Functional neural networks underlying response inhibition in adolescents and adults

PubMed Central

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

2008-01-01

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 frontostriatal 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. PMID:17467816

Fear Processing in Dental Phobia during Crossmodal Symptom Provocation: An fMRI Study

PubMed Central

Maslowski, Nina Isabel; Wittchen, Hans-Ulrich; Lueken, Ulrike

2014-01-01

While previous studies successfully identified the core neural substrates of the animal subtype of specific phobia, only few and inconsistent research is available for dental phobia. These findings might partly relate to the fact that, typically, visual stimuli were employed. The current study aimed to investigate the influence of stimulus modality on neural fear processing in dental phobia. Thirteen dental phobics (DP) and thirteen healthy controls (HC) attended a block-design functional magnetic resonance imaging (fMRI) symptom provocation paradigm encompassing both visual and auditory stimuli. Drill sounds and matched neutral sinus tones served as auditory stimuli and dentist scenes and matched neutral videos as visual stimuli. Group comparisons showed increased activation in the insula, anterior cingulate cortex, orbitofrontal cortex, and thalamus in DP compared to HC during auditory but not visual stimulation. On the contrary, no differential autonomic reactions were observed in DP. Present results are largely comparable to brain areas identified in animal phobia, but also point towards a potential downregulation of autonomic outflow by neural fear circuits in this disorder. Findings enlarge our knowledge about neural correlates of dental phobia and may help to understand the neural underpinnings of the clinical and physiological characteristics of the disorder. PMID:24738049

Online adaptive neural control of a robotic lower limb prosthesis

NASA Astrophysics Data System (ADS)

Spanias, J. A.; Simon, A. M.; Finucane, S. B.; Perreault, E. J.; Hargrove, L. J.

2018-02-01

Objective. The purpose of this study was to develop and evaluate an adaptive intent recognition algorithm that continuously learns to incorporate a lower limb amputee’s neural information (acquired via electromyography (EMG)) as they ambulate with a robotic leg prosthesis. Approach. We present a powered lower limb prosthesis that was configured to acquire the user’s neural information and kinetic/kinematic information from embedded mechanical sensors, and identify and respond to the user’s intent. We conducted an experiment with eight transfemoral amputees over multiple days. EMG and mechanical sensor data were collected while subjects using a powered knee/ankle prosthesis completed various ambulation activities such as walking on level ground, stairs, and ramps. Our adaptive intent recognition algorithm automatically transitioned the prosthesis into the different locomotion modes and continuously updated the user’s model of neural data during ambulation. Main results. Our proposed algorithm accurately and consistently identified the user’s intent over multiple days, despite changing neural signals. The algorithm incorporated 96.31% [0.91%] (mean, [standard error]) of neural information across multiple experimental sessions, and outperformed non-adaptive versions of our algorithm—with a 6.66% [3.16%] relative decrease in error rate. Significance. This study demonstrates that our adaptive intent recognition algorithm enables incorporation of neural information over long periods of use, allowing assistive robotic devices to accurately respond to the user’s intent with low error rates.

Cracking the Neural Code for Sensory Perception by Combining Statistics, Intervention, and Behavior.

PubMed

Panzeri, Stefano; Harvey, Christopher D; Piasini, Eugenio; Latham, Peter E; Fellin, Tommaso

2017-02-08

The two basic processes underlying perceptual decisions-how neural responses encode stimuli, and how they inform behavioral choices-have mainly been studied separately. Thus, although many spatiotemporal features of neural population activity, or "neural codes," have been shown to carry sensory information, it is often unknown whether the brain uses these features for perception. To address this issue, we propose a new framework centered on redefining the neural code as the neural features that carry sensory information used by the animal to drive appropriate behavior; that is, the features that have an intersection between sensory and choice information. We show how this framework leads to a new statistical analysis of neural activity recorded during behavior that can identify such neural codes, and we discuss how to combine intersection-based analysis of neural recordings with intervention on neural activity to determine definitively whether specific neural activity features are involved in a task. Copyright © 2017 Elsevier Inc. All rights reserved.

Identification of Correlated GRACE Monthly Harmonic Coefficients Using Pattern Recognition and Neural Networks

NASA Astrophysics Data System (ADS)

Piretzidis, D.; Sra, G.; Sideris, M. G.

2016-12-01

This study explores new methods for identifying correlation errors in harmonic coefficients derived from monthly solutions of the Gravity Recovery and Climate Experiment (GRACE) satellite mission using pattern recognition and neural network algorithms. These correlation errors are evidenced in the differences between monthly solutions and can be suppressed using a de-correlation filter. In all studies so far, the implementation of the de-correlation filter starts from a specific minimum order (i.e., 11 for RL04 and 38 for RL05) until the maximum order of the monthly solution examined. This implementation method has two disadvantages, namely, the omission of filtering correlated coefficients of order less than the minimum order and the filtering of uncorrelated coefficients of order higher than the minimum order. In the first case, the filtered solution is not completely free of correlated errors, whereas the second case results in a monthly solution that suffers from loss of geophysical signal. In the present study, a new method of implementing the de-correlation filter is suggested, by identifying and filtering only the coefficients that show indications of high correlation. Several numerical and geometric properties of the harmonic coefficient series of all orders are examined. Extreme cases of both correlated and uncorrelated coefficients are selected, and their corresponding properties are used to train a two-layer feed-forward neural network. The objective of the neural network is to identify and quantify the correlation by providing the probability of an order of coefficients to be correlated. Results show good performance of the neural network, both in the validation stage of the training procedure and in the subsequent use of the trained network to classify independent coefficients. The neural network is also capable of identifying correlated coefficients even when a small number of training samples and neurons are used (e.g.,100 and 10, respectively).

The use of artificial neural networks to predict the muscle behavior

NASA Astrophysics Data System (ADS)

Kutilek, Patrik; Viteckova, Slavka; Svoboda, Zdenĕk; Smrcka, Pavel

2013-09-01

The aim of this article is to introduce methods of prediction of muscle behavior of the lower extremities based on artificial neural networks, which can be used for medical purposes. Our work focuses on predicting muscletendon forces and moments during human gait with the use of angle-time diagram. A group of healthy children and children with cerebral palsy were measured using a Vicon MoCap system. The kinematic data was recorded and the OpenSim software system was used to identify the joint angles, muscle-tendon forces and joint muscle moment, which are presented graphically with time diagrams. The musculus gastrocnemius medialis that is often studied in the context of cerebral palsy have been chosen to study the method of prediction. The diagrams of mean muscle-tendon force and mean moment are plotted and the data about the force-time and moment-time dependencies are used for training neural networks. The new way of prediction of muscle-tendon forces and moments based on neural networks was tested. Neural networks predicted the muscle forces and moments of healthy children and children with cerebral palsy. The designed method of prediction by neural networks could help to identify the difference between muscle behavior of healthy subjects and diseased subjects.

Systematic review of ERP and fMRI studies investigating inhibitory control and error processing in people with substance dependence and behavioural addictions

PubMed Central

Luijten, Maartje; Machielsen, Marise W.J.; Veltman, Dick J.; Hester, Robert; de Haan, Lieuwe; Franken, Ingmar H.A.

2014-01-01

Background Several current theories emphasize the role of cognitive control in addiction. The present review evaluates neural deficits in the domains of inhibitory control and error processing in individuals with substance dependence and in those showing excessive addiction-like behaviours. The combined evaluation of event-related potential (ERP) and functional magnetic resonance imaging (fMRI) findings in the present review offers unique information on neural deficits in addicted individuals. Methods We selected 19 ERP and 22 fMRI studies using stop-signal, go/no-go or Flanker paradigms based on a search of PubMed and Embase. Results The most consistent findings in addicted individuals relative to healthy controls were lower N2, error-related negativity and error positivity amplitudes as well as hypoactivation in the anterior cingulate cortex (ACC), inferior frontal gyrus and dorsolateral prefrontal cortex. These neural deficits, however, were not always associated with impaired task performance. With regard to behavioural addictions, some evidence has been found for similar neural deficits; however, studies are scarce and results are not yet conclusive. Differences among the major classes of substances of abuse were identified and involve stronger neural responses to errors in individuals with alcohol dependence versus weaker neural responses to errors in other substance-dependent populations. Limitations Task design and analysis techniques vary across studies, thereby reducing comparability among studies and the potential of clinical use of these measures. Conclusion Current addiction theories were supported by identifying consistent abnormalities in prefrontal brain function in individuals with addiction. An integrative model is proposed, suggesting that neural deficits in the dorsal ACC may constitute a hallmark neurocognitive deficit underlying addictive behaviours, such as loss of control. PMID:24359877

Use of artificial neural networks to identify the origin of green macroalgae

NASA Astrophysics Data System (ADS)

Żbikowski, Radosław

2011-08-01

This study demonstrates application of artificial neural networks (ANNs) for identifying the origin of green macroalgae ( Enteromorpha sp. and Cladophora sp.) according to their concentrations of Cd, Cu, Ni, Zn, Mn, Pb, Na, Ca, K and Mg. Earlier studies confirmed that algae can be used for biomonitoring surveys of metal contaminants in coastal areas of the Southern Baltic. The same data sets were classified with the use of different structures of radial basis function (RBF) and multilayer perceptron (MLP) networks. The selected networks were able to classify the samples according to their geographical origin, i.e. Southern Baltic, Gulf of Gdańsk and Vistula Lagoon. Additionally in the case of macroalgae from the Gulf of Gdańsk, the networks enabled the discrimination of samples according to areas of contrasting levels of pollution. Hence this study shows that artificial neural networks can be a valuable tool in biomonitoring studies.

Neural dynamics based on the recognition of neural fingerprints

PubMed Central

Carrillo-Medina, José Luis; Latorre, Roberto

2015-01-01

Experimental evidence has revealed the existence of characteristic spiking features in different neural signals, e.g., individual neural signatures identifying the emitter or functional signatures characterizing specific tasks. These neural fingerprints may play a critical role in neural information processing, since they allow receptors to discriminate or contextualize incoming stimuli. This could be a powerful strategy for neural systems that greatly enhances the encoding and processing capacity of these networks. Nevertheless, the study of information processing based on the identification of specific neural fingerprints has attracted little attention. In this work, we study (i) the emerging collective dynamics of a network of neurons that communicate with each other by exchange of neural fingerprints and (ii) the influence of the network topology on the self-organizing properties within the network. Complex collective dynamics emerge in the network in the presence of stimuli. Predefined inputs, i.e., specific neural fingerprints, are detected and encoded into coexisting patterns of activity that propagate throughout the network with different spatial organization. The patterns evoked by a stimulus can survive after the stimulation is over, which provides memory mechanisms to the network. The results presented in this paper suggest that neural information processing based on neural fingerprints can be a plausible, flexible, and powerful strategy. PMID:25852531

Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth

PubMed Central

Just, Marcel Adam; Pan, Lisa; Cherkassky, Vladimir L.; McMakin, Dana; Cha, Christine; Nock, Matthew K.; Brent, David

2017-01-01

The clinical assessment of suicidal risk would be significantly complemented by a biologically-based measure that assesses alterations in the neural representations of concepts related to death and life in people who engage in suicidal ideation. This study used machine-learning algorithms (Gaussian Naïve Bayes) to identify such individuals (17 suicidal ideators vs 17 controls) with high (91%) accuracy, based on their altered fMRI neural signatures of death and life-related concepts. The most discriminating concepts were death, cruelty, trouble, carefree, good, and praise. A similar classification accurately (94%) discriminated 9 suicidal ideators who had made a suicide attempt from 8 who had not. Moreover, a major facet of the concept alterations was the evoked emotion, whose neural signature served as an alternative basis for accurate (85%) group classification. The study establishes a biological, neurocognitive basis for altered concept representations in participants with suicidal ideation, which enables highly accurate group membership classification. PMID:29367952

'Reading the Mind in the Eyes': an fMRI study of adolescents with autism and their siblings.

PubMed

Holt, R J; Chura, L R; Lai, M-C; Suckling, J; von dem Hagen, E; Calder, A J; Bullmore, E T; Baron-Cohen, S; Spencer, M D

2014-11-01

Mentalizing deficits are a hallmark of the autism spectrum condition (ASC) and a potential endophenotype for atypical social cognition in ASC. Differences in performance and neural activation on the 'Reading the Mind in the Eyes' task (the Eyes task) have been identified in individuals with ASC in previous studies. Performance on the Eyes task along with the associated neural activation was examined in adolescents with ASC (n = 50), their unaffected siblings (n = 40) and typically developing controls (n = 40). Based on prior literature that males and females with ASC display different cognitive and associated neural characteristics, analyses were stratified by sex. Three strategies were applied to test for endophenotypes at the level of neural activation: (1) identifying and locating conjunctions of ASC-control and sibling-control differences; (2) examining whether the sibling group is comparable to the ASC or intermediate between the ASC and control groups; and (3) examining spatial overlaps between ASC-control and sibling-control differences across multiple thresholds. Impaired behavioural performance on the Eyes task was observed in males with ASC compared to controls, but only at trend level in females; and no difference in performance was identified between sibling and same-sex control groups in both sexes. Neural activation showed a substantial endophenotype effect in the female groups but this was only modest in the male groups. Behavioural impairment on complex emotion recognition associated with mental state attribution is a phenotypic, rather than an endophenotypic, marker of ASC. However, the neural response during the Eyes task is a potential endophenotypic marker for ASC, particularly in females.

Neural Correlates of Feigned Memory Impairment are Distinguishable from Answering Randomly and Answering Incorrectly: An fMRI and Behavioral Study

ERIC Educational Resources Information Center

Liang, Chun-Yu; Xu, Zhi-Yuan; Mei, Wei; Wang, Li-Li; Xue, Li; Lu, De Jian; Zhao, Hu

2012-01-01

Previous functional magnetic resonance imaging (fMRI) studies have identified activation in the prefrontal-parietal-sub-cortical circuit during feigned memory impairment when comparing with truthful telling. Here, we used fMRI to determine whether neural activity can differentiate between answering correctly, answering randomly, answering…

A chemical screen in zebrafish embryonic cells establishes that Akt activation is required for neural crest development

PubMed Central

Ciarlo, Christie; Kaufman, Charles K; Kinikoglu, Beste; Michael, Jonathan; Yang, Song; D′Amato, Christopher; Blokzijl-Franke, Sasja; den Hertog, Jeroen; Schlaeger, Thorsten M; Zhou, Yi; Liao, Eric

2017-01-01

The neural crest is a dynamic progenitor cell population that arises at the border of neural and non-neural ectoderm. The inductive roles of FGF, Wnt, and BMP at the neural plate border are well established, but the signals required for subsequent neural crest development remain poorly characterized. Here, we conducted a screen in primary zebrafish embryo cultures for chemicals that disrupt neural crest development, as read out by crestin:EGFP expression. We found that the natural product caffeic acid phenethyl ester (CAPE) disrupts neural crest gene expression, migration, and melanocytic differentiation by reducing Sox10 activity. CAPE inhibits FGF-stimulated PI3K/Akt signaling, and neural crest defects in CAPE-treated embryos are suppressed by constitutively active Akt1. Inhibition of Akt activity by constitutively active PTEN similarly decreases crestin expression and Sox10 activity. Our study has identified Akt as a novel intracellular pathway required for neural crest differentiation. PMID:28832322

Predicting CYP2C19 Catalytic Parameters for Enantioselective Oxidations Using Artificial Neural Networks and a Chirality Code

PubMed Central

Hartman, Jessica H.; Cothren, Steven D.; Park, Sun-Ha; Yun, Chul-Ho; Darsey, Jerry A.; Miller, Grover P.

2013-01-01

Cytochromes P450 (CYP for isoforms) play a central role in biological processes especially metabolism of chiral molecules; thus, development of computational methods to predict parameters for chiral reactions is important for advancing this field. In this study, we identified the most optimal artificial neural networks using conformation-independent chirality codes to predict CYP2C19 catalytic parameters for enantioselective reactions. Optimization of the neural networks required identifying the most suitable representation of structure among a diverse array of training substrates, normalizing distribution of the corresponding catalytic parameters (kcat, Km, and kcat/Km), and determining the best topology for networks to make predictions. Among different structural descriptors, the use of partial atomic charges according to the CHelpG scheme and inclusion of hydrogens yielded the most optimal artificial neural networks. Their training also required resolution of poorly distributed output catalytic parameters using a Box-Cox transformation. End point leave-one-out cross correlations of the best neural networks revealed that predictions for individual catalytic parameters (kcat and Km) were more consistent with experimental values than those for catalytic efficiency (kcat/Km). Lastly, neural networks predicted correctly enantioselectivity and comparable catalytic parameters measured in this study for previously uncharacterized CYP2C19 substrates, R- and S-propranolol. Taken together, these seminal computational studies for CYP2C19 are the first to predict all catalytic parameters for enantioselective reactions using artificial neural networks and thus provide a foundation for expanding the prediction of cytochrome P450 reactions to chiral drugs, pollutants, and other biologically active compounds. PMID:23673224

Religious beliefs influence neural substrates of self-reflection in Tibetans

PubMed Central

Wang, Cheng; He, Xi; Mao, Lihua

2010-01-01

Previous transcultural neuroimaging studies have shown that the neural substrates of self-reflection can be shaped by different cultures. There are few studies, however, on the neural activity of self-reflection where religion is viewed as a form of cultural expression. The present study examined the self-processing of two Chinese ethnic groups (Han and Tibetan) to investigate the significant role of religion on the functional anatomy of self-representation. We replicated the previous results in Han participants with the ventral medial prefrontal cortex and left anterior cingulate cortex showing stronger activation in self-processing when compared with other-processing conditions. However, no typical self-reference pattern was identified in Tibetan participants on behavioral or neural levels. This could be explained by the minimal subjective sense of 'I-ness’ in Tibetan Buddhists. Our findings lend support to the presumed role of culture and religion in shaping the neural substrate of self. PMID:20197287

Religious beliefs influence neural substrates of self-reflection in Tibetans.

PubMed

Wu, Yanhong; Wang, Cheng; He, Xi; Mao, Lihua; Zhang, Li

2010-06-01

Previous transcultural neuroimaging studies have shown that the neural substrates of self-reflection can be shaped by different cultures. There are few studies, however, on the neural activity of self-reflection where religion is viewed as a form of cultural expression. The present study examined the self-processing of two Chinese ethnic groups (Han and Tibetan) to investigate the significant role of religion on the functional anatomy of self-representation. We replicated the previous results in Han participants with the ventral medial prefrontal cortex and left anterior cingulate cortex showing stronger activation in self-processing when compared with other-processing conditions. However, no typical self-reference pattern was identified in Tibetan participants on behavioral or neural levels. This could be explained by the minimal subjective sense of 'I-ness' in Tibetan Buddhists. Our findings lend support to the presumed role of culture and religion in shaping the neural substrate of self.

Identifying Emotions on the Basis of Neural Activation

PubMed Central

Kassam, Karim S.; Markey, Amanda R.; Cherkassky, Vladimir L.; Loewenstein, George; Just, Marcel Adam

2013-01-01

We attempt to determine the discriminability and organization of neural activation corresponding to the experience of specific emotions. Method actors were asked to self-induce nine emotional states (anger, disgust, envy, fear, happiness, lust, pride, sadness, and shame) while in an fMRI scanner. Using a Gaussian Naïve Bayes pooled variance classifier, we demonstrate the ability to identify specific emotions experienced by an individual at well over chance accuracy on the basis of: 1) neural activation of the same individual in other trials, 2) neural activation of other individuals who experienced similar trials, and 3) neural activation of the same individual to a qualitatively different type of emotion induction. Factor analysis identified valence, arousal, sociality, and lust as dimensions underlying the activation patterns. These results suggest a structure for neural representations of emotion and inform theories of emotional processing. PMID:23840392

Identifying Emotions on the Basis of Neural Activation.

PubMed

Kassam, Karim S; Markey, Amanda R; Cherkassky, Vladimir L; Loewenstein, George; Just, Marcel Adam

2013-01-01

We attempt to determine the discriminability and organization of neural activation corresponding to the experience of specific emotions. Method actors were asked to self-induce nine emotional states (anger, disgust, envy, fear, happiness, lust, pride, sadness, and shame) while in an fMRI scanner. Using a Gaussian Naïve Bayes pooled variance classifier, we demonstrate the ability to identify specific emotions experienced by an individual at well over chance accuracy on the basis of: 1) neural activation of the same individual in other trials, 2) neural activation of other individuals who experienced similar trials, and 3) neural activation of the same individual to a qualitatively different type of emotion induction. Factor analysis identified valence, arousal, sociality, and lust as dimensions underlying the activation patterns. These results suggest a structure for neural representations of emotion and inform theories of emotional processing.

Neural analysis of bovine ovaries ultrasound images in the identification process of the corpus luteum

NASA Astrophysics Data System (ADS)

Górna, K.; Jaśkowski, B. M.; Okoń, P.; Czechlowski, M.; Koszela, K.; Zaborowicz, M.; Idziaszek, P.

2017-07-01

The aim of the paper is to shown the neural image analysis as a method useful for identifying the development stage of the domestic bovine corpus luteum on digital USG (UltraSonoGraphy) images. Corpus luteum (CL) is a transient endocrine gland that develops after ovulation from the follicle secretory cells. The aim of CL is the production of progesterone, which regulates many reproductive functions. In the presented studies, identification of the corpus luteum was carried out on the basis of information contained in ultrasound digital images. Development stage of the corpus luteum was considered in two aspects: just before and middle of domination phase and luteolysis and degradation phase. Prior to the classification, the ultrasound images have been processed using a GLCM (Gray Level Co-occurence Matrix). To generate a classification model, a Neural Networks module implemented in the STATISTICA was used. Five representative parameters describing the ultrasound image were used as learner variables. On the output of the artificial neural network was generated information about the development stage of the corpus luteum. Results of this study indicate that neural image analysis combined with GLCM texture analysis may be a useful tool for identifying the bovine corpus luteum in the context of its development phase. Best-generated artificial neural network model was the structure of MLP (Multi Layer Perceptron) 5:5-17-1:1.

Neural substrates underlying fear-evoked freezing: the periaqueductal grey–cerebellar link

PubMed Central

Koutsikou, Stella; Crook, Jonathan J; Earl, Emma V; Leith, J Lianne; Watson, Thomas C; Lumb, Bridget M; Apps, Richard

2014-01-01

The central neural pathways involved in fear-evoked behaviour are highly conserved across mammalian species, and there is a consensus that understanding them is a fundamental step towards developing effective treatments for emotional disorders in man. The ventrolateral periaqueductal grey (vlPAG) has a well-established role in fear-evoked freezing behaviour. The neural pathways underlying autonomic and sensory consequences of vlPAG activation in fearful situations are well understood, but much less is known about the pathways that link vlPAG activity to distinct fear-evoked motor patterns essential for survival. In adult rats, we have identified a pathway linking the vlPAG to cerebellar cortex, which terminates as climbing fibres in lateral vermal lobule VIII (pyramis). Lesion of pyramis input–output pathways disrupted innate and fear-conditioned freezing behaviour. The disruption in freezing behaviour was strongly correlated to the reduction in the vlPAG-induced facilitation of α-motoneurone excitability observed after lesions of the pyramis. The increased excitability of α-motoneurones during vlPAG activation may therefore drive the increase in muscle tone that underlies expression of freezing behaviour. By identifying the cerebellar pyramis as a critical component of the neural network subserving emotionally related freezing behaviour, the present study identifies novel neural pathways that link the PAG to fear-evoked motor responses. PMID:24639484

Neural control of salivary glands in ixodid ticks.

PubMed

Šimo, Ladislav; Zitňan, Dušan; Park, Yoonseong

2012-04-01

Studies of tick salivary glands (SGs) and their components have produced a number of interesting discoveries over the last four decades. However, the precise neural and physiological mechanisms controlling SG secretion remain enigmatic. Major studies of SG control have identified and characterized many pharmacological and biological compounds that activate salivary secretion, including dopamine (DA), octopamine, γ-aminobutyric acid (GABA), ergot alkaloids, pilocarpine (PC), and their pharmacological relatives. Specifically, DA has shown the most robust activities in various tick species, and its effect on downstream actions in the SGs has been extensively studied. Our recent work on a SG dopamine receptor has aided new interpretations of previous pharmacological studies and provided new concepts for SG control mechanisms. Furthermore, our recent studies have suggested that multiple neuropeptides are involved in SG control. Myoinhibitory peptide (MIP) and SIFamide have been identified in the neural projections reaching the basal cells of acini types II and III. Pigment-dispersing factor (PDF)-immunoreactive neural projections reach type II acini, and RFamide- and tachykinin-immunoreactive projections reach the SG ducts, but the chemical nature of the latter three immunoreactive substances are unidentified yet. Here, we briefly review previous pharmacological studies and provide a revised summary of SG control mechanisms in ticks. Copyright © 2011 Elsevier Ltd. All rights reserved.

Neural representations of the concepts in simple sentences: Concept activation prediction and context effects.

PubMed

Just, Marcel Adam; Wang, Jing; Cherkassky, Vladimir L

2017-08-15

Although it has been possible to identify individual concepts from a concept's brain activation pattern, there have been significant obstacles to identifying a proposition from its fMRI signature. Here we demonstrate the ability to decode individual prototype sentences from readers' brain activation patterns, by using theory-driven regions of interest and semantic properties. It is possible to predict the fMRI brain activation patterns evoked by propositions and words which are entirely new to the model with reliably above-chance rank accuracy. The two core components implemented in the model that reflect the theory were the choice of intermediate semantic features and the brain regions associated with the neurosemantic dimensions. This approach also predicts the neural representation of object nouns across participants, studies, and sentence contexts. Moreover, we find that the neural representation of an agent-verb-object proto-sentence is more accurately characterized by the neural signatures of its components as they occur in a similar context than by the neural signatures of these components as they occur in isolation. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Biologically Realistic Cortical Model of Eye Movement Control in Reading

ERIC Educational Resources Information Center

Heinzle, Jakob; Hepp, Klaus; Martin, Kevan A. C.

2010-01-01

Reading is a highly complex task involving a precise integration of vision, attention, saccadic eye movements, and high-level language processing. Although there is a long history of psychological research in reading, it is only recently that imaging studies have identified some neural correlates of reading. Thus, the underlying neural mechanisms…

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

ERIC Educational Resources Information Center

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

2013-01-01

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

Neural Correlates of the Encoding of Multimodal Contextual Features

ERIC Educational Resources Information Center

Gottlieb, Lauren J.; Wong, Jenny; de Chastelaine, Marianne; Rugg, Michael D.

2012-01-01

Functional magnetic resonance imaging (fMRI) was employed to identify neural regions engaged during the encoding of contextual features belonging to different modalities. Subjects studied objects that were presented to the left or right of fixation. Each object was paired with its name, spoken in either a male or a female voice. The test…

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

PubMed

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

2018-01-01

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

Therapeutic physical exercise in neural injury: friend or foe?

PubMed

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

2015-12-01

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

Predicting Engineering Student Attrition Risk Using a Probabilistic Neural Network and Comparing Results with a Backpropagation Neural Network and Logistic Regression

ERIC Educational Resources Information Center

Mason, Cindi; Twomey, Janet; Wright, David; Whitman, Lawrence

2018-01-01

As the need for engineers continues to increase, a growing focus has been placed on recruiting students into the field of engineering and retaining the students who select engineering as their field of study. As a result of this concentration on student retention, numerous studies have been conducted to identify, understand, and confirm…

Identification of neural transcription factors required for the differentiation of three neuronal subtypes in the sea urchin embryo.

PubMed

Slota, Leslie A; McClay, David R

2018-03-15

Correct patterning of the nervous system is essential for an organism's survival and complex behavior. Embryologists have used the sea urchin as a model for decades, but our understanding of sea urchin nervous system patterning is incomplete. Previous histochemical studies identified multiple neurotransmitters in the pluteus larvae of several sea urchin species. However, little is known about how, where and when neural subtypes are differentially specified during development. Here, we examine the molecular mechanisms of neuronal subtype specification in 3 distinct neural subtypes in the Lytechinus variegatus larva. We show that these subtypes are specified through Delta/Notch signaling and identify a different transcription factor required for the development of each neural subtype. Our results show achaete-scute and neurogenin are proneural for the serotonergic neurons of the apical organ and cholinergic neurons of the ciliary band, respectively. We also show that orthopedia is not proneural but is necessary for the differentiation of the cholinergic/catecholaminergic postoral neurons. Interestingly, these transcription factors are used similarly during vertebrate neurogenesis. We believe this study is a starting point for building a neural gene regulatory network in the sea urchin and for finding conserved deuterostome neurogenic mechanisms. Copyright © 2018 Elsevier Inc. All rights reserved.

Neural Activity Patterns in the Human Brain Reflect Tactile Stickiness Perception.

PubMed

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.

Neural Activity Patterns in the Human Brain Reflect Tactile Stickiness Perception

PubMed Central

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

Study on automatic ECT data evaluation by using neural network

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

Komatsu, H.; Matsumoto, Y.; Badics, Z.

1994-12-31

At the in--service inspection of the steam generator (SG) tubings in Pressurized Water Reactor (PWR) plant, eddy current testing (ECT) has been widely used at each outage. At present, ECT data evaluation is mainly performed by ECT data analyst, therefore it has the following problems. Only ECT signal configuration on the impedance trajectory is used in the evaluation. It is an enormous time consuming process. The evaluation result is influenced by the ability and experience of the analyst. Especially, it is difficult to identify the true defect signal hidden in background signals such as lift--off noise and deposit signals. Inmore » this work, the authors performed the study on the possibility of the application of neural network to ECT data evaluation. It was demonstrated that the neural network proved to be effective to identify the nature of defect, by selecting several optimum input parameters to categorize the raw ECT signals.« less

Coupled neural systems underlie the production and comprehension of naturalistic narrative speech

PubMed Central

Silbert, Lauren J.; Honey, Christopher J.; Simony, Erez; Poeppel, David; Hasson, Uri

2014-01-01

Neuroimaging studies of language have typically focused on either production or comprehension of single speech utterances such as syllables, words, or sentences. In this study we used a new approach to functional MRI acquisition and analysis to characterize the neural responses during production and comprehension of complex real-life speech. First, using a time-warp based intrasubject correlation method, we identified all areas that are reliably activated in the brains of speakers telling a 15-min-long narrative. Next, we identified areas that are reliably activated in the brains of listeners as they comprehended that same narrative. This allowed us to identify networks of brain regions specific to production and comprehension, as well as those that are shared between the two processes. The results indicate that production of a real-life narrative is not localized to the left hemisphere but recruits an extensive bilateral network, which overlaps extensively with the comprehension system. Moreover, by directly comparing the neural activity time courses during production and comprehension of the same narrative we were able to identify not only the spatial overlap of activity but also areas in which the neural activity is coupled across the speaker’s and listener’s brains during production and comprehension of the same narrative. We demonstrate widespread bilateral coupling between production- and comprehension-related processing within both linguistic and nonlinguistic areas, exposing the surprising extent of shared processes across the two systems. PMID:25267658

Neural Network Machine Learning and Dimension Reduction for Data Visualization

NASA Technical Reports Server (NTRS)

Liles, Charles A.

2014-01-01

Neural network machine learning in computer science is a continuously developing field of study. Although neural network models have been developed which can accurately predict a numeric value or nominal classification, a general purpose method for constructing neural network architecture has yet to be developed. Computer scientists are often forced to rely on a trial-and-error process of developing and improving accurate neural network models. In many cases, models are constructed from a large number of input parameters. Understanding which input parameters have the greatest impact on the prediction of the model is often difficult to surmise, especially when the number of input variables is very high. This challenge is often labeled the "curse of dimensionality" in scientific fields. However, techniques exist for reducing the dimensionality of problems to just two dimensions. Once a problem's dimensions have been mapped to two dimensions, it can be easily plotted and understood by humans. The ability to visualize a multi-dimensional dataset can provide a means of identifying which input variables have the highest effect on determining a nominal or numeric output. Identifying these variables can provide a better means of training neural network models; models can be more easily and quickly trained using only input variables which appear to affect the outcome variable. The purpose of this project is to explore varying means of training neural networks and to utilize dimensional reduction for visualizing and understanding complex datasets.

SiNC: Saliency-injected neural codes for representation and efficient retrieval of medical radiographs

PubMed Central

Sajjad, Muhammad; Mehmood, Irfan; Baik, Sung Wook

2017-01-01

Medical image collections contain a wealth of information which can assist radiologists and medical experts in diagnosis and disease detection for making well-informed decisions. However, this objective can only be realized if efficient access is provided to semantically relevant cases from the ever-growing medical image repositories. In this paper, we present an efficient method for representing medical images by incorporating visual saliency and deep features obtained from a fine-tuned convolutional neural network (CNN) pre-trained on natural images. Saliency detector is employed to automatically identify regions of interest like tumors, fractures, and calcified spots in images prior to feature extraction. Neuronal activation features termed as neural codes from different CNN layers are comprehensively studied to identify most appropriate features for representing radiographs. This study revealed that neural codes from the last fully connected layer of the fine-tuned CNN are found to be the most suitable for representing medical images. The neural codes extracted from the entire image and salient part of the image are fused to obtain the saliency-injected neural codes (SiNC) descriptor which is used for indexing and retrieval. Finally, locality sensitive hashing techniques are applied on the SiNC descriptor to acquire short binary codes for allowing efficient retrieval in large scale image collections. Comprehensive experimental evaluations on the radiology images dataset reveal that the proposed framework achieves high retrieval accuracy and efficiency for scalable image retrieval applications and compares favorably with existing approaches. PMID:28771497

Epidemiologic and Genetic Aspects of Spina Bifida and Other Neural Tube Defects

ERIC Educational Resources Information Center

Au, Kit Sing; Ashley-Koch, Allison; Northrup, Hope

2010-01-01

The worldwide incidence of neural tube defects (NTDs) ranges from 1.0 to 10.0 per 1,000 births with almost equal frequencies between two major categories: anencephaly and spina bifida (SB). Epidemiological studies have provided valuable insight for (a) researchers to identify nongenetic and genetic factors contributing to etiology, (b) public…

Multipotent Caudal Neural Progenitors Derived from Human Pluripotent Stem Cells That Give Rise to Lineages of the Central and Peripheral Nervous System

PubMed Central

Hasegawa, Kouichi; Menheniott, Trevelyan; Rollo, Ben; Zhang, Dongcheng; Hough, Shelley; Alshawaf, Abdullah; Febbraro, Fabia; Ighaniyan, Samiramis; Leung, Jessie; Elliott, David A.; Newgreen, Donald F.; Pera, Martin F.

2015-01-01

Abstract The caudal neural plate is a distinct region of the embryo that gives rise to major progenitor lineages of the developing central and peripheral nervous system, including neural crest and floor plate cells. We show that dual inhibition of the glycogen synthase kinase 3β and activin/nodal pathways by small molecules differentiate human pluripotent stem cells (hPSCs) directly into a preneuroepithelial progenitor population we named “caudal neural progenitors” (CNPs). CNPs coexpress caudal neural plate and mesoderm markers, and, share high similarities to embryonic caudal neural plate cells in their lineage differentiation potential. Exposure of CNPs to BMP2/4, sonic hedgehog, or FGF2 signaling efficiently directs their fate to neural crest/roof plate cells, floor plate cells, and caudally specified neuroepithelial cells, respectively. Neural crest derived from CNPs differentiated to neural crest derivatives and demonstrated extensive migratory properties in vivo. Importantly, we also determined the key extrinsic factors specifying CNPs from human embryonic stem cell include FGF8, canonical WNT, and IGF1. Our studies are the first to identify a multipotent neural progenitor derived from hPSCs, that is the precursor for major neural lineages of the embryonic caudal neural tube. Stem Cells 2015;33:1759–1770 PMID:25753817

Neural priming in human frontal cortex: multiple forms of learning reduce demands on the prefrontal executive system.

PubMed

Race, Elizabeth A; Shanker, Shanti; Wagner, Anthony D

2009-09-01

Past experience is hypothesized to reduce computational demands in PFC by providing bottom-up predictive information that informs subsequent stimulus-action mapping. The present fMRI study measured cortical activity reductions ("neural priming"/"repetition suppression") during repeated stimulus classification to investigate the mechanisms through which learning from the past decreases demands on the prefrontal executive system. Manipulation of learning at three levels of representation-stimulus, decision, and response-revealed dissociable neural priming effects in distinct frontotemporal regions, supporting a multiprocess model of neural priming. Critically, three distinct patterns of neural priming were identified in lateral frontal cortex, indicating that frontal computational demands are reduced by three forms of learning: (a) cortical tuning of stimulus-specific representations, (b) retrieval of learned stimulus-decision mappings, and (c) retrieval of learned stimulus-response mappings. The topographic distribution of these neural priming effects suggests a rostrocaudal organization of executive function in lateral frontal cortex.

Neural crest specification and migration independently require NSD3-related lysine methyltransferase activity

PubMed Central

Jacques-Fricke, Bridget T.; Gammill, Laura S.

2014-01-01

Neural crest precursors express genes that cause them to become migratory, multipotent cells, distinguishing them from adjacent stationary neural progenitors in the neurepithelium. Histone methylation spatiotemporally regulates neural crest gene expression; however, the protein methyltransferases active in neural crest precursors are unknown. Moreover, the regulation of methylation during the dynamic process of neural crest migration is unclear. Here we show that the lysine methyltransferase NSD3 is abundantly and specifically expressed in premigratory and migratory neural crest cells. NSD3 expression commences before up-regulation of neural crest genes, and NSD3 is necessary for expression of the neural plate border gene Msx1, as well as the key neural crest transcription factors Sox10, Snail2, Sox9, and FoxD3, but not gene expression generally. Nevertheless, only Sox10 histone H3 lysine 36 dimethylation requires NSD3, revealing unexpected complexity in NSD3-dependent neural crest gene regulation. In addition, by temporally limiting expression of a dominant negative to migratory stages, we identify a novel, direct requirement for NSD3-related methyltransferase activity in neural crest migration. These results identify NSD3 as the first protein methyltransferase essential for neural crest gene expression during specification and show that NSD3-related methyltransferase activity independently regulates migration. PMID:25318671

Closing in on the constitution of consciousness

PubMed Central

Miller, Steven M.

2014-01-01

The science of consciousness is a nascent and thriving field of research that is founded on identifying the minimally sufficient neural correlates of consciousness. However, I have argued that it is the neural constitution of consciousness that science seeks to understand and that there are no evident strategies for distinguishing the correlates and constitution of (phenomenal) consciousness. Here I review this correlation/constitution distinction problem and challenge the existing foundations of consciousness science. I present the main analyses from a longer paper in press on this issue, focusing on recording, inhibition, stimulation, and combined inhibition/stimulation strategies, including proposal of the Jenga analogy to illustrate why identifying the minimally sufficient neural correlates of consciousness should not be considered the ultimate target of consciousness science. Thereafter I suggest that while combined inhibition and stimulation strategies might identify some constitutive neural activities—indeed minimally sufficient constitutive neural activities—such strategies fail to identify the whole neural constitution of consciousness and thus the correlation/constitution distinction problem is not fully solved. Various clarifications, potential objections and related scientific and philosophical issues are also discussed and I conclude by proposing new foundational claims for consciousness science. PMID:25452738

A Systematic Review for Functional Neuroimaging Studies of Cognitive Reserve Across the Cognitive Aging Spectrum.

PubMed

Anthony, Mia; Lin, Feng

2017-12-13

Cognitive reserve has been proposed to explain the discrepancy between clinical symptoms and the effects of aging or Alzheimer's pathology. Functional magnetic resonance imaging (fMRI) may help elucidate how neural reserve and compensation delay cognitive decline and identify brain regions associated with cognitive reserve. This systematic review evaluated neural correlates of cognitive reserve via fMRI (resting-state and task-related) studies across the cognitive aging spectrum (i.e., normal cognition, mild cognitive impairment, and Alzheimer's disease). This review examined published articles up to March 2017. There were 13 cross-sectional observational studies that met the inclusion criteria, including relevance to cognitive reserve, subjects 60 years or older with normal cognition, mild cognitive impairment, and/or Alzheimer's disease, at least one quantitative measure of cognitive reserve, and fMRI as the imaging modality. Quality assessment of included studies was conducted using the Newcastle-Ottawa Scale adapted for cross-sectional studies. Across the cognitive aging spectrum, medial temporal regions and an anterior or posterior cingulate cortex-seeded default mode network were associated with neural reserve. Frontal regions and the dorsal attentional network were related to neural compensation. Compared to neural reserve, neural compensation was more common in mild cognitive impairment and Alzheimer's disease. Neural reserve and compensation both support cognitive reserve, with compensation more common in later stages of the cognitive aging spectrum. Longitudinal and intervention studies are needed to investigate changes between neural reserve and compensation during the transition between clinical stages, and to explore the causal relationship between cognitive reserve and potential neural substrates. © The Author(s) 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Neural Tube Defects and Maternal Folate Intake Among Pregnancies Conceived After Folic Acid Fortification in the United States

PubMed Central

Mosley, Bridget S.; Cleves, Mario A.; Siega-Riz, Anna Maria; Shaw, Gary M.; Canfield, Mark A.; Waller, D. Kim; Werler, Martha M.

2009-01-01

Rates of neural tube defects have decreased since folic acid fortification of the food supply in the United States. The authors’ objective was to evaluate the associations between neural tube defects and maternal folic acid intake among pregnancies conceived after fortification. This is a multicenter, case-control study that uses data from the National Birth Defects Prevention Study, 1998–2003. Logistic regression was used to compute crude and adjusted odds ratios between cases and controls assessing maternal periconceptional use of folic acid and intake of dietary folic acid. Among 180 anencephalic cases, 385 spina bifida cases, and 3, 963 controls, 21.1%, 25.2%, and 26.1%, respectively, reported periconceptional use of folic acid supplements. Periconceptional supplement use did not reduce the risk of having a pregnancy affected by a neural tube defect. Maternal intake of dietary folate was not significantly associated with neural tube defects. In this study conducted among pregnancies conceived after mandatory folic acid fortification, the authors found little evidence of an association between neural tube defects and maternal folic acid intake. A possible explanation is that folic acid fortification reduced the occurrence of folic acid-sensitive neural tube defects. Further investigation is warranted to possibly identify women who remain at increased risk of preventable neural tube defects. PMID:18953063

Application of an artificial neural network model for diagnosing type 2 diabetes mellitus and determining the relative importance of risk factors.

PubMed

Borzouei, Shiva; Soltanian, Ali Reza

2018-01-01

To identify the most important demographic risk factors for a diagnosis of type 2 diabetes mellitus (T2DM) using a neural network model. This study was conducted on a sample of 234 individuals, in whom T2DM was diagnosed using hemoglobin A1c levels. A multilayer perceptron artificial neural network was used to identify demographic risk factors for T2DM and their importance. The DeLong method was used to compare the models by fitting in sequential steps. Variables found to be significant at a level of p<0.2 in a univariate logistic regression analysis (age, hypertension, waist circumference, body mass index [BMI], sedentary lifestyle, smoking, vegetable consumption, family history of T2DM, stress, walking, fruit consumption, and sex) were entered into the model. After 7 stages of neural network modeling, only waist circumference (100.0%), age (78.5%), BMI (78.2%), hypertension (69.4%), stress (54.2%), smoking (49.3%), and a family history of T2DM (37.2%) were identified as predictors of the diagnosis of T2DM. In this study, waist circumference and age were the most important predictors of T2DM. Due to the sensitivity, specificity, and accuracy of the final model, it is suggested that these variables should be used for T2DM risk assessment in screening tests.

