-mediated motility receptor, RHAMM) in anterior neurulation and forebrain development in Xenopus laevis. Loss of hmmr function resulted in a lack of telencephalic hemisphere separation, arising from defective roof plate formation, which in turn was caused by impaired neural tissue narrowing. hmmr regulated polarization of neural cells, a function which was dependent on the MT binding domains. hmmr cooperated with the core PCP component vangl2 in regulating cell polarity and neural morphogenesis. Disrupted cell polarization and elongation in hmmr and vangl2 morphants prevented radial intercalation (RI), a cell behavior essential for neural morphogenesis. Our results pinpoint a novel role of hmmr in anterior neural development and support the notion that RI is a major driving force for anterior neurulation and forebrain morphogenesis. Copyright © 2017 Elsevier Inc. All rights reserved.

Neural network model for the prediction of PM10 daily concentrations in two sites in the Western Mediterranean.

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de Gennaro, Gianluigi; Trizio, Livia; Di Gilio, Alessia; Pey, Jorge; Pérez, Noemi; Cusack, Michael; Alastuey, Andrés; Querol, Xavier

2013-10-01

An artificial neural network (ANN) was developed and tested to forecast PM10 daily concentration in two contrasted environments in NE Spain, a regional background site (Montseny), and an urban background site (Barcelona-CSIC), which was highly influenced by vehicular emissions. In order to predict 24-h average PM10 concentrations, the artificial neural network previously developed by Caselli et al. (2009) was improved by using hourly PM concentrations and deterministic factors such as a Saharan dust alert. In particular, the model input data for prediction were the hourly PM10 concentrations 1-day in advance, local meteorological data and information about air masses origin. The forecasted performance indexes for both sites were calculated and they showed better results for the regional background site in Montseny (R(2)=0.86, SI=0.75) than for urban site in Barcelona (R(2)=0.73, SI=0.58), influenced by local and sometimes unexpected sources. Moreover, a sensitivity analysis conducted to understand the importance of the different variables included among the input data, showed that local meteorology and air masses origin are key factors in the model forecasts. This result explains the reason for the improvement of ANN's forecasting performance at the Montseny site with respect to the Barcelona site. Moreover, the artificial neural network developed in this work could prove useful to predict PM10 concentrations, especially, at regional background sites such as those on the Mediterranean Basin which are primarily affected by long-range transports. Hence, the artificial neural network presented here could be a powerful tool for obtaining real time information on air quality status and could aid stakeholders in their development of cost-effective control strategies. © 2013 Elsevier B.V. All rights reserved.

Neural Mechanism for Mirrored Self-face Recognition.

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Sugiura, Motoaki; Miyauchi, Carlos Makoto; Kotozaki, Yuka; Akimoto, Yoritaka; Nozawa, Takayuki; Yomogida, Yukihito; Hanawa, Sugiko; Yamamoto, Yuki; Sakuma, Atsushi; Nakagawa, Seishu; Kawashima, Ryuta

2015-09-01

Self-face recognition in the mirror is considered to involve multiple processes that integrate 2 perceptual cues: temporal contingency of the visual feedback on one's action (contingency cue) and matching with self-face representation in long-term memory (figurative cue). The aim of this study was to examine the neural bases of these processes by manipulating 2 perceptual cues using a "virtual mirror" system. This system allowed online dynamic presentations of real-time and delayed self- or other facial actions. Perception-level processes were identified as responses to only a single perceptual cue. The effect of the contingency cue was identified in the cuneus. The regions sensitive to the figurative cue were subdivided by the response to a static self-face, which was identified in the right temporal, parietal, and frontal regions, but not in the bilateral occipitoparietal regions. Semantic- or integration-level processes, including amodal self-representation and belief validation, which allow modality-independent self-recognition and the resolution of potential conflicts between perceptual cues, respectively, were identified in distinct regions in the right frontal and insular cortices. The results are supportive of the multicomponent notion of self-recognition and suggest a critical role for contingency detection in the co-emergence of self-recognition and empathy in infants. © The Author 2014. Published by Oxford University Press.

Neural net generated seismic facies map and attribute facies map

International Nuclear Information System (INIS)

Addy, S.K.; Neri, P.

1998-01-01

The usefulness of 'seismic facies maps' in the analysis of an Upper Wilcox channel system in a 3-D survey shot by CGG in 1995 in Lavaca county in south Texas was discussed. A neural net-generated seismic facies map is a quick hydrocarbon exploration tool that can be applied regionally as well as on a prospect scale. The new technology is used to classify a constant interval parallel to a horizon in a 3-D seismic volume based on the shape of the wiggle traces using a neural network technology. The tool makes it possible to interpret sedimentary features of a petroleum deposit. The same technology can be used in regional mapping by making 'attribute facies maps' in which various forms of amplitude attributes, phase attributes or frequency attributes can be used

Rapid neural discrimination of communicative gestures.

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Redcay, Elizabeth; Carlson, Thomas A

2015-04-01

Humans are biased toward social interaction. Behaviorally, this bias is evident in the rapid effects that self-relevant communicative signals have on attention and perceptual systems. The processing of communicative cues recruits a wide network of brain regions, including mentalizing systems. Relatively less work, however, has examined the timing of the processing of self-relevant communicative cues. In the present study, we used multivariate pattern analysis (decoding) approach to the analysis of magnetoencephalography (MEG) to study the processing dynamics of social-communicative actions. Twenty-four participants viewed images of a woman performing actions that varied on a continuum of communicative factors including self-relevance (to the participant) and emotional valence, while their brain activity was recorded using MEG. Controlling for low-level visual factors, we found early discrimination of emotional valence (70 ms) and self-relevant communicative signals (100 ms). These data offer neural support for the robust and rapid effects of self-relevant communicative cues on behavior. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Differential neural network configuration during human path integration

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Arnold, Aiden E. G. F; Burles, Ford; Bray, Signe; Levy, Richard M.; Iaria, Giuseppe

2014-01-01

Path integration is a fundamental skill for navigation in both humans and animals. Despite recent advances in unraveling the neural basis of path integration in animal models, relatively little is known about how path integration operates at a neural level in humans. Previous attempts to characterize the neural mechanisms used by humans to visually path integrate have suggested a central role of the hippocampus in allowing accurate performance, broadly resembling results from animal data. However, in recent years both the central role of the hippocampus and the perspective that animals and humans share similar neural mechanisms for path integration has come into question. The present study uses a data driven analysis to investigate the neural systems engaged during visual path integration in humans, allowing for an unbiased estimate of neural activity across the entire brain. Our results suggest that humans employ common task control, attention and spatial working memory systems across a frontoparietal network during path integration. However, individuals differed in how these systems are configured into functional networks. High performing individuals were found to more broadly express spatial working memory systems in prefrontal cortex, while low performing individuals engaged an allocentric memory system based primarily in the medial occipito-temporal region. These findings suggest that visual path integration in humans over short distances can operate through a spatial working memory system engaging primarily the prefrontal cortex and that the differential configuration of memory systems recruited by task control networks may help explain individual biases in spatial learning strategies. PMID:24808849

A decomposition approach to analysis of competitive-cooperative neural networks with delay

International Nuclear Information System (INIS)

Chu Tianguang; Zhang Zongda; Wang Zhaolin

2003-01-01

Competitive-cooperative or inhibitory-excitatory configurations abound in neural networks. It is demonstrated here how such a configuration may be exploited to give a detailed characterization of the fixed point dynamics in general neural networks with time delay. The idea is to divide the connection weights into inhibitory and excitatory types and thereby to embed a competitive-cooperative delay neural network into an augmented cooperative delay system through a symmetric transformation. This allows for the use of the powerful monotone properties of cooperative systems. By the method, we derive several simple necessary and sufficient conditions on guaranteed trapping regions and guaranteed componentwise (exponential) convergence of the neural networks. The results relate specific decay rate and trajectory bounds to system parameters and are therefore of practical significance in designing a network with desired performance

Spatio-Temporal Pattern Estimation of PM2.5 in Beijing-Tianjin-Hebei Region Based on MODIS AOD and Meteorological Data Using the Back Propagation Neural Network

Directory of Open Access Journals (Sweden)

Xiliang Ni

2018-03-01

Full Text Available With the economic growth and increasing urbanization in the last three decades, the air quality over China has continuously degraded, which poses a great threat to human health. The concentration of fine particulate matter (PM2.5 directly affects the mortality of people living in the polluted areas where air quality is poor. The Beijing-Tianjin-Hebei (BTH region, one of the well organized urban regions in northern China, has suffered with poor air quality and atmospheric pollution due to recent growth of the industrial sector and vehicle emissions. In the present study, we used the back propagation neural network model approach to estimate the spatial distribution of PM2.5 concentration in the BTH region for the period January 2014–December 2016, combining the satellite-derived aerosol optical depth (S-DAOD and meteorological data. The results were validated using the ground PM2.5 data. The general method including all PM2.5 training data and 10-fold cross-method have been used for validation for PM2.5 estimation (R2 = 0.68, RMSE = 20.99 for general validation; R2 = 0.54, RMSE = 24.13 for cross-method validation. The study provides a new approach to monitoring the distribution of PM2.5 concentration. The results discussed in the present paper will be of great help to government agencies in developing and implementing environmental conservation policy.

Down image recognition based on deep convolutional neural network

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Wenzhu Yang

2018-06-01

Full Text Available Since of the scale and the various shapes of down in the image, it is difficult for traditional image recognition method to correctly recognize the type of down image and get the required recognition accuracy, even for the Traditional Convolutional Neural Network (TCNN. To deal with the above problems, a Deep Convolutional Neural Network (DCNN for down image classification is constructed, and a new weight initialization method is proposed. Firstly, the salient regions of a down image were cut from the image using the visual saliency model. Then, these salient regions of the image were used to train a sparse autoencoder and get a collection of convolutional filters, which accord with the statistical characteristics of dataset. At last, a DCNN with Inception module and its variants was constructed. To improve the recognition accuracy, the depth of the network is deepened. The experiment results indicate that the constructed DCNN increases the recognition accuracy by 2.7% compared to TCNN, when recognizing the down in the images. The convergence rate of the proposed DCNN with the new weight initialization method is improved by 25.5% compared to TCNN. Keywords: Deep convolutional neural network, Weight initialization, Sparse autoencoder, Visual saliency model, Image recognition

An amphioxus Msx gene expressed predominantly in the dorsal neural tube.

Science.gov (United States)

Sharman, A C; Shimeld, S M; Holland, P W

1999-04-01

Genomic and cDNA clones of an Msx class homeobox gene were isolated from amphioxus (Branchiostoma floridae). The gene, AmphiMsx, is expressed in the neural plate from late gastrulation; in later embryos it is expressed in dorsal cells of the neural tube, excluding anterior and posterior regions, in an irregular reiterated pattern. There is transient expression in dorsal cells within somites, reminiscent of migrating neural crest cells of vertebrates. In larvae, mRNA is detected in two patches of anterior ectoderm proposed to be placodes. Evolutionary analyses show there is little phylogenetic information in Msx protein sequences; however, it is likely that duplication of Msx genes occurred in the vertebrate lineage.