Classification of independent components of EEG into multiple artifact classes.

PubMed

Frølich, Laura; Andersen, Tobias S; Mørup, Morten

2015-01-01

In this study, we aim to automatically identify multiple artifact types in EEG. We used multinomial regression to classify independent components of EEG data, selecting from 65 spatial, spectral, and temporal features of independent components using forward selection. The classifier identified neural and five nonneural types of components. Between subjects within studies, high classification performances were obtained. Between studies, however, classification was more difficult. For neural versus nonneural classifications, performance was on par with previous results obtained by others. We found that automatic separation of multiple artifact classes is possible with a small feature set. Our method can reduce manual workload and allow for the selective removal of artifact classes. Identifying artifacts during EEG recording may be used to instruct subjects to refrain from activity causing them. Copyright © 2014 Society for Psychophysiological Research.

Altered Synchronizations among Neural Networks in Geriatric Depression

PubMed Central

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. PMID:26180795

Altered Synchronizations among Neural Networks in Geriatric Depression.

PubMed

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.

An Introduction to Neural Networks for Hearing Aid Noise Recognition.

ERIC Educational Resources Information Center

Kim, Jun W.; Tyler, Richard S.

1995-01-01

This article introduces the use of multilayered artificial neural networks in hearing aid noise recognition. It reviews basic principles of neural networks, and offers an example of an application in which a neural network is used to identify the presence or absence of noise in speech. The ability of neural networks to "learn" the…

Pharmacogenomic identification of small molecules for lineage specific manipulation of subventricular zone germinal activity.

PubMed

Azim, Kasum; Angonin, Diane; Marcy, Guillaume; Pieropan, Francesca; Rivera, Andrea; Donega, Vanessa; Cantù, Claudio; Williams, Gareth; Berninger, Benedikt; Butt, Arthur M; Raineteau, Olivier

2017-03-01

Strategies for promoting neural regeneration are hindered by the difficulty of manipulating desired neural fates in the brain without complex genetic methods. The subventricular zone (SVZ) is the largest germinal zone of the forebrain and is responsible for the lifelong generation of interneuron subtypes and oligodendrocytes. Here, we have performed a bioinformatics analysis of the transcriptome of dorsal and lateral SVZ in early postnatal mice, including neural stem cells (NSCs) and their immediate progenies, which generate distinct neural lineages. We identified multiple signaling pathways that trigger distinct downstream transcriptional networks to regulate the diversity of neural cells originating from the SVZ. Next, we used a novel in silico genomic analysis, searchable platform-independent expression database/connectivity map (SPIED/CMAP), to generate a catalogue of small molecules that can be used to manipulate SVZ microdomain-specific lineages. Finally, we demonstrate that compounds identified in this analysis promote the generation of specific cell lineages from NSCs in vivo, during postnatal life and adulthood, as well as in regenerative contexts. This study unravels new strategies for using small bioactive molecules to direct germinal activity in the SVZ, which has therapeutic potential in neurodegenerative diseases.

Protein S Negatively Regulates Neural Stem Cell Self-Renewal through Bmi-1 Signaling

PubMed Central

Zelentsova-Levytskyi, Katya; Talmi, Ziv; Abboud-Jarrous, Ghada; Capucha, Tal; Sapir, Tamar; Burstyn-Cohen, Tal

2017-01-01

Revealing the molecular mechanisms underlying neural stem cell self-renewal is a major goal toward understanding adult brain homeostasis. The self-renewing potential of neural stem and progenitor cells (NSPCs) must be tightly regulated to maintain brain homeostasis. We recently reported the expression of Protein S (PROS1) in adult hippocampal NSPCs, and revealed its role in regulation of NSPC quiescence and neuronal differentiation. Here, we investigate the effect of PROS1 on NSPC self-renewal and show that genetic ablation of Pros1 in neural progenitors increased NSPC self-renewal by 50%. Mechanistically, we identified the upregulation of the polycomb complex protein Bmi-1 and repression of its downstream effectors p16Ink4a and p19Arf to promote NSPC self-renewal in Pros1-ablated cells. Rescuing Pros1 expression restores normal levels of Bmi-1 signaling, and reverts the proliferation and enhanced self-renewal phenotypes observed in Pros1-deleted cells. Our study identifies PROS1 as a novel negative regulator of NSPC self-renewal. We conclude PROS1 is instructive for NSPC differentiation by negatively regulating Bmi-1 signaling in adult and embryonic neural stem cells. PMID:28512399

Puzzle Pieces: Neural Structure and Function in Prader-Willi Syndrome

PubMed Central

Manning, Katherine E.; Holland, Anthony J.

2015-01-01

Prader-Willi syndrome (PWS) is a neurodevelopmental disorder of genomic imprinting, presenting with a behavioural phenotype encompassing hyperphagia, intellectual disability, social and behavioural difficulties, and propensity to psychiatric illness. Research has tended to focus on the cognitive and behavioural investigation of these features, and, with the exception of eating behaviour, the neural physiology is currently less well understood. A systematic review was undertaken to explore findings relating to neural structure and function in PWS, using search terms designed to encompass all published articles concerning both in vivo and post-mortem studies of neural structure and function in PWS. This supported the general paucity of research in this area, with many articles reporting case studies and qualitative descriptions or focusing solely on the overeating behaviour, although a number of systematic investigations were also identified. Research to date implicates a combination of subcortical and higher order structures in PWS, including those involved in processing reward, motivation, affect and higher order cognitive functions, with both anatomical and functional investigations indicating abnormalities. It appears likely that PWS involves aberrant activity across distributed neural networks. The characterisation of neural structure and function warrants both replication and further systematic study. PMID:28943631

Puzzle Pieces: Neural Structure and Function in Prader-Willi Syndrome.

PubMed

Manning, Katherine E; Holland, Anthony J

2015-12-17

Prader-Willi syndrome (PWS) is a neurodevelopmental disorder of genomic imprinting, presenting with a behavioural phenotype encompassing hyperphagia, intellectual disability, social and behavioural difficulties, and propensity to psychiatric illness. Research has tended to focus on the cognitive and behavioural investigation of these features, and, with the exception of eating behaviour, the neural physiology is currently less well understood. A systematic review was undertaken to explore findings relating to neural structure and function in PWS, using search terms designed to encompass all published articles concerning both in vivo and post-mortem studies of neural structure and function in PWS. This supported the general paucity of research in this area, with many articles reporting case studies and qualitative descriptions or focusing solely on the overeating behaviour, although a number of systematic investigations were also identified. Research to date implicates a combination of subcortical and higher order structures in PWS, including those involved in processing reward, motivation, affect and higher order cognitive functions, with both anatomical and functional investigations indicating abnormalities. It appears likely that PWS involves aberrant activity across distributed neural networks. The characterisation of neural structure and function warrants both replication and further systematic study.

Fault detection and diagnosis using neural network approaches

NASA Technical Reports Server (NTRS)

Kramer, Mark A.

1992-01-01

Neural networks can be used to detect and identify abnormalities in real-time process data. Two basic approaches can be used, the first based on training networks using data representing both normal and abnormal modes of process behavior, and the second based on statistical characterization of the normal mode only. Given data representative of process faults, radial basis function networks can effectively identify failures. This approach is often limited by the lack of fault data, but can be facilitated by process simulation. The second approach employs elliptical and radial basis function neural networks and other models to learn the statistical distributions of process observables under normal conditions. Analytical models of failure modes can then be applied in combination with the neural network models to identify faults. Special methods can be applied to compensate for sensor failures, to produce real-time estimation of missing or failed sensors based on the correlations codified in the neural network.

Neurobiological Risk Factors for Suicide Insights from Brain Imaging

PubMed Central

Cox Lippard, Elizabeth T.; Johnston, Jennifer A.Y.; Blumberg, Hilary P.

2014-01-01

Context This article reviews neuroimaging studies on neural circuitry associated with suicide-related thoughts and behaviors to identify areas of convergence in findings. Gaps in the literature for which additional research is needed are identified. Evidence acquisition A PubMed search was conducted and articles published prior to March 2014 were reviewed that compared individuals who made suicide attempts to those with similar diagnoses who had not made attempts or to healthy comparison subjects. Articles on adults with suicidal ideation and adolescents who had made attempts, or with suicidal ideation, were also included. Reviewed imaging modalities included structural magnetic resonance imaging, diffusion tensor imaging, single photon emission computerized tomography, positron emission tomography, and functional magnetic resonance imaging. Evidence synthesis Although many studies include small samples, and subject characteristics and imaging methods vary across studies, there were convergent findings involving the structure and function of frontal neural systems and the serotonergic system. Conclusions These initial neuroimaging studies of suicide behavior have provided promising results. Future neuroimaging efforts could be strengthened by more strategic use of common data elements, and a focus on suicide risk trajectories. At-risk subgroups defined by biopsychosocial risk factors and multidimensional assessment of suicidal thoughts and behaviors may provide a clearer picture of the neural circuitry associated with risk status—both current and lifetime. Also needed are studies investigating neural changes associated with interventions that are effective in risk reduction. PMID:25145733

Investigation of Dynamic Algorithms for Pattern Recognition Identified in Cerebral Cortex

DTIC Science & Technology

1991-12-02

oscillatory and possibly chaotic activity forin the actual cortical substrate of the diverse sensory, motor, and cognitive operations now studied in...September Neural Information Processing Systems - Natural and Synthetic, Denver, Colo., November 1989 U.C. San Diego, Cognitive Science Dept...Baird. Biologically applied neural networks may foster the co-evolution of neurobiology and cognitive psychology. Brain and Behavioral Sciences, 37

Differential neural contributions to native- and foreign-language talker identification

PubMed Central

Perrachione, Tyler K.; Pierrehumbert, Janet B.; Wong, Patrick C.M.

2009-01-01

Humans are remarkably adept at identifying individuals by the sound of their voice, a behavior supported by the nervous system’s ability to integrate information from voice and speech perception. Talker-identification abilities are significantly impaired when listeners are unfamiliar with the language being spoken. Recent behavioral studies describing the language-familiarity effect implicate functionally integrated neural systems for speech and voice perception, yet specific neuroscientific evidence demonstrating the basis for such integration has not yet been shown. Listeners in the present study learned to identify voices speaking a familiar (native) or unfamiliar (foreign) language. The talker-identification performance of neural circuitry in each cerebral hemisphere was assessed using dichotic listening. To determine the relative contribution of circuitry in each hemisphere to ecological (binaural) talker identification abilities, we compared the predictive capacity of dichotic performance on binaural performance across languages. We found listeners’ right-ear (left hemisphere) performance to be a better predictor of overall accuracy in their native language than a foreign one. The enhanced predictive capacity of the classically language-dominant left-hemisphere on overall talker-identification accuracy demonstrates functionally integrated neural systems for speech and voice perception during natural talker identification. PMID:19968445

The Expression and Function of the Achaete-Scute Genes in Tribolium castaneum Reveals Conservation and Variation in Neural Pattern Formation and Cell Fate Specification

NASA Technical Reports Server (NTRS)

Wheeler, Scott R.; Carrico, Michelle L.; Wilson, Beth A.; Brown, Susan J.; Skeath, James B.

2003-01-01

SUMMARY The study of achaete-scute (ac/sc) genes has recently become a paradigm to understand the evolution and development of the arthropod nervous system. We describe the identification and characterization of the ache genes in the coleopteran insect species Tribolium castaneum. We have identified two Tribolium ache genes - achaete-scute homolog (Tc-ASH) a proneural gene and asense (Tc-ase) a neural precursor gene that reside in a gene complex. Focusing on the embryonic central nervous system we fmd that Tc-ASH is expressed in all neural precursors and the proneural clusters from which they segregate. Through RNAi and misexpression studies we show that Tc-ASH is necessary for neural precursor formation in Triboliurn and sufficient for neural precursor formation in Drosophila. Comparison of the function of the Drosophila and Triboliurn proneural ac/sc genes suggests that in the Drosophila lineage these genes have maintained their ancestral function in neural precursor formation and have acquired a new role in the fate specification of individual neural precursors. Furthermore, we find that Tc-use is expressed in all neural precursors suggesting an important and conserved role for asense genes in insect nervous system development. Our analysis of the Triboliurn ache genes indicates significant plasticity in gene number, expression and function, and implicates these modifications in the evolution of arthropod neural development.

The expression and function of the achaete-scute genes in Tribolium castaneum reveals conservation and variation in neural pattern formation and cell fate specification

NASA Technical Reports Server (NTRS)

Wheeler, Scott R.; Carrico, Michelle L.; Wilson, Beth A.; Brown, Susan J.; Skeath, James B.

2003-01-01

The study of achaete-scute (ac/sc) genes has recently become a paradigm to understand the evolution and development of the arthropod nervous system. We describe the identification and characterization of the ac/sc genes in the coleopteran insect species Tribolium castaneum. We have identified two Tribolium ac/sc genes - achaete-scute homolog (Tc-ASH) a proneural gene and asense (Tc-ase) a neural precursor gene that reside in a gene complex. Focusing on the embryonic central nervous system we find that Tc-ASH is expressed in all neural precursors and the proneural clusters from which they segregate. Through RNAi and misexpression studies we show that Tc-ASH is necessary for neural precursor formation in Tribolium and sufficient for neural precursor formation in Drosophila. Comparison of the function of the Drosophila and Tribolium proneural ac/sc genes suggests that in the Drosophila lineage these genes have maintained their ancestral function in neural precursor formation and have acquired a new role in the fate specification of individual neural precursors. Furthermore, we find that Tc-ase is expressed in all neural precursors suggesting an important and conserved role for asense genes in insect nervous system development. Our analysis of the Tribolium ac/sc genes indicates significant plasticity in gene number, expression and function, and implicates these modifications in the evolution of arthropod neural development.

Neural Bases Of Food Perception: Coordinate-Based Meta-Analyses Of Neuroimaging Studies In Multiple Modalities

PubMed Central

Huerta, Claudia I; Sarkar, Pooja R; Duong, Timothy Q.; Laird, Angela R; Fox, Peter T

2013-01-01

Objective The purpose of this study was to compare the results of the three food-cue paradigms most commonly used for functional neuroimaging studies to determine: i) commonalities and differences in the neural response patterns by paradigm; and, ii) the relative robustness and reliability of responses to each paradigm. Design and Methods functional magnetic resonance imaging (fMRI) studies using standardized stereotactic coordinates to report brain responses to food cues were identified using on-line databases. Studies were grouped by food-cue modality as: i) tastes (8 studies); ii) odors (8 studies); and, iii) images (11 studies). Activation likelihood estimation (ALE) was used to identify statistically reliable regional responses within each stimulation paradigm. Results Brain response distributions were distinctly different for the three stimulation modalities, corresponding to known differences in location of the respective primary and associative cortices. Visual stimulation induced the most robust and extensive responses. The left anterior insula was the only brain region reliably responding to all three stimulus categories. Conclusions These findings suggest visual food-cue paradigm as promising candidate for imaging studies addressing the neural substrate of therapeutic interventions. PMID:24174404

Longitudinal relationships among activity in attention redirection neural circuitry and symptom severity in youth.

PubMed

Bertocci, Michele A; Bebko, Genna; Dwojak, Amanda; Iyengar, Satish; Ladouceur, Cecile D; Fournier, Jay C; Versace, Amelia; Perlman, Susan B; Almeida, Jorge R C; Travis, Michael J; Gill, Mary Kay; Bonar, Lisa; Schirda, Claudiu; Diwadkar, Vaibhav A; Sunshine, Jeffrey L; Holland, Scott K; Kowatch, Robert A; Birmaher, Boris; Axelson, David; Horwitz, Sarah M; Frazier, Thomas; Arnold, L Eugene; Fristad, Mary A; Youngstrom, Eric A; Findling, Robert L; Phillips, Mary L

2017-05-01

Changes in neural circuitry function may be associated with longitudinal changes in psychiatric symptom severity. Identification of these relationships may aid in elucidating the neural basis of psychiatric symptom evolution over time. We aimed to distinguish these relationships using data from the Longitudinal Assessment of Manic Symptoms (LAMS) cohort. Forty-one youth completed two study visits (mean=21.3 months). Elastic-net regression (Multiple response Gaussian family) identified emotional regulation neural circuitry that changed in association with changes in depression, mania, anxiety, affect lability, and positive mood and energy dysregulation, accounting for clinical and demographic variables. Non-zero coefficients between change in the above symptom measures and change in activity over the inter-scan interval were identified in right amygdala and left ventrolateral prefrontal cortex. Differing patterns of neural activity change were associated with changes in each of the above symptoms over time. Specifically, from Scan1 to Scan2, worsening affective lability and depression severity were associated with increased right amygdala and left ventrolateral prefrontal cortical activity. Worsening anxiety and positive mood and energy dysregulation were associated with decreased right amygdala and increased left ventrolateral prefrontal cortical activity. Worsening mania was associated with increased right amygdala and decreased left ventrolateral prefrontal cortical activity. These changes in neural activity between scans accounted for 13.6% of the variance; that is 25% of the total explained variance (39.6%) in these measures. Distinct neural mechanisms underlie changes in different mood and anxiety symptoms overtime.

An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines.

PubMed

Amozegar, M; Khorasani, K

2016-04-01

In this paper, a new approach for Fault Detection and Isolation (FDI) of gas turbine engines is proposed by developing an ensemble of dynamic neural network identifiers. For health monitoring of the gas turbine engine, its dynamics is first identified by constructing three separate or individual dynamic neural network architectures. Specifically, a dynamic multi-layer perceptron (MLP), a dynamic radial-basis function (RBF) neural network, and a dynamic support vector machine (SVM) are trained to individually identify and represent the gas turbine engine dynamics. Next, three ensemble-based techniques are developed to represent the gas turbine engine dynamics, namely, two heterogeneous ensemble models and one homogeneous ensemble model. It is first shown that all ensemble approaches do significantly improve the overall performance and accuracy of the developed system identification scheme when compared to each of the stand-alone solutions. The best selected stand-alone model (i.e., the dynamic RBF network) and the best selected ensemble architecture (i.e., the heterogeneous ensemble) in terms of their performances in achieving an accurate system identification are then selected for solving the FDI task. The required residual signals are generated by using both a single model-based solution and an ensemble-based solution under various gas turbine engine health conditions. Our extensive simulation studies demonstrate that the fault detection and isolation task achieved by using the residuals that are obtained from the dynamic ensemble scheme results in a significantly more accurate and reliable performance as illustrated through detailed quantitative confusion matrix analysis and comparative studies. Copyright © 2016 Elsevier Ltd. All rights reserved.

Phenotypic chemical screening using a zebrafish neural crest EMT reporter identifies retinoic acid as an inhibitor of epithelial morphogenesis

PubMed Central

Jimenez, Laura; Wang, Jindong; Morrison, Monique A.; Whatcott, Clifford; Soh, Katherine K.; Warner, Steven; Bearss, David; Jette, Cicely A.; Stewart, Rodney A.

2016-01-01

ABSTRACT The epithelial-to-mesenchymal transition (EMT) is a highly conserved morphogenetic program essential for embryogenesis, regeneration and cancer metastasis. In cancer cells, EMT also triggers cellular reprogramming and chemoresistance, which underlie disease relapse and decreased survival. Hence, identifying compounds that block EMT is essential to prevent or eradicate disseminated tumor cells. Here, we establish a whole-animal-based EMT reporter in zebrafish for rapid drug screening, called Tg(snai1b:GFP), which labels epithelial cells undergoing EMT to produce sox10-positive neural crest (NC) cells. Time-lapse and lineage analysis of Tg(snai1b:GFP) embryos reveal that cranial NC cells delaminate from two regions: an early population delaminates adjacent to the neural plate, whereas a later population delaminates from within the dorsal neural tube. Treating Tg(snai1b:GFP) embryos with candidate small-molecule EMT-inhibiting compounds identified TP-0903, a multi-kinase inhibitor that blocked cranial NC cell delamination in both the lateral and medial populations. RNA sequencing (RNA-Seq) analysis and chemical rescue experiments show that TP-0903 acts through stimulating retinoic acid (RA) biosynthesis and RA-dependent transcription. These studies identify TP-0903 as a new therapeutic for activating RA in vivo and raise the possibility that RA-dependent inhibition of EMT contributes to its prior success in eliminating disseminated cancer cells. PMID:26794130

The method of educational assessment affects children's neural processing and performance: behavioural and fMRI Evidence

NASA Astrophysics Data System (ADS)

Howard, Steven J.; Burianová, Hana; Calleia, Alysha; Fynes-Clinton, Samuel; Kervin, Lisa; Bokosmaty, Sahar

2017-08-01

Standardised educational assessments are now widespread, yet their development has given comparatively more consideration to what to assess than how to optimally assess students' competencies. Existing evidence from behavioural studies with children and neuroscience studies with adults suggest that the method of assessment may affect neural processing and performance, but current evidence remains limited. To investigate the impact of assessment methods on neural processing and performance in young children, we used functional magnetic resonance imaging to identify and quantify the neural correlates during performance across a range of current approaches to standardised spelling assessment. Results indicated that children's test performance declined as the cognitive load of assessment method increased. Activation of neural nodes associated with working memory further suggests that this performance decline may be a consequence of a higher cognitive load, rather than the complexity of the content. These findings provide insights into principles of assessment (re)design, to ensure assessment results are an accurate reflection of students' true levels of competency.

Compounds with species and cell type specific toxicity identified in a 2000 compound drug screen of neural stem cells and rat mixed cortical neurons.

PubMed

Malik, Nasir; Efthymiou, Anastasia G; Mather, Karly; Chester, Nathaniel; Wang, Xiantao; Nath, Avindra; Rao, Mahendra S; Steiner, Joseph P

2014-12-01

Human primary neural tissue is a vital component for the quick and simple determination of chemical compound neurotoxicity in vitro. In particular, such tissue would be ideal for high-throughput screens that can be used to identify novel neurotoxic or neurotherapeutic compounds. We have previously established a high-throughput screening platform using human induced pluripotent stem cell (iPSC)-derived neural stem cells (NSCs) and neurons. In this study, we conducted a 2000 compound screen with human NSCs and rat cortical cells to identify compounds that are selectively toxic to each group. Approximately 100 of the tested compounds showed specific toxicity to human NSCs. A secondary screen of a small subset of compounds from the primary screen on human iPSCs, NSC-derived neurons, and fetal astrocytes validated the results from >80% of these compounds with some showing cell specific toxicity. Amongst those compounds were several cardiac glycosides, all of which were selectively toxic to the human cells. As the screen was able to reliably identify neurotoxicants, many with species and cell-type specificity, this study demonstrates the feasibility of this NSC-driven platform for higher-throughput neurotoxicity screens. Published by Elsevier B.V.

Brain structure and function correlates of cognitive subtypes in schizophrenia.

PubMed

Geisler, Daniel; Walton, Esther; Naylor, Melissa; Roessner, Veit; Lim, Kelvin O; Charles Schulz, S; Gollub, Randy L; Calhoun, Vince D; Sponheim, Scott R; Ehrlich, Stefan

2015-10-30

Stable neuropsychological deficits may provide a reliable basis for identifying etiological subtypes of schizophrenia. The aim of this study was to identify clusters of individuals with schizophrenia based on dimensions of neuropsychological performance, and to characterize their neural correlates. We acquired neuropsychological data as well as structural and functional magnetic resonance imaging from 129 patients with schizophrenia and 165 healthy controls. We derived eight cognitive dimensions and subsequently applied a cluster analysis to identify possible schizophrenia subtypes. Analyses suggested the following four cognitive clusters of schizophrenia: (1) Diminished Verbal Fluency, (2) Diminished Verbal Memory and Poor Motor Control, (3) Diminished Face Memory and Slowed Processing, and (4) Diminished Intellectual Function. The clusters were characterized by a specific pattern of structural brain changes in areas such as Wernicke's area, lingual gyrus and occipital face area, and hippocampus as well as differences in working memory-elicited neural activity in several fronto-parietal brain regions. Separable measures of cognitive function appear to provide a method for deriving cognitive subtypes meaningfully related to brain structure and function. Because the present study identified brain-based neural correlates of the cognitive clusters, the proposed groups of individuals with schizophrenia have some external validity. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

The Relationship of Aluminium and Silver to Neural Tube Defects; a Case Control

PubMed Central

Ramírez-Altamirano, María de Jesús; Fenton-Navarro, Patricia; Sivet-Chiñas, Elvira; Harp-Iturribarria, Flor de María; Martínez-Cruz, Ruth; Cruz, Pedro Hernández; Cruz, Margarito Martínez; Pérez-Campos, Eduardo

2012-01-01

Objective The purpose of this study was to identify the relationship of neurotoxic inorganic elements in the hair of patients with the diagnosis of Neural Tube Defects. Our initial hypothesis was that neurotoxic inorganic elements were associated with Neural Tube Defects. Methods Twenty-three samples of hair from newborns were obtained from the General Hospital, “Aurelio Valdivieso” in the city of Oaxaca, Mexico. The study group included 8 newborn infants with neural tube pathology. The control group was composed of 15 newborns without this pathology. The presence of inorganic elements in the hair samples was determined by inductively-coupled plasma spectroscopy (spectroscopic emission of the plasma). Findings The population of newborns with Neural Tube Defects showed significantly higher values of the following elements than the control group: Aluminium, Neural Tube Defects 152.77±51.06 µg/g, control group 76.24±27.89 µg/g; Silver, Neural Tube Defects 1.45±0.76, control group 0.25±0.53 µg/g; Potassium, Neural Tube Defects 553.87±77.91 µg/g, control group 341.13±205.90 µg/g. Association was found at 75 percentile between aluminium plus silver, aluminium plus potassium, silver plus potassium, and potassium plus sodium. Conclusion In the hair of newborns with Neural Tube Defects, the following metals were increased: aluminium, silver. Given the neurotoxicity of the same, and association of Neural Tube Defects with aluminum and silver, one may infer that they may be participating as factors in the development of Neural Tube Defects. PMID:23400307

The mouse that roared: neural mechanisms of social hierarchy.

PubMed

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.

Effects of Tasks on BOLD Signal Responses to Sentence Contrasts: Review and Commentary

PubMed Central

Caplan, David; Gow, David

2010-01-01

Functional neuroimaging studies of syntactic processing have been interpreted as identifying the neural locations of parsing and interpretive operations. However, current behavioral studies of sentence processing indicate that many operations occur simultaneously with parsing and interpretation. In this review, we point to issues that arise in discriminating the effects of these concurrent processes from those of the parser/interpreter in neural measures and to approaches that may help resolve them. PMID:20932562

Improved system identification using artificial neural networks and analysis of individual differences in responses of an identified neuron.

PubMed

Costalago Meruelo, Alicia; Simpson, David M; Veres, Sandor M; Newland, Philip L

2016-03-01

Mathematical modelling is used routinely to understand the coding properties and dynamics of responses of neurons and neural networks. Here we analyse the effectiveness of Artificial Neural Networks (ANNs) as a modelling tool for motor neuron responses. We used ANNs to model the synaptic responses of an identified motor neuron, the fast extensor motor neuron, of the desert locust in response to displacement of a sensory organ, the femoral chordotonal organ, which monitors movements of the tibia relative to the femur of the leg. The aim of the study was threefold: first to determine the potential value of ANNs as tools to model and investigate neural networks, second to understand the generalisation properties of ANNs across individuals and to different input signals and third, to understand individual differences in responses of an identified neuron. A metaheuristic algorithm was developed to design the ANN architectures. The performance of the models generated by the ANNs was compared with those generated through previous mathematical models of the same neuron. The results suggest that ANNs are significantly better than LNL and Wiener models in predicting specific neural responses to Gaussian White Noise, but not significantly different when tested with sinusoidal inputs. They are also able to predict responses of the same neuron in different individuals irrespective of which animal was used to develop the model, although notable differences between some individuals were evident. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

How Useful Is Electroencephalography in the Diagnosis of Autism Spectrum Disorders and the Delineation of Subtypes: A Systematic Review

PubMed Central

Gurau, Oana; Bosl, William J.; Newton, Charles R.

2017-01-01

Autism spectrum disorders (ASD) are thought to be associated with abnormal neural connectivity. Presently, neural connectivity is a theoretical construct that cannot be easily measured. Research in network science and time series analysis suggests that neural network structure, a marker of neural activity, can be measured with electroencephalography (EEG). EEG can be quantified by different methods of analysis to potentially detect brain abnormalities. The aim of this review is to examine evidence for the utility of three methods of EEG signal analysis in the ASD diagnosis and subtype delineation. We conducted a review of literature in which 40 studies were identified and classified according to the principal method of EEG analysis in three categories: functional connectivity analysis, spectral power analysis, and information dynamics. All studies identified significant differences between ASD patients and non-ASD subjects. However, due to high heterogeneity in the results, generalizations could not be inferred and none of the methods alone are currently useful as a new diagnostic tool. The lack of studies prevented the analysis of these methods as tools for ASD subtypes delineation. These results confirm EEG abnormalities in ASD, but as yet not sufficient to help in the diagnosis. Future research with larger samples and more robust study designs could allow for higher sensitivity and consistency in characterizing ASD, paving the way for developing new means of diagnosis. PMID:28747892

Learning to associate auditory and visual stimuli: behavioral and neural mechanisms.

PubMed

Altieri, Nicholas; Stevenson, Ryan A; Wallace, Mark T; Wenger, Michael J

2015-05-01

The ability to effectively combine sensory inputs across modalities is vital for acquiring a unified percept of events. For example, watching a hammer hit a nail while simultaneously identifying the sound as originating from the event requires the ability to identify spatio-temporal congruencies and statistical regularities. In this study, we applied a reaction time and hazard function measure known as capacity (e.g., Townsend and AshbyCognitive Theory 200-239, 1978) to quantify the extent to which observers learn paired associations between simple auditory and visual patterns in a model theoretic manner. As expected, results showed that learning was associated with an increase in accuracy, but more significantly, an increase in capacity. The aim of this study was to associate capacity measures of multisensory learning, with neural based measures, namely mean global field power (GFP). We observed a co-variation between an increase in capacity, and a decrease in GFP amplitude as learning occurred. This suggests that capacity constitutes a reliable behavioral index of efficient energy expenditure in the neural domain.

The Potential Neural Mechanisms of Acute Indirect Vibration

PubMed Central

2011-01-01

There is strong evidence to suggest that acute indirect vibration acts on muscle to enhance force, power, flexibility, balance and proprioception suggesting neural enhancement. Nevertheless, the neural mechanism(s) of vibration and its potentiating effect have received little attention. One proposal suggests that spinal reflexes enhance muscle contraction through a reflex activity known as tonic vibration stretch reflex (TVR), which increases muscle activation. However, TVR is based on direct, brief, and high frequency vibration (>100 Hz) which differs to indirect vibration, which is applied to the whole body or body parts at lower vibration frequency (5-45 Hz). Likewise, muscle tuning and neuromuscular aspects are other candidate mechanisms used to explain the vibration phenomenon. But there is much debate in terms of identifying which neural mechanism(s) are responsible for acute vibration; due to a number of studies using various vibration testing protocols. These protocols include: different methods of application, vibration variables, training duration, exercise types and a range of population groups. Therefore, the neural mechanism of acute vibration remain equivocal, but spinal reflexes, muscle tuning and neuromuscular aspects are all viable factors that may contribute in different ways to increasing muscular performance. Additional research is encouraged to determine which neural mechanism(s) and their contributions are responsible for acute vibration. Testing variables and vibration applications need to be standardised before reaching a consensus on which neural mechanism(s) occur during and post-vibration. Key points There is strong evidence to suggest that acute indirect vibration acts on muscle to enhance force, power, flexibility, balance and proprioception, but little attention has been given to the neural mechanism(s) of acute indirect vibration. Current findings suggest that acute vibration exposure may cause a neural response, but there is little consensus on identifying which neural mechanism(s) are specifically responsible. This is due to a number of studies using various vibration testing protocols (i.e.varying frequencies, amplitudes, durations, and methods of application). Spinal reflexes, muscle tuning and neuromuscular aspects and central motor command are all viable neuromechanical factors that may contribute at different stages to transiently increasing muscular performance. Additional research is encouraged to determine when (pre, during and post) the different neural mechanism(s) respond to direct and indirect vibration stimuli. PMID:24149291

Monoclonal Antibodies against the Drosophila Nervous System

NASA Astrophysics Data System (ADS)

Fujita, Shinobu C.; Zipursky, Stephen L.; Benzer, Seymour; Ferrus, Alberto; Shotwell, Sandra L.

1982-12-01

A panel of 148 monoclonal antibodies directed against Drosophila neural antigens has been prepared by using mice immunized with homogenates of Drosophila tissue. Antibodies were screened immunohistochemically on cryostat sections of fly heads. A large diversity of staining patterns was observed. Some antigens were broadly distributed among tissues; others were highly specific to nerve fibers, neuropil, muscle, the tracheal system, cell nuclei, photoreceptors, or other structures. The antigens for many of the antibodies have been identified on immunoblots. Monoclonal antibodies that identify specific molecules within the nervous system should prove useful in the study of the molecular genetics of neural development.

Transcriptomic profile analysis of mouse neural tube development by RNA-Seq.

PubMed

Yu, Juan; Mu, Jianbing; Guo, Qian; Yang, Lihong; Zhang, Juan; Liu, Zhizhen; Yu, Baofeng; Zhang, Ting; Xie, Jun

2017-09-01

The neural tube is the primordium of the central nervous system (CNS) in which its development is not entirely clear. Understanding the cellular and molecular basis of neural tube development could, therefore, provide vital clues to the mechanism of neural tube defects (NTDs). Here, we investigated the gene expression profiles of three different time points (embryonic day (E) 8.5, 9.5 and 10.5) of mouse neural tube by using RNA-seq approach. About 391 differentially expressed genes (DEGs) were screened during mouse neural tube development, including 45 DEGs involved in CNS development, among which Bmp2, Ascl1, Olig2, Lhx1, Wnt7b and Eomes might play the important roles. Of 45 DEGs, Foxp2, Eomes, Hoxb3, Gpr56, Hap1, Nkx2-1, Sez6l2, Wnt7b, Tbx20, Nfib, Cntn1 and Dcx had different isoforms, and the opposite expression pattern of different isoforms was observed for Gpr56, Nkx2-1 and Sez6l2. In addition, alternative splicing, such as mutually exclusive exon, retained intron, skipped exon and alternative 3' splice site was identified in 10 neural related differentially splicing genes, including Ngrn, Ddr1, Dctn1, Dnmt3b, Ect2, Map2, Mbnl1, Meis2, Vcan and App. Moreover, seven neural splicing factors, such as Nova1/2, nSR100/Srrm4, Elavl3/4, Celf3 and Rbfox1 were differentially expressed during mouse neural tube development. Interestingly, nine DEGs identified above were dysregulated in retinoic acid-induced NTDs model, indicating the possible important role of these genes in NTDs. Taken together, our study provides more comprehensive information on mouse neural tube development, which might provide new insights on NTDs occurrence. © 2017 IUBMB Life, 69(9):706-719, 2017. © 2017 International Union of Biochemistry and Molecular Biology.

Study on feed forward neural network convex optimization for LiFePO4 battery parameters

NASA Astrophysics Data System (ADS)

Liu, Xuepeng; Zhao, Dongmei

2017-08-01

Based on the modern facility agriculture automatic walking equipment LiFePO4 Battery, the parameter identification of LiFePO4 Battery is analyzed. An improved method for the process model of li battery is proposed, and the on-line estimation algorithm is presented. The parameters of the battery are identified using feed forward network neural convex optimization algorithm.

The Neural Basis of the Right Visual Field Advantage in Reading: An MEG Analysis Using Virtual Electrodes

ERIC Educational Resources Information Center

Barca, Laura; Cornelissen, Piers; Simpson, Michael; Urooj, Uzma; Woods, Will; Ellis, Andrew W.

2011-01-01

Right-handed participants respond more quickly and more accurately to written words presented in the right visual field (RVF) than in the left visual field (LVF). Previous attempts to identify the neural basis of the RVF advantage have had limited success. Experiment 1 was a behavioral study of lateralized word naming which established that the…

Identifying Predictors, Moderators, and Mediators of Antidepressant Response in Major Depressive Disorder: Neuroimaging Approaches

PubMed Central

Phillips, Mary L.; Chase, Henry W.; Sheline, Yvette I.; Etkin, Amit; Almeida, Jorge R.C.; Deckersbach, Thilo; Trivedi, Madhukar H.

2015-01-01

Objective Despite significant advances in neuroscience and treatment development, no widely accepted biomarkers are available to inform diagnostics or identify preferred treatments for individuals with major depressive disorder. Method In this critical review, the authors examine the extent to which multimodal neuroimaging techniques can identify biomarkers reflecting key pathophysiologic processes in depression and whether such biomarkers may act as predictors, moderators, and mediators of treatment response that might facilitate development of personalized treatments based on a better understanding of these processes. Results The authors first highlight the most consistent findings from neuroimaging studies using different techniques in depression, including structural and functional abnormalities in two parallel neural circuits: serotonergically modulated implicit emotion regulation circuitry, centered on the amygdala and different regions in the medial prefrontal cortex; and dopaminergically modulated reward neural circuitry, centered on the ventral striatum and medial prefrontal cortex. They then describe key findings from the relatively small number of studies indicating that specific measures of regional function and, to a lesser extent, structure in these neural circuits predict treatment response in depression. Conclusions Limitations of existing studies include small sample sizes, use of only one neuroimaging modality, and a focus on identifying predictors rather than moderators and mediators of differential treatment response. By addressing these limitations and, most importantly, capitalizing on the benefits of multimodal neuroimaging, future studies can yield moderators and mediators of treatment response in depression to facilitate significant improvements in shorter- and longer-term clinical and functional outcomes. PMID:25640931

Use of probabilistic neural networks for emitter correlation

NASA Astrophysics Data System (ADS)

Maloney, P. S.

1990-08-01

The Probabilistic Neural Network (PNN) as described by Specht''3 has been successfully applied to a number of emitter correlation problems involving operational data for training and testing of the neural net work. The PNN has been found to be a reliable classification tool for determining emitter type or even identifying specific emitter platforms given appropriate representative data sets for training con sisting only of parametric data from electronic intelligence (ELINT) reports. Four separate feasibility studies have been conducted to prove the usefulness of PNN in this application area: . Hull-to-emitter correlation (HULTEC) for identification of seagoing emitter platforms . Identification of landbased emitters from airborne sensors . Pulse sorting according to emitter of origin . Emitter typing based on a dynamically learning neural network. 1 .

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

PubMed

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 hypothesized to be modulated by cognitive control signaling, such as visual cortex. Taken together, these results illustrate the potential utility of multi-modal analyses in identifying the neural correlates of cognitive control across different indicators of brain structure and function. Copyright © 2017 Elsevier Inc. All rights reserved.

Dynamic facial expressions evoke distinct activation in the face perception network: a connectivity analysis study.

PubMed

Foley, Elaine; Rippon, Gina; Thai, Ngoc Jade; Longe, Olivia; Senior, Carl

2012-02-01

Very little is known about the neural structures involved in the perception of realistic dynamic facial expressions. In the present study, a unique set of naturalistic dynamic facial emotional expressions was created. Through fMRI and connectivity analysis, a dynamic face perception network was identified, which is demonstrated to extend Haxby et al.'s [Haxby, J. V., Hoffman, E. A., & Gobbini, M. I. The distributed human neural system for face perception. Trends in Cognitive Science, 4, 223-233, 2000] distributed neural system for face perception. This network includes early visual regions, such as the inferior occipital gyrus, which is identified as insensitive to motion or affect but sensitive to the visual stimulus, the STS, identified as specifically sensitive to motion, and the amygdala, recruited to process affect. Measures of effective connectivity between these regions revealed that dynamic facial stimuli were associated with specific increases in connectivity between early visual regions, such as the inferior occipital gyrus and the STS, along with coupling between the STS and the amygdala, as well as the inferior frontal gyrus. These findings support the presence of a distributed network of cortical regions that mediate the perception of different dynamic facial expressions.

Mood and neural correlates of excessive daytime sleepiness in Parkinson's disease.

PubMed

Wen, M-C; Chan, L L; Tan, L C S; Tan, E K

2017-08-01

For patients with Parkinson's disease (PD), excessive daytime sleepiness (PD-EDS) is a debilitating non-motor symptom and may be affected by mood symptoms, especially depression and anxiety. Few neuroimaging works have attempted to identify the neural features of PD-EDS, but various findings were reported. The purpose of this study was to systematically review the literature on mood and neuroimaging correlates of PD-EDS. A MEDLINE, PubMed, EMBASE, and PsycInfo search for peer-reviewed original research articles on depression, anxiety, and neuroimaging in PD-EDS identified 26 studies on depression, nine on anxiety, and eight on neuroimaging. Half of the studies reported greater depression in PD-EDS-positive patients compared with PD-EDS-negative patients. There was a significantly positive correlation between depression and PD-EDS. Limited studies on anxiety in PD-EDS suggested a weak correlation between anxiety and EDS. For depression and anxiety, the effect sizes were medium when EDS was subjectively measured, but became small when EDS was objective measured. Current neuroimaging studies generally suggested diminished neural structural and functional features (eg, brain volume, white matter integrity as indicated by fractional anisotropy, and cerebral metabolism) in patients with PD-EDS. Future studies should apply objective and subjective measures of mood symptoms and EDS and improve the neuroimaging methodology via using multimodal techniques and whole-brain analysis to provide new clues on the mood and neural correlates of PD-EDS. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Do Political and Economic Choices Rely on Common Neural Substrates? A Systematic Review of the Emerging Neuropolitics Literature.

PubMed

Krastev, Sekoul; McGuire, Joseph T; McNeney, Denver; Kable, Joseph W; Stolle, Dietlind; Gidengil, Elisabeth; Fellows, Lesley K

2016-01-01

The methods of cognitive neuroscience are beginning to be applied to the study of political behavior. The neural substrates of value-based decision-making have been extensively examined in economic contexts; this might provide a powerful starting point for understanding political decision-making. Here, we asked to what extent the neuropolitics literature to date has used conceptual frameworks and experimental designs that make contact with the reward-related approaches that have dominated decision neuroscience. We then asked whether the studies of political behavior that can be considered in this light implicate the brain regions that have been associated with subjective value related to "economic" reward. We performed a systematic literature review to identify papers addressing the neural substrates of political behavior and extracted the fMRI studies reporting behavioral measures of subjective value as defined in decision neuroscience studies of reward. A minority of neuropolitics studies met these criteria and relatively few brain activation foci from these studies overlapped with regions where activity has been related to subjective value. These findings show modest influence of reward-focused decision neuroscience on neuropolitics research to date. Whether the neural substrates of subjective value identified in economic choice paradigms generalize to political choice thus remains an open question. We argue that systematically addressing the commonalities and differences in these two classes of value-based choice will be important in developing a more comprehensive model of the brain basis of human decision-making.

Do Political and Economic Choices Rely on Common Neural Substrates? A Systematic Review of the Emerging Neuropolitics Literature

PubMed Central

Krastev, Sekoul; McGuire, Joseph T.; McNeney, Denver; Kable, Joseph W.; Stolle, Dietlind; Gidengil, Elisabeth; Fellows, Lesley K.

2016-01-01

The methods of cognitive neuroscience are beginning to be applied to the study of political behavior. The neural substrates of value-based decision-making have been extensively examined in economic contexts; this might provide a powerful starting point for understanding political decision-making. Here, we asked to what extent the neuropolitics literature to date has used conceptual frameworks and experimental designs that make contact with the reward-related approaches that have dominated decision neuroscience. We then asked whether the studies of political behavior that can be considered in this light implicate the brain regions that have been associated with subjective value related to “economic” reward. We performed a systematic literature review to identify papers addressing the neural substrates of political behavior and extracted the fMRI studies reporting behavioral measures of subjective value as defined in decision neuroscience studies of reward. A minority of neuropolitics studies met these criteria and relatively few brain activation foci from these studies overlapped with regions where activity has been related to subjective value. These findings show modest influence of reward-focused decision neuroscience on neuropolitics research to date. Whether the neural substrates of subjective value identified in economic choice paradigms generalize to political choice thus remains an open question. We argue that systematically addressing the commonalities and differences in these two classes of value-based choice will be important in developing a more comprehensive model of the brain basis of human decision-making. PMID:26941703

A critical appraisal of neuroimaging studies of bipolar disorder: toward a new conceptualization of underlying neural circuitry and roadmap for future research

PubMed Central

Phillips, Mary L; Swartz, Holly A.

2014-01-01

Objective This critical review appraises neuroimaging findings in bipolar disorder in emotion processing, emotion regulation, and reward processing neural circuitry, to synthesize current knowledge of the neural underpinnings of bipolar disorder, and provide a neuroimaging research “roadmap” for future studies. Method We examined findings from all major studies in bipolar disorder that used fMRI, volumetric analyses, diffusion imaging, and resting state techniques, to inform current conceptual models of larger-scale neural circuitry abnormalities in bipolar disorder Results Bipolar disorder can be conceptualized in neural circuitry terms as parallel dysfunction in bilateral prefrontal cortical (especially ventrolateral prefrontal cortical)-hippocampal-amygdala emotion processing and emotion regulation neural circuitries, together with an “overactive” left-sided ventral striatal-ventrolateral and orbitofrontal cortical reward processing circuitry, that result in characteristic behavioral abnormalities associated with bipolar disorder: emotional lability, emotional dysregulation and heightened reward sensitivity. A potential structural basis for these functional abnormalities are gray matter decreases in prefrontal and temporal cortices, amygdala and hippocampus, and fractional anisotropy decreases in white matter tracts connecting prefrontal and subcortical regions. Conclusion Neuroimaging studies of bipolar disorder clearly demonstrate abnormalities in neural circuitries supporting emotion processing, emotion regulation and reward processing, although there are several limitations to these studies. Future neuroimaging research in bipolar disorder should include studies adopting dimensional approaches; larger studies examining neurodevelopmental trajectories in bipolar disorder and at-risk youth; multimodal neuroimaging studies using integrated systems approaches; and studies using pattern recognition approaches to provide clinically useful, individual-level data. Such studies will help identify clinically-relevant biomarkers to guide diagnosis and treatment decision-making for individuals with bipolar disorder. PMID:24626773

Raman spectroscopy for discrimination of neural progenitor cells and their lineages (Conference Presentation)

NASA Astrophysics Data System (ADS)

Chen, Keren; Ong, William; Chew, Sing Yian; Liu, Quan

2017-02-01

Neurological diseases are one of the leading causes of adult disability and they are estimated to cause more deaths than cancer in the elderly population by 2040. Stem cell therapy has shown great potential in treating neurological diseases. However, before cell therapy can be widely adopted in the long term, a number of challenges need to be addressed, including the fundamental research about cellular development of neural progenitor cells. To facilitate the fundamental research of neural progenitor cells, many methods have been developed to identify neural progenitor cells. Although great progress has been made, there is still lack of an effective method to achieve fast, label-free and noninvasive differentiation of neural progenitor cells and their lineages. As a fast, label-free and noninvasive technique, spontaneous Raman spectroscopy has been conducted to characterize many types of stem cells including neural stem cells. However, to our best knowledge, it has not been studied for the discrimination of neural progenitor cells from specific lineages. Here we report the differentiation of neural progenitor cell from their lineages including astrocytes, oligodendrocytes and neurons using spontaneous Raman spectroscopy. Moreover, we also evaluate the influence of system parameters during spectral acquisition on the quality of measured Raman spectra and the accuracy of classification using the spectra, which yield a set of optimal system parameters facilitating future studies.

Magnetoencephalographic Signals Identify Stages in Real-Life Decision Processes

PubMed Central

Braeutigam, Sven; Stins, John F.; Rose, Steven P. R.; Swithenby, Stephen J.; Ambler, Tim

2001-01-01

We used magnetoencephalography (MEG) to study the dynamics of neural responses in eight subjects engaged in shopping for day-to-day items from supermarket shelves. This behavior not only has personal and economic importance but also provides an example of an experience that is both personal and shared between individuals. The shopping experience enables the exploration of neural mechanisms underlying choice based on complex memories. Choosing among different brands of closely related products activated a robust sequence of signals within the first second after the presentation of the choice images. This sequence engaged first the visual cortex (80-100 ms), then as the images were analyzed, predominantly the left temporal regions (310-340 ms). At longer latency, characteristic neural activetion was found in motor speech areas (500-520 ms) for images requiring low salience choices with respect to previous (brand) memory, and in right parietal cortex for high salience choices (850-920 ms). We argue that the neural processes associated with the particular brand-choice stimulus can be separated into identifiable stages through observation of MEG responses and knowledge of functional anatomy. PMID:12018772

Structure and function of primitive immunoglobulin superfamily neural cell adhesion molecules: a lesson from studies on planarian.

PubMed

Fusaoka, Eri; Inoue, Takeshi; Mineta, Katsuhiko; Agata, Kiyokazu; Takeuchi, Kosei

2006-05-01

Precise wiring and proper remodeling of the neural network are essential for its normal function. The freshwater planarian is an attractive animal in which to study the formation and maintenance of the neural network due to its high regenerative capability and developmental plasticity. Although a recent study revealed that homologs of netrin and its receptors are required for regeneration and maintenance of the planarian central nervous system (CNS), the roles of cell adhesion in the formation and maintenance of the planarian neural network remain poorly understood. In the present study, we found primitive immunoglobulin superfamily cell adhesion molecules (IgCAMs) in a planarian that are homologous to vertebrate neural IgCAMs. We identified planarian orthologs of NCAM, L1CAM, contactin and DSCAM, and designated them DjCAM, DjLCAM, DjCTCAM and DjDSCAM, respectively. We further confirmed that they function as cell adhesion molecules using cell aggregation assays. DjCAM and DjDSCAM were found to be differentially expressed in the CNS. Functional analyses using RNA interference revealed that DjCAM is partly involved in axon formation, and that DjDSCAM plays crucial roles in neuronal cell migration, axon outgrowth, fasciculation and projection.

DAN (NBL1) promotes collective neural crest migration by restraining uncontrolled invasion.

PubMed

McLennan, Rebecca; Bailey, Caleb M; Schumacher, Linus J; Teddy, Jessica M; Morrison, Jason A; Kasemeier-Kulesa, Jennifer C; Wolfe, Lauren A; Gogol, Madeline M; Baker, Ruth E; Maini, Philip K; Kulesa, Paul M

2017-10-02

Neural crest cells are both highly migratory and significant to vertebrate organogenesis. However, the signals that regulate neural crest cell migration remain unclear. In this study, we test the function of differential screening-selected gene aberrant in neuroblastoma (DAN), a bone morphogenetic protein (BMP) antagonist we detected by analysis of the chick cranial mesoderm. Our analysis shows that, before neural crest cell exit from the hindbrain, DAN is expressed in the mesoderm, and then it becomes absent along cell migratory pathways. Cranial neural crest and metastatic melanoma cells avoid DAN protein stripes in vitro. Addition of DAN reduces the speed of migrating cells in vivo and in vitro, respectively. In vivo loss of function of DAN results in enhanced neural crest cell migration by increasing speed and directionality. Computer model simulations support the hypothesis that DAN restrains cell migration by regulating cell speed. Collectively, our results identify DAN as a novel factor that inhibits uncontrolled neural crest and metastatic melanoma invasion and promotes collective migration in a manner consistent with the inhibition of BMP signaling. © 2017 McLennan et al.

DAN (NBL1) promotes collective neural crest migration by restraining uncontrolled invasion

PubMed Central

McLennan, Rebecca; Bailey, Caleb M.; Schumacher, Linus J.; Teddy, Jessica M.; Morrison, Jason A.; Kasemeier-Kulesa, Jennifer C.; Wolfe, Lauren A.; Gogol, Madeline M.; Baker, Ruth E.; Maini, Philip K.

2017-01-01

Neural crest cells are both highly migratory and significant to vertebrate organogenesis. However, the signals that regulate neural crest cell migration remain unclear. In this study, we test the function of differential screening-selected gene aberrant in neuroblastoma (DAN), a bone morphogenetic protein (BMP) antagonist we detected by analysis of the chick cranial mesoderm. Our analysis shows that, before neural crest cell exit from the hindbrain, DAN is expressed in the mesoderm, and then it becomes absent along cell migratory pathways. Cranial neural crest and metastatic melanoma cells avoid DAN protein stripes in vitro. Addition of DAN reduces the speed of migrating cells in vivo and in vitro, respectively. In vivo loss of function of DAN results in enhanced neural crest cell migration by increasing speed and directionality. Computer model simulations support the hypothesis that DAN restrains cell migration by regulating cell speed. Collectively, our results identify DAN as a novel factor that inhibits uncontrolled neural crest and metastatic melanoma invasion and promotes collective migration in a manner consistent with the inhibition of BMP signaling. PMID:28811280

A neural network for the identification of measured helicopter noise

NASA Technical Reports Server (NTRS)

Cabell, R. H.; Fuller, C. R.; O'Brien, W. F.

1991-01-01

The results of a preliminary study of the components of a novel acoustic helicopter identification system are described. The identification system uses the relationship between the amplitudes of the first eight harmonics in the main rotor noise spectrum to distinguish between helicopter types. Two classification algorithms are tested; a statistically optimal Bayes classifier, and a neural network adaptive classifier. The performance of these classifiers is tested using measured noise of three helicopters. The statistical classifier can correctly identify the helicopter an average of 67 percent of the time, while the neural network is correct an average of 65 percent of the time. These results indicate the need for additional study of the envelope of harmonic amplitudes as a component of a helicopter identification system. Issues concerning the implementation of the neural network classifier, such as training time and structure of the network, are discussed.

Effects of the popular food additive sodium benzoate on neural tube development in the chicken embryo.

PubMed

Emon, Selin Tural; Orakdogen, Metin; Uslu, Serap; Somay, Hakan

2015-01-01

Many more additives have been introduced with the development of processed foods. Neural tube defects are congenital malformations of the central nervous system. More than 300 000 children are born with neural tube defects every year and surviving children remain disabled for life. Sodium benzoate is used intensively in our daily lives. We therefore aimed to evaluate the effects of sodium benzoate on neural tube defects in chicken embryos. Fertile, specific pathogen-free eggs were used. The study was conducted on five groups. After 30 hours of incubation, the eggs were opened under 4x optical magnification. The embryonic disc was identified and sodium benzoate solution was injected. Eggs were closed with sterile adhesive strips and incubation was continued till the end of the 72nd hour. All eggs were then reopened and embryos were dissected from embryonic membranes and evaluated histopathologically. We found that the development of all embryos was consistent with the stage. We detected neural tube obstruction in one embryo. Neural tube defects were not detected in any embryos. This study showed that sodium benzoate as one of the widely used food preservatives has no effect to neural tube defect development in chicken embryos even at high doses.

Intergenerational neural mediators of early-life anxious temperament.

PubMed

Fox, Andrew S; Oler, Jonathan A; Shackman, Alexander J; Shelton, Steven E; Raveendran, Muthuswamy; McKay, D Reese; Converse, Alexander K; Alexander, Andrew; Davidson, Richard J; Blangero, John; Rogers, Jeffrey; Kalin, Ned H

2015-07-21

Understanding the heritability of neural systems linked to psychopathology is not sufficient to implicate them as intergenerational neural mediators. By closely examining how individual differences in neural phenotypes and psychopathology cosegregate as they fall through the family tree, we can identify the brain systems that underlie the parent-to-child transmission of psychopathology. Although research has identified genes and neural circuits that contribute to the risk of developing anxiety and depression, the specific neural systems that mediate the inborn risk for these debilitating disorders remain unknown. In a sample of 592 young rhesus monkeys that are part of an extended multigenerational pedigree, we demonstrate that metabolism within a tripartite prefrontal-limbic-midbrain circuit mediates some of the inborn risk for developing anxiety and depression. Importantly, although brain volume is highly heritable early in life, it is brain metabolism-not brain structure-that is the critical intermediary between genetics and the childhood risk to develop stress-related psychopathology.

Harmonic Training and the Formation of Pitch Representation in a Neural Network Model of the Auditory Brain

PubMed Central

Ahmad, Nasir; Higgins, Irina; Walker, Kerry M. M.; Stringer, Simon M.

2016-01-01

Attempting to explain the perceptual qualities of pitch has proven to be, and remains, a difficult problem. The wide range of sounds which elicit pitch and a lack of agreement across neurophysiological studies on how pitch is encoded by the brain have made this attempt more difficult. In describing the potential neural mechanisms by which pitch may be processed, a number of neural networks have been proposed and implemented. However, no unsupervised neural networks with biologically accurate cochlear inputs have yet been demonstrated. This paper proposes a simple system in which pitch representing neurons are produced in a biologically plausible setting. Purely unsupervised regimes of neural network learning are implemented and these prove to be sufficient in identifying the pitch of sounds with a variety of spectral profiles, including sounds with missing fundamental frequencies and iterated rippled noises. PMID:27047368

Method for Constructing Composite Response Surfaces by Combining Neural Networks with other Interpolation or Estimation Techniques

NASA Technical Reports Server (NTRS)

Rai, Man Mohan (Inventor); Madavan, Nateri K. (Inventor)

2003-01-01

A method and system for design optimization that incorporates the advantages of both traditional response surface methodology (RSM) and neural networks is disclosed. The present invention employs a unique strategy called parameter-based partitioning of the given design space. In the design procedure, a sequence of composite response surfaces based on both neural networks and polynomial fits is used to traverse the design space to identify an optimal solution. The composite response surface has both the power of neural networks and the economy of low-order polynomials (in terms of the number of simulations needed and the network training requirements). The present invention handles design problems with many more parameters than would be possible using neural networks alone and permits a designer to rapidly perform a variety of trade-off studies before arriving at the final design.

Distinctive and common neural underpinnings of major depression, social anxiety, and their comorbidity

PubMed Central

Chen, Michael C.; Waugh, Christian E.; Joormann, Jutta; Gotlib, Ian H.

2015-01-01

Assessing neural commonalities and differences among depression, anxiety and their comorbidity is critical in developing a more integrative clinical neuroscience and in evaluating currently debated categorical vs dimensional approaches to psychiatric classification. Therefore, in this study, we sought to identify patterns of anomalous neural responding to criticism and praise that are specific to and common among major depressive disorder (MDD), social anxiety disorder (SAD) and comorbid MDD-SAD. Adult females who met formal diagnostic criteria for MDD, SAD or MDD-SAD and psychiatrically healthy participants underwent functional magnetic resonance imaging as they listened to statements directing praise or criticism at them or at another person. MDD groups showed reduced responding to praise across a distributed cortical network, an effect potentially mediated by thalamic nuclei undergirding arousal-mediated attention. SAD groups showed heightened anterior insula and decreased default-mode network response to criticism. The MDD-SAD group uniquely showed reduced responding to praise in the dorsal anterior cingulate cortex. Finally, all groups with psychopathology showed heightened response to criticism in a region of the superior frontal gyrus implicated in attentional gating. The present results suggest novel neural models of anhedonia in MDD, vigilance-withdrawal behaviors in SAD, and poorer outcome in MDD-SAD. Importantly, in identifying unique and common neural substrates of MDD and SAD, these results support a formulation in which common neural components represent general risk factors for psychopathology that, due to factors that are present at illness onset, lead to distinct forms of psychopathology with unique neural signatures. PMID:25038225

Reprogramming multipotent tumor cells with the embryonic neural crest microenvironment

PubMed Central

Kasemeier-Kulesa, Jennifer C.; Teddy, Jessica M.; Postovit, Lynne-Marie; Seftor, Elisabeth A.; Seftor, Richard E.B.; Hendrix, Mary J.C.; Kulesa, Paul M.

2008-01-01

The embryonic microenvironment is an important source of signals that program multipotent cells to adopt a particular fate and migratory path, yet its potential to reprogram and restrict multipotent tumor cell fate and invasion is unrealized. Aggressive tumor cells share many characteristics with multipotent, invasive embryonic progenitors, contributing to the paradigm of tumour cell plasticity. In the vertebrate embryo, multiple cell types originate from a highly invasive cell population called the neural crest. The neural crest and the embryonic microenvironments they migrate through represent an excellent model system to study cell diversification during embryogenesis and phenotype determination. Recent exciting studies of tumor cells transplanted into various embryo models, including the neural crest rich chick microenvironment, have revealed the potential to control and revert the metastatic phenotype, suggesting further work may help to identify new targets for therapeutic intervention derived from a convergence of tumorigenic and embryonic signals. In this mini-review, we summarize markers that are common to the neural crest and highly aggressive human melanoma cells. We highlight advances in our understanding of tumor cell behaviors and plasticity studied within the chick neural crest rich microenvironment. In so doing, we honor the tremendous contributions of Professor Elizabeth D. Hay towards this important interface of developmental and cancer biology. PMID:18629870

Neural correlates of sexual cue reactivity in individuals with and without compulsive sexual behaviours.

PubMed

Voon, Valerie; Mole, Thomas B; Banca, Paula; Porter, Laura; Morris, Laurel; Mitchell, Simon; Lapa, Tatyana R; Karr, Judy; Harrison, Neil A; Potenza, Marc N; Irvine, Michael

2014-01-01

Although compulsive sexual behaviour (CSB) has been conceptualized as a "behavioural" addiction and common or overlapping neural circuits may govern the processing of natural and drug rewards, little is known regarding the responses to sexually explicit materials in individuals with and without CSB. Here, the processing of cues of varying sexual content was assessed in individuals with and without CSB, focusing on neural regions identified in prior studies of drug-cue reactivity. 19 CSB subjects and 19 healthy volunteers were assessed using functional MRI comparing sexually explicit videos with non-sexual exciting videos. Ratings of sexual desire and liking were obtained. Relative to healthy volunteers, CSB subjects had greater desire but similar liking scores in response to the sexually explicit videos. Exposure to sexually explicit cues in CSB compared to non-CSB subjects was associated with activation of the dorsal anterior cingulate, ventral striatum and amygdala. Functional connectivity of the dorsal anterior cingulate-ventral striatum-amygdala network was associated with subjective sexual desire (but not liking) to a greater degree in CSB relative to non-CSB subjects. The dissociation between desire or wanting and liking is consistent with theories of incentive motivation underlying CSB as in drug addictions. Neural differences in the processing of sexual-cue reactivity were identified in CSB subjects in regions previously implicated in drug-cue reactivity studies. The greater engagement of corticostriatal limbic circuitry in CSB following exposure to sexual cues suggests neural mechanisms underlying CSB and potential biological targets for interventions.

Neural Correlates of Sexual Cue Reactivity in Individuals with and without Compulsive Sexual Behaviours

PubMed Central

Voon, Valerie; Mole, Thomas B.; Banca, Paula; Porter, Laura; Morris, Laurel; Mitchell, Simon; Lapa, Tatyana R.; Karr, Judy; Harrison, Neil A.; Potenza, Marc N.; Irvine, Michael

2014-01-01

Although compulsive sexual behaviour (CSB) has been conceptualized as a “behavioural” addiction and common or overlapping neural circuits may govern the processing of natural and drug rewards, little is known regarding the responses to sexually explicit materials in individuals with and without CSB. Here, the processing of cues of varying sexual content was assessed in individuals with and without CSB, focusing on neural regions identified in prior studies of drug-cue reactivity. 19 CSB subjects and 19 healthy volunteers were assessed using functional MRI comparing sexually explicit videos with non-sexual exciting videos. Ratings of sexual desire and liking were obtained. Relative to healthy volunteers, CSB subjects had greater desire but similar liking scores in response to the sexually explicit videos. Exposure to sexually explicit cues in CSB compared to non-CSB subjects was associated with activation of the dorsal anterior cingulate, ventral striatum and amygdala. Functional connectivity of the dorsal anterior cingulate-ventral striatum-amygdala network was associated with subjective sexual desire (but not liking) to a greater degree in CSB relative to non-CSB subjects. The dissociation between desire or wanting and liking is consistent with theories of incentive motivation underlying CSB as in drug addictions. Neural differences in the processing of sexual-cue reactivity were identified in CSB subjects in regions previously implicated in drug-cue reactivity studies. The greater engagement of corticostriatal limbic circuitry in CSB following exposure to sexual cues suggests neural mechanisms underlying CSB and potential biological targets for interventions. PMID:25013940

Motor control in a Drosophila taste circuit

PubMed Central

Gordon, Michael D.; Scott, Kristin

2009-01-01

Tastes elicit innate behaviors critical for directing animals to ingest nutritious substances and reject toxic compounds, but the neural basis of these behaviors is not understood. Here, we use a neural silencing screen to identify neurons required for a simple Drosophila taste behavior, and characterize a neural population that controls a specific subprogram of this behavior. By silencing and activating subsets of the defined cell population, we identify the neurons involved in the taste behavior as a pair of motor neurons located in the subesophageal ganglion (SOG). The motor neurons are activated by sugar stimulation of gustatory neurons and inhibited by bitter compounds; however, experiments utilizing split-GFP detect no direct connections between the motor neurons and primary sensory neurons, indicating that further study will be necessary to elucidate the circuitry bridging these populations. Combined, these results provide a general strategy and a valuable starting point for future taste circuit analysis. PMID:19217375

Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.

PubMed

Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adeli, Hojjat

2017-09-27

An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

Neurocircuitry underlying risk and resilience to social anxiety disorder

PubMed Central

Clauss, Jacqueline A.; Avery, Suzanne N.; VanDerKlok, Ross M.; Rogers, Baxter P.; Cowan, Ronald L.; Benningfield, Margaret M.; Blackford, Jennifer Urbano

2015-01-01

Background Almost half of children with an inhibited temperament will develop social anxiety disorder by late adolescence. Importantly, this means that half of children with an inhibited temperament will not develop social anxiety disorder. Studying adults with an inhibited temperament provides a unique opportunity to identify neural signatures of both risk and resilience to social anxiety disorder. Methods Functional MRI was used to measure brain activation during the anticipation of viewing fear faces in 34 young adults (17 inhibited, 17 uninhibited). To identify neural signatures of risk, we tested for group differences in functional activation and connectivity in regions implicated in social anxiety disorder, including the prefrontal cortex, amygdala, and insula. To identify neural signatures of resilience, we tested correlations between brain activation and both emotion regulation and social anxiety scores. Results Inhibited subjects had greater activation of a prefrontal network when anticipating viewing fear faces, relative to uninhibited subjects. No group differences were identified in the amygdala. Inhibited subjects had more negative connectivity between the rostral anterior cingulate cortex (ACC) and the bilateral amygdala. Within the inhibited group, those with fewer social anxiety symptoms and better emotion regulation skills had greater ACC activation and greater functional connectivity between the ACC and amygdala. Conclusions These finding suggest that engaging regulatory prefrontal regions during anticipation may be a protective factor, or putative neural marker of resilience, in high-risk individuals. Cognitive training targeting prefrontal cortex function may provide protection against anxiety, especially in high-risk individuals, such as those with inhibited temperament. PMID:24753211

Chemically Induced Reprogramming of Somatic Cells to Pluripotent Stem Cells and Neural Cells.

PubMed

Biswas, Dhruba; Jiang, Peng

2016-02-06

The ability to generate transplantable neural cells in a large quantity in the laboratory is a critical step in the field of developing stem cell regenerative medicine for neural repair. During the last few years, groundbreaking studies have shown that cell fate of adult somatic cells can be reprogrammed through lineage specific expression of transcription factors (TFs)-and defined culture conditions. This key concept has been used to identify a number of potent small molecules that could enhance the efficiency of reprogramming with TFs. Recently, a growing number of studies have shown that small molecules targeting specific epigenetic and signaling pathways can replace all of the reprogramming TFs. Here, we provide a detailed review of the studies reporting the generation of chemically induced pluripotent stem cells (ciPSCs), neural stem cells (ciNSCs), and neurons (ciN). We also discuss the main mechanisms of actions and the pathways that the small molecules regulate during chemical reprogramming.

Methylene Blue (Tetramethylthionine Chloride) Influences the Mobility of Adult Neural Stem Cells: A Potentially Novel Therapeutic Mechanism of a Therapeutic Approach in the Treatment of Alzheimer's Disease.

PubMed

van der Ven, Amelie T; Pape, Julius C; Hermann, Dirk; Schloesser, Robert; Genius, Just; Fischer, Nadine; Mößner, Rainald; Scherbaum, Norbert; Wiltfang, Jens; Rujescu, Dan; Benninghoff, Jens

2017-01-01

An interest in neurogenesis in the adult human brain as a relevant and targetable process has emerged as a potential treatment option for Alzheimer's disease and other neurodegenerative conditions. The aim of this study was to investigate the effects of tetramethylthionine chloride (methylene blue, MB) on properties of adult murine neural stem cells. Based on recent clinical studies, MB has increasingly been discussed as a potential treatment for Alzheimer's disease. While no differences in the proliferative capacity were identified, a general potential of MB in modulating the migratory capacity of adult neural stem cells was indicated in a cell mobility assay. To our knowledge, this is the first time that MB could be associated with neural mobility. The results of this study add insight to the spectrum of features of MB within the central nervous system and may be helpful for understanding the molecular mechanisms underlying a potential therapeutic effect of MB.

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

Neocleous, C.C.; Esat, I.I.; Schizas, C.N.

The creativity phase is identified as an integral part of the design phase. The characteristics of creative persons which are relevant to designing artificial neural networks manifesting aspects of creativity, are identified. Based on these identifications, a general framework of artificial neural network characteristics to implement such a goal are proposed.

Morphological covariance in anatomical MRI scans can identify discrete neural pathways in the brain and their disturbances in persons with neuropsychiatric disorders.

PubMed

Bansal, Ravi; Hao, Xuejun; Peterson, Bradley S

2015-05-01

We hypothesize that coordinated functional activity within discrete neural circuits induces morphological organization and plasticity within those circuits. Identifying regions of morphological covariation that are independent of morphological covariation in other regions therefore may therefore allow us to identify discrete neural systems within the brain. Comparing the magnitude of these variations in individuals who have psychiatric disorders with the magnitude of variations in healthy controls may allow us to identify aberrant neural pathways in psychiatric illnesses. We measured surface morphological features by applying nonlinear, high-dimensional warping algorithms to manually defined brain regions. We transferred those measures onto the surface of a unit sphere via conformal mapping and then used spherical wavelets and their scaling coefficients to simplify the data structure representing these surface morphological features of each brain region. We used principal component analysis (PCA) to calculate covariation in these morphological measures, as represented by their scaling coefficients, across several brain regions. We then assessed whether brain subregions that covaried in morphology, as identified by large eigenvalues in the PCA, identified specific neural pathways of the brain. To do so, we spatially registered the subnuclei for each eigenvector into the coordinate space of a Diffusion Tensor Imaging dataset; we used these subnuclei as seed regions to track and compare fiber pathways with known fiber pathways identified in neuroanatomical atlases. We applied these procedures to anatomical MRI data in a cohort of 82 healthy participants (42 children, 18 males, age 10.5 ± 2.43 years; 40 adults, 22 males, age 32.42 ± 10.7 years) and 107 participants with Tourette's Syndrome (TS) (71 children, 59 males, age 11.19 ± 2.2 years; 36 adults, 21 males, age 37.34 ± 10.9 years). We evaluated the construct validity of the identified covariation in morphology using DTI data from a different set of 20 healthy adults (10 males, mean age 29.7 ± 7.7 years). The PCA identified portions of structures that covaried across the brain, the eigenvalues measuring the magnitude of the covariation in morphology along the respective eigenvectors. Our results showed that the eigenvectors, and the DTI fibers tracked from their associated brain regions, corresponded with known neural pathways in the brain. In addition, the eigenvectors that captured morphological covariation across regions, and the principal components along those eigenvectors, identified neural pathways with aberrant morphological features associated with TS. These findings suggest that covariations in brain morphology can identify aberrant neural pathways in specific neuropsychiatric disorders. Copyright © 2015. Published by Elsevier Inc.

Nonlinear neural control with power systems applications

NASA Astrophysics Data System (ADS)

Chen, Dingguo

1998-12-01

Extensive studies have been undertaken on the transient stability of large interconnected power systems with flexible ac transmission systems (FACTS) devices installed. Varieties of control methodologies have been proposed to stabilize the postfault system which would otherwise eventually lose stability without a proper control. Generally speaking, regular transient stability is well understood, but the mechanism of load-driven voltage instability or voltage collapse has not been well understood. The interaction of generator dynamics and load dynamics makes synthesis of stabilizing controllers even more challenging. There is currently increasing interest in the research of neural networks as identifiers and controllers for dealing with dynamic time-varying nonlinear systems. This study focuses on the development of novel artificial neural network architectures for identification and control with application to dynamic electric power systems so that the stability of the interconnected power systems, following large disturbances, and/or with the inclusion of uncertain loads, can be largely enhanced, and stable operations are guaranteed. The latitudinal neural network architecture is proposed for the purpose of system identification. It may be used for identification of nonlinear static/dynamic loads, which can be further used for static/dynamic voltage stability analysis. The properties associated with this architecture are investigated. A neural network methodology is proposed for dealing with load modeling and voltage stability analysis. Based on the neural network models of loads, voltage stability analysis evolves, and modal analysis is performed. Simulation results are also provided. The transient stability problem is studied with consideration of load effects. The hierarchical neural control scheme is developed. Trajectory-following policy is used so that the hierarchical neural controller performs as almost well for non-nominal cases as they do for the nominal cases. The adaptive hierarchical neural control scheme is also proposed to deal with the time-varying nature of loads. Further, adaptive neural control, which is based on the on-line updating of the weights and biases of the neural networks, is studied. Simulations provided on the faulted power systems with unknown loads suggest that the proposed adaptive hierarchical neural control schemes should be useful for practical power applications.

Distinct Neural-Functional Effects of Treatments With Selective Serotonin Reuptake Inhibitors, Electroconvulsive Therapy, and Transcranial Magnetic Stimulation and Their Relations to Regional Brain Function in Major Depression: A Meta-analysis.

PubMed

Chau, David T; Fogelman, Phoebe; Nordanskog, Pia; Drevets, Wayne C; Hamilton, J Paul

2017-05-01

Functional neuroimaging studies have examined the neural substrates of treatments for major depressive disorder (MDD). Low sample size and methodological heterogeneity, however, undermine the generalizability of findings from individual studies. We conducted a meta-analysis to identify reliable neural changes resulting from different modes of treatment for MDD and compared them with each other and with reliable neural functional abnormalities observed in depressed versus control samples. We conducted a meta-analysis of studies reporting changes in brain activity (e.g., as indexed by positron emission tomography) following treatments with selective serotonin reuptake inhibitors (SSRIs), electroconvulsive therapy (ECT), or transcranial magnetic stimulation. Additionally, we examined the statistical reliability of overlap among thresholded meta-analytic SSRI, ECT, and transcranial magnetic stimulation maps as well as a map of abnormal neural function in MDD. Our meta-analysis revealed that 1) SSRIs decrease activity in the anterior insula, 2) ECT decreases activity in central nodes of the default mode network, 3) transcranial magnetic stimulation does not result in reliable neural changes, and 4) regional effects of these modes of treatment do not significantly overlap with each other or with regions showing reliable functional abnormality in MDD. SSRIs and ECT produce neurally distinct effects relative to each other and to the functional abnormalities implicated in depression. These treatments therefore may exert antidepressant effects by diminishing neural functions not implicated in depression but that nonetheless impact mood. We discuss how the distinct neural changes resulting from SSRIs and ECT can account for both treatment effects and side effects from these therapies as well as how to individualize these treatments. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Tropical Timber Identification using Backpropagation Neural Network

NASA Astrophysics Data System (ADS)

Siregar, B.; Andayani, U.; Fatihah, N.; Hakim, L.; Fahmi, F.

2017-01-01

Each and every type of wood has different characteristics. Identifying the type of wood properly is important, especially for industries that need to know the type of timber specifically. However, it requires expertise in identifying the type of wood and only limited experts available. In addition, the manual identification even by experts is rather inefficient because it requires a lot of time and possibility of human errors. To overcome these problems, a digital image based method to identify the type of timber automatically is needed. In this study, backpropagation neural network is used as artificial intelligence component. Several stages were developed: a microscope image acquisition, pre-processing, feature extraction using gray level co-occurrence matrix and normalization of data extraction using decimal scaling features. The results showed that the proposed method was able to identify the timber with an accuracy of 94%.

Speaker verification using committee neural networks.

PubMed

Reddy, Narender P; Buch, Ojas A

2003-10-01

Security is a major problem in web based access or remote access to data bases. In the present study, the technique of committee neural networks was developed for speech based speaker verification. Speech data from the designated speaker and several imposters were obtained. Several parameters were extracted in the time and frequency domains, and fed to neural networks. Several neural networks were trained and the five best performing networks were recruited into the committee. The committee decision was based on majority voting of the member networks. The committee opinion was evaluated with further testing data. The committee correctly identified the designated speaker in (50 out of 50) 100% of the cases and rejected imposters in (150 out of 150) 100% of the cases. The committee decision was not unanimous in majority of the cases tested.

Tracking Deceased-Related Thinking with Neural Pattern Decoding of a Cortical-Basal Ganglia Circuit.

PubMed

Schneck, Noam; Haufe, Stefan; Tu, Tao; Bonanno, George A; Ochsner, Kevin; Sajda, Paul; Mann, J John

2017-07-01

Deceased-related thinking is central to grieving and potentially critical to processing of the loss. Self-report measurements might fail to capture important elements of deceased-related thinking and processing. Here, we used a machine learning approach applied to fMRI - known as neural decoding - to develop a measure of ongoing deceased-related processing. 23 subjects grieving the loss of a first-degree relative, spouse or partner within 14 months underwent two fMRI tasks. They first viewed pictures and stories related to the deceased, a living control and a demographic control figure while providing ongoing valence and arousal ratings. Second, they performed a 10-minute Sustained Attention to Response Task (SART) with thought probes every 25-35 seconds to identify deceased, living and self-related thoughts. A conjunction analysis, controlling for valence/arousal, identified neural clusters in basal ganglia, orbital prefrontal cortex and insula associated with both types of deceased-related stimuli vs. the two control conditions in the first task. This pattern was applied to fMRI data collected during the SART, and discriminated deceased-related but not living or self-related thoughts, independently of grief-severity and time since loss. Deceased-related thoughts on the SART correlated with self-reported avoidance. The neural model predicted avoidance over and above deceased-related thoughts. A neural pattern trained to identify mental representations of the deceased tracked deceased-related thinking during a sustained attention task and also predicted subject-level avoidance. This approach provides a new imaging tool to be used as an index of processing the deceased for future studies of complicated grief.

Functional and structural neural alterations in Internet gaming disorder: A systematic review and meta-analysis.

PubMed

Yao, Yuan-Wei; Liu, Lu; Ma, Shan-Shan; Shi, Xin-Hui; Zhou, Nan; Zhang, Jin-Tao; Potenza, Marc N

2017-12-01

This meta-analytic study aimed to identify the common and specific neural alterations in Internet gaming disorder (IGD) across different domains and modalities. Two separate meta-analyses for functional neural activation and gray-matter volume were conducted. Sub-meta-analyses for the domains of reward, cold-executive, and hot-executive functions were also performed, respectively. IGD subjects, compared with healthy controls, showed: (1) hyperactivation in the anterior and posterior cingulate cortices, caudate, posterior inferior frontal gyrus (IFG), which were mainly associated with studies measuring reward and cold-executive functions; and, (2) hypoactivation in the anterior IFG in relation to hot-executive function, the posterior insula, somatomotor and somatosensory cortices in relation to reward function. Furthermore, IGD subjects showed reduced gray-matter volume in the anterior cingulate, orbitofrontal, dorsolateral prefrontal, and premotor cortices. These findings suggest that IGD is associated with both functional and structural neural alterations in fronto-striatal and fronto-cingulate regions. Moreover, multi-domain assessments capture different aspects of neural alterations in IGD, which may be helpful for developing effective interventions targeting specific functions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Screening of bioactive peptides using an embryonic stem cell-based neurodifferentiation assay.

PubMed

Xu, Ruodan; Feyeux, Maxime; Julien, Stéphanie; Nemes, Csilla; Albrechtsen, Morten; Dinnyés, Andras; Krause, Karl-Heinz

2014-05-01

Differentiation of pluripotent stem cells, PSCs, towards neural lineages has attracted significant attention, given the potential use of such cells for in vitro studies and for regenerative medicine. The present experiments were designed to identify bioactive peptides which direct PSC differentiation towards neural cells. Fifteen peptides were designed based on NCAM, FGFR, and growth factors sequences. The effect of peptides was screened using a mouse embryonic stem cell line expressing luciferase dual reporter construct driven by promoters for neural tubulin and for elongation factor 1. Cell number was estimated by measuring total cellular DNA. We identified five peptides which enhanced activities of both promoters without relevant changes in cell number. We selected the two most potent peptides for further analysis: the NCAM-derived mimetic FGLL and the synthetic NCAM ligand, Plannexin. Both compounds induced phenotypic neuronal differentiation, as evidenced by increased neurite outgrowth. In summary, we used a simple, but sensitive screening approach to identify the neurogenic peptides. These peptides will not only provide new clues concerning pathways of neurogenesis, but they may also be interesting biotechnology tools for in vitro generation of neurons.

Neural Representations of Physics Concepts.

PubMed

Mason, Robert A; Just, Marcel Adam

2016-06-01

We used functional MRI (fMRI) to assess neural representations of physics concepts (momentum, energy, etc.) in juniors, seniors, and graduate students majoring in physics or engineering. Our goal was to identify the underlying neural dimensions of these representations. Using factor analysis to reduce the number of dimensions of activation, we obtained four physics-related factors that were mapped to sets of voxels. The four factors were interpretable as causal motion visualization, periodicity, algebraic form, and energy flow. The individual concepts were identifiable from their fMRI signatures with a mean rank accuracy of .75 using a machine-learning (multivoxel) classifier. Furthermore, there was commonality in participants' neural representation of physics; a classifier trained on data from all but one participant identified the concepts in the left-out participant (mean accuracy = .71 across all nine participant samples). The findings indicate that abstract scientific concepts acquired in an educational setting evoke activation patterns that are identifiable and common, indicating that science education builds abstract knowledge using inherent, repurposed brain systems. © The Author(s) 2016.

Connectivity inference from neural recording data: Challenges, mathematical bases and research directions.

PubMed

Magrans de Abril, Ildefons; Yoshimoto, Junichiro; Doya, Kenji

2018-06-01

This article presents a review of computational methods for connectivity inference from neural activity data derived from multi-electrode recordings or fluorescence imaging. We first identify biophysical and technical challenges in connectivity inference along the data processing pipeline. We then review connectivity inference methods based on two major mathematical foundations, namely, descriptive model-free approaches and generative model-based approaches. We investigate representative studies in both categories and clarify which challenges have been addressed by which method. We further identify critical open issues and possible research directions. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Incorporating deep learning with convolutional neural networks and position specific scoring matrices for identifying electron transport proteins.

PubMed

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

2017-09-05

In several years, deep learning is a modern machine learning technique using in a variety of fields with state-of-the-art performance. Therefore, utilization of deep learning to enhance performance is also an important solution for current bioinformatics field. In this study, we try to use deep learning via convolutional neural networks and position specific scoring matrices to identify electron transport proteins, which is an important molecular function in transmembrane proteins. Our deep learning method can approach a precise model for identifying of electron transport proteins with achieved sensitivity of 80.3%, specificity of 94.4%, and accuracy of 92.3%, with MCC of 0.71 for independent dataset. The proposed technique can serve as a powerful tool for identifying electron transport proteins and can help biologists understand the function of the electron transport proteins. Moreover, this study provides a basis for further research that can enrich a field of applying deep learning in bioinformatics. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Engineering platform and experimental protocol for design and evaluation of a neurally-controlled powered transfemoral prosthesis.

PubMed

Zhang, Fan; Liu, Ming; Harper, Stephen; Lee, Michael; Huang, He

2014-07-22

To enable intuitive operation of powered artificial legs, an interface between user and prosthesis that can recognize the user's movement intent is desired. A novel neural-machine interface (NMI) based on neuromuscular-mechanical fusion developed in our previous study has demonstrated a great potential to accurately identify the intended movement of transfemoral amputees. However, this interface has not yet been integrated with a powered prosthetic leg for true neural control. This study aimed to report (1) a flexible platform to implement and optimize neural control of powered lower limb prosthesis and (2) an experimental setup and protocol to evaluate neural prosthesis control on patients with lower limb amputations. First a platform based on a PC and a visual programming environment were developed to implement the prosthesis control algorithms, including NMI training algorithm, NMI online testing algorithm, and intrinsic control algorithm. To demonstrate the function of this platform, in this study the NMI based on neuromuscular-mechanical fusion was hierarchically integrated with intrinsic control of a prototypical transfemoral prosthesis. One patient with a unilateral transfemoral amputation was recruited to evaluate our implemented neural controller when performing activities, such as standing, level-ground walking, ramp ascent, and ramp descent continuously in the laboratory. A novel experimental setup and protocol were developed in order to test the new prosthesis control safely and efficiently. The presented proof-of-concept platform and experimental setup and protocol could aid the future development and application of neurally-controlled powered artificial legs.

Physical exercise increases involvement of motor networks as a compensatory mechanism during a cognitively challenging task.

PubMed

Ji, Lanxin; Pearlson, Godfrey D; Zhang, Xue; Steffens, David C; Ji, Xiaoqing; Guo, Hua; Wang, Lihong

2018-05-31

Neuroimaging studies suggest that older adults may compensate for declines in cognitive function through neural compensation and reorganization of neural resources. While neural compensation as a key component of cognitive reserve is an important factor that mediates cognitive decline, the field lacks a quantitative measure of neural compensatory ability, and little is known about factors that may modify compensation, such as physical exercise. Twenty-five healthy older adults participated in a 6-week dance training exercise program. Gait speed, cognitive function, and functional magnetic resonance imaging during a challenging memory task were measured before and after the exercise program. In this study, we used a newly proposed data-driven independent component analysis approach to measure neural compensatory ability and tested the effect of physical exercise on neural compensation through a longitudinal study. After the exercise program, participants showed significantly improved memory performance in Logical Memory Test (WMS(LM)) (P < .001) and Rey Auditory Verbal Learning Test (P = .001) and increased gait speed measured by the 6-minute walking test (P = .01). Among all identified neural networks, only the motor cortices and cerebellum showed greater involvement during the memory task after exercise. Importantly, subjects who activated the motor network only after exercise (but not before exercise) showed WMS(LM) increases. We conclude that physical exercise improved gait speed, cognitive function, and compensatory ability through increased involvement of motor-related networks. Copyright © 2018 John Wiley & Sons, Ltd.