Assessment of Convolution Neural Networks for Surficial Geology Mapping in the South Rae Geological Region, Northwest Territories, Canada

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Rasim Latifovic

2018-02-01

Full Text Available Mapping of surficial geology is an important requirement for broadening the geoscience database of northern Canada. Surficial geology maps are an integral data source for mineral and energy exploration. Moreover, they provide information such as the location of gravels and sands, which are important for infrastructure development. Currently, surficial geology maps are produced through expert interpretation of aerial photography and field data. However, interpretation is known to be subjective, labour-intensive and difficult to repeat. The expert knowledge required for interpretation can be challenging to maintain and transfer. In this research, we seek to assess the potential of deep neural networks to aid surficial geology mapping by providing an objective surficial materials initial layer that experts can modify to speed map development and improve consistency between mapped areas. Such an approach may also harness expert knowledge in a way that is transferable to unmapped areas. For this purpose, we assess the ability of convolution neural networks (CNN to predict surficial geology classes under two sampling scenarios. In the first scenario, a CNN uses samples collected over the area to be mapped. In the second, a CNN trained over one area is then applied to locations where the available samples were not used in training the network. The latter case is important, as a collection of in situ training data can be costly. The evaluation of the CNN was carried out using aerial photos, Landsat reflectance, and high-resolution digital elevation data over five areas within the South Rae geological region of Northwest Territories, Canada. The results are encouraging, with the CNN generating average accuracy of 76% when locally trained. For independent test areas (i.e., trained over one area and applied over other, accuracy dropped to 59–70% depending on the classes selected for mapping. In the South Rae region, significant confusion was found

A Neural Basis for the Acquired Capability for Suicide

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Gopikrishna Deshpande

2016-08-01

Full Text Available The high rate of fatal suicidal behavior in men is an urgent issue as highlighted in the public eye via news sources and media outlets. In this study, we have attempted to address this issue and understand the neural substrates underlying the gender differences in the rate of fatal suicidal behavior. The Interpersonal-Psychological Theory of Suicide (IPTS has proposed an explanation for the seemingly paradoxical relationship between gender and suicidal behavior, i.e. greater non-fatal suicide attempts by women but higher number of deaths by suicide in men. This theory states that possessing suicidal desire (due to conditions such as depression alone is not sufficient for a lethal suicide attempt. It is imperative for an individual to have acquired the capability for suicide (ACS along with suicidal desire in order to die by suicide. Therefore, higher levels of ACS in men may explain why men are more likely to die by suicide than women, despite being less likely to experience suicidal ideation or depression. In this study, we used activation likelihood estimation meta-analysis to investigate a potential ACS network that involves neural substrates underlying emotional stoicism, sensation seeking, pain tolerance, and fearlessness of death along with a potential depression network that involves neural substrates that underlie clinical depression. Brain regions commonly found in ACS and depression networks for males and females were further used as seeds to obtain regions functionally and structurally connected to them. We found that the male-specific networks were more widespread and diverse than the female-specific ones. Also, while the former involved motor regions such as the premotor cortex and cerebellum, the latter was dominated by limbic regions. This may support the fact that suicidal desire generally leads to fatal/decisive action in males while in females, it manifests as depression, ideation and generally non-fatal actions. The proposed

A neural network approach to the study of dynamics and structure of molecular systems

International Nuclear Information System (INIS)

Getino, C.; Sumpter, B.G.; Noid, D.W.

1994-01-01

Neural networks are used to study intramolecular energy flow in molecular systems (tetratomics to macromolecules), developing new techniques for efficient analysis of data obtained from molecular-dynamics and quantum mechanics calculations. Neural networks can map phase space points to intramolecular vibrational energies along a classical trajectory (example of complicated coordinate transformation), producing reasonably accurate values for any region of the multidimensional phase space of a tetratomic molecule. Neural network energy flow predictions are found to significantly enhance the molecular-dynamics method to longer time-scales and extensive averaging of trajectories for macromolecular systems. Pattern recognition abilities of neural networks can be used to discern phase space features. Neural networks can also expand model calculations by interpolation of costly quantum mechanical ab initio data, used to develop semiempirical potential energy functions

Application of neural network technology to setpoint control of a simulated reactor experiment loop

International Nuclear Information System (INIS)

Cordes, G.A.; Bryan, S.R.; Powell, R.H.; Chick, D.R.

1991-01-01

This paper describes the design, implementation, and application of artificial neural networks to achieve temperature and flow rate control for a simulation of a typical experiment loop in the Advanced Test Reactor (ATR) located at the Idaho National Engineering Laboratory (INEL). The goal of the project was to research multivariate, nonlinear control using neural networks. A loop simulation code was adapted for the project and used to create a training set and test the neural network controller for comparison with the existing loop controllers. The results for the best neural network design are documented and compared with existing loop controller action. The neural network was shown to be as accurate at loop control as the classical controllers in the operating region represented by the training set. 5 refs., 8 figs., 3 tabs

Neural Crossroads in the Hematopoietic Stem Cell Niche.

Science.gov (United States)

Agarwala, Sobhika; Tamplin, Owen J

2018-05-29

The hematopoietic stem cell (HSC) niche supports steady-state hematopoiesis and responds to changing needs during stress and disease. The nervous system is an important regulator of the niche, and its influence is established early in development when stem cells are specified. Most research has focused on direct innervation of the niche, however recent findings show there are different modes of neural control, including globally by the central nervous system (CNS) and hormone release, locally by neural crest-derived mesenchymal stem cells, and intrinsically by hematopoietic cells that express neural receptors and neurotransmitters. Dysregulation between neural and hematopoietic systems can contribute to disease, however new therapeutic opportunities may be found among neuroregulator drugs repurposed to support hematopoiesis. Copyright © 2018 Elsevier Ltd. All rights reserved.

Neural correlates of a single-session massage treatment.

Science.gov (United States)

Sliz, D; Smith, A; Wiebking, C; Northoff, G; Hayley, S

2012-03-01

The current study investigated the immediate neurophysiological effects of different types of massage in healthy adults using functional magnetic resonance imaging (fMRI). Much attention has been given to the default mode network, a set of brain regions showing greater activity in the resting state. These regions (i.e. insula, posterior and anterior cingulate, inferior parietal and medial prefrontal cortices) have been postulated to be involved in the neural correlates of consciousness, specifically in arousal and awareness. We posit that massage would modulate these same regions given the benefits and pleasant affective properties of touch. To this end, healthy participants were randomly assigned to one of four conditions: 1. Swedish massage, 2. reflexology, 3. massage with an object or 4. a resting control condition. The right foot was massaged while each participant performed a cognitive association task in the scanner. We found that the Swedish massage treatment activated the subgenual anterior and retrosplenial/posterior cingulate cortices. This increased blood oxygen level dependent (BOLD) signal was maintained only in the former brain region during performance of the cognitive task. Interestingly, the reflexology massage condition selectively affected the retrosplenial/posterior cingulate in the resting state, whereas massage with the object augmented the BOLD response in this region during the cognitive task performance. These findings should have implications for better understanding how alternative treatments might affect resting state neural activity and could ultimately be important for devising new targets in the management of mood disorders.

Multiscale Convolutional Neural Networks for Hand Detection

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Shiyang Yan

2017-01-01

Full Text Available Unconstrained hand detection in still images plays an important role in many hand-related vision problems, for example, hand tracking, gesture analysis, human action recognition and human-machine interaction, and sign language recognition. Although hand detection has been extensively studied for decades, it is still a challenging task with many problems to be tackled. The contributing factors for this complexity include heavy occlusion, low resolution, varying illumination conditions, different hand gestures, and the complex interactions between hands and objects or other hands. In this paper, we propose a multiscale deep learning model for unconstrained hand detection in still images. Deep learning models, and deep convolutional neural networks (CNNs in particular, have achieved state-of-the-art performances in many vision benchmarks. Developed from the region-based CNN (R-CNN model, we propose a hand detection scheme based on candidate regions generated by a generic region proposal algorithm, followed by multiscale information fusion from the popular VGG16 model. Two benchmark datasets were applied to validate the proposed method, namely, the Oxford Hand Detection Dataset and the VIVA Hand Detection Challenge. We achieved state-of-the-art results on the Oxford Hand Detection Dataset and had satisfactory performance in the VIVA Hand Detection Challenge.

Abnormal neural activities of directional brain networks in patients with long-term bilateral hearing loss.

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Xu, Long-Chun; Zhang, Gang; Zou, Yue; Zhang, Min-Feng; Zhang, Dong-Sheng; Ma, Hua; Zhao, Wen-Bo; Zhang, Guang-Yu

2017-10-13

The objective of the study is to provide some implications for rehabilitation of hearing impairment by investigating changes of neural activities of directional brain networks in patients with long-term bilateral hearing loss. Firstly, we implemented neuropsychological tests of 21 subjects (11 patients with long-term bilateral hearing loss, and 10 subjects with normal hearing), and these tests revealed significant differences between the deaf group and the controls. Then we constructed the individual specific virtual brain based on functional magnetic resonance data of participants by utilizing effective connectivity and multivariate regression methods. We exerted the stimulating signal to the primary auditory cortices of the virtual brain and observed the brain region activations. We found that patients with long-term bilateral hearing loss presented weaker brain region activations in the auditory and language networks, but enhanced neural activities in the default mode network as compared with normally hearing subjects. Especially, the right cerebral hemisphere presented more changes than the left. Additionally, weaker neural activities in the primary auditor cortices were also strongly associated with poorer cognitive performance. Finally, causal analysis revealed several interactional circuits among activated brain regions, and these interregional causal interactions implied that abnormal neural activities of the directional brain networks in the deaf patients impacted cognitive function.

Landslide susceptibility assessment of SE Bartin (West Black Sea region, Turkey by artificial neural networks

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M. Ercanoglu

2005-01-01

Full Text Available Landslides are significant natural hazards in Turkey, second only to earthquakes with respect to economic losses and casualties. The West Black Sea region of Turkey is known as one of the most landslide-prone regions in the country. The work presented in this paper is aimed at evaluating landslide susceptibility in a selected area in the West Black Sea region using Artificial Neural Network (ANN method. A total of 317 landslides were identified and mapped in the area by extensive field work and by use of air photo interpretations to build a landslide inventory map. A landslide database was then derived automatically from the landslide inventory map. To evaluate landslide susceptibility, six input parameters (slope angle, slope aspect, topographical elevation, topographical shape, wetness index, and vegetation index were used. To obtain maps of these parameters, Digital Elevation Model (DEM and ASTER satellite imagery of the study area were used. At the first stage, all data were normalized in [0, 1] interval, and parameter effects on landslide occurrence were expressed using Statistical Index values (Wi. Then, landslide susceptibility analyses were performed using an ANN. Finally, performance of the resulting map and the applied methodology is discussed relative to performance indicators, such as predicted areal extent of landslides and the strength of relation (rij value. Much of the areal extents of the landslides (87.2% were classified as susceptible to landsliding, and rij value of 0.85 showed a high degree of similarity. In addition to these, at the final stage, an independent validation strategy was followed by dividing the landslide data set into two parts and 82.5% of the validation data set was found to be correctly classified as landslide susceptible areas. According to these results, it is concluded that the map produced by the ANN is reliable and methodology applied in the study produced high performance, and satisfactory results.

Neural Blockade for Persistent Pain After Breast Cancer Surgery

DEFF Research Database (Denmark)

Wijayasinghe, Nelun; Andersen, Kenneth Geving; Kehlet, Henrik

2014-01-01

involved in neuropathic pain syndromes or to be used as a treatment in its own right. The purpose of this review was to examine the evidence for neural blockade as a potential diagnostic tool or treatment for persistent pain after breast cancer surgery. In this systematic review, we found only 7 studies (n......Persistent pain after breast cancer surgery is predominantly a neuropathic pain syndrome affecting 25% to 60% of patients and related to injury of the intercostobrachial nerve, intercostal nerves, and other nerves in the region. Neural blockade can be useful for the identification of nerves...

Neural correlates of attitude change following positive and negative advertisements

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Junko Kato

2009-05-01

Full Text Available Understanding changes in attitudes towards others is critical to understanding human behaviour. Neuropolitical studies have found that the activation of emotion-related areas in the brain is linked to resilient political preferences, and neuroeconomic research has analysed the neural correlates of social preferences that favour or oppose consideration of intrinsic rewards. This study aims to identify the neural correlates in the prefrontal cortices of changes in political attitudes toward others that are linked to social cognition. Functional magnetic resonance imaging (fMRI experiments have presented videos from previous electoral campaigns and television commercials for major cola brands and then used the subjects’ self-rated affinity toward political candidates as behavioural indicators. After viewing negative campaign videos, subjects showing stronger fMRI activation in the dorsolateral prefrontal cortex lowered their ratings of the candidate they originally supported more than did those with smaller fMRI signal changes in the same region. Subjects showing stronger activation in the medial prefrontal cortex tended to increase their ratings more than did those with less activation. The same regions were not activated by viewing negative advertisements for cola. Correlations between the self-rated values and the neural signal changes underscore the metric representation of observed decisions (i.e., whether to support or not in the brain. This indicates that neurometric analysis may contribute to the exploration of the neural correlates of daily social behaviour.

Connecting Neural Coding to Number Cognition: A Computational Account

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Prather, Richard W.

2012-01-01

The current study presents a series of computational simulations that demonstrate how the neural coding of numerical magnitude may influence number cognition and development. This includes behavioral phenomena cataloged in cognitive literature such as the development of numerical estimation and operational momentum. Though neural research has…

Time Series Neural Network Model for Part-of-Speech Tagging Indonesian Language

Science.gov (United States)

Tanadi, Theo

2018-03-01

Part-of-speech tagging (POS tagging) is an important part in natural language processing. Many methods have been used to do this task, including neural network. This paper models a neural network that attempts to do POS tagging. A time series neural network is modelled to solve the problems that a basic neural network faces when attempting to do POS tagging. In order to enable the neural network to have text data input, the text data will get clustered first using Brown Clustering, resulting a binary dictionary that the neural network can use. To further the accuracy of the neural network, other features such as the POS tag, suffix, and affix of previous words would also be fed to the neural network.