New application of intelligent agents in sporadic amyotrophic lateral sclerosis identifies unexpected specific genetic background.

PubMed

Penco, Silvana; Buscema, Massimo; Patrosso, Maria Cristina; Marocchi, Alessandro; Grossi, Enzo

2008-05-30

Few genetic factors predisposing to the sporadic form of amyotrophic lateral sclerosis (ALS) have been identified, but the pathology itself seems to be a true multifactorial disease in which complex interactions between environmental and genetic susceptibility factors take place. The purpose of this study was to approach genetic data with an innovative statistical method such as artificial neural networks to identify a possible genetic background predisposing to the disease. A DNA multiarray panel was applied to genotype more than 60 polymorphisms within 35 genes selected from pathways of lipid and homocysteine metabolism, regulation of blood pressure, coagulation, inflammation, cellular adhesion and matrix integrity, in 54 sporadic ALS patients and 208 controls. Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis. An unexpected discovery of a strong genetic background in sporadic ALS using a DNA multiarray panel and analytical processing of the data with advanced artificial neural networks was found. The predictive accuracy obtained with Linear Discriminant Analysis and Standard Artificial Neural Networks ranged from 70% to 79% (average 75.31%) and from 69.1 to 86.2% (average 76.6%) respectively. The corresponding value obtained with Advanced Intelligent Systems reached an average of 96.0% (range 94.4 to 97.6%). This latter approach allowed the identification of seven genetic variants essential to differentiate cases from controls: apolipoprotein E arg158cys; hepatic lipase -480 C/T; endothelial nitric oxide synthase 690 C/T and glu298asp; vitamin K-dependent coagulation factor seven arg353glu, glycoprotein Ia/IIa 873 G/A and E-selectin ser128arg. This study provides an alternative and reliable method to approach complex diseases. Indeed, the application of a novel artificial intelligence-based method offers a new insight into genetic markers of sporadic ALS pointing out the existence of a strong genetic background.

Neural underpinnings of the identifiable victim effect: affect shifts preferences for giving.

PubMed

Genevsky, Alexander; Västfjäll, Daniel; Slovic, Paul; Knutson, Brian

2013-10-23

The "identifiable victim effect" refers to peoples' tendency to preferentially give to identified versus anonymous victims of misfortune, and has been proposed to partly depend on affect. By soliciting charitable donations from human subjects during behavioral and neural (i.e., functional magnetic resonance imaging) experiments, we sought to determine whether and how affect might promote the identifiable victim effect. Behaviorally, subjects gave more to orphans depicted by photographs versus silhouettes, and their shift in preferences was mediated by photograph-induced feelings of positive arousal, but not negative arousal. Neurally, while photographs versus silhouettes elicited activity in widespread circuits associated with facial and affective processing, only nucleus accumbens activity predicted and could statistically account for increased donations. Together, these findings suggest that presenting evaluable identifiable information can recruit positive arousal, which then promotes giving. We propose that affect elicited by identifiable stimuli can compel people to give more to strangers, even despite costs to the self.

Distinctive and common neural underpinnings of major depression, social anxiety, and their comorbidity.

PubMed

Hamilton, J Paul; Chen, Michael C; Waugh, Christian E; Joormann, Jutta; Gotlib, Ian H

2015-04-01

Assessing neural commonalities and differences among depression, anxiety and their comorbidity is critical in developing a more integrative clinical neuroscience and in evaluating currently debated categorical vs dimensional approaches to psychiatric classification. Therefore, in this study, we sought to identify patterns of anomalous neural responding to criticism and praise that are specific to and common among major depressive disorder (MDD), social anxiety disorder (SAD) and comorbid MDD-SAD. Adult females who met formal diagnostic criteria for MDD, SAD or MDD-SAD and psychiatrically healthy participants underwent functional magnetic resonance imaging as they listened to statements directing praise or criticism at them or at another person. MDD groups showed reduced responding to praise across a distributed cortical network, an effect potentially mediated by thalamic nuclei undergirding arousal-mediated attention. SAD groups showed heightened anterior insula and decreased default-mode network response to criticism. The MDD-SAD group uniquely showed reduced responding to praise in the dorsal anterior cingulate cortex. Finally, all groups with psychopathology showed heightened response to criticism in a region of the superior frontal gyrus implicated in attentional gating. The present results suggest novel neural models of anhedonia in MDD, vigilance-withdrawal behaviors in SAD, and poorer outcome in MDD-SAD. Importantly, in identifying unique and common neural substrates of MDD and SAD, these results support a formulation in which common neural components represent general risk factors for psychopathology that, due to factors that are present at illness onset, lead to distinct forms of psychopathology with unique neural signatures. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Predicting General Academic Performance and Identifying the Differential Contribution of Participating Variables Using Artificial Neural Networks

ERIC Educational Resources Information Center

Musso, Mariel F.; Kyndt, Eva; Cascallar, Eduardo C.; Dochy, Filip

2013-01-01

Many studies have explored the contribution of different factors from diverse theoretical perspectives to the explanation of academic performance. These factors have been identified as having important implications not only for the study of learning processes, but also as tools for improving curriculum designs, tutorial systems, and students'…

The neural correlates of risk propensity in males and females using resting-state fMRI

PubMed Central

Zhou, Yuan; Li, Shu; Dunn, John; Li, Huandong; Qin, Wen; Zhu, Maohu; Rao, Li-Lin; Song, Ming; Yu, Chunshui; Jiang, Tianzi

2014-01-01

Men are more risk prone than women, but the underlying basis remains unclear. To investigate this question, we developed a trait-like measure of risk propensity which we correlated with resting-state functional connectivity to identify sex differences. Specifically, we used short- and long-range functional connectivity densities to identify associated brain regions and examined their functional connectivities in resting-state functional magnetic resonance imaging (fMRI) data collected from a large sample of healthy young volunteers. We found that men had a higher level of general risk propensity (GRP) than women. At the neural level, although they shared a common neural correlate of GRP in a network centered at the right inferior frontal gyrus, men and women differed in a network centered at the right secondary somatosensory cortex, which included the bilateral dorsal anterior/middle insular cortices and the dorsal anterior cingulate cortex. In addition, men and women differed in a local network centered at the left inferior orbitofrontal cortex. Most of the regions identified by this resting-state fMRI study have been previously implicated in risk processing when people make risky decisions. This study provides a new perspective on the brain-behavioral relationships in risky decision making and contributes to our understanding of sex differences in risk propensity. PMID:24478649

Hourly air pollution concentrations and their important predictors over Houston, Texas using deep neural networks: case study of DISCOVER-AQ time period

NASA Astrophysics Data System (ADS)

Eslami, E.; Choi, Y.; Roy, A.

2017-12-01

Air quality forecasting carried out by chemical transport models often show significant error. This study uses a deep-learning approach over the Houston-Galveston-Brazoria (HGB) area to overcome this forecasting challenge, for the DISCOVER-AQ period (September 2013). Two approaches, deep neural network (DNN) using a Multi-Layer Perceptron (MLP) and Restricted Boltzmann Machine (RBM) were utilized. The proposed approaches analyzed input data by identifying features abstracted from its previous layer using a stepwise method. The approaches predicted hourly ozone and PM in September 2013 using several predictors of prior three days, including wind fields, temperature, relative humidity, cloud fraction, precipitation along with PM, ozone, and NOx concentrations. Model-measurement comparisons for available monitoring sites reported Indexes of Agreement (IOA) of around 0.95 for both DNN and RBM. A standard artificial neural network (ANN) (IOA=0.90) with similar architecture showed poorer performance than the deep networks, clearly demonstrating the superiority of the deep approaches. Additionally, each network (both deep and standard) performed significantly better than a previous CMAQ study, which showed an IOA of less than 0.80. The most influential input variables were identified using their associated weights, which represented the sensitivity of ozone to input parameters. The results indicate deep learning approaches can achieve more accurate ozone forecasting and identify the important input variables for ozone predictions in metropolitan areas.

Enhanced immunohistochemical detection of neural infiltration in primary melanoma: is there a clinical value?

PubMed

Scanlon, Patrick; Tian, Jaiying; Zhong, Judy; Silva, Ines; Shapiro, Richard; Pavlick, Anna; Berman, Russell; Osman, Iman; Darvishian, Farbod

2014-08-01

Neural infiltration in primary melanoma is a histopathologic feature that has been associated with desmoplastic histopathologic subtype and local recurrence in the literature. We tested the hypothesis that improved detection and characterization of neural infiltration into peritumoral or intratumoral location and perineural or intraneural involvement could have a prognostic relevance. We studied 128 primary melanoma cases prospectively accrued and followed at New York University using immunohistochemical detection with antihuman neurofilament protein and routine histology with hematoxylin and eosin. Neural infiltration, defined as the presence of tumor cells involving or immediately surrounding nerve foci, was identified and characterized using both detection methods. Neural infiltration rate of detection was enhanced by immunohistochemistry for neurofilament in matched-pair design (47% by immunohistochemistry versus 25% by routine histology). Immunohistochemical detection of neural infiltration was significantly associated with ulceration (P = .021), desmoplastic and acral lentiginous histologic subtype (P = .008), and head/neck/hands/feet tumor location (P = .037). Routinely detected neural infiltration was significantly associated with local recurrence (P = .010). Immunohistochemistry detected more intratumoral neural infiltration cases compared with routine histology (30% versus 3%, respectively). Peritumoral and intratumoral nerve location had no impact on clinical outcomes. Using a multivariate model controlling for stage, neither routinely detected neural infiltration nor enhanced immunohistochemical characterization of neural infiltration was significantly associated with disease-free or overall survival. Our data demonstrate that routinely detected neural infiltration is associated with local recurrence in all histologic subtypes but that improved detection and characterization of neural infiltration with immunohistochemistry in primary melanoma does not add to prognostic relevance. Copyright © 2014 Elsevier Inc. All rights reserved.

Application of neural networks to software quality modeling of a very large telecommunications system.

PubMed

Khoshgoftaar, T M; Allen, E B; Hudepohl, J P; Aud, S J

1997-01-01

Society relies on telecommunications to such an extent that telecommunications software must have high reliability. Enhanced measurement for early risk assessment of latent defects (EMERALD) is a joint project of Nortel and Bell Canada for improving the reliability of telecommunications software products. This paper reports a case study of neural-network modeling techniques developed for the EMERALD system. The resulting neural network is currently in the prototype testing phase at Nortel. Neural-network models can be used to identify fault-prone modules for extra attention early in development, and thus reduce the risk of operational problems with those modules. We modeled a subset of modules representing over seven million lines of code from a very large telecommunications software system. The set consisted of those modules reused with changes from the previous release. The dependent variable was membership in the class of fault-prone modules. The independent variables were principal components of nine measures of software design attributes. We compared the neural-network model with a nonparametric discriminant model and found the neural-network model had better predictive accuracy.

Neural Correlates of Biased Responses: The Negative Method Effect in the Rosenberg Self-Esteem Scale Is Associated with Right Amygdala Volume.

PubMed

Wang, Yinan; Kong, Feng; Huang, Lijie; Liu, Jia

2016-10-01

Self-esteem is a widely studied construct in psychology that is typically measured by the Rosenberg Self-Esteem Scale (RSES). However, a series of cross-sectional and longitudinal studies have suggested that a simple and widely used unidimensional factor model does not provide an adequate explanation of RSES responses due to method effects. To identify the neural correlates of the method effect, we sought to determine whether and how method effects were associated with the RSES and investigate the neural basis of these effects. Two hundred and eighty Chinese college students (130 males; mean age = 22.64 years) completed the RSES and underwent magnetic resonance imaging (MRI). Behaviorally, method effects were linked to both positively and negatively worded items in the RSES. Neurally, the right amygdala volume negatively correlated with the negative method factor, while the hippocampal volume positively correlated with the general self-esteem factor in the RSES. The neural dissociation between the general self-esteem factor and negative method factor suggests that there are different neural mechanisms underlying them. The amygdala is involved in modulating negative affectivity; therefore, the current study sheds light on the nature of method effects that are related to self-report with a mix of positively and negatively worded items. © 2015 Wiley Periodicals, Inc.

NGF reprograms metastatic melanoma to a bipotent glial-melanocyte neural crest-like precursor

PubMed Central

Kasemeier-Kulesa, Jennifer C.; Romine, Morgan H.; Morrison, Jason A.; Bailey, Caleb M.; Welch, Danny R.

2018-01-01

ABSTRACT Melanoma pathogenesis from normal neural crest-derived melanocytes is often fatal due to aggressive cell invasion throughout the body. The identification of signals that reprogram de-differentiated, metastatic melanoma cells to a less aggressive and stable phenotype would provide a novel strategy to limit disease progression. In this study, we identify and test the function of developmental signals within the chick embryonic neural crest microenvironment to reprogram and sustain the transition of human metastatic melanoma to a neural crest cell-like phenotype. Results reveal that co-culture of the highly aggressive and metastatic human melanoma cell line C8161 upregulate a marker of melanosome formation (Mart-1) in the presence of embryonic day 3.5 chick trunk dorsal root ganglia. We identify nerve growth factor (NGF) as the signal within this tissue driving Mart-1 re-expression and show that NGF receptors trkA and p75 cooperate to induce Mart-1 re-expression. Furthermore, Mart-1 expressing C8161 cells acquire a gene signature of poorly aggressive C81-61 cells. These data suggest that targeting NGF signaling may yield a novel strategy to reprogram metastatic melanoma toward a benign cell type. PMID:29175861

Signaling mechanisms regulating adult neural stem cells and neurogenesis

PubMed Central

Faigle, Roland; Song, Hongjun

2012-01-01

Background Adult neurogenesis occurs throughout life in discrete regions of the mammalian brain and is tightly regulated via both extrinsic environmental influences and intrinsic genetic factors. In recent years, several crucial signaling pathways have been identified in regulating self-renewal, proliferation, and differentiation of neural stem cells, as well as migration and functional integration of developing neurons in the adult brain. Scope of review Here we review our current understanding of signaling mechanisms, including Wnt, notch, sonic hedgehog, growth and neurotrophic factors, bone morphogenetic proteins, neurotransmitters, transcription factors, and epigenetic modulators, and crosstalk between these signaling pathways in the regulation of adult neurogenesis. We also highlight emerging principles in the vastly growing field of adult neural stem cell biology and neural plasticity. Major conclusions Recent methodological advances have enabled the field to identify signaling mechanisms that fine-tune and coordinate neurogenesis in the adult brain, leading to a better characterization of both cell-intrinsic and environmental cues defining the neurogenic niche. Significant questions related to niche cell identity and underlying regulatory mechanisms remain to be fully addressed and will be the focus of future studies. General significance A full understanding of the role and function of individual signaling pathways in regulating neural stem cells and generation and integration of newborn neurons in the adult brain may lead to targeted new therapies for neurological diseases in humans. PMID:22982587

Neural signatures of social conformity: A coordinate-based activation likelihood estimation meta-analysis of functional brain imaging studies.

PubMed

Wu, Haiyan; Luo, Yi; Feng, Chunliang

2016-12-01

People often align their behaviors with group opinions, known as social conformity. Many neuroscience studies have explored the neuropsychological mechanisms underlying social conformity. Here we employed a coordinate-based meta-analysis on neuroimaging studies of social conformity with the purpose to reveal the convergence of the underlying neural architecture. We identified a convergence of reported activation foci in regions associated with normative decision-making, including ventral striatum (VS), dorsal posterior medial frontal cortex (dorsal pMFC), and anterior insula (AI). Specifically, consistent deactivation of VS and activation of dorsal pMFC and AI are identified when people's responses deviate from group opinions. In addition, the deviation-related responses in dorsal pMFC predict people's conforming behavioral adjustments. These are consistent with current models that disagreement with others might evoke "error" signals, cognitive imbalance, and/or aversive feelings, which are plausibly detected in these brain regions as control signals to facilitate subsequent conforming behaviors. Finally, group opinions result in altered neural correlates of valuation, manifested as stronger responses of VS to stimuli endorsed than disliked by others. Copyright © 2016 Elsevier Ltd. All rights reserved.

Neural activity to intense positive versus negative stimuli can help differentiate bipolar disorder from unipolar major depressive disorder in depressed adolescents: a pilot fMRI study.

PubMed

Diler, Rasim Somer; de Almeida, Jorge Renner Cardoso; Ladouceur, Cecile; Birmaher, Boris; Axelson, David; Phillips, Mary

2013-12-30

Failure to distinguish bipolar depression (BDd) from the unipolar depression of major depressive disorder (UDd) in adolescents has significant clinical consequences. We aimed to identify differential patterns of functional neural activity in BDd versus UDd and employed two (fearful and happy) facial expression/ gender labeling functional magnetic resonance imaging (fMRI) experiments to study emotion processing in 10 BDd (8 females, mean age=15.1 ± 1.1) compared to age- and gender-matched 10 UDd and 10 healthy control (HC) adolescents who were age- and gender-matched to the BDd group. BDd adolescents, relative to UDd, showed significantly lower activity to both intense happy (e.g., insula and temporal cortex) and intense fearful faces (e.g., frontal precentral cortex). Although the neural regions recruited in each group were not the same, both BDd and UDd adolescents, relative to HC, showed significantly lower neural activity to intense happy and mild happy faces, but elevated neural activity to mild fearful faces. Our results indicated that patterns of neural activity to intense positive and negative emotional stimuli can help differentiate BDd from UDd in adolescents. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Deep Neural Network Detects Quantum Phase Transition

NASA Astrophysics Data System (ADS)

Arai, Shunta; Ohzeki, Masayuki; Tanaka, Kazuyuki

2018-03-01

We detect the quantum phase transition of a quantum many-body system by mapping the observed results of the quantum state onto a neural network. In the present study, we utilized the simplest case of a quantum many-body system, namely a one-dimensional chain of Ising spins with the transverse Ising model. We prepared several spin configurations, which were obtained using repeated observations of the model for a particular strength of the transverse field, as input data for the neural network. Although the proposed method can be employed using experimental observations of quantum many-body systems, we tested our technique with spin configurations generated by a quantum Monte Carlo simulation without initial relaxation. The neural network successfully identified the strength of transverse field only from the spin configurations, leading to consistent estimations of the critical point of our model Γc = J.

[Mucosal Schwann cells hamartoma: Review of a recently described entity].

PubMed

García-Molina, Francisco; Ruíz-Macia, José Antonio; Sola, Joaquin

Neural lesions of the colon may be masses (schwannomas and neurofibromas) or, more frequently, small polyps including perineuromas, ganglioneuromas and granular cell tumors. Some neural lesions are associated with congenital syndromes (neurofibromatosis-1, multiple endocrine neoplasia-2B). Recently, a new entity has been described named mucosal Schwann cell hamartoma, consisting of an intramucosal neural proliferation; to date, less than forty cases have been reported. We report a further case in a patient from whom a polyp was extirpated during colonoscopy screening. Histologically, the polyp showed a lamina propia that contained spindle-shaped cells of neural aspect which could only be identified after a histochemical and immunohistochemical study. Copyright © 2017 Sociedad Española de Anatomía Patológica. Publicado por Elsevier España, S.L.U. All rights reserved.

A quantitative framework to evaluate modeling of cortical development by neural stem cells

PubMed Central

Stein, Jason L.; de la Torre-Ubieta, Luis; Tian, Yuan; Parikshak, Neelroop N.; Hernandez, Israel A.; Marchetto, Maria C.; Baker, Dylan K.; Lu, Daning; Hinman, Cassidy R.; Lowe, Jennifer K.; Wexler, Eric M.; Muotri, Alysson R.; Gage, Fred H.; Kosik, Kenneth S.; Geschwind, Daniel H.

2014-01-01

Summary Neural stem cells have been adopted to model a wide range of neuropsychiatric conditions in vitro. However, how well such models correspond to in vivo brain has not been evaluated in an unbiased, comprehensive manner. We used transcriptomic analyses to compare in vitro systems to developing human fetal brain and observed strong conservation of in vivo gene expression and network architecture in differentiating primary human neural progenitor cells (phNPCs). Conserved modules are enriched in genes associated with ASD, supporting the utility of phNPCs for studying neuropsychiatric disease. We also developed and validated a machine learning approach called CoNTExT that identifies the developmental maturity and regional identity of in vitro models. We observed strong differences between in vitro models, including hiPSC-derived neural progenitors from multiple laboratories. This work provides a systems biology framework for evaluating in vitro systems and supports their value in studying the molecular mechanisms of human neurodevelopmental disease. PMID:24991955

Predicting Internet/E-Commerce Use.

ERIC Educational Resources Information Center

Sexton, Randall S.; Johnson, Richard A.; Hignite, Michael A.

2002-01-01

Describes a study that analyzed variables in order to identify accurate predictors of individuals' use of the Internet and e-commerce. Results of survey research and a neural network identifies gender, overall computer use, job-related use, and home access as important characteristics that should influence use of the Internet and e-commerce.…

Functional Roles of Neural Preparatory Processes in a Cued Stroop Task Revealed by Linking Electrophysiology with Behavioral Performance.

PubMed

Wang, Chao; Ding, Mingzhou; Kluger, Benzi M

2015-01-01

It is well established that cuing facilitates behavioral performance and that different aspects of instructional cues evoke specific neural preparatory processes in cued task-switching paradigms. To deduce the functional role of these neural preparatory processes the majority of studies vary aspects of the experimental paradigm and describe how these variations alter markers of neural preparatory processes. Although these studies provide important insights, they also have notable limitations, particularly in terms of understanding the causal or functional relationship of neural markers to cognitive and behavioral processes. In this study, we sought to address these limitations and uncover the functional roles of neural processes by examining how variability in the amplitude of neural preparatory processes predicts behavioral performance to subsequent stimuli. To achieve this objective 16 young adults were recruited to perform a cued Stroop task while their brain activity was measured using high-density electroencephalography. Four temporally overlapping but functionally and topographically distinct cue-triggered event related potentials (ERPs) were identified: 1) A left-frontotemporal negativity (250-700 ms) that was positively associated with word-reading performance; 2) a midline-frontal negativity (450-800 ms) that was positively associated with color-naming and incongruent performance; 3) a left-frontal negativity (450-800 ms) that was positively associated with switch trial performance; and 4) a centroparietal positivity (450-800 ms) that was positively associated with performance for almost all trial types. These results suggest that at least four dissociable cognitive processes are evoked by instructional cues in the present task, including: 1) domain-specific task facilitation; 2) switch-specific task-set reconfiguration; 3) preparation for response conflict; and 4) proactive attentional control. Examining the relationship between ERPs and behavioral performance provides a functional link between neural markers and the cognitive processes they index.

Inference in the brain: Statistics flowing in redundant population codes

PubMed Central

Pitkow, Xaq; Angelaki, Dora E

2017-01-01

It is widely believed that the brain performs approximate probabilistic inference to estimate causal variables in the world from ambiguous sensory data. To understand these computations, we need to analyze how information is represented and transformed by the actions of nonlinear recurrent neural networks. We propose that these probabilistic computations function by a message-passing algorithm operating at the level of redundant neural populations. To explain this framework, we review its underlying concepts, including graphical models, sufficient statistics, and message-passing, and then describe how these concepts could be implemented by recurrently connected probabilistic population codes. The relevant information flow in these networks will be most interpretable at the population level, particularly for redundant neural codes. We therefore outline a general approach to identify the essential features of a neural message-passing algorithm. Finally, we argue that to reveal the most important aspects of these neural computations, we must study large-scale activity patterns during moderately complex, naturalistic behaviors. PMID:28595050

Neural network application to comprehensive engine diagnostics

NASA Technical Reports Server (NTRS)

Marko, Kenneth A.

1994-01-01

We have previously reported on the use of neural networks for detection and identification of faults in complex microprocessor controlled powertrain systems. The data analyzed in those studies consisted of the full spectrum of signals passing between the engine and the real-time microprocessor controller. The specific task of the classification system was to classify system operation as nominal or abnormal and to identify the fault present. The primary concern in earlier work was the identification of faults, in sensors or actuators in the powertrain system as it was exercised over its full operating range. The use of data from a variety of sources, each contributing some potentially useful information to the classification task, is commonly referred to as sensor fusion and typifies the type of problems successfully addressed using neural networks. In this work we explore the application of neural networks to a different diagnostic problem, the diagnosis of faults in newly manufactured engines and the utility of neural networks for process control.

Identification of Phragmites australis and Spartina alterniflora in the Yangtze Estuary between Bayes and BP neural network using hyper-spectral data

NASA Astrophysics Data System (ADS)

Liu, Pudong; Zhou, Jiayuan; Shi, Runhe; Zhang, Chao; Liu, Chaoshun; Sun, Zhibin; Gao, Wei

2016-09-01

The aim of this work was to identify the coastal wetland plants between Bayes and BP neural network using hyperspectral data in order to optimize the classification method. For this purpose, we chose two dominant plants (invasive S. alterniflora and native P. australis) in the Yangtze Estuary, the leaf spectral reflectance of P. australis and S. alterniflora were measured by ASD field spectral machine. We tested the Bayes method and BP neural network for the identification of these two species. Results showed that three different bands (i.e., 555 nm 711 nm and 920 nm) could be identified as the sensitive bands for the input parameters for the two methods. Bayes method and BP neural network prediction model both performed well (Bayes prediction for 88.57% accuracy, BP neural network model prediction for about 80% accuracy), but Bayes theorem method could give higher accuracy and stability.

Modeling Anterior Development in Mice: Diet as Modulator of Risk for Neural Tube Defects

PubMed Central

Kappen, Claudia

2014-01-01

Head morphogenesis is a complex process that is controlled by multiple signaling centers. The most common defects of cranial development are craniofacial defects, such as cleft lip and cleft palate, and neural tube defects, such as anencephaly and encephalocoele in humans. More than 400 genes that contribute to proper neural tube closure have been identified in experimental animals, but only very few causative gene mutations have been identified in humans, supporting the notion that environmental influences are critical. The intrauterine environment is influenced by maternal nutrition, and hence, maternal diet can modulate the risk for cranial and neural tube defects. This article reviews recent progress toward a better understanding of nutrients during pregnancy, with particular focus on mouse models for defective neural tube closure. At least four major patterns of nutrient responses are apparent, suggesting that multiple pathways are involved in the response, and likely in the underlying pathogenesis of the defects. Folic acid has been the most widely studied nutrient, and the diverse responses of the mouse models to folic acid supplementation indicate that folic acid is not universally beneficial, but that the effect is dependent on genetic configuration. If this is the case for other nutrients as well, efforts to prevent neural tube defects with nutritional supplementation may need to become more specifically targeted than previously appreciated. Mouse models are indispensable for a better understanding of nutrient–gene interactions in normal pregnancies, as well as in those affected by metabolic diseases, such as diabetes and obesity. PMID:24124024

Informatic selection of a neural crest-melanocyte cDNA set for microarray analysis

PubMed Central

Loftus, S. K.; Chen, Y.; Gooden, G.; Ryan, J. F.; Birznieks, G.; Hilliard, M.; Baxevanis, A. D.; Bittner, M.; Meltzer, P.; Trent, J.; Pavan, W.

1999-01-01

With cDNA microarrays, it is now possible to compare the expression of many genes simultaneously. To maximize the likelihood of finding genes whose expression is altered under the experimental conditions, it would be advantageous to be able to select clones for tissue-appropriate cDNA sets. We have taken advantage of the extensive sequence information in the dbEST expressed sequence tag (EST) database to identify a neural crest-derived melanocyte cDNA set for microarray analysis. Analysis of characterized genes with dbEST identified one library that contained ESTs representing 21 neural crest-expressed genes (library 198). The distribution of the ESTs corresponding to these genes was biased toward being derived from library 198. This is in contrast to the EST distribution profile for a set of control genes, characterized to be more ubiquitously expressed in multiple tissues (P < 1 × 10−9). From library 198, a subset of 852 clustered ESTs were selected that have a library distribution profile similar to that of the 21 neural crest-expressed genes. Microarray analysis demonstrated the majority of the neural crest-selected 852 ESTs (Mel1 array) were differentially expressed in melanoma cell lines compared with a non-neural crest kidney epithelial cell line (P < 1 × 10−8). This was not observed with an array of 1,238 ESTs that was selected without library origin bias (P = 0.204). This study presents an approach for selecting tissue-appropriate cDNAs that can be used to examine the expression profiles of developmental processes and diseases. PMID:10430933

Motor Cortex Reorganization across the Lifespan

ERIC Educational Resources Information Center

Plowman, Emily K.; Kleim, Jeffrey A.

2010-01-01

The brain is a highly dynamic structure with the capacity for profound structural and functional change. Such neural plasticity has been well characterized within motor cortex and is believed to represent one of the neural mechanisms for acquiring and modifying motor behaviors. A number of behavioral and neural signals have been identified that…

Reward-related neural activity and structure predict future substance use in dysregulated youth.

PubMed

Bertocci, M A; Bebko, G; Versace, A; Iyengar, S; Bonar, L; Forbes, E E; Almeida, J R C; Perlman, S B; Schirda, C; Travis, M J; Gill, M K; Diwadkar, V A; Sunshine, J L; Holland, S K; Kowatch, R A; Birmaher, B; Axelson, D A; Frazier, T W; Arnold, L E; Fristad, M A; Youngstrom, E A; Horwitz, S M; Findling, R L; Phillips, M L

2017-06-01

Identifying youth who may engage in future substance use could facilitate early identification of substance use disorder vulnerability. We aimed to identify biomarkers that predicted future substance use in psychiatrically un-well youth. LASSO regression for variable selection was used to predict substance use 24.3 months after neuroimaging assessment in 73 behaviorally and emotionally dysregulated youth aged 13.9 (s.d. = 2.0) years, 30 female, from three clinical sites in the Longitudinal Assessment of Manic Symptoms (LAMS) study. Predictor variables included neural activity during a reward task, cortical thickness, and clinical and demographic variables. Future substance use was associated with higher left middle prefrontal cortex activity, lower left ventral anterior insula activity, thicker caudal anterior cingulate cortex, higher depression and lower mania scores, not using antipsychotic medication, more parental stress, older age. This combination of variables explained 60.4% of the variance in future substance use, and accurately classified 83.6%. These variables explained a large proportion of the variance, were useful classifiers of future substance use, and showed the value of combining multiple domains to provide a comprehensive understanding of substance use development. This may be a step toward identifying neural measures that can identify future substance use disorder risk, and act as targets for therapeutic interventions.

An investigation into vocal expressions of emotions: the roles of valence, culture, and acoustic factors

NASA Astrophysics Data System (ADS)

Sauter, Disa

This PhD is an investigation of vocal expressions of emotions, mainly focusing on non-verbal sounds such as laughter, cries and sighs. The research examines the roles of categorical and dimensional factors, the contributions of a number of acoustic cues, and the influence of culture. A series of studies established that naive listeners can reliably identify non-verbal vocalisations of positive and negative emotions in forced-choice and rating tasks. Some evidence for underlying dimensions of arousal and valence is found, although each emotion had a discrete expression. The role of acoustic characteristics of the sounds is investigated experimentally and analytically. This work shows that the cues used to identify different emotions vary, although pitch and pitch variation play a central role. The cues used to identify emotions in non-verbal vocalisations differ from the cues used when comprehending speech. An additional set of studies using stimuli consisting of emotional speech demonstrates that these sounds can also be reliably identified, and rely on similar acoustic cues. A series of studies with a pre-literate Namibian tribe shows that non-verbal vocalisations can be recognized across cultures. An fMRI study carried out to investigate the neural processing of non-verbal vocalisations of emotions is presented. The results show activation in pre-motor regions arising from passive listening to non-verbal emotional vocalisations, suggesting neural auditory-motor interactions in the perception of these sounds. In sum, this thesis demonstrates that non-verbal vocalisations of emotions are reliably identifiable tokens of information that belong to discrete categories. These vocalisations are recognisable across vastly different cultures and thus seem to, like facial expressions of emotions, comprise human universals. Listeners rely mainly on pitch and pitch variation to identify emotions in non verbal vocalisations, which differs with the cues used to comprehend speech. When listening to others' emotional vocalisations, a neural system of preparatory motor activation is engaged.

Modeling Training Site Vegetation Coverage Probability with a Random Optimizing Procedure: An Artificial Neural Network Approach.

DTIC Science & Technology

1998-05-01

Coverage Probability with a Random Optimization Procedure: An Artificial Neural Network Approach by Biing T. Guan, George Z. Gertner, and Alan B...Modeling Training Site Vegetation Coverage Probability with a Random Optimizing Procedure: An Artificial Neural Network Approach 6. AUTHOR(S) Biing...coverage based on past coverage. Approach A literature survey was conducted to identify artificial neural network analysis techniques applicable for

Still searching for the engram.

PubMed

Eichenbaum, Howard

2016-09-01

For nearly a century, neurobiologists have searched for the engram-the neural representation of a memory. Early studies showed that the engram is widely distributed both within and across brain areas and is supported by interactions among large networks of neurons. Subsequent research has identified engrams that support memory within dedicated functional systems for habit learning and emotional memory, but the engram for declarative memories has been elusive. Nevertheless, recent years have brought progress from molecular biological approaches that identify neurons and networks that are necessary and sufficient to support memory, and from recording approaches and population analyses that characterize the information coded by large neural networks. These new directions offer the promise of revealing the engrams for episodic and semantic memories.

The Neural Correlates of Driving Performance Identified Using Positron Emission Tomography

ERIC Educational Resources Information Center

Horikawa, E.; Okamura, N.; Tashiro, M.; Sakurada, Y.; Maruyama, M.; Arai, H.; Yamaguchi, K.; Sasaki, H.; Yanai, K.; Itoh, M.

2005-01-01

Driving is a complex behavior involving multiple cognitive domains. To identify neural correlates of driving performance, [^1^5O]H"2O positron emission tomography was performed using a simulated driving task. Compared with the resting condition, simulated driving increased regional cerebral blood flow (rCBF) in the cerebellum, occipital, and…

Applying gene regulatory network logic to the evolution of social behavior.

PubMed

Baran, Nicole M; McGrath, Patrick T; Streelman, J Todd

2017-06-06

Animal behavior is ultimately the product of gene regulatory networks (GRNs) for brain development and neural networks for brain function. The GRN approach has advanced the fields of genomics and development, and we identify organizational similarities between networks of genes that build the brain and networks of neurons that encode brain function. In this perspective, we engage the analogy between developmental networks and neural networks, exploring the advantages of using GRN logic to study behavior. Applying the GRN approach to the brain and behavior provides a quantitative and manipulative framework for discovery. We illustrate features of this framework using the example of social behavior and the neural circuitry of aggression.

At the interface: convergence of neural regeneration and neural prostheses for restoration of function.

PubMed

Grill, W M; McDonald, J W; Peckham, P H; Heetderks, W; Kocsis, J; Weinrich, M

2001-01-01

The rapid pace of recent advances in development and application of electrical stimulation of the nervous system and in neural regeneration has created opportunities to combine these two approaches to restoration of function. This paper relates the discussion on this topic from a workshop at the International Functional Electrical Stimulation Society. The goals of this workshop were to discuss the current state of interaction between the fields of neural regeneration and neural prostheses and to identify potential areas of future research that would have the greatest impact on achieving the common goal of restoring function after neurological damage. Identified areas include enhancement of axonal regeneration with applied electric fields, development of hybrid neural interfaces combining synthetic silicon and biologically derived elements, and investigation of the role of patterned neural activity in regulating various neuronal processes and neurorehabilitation. Increased communication and cooperation between the two communities and recognition by each field that the other has something to contribute to their efforts are needed to take advantage of these opportunities. In addition, creative grants combining the two approaches and more flexible funding mechanisms to support the convergence of their perspectives are necessary to achieve common objectives.

Decoding power-spectral profiles from FMRI brain activities during naturalistic auditory experience.

PubMed

Hu, Xintao; Guo, Lei; Han, Junwei; Liu, Tianming

2017-02-01

Recent studies have demonstrated a close relationship between computational acoustic features and neural brain activities, and have largely advanced our understanding of auditory information processing in the human brain. Along this line, we proposed a multidisciplinary study to examine whether power spectral density (PSD) profiles can be decoded from brain activities during naturalistic auditory experience. The study was performed on a high resolution functional magnetic resonance imaging (fMRI) dataset acquired when participants freely listened to the audio-description of the movie "Forrest Gump". Representative PSD profiles existing in the audio-movie were identified by clustering the audio samples according to their PSD descriptors. Support vector machine (SVM) classifiers were trained to differentiate the representative PSD profiles using corresponding fMRI brain activities. Based on PSD profile decoding, we explored how the neural decodability correlated to power intensity and frequency deviants. Our experimental results demonstrated that PSD profiles can be reliably decoded from brain activities. We also suggested a sigmoidal relationship between the neural decodability and power intensity deviants of PSD profiles. Our study in addition substantiates the feasibility and advantage of naturalistic paradigm for studying neural encoding of complex auditory information.

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

PubMed

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

2017-10-21

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

Neural correlates of monocular and binocular depth cues based on natural images: a LORETA analysis.

PubMed

Fischmeister, Florian Ph S; Bauer, Herbert

2006-10-01

Functional imaging studies investigating perception of depth rely solely on one type of depth cue based on non-natural stimulus material. To overcome these limitations and to provide a more realistic and complete set of depth cues natural stereoscopic images were used in this study. Using slow cortical potentials and source localization we aimed to identify the neural correlates of monocular and binocular depth cues. This study confirms and extends functional imaging studies, showing that natural images provide a good, reliable, and more realistic alternative to artificial stimuli, and demonstrates the possibility to separate the processing of different depth cues.

Induction of specific neuron types by overexpression of single transcription factors.

PubMed

Teratani-Ota, Yusuke; Yamamizu, Kohei; Piao, Yulan; Sharova, Lioudmila; Amano, Misa; Yu, Hong; Schlessinger, David; Ko, Minoru S H; Sharov, Alexei A

2016-10-01

Specific neuronal types derived from embryonic stem cells (ESCs) can facilitate mechanistic studies and potentially aid in regenerative medicine. Existing induction methods, however, mostly rely on the effects of the combined action of multiple added growth factors, which generally tend to result in mixed populations of neurons. Here, we report that overexpression of specific transcription factors (TFs) in ESCs can rather guide the differentiation of ESCs towards specific neuron lineages. Analysis of data on gene expression changes 2 d after induction of each of 185 TFs implicated candidate TFs for further ESC differentiation studies. Induction of 23 TFs (out of 49 TFs tested) for 6 d facilitated neural differentiation of ESCs as inferred from increased proportion of cells with neural progenitor marker PSA-NCAM. We identified early activation of the Notch signaling pathway as a common feature of most potent inducers of neural differentiation. The majority of neuron-like cells generated by induction of Ascl1, Smad7, Nr2f1, Dlx2, Dlx4, Nr2f2, Barhl2, and Lhx1 were GABA-positive and expressed other markers of GABAergic neurons. In the same way, we identified Lmx1a and Nr4a2 as inducers for neurons bearing dopaminergic markers and Isl1, Fezf2, and St18 for cholinergic motor neurons. A time-course experiment with induction of Ascl1 showed early upregulation of most neural-specific messenger RNA (mRNA) and microRNAs (miRNAs). Sets of Ascl1-induced mRNAs and miRNAs were enriched in Ascl1 targets. In further studies, enrichment of cells obtained with the induction of Ascl1, Smad7, and Nr2f1 using microbeads resulted in essentially pure population of neuron-like cells with expression profiles similar to neural tissues and expressed markers of GABAergic neurons. In summary, this study indicates that induction of transcription factors is a promising approach to generate cultures that show the transcription profiles characteristic of specific neural cell types.

The Topographical Mapping in Drosophila Central Complex Network and Its Signal Routing

PubMed Central

Chang, Po-Yen; Su, Ta-Shun; Shih, Chi-Tin; Lo, Chung-Chuan

2017-01-01

Neural networks regulate brain functions by routing signals. Therefore, investigating the detailed organization of a neural circuit at the cellular levels is a crucial step toward understanding the neural mechanisms of brain functions. To study how a complicated neural circuit is organized, we analyzed recently published data on the neural circuit of the Drosophila central complex, a brain structure associated with a variety of functions including sensory integration and coordination of locomotion. We discovered that, except for a small number of “atypical” neuron types, the network structure formed by the identified 194 neuron types can be described by only a few simple mathematical rules. Specifically, the topological mapping formed by these neurons can be reconstructed by applying a generation matrix on a small set of initial neurons. By analyzing how information flows propagate with or without the atypical neurons, we found that while the general pattern of signal propagation in the central complex follows the simple topological mapping formed by the “typical” neurons, some atypical neurons can substantially re-route the signal pathways, implying specific roles of these neurons in sensory signal integration. The present study provides insights into the organization principle and signal integration in the central complex. PMID:28443014

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

PubMed

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.

Functional neuroimaging of psychotherapeutic processes in anxiety and depression: from mechanisms to predictions.

PubMed

Lueken, Ulrike; Hahn, Tim

2016-01-01

The review provides an update of functional neuroimaging studies that identify neural processes underlying psychotherapy and predict outcomes following psychotherapeutic treatment in anxiety and depressive disorders. Following current developments in this field, studies were classified as 'mechanistic' or 'predictor' studies (i.e., informing neurobiological models about putative mechanisms versus aiming to provide predictive information). Mechanistic evidence points toward a dual-process model of psychotherapy in anxiety disorders with abnormally increased limbic activation being decreased, while prefrontal activity is increased. Partly overlapping findings are reported for depression, albeit with a stronger focus on prefrontal activation following treatment. No studies directly comparing neural pathways of psychotherapy between anxiety and depression were detected. Consensus is accumulating for an overarching role of the anterior cingulate cortex in modulating treatment response across disorders. When aiming to quantify clinical utility, the need for single-subject predictions is increasingly recognized and predictions based on machine learning approaches show high translational potential. Present findings encourage the search for predictors providing clinically meaningful information for single patients. However, independent validation as a crucial prerequisite for clinical use is still needed. Identifying nonresponders a priori creates the need for alternative treatment options that can be developed based on an improved understanding of those neural mechanisms underlying effective interventions.

Genetic Rescue of Functional Senescence in Synaptic and Behavioral Plasticity

PubMed Central

Donlea, Jeffrey M.; Ramanan, Narendrakumar; Silverman, Neal; Shaw, Paul J.

2014-01-01

Study Objectives: Aging has been linked with decreased neural plasticity and memory formation in humans and in laboratory model species such as the fruit fly, Drosophila melanogaster. Here, we examine plastic responses following social experience in Drosophila as a high-throughput method to identify interventions that prevent these impairments. Patients or Participants: Wild-type and transgenic Drosophila melanogaster. Design and Interventions: Young (5-day old) or aged (20-day old) adult female Drosophila were housed in socially enriched (n = 35-40) or isolated environments, then assayed for changes in sleep and for structural markers of synaptic terminal growth in the ventral lateral neurons (LNVs) of the circadian clock. Measurements and Results: When young flies are housed in a socially enriched environment, they exhibit synaptic elaboration within a component of the circadian circuitry, the LNVs, which is followed by increased sleep. Aged flies, however, no longer exhibit either of these plastic changes. Because of the tight correlation between neural plasticity and ensuing increases in sleep, we use sleep after enrichment as a high-throughput marker for neural plasticity to identify interventions that prolong youthful plasticity in aged flies. To validate this strategy, we find three independent genetic manipulations that delay age-related losses in plasticity: (1) elevation of dopaminergic signaling, (2) over-expression of the transcription factor blistered (bs) in the LNVs, and (3) reduction of the Imd immune signaling pathway. These findings provide proof-of-principle evidence that measuring changes in sleep in flies after social enrichment may provide a highly scalable assay for the study of age-related deficits in synaptic plasticity. Conclusions: These studies demonstrate that Drosophila provides a promising model for the study of age-related loss of neural plasticity and begin to identify genes that might be manipulated to delay the onset of functional senescence. Citation: Donlea JM, Ramanan N, Silverman N, Shaw PJ. Genetic rescue of functional senescence in synaptic and behavioral plasticity. SLEEP 2014;37(9):1427-1437. PMID:25142573

Neuroanatomical substrates involved in unrelated false facial recognition.

PubMed

Ronzon-Gonzalez, Eliane; Hernandez-Castillo, Carlos R; Pasaye, Erick H; Vaca-Palomares, Israel; Fernandez-Ruiz, Juan

2017-11-22

Identifying faces is a process central for social interaction and a relevant factor in eyewitness theory. False recognition is a critical mistake during an eyewitness's identification scenario because it can lead to a wrongful conviction. Previous studies have described neural areas related to false facial recognition using the standard Deese/Roediger-McDermott (DRM) paradigm, triggering related false recognition. Nonetheless, misidentification of faces without trying to elicit false memories (unrelated false recognition) in a police lineup could involve different cognitive processes, and distinct neural areas. To delve into the neural circuitry of unrelated false recognition, we evaluated the memory and response confidence of participants while watching faces photographs in an fMRI task. Functional activations of unrelated false recognition were identified by contrasting the activation on this condition vs. the activations related to recognition (hits) and correct rejections. The results identified the right precentral and cingulate gyri as areas with distinctive activations during false recognition events suggesting a conflict resulting in a dysfunction during memory retrieval. High confidence suggested that about 50% of misidentifications may be related to an unconscious process. These findings add to our understanding of the construction of facial memories and its biological basis, and the fallibility of the eyewitness testimony.

GUI Type Fault Diagnostic Program for a Turboshaft Engine Using Fuzzy and Neural Networks

NASA Astrophysics Data System (ADS)

Kong, Changduk; Koo, Youngju

2011-04-01

The helicopter to be operated in a severe flight environmental condition must have a very reliable propulsion system. On-line condition monitoring and fault detection of the engine can promote reliability and availability of the helicopter propulsion system. A hybrid health monitoring program using Fuzzy Logic and Neural Network Algorithms can be proposed. In this hybrid method, the Fuzzy Logic identifies easily the faulted components from engine measuring parameter changes, and the Neural Networks can quantify accurately its identified faults. In order to use effectively the fault diagnostic system, a GUI (Graphical User Interface) type program is newly proposed. This program is composed of the real time monitoring part, the engine condition monitoring part and the fault diagnostic part. The real time monitoring part can display measuring parameters of the study turboshaft engine such as power turbine inlet temperature, exhaust gas temperature, fuel flow, torque and gas generator speed. The engine condition monitoring part can evaluate the engine condition through comparison between monitoring performance parameters the base performance parameters analyzed by the base performance analysis program using look-up tables. The fault diagnostic part can identify and quantify the single faults the multiple faults from the monitoring parameters using hybrid method.

Neural Underpinnings of the Identifiable Victim Effect: Affect Shifts Preferences for Giving

PubMed Central

Västfjäll, Daniel; Slovic, Paul; Knutson, Brian

2013-01-01

The “identifiable victim effect” refers to peoples' tendency to preferentially give to identified versus anonymous victims of misfortune, and has been proposed to partly depend on affect. By soliciting charitable donations from human subjects during behavioral and neural (i.e., functional magnetic resonance imaging) experiments, we sought to determine whether and how affect might promote the identifiable victim effect. Behaviorally, subjects gave more to orphans depicted by photographs versus silhouettes, and their shift in preferences was mediated by photograph-induced feelings of positive arousal, but not negative arousal. Neurally, while photographs versus silhouettes elicited activity in widespread circuits associated with facial and affective processing, only nucleus accumbens activity predicted and could statistically account for increased donations. Together, these findings suggest that presenting evaluable identifiable information can recruit positive arousal, which then promotes giving. We propose that affect elicited by identifiable stimuli can compel people to give more to strangers, even despite costs to the self. PMID:24155323

Empirical study on neural network based predictive techniques for automatic number plate recognition

NASA Astrophysics Data System (ADS)

Shashidhara, M. S.; Indrakumar, S. S.

2011-10-01

The objective of this study is to provide an easy, accurate and effective technology for the Bangalore city traffic control. This is based on the techniques of image processing and laser beam technology. The core concept chosen here is an image processing technology by the method of automatic number plate recognition system. First number plate is recognized if any vehicle breaks the traffic rules in the signals. The number is fetched from the database of the RTO office by the process of automatic database fetching. Next this sends the notice and penalty related information to the vehicle owner email-id and an SMS sent to vehicle owner. In this paper, we use of cameras with zooming options & laser beams to get accurate pictures further applied image processing techniques such as Edge detection to understand the vehicle, Identifying the location of the number plate, Identifying the number plate for further use, Plain plate number, Number plate with additional information, Number plates in the different fonts. Accessing the database of the vehicle registration office to identify the name and address and other information of the vehicle number. The updates to be made to the database for the recording of the violation and penalty issues. A feed forward artificial neural network is used for OCR. This procedure is particularly important for glyphs that are visually similar such as '8' and '9' and results in training sets of between 25,000 and 40,000 training samples. Over training of the neural network is prevented by Bayesian regularization. The neural network output value is set to 0.05 when the input is not desired glyph, and 0.95 for correct input.

Sex/Gender Differences in Neural Correlates of Food Stimuli: A Systematic Review of Functional Neuroimaging Studies

PubMed Central

Chao, Ariana M.; Loughead, James; Bakizada, Zayna M.; Hopkins, Christina M.; Geliebter, Allan; Gur, Ruben C.; Wadden, Thomas A.

2017-01-01

Sex and gender differences in food perceptions and eating behaviors have been reported in psychological and behavioral studies. The aim of this systematic review was to synthesize studies that examined sex/gender differences in neural correlates of food stimuli, as assessed by functional neuroimaging. Published studies to 2016 were retrieved and included if they used food or eating stimuli, assessed patients with functional magnetic resonance imaging (fMRI) or positron emission tomography (PET), and compared activation between males and females. Fifteen studies were identified. In response to visual food cues, females, compared to males, showed increased activation in the frontal, limbic, and striatal areas of the brain as well as the fusiform gyrus. Differences in neural response to gustatory stimuli were inconsistent. This body of literature suggests that females may be more reactive to visual food stimuli. However, findings are based on a small number of studies and additional research is needed to establish a more definitive explanation and conclusion. PMID:28371180

A Survey of Neural Network Publications.

ERIC Educational Resources Information Center

Vijayaraman, Bindiganavale S.; Osyk, Barbara

This paper is a survey of publications on artificial neural networks published in business journals for the period ending July 1996. Its purpose is to identify and analyze trends in neural network research during that period. This paper shows which topics have been heavily researched, when these topics were researched, and how that research has…

Identifying apple surface defects using principal components analysis and artifical neural networks

USDA-ARS?s Scientific Manuscript database

Artificial neural networks and principal components were used to detect surface defects on apples in near-infrared images. Neural networks were trained and tested on sets of principal components derived from columns of pixels from images of apples acquired at two wavelengths (740 nm and 950 nm). I...

Neural basis of postural instability identified by VTC and EEG

PubMed Central

Cao, Cheng; Jaiswal, Niharika; Newell, Karl M.

2010-01-01

In this study, we investigated the neural basis of virtual time to contact (VTC) and the hypothesis that VTC provides predictive information for future postural instability. A novel approach to differentiate stable pre-falling and transition-to-instability stages within a single postural trial while a subject was performing a challenging single leg stance with eyes closed was developed. Specifically, we utilized wavelet transform and stage segmentation algorithms using VTC time series data set as an input. The VTC time series was time-locked with multichannel (n = 64) EEG signals to examine its underlying neural substrates. To identify the focal sources of neural substrates of VTC, a two-step approach was designed combining the independent component analysis (ICA) and low-resolution tomography (LORETA) of multichannel EEG. There were two major findings: (1) a significant increase of VTC minimal values (along with enhanced variability of VTC) was observed during the transition-to-instability stage with progression to ultimate loss of balance and falling; and (2) this VTC dynamics was associated with pronounced modulation of EEG predominantly within theta, alpha and gamma frequency bands. The sources of this EEG modulation were identified at the cingulate cortex (ACC) and the junction of precuneus and parietal lobe, as well as at the occipital cortex. The findings support the hypothesis that the systematic increase of minimal values of VTC concomitant with modulation of EEG signals at the frontal-central and parietal–occipital areas serve collectively to predict the future instability in posture. PMID:19655130

Appetitive traits from infancy to adolescence: Using behavioral and neural measures to investigate obesity risk

PubMed Central

Carnell, Susan; Benson, Leora; Pryor, Katherine; Driggin, Elissa

2013-01-01

We come into the world with enduring predispositions towards food, which interact with environmental factors to influence our eating behaviors and weight trajectories. But our fates are not sealed – by learning more about this process we can identify ways to intervene. To advance this goal this we need to be able to assess appetitive traits such as food cue responsiveness and satiety sensitivity at different developmental stages. Assessment methods might include behavioral measures (e.g. eating behavior tests, psychometric questionnaires), but also biomarkers such as brain responses to food cues measured using fMRI. Evidence from infants, children and adolescents suggests that these indices of appetite differ not only with body weight, but also with familial obesity risk as assessed by parent weight, which reflects both genetic and environmental influences, and may provide a useful predictor of obesity development. Behavioral and neural approaches have great potential to inform each other: examining eating behavior can help us identify meaningful appetitive endophenotypes whose neural bases can be probed, while increasing knowledge of the shared neurobiology underlying appetite, obesity, and related behaviors and disorders may ultimately lead to innovative generalized interventions. Another challenge will be to combine comprehensive behavioral and neural assessments of appetitive traits with measures of relevant genetic and environmental factors within long-term prospective studies. This approach may help to identify the biobehavioral precursors of obesity, and lay the foundations for targeted neurobehavioral interventions that can interrupt the pathway to excess weight. PMID:23458627

The Effects of Spaceflight on Neurocognitive Performance: Extent, Longevity, and Neural Bases

NASA Technical Reports Server (NTRS)

Seidler, Rachael D.; Bloomberg, Jacob; Wood, Scott; Mason, Sara; Mulavara, Ajit; Kofman, Igor; De Dios, Yiri; Gadd, Nicole; Stepanyan, Vahagn; Szecsy, Darcy

2017-01-01

Spaceflight effects on gait, balance, & manual motor control have been well studied; some evidence for cognitive deficits. Rodent cortical motor & sensory systems show neural structural alterations with spaceflight. We found extensive changes in behavior, brain structure & brain function following 70 days of HDBR. Specific Aim: Aim 1-Identify changes in brain structure, function, and network integrity as a function of spaceflight and characterize their time course. Aim 2-Specify relationships between structural and functional brain changes and performance and characterize their time course.

The Effects of Spaceflight and Head Down Tilt Bed Rest on Neurocognitive Performance: Extent, Longevity, and Neural Bases

NASA Technical Reports Server (NTRS)

Seidler, Rachael D.; Bloomberg, Jacob; Wood, Scott; Mulavara, Ajit; Kofman, Igor; De Dios, Yiri; Gadd, Nicole; Stepanyan, Vahagn

2017-01-01

Spaceflight effects on gait, balance, & manual motor control have been well studied; some evidence for cognitive deficits. Rodent cortical motor & sensory systems show neural structural alterations with spaceflight. specific Aims: Aim 1-Identify changes in brain structure, function, and network integrity as a function of head down tilt bed rest and spaceflight, and characterize their time course. Aim 2-Specify relationships between structural and functional brain changes and performance and characterize their time course.

From sensation to perception: Using multivariate classification of visual illusions to identify neural correlates of conscious awareness in space and time.

PubMed

Hogendoorn, Hinze

2015-01-01

An important goal of cognitive neuroscience is understanding the neural underpinnings of conscious awareness. Although the low-level processing of sensory input is well understood in most modalities, it remains a challenge to understand how the brain translates such input into conscious awareness. Here, I argue that the application of multivariate pattern classification techniques to neuroimaging data acquired while observers experience perceptual illusions provides a unique way to dissociate sensory mechanisms from mechanisms underlying conscious awareness. Using this approach, it is possible to directly compare patterns of neural activity that correspond to the contents of awareness, independent from changes in sensory input, and to track these neural representations over time at high temporal resolution. I highlight five recent studies using this approach, and provide practical considerations and limitations for future implementations.

Fuzzy wavelet plus a quantum neural network as a design base for power system stability enhancement.

PubMed

Ganjefar, Soheil; Tofighi, Morteza; Karami, Hamidreza

2015-11-01

In this study, we introduce an indirect adaptive fuzzy wavelet neural controller (IAFWNC) as a power system stabilizer to damp inter-area modes of oscillations in a multi-machine power system. Quantum computing is an efficient method for improving the computational efficiency of neural networks, so we developed an identifier based on a quantum neural network (QNN) to train the IAFWNC in the proposed scheme. All of the controller parameters are tuned online based on the Lyapunov stability theory to guarantee the closed-loop stability. A two-machine, two-area power system equipped with a static synchronous series compensator as a series flexible ac transmission system was used to demonstrate the effectiveness of the proposed controller. The simulation and experimental results demonstrated that the proposed IAFWNC scheme can achieve favorable control performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Neural Representation of Consonant-Vowel Transitions in Adults Who Wear Hearing Aids

PubMed Central

Tremblay, Kelly L.; Kalstein, Laura; Billings, Cuttis J.; Souza, Pamela E.

2006-01-01

Hearing aids help compensate for disorders of the ear by amplifying sound; however, their effectiveness also depends on the central auditory system's ability to represent and integrate spectral and temporal information delivered by the hearing aid. The authors report that the neural detection of time-varying acoustic cues contained in speech can be recorded in adult hearing aid users using the acoustic change complex (ACC). Seven adults (50–76 years) with mild to severe sensorineural hearing participated in the study. When presented with 2 identifiable consonant-vowel (CV) syllables (“shee” and “see”), the neural detection of CV transitions (as indicated by the presence of a P1-N1-P2 response) was different for each speech sound. More specifically, the latency of the evoked neural response coincided in time with the onset of the vowel, similar to the latency patterns the authors previously reported in normal-hearing listeners. PMID:16959736

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

PubMed

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/; NCT00025935 and NCT00006177. Published by Elsevier Inc.

An integrated epidemiological and neural net model of the warfarin effect in managed care patients.

PubMed

Jacobs, David M; Stefanovic, Filip; Wilton, Greg; Gomez-Caminero, Andres; Schentag, Jerome J

2017-01-01

Risk assessment tools are utilized to estimate the risk for stroke and need of anticoagulation therapy for patients with atrial fibrillation (AF). These risk stratification scores are limited by the information inputted into them and a reliance on time-independent variables. The objective of this study was to develop a time-dependent neural net model to identify AF populations at high risk of poor clinical outcomes and evaluate the discriminatory ability of the model in a managed care population. We performed a longitudinal, cohort study within a health-maintenance organization from 1997 to 2008. Participants were identified with incident AF irrespective of warfarin status and followed through their duration within the database. Three clinical outcome measures were evaluated including stroke, myocardial infarction, and hemorrhage. A neural net model was developed to identify patients at high risk of clinical events and defined to be an "enriched" patient. The model defines the enrichment based on the top 10 minimum mean square error output parameters that describe the three clinical outcomes. Cox proportional hazard models were utilized to evaluate the outcome measures. Among 285 patients, the mean age was 74±12 years with a mean follow-up of 4.3±2.6 years, and 154 (54%) were treated with warfarin. After propensity score adjustment, warfarin use was associated with a slightly increased risk of adverse outcomes (including stroke, myocardial infarction, and hemorrhage), though it did not attain statistical significance (adjusted hazard ratio [aHR] =1.22; 95% confidence interval [CI] 0.75-1.97; p =0.42). Within the neural net model, subjects at high risk of adverse outcomes were identified and labeled as "enriched." Following propensity score adjustment, enriched subjects were associated with an 81% higher risk of adverse outcomes as compared to nonenriched subjects (aHR=1.81; 95% CI, 1.15-2.88; p =0.01). Enrichment methodology improves the statistical discrimination of meaningful endpoints when used in a health records-based analysis.

Induced Pluripotent Stem Cells for Disease Modeling and Evaluation of Therapeutics for Niemann-Pick Disease Type A.

PubMed

Long, Yan; Xu, Miao; Li, Rong; Dai, Sheng; Beers, Jeanette; Chen, Guokai; Soheilian, Ferri; Baxa, Ulrich; Wang, Mengqiao; Marugan, Juan J; Muro, Silvia; Li, Zhiyuan; Brady, Roscoe; Zheng, Wei

2016-12-01

: Niemann-Pick disease type A (NPA) is a lysosomal storage disease caused by mutations in the SMPD1 gene that encodes acid sphingomyelinase (ASM). Deficiency in ASM function results in lysosomal accumulation of sphingomyelin and neurodegeneration. Currently, there is no effective treatment for NPA. To accelerate drug discovery for treatment of NPA, we generated induced pluripotent stem cells from two patient dermal fibroblast lines and differentiated them into neural stem cells. The NPA neural stem cells exhibit a disease phenotype of lysosomal sphingomyelin accumulation and enlarged lysosomes. By using this disease model, we also evaluated three compounds that reportedly reduced lysosomal lipid accumulation in Niemann-Pick disease type C as well as enzyme replacement therapy with ASM. We found that α-tocopherol, δ-tocopherol, hydroxypropyl-β-cyclodextrin, and ASM reduced sphingomyelin accumulation and enlarged lysosomes in NPA neural stem cells. Therefore, the NPA neural stem cells possess the characteristic NPA disease phenotype that can be ameliorated by tocopherols, cyclodextrin, and ASM. Our results demonstrate the efficacies of cyclodextrin and tocopherols in the NPA cell-based model. Our data also indicate that the NPA neural stem cells can be used as a new cell-based disease model for further study of disease pathophysiology and for high-throughput screening to identify new lead compounds for drug development. Currently, there is no effective treatment for Niemann-Pick disease type A (NPA). To accelerate drug discovery for treatment of NPA, NPA-induced pluripotent stem cells were generated from patient dermal fibroblasts and differentiated into neural stem cells. By using the differentiated NPA neuronal cells as a cell-based disease model system, α-tocopherol, δ-tocopherol, and hydroxypropyl-β-cyclodextrin significantly reduced sphingomyelin accumulation in these NPA neuronal cells. Therefore, this cell-based NPA model can be used for further study of disease pathophysiology and for high-throughput screening of compound libraries to identify lead compounds for drug development. ©AlphaMed Press.

Parameter diagnostics of phases and phase transition learning by neural networks

NASA Astrophysics Data System (ADS)

Suchsland, Philippe; Wessel, Stefan

2018-05-01

We present an analysis of neural network-based machine learning schemes for phases and phase transitions in theoretical condensed matter research, focusing on neural networks with a single hidden layer. Such shallow neural networks were previously found to be efficient in classifying phases and locating phase transitions of various basic model systems. In order to rationalize the emergence of the classification process and for identifying any underlying physical quantities, it is feasible to examine the weight matrices and the convolutional filter kernels that result from the learning process of such shallow networks. Furthermore, we demonstrate how the learning-by-confusing scheme can be used, in combination with a simple threshold-value classification method, to diagnose the learning parameters of neural networks. In particular, we study the classification process of both fully-connected and convolutional neural networks for the two-dimensional Ising model with extended domain wall configurations included in the low-temperature regime. Moreover, we consider the two-dimensional XY model and contrast the performance of the learning-by-confusing scheme and convolutional neural networks trained on bare spin configurations to the case of preprocessed samples with respect to vortex configurations. We discuss these findings in relation to similar recent investigations and possible further applications.

A neural network device for on-line particle identification in cosmic ray experiments

NASA Astrophysics Data System (ADS)

Scrimaglio, R.; Finetti, N.; D'Altorio, L.; Rantucci, E.; Raso, M.; Segreto, E.; Tassoni, A.; Cardarilli, G. C.

2004-05-01

On-line particle identification is one of the main goals of many experiments in space both for rare event studies and for optimizing measurements along the orbital trajectory. Neural networks can be a useful tool for signal processing and real time data analysis in such experiments. In this document we report on the performances of a programmable neural device which was developed in VLSI analog/digital technology. Neurons and synapses were accomplished by making use of Operational Transconductance Amplifier (OTA) structures. In this paper we report on the results of measurements performed in order to verify the agreement of the characteristic curves of each elementary cell with simulations and on the device performances obtained by implementing simple neural structures on the VLSI chip. A feed-forward neural network (Multi-Layer Perceptron, MLP) was implemented on the VLSI chip and trained to identify particles by processing the signals of two-dimensional position-sensitive Si detectors. The radiation monitoring device consisted of three double-sided silicon strip detectors. From the analysis of a set of simulated data it was found that the MLP implemented on the neural device gave results comparable with those obtained with the standard method of analysis confirming that the implemented neural network could be employed for real time particle identification.

Artificial neural network with backpropagation learning to predict mean monthly total ozone in Arosa, Switzerland

NASA Astrophysics Data System (ADS)

Chattopadhyay, Surajit; Bandyopadhyay, Goutami

2007-01-01

Present study deals with the mean monthly total ozone time series over Arosa, Switzerland. The study period is 1932-1971. First of all, the total ozone time series has been identified as a complex system and then Artificial Neural Networks models in the form of Multilayer Perceptron with back propagation learning have been developed. The models are Single-hidden-layer and Two-hidden-layer Perceptrons with sigmoid activation function. After sequential learning with learning rate 0.9 the peak total ozone period (February-May) concentrations of mean monthly total ozone have been predicted by the two neural net models. After training and validation, both of the models are found skillful. But, Two-hidden-layer Perceptron is found to be more adroit in predicting the mean monthly total ozone concentrations over the aforesaid period.

The Effects of Spaceflight and a Spaceflight Analog on Neurocognitive Perfonnance: Extent, Longevity, and Neural Bases

NASA Technical Reports Server (NTRS)

Seidler, R. D.; Mulavara, A. P.; Koppelmans, V.; Erdeniz, B.; Kofman, I. S.; DeDios, Y. E.; Szecsy, D. L.; Riascos-Castaneda, R. F.; Wood, S. J.; Bloomberg, J. J.

2014-01-01

We are conducting ongoing experiments in which we are performing structural and functional magnetic resonance brain imaging to identify the relationships between changes in neurocognitive function and neural structural alterations following a six month International Space Station mission and following 70 days exposure to a spaceflight analog, head down tilt bedrest. Our central hypothesis is that measures of brain structure, function, and network integrity will change from pre to post intervention (spaceflight, bedrest). Moreover, we predict that these changes will correlate with indices of cognitive, sensory, and motor function in a neuroanatomically selective fashion. Our interdisciplinary approach utilizes cutting edge neuroimaging techniques and a broad ranging battery of sensory, motor, and cognitive assessments that will be conducted pre flight, during flight, and post flight to investigate potential neuroplastic and maladaptive brain changes in crewmembers following long-duration spaceflight. Success in this endeavor would 1) result in identification of the underlying neural mechanisms and operational risks of spaceflight-induced changes in behavior, and 2) identify whether a return to normative behavioral function following re-adaptation to Earth's gravitational environment is associated with a restitution of brain structure and function or instead is supported by substitution with compensatory brain processes. With the bedrest study, we will be able to determine the neural and neurocognitive effects of extended duration unloading, reduced sensory inputs, and increased cephalic fluid distribution. This will enable us to parse out the multiple mechanisms contributing to any spaceflight-induced neural structural and behavioral changes that we observe in the flight study. In this presentation I will discuss preliminary results from six participants who have undergone the bed rest protocol. These individuals show decrements in balance and functional mobility, and alterations in brain structure and function, in association with extended bed rest.

Neural Stem Cells (NSCs) and Proteomics*

PubMed Central

Shoemaker, Lorelei D.; Kornblum, Harley I.

2016-01-01

Neural stem cells (NSCs) can self-renew and give rise to the major cell types of the CNS. Studies of NSCs include the investigation of primary, CNS-derived cells as well as animal and human embryonic stem cell (ESC)-derived and induced pluripotent stem cell (iPSC)-derived sources. NSCs provide a means with which to study normal neural development, neurodegeneration, and neurological disease and are clinically relevant sources for cellular repair to the damaged and diseased CNS. Proteomics studies of NSCs have the potential to delineate molecules and pathways critical for NSC biology and the means by which NSCs can participate in neural repair. In this review, we provide a background to NSC biology, including the means to obtain them and the caveats to these processes. We then focus on advances in the proteomic interrogation of NSCs. This includes the analysis of posttranslational modifications (PTMs); approaches to analyzing different proteomic compartments, such the secretome; as well as approaches to analyzing temporal differences in the proteome to elucidate mechanisms of differentiation. We also discuss some of the methods that will undoubtedly be useful in the investigation of NSCs but which have not yet been applied to the field. While many proteomics studies of NSCs have largely catalogued the proteome or posttranslational modifications of specific cellular states, without delving into specific functions, some have led to understandings of functional processes or identified markers that could not have been identified via other means. Many challenges remain in the field, including the precise identification and standardization of NSCs used for proteomic analyses, as well as how to translate fundamental proteomics studies to functional biology. The next level of investigation will require interdisciplinary approaches, combining the skills of those interested in the biochemistry of proteomics with those interested in modulating NSC function. PMID:26494823

Particle identification with neural networks using a rotational invariant moment representation

NASA Astrophysics Data System (ADS)

Sinkus, R.; Voss, T.

1997-02-01

A feed-forward neural network is used to identify electromagnetic particles based upon their showering properties within a segmented calorimeter. The novel feature is the expansion of the energy distribution in terms of moments of the so-called Zernike functions which are invariant under rotation. The multidimensional input distribution for the neural network is transformed via a principle component analysis and rescaled by its respective variances to ensure input values of the order of one. This results is a better performance in identifying and separating electromagnetic from hadronic particles, especially at low energies.

The Differential Role of Verbal and Spatial Working Memory in the Neural Basis of Arithmetic

PubMed Central

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

2014-01-01

We examine the relations of verbal and spatial WM ability to the neural bases of arithmetic in school-age children. We independently localize brain regions subserving verbal versus spatial representations. For multiplication, higher verbal WM ability is associated with greater recruitment of the left temporal cortex, identified by the verbal localizer. For multiplication and subtraction, higher spatial WM ability is associated with greater recruitment of right parietal cortex, identified by the spatial localizer. Depending on their WM ability, children engage different neural systems that manipulate different representations to solve arithmetic problems. PMID:25144257

Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe

PubMed Central

Shlizerman, Eli; Riffell, Jeffrey A.; Kutz, J. Nathan

2014-01-01

The antennal lobe (AL), olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units), and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (1) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (2) characterize scent recognition, i.e., decision-making based on olfactory signals and (3) infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns. PMID:25165442

Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: A theoretical review of the evidence and future directions for clinical translation

PubMed Central

Williams, Leanne M

2016-01-01

Complex emotional, cognitive and self-reflective functions rely on the activation and connectivity of large-scale neural circuits. These circuits offer a relevant scale of focus for conceptualizing a taxonomy for depression and anxiety based on specific profiles (or biotypes) of neural circuit dysfunction. Here, the theoretical review first outlined the current consensus as to what constitutes the organization of large-scale circuits in the human brain identified using parcellation and meta-analysis. The focus is on neural circuits implicated in resting reflection (“default mode”), detection of “salience”, affective processing (“threat” and “reward”), “attention” and “cognitive control”. Next, the current evidence regarding which type of dysfunctions in these circuits characterize depression and anxiety disorders was reviewed, with an emphasis on published meta-analyses and reviews of circuit dysfunctions that have been identified in at least two well-powered case:control studies. Grounded in the review of these topics, a conceptual framework is proposed for considering neural circuit-defined “biotypes”. In this framework, biotypes are defined by profiles of extent of dysfunction on each large-scale circuit. The clinical implications of a biotype approach for guiding classification and treatment of depression and anxiety is considered. Future research directions will develop the validity and clinical utility of a neural circuit biotype model that spans diagnostic categories and helps to translate neuroscience into clinical practice in the real world. PMID:27653321

Convergence of inhibitory neural inputs regulate motor activity in the murine and monkey stomach

PubMed Central

Shaylor, Lara A.; Hwang, Sung Jin; Sanders, Kenton M.

2016-01-01

Inhibitory motor neurons regulate several gastric motility patterns including receptive relaxation, gastric peristaltic motor patterns, and pyloric sphincter opening. Nitric oxide (NO) and purines have been identified as likely candidates that mediate inhibitory neural responses. However, the contribution from each neurotransmitter has received little attention in the distal stomach. The aims of this study were to identify the roles played by NO and purines in inhibitory motor responses in the antrums of mice and monkeys. By using wild-type mice and mutants with genetically deleted neural nitric oxide synthase (Nos1−/−) and P2Y1 receptors (P2ry1−/−) we examined the roles of NO and purines in postjunctional inhibitory responses in the distal stomach and compared these responses to those in primate stomach. Activation of inhibitory motor nerves using electrical field stimulation (EFS) produced frequency-dependent inhibitory junction potentials (IJPs) that produced muscle relaxations in both species. Stimulation of inhibitory nerves during slow waves terminated pacemaker events and associated contractions. In Nos1−/− mice IJPs and relaxations persisted whereas in P2ry1−/− mice IJPs were absent but relaxations persisted. In the gastric antrum of the non-human primate model Macaca fascicularis, similar NO and purine neural components contributed to inhibition of gastric motor activity. These data support a role of convergent inhibitory neural responses in the regulation of gastric motor activity across diverse species. PMID:27634009

Convergence of inhibitory neural inputs regulate motor activity in the murine and monkey stomach.

PubMed

Shaylor, Lara A; Hwang, Sung Jin; Sanders, Kenton M; Ward, Sean M

2016-11-01

Inhibitory motor neurons regulate several gastric motility patterns including receptive relaxation, gastric peristaltic motor patterns, and pyloric sphincter opening. Nitric oxide (NO) and purines have been identified as likely candidates that mediate inhibitory neural responses. However, the contribution from each neurotransmitter has received little attention in the distal stomach. The aims of this study were to identify the roles played by NO and purines in inhibitory motor responses in the antrums of mice and monkeys. By using wild-type mice and mutants with genetically deleted neural nitric oxide synthase (Nos1 -/- ) and P2Y1 receptors (P2ry1 -/- ) we examined the roles of NO and purines in postjunctional inhibitory responses in the distal stomach and compared these responses to those in primate stomach. Activation of inhibitory motor nerves using electrical field stimulation (EFS) produced frequency-dependent inhibitory junction potentials (IJPs) that produced muscle relaxations in both species. Stimulation of inhibitory nerves during slow waves terminated pacemaker events and associated contractions. In Nos1 -/- mice IJPs and relaxations persisted whereas in P2ry1 -/- mice IJPs were absent but relaxations persisted. In the gastric antrum of the non-human primate model Macaca fascicularis, similar NO and purine neural components contributed to inhibition of gastric motor activity. These data support a role of convergent inhibitory neural responses in the regulation of gastric motor activity across diverse species. Copyright © 2016 the American Physiological Society.

A Role of Phase-Resetting in Coordinating Large Scale Neural Networks During Attention and Goal-Directed Behavior

PubMed Central

Voloh, Benjamin; Womelsdorf, Thilo

2016-01-01

Short periods of oscillatory activation are ubiquitous signatures of neural circuits. A broad range of studies documents not only their circuit origins, but also a fundamental role for oscillatory activity in coordinating information transfer during goal directed behavior. Recent studies suggest that resetting the phase of ongoing oscillatory activity to endogenous or exogenous cues facilitates coordinated information transfer within circuits and between distributed brain areas. Here, we review evidence that pinpoints phase resetting as a critical marker of dynamic state changes of functional networks. Phase resets: (1) set a “neural context” in terms of narrow band frequencies that uniquely characterizes the activated circuits; (2) impose coherent low frequency phases to which high frequency activations can synchronize, identifiable as cross-frequency correlations across large anatomical distances; (3) are critical for neural coding models that depend on phase, increasing the informational content of neural representations; and (4) likely originate from the dynamics of canonical E-I circuits that are anatomically ubiquitous. These multiple signatures of phase resets are directly linked to enhanced information transfer and behavioral success. We survey how phase resets re-organize oscillations in diverse task contexts, including sensory perception, attentional stimulus selection, cross-modal integration, Pavlovian conditioning, and spatial navigation. The evidence we consider suggests that phase-resets can drive changes in neural excitability, ensemble organization, functional networks, and ultimately, overt behavior. PMID:27013986

Largely overlapping neuronal substrates of reactivity to drug, gambling, food and sexual cues: A comprehensive meta-analysis.

PubMed

Noori, Hamid R; Cosa Linan, Alejandro; Spanagel, Rainer

2016-09-01

Cue reactivity to natural and social rewards is essential for motivational behavior. However, cue reactivity to drug rewards can also elicit craving in addicted subjects. The degree to which drug and natural rewards share neural substrates is not known. The objective of this study is to conduct a comprehensive meta-analysis of neuroimaging studies on drug, gambling and natural stimuli (food and sex) to identify the common and distinct neural substrates of cue reactivity to drug and natural rewards. Neural cue reactivity studies were selected for the meta-analysis by means of activation likelihood estimations, followed by sensitivity and clustering analyses of averaged neuronal response patterns. Data from 176 studies (5573 individuals) suggests largely overlapping neural response patterns towards all tested reward modalities. Common cue reactivity to natural and drug rewards was expressed by bilateral neural responses within anterior cingulate gyrus, insula, caudate head, inferior frontal gyrus, middle frontal gyrus and cerebellum. However, drug cues also generated distinct activation patterns in medial frontal gyrus, middle temporal gyrus, posterior cingulate gyrus, caudate body and putamen. Natural (sexual) reward cues induced unique activation of the pulvinar in thalamus. Neural substrates of cue reactivity to alcohol, drugs of abuse, food, sex and gambling are largely overlapping and comprise a network that processes reward, emotional responses and habit formation. This suggests that cue-mediated craving involves mechanisms that are not exclusive for addictive disorders but rather resemble the intersection of information pathways for processing reward, emotional responses, non-declarative memory and obsessive-compulsive behavior. Copyright © 2016 Elsevier B.V. and ECNP. All rights reserved.

A study on using texture analysis methods for identifying lobar fissure regions in isotropic CT images.

PubMed

Wei, Q; Hu, Y

2009-01-01

The major hurdle for segmenting lung lobes in computed tomographic (CT) images is to identify fissure regions, which encase lobar fissures. Accurate identification of these regions is difficult due to the variable shape and appearance of the fissures, along with the low contrast and high noise associated with CT images. This paper studies the effectiveness of two texture analysis methods - the gray level co-occurrence matrix (GLCM) and the gray level run length matrix (GLRLM) - in identifying fissure regions from isotropic CT image stacks. To classify GLCM and GLRLM texture features, we applied a feed-forward back-propagation neural network and achieved the best classification accuracy utilizing 16 quantized levels for computing the GLCM and GLRLM texture features and 64 neurons in the input/hidden layers of the neural network. Tested on isotropic CT image stacks of 24 patients with the pathologic lungs, we obtained accuracies of 86% and 87% for identifying fissure regions using the GLCM and GLRLM methods, respectively. These accuracies compare favorably with surgeons/radiologists' accuracy of 80% for identifying fissure regions in clinical settings. This shows promising potential for segmenting lung lobes using the GLCM and GLRLM methods.

Why Some People Discount More than Others: Baseline Activation in the Dorsal PFC Mediates the Link between COMT Genotype and Impatient Choice

PubMed Central

Gianotti, Lorena R. R.; Figner, Bernd; Ebstein, Richard P.; Knoch, Daria

2012-01-01

Individuals differ widely in how steeply they discount future rewards. The sources of these stable individual differences in delay discounting (DD) are largely unknown. One candidate is the COMT Val158Met polymorphism, known to modulate prefrontal dopamine levels and affect DD. To identify possible neural mechanisms by which this polymorphism may contribute to stable individual DD differences, we measured 73 participants’ neural baseline activation using resting electroencephalogram (EEG). Such neural baseline activation measures are highly heritable and stable over time, thus an ideal endophenotype candidate to explain how genes may influence behavior via individual differences in neural function. After EEG-recording, participants made a series of incentive-compatible intertemporal choices to determine the steepness of their DD. We found that COMT significantly affected DD and that this effect was mediated by baseline activation level in the left dorsal prefrontal cortex (DPFC): (i) COMT had a significant effect on DD such that the number of Val alleles was positively correlated with steeper DD (higher numbers of Val alleles means greater COMT activity and thus lower dopamine levels). (ii) A whole-brain search identified a cluster in left DPFC where baseline activation was correlated with DD; lower activation was associated with steeper DD. (iii) COMT had a significant effect on the baseline activation level in this left DPFC cluster such that a higher number of Val alleles was associated with lower baseline activation. (iv) The effect of COMT on DD was explained by the mediating effect of neural baseline activation in the left DPFC cluster. Our study thus establishes baseline activation level in left DPFC as salient neural signature in the form of an endophenotype that mediates the link between COMT and DD. PMID:22586360

Localization and identification of structural nonlinearities using cascaded optimization and neural networks

NASA Astrophysics Data System (ADS)

Koyuncu, A.; Cigeroglu, E.; Özgüven, H. N.

2017-10-01

In this study, a new approach is proposed for identification of structural nonlinearities by employing cascaded optimization and neural networks. Linear finite element model of the system and frequency response functions measured at arbitrary locations of the system are used in this approach. Using the finite element model, a training data set is created, which appropriately spans the possible nonlinear configurations space of the system. A classification neural network trained on these data sets then localizes and determines the types of all nonlinearities associated with the nonlinear degrees of freedom in the system. A new training data set spanning the parametric space associated with the determined nonlinearities is created to facilitate parametric identification. Utilizing this data set, initially, a feed forward regression neural network is trained, which parametrically identifies the classified nonlinearities. Then, the results obtained are further improved by carrying out an optimization which uses network identified values as starting points. Unlike identification methods available in literature, the proposed approach does not require data collection from the degrees of freedoms where nonlinear elements are attached, and furthermore, it is sufficiently accurate even in the presence of measurement noise. The application of the proposed approach is demonstrated on an example system with nonlinear elements and on a real life experimental setup with a local nonlinearity.

Applications of neural networks in training science.

PubMed

Pfeiffer, Mark; Hohmann, Andreas

2012-04-01

Training science views itself as an integrated and applied science, developing practical measures founded on scientific method. Therefore, it demands consideration of a wide spectrum of approaches and methods. Especially in the field of competitive sports, research questions are usually located in complex environments, so that mainly field studies are drawn upon to obtain broad external validity. Here, the interrelations between different variables or variable sets are mostly of a nonlinear character. In these cases, methods like neural networks, e.g., the pattern recognizing methods of Self-Organizing Kohonen Feature Maps or similar instruments to identify interactions might be successfully applied to analyze data. Following on from a classification of data analysis methods in training-science research, the aim of the contribution is to give examples of varied sports in which network approaches can be effectually used in training science. First, two examples are given in which neural networks are employed for pattern recognition. While one investigation deals with the detection of sporting talent in swimming, the other is located in game sports research, identifying tactical patterns in team handball. The third and last example shows how an artificial neural network can be used to predict competitive performance in swimming. Copyright © 2011 Elsevier B.V. All rights reserved.

Neural Entrainment to Polyrhythms: A Comparison of Musicians and Non-musicians.

PubMed

Stupacher, Jan; Wood, Guilherme; Witte, Matthias

2017-01-01

Music can be thought of as a dynamic path over time. In most cases, the rhythmic structure of this path, such as specific sequences of strong and weak beats or recurring patterns, allows us to predict what and particularly when sounds are going to happen. Without this ability we would not be able to entrain body movements to music, like we do when we dance. By combining EEG and behavioral measures, the current study provides evidence illustrating the importance of ongoing neural oscillations at beat-related frequencies-i.e., neural entrainment-for tracking and predicting musical rhythms. Participants (13 musicians and 13 non-musicians) listened to drum rhythms that switched from a quadruple rhythm to a 3-over-4 polyrhythm. After a silent period of ~2-3 s, participants had to decide whether a target stimulus was presented on time with the triple beat of the polyrhythm, too early, or too late. Results showed that neural oscillations reflected the rhythmic structure of both the simple quadruple rhythm and the more complex polyrhythm with no differences between musicians and non-musicians. During silent periods, the observation of time-frequency plots and more commonly used frequency spectra analyses suggest that beat-related neural oscillations were more pronounced in musicians compared to non-musicians. Neural oscillations during silent periods are not driven by an external input and therefore are thought to reflect top-down controlled endogenous neural entrainment. The functional relevance of endogenous neural entrainment was demonstrated by a positive correlation between the amplitude of task-relevant neural oscillations during silent periods and the number of correctly identified target stimuli. In sum, our findings add to the evidence supporting the neural resonance theory of pulse and meter. Furthermore, they indicate that beat-related top-down controlled neural oscillations can exist without external stimulation and suggest that those endogenous oscillations are strengthened by musical expertise. Finally, this study shows that the analysis of neural oscillations can be a useful tool to assess how we perceive and process complex auditory stimuli such as polyrhythms.

Still searching for the engram

PubMed Central

Eichenbaum, Howard

2016-01-01

For nearly a century neurobiologists have searched for the engram - the neural representation of a memory. Early studies showed that the engram is widely distributed both within and across brain areas and is supported by interactions among large networks of neurons. Subsequent research has identified engrams that support memory within dedicated functional systems for habit learning and emotional memory, but the engram for declarative memories has been elusive. Nevertheless, recent years have brought progress from molecular biological approaches that identify neurons and networks that are necessary and sufficient to support memory, and from recording approaches and population analyses that characterize the information coded by large neural networks. These new directions offer the promise of revealing the engrams for episodic and semantic memories. PMID:26944423

Vocal learning in elephants: neural bases and adaptive context

PubMed Central

Stoeger, Angela S; Manger, Paul

2014-01-01

In the last decade clear evidence has accumulated that elephants are capable of vocal production learning. Examples of vocal imitation are documented in African (Loxodonta africana) and Asian (Elephas maximus) elephants, but little is known about the function of vocal learning within the natural communication systems of either species. We are also just starting to identify the neural basis of elephant vocalizations. The African elephant diencephalon and brainstem possess specializations related to aspects of neural information processing in the motor system (affecting the timing and learning of trunk movements) and the auditory and vocalization system. Comparative interdisciplinary (from behavioral to neuroanatomical) studies are strongly warranted to increase our understanding of both vocal learning and vocal behavior in elephants. PMID:25062469

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

PubMed

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

2013-09-01

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

Neural correlates of the difference between working memory speed and simple sensorimotor speed: an fMRI study.