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

Science.gov (United States)

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

Neural networks

International Nuclear Information System (INIS)

Denby, Bruce; Lindsey, Clark; Lyons, Louis

1992-01-01

The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive

Neural substrates of sublexical processing for spelling.

Science.gov (United States)

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

2017-01-01

We used fMRI to examine the neural substrates of sublexical phoneme-grapheme conversion during spelling in a group of healthy young adults. Participants performed a writing-to-dictation task involving irregular words (e.g., choir), plausible nonwords (e.g., kroid), and a control task of drawing familiar geometric shapes (e.g., squares). Written production of both irregular words and nonwords engaged a left-hemisphere perisylvian network associated with reading/spelling and phonological processing skills. Effects of lexicality, manifested by increased activation during nonword relative to irregular word spelling, were noted in anterior perisylvian regions (posterior inferior frontal gyrus/operculum/precentral gyrus/insula), and in left ventral occipito-temporal cortex. In addition to enhanced neural responses within domain-specific components of the language network, the increased cognitive demands associated with spelling nonwords engaged domain-general frontoparietal cortical networks involved in selective attention and executive control. These results elucidate the neural substrates of sublexical processing during written language production and complement lesion-deficit correlation studies of phonological agraphia. Copyright © 2016 Elsevier Inc. All rights reserved.

Assessing Rainfall Erosivity with Artificial Neural Networks for the Ribeira Valley, Brazil

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Reginald B. Silva

2010-01-01

Full Text Available Soil loss is one of the main causes of pauperization and alteration of agricultural soil properties. Various empirical models (e.g., USLE are used to predict soil losses from climate variables which in general have to be derived from spatial interpolation of point measurements. Alternatively, Artificial Neural Networks may be used as a powerful option to obtain site-specific climate data from independent factors. This study aimed to develop an artificial neural network to estimate rainfall erosivity in the Ribeira Valley and Coastal region of the State of São Paulo. In the development of the Artificial Neural Networks the input variables were latitude, longitude, and annual rainfall and a mathematical equation of the activation function for use in the study area as the output variable. It was found among other things that the Artificial Neural Networks can be used in the interpolation of rainfall erosivity values for the Ribeira Valley and Coastal region of the State of São Paulo to a satisfactory degree of precision in the estimation of erosion. The equation performance has been demonstrated by comparison with the mathematical equation of the activation function adjusted to the specific conditions of the study area.

Fatty acid-induced gut-brain signaling attenuates neural and behavioral effects of sad emotion in humans.

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Van Oudenhove, Lukas; McKie, Shane; Lassman, Daniel; Uddin, Bilal; Paine, Peter; Coen, Steven; Gregory, Lloyd; Tack, Jan; Aziz, Qasim

2011-08-01

Although a relationship between emotional state and feeding behavior is known to exist, the interactions between signaling initiated by stimuli in the gut and exteroceptively generated emotions remain incompletely understood. Here, we investigated the interaction between nutrient-induced gut-brain signaling and sad emotion induced by musical and visual cues at the behavioral and neural level in healthy nonobese subjects undergoing functional magnetic resonance imaging. Subjects received an intragastric infusion of fatty acid solution or saline during neutral or sad emotion induction and rated sensations of hunger, fullness, and mood. We found an interaction between fatty acid infusion and emotion induction both in the behavioral readouts (hunger, mood) and at the level of neural activity in multiple pre-hypothesized regions of interest. Specifically, the behavioral and neural responses to sad emotion induction were attenuated by fatty acid infusion. These findings increase our understanding of the interplay among emotions, hunger, food intake, and meal-induced sensations in health, which may have important implications for a wide range of disorders, including obesity, eating disorders, and depression.

The neural network z-vertex trigger for the Belle II detector

Energy Technology Data Exchange (ETDEWEB)

Skambraks, Sebastian; Neuhaus, Sara [Technische Universitaet Muenchen (Germany); Chen, Yang; Kiesling, Christian [Max-Planck-Institut fuer Physik, Muenchen (Germany); Collaboration: Belle II-Collaboration

2016-07-01

We present a neural network based first level track trigger for the upcoming Belle II detector at the high luminosity SuperKEKB flavor factory. Using hit and drift time information from the Central Drift Chamber (CDC), neural networks estimate the z-coordinates of single track vertex positions. Especially beam induced background, with vertices outside of the interaction region, can clearly be rejected. This allows to relax the track trigger conditions and thus enhances the efficiency for events with a low track multiplicity. In the CDC trigger pipeline, the preceding 2D pattern recognition enables a unique per track input representation and a sectorization of the track parameter phase space. The precise z-vertices are then estimated by an ensemble of sector-specific local expert neural networks. After an introduction to the neural trigger system, the benefits of an improved 3D pattern recognition are discussed.

Neural responses to exclusion predict susceptibility to social influence.

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Falk, Emily B; Cascio, Christopher N; O'Donnell, Matthew Brook; Carp, Joshua; Tinney, Francis J; Bingham, C Raymond; Shope, Jean T; Ouimet, Marie Claude; Pradhan, Anuj K; Simons-Morton, Bruce G

2014-05-01

Social influence is prominent across the lifespan, but sensitivity to influence is especially high during adolescence and is often associated with increased risk taking. Such risk taking can have dire consequences. For example, in American adolescents, traffic-related crashes are leading causes of nonfatal injury and death. Neural measures may be especially useful in understanding the basic mechanisms of adolescents' vulnerability to peer influence. We examined neural responses to social exclusion as potential predictors of risk taking in the presence of peers in recently licensed adolescent drivers. Risk taking was assessed in a driving simulator session occurring approximately 1 week after the neuroimaging session. Increased activity in neural systems associated with the distress of social exclusion and mentalizing during an exclusion episode predicted increased risk taking in the presence of a peer (controlling for solo risk behavior) during a driving simulator session outside the neuroimaging laboratory 1 week later. These neural measures predicted risky driving behavior above and beyond self-reports of susceptibility to peer pressure and distress during exclusion. These results address the neural bases of social influence and risk taking; contribute to our understanding of social and emotional function in the adolescent brain; and link neural activity in specific, hypothesized, regions to risk-relevant outcomes beyond the neuroimaging laboratory. Results of this investigation are discussed in terms of the mechanisms underlying risk taking in adolescents and the public health implications for adolescent driving. Copyright © 2014 Society for Adolescent Health and Medicine. All rights reserved.

Neural circuits of disgust induced by sexual stimuli in homosexual and heterosexual men: An fMRI study

International Nuclear Information System (INIS)

Zhang Minming; Hu Shaohua; Xu Lijuan; Wang Qidong; Xu Xiaojun; Wei Erqing; Yan Leqin; Hu Jianbo; Wei Ning; Zhou Weihua; Huang Manli; Xu Yi

2011-01-01

Few studies demonstrated neural circuits related to disgust were influenced by internal sexual orientation in male. Here we used fMRI to study the neural responses to disgust in homosexual and heterosexual men to investigate that issue. Thirty-two healthy male volunteers (sixteen homosexual and sixteen heterosexual) were scanned while viewing alternating blocks of three types of erotic film: heterosexual couples (F-M), male homosexual couples (M-M), and female homosexual couples (F-F) engaged in sexual activity. All the participants rated their level of disgust and sexual arousal as well. The F-F and M-M stimuli induced disgust in homosexual and heterosexual men, respectively. The common activations related to disgusting stimuli included: bilateral frontal gyrus and occipital gyrus, right middle temporal gyrus, left superior temporal gyrus, right cerebellum, and right thalamus. Homosexual men had greater neural responses in the left medial frontal gyrus than did heterosexual men to the sexual disgusting stimuli; in contrast, heterosexual men showed significantly greater activation than homosexual men in the left cuneus. ROI analysis showed that negative correlation were found between the magnitude of MRI signals in the left medial frontal gyrus and scores of disgust in homosexual subjects (p < 0.05). This study indicated that there were regions in common as well as regions specific for each type of erotic stimuli during disgust of homosexual and heterosexual men.

Neural circuits of disgust induced by sexual stimuli in homosexual and heterosexual men: An fMRI study

Energy Technology Data Exchange (ETDEWEB)

Zhang Minming [Department of Radiology, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou (China); Hu Shaohua [Department of Mental Health, First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qing Chun Road, Hangzhou, Zhejiang Province 310003 (China); Xu Lijuan [National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing (China); Wang Qidong [Department of Radiology, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou (China); Xu Xiaojun [Department of Radiology, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou (China); Wei Erqing [College of Pharmacology, Zhejiang University (China); Yan Leqin [MD Anderson Cancer Center, Virginia Harris Cockrell Cancer Research Center, University of Texas, Austin (United States); Hu Jianbo; Wei Ning; Zhou Weihua; Huang Manli [Department of Mental Health, First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qing Chun Road, Hangzhou, Zhejiang Province 310003 (China); Xu Yi, E-mail: xuyi61@yahoo.com.cn [Department of Mental Health, First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qing Chun Road, Hangzhou, Zhejiang Province 310003 (China)

2011-11-15

Few studies demonstrated neural circuits related to disgust were influenced by internal sexual orientation in male. Here we used fMRI to study the neural responses to disgust in homosexual and heterosexual men to investigate that issue. Thirty-two healthy male volunteers (sixteen homosexual and sixteen heterosexual) were scanned while viewing alternating blocks of three types of erotic film: heterosexual couples (F-M), male homosexual couples (M-M), and female homosexual couples (F-F) engaged in sexual activity. All the participants rated their level of disgust and sexual arousal as well. The F-F and M-M stimuli induced disgust in homosexual and heterosexual men, respectively. The common activations related to disgusting stimuli included: bilateral frontal gyrus and occipital gyrus, right middle temporal gyrus, left superior temporal gyrus, right cerebellum, and right thalamus. Homosexual men had greater neural responses in the left medial frontal gyrus than did heterosexual men to the sexual disgusting stimuli; in contrast, heterosexual men showed significantly greater activation than homosexual men in the left cuneus. ROI analysis showed that negative correlation were found between the magnitude of MRI signals in the left medial frontal gyrus and scores of disgust in homosexual subjects (p < 0.05). This study indicated that there were regions in common as well as regions specific for each type of erotic stimuli during disgust of homosexual and heterosexual men.

Neural changes associated with semantic processing in healthy aging despite intact behavioral performance.

Science.gov (United States)

Lacombe, Jacinthe; Jolicoeur, Pierre; Grimault, Stephan; Pineault, Jessica; Joubert, Sven

2015-10-01

Semantic memory recruits an extensive neural network including the left inferior prefrontal cortex (IPC) and the left temporoparietal region, which are involved in semantic control processes, as well as the anterior temporal lobe region (ATL) which is considered to be involved in processing semantic information at a central level. However, little is known about the underlying neuronal integrity of the semantic network in normal aging. Young and older healthy adults carried out a semantic judgment task while their cortical activity was recorded using magnetoencephalography (MEG). Despite equivalent behavioral performance, young adults activated the left IPC to a greater extent than older adults, while the latter group recruited the temporoparietal region bilaterally and the left ATL to a greater extent than younger adults. Results indicate that significant neuronal changes occur in normal aging, mainly in regions underlying semantic control processes, despite an apparent stability in performance at the behavioral level. Copyright © 2015 Elsevier Inc. All rights reserved.

Load matters: neural correlates of verbal working memory in children with autism spectrum disorder.

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Vogan, Vanessa M; Francis, Kaitlyn E; Morgan, Benjamin R; Smith, Mary Lou; Taylor, Margot J

2018-06-01

Autism spectrum disorder (ASD) is a pervasive neurodevelopmental disorder characterised by diminished social reciprocity and communication skills and the presence of stereotyped and restricted behaviours. Executive functioning deficits, such as working memory, are associated with core ASD symptoms. Working memory allows for temporary storage and manipulation of information and relies heavily on frontal-parietal networks of the brain. There are few reports on the neural correlates of working memory in youth with ASD. The current study identified the neural systems underlying verbal working memory capacity in youth with and without ASD using functional magnetic resonance imaging (fMRI). Fifty-seven youth, 27 with ASD and 30 sex- and age-matched typically developing (TD) controls (9-16 years), completed a one-back letter matching task (LMT) with four levels of difficulty (i.e. cognitive load) while fMRI data were recorded. Linear trend analyses were conducted to examine brain regions that were recruited as a function of increasing cognitive load. We found similar behavioural performance on the LMT in terms of reaction times, but in the two higher load conditions, the ASD youth had lower accuracy than the TD group. Neural patterns of activations differed significantly between TD and ASD groups. In TD youth, areas classically used for working memory, including the lateral and medial frontal, as well as superior parietal brain regions, increased in activation with increasing task difficulty, while areas related to the default mode network (DMN) showed decreasing activation (i.e., deactivation). The youth with ASD did not appear to use this opposing cognitive processing system; they showed little recruitment of frontal and parietal regions across the load but did show similar modulation of the DMN. In a working memory task, where the load was manipulated without changing executive demands, TD youth showed increasing recruitment with increasing load of the classic fronto

A One-Layer Recurrent Neural Network for Pseudoconvex Optimization Problems With Equality and Inequality Constraints.

Science.gov (United States)

Qin, Sitian; Yang, Xiudong; Xue, Xiaoping; Song, Jiahui

2017-10-01

Pseudoconvex optimization problem, as an important nonconvex optimization problem, plays an important role in scientific and engineering applications. In this paper, a recurrent one-layer neural network is proposed for solving the pseudoconvex optimization problem with equality and inequality constraints. It is proved that from any initial state, the state of the proposed neural network reaches the feasible region in finite time and stays there thereafter. It is also proved that the state of the proposed neural network is convergent to an optimal solution of the related problem. Compared with the related existing recurrent neural networks for the pseudoconvex optimization problems, the proposed neural network in this paper does not need the penalty parameters and has a better convergence. Meanwhile, the proposed neural network is used to solve three nonsmooth optimization problems, and we make some detailed comparisons with the known related conclusions. In the end, some numerical examples are provided to illustrate the effectiveness of the performance of the proposed neural network.

Including capabilities of local actors in regional economic development: Empirical results of local seaweed industries in Sulawesi

Directory of Open Access Journals (Sweden)

Mark T.J. Vredegoor

2013-11-01

Full Text Available Stimson, et al. (2009 developed one of the most relevant and well known model for Regional Economic Development. This model covers the most important factors related to economic development question. However, this model excludes the social components of development. Local community should be included in terms of the development of a region. This paper introduced to the Stimson model “Skills” and “Knowledge” at the individual level for local actors indicating the capabilities at the individual level and introduced “Human Coordination” for the capabilities at the collective level. In our empirical research we looked at the Indonesian seaweed market with a specific focus on the region of Baubau. This region was chosen because there are hardly any economic developments. Furthermore this study focuses on the poorer community who are trying to improve their situation by the cultivation of Seaweed. Eighteen local informants was interviewed besides additional interviews of informants from educational and governmental institutions in the cities of Jakarta, Bandung and Yogyakarta. The informants selected had a direct or indirect relationship with the region of Baubau. With the support of the empirical data from this region we can confirm that it is worthwhile to include the local community in the model for regional economic development.  The newly added variables: at the individual level; Skills and Knowledge and at the level of the collective: Human Coordination was supported by the empirical material. It is an indication that including the new variables can give regional economic an extra dimension.  In this way we think that it becomes more explicit that “endogenous” means that the people, or variables closely related to them, should be more explicitly included in models trying to capture Regional Economic Development or rephrased as Local Economic Development Keywords:Regional and endogenous development; Fisheries and seaweed

Morphological self-organizing feature map neural network with applications to automatic target recognition

Science.gov (United States)

Zhang, Shijun; Jing, Zhongliang; Li, Jianxun

2005-01-01

The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

Autonomous Navigation Apparatus With Neural Network for a Mobile Vehicle

Science.gov (United States)

Quraishi, Naveed (Inventor)

1996-01-01

An autonomous navigation system for a mobile vehicle arranged to move within an environment includes a plurality of sensors arranged on the vehicle and at least one neural network including an input layer coupled to the sensors, a hidden layer coupled to the input layer, and an output layer coupled to the hidden layer. The neural network produces output signals representing respective positions of the vehicle, such as the X coordinate, the Y coordinate, and the angular orientation of the vehicle. A plurality of patch locations within the environment are used to train the neural networks to produce the correct outputs in response to the distances sensed.

Neural basis of moral elevation demonstrated through inter-subject synchronization of cortical activity during free-viewing.

Directory of Open Access Journals (Sweden)

Zoë A Englander

Full Text Available Most research investigating the neural basis of social emotions has examined emotions that give rise to negative evaluations of others (e.g. anger, disgust. Emotions triggered by the virtues and excellences of others have been largely ignored. Using fMRI, we investigated the neural basis of two "other-praising" emotions--Moral Elevation (a response to witnessing acts of moral beauty, and Admiration (which we restricted to admiration for physical skill.Ten participants viewed the same nine video clips. Three clips elicited moral elevation, three elicited admiration, and three were emotionally neutral. We then performed pair-wise voxel-by-voxel correlations of the BOLD signal between individuals for each video clip and a separate resting-state run. We observed a high degree of inter-subject synchronization, regardless of stimulus type, across several brain regions during free-viewing of videos. Videos in the elevation condition evoked significant inter-subject synchronization in brain regions previously implicated in self-referential and interoceptive processes, including the medial prefrontal cortex, precuneus, and insula. The degree of synchronization was highly variable over the course of the videos, with the strongest synchrony occurring during portions of the videos that were independently rated as most emotionally arousing. Synchrony in these same brain regions was not consistently observed during the admiration videos, and was absent for the neutral videos.Results suggest that the neural systems supporting moral elevation are remarkably consistent across subjects viewing the same emotional content. We demonstrate that model-free techniques such as inter-subject synchronization may be a useful tool for studying complex, context dependent emotions such as self-transcendent emotion.

Computationally efficient model predictive control algorithms a neural network approach

CERN Document Server

Ławryńczuk, Maciej

2014-01-01

This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: ·         A few types of suboptimal MPC algorithms in which a linear approximation of the model or of the predicted trajectory is successively calculated on-line and used for prediction. ·         Implementation details of the MPC algorithms for feedforward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. ·         The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). ·         The MPC algorithms with neural approximation with no on-line linearization. ·         The MPC algorithms with guaranteed stability and robustness. ·         Cooperation between the MPC algorithms and set-point optimization. Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require d...

Led into temptation? Rewarding brand logos bias the neural encoding of incidental economic decisions.

Directory of Open Access Journals (Sweden)

Carsten Murawski

Full Text Available Human decision-making is driven by subjective values assigned to alternative choice options. These valuations are based on reward cues. It is unknown, however, whether complex reward cues, such as brand logos, may bias the neural encoding of subjective value in unrelated decisions. In this functional magnetic resonance imaging (fMRI study, we subliminally presented brand logos preceding intertemporal choices. We demonstrated that priming biased participants' preferences towards more immediate rewards in the subsequent temporal discounting task. This was associated with modulations of the neural encoding of subjective values of choice options in a network of brain regions, including but not restricted to medial prefrontal cortex. Our findings demonstrate the general susceptibility of the human decision making system to apparently incidental contextual information. We conclude that the brain incorporates seemingly unrelated value information that modifies decision making outside the decision-maker's awareness.

Neural networks mediating sentence reading in the deaf

Directory of Open Access Journals (Sweden)

Elizabeth Ann Hirshorn

2014-06-01

Full Text Available The present work addresses the neural bases of sentence reading in deaf populations. To better understand the relative role of deafness and English knowledge in shaping the neural networks that mediate sentence reading, three populations with different degrees of English knowledge and depth of hearing loss were included – deaf signers, oral deaf and hearing individuals. The three groups were matched for reading comprehension and scanned while reading sentences. A similar neural network of left perisylvian areas was observed, supporting the view of a shared network of areas for reading despite differences in hearing and English knowledge. However, differences were observed, in particular in the auditory cortex, with deaf signers and oral deaf showing greatest bilateral superior temporal gyrus (STG recruitment as compared to hearing individuals. Importantly, within deaf individuals, the same STG area in the left hemisphere showed greater recruitment as hearing loss increased. To further understand the functional role of such auditory cortex re-organization after deafness, connectivity analyses were performed from the STG regions identified above. Connectivity from the left STG toward areas typically associated with semantic processing (BA45 and thalami was greater in deaf signers and in oral deaf as compared to hearing. In contrast, connectivity from left STG toward areas identified with speech-based processing was greater in hearing and in oral deaf as compared to deaf signers. These results support the growing literature indicating recruitment of auditory areas after congenital deafness for visually-mediated language functions, and establish that both auditory deprivation and language experience shape its functional reorganization. Implications for differential reliance on semantic vs. phonological pathways during reading in the three groups is discussed.

Child Maltreatment and Neural Systems Underlying Emotion Regulation.

Science.gov (United States)

McLaughlin, Katie A; Peverill, Matthew; Gold, Andrea L; Alves, Sonia; Sheridan, Margaret A

2015-09-01

The strong associations between child maltreatment and psychopathology have generated interest in identifying neurodevelopmental processes that are disrupted following maltreatment. Previous research has focused largely on neural response to negative facial emotion. We determined whether child maltreatment was associated with neural responses during passive viewing of negative and positive emotional stimuli and effortful attempts to regulate emotional responses. A total of 42 adolescents aged 13 to 19 years, half with exposure to physical and/or sexual abuse, participated. Blood oxygen level-dependent (BOLD) response was measured during passive viewing of negative and positive emotional stimuli and attempts to modulate emotional responses using cognitive reappraisal. Maltreated adolescents exhibited heightened response in multiple nodes of the salience network, including amygdala, putamen, and anterior insula, to negative relative to neutral stimuli. During attempts to decrease responses to negative stimuli relative to passive viewing, maltreatment was associated with greater recruitment of superior frontal gyrus, dorsal anterior cingulate cortex, and frontal pole; adolescents with and without maltreatment down-regulated amygdala response to a similar degree. No associations were observed between maltreatment and neural response to positive emotional stimuli during passive viewing or effortful regulation. Child maltreatment heightens the salience of negative emotional stimuli. Although maltreated adolescents modulate amygdala responses to negative cues to a degree similar to that of non-maltreated youths, they use regions involved in effortful control to a greater degree to do so, potentially because greater effort is required to modulate heightened amygdala responses. These findings are promising, given the centrality of cognitive restructuring in trauma-focused treatments for children. Copyright © 2015 American Academy of Child and Adolescent Psychiatry

Neural mechanisms underlying melodic perception and memory for pitch.

Science.gov (United States)

Zatorre, R J; Evans, A C; Meyer, E

1994-04-01

The neural correlates of music perception were studied by measuring cerebral blood flow (CBF) changes with positron emission tomography (PET). Twelve volunteers were scanned using the bolus water method under four separate conditions: (1) listening to a sequence of noise bursts, (2) listening to unfamiliar tonal melodies, (3) comparing the pitch of the first two notes of the same set of melodies, and (4) comparing the pitch of the first and last notes of the melodies. The latter two conditions were designed to investigate short-term pitch retention under low or high memory load, respectively. Subtraction of the obtained PET images, superimposed on matched MRI scans, provides anatomical localization of CBF changes associated with specific cognitive functions. Listening to melodies, relative to acoustically matched noise sequences, resulted in CBF increases in the right superior temporal and right occipital cortices. Pitch judgments of the first two notes of each melody, relative to passive listening to the same stimuli, resulted in right frontal-lobe activation. Analysis of the high memory load condition relative to passive listening revealed the participation of a number of cortical and subcortical regions, notably in the right frontal and right temporal lobes, as well as in parietal and insular cortex. Both pitch judgment conditions also revealed CBF decreases within the left primary auditory cortex. We conclude that specialized neural systems in the right superior temporal cortex participate in perceptual analysis of melodies; pitch comparisons are effected via a neural network that includes right prefrontal cortex, but active retention of pitch involves the interaction of right temporal and frontal cortices.

An Overview of Bayesian Methods for Neural Spike Train Analysis

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Zhe Chen

2013-01-01

Full Text Available Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.

A review of organic and inorganic biomaterials for neural interfaces.

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Fattahi, Pouria; Yang, Guang; Kim, Gloria; Abidian, Mohammad Reza

2014-03-26

Recent advances in nanotechnology have generated wide interest in applying nanomaterials for neural prostheses. An ideal neural interface should create seamless integration into the nervous system and performs reliably for long periods of time. As a result, many nanoscale materials not originally developed for neural interfaces become attractive candidates to detect neural signals and stimulate neurons. In this comprehensive review, an overview of state-of-the-art microelectrode technologies provided fi rst, with focus on the material properties of these microdevices. The advancements in electro active nanomaterials are then reviewed, including conducting polymers, carbon nanotubes, graphene, silicon nanowires, and hybrid organic-inorganic nanomaterials, for neural recording, stimulation, and growth. Finally, technical and scientific challenges are discussed regarding biocompatibility, mechanical mismatch, and electrical properties faced by these nanomaterials for the development of long-lasting functional neural interfaces.