PubMed

Takeuchi, Hikaru; Sugiura, Motoaki; Sassa, Yuko; Sekiguchi, Atsushi; Yomogida, Yukihito; Taki, Yasuyuki; Kawashima, Ryuta

2012-01-01

The difference between the speed of simple cognitive processes and the speed of complex cognitive processes has various psychological correlates. However, the neural correlates of this difference have not yet been investigated. In this study, we focused on working memory (WM) for typical complex cognitive processes. Functional magnetic resonance imaging data were acquired during the performance of an N-back task, which is a measure of WM for typical complex cognitive processes. In our N-back task, task speed and memory load were varied to identify the neural correlates responsible for the difference between the speed of simple cognitive processes (estimated from the 0-back task) and the speed of WM. Our findings showed that this difference was characterized by the increased activation in the right dorsolateral prefrontal cortex (DLPFC) and the increased functional interaction between the right DLPFC and right superior parietal lobe. Furthermore, the local gray matter volume of the right DLPFC was correlated with participants' accuracy during fast WM tasks, which in turn correlated with a psychometric measure of participants' intelligence. Our findings indicate that the right DLPFC and its related network are responsible for the execution of the fast cognitive processes involved in WM. Identified neural bases may underlie the psychometric differences between the speed with which subjects perform simple cognitive tasks and the speed with which subjects perform more complex cognitive tasks, and explain the previous traditional psychological findings.

Combining Computational Modeling and Neuroimaging to Examine Multiple Category Learning Systems in the Brain

PubMed Central

Nomura, Emi M.; Reber, Paul J.

2012-01-01

Considerable evidence has argued in favor of multiple neural systems supporting human category learning, one based on conscious rule inference and one based on implicit information integration. However, there have been few attempts to study potential system interactions during category learning. The PINNACLE (Parallel Interactive Neural Networks Active in Category Learning) model incorporates multiple categorization systems that compete to provide categorization judgments about visual stimuli. Incorporating competing systems requires inclusion of cognitive mechanisms associated with resolving this competition and creates a potential credit assignment problem in handling feedback. The hypothesized mechanisms make predictions about internal mental states that are not always reflected in choice behavior, but may be reflected in neural activity. Two prior functional magnetic resonance imaging (fMRI) studies of category learning were re-analyzed using PINNACLE to identify neural correlates of internal cognitive states on each trial. These analyses identified additional brain regions supporting the two types of category learning, regions particularly active when the systems are hypothesized to be in maximal competition, and found evidence of covert learning activity in the “off system” (the category learning system not currently driving behavior). These results suggest that PINNACLE provides a plausible framework for how competing multiple category learning systems are organized in the brain and shows how computational modeling approaches and fMRI can be used synergistically to gain access to cognitive processes that support complex decision-making machinery. PMID:24962771

An RNA-binding protein, Qki5, regulates embryonic neural stem cells through pre-mRNA processing in cell adhesion signaling.

PubMed

Hayakawa-Yano, Yoshika; Suyama, Satoshi; Nogami, Masahiro; Yugami, Masato; Koya, Ikuko; Furukawa, Takako; Zhou, Li; Abe, Manabu; Sakimura, Kenji; Takebayashi, Hirohide; Nakanishi, Atsushi; Okano, Hideyuki; Yano, Masato

2017-09-15

Cell type-specific transcriptomes are enabled by the action of multiple regulators, which are frequently expressed within restricted tissue regions. In the present study, we identify one such regulator, Quaking 5 (Qki5), as an RNA-binding protein (RNABP) that is expressed in early embryonic neural stem cells and subsequently down-regulated during neurogenesis. mRNA sequencing analysis in neural stem cell culture indicates that Qki proteins play supporting roles in the neural stem cell transcriptome and various forms of mRNA processing that may result from regionally restricted expression and subcellular localization. Also, our in utero electroporation gain-of-function study suggests that the nuclear-type Qki isoform Qki5 supports the neural stem cell state. We next performed in vivo transcriptome-wide protein-RNA interaction mapping to search for direct targets of Qki5 and elucidate how Qki5 regulates neural stem cell function. Combined with our transcriptome analysis, this mapping analysis yielded a bona fide map of Qki5-RNA interaction at single-nucleotide resolution, the identification of 892 Qki5 direct target genes, and an accurate Qki5-dependent alternative splicing rule in the developing brain. Last, our target gene list provides the first compelling evidence that Qki5 is associated with specific biological events; namely, cell-cell adhesion. This prediction was confirmed by histological analysis of mice in which Qki proteins were genetically ablated, which revealed disruption of the apical surface of the lateral wall in the developing brain. These data collectively indicate that Qki5 regulates communication between neural stem cells by mediating numerous RNA processing events and suggest new links between splicing regulation and neural stem cell states. © 2017 Hayakawa-Yano et al.; Published by Cold Spring Harbor Laboratory Press.

Towards a model-based cognitive neuroscience of stopping - a neuroimaging perspective.

PubMed

Sebastian, Alexandra; Forstmann, Birte U; Matzke, Dora

2018-07-01

Our understanding of the neural correlates of response inhibition has greatly advanced over the last decade. Nevertheless the specific function of regions within this stopping network remains controversial. The traditional neuroimaging approach cannot capture many processes affecting stopping performance. Despite the shortcomings of the traditional neuroimaging approach and a great progress in mathematical and computational models of stopping, model-based cognitive neuroscience approaches in human neuroimaging studies are largely lacking. To foster model-based approaches to ultimately gain a deeper understanding of the neural signature of stopping, we outline the most prominent models of response inhibition and recent advances in the field. We highlight how a model-based approach in clinical samples has improved our understanding of altered cognitive functions in these disorders. Moreover, we show how linking evidence-accumulation models and neuroimaging data improves the identification of neural pathways involved in the stopping process and helps to delineate these from neural networks of related but distinct functions. In conclusion, adopting a model-based approach is indispensable to identifying the actual neural processes underlying stopping. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Age-induced differences in brain neural activation elicited by visual emotional stimuli: A high-density EEG study.

PubMed

Tsolaki, Anthoula C; Kosmidou, Vasiliki E; Kompatsiaris, Ioannis Yiannis; Papadaniil, Chrysa; Hadjileontiadis, Leontios; Tsolaki, Magda

2017-01-06

Identifying the brain sources of neural activation during processing of emotional information remains a very challenging task. In this work, we investigated the response to different emotional stimuli and the effect of age on the neuronal activation. Two negative emotion conditions, i.e., 'anger' and 'fear' faces were presented to 22 adult female participants (11 young and 11 elderly) while acquiring high-density electroencephalogram (EEG) data of 256 channels. Brain source localization was utilized to study the modulations in the early N170 event-related-potential component. The results revealed alterations in the amplitude of N170 and the localization of areas with maximum neural activation. Furthermore, age-induced differences are shown in the topographic maps and the neural activation for both emotional stimuli. Overall, aging appeared to affect the limbic area and its implication to emotional processing. These findings can serve as a step toward the understanding of the way the brain functions and evolves with age which is a significant element in the design of assistive environments. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Neural correlates of nesting behavior in zebra finches (Taeniopygia guttata).

PubMed

Hall, Zachary J; Bertin, Marion; Bailey, Ida E; Meddle, Simone L; Healy, Susan D

2014-05-01

Nest building in birds involves a behavioral sequence (nest material collection and deposition in the nest) that offers a unique model for addressing how the brain sequences motor actions. In this study, we identified brain regions involved in nesting behavior in male and female zebra finches (Taeniopygia guttata). We used Fos immunohistochemistry to quantify production of the immediate early gene protein product Fos (a molecular indicator of neuronal activity) in the brain correlated this expression with the variation in nesting behavior. Using this technique, we found that neural circuitry involved in motor sequencing, social behavior, reward and motivation were active during nesting. Within pairs of nesting birds, the number of times a male picked up or deposited nesting material and the amount of time a female spent in the nest explained the variation in Fos expression in the anterior motor pathway, social behavior network, and reward neural circuits. Identification of the brain regions that are involved in nesting enables us to begin studying the roles of motor sequencing, context, and reward in construction behavior at the neural level. Copyright © 2014 Elsevier B.V. All rights reserved.

Neural correlates of nesting behavior in zebra finches (Taeniopygia guttata)

PubMed Central

Hall, Zachary J.; Bertin, Marion; Bailey, Ida E.; Meddle, Simone L.; Healy, Susan D.

2014-01-01

Nest building in birds involves a behavioral sequence (nest material collection and deposition in the nest) that offers a unique model for addressing how the brain sequences motor actions. In this study, we identified brain regions involved in nesting behavior in male and female zebra finches (Taeniopygia guttata). We used Fos immunohistochemistry to quantify production of the immediate early gene protein product Fos (a molecular indicator of neuronal activity) in the brain correlated this expression with the variation in nesting behavior. Using this technique, we found that neural circuitry involved in motor sequencing, social behavior, reward and motivation were active during nesting. Within pairs of nesting birds, the number of times a male picked up or deposited nesting material and the amount of time a female spent in the nest explained the variation in Fos expression in the anterior motor pathway, social behavior network, and reward neural circuits. Identification of the brain regions that are involved in nesting enables us to begin studying the roles of motor sequencing, context, and reward in construction behavior at the neural level. PMID:24508238

Synaptic transmission at functionally identified synapses in the enteric nervous system: roles for both ionotropic and metabotropic receptors.

PubMed

Gwynne, R M; Bornstein, J C

2007-03-01

Digestion and absorption of nutrients and the secretion and reabsorption of fluid in the gastrointestinal tract are regulated by neurons of the enteric nervous system (ENS), the extensive peripheral nerve network contained within the intestinal wall. The ENS is an important physiological model for the study of neural networks since it is both complex and accessible. At least 20 different neurochemically and functionally distinct classes of enteric neurons have been identified in the guinea pig ileum. These neurons express a wide range of ionotropic and metabotropic receptors. Synaptic potentials mediated by ionotropic receptors such as the nicotinic acetylcholine receptor, P2X purinoceptors and 5-HT(3) receptors are seen in many enteric neurons. However, prominent synaptic potentials mediated by metabotropic receptors, like the P2Y(1) receptor and the NK(1) receptor, are also seen in these neurons. Studies of synaptic transmission between the different neuron classes within the enteric neural pathways have shown that both ionotropic and metabotropic synaptic potentials play major roles at distinct synapses within simple reflex pathways. However, there are still functional synapses at which no known transmitter or receptor has been identified. This review describes the identified roles for both ionotropic and metabotropic neurotransmission at functionally defined synapses within the guinea pig ileum ENS. It is concluded that metabotropic synaptic potentials act as primary transmitters at some synapses. It is suggested identification of the interactions between different synaptic potentials in the production of complex behaviours will require the use of well validated computer models of the enteric neural circuitry.

Designing an Adaptive Web-Based Learning System Based on Students' Cognitive Styles Identified Online

ERIC Educational Resources Information Center

Lo, Jia-Jiunn; Chan, Ya-Chen; Yeh, Shiou-Wen

2012-01-01

This study developed an adaptive web-based learning system focusing on students' cognitive styles. The system is composed of a student model and an adaptation model. It collected students' browsing behaviors to update the student model for unobtrusively identifying student cognitive styles through a multi-layer feed-forward neural network (MLFF).…

Neural Crest Origins of the Neck and Shoulder

PubMed Central

Matsuoka, Toshiyuki; Ahlberg, Per E.; Kessaris, Nicoletta; Iannarelli, Palma; Dennehy, Ulla; Richardson, William D.; McMahon, Andrew P.; Koentges, Georgy

2005-01-01

Summary The neck and shoulder region of vertebrates has undergone a complex evolutionary history. In order to identify its underlying mechanisms we map the destinations of embryonic neural crest and mesodermal stem cells using novel Cre-recombinase mediated transgenesis. The single-cell resolution of this genetic labelling reveals cryptic cell boundaries traversing seemingly homogeneous skeleton of neck and shoulders. Within this complex assembly of bones and muscles we discern a precise code of connectivity that mesenchymal stem cells of neural crest and mesodermal origin both obey as they form muscle scaffolds. Neural crest anchors the head onto the anterior lining of the shoulder girdle, while a Hox gene controlled mesoderm links trunk muscles to the posterior neck and shoulder skeleton. The skeleton that we identify as neural crest is specifically affected in human Klippel-Feil syndrome, Sprengel’s deformity and Arnold-Chiari I/II malformation, providing first insights into their likely aetiology. We identify genes involved in the cellular modularity of neck and shoulder skeleton and propose a new methodology for determining skeletal homologies that is based on muscle attachments. This has allowed us to trace the whereabouts of the cleithrum, the major shoulder bone of extinct land vertebrate ancestors which appears to survive as the scapular spine in living mammals. PMID:16034409

The neuronal correlates of mirror therapy: A functional magnetic resonance imaging study on mirror-induced visual illusions of ankle movements.

PubMed

Guo, Feng; Xu, Qun; Abo Salem, Hassan M; Yao, Yihao; Lou, Jicheng; Huang, Xiaolin

2016-05-15

Recovery in stroke is mediated by neural plasticity. Mirror therapy is an effective method in the rehabilitation of stroke patients, but the mechanism is still obscure. To identify the neural networks associated with the effect of lower-limbs mirror therapy, we investigated a functional magnetic resonance imaging (fMRI) study of mirror-induced visual illusion of ankle movements. Five healthy controls and five left hemiplegic stroke patients performed tasks related to mirror therapy in the fMRI study. Neural activation was compared in a no-mirror condition and a mirror condition. All subjects in the experiment performed the task of flexing and extending the right ankle. In a mirror condition, movement of the left ankle was simulated by mirror reflection of right ankle movement. Changes in neural activation in response to mirror therapy were assessed both in healthy controls and stroke patients. We found strong activation of the motor cortex bilaterally in healthy controls, as well as significant activation of the ipsilateral sensorimotor cortex, the occipital gyrus, and the anterior prefrontal gyrus in stroke patients with respect to the non-mirror condition. We concluded that mirror therapy of ankle movements may induce neural activation of the ipsilesional sensorimotor cortex, and that cortical reorganization may be useful for motor rehabilitation in stroke. Copyright © 2016 Elsevier B.V. All rights reserved.

Individual differences in neural mechanisms of selective auditory attention in preschoolers from lower socioeconomic status backgrounds: an event-related potentials study.

PubMed

Isbell, Elif; Wray, Amanda Hampton; Neville, Helen J

2016-11-01

Selective attention, the ability to enhance the processing of particular input while suppressing the information from other concurrent sources, has been postulated to be a foundational skill for learning and academic achievement. The neural mechanisms of this foundational ability are both vulnerable and enhanceable in children from lower socioeconomic status (SES) families. In the current study, we assessed individual differences in neural mechanisms of this malleable brain function in children from lower SES families. Specifically, we investigated the extent to which individual differences in neural mechanisms of selective auditory attention accounted for variability in nonverbal cognitive abilities in lower SES preschoolers. We recorded event-related potentials (ERPs) during a dichotic listening task and administered nonverbal IQ tasks to 124 lower SES children (77 females) between the ages of 40 and 67 months. The attention effect, i.e., the difference in ERP mean amplitudes elicited by identical probes embedded in stories when attended versus unattended, was significantly correlated with nonverbal IQ scores. Larger, more positive attention effects over the anterior and central electrode locations were associated with superior nonverbal IQ performance. Our findings provide initial evidence for prominent individual differences in neural indices of selective attention in lower SES children. Furthermore, our results indicate a noteworthy relationship between neural mechanisms of selective attention and nonverbal IQ performance in lower SES preschoolers. These findings provide the basis for future research to identify the factors that contribute to such individual differences in neural mechanisms of selective attention. © 2015 John Wiley & Sons Ltd.

Major transcriptome re-organisation and abrupt changes in signalling, cell cycle and chromatin regulation at neural differentiation in vivo.

PubMed

Olivera-Martinez, Isabel; Schurch, Nick; Li, Roman A; Song, Junfang; Halley, Pamela A; Das, Raman M; Burt, Dave W; Barton, Geoffrey J; Storey, Kate G

2014-08-01

Here, we exploit the spatial separation of temporal events of neural differentiation in the elongating chick body axis to provide the first analysis of transcriptome change in progressively more differentiated neural cell populations in vivo. Microarray data, validated against direct RNA sequencing, identified: (1) a gene cohort characteristic of the multi-potent stem zone epiblast, which contains neuro-mesodermal progenitors that progressively generate the spinal cord; (2) a major transcriptome re-organisation as cells then adopt a neural fate; and (3) increasing diversity as neural patterning and neuron production begin. Focussing on the transition from multi-potent to neural state cells, we capture changes in major signalling pathways, uncover novel Wnt and Notch signalling dynamics, and implicate new pathways (mevalonate pathway/steroid biogenesis and TGFβ). This analysis further predicts changes in cellular processes, cell cycle, RNA-processing and protein turnover as cells acquire neural fate. We show that these changes are conserved across species and provide biological evidence for reduced proteasome efficiency and a novel lengthening of S phase. This latter step may provide time for epigenetic events to mediate large-scale transcriptome re-organisation; consistent with this, we uncover simultaneous downregulation of major chromatin modifiers as the neural programme is established. We further demonstrate that transcription of one such gene, HDAC1, is dependent on FGF signalling, making a novel link between signals that control neural differentiation and transcription of a core regulator of chromatin organisation. Our work implicates new signalling pathways and dynamics, cellular processes and epigenetic modifiers in neural differentiation in vivo, identifying multiple new potential cellular and molecular mechanisms that direct differentiation. © 2014. Published by The Company of Biologists Ltd.

Theory of mind in schizophrenia: Exploring neural mechanisms of belief attribution

PubMed Central

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

2014-01-01

Background 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. Methods In the scanner, 12 schizophrenia patients and 13 healthy control subjects performed the Belief Attribution Task with 3 conditions: a false belief condition, a false photograph condition, and a simple reading condition. Results For the false belief vs. simple reading conditions, schizophrenia patients showed reduced neural activation in areas including the temporo-parietal junction (TPJ) and medial prefrontal cortex (MPFC) compared with controls. Further, during the false belief vs. 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 vs. simple reading condition, both groups showed comparable neural activations. Conclusions 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. PMID:22050432

A convolutional neural network to filter artifacts in spectroscopic MRI.

PubMed

Gurbani, Saumya S; Schreibmann, Eduard; Maudsley, Andrew A; Cordova, James Scott; Soher, Brian J; Poptani, Harish; Verma, Gaurav; Barker, Peter B; Shim, Hyunsuk; Cooper, Lee A D

2018-03-09

Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viable imaging modality for studying several neuropathologies. However, a key hurdle in the routine clinical adoption of MRSI is the presence of spectral artifacts that can arise from a number of sources, possibly leading to false information. A deep learning model was developed that was capable of identifying and filtering out poor quality spectra. The core of the model used a tiled convolutional neural network that analyzed frequency-domain spectra to detect artifacts. When compared with a panel of MRS experts, our convolutional neural network achieved high sensitivity and specificity with an area under the curve of 0.95. A visualization scheme was implemented to better understand how the convolutional neural network made its judgement on single-voxel or multivoxel MRSI, and the convolutional neural network was embedded into a pipeline capable of producing whole-brain spectroscopic MRI volumes in real time. The fully automated method for assessment of spectral quality provides a valuable tool to support clinical MRSI or spectroscopic MRI studies for use in fields such as adaptive radiation therapy planning. © 2018 International Society for Magnetic Resonance in Medicine.

Neural networks for Higgs physics

NASA Astrophysics Data System (ADS)

Tentindo-Repond, Silvia; Bhat, Pushpalatha C.; Prosper, Harrison B.

2001-08-01

The main application of neural networks (NN) in Higgs physics so far has been to optimize the signal over background ratio. The positive result obtained imply that the use of NN will lead to a big reduction in the integrated luminosity required for the discovery of the Higgs in RunII. Neural Networks have also been recently used in Higgs physics to set up tagging algorithms to identify the heavy flavor content of jets. Whereas in the previous studies the NN b-tagging methods used are channel-independent, a channel-dependent method has been used in the present work. The signal pp¯→WH→lνbb¯ has been studied against the dominant background pp¯→Wbb¯, in an attempt to improve the signal over background ratio by trying to push the invariant mass of the background events further away from the signal. This result would get the equivalent effect of an improved mass resolution.

Using c-Jun to identify fear extinction learning-specific patterns of neural activity that are affected by single prolonged stress.

PubMed

Knox, Dayan; Stanfield, Briana R; Staib, Jennifer M; David, Nina P; DePietro, Thomas; Chamness, Marisa; Schneider, Elizabeth K; Keller, Samantha M; Lawless, Caroline

2018-04-02

Neural circuits via which stress leads to disruptions in fear extinction is often explored in animal stress models. Using the single prolonged stress (SPS) model of post traumatic stress disorder and the immediate early gene (IEG) c-Fos as a measure of neural activity, we previously identified patterns of neural activity through which SPS disrupts extinction retention. However, none of these stress effects were specific to fear or extinction learning and memory. C-Jun is another IEG that is sometimes regulated in a different manner to c-Fos and could be used to identify emotional learning/memory specific patterns of neural activity that are sensitive to SPS. Animals were either fear conditioned (CS-fear) or presented with CSs only (CS-only) then subjected to extinction training and testing. C-Jun was then assayed within neural substrates critical for extinction memory. Inhibited c-Jun levels in the hippocampus (Hipp) and enhanced functional connectivity between the ventromedial prefrontal cortex (vmPFC) and basolateral amygdala (BLA) during extinction training was disrupted by SPS in the CS-fear group only. As a result, these effects were specific to emotional learning/memory. SPS also disrupted inhibited Hipp c-Jun levels, enhanced BLA c-Jun levels, and altered functional connectivity among the vmPFC, BLA, and Hipp during extinction testing in SPS rats in the CS-fear and CS-only groups. As a result, these effects were not specific to emotional learning/memory. Our findings suggest that SPS disrupts neural activity specific to extinction memory, but may also disrupt the retention of fear extinction by mechanisms that do not involve emotional learning/memory. Copyright © 2017 Elsevier B.V. All rights reserved.

Zebrafish narrowminded suggests a genetic link between formation of neural crest and primary sensory neurons

PubMed Central

Bruk Artinger, Kristin; Chitnis, Ajay B.; Mercola, Mark; Driever, Wolfgang

2014-01-01

SUMMARY In the developing vertebrate nervous system, both neural crest and sensory neurons form at the boundary between non-neural ectoderm and the neural plate. From an in situ hybridization based expression analysis screen, we have identified a novel zebrafish mutation, narrowminded (nrd), which reduces the number of early neural crest cells and eliminates Rohon-Beard (RB) sensory neurons. Mosaic analysis has shown that the mutation acts cell autonomously suggesting that nrd is involved in either the reception or interpretation of signals at the lateral neural plate boundary. Characterization of the mutant phenotype indicates that nrd is required for a primary wave of neural crest cell formation during which progenitors generate both RB sensory neurons and neural crest cells. Moreover, the early deficit in neural crest cells in nrd homozygotes is compensated later in development. Thus, we propose that a later wave can compensate for the loss of early neural crest cells but, interestingly, not the RB sensory neurons. We discuss the implications of these findings for the possibility that RB sensory neurons and neural crest cells share a common evolutionary origin. PMID:10457007

Functional Roles of Neural Preparatory Processes in a Cued Stroop Task Revealed by Linking Electrophysiology with Behavioral Performance

PubMed Central

Wang, Chao; Ding, Mingzhou; Kluger, Benzi M.

2015-01-01

It is well established that cuing facilitates behavioral performance and that different aspects of instructional cues evoke specific neural preparatory processes in cued task-switching paradigms. To deduce the functional role of these neural preparatory processes the majority of studies vary aspects of the experimental paradigm and describe how these variations alter markers of neural preparatory processes. Although these studies provide important insights, they also have notable limitations, particularly in terms of understanding the causal or functional relationship of neural markers to cognitive and behavioral processes. In this study, we sought to address these limitations and uncover the functional roles of neural processes by examining how variability in the amplitude of neural preparatory processes predicts behavioral performance to subsequent stimuli. To achieve this objective 16 young adults were recruited to perform a cued Stroop task while their brain activity was measured using high-density electroencephalography. Four temporally overlapping but functionally and topographically distinct cue-triggered event related potentials (ERPs) were identified: 1) A left-frontotemporal negativity (250-700 ms) that was positively associated with word-reading performance; 2) a midline-frontal negativity (450-800 ms) that was positively associated with color-naming and incongruent performance; 3) a left-frontal negativity (450-800 ms) that was positively associated with switch trial performance; and 4) a centroparietal positivity (450-800 ms) that was positively associated with performance for almost all trial types. These results suggest that at least four dissociable cognitive processes are evoked by instructional cues in the present task, including: 1) domain-specific task facilitation; 2) switch-specific task-set reconfiguration; 3) preparation for response conflict; and 4) proactive attentional control. Examining the relationship between ERPs and behavioral performance provides a functional link between neural markers and the cognitive processes they index. PMID:26230662

The association between brain activity and motor imagery during motor illusion induction by vibratory stimulation.

PubMed

Kodama, Takayuki; Nakano, Hideki; Katayama, Osamu; Murata, Shin

2017-01-01

The association between motor imagery ability and brain neural activity that leads to the manifestation of a motor illusion remains unclear. In this study, we examined the association between the ability to generate motor imagery and brain neural activity leading to the induction of a motor illusion by vibratory stimulation. The sample consisted of 20 healthy individuals who did not have movement or sensory disorders. We measured the time between the starting and ending points of a motor illusion (the time to illusion induction, TII) and performed electroencephalography (EEG). We conducted a temporo-spatial analysis on brain activity leading to the induction of motor illusions using the EEG microstate segmentation method. Additionally, we assessed the ability to generate motor imagery using the Japanese version of the Movement Imagery Questionnaire-Revised (JMIQ-R) prior to performing the task and examined the associations among brain neural activity levels as identified by microstate segmentation method, TII, and the JMIQ-R scores. The results showed four typical microstates during TII and significantly higher neural activity in the ventrolateral prefrontal cortex, primary sensorimotor area, supplementary motor area (SMA), and inferior parietal lobule (IPL). Moreover, there were significant negative correlations between the neural activity of the primary motor cortex (MI), SMA, IPL, and TII, and a significant positive correlation between the neural activity of the SMA and the JMIQ-R scores. These findings suggest the possibility that a neural network primarily comprised of the neural activity of SMA and M1, which are involved in generating motor imagery, may be the neural basis for inducing motor illusions. This may aid in creating a new approach to neurorehabilitation that enables a more robust reorganization of the neural base for patients with brain dysfunction with a motor function disorder.

Design development of a neural network-based telemetry monitor

NASA Technical Reports Server (NTRS)

Lembeck, Michael F.

1992-01-01

This paper identifies the requirements and describes an architectural framework for an artificial neural network-based system that is capable of fulfilling monitoring and control requirements of future aerospace missions. Incorporated into this framework are a newly developed training algorithm and the concept of cooperative network architectures. The feasibility of such an approach is demonstrated for its ability to identify faults in low frequency waveforms.

Short-time windowed covariance: A metric for identifying non-stationary, event-related covariant cortical sites

PubMed Central

Blakely, Timothy; Ojemann, Jeffrey G.; Rao, Rajesh P.N.

2014-01-01

Background Electrocorticography (ECoG) signals can provide high spatio-temporal resolution and high signal to noise ratio recordings of local neural activity from the surface of the brain. Previous studies have shown that broad-band, spatially focal, high-frequency increases in ECoG signals are highly correlated with movement and other cognitive tasks and can be volitionally modulated. However, significant additional information may be present in inter-electrode interactions, but adding additional higher order inter-electrode interactions can be impractical from a computational aspect, if not impossible. New method In this paper we present a new method of calculating high frequency interactions between electrodes called Short-Time Windowed Covariance (STWC) that builds on mathematical techniques currently used in neural signal analysis, along with an implementation that accelerates the algorithm by orders of magnitude by leveraging commodity, off-the-shelf graphics processing unit (GPU) hardware. Results Using the hardware-accelerated implementation of STWC, we identify many types of event-related inter-electrode interactions from human ECoG recordings on global and local scales that have not been identified by previous methods. Unique temporal patterns are observed for digit flexion in both low- (10 mm spacing) and high-resolution (3 mm spacing) electrode arrays. Comparison with existing methods Covariance is a commonly used metric for identifying correlated signals, but the standard covariance calculations do not allow for temporally varying covariance. In contrast STWC allows and identifies event-driven changes in covariance without identifying spurious noise correlations. Conclusions: STWC can be used to identify event-related neural interactions whose high computational load is well suited to GPU capabilities. PMID:24211499

Obesity-specific neural cost of maintaining gait performance under complex conditions in community-dwelling older adults.

PubMed

Osofundiya, Olufunmilola; Benden, Mark E; Dowdy, Diane; Mehta, Ranjana K

2016-06-01

Recent evidence of obesity-related changes in the prefrontal cortex during cognitive and seated motor activities has surfaced; however, the impact of obesity on neural activity during ambulation remains unclear. The purpose of this study was to determine obesity-specific neural cost of simple and complex ambulation in older adults. Twenty non-obese and obese individuals, 65years and older, performed three tasks varying in the types of complexity of ambulation (simple walking, walking+cognitive dual-task, and precision walking). Maximum oxygenated hemoglobin, a measure of neural activity, was measured bilaterally using a portable functional near infrared spectroscopy system, and gait speed and performance on the complex tasks were also obtained. Complex ambulatory tasks were associated with ~2-3.5 times greater cerebral oxygenation levels and ~30-40% slower gait speeds when compared to the simple walking task. Additionally, obesity was associated with three times greater oxygenation levels, particularly during the precision gait task, despite obese adults demonstrating similar gait speeds and performances on the complex gait tasks as non-obese adults. Compared to existing studies that focus solely on biomechanical outcomes, the present study is one of the first to examine obesity-related differences in neural activity during ambulation in older adults. In order to maintain gait performance, obesity was associated with higher neural costs, and this was augmented during ambulatory tasks requiring greater precision control. These preliminary findings have clinical implications in identifying individuals who are at greater risk of mobility limitations, particularly when performing complex ambulatory tasks. Copyright © 2016 Elsevier Ltd. All rights reserved.

Diabetic retinopathy screening using deep neural network.

PubMed

Ramachandran, Nishanthan; Hong, Sheng Chiong; Sime, Mary J; Wilson, Graham A

2017-09-07

There is a burgeoning interest in the use of deep neural network in diabetic retinal screening. To determine whether a deep neural network could satisfactorily detect diabetic retinopathy that requires referral to an ophthalmologist from a local diabetic retinal screening programme and an international database. Retrospective audit. Diabetic retinal photos from Otago database photographed during October 2016 (485 photos), and 1200 photos from Messidor international database. Receiver operating characteristic curve to illustrate the ability of a deep neural network to identify referable diabetic retinopathy (moderate or worse diabetic retinopathy or exudates within one disc diameter of the fovea). Area under the receiver operating characteristic curve, sensitivity and specificity. For detecting referable diabetic retinopathy, the deep neural network had an area under receiver operating characteristic curve of 0.901 (95% confidence interval 0.807-0.995), with 84.6% sensitivity and 79.7% specificity for Otago and 0.980 (95% confidence interval 0.973-0.986), with 96.0% sensitivity and 90.0% specificity for Messidor. This study has shown that a deep neural network can detect referable diabetic retinopathy with sensitivities and specificities close to or better than 80% from both an international and a domestic (New Zealand) database. We believe that deep neural networks can be integrated into community screening once they can successfully detect both diabetic retinopathy and diabetic macular oedema. © 2017 Royal Australian and New Zealand College of Ophthalmologists.

The experimental identification of magnetorheological dampers and evaluation of their controllers

NASA Astrophysics Data System (ADS)

Metered, H.; Bonello, P.; Oyadiji, S. O.

2010-05-01

Magnetorheological (MR) fluid dampers are semi-active control devices that have been applied over a wide range of practical vibration control applications. This paper concerns the experimental identification of the dynamic behaviour of an MR damper and the use of the identified parameters in the control of such a damper. Feed-forward and recurrent neural networks are used to model both the direct and inverse dynamics of the damper. Training and validation of the proposed neural networks are achieved by using the data generated through dynamic tests with the damper mounted on a tensile testing machine. The validation test results clearly show that the proposed neural networks can reliably represent both the direct and inverse dynamic behaviours of an MR damper. The effect of the cylinder's surface temperature on both the direct and inverse dynamics of the damper is studied, and the neural network model is shown to be reasonably robust against significant temperature variation. The inverse recurrent neural network model is introduced as a damper controller and experimentally evaluated against alternative controllers proposed in the literature. The results reveal that the neural-based damper controller offers superior damper control. This observation and the added advantages of low-power requirement, extended service life of the damper and the minimal use of sensors, indicate that a neural-based damper controller potentially offers the most cost-effective vibration control solution among the controllers investigated.

Hierarchical Process Control of Chemical Vapor Infiltration.

DTIC Science & Technology

1995-05-31

convergence artificial neural network and used it to discover improved regions of the CVI processing parameter space; also, the Technology Assessment...identify in situ process sensors of considerable promise and as artificial neural network training pairs.

A Hybrid Neural Network and Feature Extraction Technique for Target Recognition.

DTIC Science & Technology

target features are extracted, the extracted data being evaluated in an artificial neural network to identify a target at a location within the image scene from which the different viewing angles extend.

Using FMRI brain activation to identify cognitive states associated with perception of tools and dwellings.

PubMed

Shinkareva, Svetlana V; Mason, Robert A; Malave, Vicente L; Wang, Wei; Mitchell, Tom M; Just, Marcel Adam

2008-01-02

Previous studies have succeeded in identifying the cognitive state corresponding to the perception of a set of depicted categories, such as tools, by analyzing the accompanying pattern of brain activity, measured with fMRI. The current research focused on identifying the cognitive state associated with a 4s viewing of an individual line drawing (1 of 10 familiar objects, 5 tools and 5 dwellings, such as a hammer or a castle). Here we demonstrate the ability to reliably (1) identify which of the 10 drawings a participant was viewing, based on that participant's characteristic whole-brain neural activation patterns, excluding visual areas; (2) identify the category of the object with even higher accuracy, based on that participant's activation; and (3) identify, for the first time, both individual objects and the category of the object the participant was viewing, based only on other participants' activation patterns. The voxels important for category identification were located similarly across participants, and distributed throughout the cortex, focused in ventral temporal perceptual areas but also including more frontal association areas (and somewhat left-lateralized). These findings indicate the presence of stable, distributed, communal, and identifiable neural states corresponding to object concepts.

Zfp488 promotes oligodendrocyte differentiation of neural progenitor cells in adult mice after demyelination

PubMed Central

Soundarapandian, Mangala M.; Selvaraj, Vimal; Lo, U-Ging; Golub, Mari S.; Feldman, Daniel H.; Pleasure, David E.; Deng, Wenbin

2011-01-01

Basic helix-loop-helix transcription factors Olig1 and Olig2 critically regulate oligodendrocyte development. Initially identified as a downstream effector of Olig1, an oligodendrocyte-specific zinc finger transcription repressor, Zfp488, cooperates with Olig2 function. Although Zfp488 is required for oligodendrocyte precursor formation and differentiation during embryonic development, its role in oligodendrogenesis of adult neural progenitor cells is not known. In this study, we tested whether Zfp488 could promote an oligodendrogenic fate in adult subventricular zone (SVZ) neural stem/progenitor cells (NSPCs). Using a cuprizone-induced demyelination model in mice, we examined the effect of retrovirus-mediated Zfp488 overexpression in SVZ NSPCs. Our results showed that Zfp488 efficiently promoted the differentiation of the SVZ NSPCs into mature oligodendrocytes in vivo. After cuprizone-induced demyelination injury, Zfp488-transduced mice also showed significant restoration of motor function to levels comparable to control mice. Together, these findings identify a previously unreported role for Zfp488 in adult oligodendrogenesis and functional remyelination after injury. PMID:22355521

Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays

PubMed Central

Grosberg, Lauren E.; Madugula, Sasidhar; Litke, Alan; Cunningham, John; Chichilnisky, E. J.; Paninski, Liam

2017-01-01

Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible. PMID:29131818

Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays.

PubMed

Mena, Gonzalo E; Grosberg, Lauren E; Madugula, Sasidhar; Hottowy, Paweł; Litke, Alan; Cunningham, John; Chichilnisky, E J; Paninski, Liam

2017-11-01

Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible.

Regression-Based Identification of Behavior-Encoding Neurons During Large-Scale Optical Imaging of Neural Activity at Cellular Resolution

PubMed Central

Miri, Andrew; Daie, Kayvon; Burdine, Rebecca D.; Aksay, Emre

2011-01-01

The advent of methods for optical imaging of large-scale neural activity at cellular resolution in behaving animals presents the problem of identifying behavior-encoding cells within the resulting image time series. Rapid and precise identification of cells with particular neural encoding would facilitate targeted activity measurements and perturbations useful in characterizing the operating principles of neural circuits. Here we report a regression-based approach to semiautomatically identify neurons that is based on the correlation of fluorescence time series with quantitative measurements of behavior. The approach is illustrated with a novel preparation allowing synchronous eye tracking and two-photon laser scanning fluorescence imaging of calcium changes in populations of hindbrain neurons during spontaneous eye movement in the larval zebrafish. Putative velocity-to-position oculomotor integrator neurons were identified that showed a broad spatial distribution and diversity of encoding. Optical identification of integrator neurons was confirmed with targeted loose-patch electrical recording and laser ablation. The general regression-based approach we demonstrate should be widely applicable to calcium imaging time series in behaving animals. PMID:21084686

Infrared neural stimulation of human spinal nerve roots in vivo.

PubMed

Cayce, Jonathan M; Wells, Jonathon D; Malphrus, Jonathan D; Kao, Chris; Thomsen, Sharon; Tulipan, Noel B; Konrad, Peter E; Jansen, E Duco; Mahadevan-Jansen, Anita

2015-01-01

Infrared neural stimulation (INS) is a neurostimulation modality that uses pulsed infrared light to evoke artifact-free, spatially precise neural activity with a noncontact interface; however, the technique has not been demonstrated in humans. The objective of this study is to demonstrate the safety and efficacy of INS in humans in vivo. The feasibility of INS in humans was assessed in patients ([Formula: see text]) undergoing selective dorsal root rhizotomy, where hyperactive dorsal roots, identified for transection, were stimulated in vivo with INS on two to three sites per nerve with electromyogram recordings acquired throughout the stimulation. The stimulated dorsal root was removed and histology was performed to determine thermal damage thresholds of INS. Threshold activation of human dorsal rootlets occurred in 63% of nerves for radiant exposures between 0.53 and [Formula: see text]. In all cases, only one or two monitored muscle groups were activated from INS stimulation of a hyperactive spinal root identified by electrical stimulation. Thermal damage was first noted at [Formula: see text] and a [Formula: see text] safety ratio was identified. These findings demonstrate the success of INS as a fresh approach for activating human nerves in vivo and providing the necessary safety data needed to pursue clinically driven therapeutic and diagnostic applications of INS in humans.

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

PubMed

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.

Modeling neural circuits in Parkinson's disease.

PubMed

Psiha, Maria; Vlamos, Panayiotis

2015-01-01

Parkinson's disease (PD) is caused by abnormal neural activity of the basal ganglia which are connected to the cerebral cortex in the brain surface through complex neural circuits. For a better understanding of the pathophysiological mechanisms of PD, it is important to identify the underlying PD neural circuits, and to pinpoint the precise nature of the crucial aberrations in these circuits. In this paper, the general architecture of a hybrid Multilayer Perceptron (MLP) network for modeling the neural circuits in PD is presented. The main idea of the proposed approach is to divide the parkinsonian neural circuitry system into three discrete subsystems: the external stimuli subsystem, the life-threatening events subsystem, and the basal ganglia subsystem. The proposed model, which includes the key roles of brain neural circuit in PD, is based on both feed-back and feed-forward neural networks. Specifically, a three-layer MLP neural network with feedback in the second layer was designed. The feedback in the second layer of this model simulates the dopamine modulatory effect of compacta on striatum.

Real-Time Decentralized Neural Control via Backstepping for a Robotic Arm Powered by Industrial Servomotors.

PubMed

Vazquez, Luis A; Jurado, Francisco; Castaneda, Carlos E; Santibanez, Victor

2018-02-01

This paper presents a continuous-time decentralized neural control scheme for trajectory tracking of a two degrees of freedom direct drive vertical robotic arm. A decentralized recurrent high-order neural network (RHONN) structure is proposed to identify online, in a series-parallel configuration and using the filtered error learning law, the dynamics of the plant. Based on the RHONN subsystems, a local neural controller is derived via backstepping approach. The effectiveness of the decentralized neural controller is validated on a robotic arm platform, of our own design and unknown parameters, which uses industrial servomotors to drive the joints.

Different Neural Processes Accompany Self-Recognition in Photographs Across the Lifespan: An ERP Study Using Dizygotic Twins

PubMed Central

Butler, David L.; Mattingley, Jason B.; Cunnington, Ross; Suddendorf, Thomas

2013-01-01

Our appearance changes over time, yet we can recognize ourselves in photographs from across the lifespan. Researchers have extensively studied self-recognition in photographs and have proposed that specific neural correlates are involved, but few studies have examined self-recognition using images from different periods of life. Here we compared ERP responses to photographs of participants when they were 5–15, 16–25, and 26–45 years old. We found marked differences between the responses to photographs from these time periods in terms of the neural markers generally assumed to reflect (i) the configural processing of faces (i.e., the N170), (ii) the matching of the currently perceived face to a representation already stored in memory (i.e., the P250), and (iii) the retrieval of information about the person being recognized (i.e., the N400). There was no uniform neural signature of visual self-recognition. To test whether there was anything specific to self-recognition in these brain responses, we also asked participants to identify photographs of their dizygotic twins taken from the same time periods. Critically, this allowed us to minimize the confounding effects of exposure, for it is likely that participants have been similarly exposed to each other's faces over the lifespan. The same pattern of neural response emerged with only one exception: the neural marker reflecting the retrieval of mnemonic information (N400) differed across the lifespan for self but not for twin. These results, as well as our novel approach using twins and photographs from across the lifespan, have wide-ranging consequences for the study of self-recognition and the nature of our personal identity through time. PMID:24069151

Different neural processes accompany self-recognition in photographs across the lifespan: an ERP study using dizygotic twins.