A systematic review of the neural correlates of positive emotions

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Leonardo Machado

Full Text Available Objective: To conduct a systematic literature review of human studies reporting neural correlates of positive emotions. Methods: The PubMed and Web of Science databases were searched in January 2016 for scientific papers written in English. No restrictions were placed on year of publication. Results: Twenty-two articles were identified and 12 met the established criteria. Five had been published during the last 4 years. Formation and regulation of positive emotions, including happiness, are associated with significant reductions in activity in the right prefrontal cortex and bilaterally in the temporoparietal cortex, as well as with increased activity in the left prefrontal regions. They are also associated with increased activity in the cingulate gyrus, inferior and middle temporal gyri, amygdalae, and ventral striatum. Conclusion: It is too early to claim that there is an established understanding of the neuroscience of positive emotions and happiness. However, despite overlap in the brain regions involved in the formation and regulation of positive and negative emotions, we can conclude that positive emotions such as happiness activate specific brain regions.

[Neural Mechanisms That Facilitate Adaptive Behavior Based on Acquired Stimulus-Outcome Information].

Science.gov (United States)

Ogawa, Masaaki

2017-11-01

In response to changing internal and external situations, we always need to adapt our behavior based on previous experiences, particularly, acquired stimulus-outcome information. The orbitofrontal cortex (OFC), a prefrontal cortical region, is critical for this type of decision-making. The current understanding of the fundamental functions of the OFC has been reviewed by introducing, as an example, how the OFC contributes to the processing of uncertain rewards. Furthermore, the importance of revealing context and temporally specific causal roles of neural circuits including the OFC in decision-making, as well as the techniques to achieve the goal, have been discussed.

Learning and adaptation: neural and behavioural mechanisms behind behaviour change

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Lowe, Robert; Sandamirskaya, Yulia

2018-01-01

This special issue presents perspectives on learning and adaptation as they apply to a number of cognitive phenomena including pupil dilation in humans and attention in robots, natural language acquisition and production in embodied agents (robots), human-robot game play and social interaction, neural-dynamic modelling of active perception and neural-dynamic modelling of infant development in the Piagetian A-not-B task. The aim of the special issue, through its contributions, is to highlight some of the critical neural-dynamic and behavioural aspects of learning as it grounds adaptive responses in robotic- and neural-dynamic systems.

Utility of Phox2b immunohistochemical stain in neural crest tumours and non-neural crest tumours in paediatric patients.

Science.gov (United States)

Warren, Mikako; Matsuno, Ryosuke; Tran, Henry; Shimada, Hiroyuki

2018-03-01

This study evaluated the utility of Phox2b in paediatric tumours. Previously, tyrosine hydroxylase (TH) was the most widely utilised sympathoadrenal marker specific for neural crest tumours with neuronal/neuroendocrine differentiation. However, its sensitivity is insufficient. Recently Phox2b has emerged as another specific marker for this entity. Phox2b immunohistochemistry (IHC) was performed on 159 paediatric tumours, including (group 1) 65 neural crest tumours with neuronal differentiation [peripheral neuroblastic tumours (pNT)]: 15 neuroblastoma undifferentiated (NB-UD), 10 NB poorly differentiated (NB-PD), 10 NB differentiating (NB-D), 10 ganglioneuroblastoma intermixed (GNBi), 10 GNB nodular (GNBn) and 10 ganglioneuroma (GN); (group 2) 23 neural crest tumours with neuroendocrine differentiation [pheochromocytoma/paraganglioma (PCC/PG)]; (group 3) 27 other neural crest tumours including one composite rhabdomyosarcoma/neuroblastoma; and (group 4) 44 non-neural crest tumours. TH IHC was performed on groups 1, 2 and 3. Phox2b was expressed diffusely in pNT (n = 65 of 65), strongly in NB-UD and NB-PD and with less intensity in NB-D, GNB and GN. Diffuse TH was seen in all NB-PD, NB-D, GNB and GN, but nine of 15 NB-UD and a nodule in GNBn did not express TH (n = 55 of 65). PCC/PG expressed diffuse Phox2b (n = 23 of 23) and diffuse TH, except for one tumour (n = 22 of 23). In composite rhabdomyosarcoma, TH was expressed only in neuroblastic cells and Phox2b was diffusely positive in neuroblastic cells and focally in rhabdomyosarcoma. All other tumours were negative for Phox2b (n = none of 44). Phox2b was a specific and sensitive marker for pNT and PCC/PG, especially useful for identifying NB-UD often lacking TH. Our study also presented a composite rhabdomyosarcoma/neuroblastoma of neural crest origin. © 2017 John Wiley & Sons Ltd.

A One-Layer Recurrent Neural Network for Real-Time Portfolio Optimization With Probability Criterion.

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Liu, Qingshan; Dang, Chuangyin; Huang, Tingwen

2013-02-01

This paper presents a decision-making model described by a recurrent neural network for dynamic portfolio optimization. The portfolio-optimization problem is first converted into a constrained fractional programming problem. Since the objective function in the programming problem is not convex, the traditional optimization techniques are no longer applicable for solving this problem. Fortunately, the objective function in the fractional programming is pseudoconvex on the feasible region. It leads to a one-layer recurrent neural network modeled by means of a discontinuous dynamic system. To ensure the optimal solutions for portfolio optimization, the convergence of the proposed neural network is analyzed and proved. In fact, the neural network guarantees to get the optimal solutions for portfolio-investment advice if some mild conditions are satisfied. A numerical example with simulation results substantiates the effectiveness and illustrates the characteristics of the proposed neural network.

Artificial neural networks for stiffness estimation in magnetic resonance elastography.

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Murphy, Matthew C; Manduca, Armando; Trzasko, Joshua D; Glaser, Kevin J; Huston, John; Ehman, Richard L

2018-07-01

To investigate the feasibility of using artificial neural networks to estimate stiffness from MR elastography (MRE) data. Artificial neural networks were fit using model-based training patterns to estimate stiffness from images of displacement using a patch size of ∼1 cm in each dimension. These neural network inversions (NNIs) were then evaluated in a set of simulation experiments designed to investigate the effects of wave interference and noise on NNI accuracy. NNI was also tested in vivo, comparing NNI results against currently used methods. In 4 simulation experiments, NNI performed as well or better than direct inversion (DI) for predicting the known stiffness of the data. Summary NNI results were also shown to be significantly correlated with DI results in the liver (R 2  = 0.974) and in the brain (R 2  = 0.915), and also correlated with established biological effects including fibrosis stage in the liver and age in the brain. Finally, repeatability error was lower in the brain using NNI compared to DI, and voxel-wise modeling using NNI stiffness maps detected larger effects than using DI maps with similar levels of smoothing. Artificial neural networks represent a new approach to inversion of MRE data. Summary results from NNI and DI are highly correlated and both are capable of detecting biologically relevant signals. Preliminary evidence suggests that NNI stiffness estimates may be more resistant to noise than an algebraic DI approach. Taken together, these results merit future investigation into NNIs to improve the estimation of stiffness in small regions. Magn Reson Med 80:351-360, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

DWI-based neural fingerprinting technology: a preliminary study on stroke analysis.

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Ye, Chenfei; Ma, Heather Ting; Wu, Jun; Yang, Pengfei; Chen, Xuhui; Yang, Zhengyi; Ma, Jingbo

2014-01-01

Stroke is a common neural disorder in neurology clinics. Magnetic resonance imaging (MRI) has become an important tool to assess the neural physiological changes under stroke, such as diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI). Quantitative analysis of MRI images would help medical doctors to localize the stroke area in the diagnosis in terms of structural information and physiological characterization. However, current quantitative approaches can only provide localization of the disorder rather than measure physiological variation of subtypes of ischemic stroke. In the current study, we hypothesize that each kind of neural disorder would have its unique physiological characteristics, which could be reflected by DWI images on different gradients. Based on this hypothesis, a DWI-based neural fingerprinting technology was proposed to classify subtypes of ischemic stroke. The neural fingerprint was constructed by the signal intensity of the region of interest (ROI) on the DWI images under different gradients. The fingerprint derived from the manually drawn ROI could classify the subtypes with accuracy 100%. However, the classification accuracy was worse when using semiautomatic and automatic method in ROI segmentation. The preliminary results showed promising potential of DWI-based neural fingerprinting technology in stroke subtype classification. Further studies will be carried out for enhancing the fingerprinting accuracy and its application in other clinical practices.

Effect of Explicit Evaluation on Neural Connectivity Related to Listening to Unfamiliar Music

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Liu, Chao; Brattico, Elvira; Abu-jamous, Basel; Pereira, Carlos S.; Jacobsen, Thomas; Nandi, Asoke K.

2017-01-01

People can experience different emotions when listening to music. A growing number of studies have investigated the brain structures and neural connectivities associated with perceived emotions. However, very little is known about the effect of an explicit act of judgment on the neural processing of emotionally-valenced music. In this study, we adopted the novel consensus clustering paradigm, called binarisation of consensus partition matrices (Bi-CoPaM), to study whether and how the conscious aesthetic evaluation of the music would modulate brain connectivity networks related to emotion and reward processing. Participants listened to music under three conditions – one involving a non-evaluative judgment, one involving an explicit evaluative aesthetic judgment, and one involving no judgment at all (passive listening only). During non-evaluative attentive listening we obtained auditory-limbic connectivity whereas when participants were asked to decide explicitly whether they liked or disliked the music excerpt, only two clusters of intercommunicating brain regions were found: one including areas related to auditory processing and action observation, and the other comprising higher-order structures involved with visual processing. Results indicate that explicit evaluative judgment has an impact on the neural auditory-limbic connectivity during affective processing of music. PMID:29311874

Potential applications of neural networks to nuclear power plants

International Nuclear Information System (INIS)

Uhrig, R.E.

1991-01-01

Application of neural networks to the operation of nuclear power plants is being investigated under a US Department of Energy sponsored program at the University of Tennessee. Projects include the feasibility of using neural networks for the following tasks: diagnosing specific abnormal conditions, detection of the change of mode of operation, signal validation, monitoring of check valves, plant-wide monitoring using autoassociative neural networks, modeling of the plant thermodynamics, emulation of core reload calculations, monitoring of plant parameters, and analysis of plant vibrations. Each of these projects and its status are described briefly in this article. The objective of each of these projects is to enhance the safety and performance of nuclear plants through the use of neural networks

Neural 17β-estradiol facilitates long-term potentiation in the hippocampal CA1 region.

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Grassi, S; Tozzi, A; Costa, C; Tantucci, M; Colcelli, E; Scarduzio, M; Calabresi, P; Pettorossi, V E

2011-09-29

In the hippocampal formation many neuromodulators are possibly implied in the synaptic plasticity such as the long-term potentiation (LTP) induced by high-frequency stimulation (HFS) of afferent fibers. We investigated the involvement of locally synthesized neural 17β-estradiol (nE(2)) in the induction of HFS-LTP in hippocampal slices from male rats by stimulating the Schaffer collateral fibers and recording the evoked field excitatory postsynaptic potential (fEPSP) in the CA1 region. We demonstrated that either the blockade of nE(2) synthesis by the aromatase inhibitor letrozole, or the antagonism of E(2) receptors (ERs) by ICI 182,780 did not prevent the induction of HFS-LTP, but reduced its amplitude by ∼60%, without influencing its maintenance. Moreover, letrozole and ICI 182,780 did not affect the first short-term post-tetanic component of LTP and the paired-pulse facilitation (PPF). These findings demonstrate that nE(2) plays an important role in the induction phase of HFS-dependent LTP. Since the basal responses were not affected by the blocking agents, we suggest that the synthesis of nE(2) is induced or enhanced by HFS through aromatase activation. In this context, the local production of nE(2) seems to be a very effective mechanism to modulate the amplitude of LTP. Copyright © 2011 IBRO. Published by Elsevier Ltd. All rights reserved.

The murine homeobox gene Msx-3 shows highly restricted expression in the developing neural tube.

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Shimeld, S M; McKay, I J; Sharpe, P T

1996-04-01

The mouse homeobox-genes Msx-1 and Msx-2 are expressed in several areas of the developing embryo, including the neural tube, neural crest, facial processes and limb buds. Here we report the characterisation of a third mouse Msx gene, which we designate Msx-3. The embryonic expression of Msx-3 was found to differ from that of Msx-1 and -2 in that it was confined to the dorsal neural tube. In embryos with 5-8 somites a segmental pattern of expression was observed in the hindbrain, with rhombomeres 3 and 5 lacking Msx-3 while other rhombomeres expressed Msx-3. This pattern was transient, however, such that in embryos with 18 or more somites expression was continuous throughout the dorsal hindbrain and anterior dorsal spinal cord. Differentiation of dorsal cell types in the neural tube can be induced by addition of members of the Tgf-beta family. Additionally, Msx-1 and -2 have been shown to be activated by addition of the Tgf-beta family member Bmp-4. To determine if Bmp-4 could activate Msx-3, we incubated embryonic hindbrain explants with exogenous Bmp-4. The dorsal expression of Msx-3 was seen to expand into more ventral regions of the neurectoderm in Bmp-4-treated cultures, implying that Bmp-4 may be able to mimic an in vivo signal that induces Msx-3.

A model for integrating elementary neural functions into delayed-response behavior.

Directory of Open Access Journals (Sweden)

Thomas Gisiger

2006-04-01

Full Text Available It is well established that various cortical regions can implement a wide array of neural processes, yet the mechanisms which integrate these processes into behavior-producing, brain-scale activity remain elusive. We propose that an important role in this respect might be played by executive structures controlling the traffic of information between the cortical regions involved. To illustrate this hypothesis, we present a neural network model comprising a set of interconnected structures harboring stimulus-related activity (visual representation, working memory, and planning, and a group of executive units with task-related activity patterns that manage the information flowing between them. The resulting dynamics allows the network to perform the dual task of either retaining an image during a delay (delayed-matching to sample task, or recalling from this image another one that has been associated with it during training (delayed-pair association task. The model reproduces behavioral and electrophysiological data gathered on the inferior temporal and prefrontal cortices of primates performing these same tasks. It also makes predictions on how neural activity coding for the recall of the image associated with the sample emerges and becomes prospective during the training phase. The network dynamics proves to be very stable against perturbations, and it exhibits signs of scale-invariant organization and cooperativity. The present network represents a possible neural implementation for active, top-down, prospective memory retrieval in primates. The model suggests that brain activity leading to performance of cognitive tasks might be organized in modular fashion, simple neural functions becoming integrated into more complex behavior by executive structures harbored in prefrontal cortex and/or basal ganglia.

A model for integrating elementary neural functions into delayed-response behavior.

Science.gov (United States)

Gisiger, Thomas; Kerszberg, Michel

2006-04-01

It is well established that various cortical regions can implement a wide array of neural processes, yet the mechanisms which integrate these processes into behavior-producing, brain-scale activity remain elusive. We propose that an important role in this respect might be played by executive structures controlling the traffic of information between the cortical regions involved. To illustrate this hypothesis, we present a neural network model comprising a set of interconnected structures harboring stimulus-related activity (visual representation, working memory, and planning), and a group of executive units with task-related activity patterns that manage the information flowing between them. The resulting dynamics allows the network to perform the dual task of either retaining an image during a delay (delayed-matching to sample task), or recalling from this image another one that has been associated with it during training (delayed-pair association task). The model reproduces behavioral and electrophysiological data gathered on the inferior temporal and prefrontal cortices of primates performing these same tasks. It also makes predictions on how neural activity coding for the recall of the image associated with the sample emerges and becomes prospective during the training phase. The network dynamics proves to be very stable against perturbations, and it exhibits signs of scale-invariant organization and cooperativity. The present network represents a possible neural implementation for active, top-down, prospective memory retrieval in primates. The model suggests that brain activity leading to performance of cognitive tasks might be organized in modular fashion, simple neural functions becoming integrated into more complex behavior by executive structures harbored in prefrontal cortex and/or basal ganglia.

Neural representations of social status hierarchy in human inferior parietal cortex.

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Chiao, Joan Y; Harada, Tokiko; Oby, Emily R; Li, Zhang; Parrish, Todd; Bridge, Donna J

2009-01-01

Mental representations of social status hierarchy share properties with that of numbers. Previous neuroimaging studies have shown that the neural representation of numerical magnitude lies within a network of regions within inferior parietal cortex. However the neural basis of social status hierarchy remains unknown. Using fMRI, we studied subjects while they compared social status magnitude of people, objects and symbols, as well as numerical magnitude. Both social status and number comparisons recruited bilateral intraparietal sulci. We also observed a semantic distance effect whereby neural activity within bilateral intraparietal sulci increased for semantically close relative to far numerical and social status comparisons. These results demonstrate that social status and number comparisons recruit distinct and overlapping neuronal representations within human inferior parietal cortex.

Transformed Neural Pattern Reinstatement during Episodic Memory Retrieval.

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Xiao, Xiaoqian; Dong, Qi; Gao, Jiahong; Men, Weiwei; Poldrack, Russell A; Xue, Gui

2017-03-15

Contemporary models of episodic memory posit that remembering involves the reenactment of encoding processes. Although encoding-retrieval similarity has been consistently reported and linked to memory success, the nature of neural pattern reinstatement is poorly understood. Using high-resolution fMRI on human subjects, our results obtained clear evidence for item-specific pattern reinstatement in the frontoparietal cortex, even when the encoding-retrieval pairs shared no perceptual similarity. No item-specific pattern reinstatement was found in the ventral visual cortex. Importantly, the brain regions and voxels carrying item-specific representation differed significantly between encoding and retrieval, and the item specificity for encoding-retrieval similarity was smaller than that for encoding or retrieval, suggesting different nature of representations between encoding and retrieval. Moreover, cross-region representational similarity analysis suggests that the encoded representation in the ventral visual cortex was reinstated in the frontoparietal cortex during retrieval. Together, these results suggest that, in addition to reinstatement of the originally encoded pattern in the brain regions that perform encoding processes, retrieval may also involve the reinstatement of a transformed representation of the encoded information. These results emphasize the constructive nature of memory retrieval that helps to serve important adaptive functions. SIGNIFICANCE STATEMENT Episodic memory enables humans to vividly reexperience past events, yet how this is achieved at the neural level is barely understood. A long-standing hypothesis posits that memory retrieval involves the faithful reinstatement of encoding-related activity. We tested this hypothesis by comparing the neural representations during encoding and retrieval. We found strong pattern reinstatement in the frontoparietal cortex, but not in the ventral visual cortex, that represents visual details. Critically

Neural networks in signal processing

International Nuclear Information System (INIS)

Govil, R.

2000-01-01

Nuclear Engineering has matured during the last decade. In research and design, control, supervision, maintenance and production, mathematical models and theories are used extensively. In all such applications signal processing is embedded in the process. Artificial Neural Networks (ANN), because of their nonlinear, adaptive nature are well suited to such applications where the classical assumptions of linearity and second order Gaussian noise statistics cannot be made. ANN's can be treated as nonparametric techniques, which can model an underlying process from example data. They can also adopt their model parameters to statistical change with time. Algorithms in the framework of Neural Networks in Signal processing have found new applications potentials in the field of Nuclear Engineering. This paper reviews the fundamentals of Neural Networks in signal processing and their applications in tasks such as recognition/identification and control. The topics covered include dynamic modeling, model based ANN's, statistical learning, eigen structure based processing and generalization structures. (orig.)

Abnormal neural responses to social exclusion in schizophrenia.

Directory of Open Access Journals (Sweden)

Victoria B Gradin

Full Text Available Social exclusion is an influential concept in politics, mental health and social psychology. Studies on healthy subjects have implicated the medial prefrontal cortex (mPFC, a region involved in emotional and social information processing, in neural responses to social exclusion. Impairments in social interactions are common in schizophrenia and are associated with reduced quality of life. Core symptoms such as delusions usually have a social content. However little is known about the neural underpinnings of social abnormalities. The aim of this study was to investigate the neural substrates of social exclusion in schizophrenia. Patients with schizophrenia and healthy controls underwent fMRI while participating in a popular social exclusion paradigm. This task involves passing a 'ball' between the participant and two cartoon representations of other subjects. The extent of social exclusion (ball not being passed to the participant was parametrically varied throughout the task. Replicating previous findings, increasing social exclusion activated the mPFC in controls. In contrast, patients with schizophrenia failed to modulate mPFC responses with increasing exclusion. Furthermore, the blunted response to exclusion correlated with increased severity of positive symptoms. These data support the hypothesis that the neural response to social exclusion differs in schizophrenia, highlighting the mPFC as a potential substrate of impaired social interactions.

A common neural code for perceived and inferred emotion.

Science.gov (United States)

Skerry, Amy E; Saxe, Rebecca

2014-11-26

Although the emotions of other people can often be perceived from overt reactions (e.g., facial or vocal expressions), they can also be inferred from situational information in the absence of observable expressions. How does the human brain make use of these diverse forms of evidence to generate a common representation of a target's emotional state? In the present research, we identify neural patterns that correspond to emotions inferred from contextual information and find that these patterns generalize across different cues from which an emotion can be attributed. Specifically, we use functional neuroimaging to measure neural responses to dynamic facial expressions with positive and negative valence and to short animations in which the valence of a character's emotion could be identified only from the situation. Using multivoxel pattern analysis, we test for regions that contain information about the target's emotional state, identifying representations specific to a single stimulus type and representations that generalize across stimulus types. In regions of medial prefrontal cortex (MPFC), a classifier trained to discriminate emotional valence for one stimulus (e.g., animated situations) could successfully discriminate valence for the remaining stimulus (e.g., facial expressions), indicating a representation of valence that abstracts away from perceptual features and generalizes across different forms of evidence. Moreover, in a subregion of MPFC, this neural representation generalized to trials involving subjectively experienced emotional events, suggesting partial overlap in neural responses to attributed and experienced emotions. These data provide a step toward understanding how the brain transforms stimulus-bound inputs into abstract representations of emotion. Copyright © 2014 the authors 0270-6474/14/3315997-12$15.00/0.

Evolvable Neural Software System

Science.gov (United States)

Curtis, Steven A.

2009-01-01

The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

Neural correlate of human reciprocity in social interactions.

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Sakaiya, Shiro; Shiraito, Yuki; Kato, Junko; Ide, Hiroko; Okada, Kensuke; Takano, Kouji; Kansaku, Kenji

2013-01-01

Reciprocity plays a key role maintaining cooperation in society. However, little is known about the neural process that underpins human reciprocity during social interactions. Our neuroimaging study manipulated partner identity (computer, human) and strategy (random, tit-for-tat) in repeated prisoner's dilemma games and investigated the neural correlate of reciprocal interaction with humans. Reciprocal cooperation with humans but exploitation of computers by defection was associated with activation in the left amygdala. Amygdala activation was also positively and negatively correlated with a preference change for human partners following tit-for-tat and random strategies, respectively. The correlated activation represented the intensity of positive feeling toward reciprocal and negative feeling toward non-reciprocal partners, and so reflected reciprocity in social interaction. Reciprocity in social interaction, however, might plausibly be misinterpreted and so we also examined the neural coding of insight into the reciprocity of partners. Those with and without insight revealed differential brain activation across the reward-related circuitry (i.e., the right middle dorsolateral prefrontal cortex and dorsal caudate) and theory of mind (ToM) regions [i.e., ventromedial prefrontal cortex (VMPFC) and precuneus]. Among differential activations, activation in the precuneus, which accompanied deactivation of the VMPFC, was specific to those without insight into human partners who were engaged in a tit-for-tat strategy. This asymmetric (de)activation might involve specific contributions of ToM regions to the human search for reciprocity. Consequently, the intensity of emotion attached to human reciprocity was represented in the amygdala, whereas insight into the reciprocity of others was reflected in activation across the reward-related and ToM regions. This suggests the critical role of mentalizing, which was not equated with reward expectation during social interactions.