PubMed

Butler, David L; Mattingley, Jason B; Cunnington, Ross; Suddendorf, Thomas

2013-01-01

Our appearance changes over time, yet we can recognize ourselves in photographs from across the lifespan. Researchers have extensively studied self-recognition in photographs and have proposed that specific neural correlates are involved, but few studies have examined self-recognition using images from different periods of life. Here we compared ERP responses to photographs of participants when they were 5-15, 16-25, and 26-45 years old. We found marked differences between the responses to photographs from these time periods in terms of the neural markers generally assumed to reflect (i) the configural processing of faces (i.e., the N170), (ii) the matching of the currently perceived face to a representation already stored in memory (i.e., the P250), and (iii) the retrieval of information about the person being recognized (i.e., the N400). There was no uniform neural signature of visual self-recognition. To test whether there was anything specific to self-recognition in these brain responses, we also asked participants to identify photographs of their dizygotic twins taken from the same time periods. Critically, this allowed us to minimize the confounding effects of exposure, for it is likely that participants have been similarly exposed to each other's faces over the lifespan. The same pattern of neural response emerged with only one exception: the neural marker reflecting the retrieval of mnemonic information (N400) differed across the lifespan for self but not for twin. These results, as well as our novel approach using twins and photographs from across the lifespan, have wide-ranging consequences for the study of self-recognition and the nature of our personal identity through time.

A cortical neural prosthesis for restoring and enhancing memory

NASA Astrophysics Data System (ADS)

Berger, Theodore W.; Hampson, Robert E.; Song, Dong; Goonawardena, Anushka; Marmarelis, Vasilis Z.; Deadwyler, Sam A.

2011-08-01

A primary objective in developing a neural prosthesis is to replace neural circuitry in the brain that no longer functions appropriately. Such a goal requires artificial reconstruction of neuron-to-neuron connections in a way that can be recognized by the remaining normal circuitry, and that promotes appropriate interaction. In this study, the application of a specially designed neural prosthesis using a multi-input/multi-output (MIMO) nonlinear model is demonstrated by using trains of electrical stimulation pulses to substitute for MIMO model derived ensemble firing patterns. Ensembles of CA3 and CA1 hippocampal neurons, recorded from rats performing a delayed-nonmatch-to-sample (DNMS) memory task, exhibited successful encoding of trial-specific sample lever information in the form of different spatiotemporal firing patterns. MIMO patterns, identified online and in real-time, were employed within a closed-loop behavioral paradigm. Results showed that the model was able to predict successful performance on the same trial. Also, MIMO model-derived patterns, delivered as electrical stimulation to the same electrodes, improved performance under normal testing conditions and, more importantly, were capable of recovering performance when delivered to animals with ensemble hippocampal activity compromised by pharmacologic blockade of synaptic transmission. These integrated experimental-modeling studies show for the first time that, with sufficient information about the neural coding of memories, a neural prosthesis capable of real-time diagnosis and manipulation of the encoding process can restore and even enhance cognitive, mnemonic processes.

Disambiguating ventral striatum fMRI-related bold signal during reward prediction in schizophrenia

PubMed Central

Morris, R W; Vercammen, A; Lenroot, R; Moore, L; Langton, J M; Short, B; Kulkarni, J; Curtis, J; O'Donnell, M; Weickert, C S; Weickert, T W

2012-01-01

Reward detection, surprise detection and prediction-error signaling have all been proposed as roles for the ventral striatum (vStr). Previous neuroimaging studies of striatal function in schizophrenia have found attenuated neural responses to reward-related prediction errors; however, as prediction errors represent a discrepancy in mesolimbic neural activity between expected and actual events, it is critical to examine responses to both expected and unexpected rewards (URs) in conjunction with expected and UR omissions in order to clarify the nature of ventral striatal dysfunction in schizophrenia. In the present study, healthy adults and people with schizophrenia were tested with a reward-related prediction-error task during functional magnetic resonance imaging to determine whether schizophrenia is associated with altered neural responses in the vStr to rewards, surprise prediction errors or all three factors. In healthy adults, we found neural responses in the vStr were correlated more specifically with prediction errors than to surprising events or reward stimuli alone. People with schizophrenia did not display the normal differential activation between expected and URs, which was partially due to exaggerated ventral striatal responses to expected rewards (right vStr) but also included blunted responses to unexpected outcomes (left vStr). This finding shows that neural responses, which typically are elicited by surprise, can also occur to well-predicted events in schizophrenia and identifies aberrant activity in the vStr as a key node of dysfunction in the neural circuitry used to differentiate expected and unexpected feedback in schizophrenia. PMID:21709684

Applying artificial neural networks to predict communication risks in the emergency department.

PubMed

Bagnasco, Annamaria; Siri, Anna; Aleo, Giuseppe; Rocco, Gennaro; Sasso, Loredana

2015-10-01

To describe the utility of artificial neural networks in predicting communication risks. In health care, effective communication reduces the risk of error. Therefore, it is important to identify the predictive factors of effective communication. Non-technical skills are needed to achieve effective communication. This study explores how artificial neural networks can be applied to predict the risk of communication failures in emergency departments. A multicentre observational study. Data were collected between March-May 2011 by observing the communication interactions of 840 nurses with their patients during their routine activities in emergency departments. The tools used for our observation were a questionnaire to collect personal and descriptive data, level of training and experience and Guilbert's observation grid, applying the Situation-Background-Assessment-Recommendation technique to communication in emergency departments. A total of 840 observations were made on the nurses working in the emergency departments. Based on Guilbert's observation grid, the output variables is likely to influence the risk of communication failure were 'terminology'; 'listening'; 'attention' and 'clarity', whereas nurses' personal characteristics were used as input variables in the artificial neural network model. A model based on the multilayer perceptron topology was developed and trained. The receiver operator characteristic analysis confirmed that the artificial neural network model correctly predicted the performance of more than 80% of the communication failures. The application of the artificial neural network model could offer a valid tool to forecast and prevent harmful communication errors in the emergency department. © 2015 John Wiley & Sons Ltd.

Modulation of task demands suggests that semantic processing interferes with the formation of episodic associations

PubMed Central

Long, Nicole M.; Kahana, Michael J.

2016-01-01

Although episodic and semantic memory share overlapping neural mechanisms, it remains unclear how our pre-existing semantic associations modulate the formation of new, episodic associations. When freely recalling recently studied words, people rely on both episodic and semantic associations, shown through temporal and semantic clustering of responses. We asked whether orienting participants toward semantic associations interferes with or facilitates the formation of episodic associations. We compared electroencephalographic (EEG) activity recorded during the encoding of subsequently recalled words that were either temporally or semantically clustered. Participants studied words with or without a concurrent semantic orienting task. We identified a neural signature of successful episodic association formation whereby high frequency EEG activity (HFA, 44 – 100 Hz) overlying left prefrontal regions increased for subsequently temporally clustered words, but only for those words studied without a concurrent semantic orienting task. To confirm that this disruption in the formation of episodic associations was driven by increased semantic processing, we measured the neural correlates of subsequent semantic clustering. We found that HFA increased for subsequently semantically clustered words only for lists with a concurrent semantic orienting task. This dissociation suggests that increased semantic processing of studied items interferes with the neural processes that support the formation of novel episodic associations. PMID:27617775

Modulation of task demands suggests that semantic processing interferes with the formation of episodic associations.

PubMed

Long, Nicole M; Kahana, Michael J

2017-02-01

Although episodic and semantic memory share overlapping neural mechanisms, it remains unclear how our pre-existing semantic associations modulate the formation of new, episodic associations. When freely recalling recently studied words, people rely on both episodic and semantic associations, shown through temporal and semantic clustering of responses. We asked whether orienting participants toward semantic associations interferes with or facilitates the formation of episodic associations. We compared electroencephalographic (EEG) activity recorded during the encoding of subsequently recalled words that were either temporally or semantically clustered. Participants studied words with or without a concurrent semantic orienting task. We identified a neural signature of successful episodic association formation whereby high-frequency EEG activity (HFA, 44-100 Hz) overlying left prefrontal regions increased for subsequently temporally clustered words, but only for those words studied without a concurrent semantic orienting task. To confirm that this disruption in the formation of episodic associations was driven by increased semantic processing, we measured the neural correlates of subsequent semantic clustering. We found that HFA increased for subsequently semantically clustered words only for lists with a concurrent semantic orienting task. This dissociation suggests that increased semantic processing of studied items interferes with the neural processes that support the formation of novel episodic associations. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A sleep state in Drosophila larvae required for neural stem cell proliferation

PubMed Central

Szuperak, Milan; Churgin, Matthew A; Borja, Austin J; Raizen, David M; Fang-Yen, Christopher

2018-01-01

Sleep during development is involved in refining brain circuitry, but a role for sleep in the earliest periods of nervous system elaboration, when neurons are first being born, has not been explored. Here we identify a sleep state in Drosophila larvae that coincides with a major wave of neurogenesis. Mechanisms controlling larval sleep are partially distinct from adult sleep: octopamine, the Drosophila analog of mammalian norepinephrine, is the major arousal neuromodulator in larvae, but dopamine is not required. Using real-time behavioral monitoring in a closed-loop sleep deprivation system, we find that sleep loss in larvae impairs cell division of neural progenitors. This work establishes a system uniquely suited for studying sleep during nascent periods, and demonstrates that sleep in early life regulates neural stem cell proliferation. PMID:29424688

Long-Latency Reductions in Gamma Power Predict Hemodynamic Changes That Underlie the Negative BOLD Signal

PubMed Central

Harris, Samuel; Bruyns-Haylett, Michael; Kennerley, Aneurin; Zheng, Ying; Martin, Chris; Jones, Myles; Redgrave, Peter; Berwick, Jason

2015-01-01

Studies that use prolonged periods of sensory stimulation report associations between regional reductions in neural activity and negative blood oxygenation level-dependent (BOLD) signaling. However, the neural generators of the negative BOLD response remain to be characterized. Here, we use single-impulse electrical stimulation of the whisker pad in the anesthetized rat to identify components of the neural response that are related to “negative” hemodynamic changes in the brain. Laminar multiunit activity and local field potential recordings of neural activity were performed concurrently with two-dimensional optical imaging spectroscopy measuring hemodynamic changes. Repeated measurements over multiple stimulation trials revealed significant variations in neural responses across session and animal datasets. Within this variation, we found robust long-latency decreases (300 and 2000 ms after stimulus presentation) in gamma-band power (30–80 Hz) in the middle-superficial cortical layers in regions surrounding the activated whisker barrel cortex. This reduction in gamma frequency activity was associated with corresponding decreases in the hemodynamic responses that drive the negative BOLD signal. These findings suggest a close relationship between BOLD responses and neural events that operate over time scales that outlast the initiating sensory stimulus, and provide important insights into the neurophysiological basis of negative neuroimaging signals. PMID:25788681

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

PubMed

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.

Emotion identification and aging: Behavioral and neural age-related changes.

PubMed

Gonçalves, Ana R; Fernandes, Carina; Pasion, Rita; Ferreira-Santos, Fernando; Barbosa, Fernando; Marques-Teixeira, João

2018-05-01

Aging is known to alter the processing of facial expressions of emotion (FEE), however the impact of this alteration is less clear. Additionally, there is little information about the temporal dynamics of the neural processing of facial affect. We examined behavioral and neural age-related changes in the identification of FEE using event-related potentials. Furthermore, we analyze the relationship between behavioral/neural responses and neuropsychological functioning. To this purpose, 30 younger adults, 29 middle-aged adults and 26 older adults identified FEE. The behavioral results showed a similar performance between groups. The neural results showed no significant differences between groups for the P100 component and an increased N170 amplitude in the older group. Furthermore, a pattern of asymmetric activation was evident in the N170 component. Results also suggest deficits in facial feature decoding abilities, reflected by a reduced N250 amplitude in older adults. Neuropsychological functioning predicts P100 modulation, but does not seem to influence emotion identification ability. The findings suggest the existence of a compensatory function that would explain the age-equivalent performance in emotion identification. The study may help future research addressing behavioral and neural processes involved on processing of FEE in neurodegenerative conditions. Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Long-latency reductions in gamma power predict hemodynamic changes that underlie the negative BOLD signal.

PubMed

Boorman, Luke; Harris, Samuel; Bruyns-Haylett, Michael; Kennerley, Aneurin; Zheng, Ying; Martin, Chris; Jones, Myles; Redgrave, Peter; Berwick, Jason

2015-03-18

Studies that use prolonged periods of sensory stimulation report associations between regional reductions in neural activity and negative blood oxygenation level-dependent (BOLD) signaling. However, the neural generators of the negative BOLD response remain to be characterized. Here, we use single-impulse electrical stimulation of the whisker pad in the anesthetized rat to identify components of the neural response that are related to "negative" hemodynamic changes in the brain. Laminar multiunit activity and local field potential recordings of neural activity were performed concurrently with two-dimensional optical imaging spectroscopy measuring hemodynamic changes. Repeated measurements over multiple stimulation trials revealed significant variations in neural responses across session and animal datasets. Within this variation, we found robust long-latency decreases (300 and 2000 ms after stimulus presentation) in gamma-band power (30-80 Hz) in the middle-superficial cortical layers in regions surrounding the activated whisker barrel cortex. This reduction in gamma frequency activity was associated with corresponding decreases in the hemodynamic responses that drive the negative BOLD signal. These findings suggest a close relationship between BOLD responses and neural events that operate over time scales that outlast the initiating sensory stimulus, and provide important insights into the neurophysiological basis of negative neuroimaging signals. Copyright © 2015 Boorman et al.

A differential neural response to threatening and non-threatening negative facial expressions in paranoid and non-paranoid schizophrenics.

PubMed

Phillips, M L; Williams, L; Senior, C; Bullmore, E T; Brammer, M J; Andrew, C; Williams, S C; David, A S

1999-11-08

Several studies have demonstrated impaired facial expression recognition in schizophrenia. Few have examined the neural basis for this; none have compared the neural correlates of facial expression perception in different schizophrenic patient subgroups. We compared neural responses to facial expressions in 10 right-handed schizophrenic patients (five paranoid and five non-paranoid) and five normal volunteers using functional Magnetic Resonance Imaging (fMRI). In three 5-min experiments, subjects viewed alternating 30-s blocks of black-and-white facial expressions of either fear, anger or disgust contrasted with expressions of mild happiness. After scanning, subjects categorised each expression. All patients were less accurate in identifying expressions, and showed less activation to these stimuli than normals. Non-paranoids performed poorly in the identification task and failed to activate neural regions that are normally linked with perception of these stimuli. They categorised disgust as either anger or fear more frequently than paranoids, and demonstrated in response to disgust expressions activation in the amygdala, a region associated with perception of fearful faces. Paranoids were more accurate in recognising expressions, and demonstrated greater activation than non-paranoids to most stimuli. We provide the first evidence for a distinction between two schizophrenic patient subgroups on the basis of recognition of and neural response to different negative facial expressions.

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

PubMed

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.

Monitoring Neural Activity with Bioluminescence during Natural Behavior

PubMed Central

Naumann, Eva A.; Kampff, Adam R.; Prober, David A.; Schier, Alexander F.; Engert, Florian

2010-01-01

Existing techniques for monitoring neural activity in awake, freely behaving vertebrates are invasive and difficult to target to genetically identified neurons. Here we describe the use of bioluminescence to non-invasively monitor the activity of genetically specified neurons in freely behaving zebrafish. Transgenic fish expressing the Ca2+-sensitive photoprotein GFP-apoAequorin (GA) in most neurons generated large and fast bioluminescent signals related to neural activity, neuroluminescence, that could be recorded continuously for many days. To test the limits of this technique, GA was specifically targeted to the hypocretin-positive neurons of the hypothalamus. We found that neuroluminescence generated by this group of ~20 neurons was associated with periods of increased locomotor activity and identified two classes of neural activity corresponding to distinct swim latencies. Thus, our neuroluminescence assay can report, with high temporal resolution and sensitivity, the activity of small subsets of neurons during unrestrained behavior. PMID:20305645

Can beneficial ends justify lying? Neural responses to the passive reception of lies and truth-telling with beneficial and harmful monetary outcomes

PubMed Central

Weber, Bernd

2016-01-01

Can beneficial ends justify morally questionable means? To investigate how monetary outcomes influence the neural responses to lying, we used a modified, cheap talk sender–receiver game in which participants were the direct recipients of lies and truthful statements resulting in either beneficial or harmful monetary outcomes. Both truth-telling (vs lying) as well as beneficial (vs harmful) outcomes elicited higher activity in the nucleus accumbens. Lying (vs truth-telling) elicited higher activity in the supplementary motor area, right inferior frontal gyrus, superior temporal sulcus and left anterior insula. Moreover, the significant interaction effect was found in the left amygdala, which showed that the monetary outcomes modulated the neural activity in the left amygdala only when truth-telling rather than lying. Our study identified a neural network associated with the reception of lies and truth, including the regions linked to the reward process, recognition and emotional experiences of being treated (dis)honestly. PMID:26454816

Neural image analysis for estimating aerobic and anaerobic decomposition of organic matter based on the example of straw decomposition

NASA Astrophysics Data System (ADS)

Boniecki, P.; Nowakowski, K.; Slosarz, P.; Dach, J.; Pilarski, K.

2012-04-01

The purpose of the project was to identify the degree of organic matter decomposition by means of a neural model based on graphical information derived from image analysis. Empirical data (photographs of compost content at various stages of maturation) were used to generate an optimal neural classifier (Boniecki et al. 2009, Nowakowski et al. 2009). The best classification properties were found in an RBF (Radial Basis Function) artificial neural network, which demonstrates that the process is non-linear.

Identification of lithofacies using Kohonen self-organizing maps

USGS Publications Warehouse

Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.

2002-01-01

Lithofacies identification is a primary task in reservoir characterization. Traditional techniques of lithofacies identification from core data are costly, and it is difficult to extrapolate to non-cored wells. We present a low-cost automated technique using Kohonen self-organizing maps (SOMs) to identify systematically and objectively lithofacies from well log data. SOMs are unsupervised artificial neural networks that map the input space into clusters in a topological form whose organization is related to trends in the input data. A case study used five wells located in Appleton Field, Escambia County, Alabama (Smackover Formation, limestone and dolomite, Oxfordian, Jurassic). A five-input, one-dimensional output approach is employed, assuming the lithofacies are in ascending/descending order with respect to paleoenvironmental energy levels. To consider the possible appearance of new logfacies not seen in training mode, which may potentially appear in test wells, the maximum number of outputs is set to 20 instead of four, the designated number of lithosfacies in the study area. This study found eleven major clusters. The clusters were compared to depositional lithofacies identified by manual core examination. The clusters were ordered by the SOM in a pattern consistent with environmental gradients inferred from core examination: bind/boundstone, grainstone, packstone, and wackestone. This new approach predicted lithofacies identity from well log data with 78.8% accuracy which is more accurate than using a backpropagation neural network (57.3%). The clusters produced by the SOM are ordered with respect to paleoenvironmental energy levels. This energy-related clustering provides geologists and petroleum engineers with valuable geologic information about the logfacies and their interrelationships. This advantage is not obtained in backpropagation neural networks and adaptive resonance theory neural networks. ?? 2002 Elsevier Science Ltd. All rights reserved.

Adult Olfactory Bulb Interneuron Phenotypes Identified by Targeting Embryonic and Postnatal Neural Progenitors

PubMed Central

Figueres-Oñate, Maria; López-Mascaraque, Laura

2016-01-01

Neurons are generated during embryonic development and in adulthood, although adult neurogenesis is restricted to two main brain regions, the hippocampus and olfactory bulb. The subventricular zone (SVZ) of the lateral ventricles generates neural stem/progenitor cells that continually provide the olfactory bulb (OB) with new granule or periglomerular neurons, cells that arrive from the SVZ via the rostral migratory stream. The continued neurogenesis and the adequate integration of these newly generated interneurons is essential to maintain homeostasis in the olfactory bulb, where the differentiation of these cells into specific neural cell types is strongly influenced by temporal cues. Therefore, identifying the critical features that control the generation of adult OB interneurons at either pre- or post-natal stages is important to understand the dynamic contribution of neural stem cells. Here, we used in utero and neonatal SVZ electroporation along with a transposase-mediated stable integration plasmid, in order to track interneurons and glial lineages in the OB. These plasmids are valuable tools to study the development of OB interneurons from embryonic and post-natal SVZ progenitors. Accordingly, we examined the location and identity of the adult progeny of embryonic and post-natally transfected progenitors by examining neurochemical markers in the adult OB. These data reveal the different cell types in the olfactory bulb that are generated in function of age and different electroporation conditions. PMID:27242400

Spaceflight Effects on Neurocognitive Performance: Extent, Longevity and Neural Bases

NASA Technical Reports Server (NTRS)

Seidler, R. D.; Mulavara, A. P.; Koppelmans, V.; Kofman, I. S.; Cassady, K.; Yuan, P.; De Dios, Y. E.; Gadd, N.; Riascos, R. F.; Wood, S. J.;

Massively parallel neural circuits for stereoscopic color vision: encoding, decoding and identification.

PubMed

Lazar, Aurel A; Slutskiy, Yevgeniy B; Zhou, Yiyin

2015-03-01

Past work demonstrated how monochromatic visual stimuli could be faithfully encoded and decoded under Nyquist-type rate conditions. Color visual stimuli were then traditionally encoded and decoded in multiple separate monochromatic channels. The brain, however, appears to mix information about color channels at the earliest stages of the visual system, including the retina itself. If information about color is mixed and encoded by a common pool of neurons, how can colors be demixed and perceived? We present Color Video Time Encoding Machines (Color Video TEMs) for encoding color visual stimuli that take into account a variety of color representations within a single neural circuit. We then derive a Color Video Time Decoding Machine (Color Video TDM) algorithm for color demixing and reconstruction of color visual scenes from spikes produced by a population of visual neurons. In addition, we formulate Color Video Channel Identification Machines (Color Video CIMs) for functionally identifying color visual processing performed by a spiking neural circuit. Furthermore, we derive a duality between TDMs and CIMs that unifies the two and leads to a general theory of neural information representation for stereoscopic color vision. We provide examples demonstrating that a massively parallel color visual neural circuit can be first identified with arbitrary precision and its spike trains can be subsequently used to reconstruct the encoded stimuli. We argue that evaluation of the functional identification methodology can be effectively and intuitively performed in the stimulus space. In this space, a signal reconstructed from spike trains generated by the identified neural circuit can be compared to the original stimulus. Copyright © 2014 Elsevier Ltd. All rights reserved.

Detectability of neural tracts and nuclei in the brainstem utilizing 3DAC-PROPELLER.

PubMed

Nishikawa, Taro; Okamoto, Kouichirou; Matsuzawa, Hitoshi; Terumitsu, Makoto; Nakada, Tsutomu; Fujii, Yukihiko

2014-01-01

Despite clinical importance of identifying exact anatomical location of neural tracts and nuclei in the brainstem, no neuroimaging studies have validated the detectability of these structures. The aim of this study was to assess the detectability of the structures using three-dimensional anisotropy contrast-periodically rotated overlapping parallel lines with enhanced reconstruction (3DAC-PROPELLER) imaging. Forty healthy volunteers (21 males, 19 females; 19-53 years, average 23.4 years) participated in this study. 3DAC-PROPELLER axial images were obtained with a 3T-MR system at four levels of the brainstem: the lower midbrain, upper and lower pons, and medulla oblongata. Three experts independently judged whether five tracts (corticospinal tract, medial lemniscus, medial longitudinal fasciculus, central tegmental and spinothalamic tracts) and 10 nuclei (oculomotor and trochlear nuclei, spinal trigeminal, abducens, facial, vestibular, hypoglossal, prepositus, and solitary nuclei, locus ceruleus, superior and inferior olives) on each side could be identified. In total, 240 assessments were made. The five tracts and eight nuclei were identified in all the corresponding assessments, whereas the locus ceruleus and superior olive could not be identified in 3 (1.3%) and 16 (6.7%) assessments, respectively. 3DAC-PROPELLER seems extremely valuable imaging method for mapping out surgical strategies for brainstem lesions. Copyright © 2013 by the American Society of Neuroimaging.

Studies on the detection and identification of the explosives in the terahertz range

NASA Astrophysics Data System (ADS)

Zhou, Qing-li; Zhang, Cun-lin; Li, Wei-Wei; Mu, Kai-jun; Feng, Rui-shu

2008-03-01

The sensing of the explosives and the related compounds is very important for homeland security and defense. Based on the non-invasive terahertz (THz) technology, we have studied some pure and mixed explosives by using the THz time-domain spectroscopy and have obtained the absorption spectra of those samples. The obtained results show that those explosives can be identified due to their different characterized finger-prints in the terahertz frequency region of 0.2-2.5 THz. Furthermore, the spectra analyses indicate that the shape and peak positions of the spectra for these mixed explosive are mainly determined by their explosive components. In order to identify those different kinds of explosives, we have applied the artificial neural network, which is a mathematical device for modeling complex and non-linear functionalities, to our present work. After the repetitive modeling and adequate training with the known input-output data, the identification of the explosive is realized roughly on a multi-hidden-layers model. It is shown that the neural network analyses of the THz spectra would positively identify the explosives and reduce false alarm rates.

Patient-centered interviewing is associated with decreased responses to painful stimuli: an initial fMRI study.

PubMed

Sarinopoulos, Issidoros; Hesson, Ashley M; Gordon, Chelsea; Lee, Seungcheol A; Wang, Lu; Dwamena, Francesca; Smith, Robert C

2013-02-01

To identify the functional magnetic resonance imaging (fMRI) changes associated with a patient-centered interview (PCI) and a positive provider-patient relationship (PPR). Nine female patients participated, five randomly selected to undergo a replicable, evidence-based PCI, the other four receiving standard clinician-centered interviews (CCI). To verify that PCI differed from CCI, we rated the interviews and administered a patient satisfaction with the provider-patient relationship (PPR) questionnaire. Patients were then scanned as they received painful stimulation while viewing pictures of the interviewing doctor and control images (unknown doctor). Interview ratings and questionnaire results confirmed that PCIs and CCIs were performed as planned and PCIs led to a much more positive PPR. We found significantly reduced pain-related neural activation in the left anterior insula region in the PCI group when the interviewing doctor's picture was shown. This study identifies an association between a PCI that produced a positive PPR and reduced pain-related neural responses in the anterior insula. This is an initial step in understanding the neural underpinnings of a PCI. If confirmed, our results indicate one neurobiological underpinning of an effective PCI, providing an additional scientific rationale for its use clinically. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Simulating reading acquisition: The link between reading outcome and multimodal brain signatures of letter-speech sound learning in prereaders.

PubMed

Karipidis, Iliana I; Pleisch, Georgette; Brandeis, Daniel; Roth, Alexander; Röthlisberger, Martina; Schneebeli, Maya; Walitza, Susanne; Brem, Silvia

2018-05-08

During reading acquisition, neural reorganization of the human brain facilitates the integration of letters and speech sounds, which enables successful reading. Neuroimaging and behavioural studies have established that impaired audiovisual integration of letters and speech sounds is a core deficit in individuals with developmental dyslexia. This longitudinal study aimed to identify neural and behavioural markers of audiovisual integration that are related to future reading fluency. We simulated the first step of reading acquisition by performing artificial-letter training with prereading children at risk for dyslexia. Multiple logistic regressions revealed that our training provides new precursors of reading fluency at the beginning of reading acquisition. In addition, an event-related potential around 400 ms and functional magnetic resonance imaging activation patterns in the left planum temporale to audiovisual correspondences improved cross-validated prediction of future poor readers. Finally, an exploratory analysis combining simultaneously acquired electroencephalography and hemodynamic data suggested that modulation of temporoparietal brain regions depended on future reading skills. The multimodal approach demonstrates neural adaptations to audiovisual integration in the developing brain that are related to reading outcome. Despite potential limitations arising from the restricted sample size, our results may have promising implications both for identifying poor-reading children and for monitoring early interventions.

[Terahertz Spectroscopic Identification with Deep Belief Network].

PubMed

Ma, Shuai; Shen, Tao; Wang, Rui-qi; Lai, Hua; Yu, Zheng-tao

2015-12-01

Feature extraction and classification are the key issues of terahertz spectroscopy identification. Because many materials have no apparent absorption peaks in the terahertz band, it is difficult to extract theirs terahertz spectroscopy feature and identify. To this end, a novel of identify terahertz spectroscopy approach with Deep Belief Network (DBN) was studied in this paper, which combines the advantages of DBN and K-Nearest Neighbors (KNN) classifier. Firstly, cubic spline interpolation and S-G filter were used to normalize the eight kinds of substances (ATP, Acetylcholine Bromide, Bifenthrin, Buprofezin, Carbazole, Bleomycin, Buckminster and Cylotriphosphazene) terahertz transmission spectra in the range of 0.9-6 THz. Secondly, the DBN model was built by two restricted Boltzmann machine (RBM) and then trained layer by layer using unsupervised approach. Instead of using handmade features, the DBN was employed to learn suitable features automatically with raw input data. Finally, a KNN classifier was applied to identify the terahertz spectrum. Experimental results show that using the feature learned by DBN can identify the terahertz spectrum of different substances with the recognition rate of over 90%, which demonstrates that the proposed method can automatically extract the effective features of terahertz spectrum. Furthermore, this KNN classifier was compared with others (BP neural network, SOM neural network and RBF neural network). Comparisons showed that the recognition rate of KNN classifier is better than the other three classifiers. Using the approach that automatic extract terahertz spectrum features by DBN can greatly reduce the workload of feature extraction. This proposed method shows a promising future in the application of identifying the mass terahertz spectroscopy.

Effects of Tasks on BOLD Signal Responses to Sentence Contrasts: Review and Commentary

ERIC Educational Resources Information Center

Caplan, David; Gow, David

2012-01-01

Functional neuroimaging studies of syntactic processing have been interpreted as identifying the neural locations of parsing and interpretive operations. However, current behavioral studies of sentence processing indicate that many operations occur simultaneously with parsing and interpretation. In this review, we point to issues that arise in…

Homology and homoplasy of swimming behaviors and neural circuits in the Nudipleura (Mollusca, Gastropoda, Opisthobranchia)

PubMed Central

Newcomb, James M.; Sakurai, Akira; Lillvis, Joshua L.; Gunaratne, Charuni A.; Katz, Paul S.

2012-01-01

How neural circuit evolution relates to behavioral evolution is not well understood. Here the relationship between neural circuits and behavior is explored with respect to the swimming behaviors of the Nudipleura (Mollusca, Gastropoda, Opithobranchia). Nudipleura is a diverse monophyletic clade of sea slugs among which only a small percentage of species can swim. Swimming falls into a limited number of categories, the most prevalent of which are rhythmic left–right body flexions (LR) and rhythmic dorsal–ventral body flexions (DV). The phylogenetic distribution of these behaviors suggests a high degree of homoplasy. The central pattern generator (CPG) underlying DV swimming has been well characterized in Tritonia diomedea and in Pleurobranchaea californica. The CPG for LR swimming has been elucidated in Melibe leonina and Dendronotus iris, which are more closely related. The CPGs for the categorically distinct DV and LR swimming behaviors consist of nonoverlapping sets of homologous identified neurons, whereas the categorically similar behaviors share some homologous identified neurons, although the exact composition of neurons and synapses in the neural circuits differ. The roles played by homologous identified neurons in categorically distinct behaviors differ. However, homologous identified neurons also play different roles even in the swim CPGs of the two LR swimming species. Individual neurons can be multifunctional within a species. Some of those functions are shared across species, whereas others are not. The pattern of use and reuse of homologous neurons in various forms of swimming and other behaviors further demonstrates that the composition of neural circuits influences the evolution of behaviors. PMID:22723353

Efficacy of identifying neural components in the face and emotion processing system in schizophrenia using a dynamic functional localizer.

PubMed

Arnold, Aiden E G F; Iaria, Giuseppe; Goghari, Vina M

2016-02-28

Schizophrenia is associated with deficits in face perception and emotion recognition. Despite consistent behavioural results, the neural mechanisms underlying these cognitive abilities have been difficult to isolate, in part due to differences in neuroimaging methods used between studies for identifying regions in the face processing system. Given this problem, we aimed to validate a recently developed fMRI-based dynamic functional localizer task for use in studies of psychiatric populations and specifically schizophrenia. Previously, this functional localizer successfully identified each of the core face processing regions (i.e. fusiform face area, occipital face area, superior temporal sulcus), and regions within an extended system (e.g. amygdala) in healthy individuals. In this study, we tested the functional localizer success rate in 27 schizophrenia patients and in 24 community controls. Overall, the core face processing regions were localized equally between both the schizophrenia and control group. Additionally, the amygdala, a candidate brain region from the extended system, was identified in nearly half the participants from both groups. These results indicate the effectiveness of a dynamic functional localizer at identifying regions of interest associated with face perception and emotion recognition in schizophrenia. The use of dynamic functional localizers may help standardize the investigation of the facial and emotion processing system in this and other clinical populations. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Lenticular nucleus correlates of general self-efficacy in young adults.

PubMed

Nakagawa, Seishu; Takeuchi, Hikaru; Taki, Yasuyuki; Nouchi, Rui; Kotozaki, Yuka; Shinada, Takamitsu; Maruyama, Tsukasa; Sekiguchi, Atsushi; Iizuka, Kunio; Yokoyama, Ryoichi; Yamamoto, Yuki; Hanawa, Sugiko; Araki, Tsuyoshi; Miyauchi, Carlos Makoto; Magistro, Daniele; Sakaki, Kohei; Jeong, Hyeonjeong; Sasaki, Yukako; Kawashima, Ryuta

2017-09-01

General self-efficacy (GSE) is an important factor in education, social participation, and medical treatment. However, the only study that has investigated the direct association between GSE and a neural correlate did not identify specific brain regions, rather only assessed brain structures, and included older adult subjects. GSE is related to motivation, physical activity, learning, the willingness to initiate behaviour and expend effort, and adjustment. Thus, it was hypothesized in the present study that the neural correlates of GSE might be related to changes in the basal ganglia, which is a region related to the abovementioned self-efficacy factors. This study aimed to identify the brain structures associated with GSE in healthy young adults (n = 1204, 691 males and 513 females, age 20.7 ± 1.8 years) using regional grey matter density and volume (rGMD and rGMV), fractional anisotropy (FA) and mean diffusivity (MD) analyses of magnetic resonance imaging (MRI) data. The findings showed that scores on the GSE Scale (GSES) were associated with a lower MD value in regions from the right putamen to the globus pallidum; however, there were no significant association between GSES scores and regional brain structures using the other analyses (rGMD, rGMV, and FA). Thus, the present findings indicated that the lenticular nucleus is a neural correlate of GSE.

Neural-activity mapping of memory-based dominance in the crow: neural networks integrating individual discrimination and social behaviour control.

PubMed

Nishizawa, K; Izawa, E-I; Watanabe, S

2011-12-01

Large-billed crows (Corvus macrorhynchos), highly social birds, form stable dominance relationships based on the memory of win/loss outcomes of first encounters and on individual discrimination. This socio-cognitive behaviour predicts the existence of neural mechanisms for integration of social behaviour control and individual discrimination. This study aimed to elucidate the neural substrates of memory-based dominance in crows. First, the formation of dominance relationships was confirmed between males in a dyadic encounter paradigm. Next, we examined whether neural activities in 22 focal nuclei of pallium and subpallium were correlated with social behaviour and stimulus familiarity after exposure to dominant/subordinate familiar individuals and unfamiliar conspecifics. Neural activity was determined by measuring expression level of the immediate-early-gene (IEG) protein Zenk. Crows displayed aggressive and/or submissive behaviour to opponents less frequently but more discriminatively in subsequent encounters, suggesting stable dominance based on memory, including win/loss outcomes of the first encounters and individual discrimination. Neural correlates of aggressive and submissive behaviour were found in limbic subpallium including septum, bed nucleus of the striae terminalis (BST), and nucleus taeniae of amygdala (TnA), but also those to familiarity factor in BST and TnA. Contrastingly, correlates of social behaviour were little in pallium and those of familiarity with exposed individuals were identified in hippocampus, medial meso-/nidopallium, and ventro-caudal nidopallium. Given the anatomical connection and neural response patterns of the focal nuclei, neural networks connecting pallium and limbic subpallium via hippocampus could be involved in the integration of individual discrimination and social behaviour control in memory-based dominance in the crow. Copyright © 2011 IBRO. Published by Elsevier Ltd. All rights reserved.

Adult Palatum as a Novel Source of Neural Crest-Related Stem Cells

PubMed Central

Widera, Darius; Zander, Christin; Heidbreder, Meike; Kasperek, Yvonne; Noll, Thomas; Seitz, Oliver; Saldamli, Belma; Sudhoff, Holger; Sader, Robert; Kaltschmidt, Christian; Kaltschmidt, Barbara

2009-01-01

Somatic neural and neural crest stem cells are promising sources for cellular therapy of several neurodegenerative diseases. However, because of practical considerations such as inadequate accessibility of the source material, the application of neural crest stem cells is strictly limited. The secondary palate is a highly regenerative and heavily innervated tissue, which develops embryonically under direct contribution of neural crest cells. Here, we describe for the first time the presence of nestin-positive neural crest-related stem cells within Meissner corpuscles and Merkel cell-neurite complexes located in the hard palate of adult Wistar rats. After isolation, palatal neural crest-related stem cells (pNC-SCs) were cultivated in the presence of epidermal growth factor and fibroblast growth factor under serum-free conditions, resulting in large amounts of neurospheres. We used immunocytochemical techniques and reverse transcriptase-polymerase chain reaction to assess the expression profile of pNC-SCs. In addition to the expression of neural crest stem cell markers such as Nestin, Sox2, and p75, we detected the expression of Klf4, Oct4, and c-Myc. pNC-SCs differentiated efficiently into neuronal and glial cells. Finally, we investigated the potential expression of stemness markers within the human palate. We identified expression of stem cell markers nestin and CD133 and the transcription factors needed for reprogramming of somatic cells into pluripotent cells: Sox2, Oct4, Klf4, and c-Myc. These data show that cells isolated from palatal rugae form neurospheres, are highly plastic, and express neural crest stem cell markers. In addition, pNC-SCs may have the ability to differentiate into functional neurons and glial cells, serving as a starting point for therapeutic studies. Stem Cells 2009;27:1899–1910 PMID:19544446

Maternal vitamin B12 status and risk of neural tube defects in a population with high neural tube defect prevalence and no folic Acid fortification.

PubMed

Molloy, Anne M; Kirke, Peadar N; Troendle, James F; Burke, Helen; Sutton, Marie; Brody, Lawrence C; Scott, John M; Mills, James L

2009-03-01

Folic acid fortification has reduced neural tube defect prevalence by 50% to 70%. It is unlikely that fortification levels will be increased to reduce neural tube defect prevalence further. Therefore, it is important to identify other modifiable risk factors. Vitamin B(12) is metabolically related to folate; moreover, previous studies have found low B(12) status in mothers of children affected by neural tube defect. Our objective was to quantify the effect of low B(12) status on neural tube defect risk in a high-prevalence, unfortified population. We assessed pregnancy vitamin B(12) status concentrations in blood samples taken at an average of 15 weeks' gestation from 3 independent nested case-control groups of Irish women within population-based cohorts, at a time when vitamin supplementation or food fortification was rare. Group 1 blood samples were from 95 women during a neural tube defect-affected pregnancy and 265 control subjects. Group 2 included blood samples from 107 women who had a previous neural tube defect birth but whose current pregnancy was not affected and 414 control subjects. Group 3 samples were from 76 women during an affected pregnancy and 222 control subjects. Mothers of children affected by neural tube defect had significantly lower B(12) status. In all 3 groups those in the lowest B(12) quartiles, compared with the highest, had between two and threefold higher adjusted odds ratios for being the mother of a child affected by neural tube defect. Pregnancy blood B(12) concentrations of <250 ng/L were associated with the highest risks. Deficient or inadequate maternal vitamin B(12) status is associated with a significantly increased risk for neural tube defects. We suggest that women have vitamin B(12) levels of >300 ng/L (221 pmol/L) before becoming pregnant. Improving B(12) status beyond this level may afford a further reduction in risk, but this is uncertain.

A review on cluster estimation methods and their application to neural spike data.

PubMed

Zhang, James; Nguyen, Thanh; Cogill, Steven; Bhatti, Asim; Luo, Lingkun; Yang, Samuel; Nahavandi, Saeid

2018-06-01

The extracellular action potentials recorded on an electrode result from the collective simultaneous electrophysiological activity of an unknown number of neurons. Identifying and assigning these action potentials to their firing neurons-'spike sorting'-is an indispensable step in studying the function and the response of an individual or ensemble of neurons to certain stimuli. Given the task of neural spike sorting, the determination of the number of clusters (neurons) is arguably the most difficult and challenging issue, due to the existence of background noise and the overlap and interactions among neurons in neighbouring regions. It is not surprising that some researchers still rely on visual inspection by experts to estimate the number of clusters in neural spike sorting. Manual inspection, however, is not suitable to processing the vast, ever-growing amount of neural data. To address this pressing need, in this paper, thirty-three clustering validity indices have been comprehensively reviewed and implemented to determine the number of clusters in neural datasets. To gauge the suitability of the indices to neural spike data, and inform the selection process, we then calculated the indices by applying k-means clustering to twenty widely used synthetic neural datasets and one empirical dataset, and compared the performance of these indices against pre-existing ground truth labels. The results showed that the top five validity indices work consistently well across variations in noise level, both for the synthetic datasets and the real dataset. Using these top performing indices provides strong support for the determination of the number of neural clusters, which is essential in the spike sorting process.

A review on cluster estimation methods and their application to neural spike data

NASA Astrophysics Data System (ADS)

Zhang, James; Nguyen, Thanh; Cogill, Steven; Bhatti, Asim; Luo, Lingkun; Yang, Samuel; Nahavandi, Saeid

2018-06-01

The extracellular action potentials recorded on an electrode result from the collective simultaneous electrophysiological activity of an unknown number of neurons. Identifying and assigning these action potentials to their firing neurons—‘spike sorting’—is an indispensable step in studying the function and the response of an individual or ensemble of neurons to certain stimuli. Given the task of neural spike sorting, the determination of the number of clusters (neurons) is arguably the most difficult and challenging issue, due to the existence of background noise and the overlap and interactions among neurons in neighbouring regions. It is not surprising that some researchers still rely on visual inspection by experts to estimate the number of clusters in neural spike sorting. Manual inspection, however, is not suitable to processing the vast, ever-growing amount of neural data. To address this pressing need, in this paper, thirty-three clustering validity indices have been comprehensively reviewed and implemented to determine the number of clusters in neural datasets. To gauge the suitability of the indices to neural spike data, and inform the selection process, we then calculated the indices by applying k-means clustering to twenty widely used synthetic neural datasets and one empirical dataset, and compared the performance of these indices against pre-existing ground truth labels. The results showed that the top five validity indices work consistently well across variations in noise level, both for the synthetic datasets and the real dataset. Using these top performing indices provides strong support for the determination of the number of neural clusters, which is essential in the spike sorting process.

The neural signature of emotional memories in serial crimes.

PubMed

Chassy, Philippe

2017-10-01

Neural plasticity is the process whereby semantic information and emotional responses are stored in neural networks. It is hypothesized that the neural networks built over time to encode the sexual fantasies that motivate serial killers to act should display a unique, detectable activation pattern. The pathological neural watermark hypothesis posits that such networks comprise activation of brain sites that reflect four cognitive components: autobiographical memory, sexual arousal, aggression, and control over aggression. The neural sites performing these cognitive functions have been successfully identified by previous research. The key findings are reviewed to hypothesise the typical pattern of activity that serial killers should display. Through the integration of biological findings into one framework, the neural approach proposed in this paper is in stark contrast with the many theories accounting for serial killers that offer non-medical taxonomies. The pathological neural watermark hypothesis offers a new framework to understand and detect deviant individuals. The technical and legal issues are briefly discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

The lamprey: a jawless vertebrate model system for examining origin of the neural crest and other vertebrate traits.