Neural correlate of human reciprocity in social interactions

Directory of Open Access Journals (Sweden)

Shiro eSakaiya

2013-12-01

Full Text Available Reciprocity plays a key role maintaining cooperation in society. However, little is known about the neural process that underpins human reciprocity during social interactions. Our neuroimaging study manipulated partner identity (computer, human and strategy (random, tit-for-tat in repeated prisoner’s dilemma games and investigated the neural correlate of reciprocal interaction with humans. Reciprocal cooperation with humans but exploitation of computers by defection was associated with activation in the left amygdala. Amygdala activation was also positively and negatively correlated with a preference change for human partners following tit-for-tat and random strategies, respectively. The correlated activation represented the intensity of positive feeling toward reciprocal and negative feeling toward non-reciprocal partners, and so reflected reciprocity in social interaction. Reciprocity in social interaction, however, might plausibly be misinterpreted and so we also examined the neural coding of insight into the reciprocity of partners. Those with and without insight revealed differential brain activation across the reward-related circuitry (i.e., the right middle dorsolateral prefrontal cortex and dorsal caudate and theory of mind (ToM regions (i.e., ventromedial prefrontal cortex [VMPFC] and precuneus. Among differential activations, activation in the precuneus, which accompanied deactivation of the VMPFC, was specific to those without insight into human partners who were engaged in a tit-for-tat strategy. This asymmetric (deactivation might involve specific contributions of ToM regions to the human search for reciprocity. Consequently, the intensity of emotion attached to human reciprocity was represented in the amygdala, whereas insight into the reciprocity of others was reflected in activation across the reward-related and ToM regions. This suggests the critical role of mentalizing, which was not equated with reward expectation during

Neural activity when people solve verbal problems with insight.

Directory of Open Access Journals (Sweden)

Mark Jung-Beeman

2004-04-01

Full Text Available People sometimes solve problems with a unique process called insight, accompanied by an "Aha!" experience. It has long been unclear whether different cognitive and neural processes lead to insight versus noninsight solutions, or if solutions differ only in subsequent subjective feeling. Recent behavioral studies indicate distinct patterns of performance and suggest differential hemispheric involvement for insight and noninsight solutions. Subjects solved verbal problems, and after each correct solution indicated whether they solved with or without insight. We observed two objective neural correlates of insight. Functional magnetic resonance imaging (Experiment 1 revealed increased activity in the right hemisphere anterior superior temporal gyrus for insight relative to noninsight solutions. The same region was active during initial solving efforts. Scalp electroencephalogram recordings (Experiment 2 revealed a sudden burst of high-frequency (gamma-band neural activity in the same area beginning 0.3 s prior to insight solutions. This right anterior temporal area is associated with making connections across distantly related information during comprehension. Although all problem solving relies on a largely shared cortical network, the sudden flash of insight occurs when solvers engage distinct neural and cognitive processes that allow them to see connections that previously eluded them.

Deep Neural Network Based Demand Side Short Term Load Forecasting

Directory of Open Access Journals (Sweden)

Seunghyoung Ryu

2016-12-01

Full Text Available In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN, a double seasonal Holt–Winters (DSHW model and the autoregressive integrated moving average (ARIMA. The mean absolute percentage error (MAPE and relative root mean square error (RRMSE are used for verification. Our results show that DNNs exhibit accurate and robust predictions compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN and 9% and 29% compared to DSHW.

Lifelong bilingualism maintains neural efficiency for cognitive control in aging.

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Gold, Brian T; Kim, Chobok; Johnson, Nathan F; Kryscio, Richard J; Smith, Charles D

2013-01-09

Recent behavioral data have shown that lifelong bilingualism can maintain youthful cognitive control abilities in aging. Here, we provide the first direct evidence of a neural basis for the bilingual cognitive control boost in aging. Two experiments were conducted, using a perceptual task-switching paradigm, including a total of 110 participants. In Experiment 1, older adult bilinguals showed better perceptual switching performance than their monolingual peers. In Experiment 2, younger and older adult monolinguals and bilinguals completed the same perceptual task-switching experiment while functional magnetic resonance imaging (fMRI) was performed. Typical age-related performance reductions and fMRI activation increases were observed. However, like younger adults, bilingual older adults outperformed their monolingual peers while displaying decreased activation in left lateral frontal cortex and cingulate cortex. Critically, this attenuation of age-related over-recruitment associated with bilingualism was directly correlated with better task-switching performance. In addition, the lower blood oxygenation level-dependent response in frontal regions accounted for 82% of the variance in the bilingual task-switching reaction time advantage. These results suggest that lifelong bilingualism offsets age-related declines in the neural efficiency for cognitive control processes.

Radial basis function neural network in fault detection of automotive ...

African Journals Online (AJOL)

Radial basis function neural network in fault detection of automotive engines. ... Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The three sensor faults ... Keywords: Automotive engine, independent RBFNN model, RBF neural network, fault detection

The neural correlates of internal and external comparisons: an fMRI study.

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Wen, Xue; Xiang, Yanhui; Cant, Jonathan S; Wang, Tingting; Cupchik, Gerald; Huang, Ruiwang; Mo, Lei

2017-01-01

Many previous studies have suggested that various comparisons rely on the same cognitive and neural mechanisms. However, little attention has been paid to exploring the commonalities and differences between the internal comparison based on concepts or rules and the external comparison based on perception. In the present experiment, moral beauty comparison and facial beauty comparison were selected as the representatives of internal comparison and external comparison, respectively. Functional magnetic resonance imaging (fMRI) was used to record brain activity while participants compared the level of moral beauty of two scene drawings containing moral acts or the level of facial beauty of two face photos. In addition, a physical size comparison task with the same stimuli as the beauty comparison was included. We observed that both the internal moral beauty comparison and external facial beauty comparison obeyed a typical distance effect and this behavioral effect recruited a common frontoparietal network involved in comparisons of simple physical magnitudes such as size. In addition, compared to external facial beauty comparison, internal moral beauty comparison induced greater activity in more advanced and complex cortical regions, such as the bilateral middle temporal gyrus and middle occipital gyrus, but weaker activity in the putamen, a subcortical region. Our results provide novel neural evidence for the comparative process and suggest that different comparisons may rely on both common cognitive processes as well as distinct and specific cognitive components.

Intrusive images in psychological disorders: characteristics, neural mechanisms, and treatment implications.

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Brewin, Chris R; Gregory, James D; Lipton, Michelle; Burgess, Neil

2010-01-01

Involuntary images and visual memories are prominent in many types of psychopathology. Patients with posttraumatic stress disorder, other anxiety disorders, depression, eating disorders, and psychosis frequently report repeated visual intrusions corresponding to a small number of real or imaginary events, usually extremely vivid, detailed, and with highly distressing content. Both memory and imagery appear to rely on common networks involving medial prefrontal regions, posterior regions in the medial and lateral parietal cortices, the lateral temporal cortex, and the medial temporal lobe. Evidence from cognitive psychology and neuroscience implies distinct neural bases to abstract, flexible, contextualized representations (C-reps) and to inflexible, sensory-bound representations (S-reps). We revise our previous dual representation theory of posttraumatic stress disorder to place it within a neural systems model of healthy memory and imagery. The revised model is used to explain how the different types of distressing visual intrusions associated with clinical disorders arise, in terms of the need for correct interaction between the neural systems supporting S-reps and C-reps via visuospatial working memory. Finally, we discuss the treatment implications of the new model and relate it to existing forms of psychological therapy.

The neural correlates of cognitive reappraisal during emotional autobiographical memory recall.

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Holland, Alisha C; Kensinger, Elizabeth A

2013-01-01

We used fMRI to investigate the neural processes engaged as individuals down- and up-regulated the emotions associated with negative autobiographical memories (AMs) using cognitive reappraisal strategies. Our analyses examined neural activity during three separate phases, as participants (a) viewed a reappraisal instruction (i.e., Decrease, Increase, Maintain), (b) searched for an AM referenced by a self-generated cue, and (c) elaborated upon the details of the AM being held in mind. Decreasing emotional intensity primarily engaged activity in regions previously implicated in cognitive control (e.g., dorsal and ventral lateral pFC), emotion generation and processing (e.g., amygdala, insula), and visual imagery (e.g., precuneus) as participants searched for and retrieved events. In contrast, increasing emotional intensity engaged similar regions during the instruction phase (i.e., before a memory cue was presented) and again as individuals later elaborated upon the details of the events they had recalled. These findings confirm that reappraisal can modulate neural activity during the recall of personally relevant events, although the time course of this modulation appears to depend on whether individuals are attempting to down- or up-regulate their emotions.

Modeling Distillation Column Using ARX Model Structure and Artificial Neural Networks

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Reza Pirmoradi

2012-04-01

Full Text Available Distillation is a complex and highly nonlinear industrial process. In general it is not always possible to obtain accurate first principles models for high-purity distillation columns. On the other hand the development of first principles models is usually time consuming and expensive. To overcome these problems, empirical models such as neural networks can be used. One major drawback of empirical models is that the prediction is valid only inside the data domain that is sufficiently covered by measurement data. Modeling distillation columns by means of neural networks is reported in literature by using recursive networks. The recursive networks are proper for modeling purpose, but such models have the problems of high complexity and high computational cost. The objective of this paper is to propose a simple and reliable model for distillation column. The proposed model uses feed forward neural networks which results in a simple model with less parameters and faster training time. Simulation results demonstrate that predictions of the proposed model in all regions are close to outputs of the dynamic model and the error in negligible. This implies that the model is reliable in all regions.

Oscillations, neural computations and learning during wake and sleep.

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Penagos, Hector; Varela, Carmen; Wilson, Matthew A

2017-06-01

Learning and memory theories consider sleep and the reactivation of waking hippocampal neural patterns to be crucial for the long-term consolidation of memories. Here we propose that precisely coordinated representations across brain regions allow the inference and evaluation of causal relationships to train an internal generative model of the world. This training starts during wakefulness and strongly benefits from sleep because its recurring nested oscillations may reflect compositional operations that facilitate a hierarchical processing of information, potentially including behavioral policy evaluations. This suggests that an important function of sleep activity is to provide conditions conducive to general inference, prediction and insight, which contribute to a more robust internal model that underlies generalization and adaptive behavior. Copyright © 2017 Elsevier Ltd. All rights reserved.

Neural Tube Defects and Pregnancy

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Emine Çoşar

2009-09-01

Full Text Available OBJECTIVE: Neural tube defects are congenital malformations those mostly causing life-long morbidities. They are prevented by the periconseptional folic acid usage and prenatal diagnostic methods. MATERIALS-METHODS: Pregnants from Afyonkarahisar and neighbourhood cities applied to our hospital and determined NTD, were investigated. RESULTS: In our obstetrics clinic 1403 delivery were made and 43 of them had fetus with NTD. Among these fetuses 41.3% had meningomyelocel, 17.4% had meningocel, 21.7% had encephalocel, 8.7% had unencephali and 4.3% had iniencephali. CONCLUSION: Incidence of NTD is high in our region and geographic region, nutrition and other socioeconomic factors may be related to the high incidence. Education of the mother and periconceptional folic acid usage may reduce teh incidence of NTD.

Engaged listeners: shared neural processing of powerful political speeches.

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Schmälzle, Ralf; Häcker, Frank E K; Honey, Christopher J; Hasson, Uri

2015-08-01

Powerful speeches can captivate audiences, whereas weaker speeches fail to engage their listeners. What is happening in the brains of a captivated audience? Here, we assess audience-wide functional brain dynamics during listening to speeches of varying rhetorical quality. The speeches were given by German politicians and evaluated as rhetorically powerful or weak. Listening to each of the speeches induced similar neural response time courses, as measured by inter-subject correlation analysis, in widespread brain regions involved in spoken language processing. Crucially, alignment of the time course across listeners was stronger for rhetorically powerful speeches, especially for bilateral regions of the superior temporal gyri and medial prefrontal cortex. Thus, during powerful speeches, listeners as a group are more coupled to each other, suggesting that powerful speeches are more potent in taking control of the listeners' brain responses. Weaker speeches were processed more heterogeneously, although they still prompted substantially correlated responses. These patterns of coupled neural responses bear resemblance to metaphors of resonance, which are often invoked in discussions of speech impact, and contribute to the literature on auditory attention under natural circumstances. Overall, this approach opens up possibilities for research on the neural mechanisms mediating the reception of entertaining or persuasive messages. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Chaotic diagonal recurrent neural network

International Nuclear Information System (INIS)

Wang Xing-Yuan; Zhang Yi

2012-01-01

We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. (interdisciplinary physics and related areas of science and technology)

Computational modeling of neural plasticity for self-organization of neural networks.

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Chrol-Cannon, Joseph; Jin, Yaochu

2014-11-01

Self-organization in biological nervous systems during the lifetime is known to largely occur through a process of plasticity that is dependent upon the spike-timing activity in connected neurons. In the field of computational neuroscience, much effort has been dedicated to building up computational models of neural plasticity to replicate experimental data. Most recently, increasing attention has been paid to understanding the role of neural plasticity in functional and structural neural self-organization, as well as its influence on the learning performance of neural networks for accomplishing machine learning tasks such as classification and regression. Although many ideas and hypothesis have been suggested, the relationship between the structure, dynamics and learning performance of neural networks remains elusive. The purpose of this article is to review the most important computational models for neural plasticity and discuss various ideas about neural plasticity's role. Finally, we suggest a few promising research directions, in particular those along the line that combines findings in computational neuroscience and systems biology, and their synergetic roles in understanding learning, memory and cognition, thereby bridging the gap between computational neuroscience, systems biology and computational intelligence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Neural correlates of "Theory of Mind" in very preterm born children.

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Mossad, Sarah I; Smith, Mary Lou; Pang, Elizabeth W; Taylor, Margot J

2017-11-01

Very preterm (VPT) birth (Theory of Mind (ToM); the ability to attribute mental states to others and understand that those beliefs can differ from one's own or reality. The neural bases for ToM deficits in VPT born children have not been examined. We used magnetoencephalography (MEG) for its excellent spatial and temporal resolution to determine the neural underpinnings of ToM in 24 VPT and 24 full-term born (FT) children (7-13 years). VPT children performed more poorly on neuropsychological measures of ToM but not inhibition. In the MEG task, both FT children and VPT children recruited regions involved in false belief processing such as the rIFG (VPT: 275-350 ms, FT: 250-375 ms) and left inferior temporal gyrus (VPT: 375-450 ms, FT: 325-375 ms) and right fusiform gyrus (VPT: 150-200 ms, FT: 175-250 ms). The rIPL (included in the temporal-parietal junction) was recruited in FT children (475-575 ms) and the lTPJ in VPT children (500-575 ms). However, activations in all regions were reduced in the VPT compared to the FT group. We suggest that with increasing social-cognitive demands such as varying the type of scenarios in the standardized measure of ToM, reduced activations in the rIFG and TPJ in the VPT group may reflect the decreased performance. With access to both spatial and temporal information, we discuss the role of domain general and specific regions of the ToM network in both groups. Hum Brain Mapp 38:5577-5589, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Dynamics in a delayed-neural network

International Nuclear Information System (INIS)

Yuan Yuan

2007-01-01

In this paper, we consider a neural network of four identical neurons with time-delayed connections. Some parameter regions are given for global, local stability and synchronization using the theory of functional differential equations. The root distributions in the corresponding characteristic transcendental equation are analyzed, Pitchfork bifurcation, Hopf and equivariant Hopf bifurcations are investigated by revealing the center manifolds and normal forms. Numerical simulations are shown the agreements with the theoretical results

Neural mechanisms underlying morphine withdrawal in addicted patients: a review

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Nima Babhadiashar

2015-06-01

Full Text Available Morphine is one of the most potent alkaloid in opium, which has substantial medical uses and needs and it is the first active principle purified from herbal source. Morphine has commonly been used for relief of moderate to severe pain as it acts directly on the central nervous system; nonetheless, its chronic abuse increases tolerance and physical dependence, which is commonly known as opiate addiction. Morphine withdrawal syndrome is physiological and behavioral symptoms that stem from prolonged exposure to morphine. A majority of brain regions are hypofunctional over prolonged abstinence and acute morphine withdrawal. Furthermore, several neural mechanisms are likely to contribute to morphine withdrawal. The present review summarizes the literature pertaining to neural mechanisms underlying morphine withdrawal. Despite the fact that morphine withdrawal is a complex process, it is suggested that neural mechanisms play key roles in morphine withdrawal.

Speech comprehension aided by multiple modalities: behavioural and neural interactions

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McGettigan, Carolyn; Faulkner, Andrew; Altarelli, Irene; Obleser, Jonas; Baverstock, Harriet; Scott, Sophie K.

2014-01-01

Speech comprehension is a complex human skill, the performance of which requires the perceiver to combine information from several sources – e.g. voice, face, gesture, linguistic context – to achieve an intelligible and interpretable percept. We describe a functional imaging investigation of how auditory, visual and linguistic information interact to facilitate comprehension. Our specific aims were to investigate the neural responses to these different information sources, alone and in interaction, and further to use behavioural speech comprehension scores to address sites of intelligibility-related activation in multifactorial speech comprehension. In fMRI, participants passively watched videos of spoken sentences, in which we varied Auditory Clarity (with noise-vocoding), Visual Clarity (with Gaussian blurring) and Linguistic Predictability. Main effects of enhanced signal with increased auditory and visual clarity were observed in overlapping regions of posterior STS. Two-way interactions of the factors (auditory × visual, auditory × predictability) in the neural data were observed outside temporal cortex, where positive signal change in response to clearer facial information and greater semantic predictability was greatest at intermediate levels of auditory clarity. Overall changes in stimulus intelligibility by condition (as determined using an independent behavioural experiment) were reflected in the neural data by increased activation predominantly in bilateral dorsolateral temporal cortex, as well as inferior frontal cortex and left fusiform gyrus. Specific investigation of intelligibility changes at intermediate auditory clarity revealed a set of regions, including posterior STS and fusiform gyrus, showing enhanced responses to both visual and linguistic information. Finally, an individual differences analysis showed that greater comprehension performance in the scanning participants (measured in a post-scan behavioural test) were associated with

Multi-Connection Pattern Analysis: Decoding the representational content of neural communication.

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Li, Yuanning; Richardson, Robert Mark; Ghuman, Avniel Singh

2017-11-15

The lack of multivariate methods for decoding the representational content of interregional neural communication has left it difficult to know what information is represented in distributed brain circuit interactions. Here we present Multi-Connection Pattern Analysis (MCPA), which works by learning mappings between the activity patterns of the populations as a factor of the information being processed. These maps are used to predict the activity from one neural population based on the activity from the other population. Successful MCPA-based decoding indicates the involvement of distributed computational processing and provides a framework for probing the representational structure of the interaction. Simulations demonstrate the efficacy of MCPA in realistic circumstances. In addition, we demonstrate that MCPA can be applied to different signal modalities to evaluate a variety of hypothesis associated with information coding in neural communications. We apply MCPA to fMRI and human intracranial electrophysiological data to provide a proof-of-concept of the utility of this method for decoding individual natural images and faces in functional connectivity data. We further use a MCPA-based representational similarity analysis to illustrate how MCPA may be used to test computational models of information transfer among regions of the visual processing stream. Thus, MCPA can be used to assess the information represented in the coupled activity of interacting neural circuits and probe the underlying principles of information transformation between regions. Copyright © 2017 Elsevier Inc. All rights reserved.

The neural subjective frame: from bodily signals to perceptual consciousness.

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Park, Hyeong-Dong; Tallon-Baudry, Catherine

2014-05-05

The report 'I saw the stimulus' operationally defines visual consciousness, but where does the 'I' come from? To account for the subjective dimension of perceptual experience, we introduce the concept of the neural subjective frame. The neural subjective frame would be based on the constantly updated neural maps of the internal state of the body and constitute a neural referential from which first person experience can be created. We propose to root the neural subjective frame in the neural representation of visceral information which is transmitted through multiple anatomical pathways to a number of target sites, including posterior insula, ventral anterior cingulate cortex, amygdala and somatosensory cortex. We review existing experimental evidence showing that the processing of external stimuli can interact with visceral function. The neural subjective frame is a low-level building block of subjective experience which is not explicitly experienced by itself which is necessary but not sufficient for perceptual experience. It could also underlie other types of subjective experiences such as self-consciousness and emotional feelings. Because the neural subjective frame is tightly linked to homeostatic regulations involved in vigilance, it could also make a link between state and content consciousness.

Neural networks and applications tutorial

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Guyon, I.

1991-09-01

The importance of neural networks has grown dramatically during this decade. While only a few years ago they were primarily of academic interest, now dozens of companies and many universities are investigating the potential use of these systems and products are beginning to appear. The idea of building a machine whose architecture is inspired by that of the brain has roots which go far back in history. Nowadays, technological advances of computers and the availability of custom integrated circuits, permit simulations of hundreds or even thousands of neurons. In conjunction, the growing interest in learning machines, non-linear dynamics and parallel computation spurred renewed attention in artificial neural networks. Many tentative applications have been proposed, including decision systems (associative memories, classifiers, data compressors and optimizers), or parametric models for signal processing purposes (system identification, automatic control, noise canceling, etc.). While they do not always outperform standard methods, neural network approaches are already used in some real world applications for pattern recognition and signal processing tasks. The tutorial is divided into six lectures, that where presented at the Third Graduate Summer Course on Computational Physics (September 3-7, 1990) on Parallel Architectures and Applications, organized by the European Physical Society: (1) Introduction: machine learning and biological computation. (2) Adaptive artificial neurons (perceptron, ADALINE, sigmoid units, etc.): learning rules and implementations. (3) Neural network systems: architectures, learning algorithms. (4) Applications: pattern recognition, signal processing, etc. (5) Elements of learning theory: how to build networks which generalize. (6) A case study: a neural network for on-line recognition of handwritten alphanumeric characters.

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

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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

Investigating the Neural Correlates of Emotion–Cognition Interaction Using an Affective Stroop Task

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Nora M. Raschle

2017-09-01

Full Text Available The human brain has the capacity to integrate various sources of information and continuously adapts our behavior according to situational needs in order to allow a healthy functioning. Emotion–cognition interactions are a key example for such integrative processing. However, the neuronal correlates investigating the effects of emotion on cognition remain to be explored and replication studies are needed. Previous neuroimaging studies have indicated an involvement of emotion and cognition related brain structures including parietal and prefrontal cortices and limbic brain regions. Here, we employed whole brain event-related functional magnetic resonance imaging (fMRI during an affective number Stroop task and aimed at replicating previous findings using an adaptation of an existing task design in 30 healthy young adults. The Stroop task is an indicator of cognitive control and enables the quantification of interference in relation to variations in cognitive load. By the use of emotional primes (negative/neutral prior to Stroop task performance, an emotional variation is added as well. Behavioral in-scanner data showed that negative primes delayed and disrupted cognitive processing. Trials with high cognitive demand furthermore negatively influenced cognitive control mechanisms. Neuronally, the emotional primes consistently activated emotion-related brain regions (e.g., amygdala, insula, and prefrontal brain regions while Stroop task performance lead to activations in cognition networks of the brain (prefrontal cortices, superior temporal lobe, and insula. When assessing the effect of emotion on cognition, increased cognitive demand led to decreases in neural activation in response to emotional stimuli (negative > neutral within prefrontal cortex, amygdala, and insular cortex. Overall, these results suggest that emotional primes significantly impact cognitive performance and increasing cognitive demand leads to reduced neuronal activation in

Structural neural correlates of multitasking: A voxel-based morphometry study.

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Zhang, Rui-Ting; Yang, Tian-Xiao; Wang, Yi; Sui, Yuxiu; Yao, Jingjing; Zhang, Chen-Yuan; Cheung, Eric F C; Chan, Raymond C K

2016-12-01

Multitasking refers to the ability to organize assorted tasks efficiently in a short period of time, which plays an important role in daily life. However, the structural neural correlates of multitasking performance remain unclear. The present study aimed at exploring the brain regions associated with multitasking performance using global correlation analysis. Twenty-six healthy participants first underwent structural brain scans and then performed the modified Six Element Test, which required participants to attempt six subtasks in 10 min while obeying a specific rule. Voxel-based morphometry of the whole brain was used to detect the structural correlates of multitasking ability. Grey matter volume of the anterior cingulate cortex (ACC) was positively correlated with the overall performance and time monitoring in multitasking. In addition, white matter volume of the anterior thalamic radiation (ATR) was also positively correlated with time monitoring during multitasking. Other related brain regions associated with multitasking included the superior frontal gyrus, the inferior occipital gyrus, the lingual gyrus, and the inferior longitudinal fasciculus. No significant correlation was found between grey matter volume of the prefrontal cortex (Brodmann Area 10) and multitasking performance. Using a global correlation analysis to examine various aspects of multitasking performance, this study provided new insights into the structural neural correlates of multitasking ability. In particular, the ACC was identified as an important brain region that played both a general and a specific time-monitoring role in multitasking, extending the role of the ACC from lesioned populations to healthy populations. The present findings also support the view that the ATR may influence multitasking performance by affecting time-monitoring abilities. © 2016 The Institute of Psychology, Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

Optimization of blanking process using neural network simulation

International Nuclear Information System (INIS)

Hambli, R.

2005-01-01

The present work describes a methodology using the finite element method and neural network simulation in order to predict the optimum punch-die clearance during sheet metal blanking processes. A damage model is used in order to describe crack initiation and propagation into the sheet. The proposed approach combines predictive finite element and neural network modeling of the leading blanking parameters. Numerical results obtained by finite element computation including damage and fracture modeling were utilized to train the developed simulation environment based on back propagation neural network modeling. The comparative study between the numerical results and the experimental ones shows the good agreement. (author)