PubMed

Green, Stephen A; Bronner, Marianne E

2014-01-01

Lampreys are a group of jawless fishes that serve as an important point of comparison for studies of vertebrate evolution. Lampreys and hagfishes are agnathan fishes, the cyclostomes, which sit at a crucial phylogenetic position as the only living sister group of the jawed vertebrates. Comparisons between cyclostomes and jawed vertebrates can help identify shared derived (i.e. synapomorphic) traits that might have been inherited from ancestral early vertebrates, if unlikely to have arisen convergently by chance. One example of a uniquely vertebrate trait is the neural crest, an embryonic tissue that produces many cell types crucial to vertebrate features, such as the craniofacial skeleton, pigmentation of the skin, and much of the peripheral nervous system (Gans and Northcutt, 1983). Invertebrate chordates arguably lack unambiguous neural crest homologs, yet have cells with some similarities, making comparisons with lampreys and jawed vertebrates essential for inferring characteristics of development in early vertebrates, and how they may have evolved from nonvertebrate chordates. Here we review recent research on cyclostome neural crest development, including research on lamprey gene regulatory networks and differentiated neural crest fates. Copyright © 2014 International Society of Differentiation. Published by Elsevier B.V. All rights reserved.

Patterns of neural response to emotive stimuli distinguish the different symptom dimensions of obsessive-compulsive disorder.

PubMed

Phillips, Mary L; Mataix-Cols, David

2004-04-01

Despite its heterogeneous symptomatology, obsessive-compulsive disorder (OCD) is currently conceptualized as a unitary diagnostic entity. Recent factor-analytic studies have identified several OCD symptom dimensions that are associated with different demographic variables, comorbidity, patterns of genetic transmission, and treatment response. Functional abnormalities in neural systems important for emotion perception, including the orbitofrontal cortex, lateral prefrontal cortex, anterior cingulate gyrus, and limbic regions, have been reported in OCD. In this review, we discuss the extent to which neurobiological markers may distinguish these different symptom dimensions and whether specific symptom dimensions, such as contamination/washing, are associated with abnormalities in emotion and, in particular, disgust, perception in OCD. Also discussed are findings that indicate that anxiety can be induced in healthy volunteers in response to OCD symptom-related material, and that associated increases in activity within neural systems important for emotion perception occur to washing- and hoarding-related material in particular in these subjects. Further examination of neural responses during provocation of different symptom dimensions in OCD patients will help determine the extent to which specific abnormalities in neural systems underlying emotion perception are associated with different symptom dimensions and predict treatment response in OCD.

Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre-Incision Variables

PubMed Central

Pothula, Venu M.; Yuan, Stanley C.; Maerz, David A.; Montes, Lucresia; Oleszkiewicz, Stephen M.; Yusupov, Albert; Perline, Richard

2015-01-01

Background Advanced predictive analytical techniques are being increasingly applied to clinical risk assessment. This study compared a neural network model to several other models in predicting the length of stay (LOS) in the cardiac surgical intensive care unit (ICU) based on pre-incision patient characteristics. Methods Thirty six variables collected from 185 cardiac surgical patients were analyzed for contribution to ICU LOS. The Automatic Linear Modeling (ALM) module of IBM-SPSS software identified 8 factors with statistically significant associations with ICU LOS; these factors were also analyzed with the Artificial Neural Network (ANN) module of the same software. The weighted contributions of each factor (“trained” data) were then applied to data for a “new” patient to predict ICU LOS for that individual. Results Factors identified in the ALM model were: use of an intra-aortic balloon pump; O2 delivery index; age; use of positive cardiac inotropic agents; hematocrit; serum creatinine ≥ 1.3 mg/deciliter; gender; arterial pCO2. The r2 value for ALM prediction of ICU LOS in the initial (training) model was 0.356, p <0.0001. Cross validation in prediction of a “new” patient yielded r2 = 0.200, p <0.0001. The same 8 factors analyzed with ANN yielded a training prediction r2 of 0.535 (p <0.0001) and a cross validation prediction r2 of 0.410, p <0.0001. Two additional predictive algorithms were studied, but they had lower prediction accuracies. Our validated neural network model identified the upper quartile of ICU LOS with an odds ratio of 9.8(p <0.0001). Conclusions ANN demonstrated a 2-fold greater accuracy than ALM in prediction of observed ICU LOS. This greater accuracy would be presumed to result from the capacity of ANN to capture nonlinear effects and higher order interactions. Predictive modeling may be of value in early anticipation of risks of post-operative morbidity and utilization of ICU facilities. PMID:26710254

Genome-wide association study identifies 74 loci associated with educational attainment.

PubMed

Okbay, Aysu; Beauchamp, Jonathan P; Fontana, Mark Alan; Lee, James J; Pers, Tune H; Rietveld, Cornelius A; Turley, Patrick; Chen, Guo-Bo; Emilsson, Valur; Meddens, S Fleur W; Oskarsson, Sven; Pickrell, Joseph K; Thom, Kevin; Timshel, Pascal; de Vlaming, Ronald; Abdellaoui, Abdel; Ahluwalia, Tarunveer S; Bacelis, Jonas; Baumbach, Clemens; Bjornsdottir, Gyda; Brandsma, Johannes H; Pina Concas, Maria; Derringer, Jaime; Furlotte, Nicholas A; Galesloot, Tessel E; Girotto, Giorgia; Gupta, Richa; Hall, Leanne M; Harris, Sarah E; Hofer, Edith; Horikoshi, Momoko; Huffman, Jennifer E; Kaasik, Kadri; Kalafati, Ioanna P; Karlsson, Robert; Kong, Augustine; Lahti, Jari; van der Lee, Sven J; deLeeuw, Christiaan; Lind, Penelope A; Lindgren, Karl-Oskar; Liu, Tian; Mangino, Massimo; Marten, Jonathan; Mihailov, Evelin; Miller, Michael B; van der Most, Peter J; Oldmeadow, Christopher; Payton, Antony; Pervjakova, Natalia; Peyrot, Wouter J; Qian, Yong; Raitakari, Olli; Rueedi, Rico; Salvi, Erika; Schmidt, Börge; Schraut, Katharina E; Shi, Jianxin; Smith, Albert V; Poot, Raymond A; St Pourcain, Beate; Teumer, Alexander; Thorleifsson, Gudmar; Verweij, Niek; Vuckovic, Dragana; Wellmann, Juergen; Westra, Harm-Jan; Yang, Jingyun; Zhao, Wei; Zhu, Zhihong; Alizadeh, Behrooz Z; Amin, Najaf; Bakshi, Andrew; Baumeister, Sebastian E; Biino, Ginevra; Bønnelykke, Klaus; Boyle, Patricia A; Campbell, Harry; Cappuccio, Francesco P; Davies, Gail; De Neve, Jan-Emmanuel; Deloukas, Panos; Demuth, Ilja; Ding, Jun; Eibich, Peter; Eisele, Lewin; Eklund, Niina; Evans, David M; Faul, Jessica D; Feitosa, Mary F; Forstner, Andreas J; Gandin, Ilaria; Gunnarsson, Bjarni; Halldórsson, Bjarni V; Harris, Tamara B; Heath, Andrew C; Hocking, Lynne J; Holliday, Elizabeth G; Homuth, Georg; Horan, Michael A; Hottenga, Jouke-Jan; de Jager, Philip L; Joshi, Peter K; Jugessur, Astanand; Kaakinen, Marika A; Kähönen, Mika; Kanoni, Stavroula; Keltigangas-Järvinen, Liisa; Kiemeney, Lambertus A L M; Kolcic, Ivana; Koskinen, Seppo; Kraja, Aldi T; Kroh, Martin; Kutalik, Zoltan; Latvala, Antti; Launer, Lenore J; Lebreton, Maël P; Levinson, Douglas F; Lichtenstein, Paul; Lichtner, Peter; Liewald, David C M; Loukola, Anu; Madden, Pamela A; Mägi, Reedik; Mäki-Opas, Tomi; Marioni, Riccardo E; Marques-Vidal, Pedro; Meddens, Gerardus A; McMahon, George; Meisinger, Christa; Meitinger, Thomas; Milaneschi, Yusplitri; Milani, Lili; Montgomery, Grant W; Myhre, Ronny; Nelson, Christopher P; Nyholt, Dale R; Ollier, William E R; Palotie, Aarno; Paternoster, Lavinia; Pedersen, Nancy L; Petrovic, Katja E; Porteous, David J; Räikkönen, Katri; Ring, Susan M; Robino, Antonietta; Rostapshova, Olga; Rudan, Igor; Rustichini, Aldo; Salomaa, Veikko; Sanders, Alan R; Sarin, Antti-Pekka; Schmidt, Helena; Scott, Rodney J; Smith, Blair H; Smith, Jennifer A; Staessen, Jan A; Steinhagen-Thiessen, Elisabeth; Strauch, Konstantin; Terracciano, Antonio; Tobin, Martin D; Ulivi, Sheila; Vaccargiu, Simona; Quaye, Lydia; van Rooij, Frank J A; Venturini, Cristina; Vinkhuyzen, Anna A E; Völker, Uwe; Völzke, Henry; Vonk, Judith M; Vozzi, Diego; Waage, Johannes; Ware, Erin B; Willemsen, Gonneke; Attia, John R; Bennett, David A; Berger, Klaus; Bertram, Lars; Bisgaard, Hans; Boomsma, Dorret I; Borecki, Ingrid B; Bültmann, Ute; Chabris, Christopher F; Cucca, Francesco; Cusi, Daniele; Deary, Ian J; Dedoussis, George V; van Duijn, Cornelia M; Eriksson, Johan G; Franke, Barbara; Franke, Lude; Gasparini, Paolo; Gejman, Pablo V; Gieger, Christian; Grabe, Hans-Jörgen; Gratten, Jacob; Groenen, Patrick J F; Gudnason, Vilmundur; van der Harst, Pim; Hayward, Caroline; Hinds, David A; Hoffmann, Wolfgang; Hyppönen, Elina; Iacono, William G; Jacobsson, Bo; Järvelin, Marjo-Riitta; Jöckel, Karl-Heinz; Kaprio, Jaakko; Kardia, Sharon L R; Lehtimäki, Terho; Lehrer, Steven F; Magnusson, Patrik K E; Martin, Nicholas G; McGue, Matt; Metspalu, Andres; Pendleton, Neil; Penninx, Brenda W J H; Perola, Markus; Pirastu, Nicola; Pirastu, Mario; Polasek, Ozren; Posthuma, Danielle; Power, Christine; Province, Michael A; Samani, Nilesh J; Schlessinger, David; Schmidt, Reinhold; Sørensen, Thorkild I A; Spector, Tim D; Stefansson, Kari; Thorsteinsdottir, Unnur; Thurik, A Roy; Timpson, Nicholas J; Tiemeier, Henning; Tung, Joyce Y; Uitterlinden, André G; Vitart, Veronique; Vollenweider, Peter; Weir, David R; Wilson, James F; Wright, Alan F; Conley, Dalton C; Krueger, Robert F; Davey Smith, George; Hofman, Albert; Laibson, David I; Medland, Sarah E; Meyer, Michelle N; Yang, Jian; Johannesson, Magnus; Visscher, Peter M; Esko, Tõnu; Koellinger, Philipp D; Cesarini, David; Benjamin, Daniel J

2016-05-26

Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals. Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide significant loci associated with the number of years of schooling completed. Single-nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric diseases.

Microvessel prediction in H&E Stained Pathology Images using fully convolutional neural networks.

PubMed

Yi, Faliu; Yang, Lin; Wang, Shidan; Guo, Lei; Huang, Chenglong; Xie, Yang; Xiao, Guanghua

2018-02-27

Pathological angiogenesis has been identified in many malignancies as a potential prognostic factor and target for therapy. In most cases, angiogenic analysis is based on the measurement of microvessel density (MVD) detected by immunostaining of CD31 or CD34. However, most retrievable public data is generally composed of Hematoxylin and Eosin (H&E)-stained pathology images, for which is difficult to get the corresponding immunohistochemistry images. The role of microvessels in H&E stained images has not been widely studied due to their complexity and heterogeneity. Furthermore, identifying microvessels manually for study is a labor-intensive task for pathologists, with high inter- and intra-observer variation. Therefore, it is important to develop automated microvessel-detection algorithms in H&E stained pathology images for clinical association analysis. In this paper, we propose a microvessel prediction method using fully convolutional neural networks. The feasibility of our proposed algorithm is demonstrated through experimental results on H&E stained images. Furthermore, the identified microvessel features were significantly associated with the patient clinical outcomes. This is the first study to develop an algorithm for automated microvessel detection in H&E stained pathology images.

Adaptive online inverse control of a shape memory alloy wire actuator using a dynamic neural network

NASA Astrophysics Data System (ADS)

Mai, Huanhuan; Song, Gangbing; Liao, Xiaofeng

2013-01-01

Shape memory alloy (SMA) actuators exhibit severe hysteresis, a nonlinear behavior, which complicates control strategies and limits their applications. This paper presents a new approach to controlling an SMA actuator through an adaptive inverse model based controller that consists of a dynamic neural network (DNN) identifier, a copy dynamic neural network (CDNN) feedforward term and a proportional (P) feedback action. Unlike fixed hysteresis models used in most inverse controllers, the proposed one uses a DNN to identify online the relationship between the applied voltage to the actuator and the displacement (the inverse model). Even without a priori knowledge of the SMA hysteresis and without pre-training, the proposed controller can precisely control the SMA wire actuator in various tracking tasks by identifying online the inverse model of the SMA actuator. Experiments were conducted, and experimental results demonstrated real-time modeling capabilities of DNN and the performance of the adaptive inverse controller.

Predicting Length of Stay in Intensive Care Units after Cardiac Surgery: Comparison of Artificial Neural Networks and Adaptive Neuro-fuzzy System.

PubMed

Maharlou, Hamidreza; Niakan Kalhori, Sharareh R; Shahbazi, Shahrbanoo; Ravangard, Ramin

2018-04-01

Accurate prediction of patients' length of stay is highly important. This study compared the performance of artificial neural network and adaptive neuro-fuzzy system algorithms to predict patients' length of stay in intensive care units (ICU) after cardiac surgery. A cross-sectional, analytical, and applied study was conducted. The required data were collected from 311 cardiac patients admitted to intensive care units after surgery at three hospitals of Shiraz, Iran, through a non-random convenience sampling method during the second quarter of 2016. Following the initial processing of influential factors, models were created and evaluated. The results showed that the adaptive neuro-fuzzy algorithm (with mean squared error [MSE] = 7 and R = 0.88) resulted in the creation of a more precise model than the artificial neural network (with MSE = 21 and R = 0.60). The adaptive neuro-fuzzy algorithm produces a more accurate model as it applies both the capabilities of a neural network architecture and experts' knowledge as a hybrid algorithm. It identifies nonlinear components, yielding remarkable results for prediction the length of stay, which is a useful calculation output to support ICU management, enabling higher quality of administration and cost reduction.

The neural basis of intuitive and counterintuitive moral judgment.

PubMed

Kahane, Guy; Wiech, Katja; Shackel, Nicholas; Farias, Miguel; Savulescu, Julian; Tracey, Irene

2012-04-01

Neuroimaging studies on moral decision-making have thus far largely focused on differences between moral judgments with opposing utilitarian (well-being maximizing) and deontological (duty-based) content. However, these studies have investigated moral dilemmas involving extreme situations, and did not control for two distinct dimensions of moral judgment: whether or not it is intuitive (immediately compelling to most people) and whether it is utilitarian or deontological in content. By contrasting dilemmas where utilitarian judgments are counterintuitive with dilemmas in which they are intuitive, we were able to use functional magnetic resonance imaging to identify the neural correlates of intuitive and counterintuitive judgments across a range of moral situations. Irrespective of content (utilitarian/deontological), counterintuitive moral judgments were associated with greater difficulty and with activation in the rostral anterior cingulate cortex, suggesting that such judgments may involve emotional conflict; intuitive judgments were linked to activation in the visual and premotor cortex. In addition, we obtained evidence that neural differences in moral judgment in such dilemmas are largely due to whether they are intuitive and not, as previously assumed, to differences between utilitarian and deontological judgments. Our findings therefore do not support theories that have generally associated utilitarian and deontological judgments with distinct neural systems.

The neural basis of intuitive and counterintuitive moral judgment

PubMed Central

Wiech, Katja; Shackel, Nicholas; Farias, Miguel; Savulescu, Julian; Tracey, Irene

2012-01-01

Neuroimaging studies on moral decision-making have thus far largely focused on differences between moral judgments with opposing utilitarian (well-being maximizing) and deontological (duty-based) content. However, these studies have investigated moral dilemmas involving extreme situations, and did not control for two distinct dimensions of moral judgment: whether or not it is intuitive (immediately compelling to most people) and whether it is utilitarian or deontological in content. By contrasting dilemmas where utilitarian judgments are counterintuitive with dilemmas in which they are intuitive, we were able to use functional magnetic resonance imaging to identify the neural correlates of intuitive and counterintuitive judgments across a range of moral situations. Irrespective of content (utilitarian/deontological), counterintuitive moral judgments were associated with greater difficulty and with activation in the rostral anterior cingulate cortex, suggesting that such judgments may involve emotional conflict; intuitive judgments were linked to activation in the visual and premotor cortex. In addition, we obtained evidence that neural differences in moral judgment in such dilemmas are largely due to whether they are intuitive and not, as previously assumed, to differences between utilitarian and deontological judgments. Our findings therefore do not support theories that have generally associated utilitarian and deontological judgments with distinct neural systems. PMID:21421730

Examining Neural Correlates of Psychopathology Using a Lesion-Based Approach.

PubMed

Calamia, Matthew; Markon, Kristian E; Sutterer, Matthew J; Tranel, Daniel

2018-06-22

Studies of individuals with focal brain damage have long been used to expand understanding of the neural basis of psychopathology. However, most previous studies were conducted using small sample sizes and relatively coarse methods for measuring psychopathology or mapping brain-behavior relationships. Here, we examined the factor structure and neural correlates of psychopathology in 232 individuals with focal brain damage, using their responses to the Minnesota Multiphasic Personality Inventory-2-Restructured Form (MMPI-2-RF). Factor analysis and voxel-based lesion symptom mapping were used to examine the structure and neural correlates of psychopathology in this sample. Consistent with existing MMPI-2-RF literature, separate internalizing, externalizing, and psychotic symptom dimensions were found. In addition, a somatic dimension likely reflecting neurological symptoms was identified. Damage to the medial temporal lobe, including the hippocampus, was associated with scales related to both internalizing problems and psychoticism. Damage to the medial temporal lobe and orbitofrontal cortex was associated with both a general distrust of others and beliefs that one is being personally targeted by others. These findings provide evidence for the critical role of dysfunction in specific frontal and temporal regions in the development of psychopathology. Copyright © 2018. Published by Elsevier Ltd.

Neural activity and emotional processing following military deployment: Effects of mild traumatic brain injury and posttraumatic stress disorder.

PubMed

Zuj, Daniel V; Felmingham, Kim L; Palmer, Matthew A; Lawrence-Wood, Ellie; Van Hooff, Miranda; Lawrence, Andrew J; Bryant, Richard A; McFarlane, Alexander C

2017-11-01

Posttraumatic Stress Disorder (PTSD) and mild traumatic brain injury (mTBI) are common comorbidities during military deployment that affect emotional brain processing, yet few studies have examined the independent effects of mTBI and PTSD. The purpose of this study was to examine distinct differences in neural responses to emotional faces in mTBI and PTSD. Twenty-one soldiers reporting high PTSD symptoms were compared to 21 soldiers with low symptoms, and 16 soldiers who reported mTBI-consistent injury and symptoms were compared with 16 soldiers who did not sustain an mTBI. Participants viewed emotional face expressions while their neural activity was recorded (via event-related potentials) prior to and following deployment. The high-PTSD group displayed increased P1 and P2 amplitudes to threatening faces at post-deployment compared to the low-PTSD group. In contrast, the mTBI group displayed reduced face-specific processing (N170 amplitude) to all facial expressions compared to the no-mTBI group. Here, we identified distinctive neural patterns of emotional face processing, with attentional biases towards threatening faces in PTSD, and reduced emotional face processing in mTBI. Copyright © 2017 Elsevier Inc. All rights reserved.

A systematic review of fMRI reward paradigms used in studies of adolescents vs. adults: the impact of task design and implications for understanding neurodevelopment.

PubMed

Richards, Jessica M; Plate, Rista C; Ernst, Monique

2013-06-01

The neural systems underlying reward-related behaviors across development have recently generated a great amount of interest. Yet, the neurodevelopmental literature on reward processing is marked by inconsistencies due to the heterogeneity of the reward paradigms used, the complexity of the behaviors being studied, and the developing brain itself as a moving target. The present review will examine task design as one source of variability across findings by compiling this literature along three dimensions: (1) task structures, (2) cognitive processes, and (3) neural systems. We start with the presentation of a heuristic neural systems model, the Triadic Model, as a way to provide a theoretical framework for the neuroscience research on motivated behaviors. We then discuss the principles guiding reward task development. Finally, we review the extant developmental neuroimaging literature on reward-related processing, organized by reward task type. We hope that this approach will help to clarify the literature on the functional neurodevelopment of reward-related neural systems, and to identify the role of the experimental parameters that significantly influence these findings. Published by Elsevier Ltd.

Is Neural Processing of Negative Stimuli Altered in Addiction Independent of Drug Effects? Findings From Drug-Naïve Youth with Internet Gaming Disorder.

PubMed

Yip, Sarah W; Gross, James J; Chawla, Megha; Ma, Shan-Shan; Shi, Xing-Hui; Liu, Lu; Yao, Yuan-Wei; Zhu, Lei; Worhunsky, Patrick D; Zhang, Jintao

2018-05-01

Difficulties in emotion regulation are commonly reported among individuals with alcohol and drug addictions and contribute to the acquisition and maintenance of addictive behaviors. Alterations in neural processing of negative affective stimuli have further been demonstrated among individuals with addictions. However, it is unclear whether these alterations are a general feature of addictions or are a result of prolonged exposure to drugs of abuse. To test the hypothesis of altered negative affect processing independent of drug effects, this study assessed neural function among drug-naïve youth with a behavioral addiction-Internet gaming disorder (IGD). Fifty-six young adults (28 with IGD, 28 matched controls) participated in fMRI scanning during performance of a well-validated emotion regulation task. Between-group differences in neural activity during task performance were assessed using a whole-brain, mixed-effects ANOVA with correction for multiple comparisons at currently recommended thresholds (voxel-level p<0.001, pFWE<0.05). Compared to controls, youth with IGD exhibited significantly blunted neural responses within distributed subcortical and cortical regions including the striatum, insula, lateral prefrontal cortex and anterior cingulate in response to negative affective cues, as well as during emotion regulation. Independent component analysis (ICA) further identified between-group differences in engagement of a fronto-cingulo-parietal network, involving decreased engagement in IGD youth relative to controls. Study findings are largely consistent with those from prior neuroimaging studies in substance-use disorders, thus raising the possibility that neural processing of negative affect may be blunted across drug and behavioral addictions independent of acute or chronic drug effects.

Psychophysiology of neural, cognitive and affective integration: fMRI and autonomic indicants

PubMed Central

Critchley, Hugo D.

2009-01-01

Behaviour is shaped by environmental challenge in the context of homoeostatic need. Emotional and cognitive processes evoke patterned changes in bodily state that may signal emotional state to others. This dynamic modulation of visceral state is neurally mediated by sympathetic and parasympathetic divisions of the autonomic nervous system. Moreover neural afferents convey representations of the internal state of the body back to the brain to further influence emotion and cognition. Neuroimaging and lesion studies implicate specific regions of limbic forebrain in the behavioural generation of autonomic arousal states. Activity within these regions may predict emotion-specific autonomic response patterns within and between bodily organs, with implications for psychosomatic medicine. Feedback from the viscera is mapped hierarchically in the brain to influence efferent signals, and ultimately at the cortical level to engender and reinforce affective responses and subjective feeling states. Again neuroimaging and patient studies suggest discrete neural substrates for these representations, notably regions of insula and orbitofrontal cortex. Individual differences in conscious access to these interoceptive representations predict differences in emotional experience, but equally the misperception of heightened arousal level may evoke changes in emotional behaviour through engagement of the same neural centres. Perturbation of feedback may impair emotional reactivity and, in the context of inflammatory states give rise to cognitive, affective and psychomotor expressions of illness. Changes in visceral state during emotion may be mirrored in the responses of others, permitting a corresponding representation in the observer. The degree to which individuals are susceptible to this ‘contagion’ predicts individual differences in questionnaire ratings of empathy. Together these neuroimaging and clinical studies highlight the dynamic relationship between mind and body and help identify neural substrates that may translate thoughts into autonomic arousal and bodily states into feelings that can be shared. PMID:19414044

Neural Correlates of Attitude Change Following Positive and Negative Advertisements

PubMed Central

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

Abnormal resting-state connectivity of motor and cognitive networks in early manifest Huntington's disease.

PubMed

Wolf, R C; Sambataro, F; Vasic, N; Depping, M S; Thomann, P A; Landwehrmeyer, G B; Süssmuth, S D; Orth, M

2014-11-01

Functional magnetic resonance imaging (fMRI) of multiple neural networks during the brain's 'resting state' could facilitate biomarker development in patients with Huntington's disease (HD) and may provide new insights into the relationship between neural dysfunction and clinical symptoms. To date, however, very few studies have examined the functional integrity of multiple resting state networks (RSNs) in manifest HD, and even less is known about whether concomitant brain atrophy affects neural activity in patients. Using MRI, we investigated brain structure and RSN function in patients with early HD (n = 20) and healthy controls (n = 20). For resting-state fMRI data a group-independent component analysis identified spatiotemporally distinct patterns of motor and prefrontal RSNs of interest. We used voxel-based morphometry to assess regional brain atrophy, and 'biological parametric mapping' analyses to investigate the impact of atrophy on neural activity. Compared with controls, patients showed connectivity changes within distinct neural systems including lateral prefrontal, supplementary motor, thalamic, cingulate, temporal and parietal regions. In patients, supplementary motor area and cingulate cortex connectivity indices were associated with measures of motor function, whereas lateral prefrontal connectivity was associated with cognition. This study provides evidence for aberrant connectivity of RSNs associated with motor function and cognition in early manifest HD when controlling for brain atrophy. This suggests clinically relevant changes of RSN activity in the presence of HD-associated cortical and subcortical structural abnormalities.

Revealing unobserved factors underlying cortical activity with a rectified latent variable model applied to neural population recordings.

PubMed

Whiteway, Matthew R; Butts, Daniel A

2017-03-01

The activity of sensory cortical neurons is not only driven by external stimuli but also shaped by other sources of input to the cortex. Unlike external stimuli, these other sources of input are challenging to experimentally control, or even observe, and as a result contribute to variability of neural responses to sensory stimuli. However, such sources of input are likely not "noise" and may play an integral role in sensory cortex function. Here we introduce the rectified latent variable model (RLVM) in order to identify these sources of input using simultaneously recorded cortical neuron populations. The RLVM is novel in that it employs nonnegative (rectified) latent variables and is much less restrictive in the mathematical constraints on solutions because of the use of an autoencoder neural network to initialize model parameters. We show that the RLVM outperforms principal component analysis, factor analysis, and independent component analysis, using simulated data across a range of conditions. We then apply this model to two-photon imaging of hundreds of simultaneously recorded neurons in mouse primary somatosensory cortex during a tactile discrimination task. Across many experiments, the RLVM identifies latent variables related to both the tactile stimulation as well as nonstimulus aspects of the behavioral task, with a majority of activity explained by the latter. These results suggest that properly identifying such latent variables is necessary for a full understanding of sensory cortical function and demonstrate novel methods for leveraging large population recordings to this end. NEW & NOTEWORTHY The rapid development of neural recording technologies presents new opportunities for understanding patterns of activity across neural populations. Here we show how a latent variable model with appropriate nonlinear form can be used to identify sources of input to a neural population and infer their time courses. Furthermore, we demonstrate how these sources are related to behavioral contexts outside of direct experimental control. Copyright © 2017 the American Physiological Society.

The extraction of neural strategies from the surface EMG: an update

PubMed Central

Merletti, Roberto; Enoka, Roger M.

2014-01-01

A surface EMG signal represents the linear transformation of motor neuron discharge times by the compound action potentials of the innervated muscle fibers and is often used as a source of information about neural activation of muscle. However, retrieving the embedded neural code from a surface EMG signal is extremely challenging. Most studies use indirect approaches in which selected features of the signal are interpreted as indicating certain characteristics of the neural code. These indirect associations are constrained by limitations that have been detailed previously (Farina D, Merletti R, Enoka RM. J Appl Physiol 96: 1486–1495, 2004) and are generally difficult to overcome. In an update on these issues, the current review extends the discussion to EMG-based coherence methods for assessing neural connectivity. We focus first on EMG amplitude cancellation, which intrinsically limits the association between EMG amplitude and the intensity of the neural activation and then discuss the limitations of coherence methods (EEG-EMG, EMG-EMG) as a way to assess the strength of the transmission of synaptic inputs into trains of motor unit action potentials. The debated influence of rectification on EMG spectral analysis and coherence measures is also discussed. Alternatively, there have been a number of attempts to identify the neural information directly by decomposing surface EMG signals into the discharge times of motor unit action potentials. The application of this approach is extremely powerful, but validation remains a central issue. PMID:25277737

Identifying a Network of Brain Regions Involved in Aversion-Related Processing: A Cross-Species Translational Investigation

PubMed Central

Hayes, Dave J.; Northoff, Georg

2011-01-01

The ability to detect and respond appropriately to aversive stimuli is essential for all organisms, from fruit flies to humans. This suggests the existence of a core neural network which mediates aversion-related processing. Human imaging studies on aversion have highlighted the involvement of various cortical regions, such as the prefrontal cortex, while animal studies have focused largely on subcortical regions like the periaqueductal gray and hypothalamus. However, whether and how these regions form a core neural network of aversion remains unclear. To help determine this, a translational cross-species investigation in humans (i.e., meta-analysis) and other animals (i.e., systematic review of functional neuroanatomy) was performed. Our results highlighted the recruitment of the anterior cingulate cortex, the anterior insula, and the amygdala as well as other subcortical (e.g., thalamus, midbrain) and cortical (e.g., orbitofrontal) regions in both animals and humans. Importantly, involvement of these regions remained independent of sensory modality. This study provides evidence for a core neural network mediating aversion in both animals and humans. This not only contributes to our understanding of the trans-species neural correlates of aversion but may also carry important implications for psychiatric disorders where abnormal aversive behavior can often be observed. PMID:22102836

Socioeconomic influences on brain function: implications for health.

PubMed

Muscatell, Keely A

2018-06-27

Socioeconomic-based disparities in physical health outcomes are well established, with individuals from lower socioeconomic status (SES) backgrounds being more likely to experience chronic disease morbidity and early mortality compared to those from higher SES strata. While numerous studies in recent decades have focused on understanding the contextual, psychosocial, and biological mechanisms linking SES and health, the neural pathways that contribute to this relationship are currently underinvestigated. The present paper reviews and synthesizes the small number of published studies that have explored links between SES and health-relevant neural functioning. Specifically, current knowledge of the relationship between socioeconomic factors and neural systems that may be affected by low SES contexts, including those related to processing threat and stress, responding to reward, and engaging in emotion regulation, is reviewed. Gaps in our knowledge that could be filled by health neuroscience research are emphasized, in an effort to catalyze future studies in this area. Understanding the neural mechanisms linking SES and health is crucial for building comprehensive models of the pathways by which social inequalities become health inequalities and may help identify novel targets for intervention to prevent health disparities. Health neuroscience research has a critical role to play in this important area of research. © 2018 New York Academy of Sciences.

Intraoperative neural monitoring in thyroid surgery: lessons learned from animal studies

PubMed Central

Randolph, Gregory W.; Lu, I-Cheng; Chang, Pi-Ying; Chen, Yi-Ting; Hun, Pao-Chu; Lin, Yi-Chu; Dionigi, Gianlorenzo; Chiang, Feng-Yu

2016-01-01

Recurrent laryngeal nerve (RLN) injury remains a significant morbidity associated with thyroid and parathyroid surgery. In the past decade, surgeons have increasingly used intraoperative neural monitoring (IONM) as an adjunct technique for localizing and identifying the RLN, detecting RLN injury, and predicting the outcome of vocal cord function. In recent years, many animal studies have investigated common pitfalls and new applications of IONM. For example, the use of IONM technology in animal models has proven valuable in studies of the electrophysiology of RLN injury. The advent of animal studies has substantially improved understanding of IONM technology. Lessons learned from animal studies have immediate clinical applications in establishing reliable strategies for preventing intraoperative RLN injury. This article gives an overview of the research progress on IONM-relevant animal models. PMID:27867861

Comparison of the neuropoietic activity of gene-modified versus parental mesenchymal stromal cells and the identification of soluble and extracellular matrix-related neuropoietic mediators

PubMed Central

2014-01-01

Introduction Transplanting mesenchymal stromal cells (MSCs) or their derivatives into a neurodegenerative environment is believed to be beneficial because of the trophic support, migratory guidance, immunosuppression, and neurogenic stimuli they provide. SB623, a cell therapy for the treatment of chronic stroke, currently in a clinical trial, is derived from bone marrow MSCs by using transient transfection with a vector encoding the human Notch1 intracellular domain. This creates a new phenotype, which is effective in experimental stroke, exhibits immunosuppressive and angiogenic activity equal or superior to parental MSCs in vitro, and produces extracellular matrix (ECM) that is exceptionally supportive for neural cell growth. The neuropoietic activity of SB623 and parental MSCs has not been compared, and the SB623-derived neuropoietic mediators have not been identified. Methods SB623 or parental MSCs were cocultured with rat embryonic brain cortex cells on cell-derived ECM in a previously characterized quantitative neuropoiesis assay. Changes in expression of rat neural differentiation markers were quantified by using rat-specific qRT-PCR. Human mediators were identified by using expression profiling, an enzymatic crosslinking activity, and functional interference studies by means of blocking antibodies, biologic inhibitors, and siRNA. Cocultures were immunolabeled for presynaptic vesicular transporters to assess neuronal specialization. Results Among six MSC/SB623 pairs, SB623 induced expression of rat neural precursor, oligodendrocyte, and astrocyte markers on average 2.6 to 3 times stronger than did their parental MSCs. SB623 expressed significantly higher FGF2, FGF1, and BMP4, and lower FGFR1 and FGFR2 levels; and human FGF1, FGF2, BMPs, and HGF were implicated as neuropoietic mediators. Neural precursors grew faster on SB623- than on MSC-derived ECM. SB623 exhibited higher expression levels and crosslinking activity of tissue transglutaminase (TGM2). TGM2 silencing reduced neural precursor growth on SB623-ECM. SB623 also promoted the induction of GABA-ergic, but not glutamatergic, neurons more effectively than did MSCs. Conclusions These data demonstrate that SB623 cells tend to support neural cell growth more effectively than their parental MSCs and identify both soluble and insoluble mediators responsible, at least in part, for enhanced neuropoietic potency of SB623. The neuropoiesis assay is a useful tool for identifying beneficial factors produced by MSCs and their derivatives. PMID:24572070

Neural Stem Cells (NSCs) and Proteomics.

PubMed

Shoemaker, Lorelei D; Kornblum, Harley I

2016-02-01

Neural stem cells (NSCs) can self-renew and give rise to the major cell types of the CNS. Studies of NSCs include the investigation of primary, CNS-derived cells as well as animal and human embryonic stem cell (ESC)-derived and induced pluripotent stem cell (iPSC)-derived sources. NSCs provide a means with which to study normal neural development, neurodegeneration, and neurological disease and are clinically relevant sources for cellular repair to the damaged and diseased CNS. Proteomics studies of NSCs have the potential to delineate molecules and pathways critical for NSC biology and the means by which NSCs can participate in neural repair. In this review, we provide a background to NSC biology, including the means to obtain them and the caveats to these processes. We then focus on advances in the proteomic interrogation of NSCs. This includes the analysis of posttranslational modifications (PTMs); approaches to analyzing different proteomic compartments, such the secretome; as well as approaches to analyzing temporal differences in the proteome to elucidate mechanisms of differentiation. We also discuss some of the methods that will undoubtedly be useful in the investigation of NSCs but which have not yet been applied to the field. While many proteomics studies of NSCs have largely catalogued the proteome or posttranslational modifications of specific cellular states, without delving into specific functions, some have led to understandings of functional processes or identified markers that could not have been identified via other means. Many challenges remain in the field, including the precise identification and standardization of NSCs used for proteomic analyses, as well as how to translate fundamental proteomics studies to functional biology. The next level of investigation will require interdisciplinary approaches, combining the skills of those interested in the biochemistry of proteomics with those interested in modulating NSC function. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

Gustatory processing and taste memory in Drosophila

PubMed Central

Masek, Pavel; Keene, Alex C.

2018-01-01

Taste allows animals to discriminate the value and potential toxicity of food prior to ingestion. Many tastants elicit an innate attractive or avoidance response that is modifiable with nutritional state and prior experience. A powerful genetic tool kit, well-characterized gustatory system, and standardized behavioral assays make the fruit fly, Drosophila melanogaster, an excellent system for investigating taste processing and memory. Recent studies have used this system to identify the neural basis for acquired taste preference. These studies have revealed a role for dopamine-mediated plasticity of the mushroom bodies that modulate the threshold of response to appetitive tastants. The identification of neural circuitry regulating taste memory provides a system to study the genetic and physiological processes that govern plasticity within a defined memory circuit. PMID:27328844

On Design and Implementation of Neural-Machine Interface for Artificial Legs

PubMed Central

Zhang, Xiaorong; Liu, Yuhong; Zhang, Fan; Ren, Jin; Sun, Yan (Lindsay); Yang, Qing

2011-01-01

The quality of life of leg amputees can be improved dramatically by using a cyber physical system (CPS) that controls artificial legs based on neural signals representing amputees’ intended movements. The key to the CPS is the neural-machine interface (NMI) that senses electromyographic (EMG) signals to make control decisions. This paper presents a design and implementation of a novel NMI using an embedded computer system to collect neural signals from a physical system - a leg amputee, provide adequate computational capability to interpret such signals, and make decisions to identify user’s intent for prostheses control in real time. A new deciphering algorithm, composed of an EMG pattern classifier and a post-processing scheme, was developed to identify the user’s intended lower limb movements. To deal with environmental uncertainty, a trust management mechanism was designed to handle unexpected sensor failures and signal disturbances. Integrating the neural deciphering algorithm with the trust management mechanism resulted in a highly accurate and reliable software system for neural control of artificial legs. The software was then embedded in a newly designed hardware platform based on an embedded microcontroller and a graphic processing unit (GPU) to form a complete NMI for real time testing. Real time experiments on a leg amputee subject and an able-bodied subject have been carried out to test the control accuracy of the new NMI. Our extensive experiments have shown promising results on both subjects, paving the way for clinical feasibility of neural controlled artificial legs. PMID:22389637

A computational neural approach to support the discovery of gene function and classes of cancer.

PubMed

Azuaje, F

2001-03-01

Advances in molecular classification of tumours may play a central role in cancer treatment. Here, a novel approach to genome expression pattern interpretation is described and applied to the recognition of B-cell malignancies as a test set. Using cDNA microarrays data generated by a previous study, a neural network model known as simplified fuzzy ARTMAP is able to identify normal and diffuse large B-cell lymphoma (DLBCL) patients. Furthermore, it discovers the distinction between patients with molecularly distinct forms of DLBCL without previous knowledge of those subtypes.

The neural bases for valuing social equality.

PubMed

Aoki, Ryuta; Yomogida, Yukihito; Matsumoto, Kenji

2015-01-01

The neural basis of how humans value and pursue social equality has become a major topic in social neuroscience research. Although recent studies have identified a set of brain regions and possible mechanisms that are involved in the neural processing of equality of outcome between individuals, how the human brain processes equality of opportunity remains unknown. In this review article, first we describe the importance of the distinction between equality of outcome and equality of opportunity, which has been emphasized in philosophy and economics. Next, we discuss possible approaches for empirical characterization of human valuation of equality of opportunity vs. equality of outcome. Understanding how these two concepts are distinct and interact with each other may provide a better explanation of complex human behaviors concerning fairness and social equality. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

Patterns recognition of electric brain activity using artificial neural networks

NASA Astrophysics Data System (ADS)

Musatov, V. Yu.; Pchelintseva, S. V.; Runnova, A. E.; Hramov, A. E.

2017-04-01

An approach for the recognition of various cognitive processes in the brain activity in the perception of ambiguous images. On the basis of developed theoretical background and the experimental data, we propose a new classification of oscillating patterns in the human EEG by using an artificial neural network approach. After learning of the artificial neural network reliably identified cube recognition processes, for example, left-handed or right-oriented Necker cube with different intensity of their edges, construct an artificial neural network based on Perceptron architecture and demonstrate its effectiveness in the pattern recognition of the EEG in the experimental.

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

PubMed

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.

Relationship between neural rhythm generation disorders and physical disabilities in Parkinson's disease patients' walking.

PubMed

Ota, Leo; Uchitomi, Hirotaka; Ogawa, Ken-ichiro; Orimo, Satoshi; Miyake, Yoshihiro

2014-01-01

Walking is generated by the interaction between neural rhythmic and physical activities. In fact, Parkinson's disease (PD), which is an example of disease, causes not only neural rhythm generation disorders but also physical disabilities. However, the relationship between neural rhythm generation disorders and physical disabilities has not been determined. The aim of this study was to identify the mechanism of gait rhythm generation. In former research, neural rhythm generation disorders in PD patients' walking were characterized by stride intervals, which are more variable and fluctuate randomly. The variability and fluctuation property were quantified using the coefficient of variation (CV) and scaling exponent α. Conversely, because walking is a dynamic process, postural reflex disorder (PRD) is considered the best way to estimate physical disabilities in walking. Therefore, we classified the severity of PRD using CV and α. Specifically, PD patients and healthy elderly were classified into three groups: no-PRD, mild-PRD, and obvious-PRD. We compared the contributions of CV and α to the accuracy of this classification. In this study, 45 PD patients and 17 healthy elderly people walked 200 m. The severity of PRD was determined using the modified Hoehn-Yahr scale (mH-Y). People with mH-Y scores of 2.5 and 3 had mild-PRD and obvious-PRD, respectively. As a result, CV differentiated no-PRD from PRD, indicating the correlation between CV and PRD. Considering that PRD is independent of neural rhythm generation, this result suggests the existence of feedback process from physical activities to neural rhythmic activities. Moreover, α differentiated obvious-PRD from mild-PRD. Considering α reflects the intensity of interaction between factors, this result suggests the change of the interaction. Therefore, the interaction between neural rhythmic and physical activities is thought to plays an important role for gait rhythm generation. These characteristics have potential to evaluate the symptoms of PD.

Surface electromyographic amplitude does not identify differences in neural drive to synergistic muscles.

PubMed

Martinez-Valdes, Eduardo; Negro, Francesco; Falla, Deborah; De Nunzio, Alessandro Marco; Farina, Dario

2018-04-01

Surface electromyographic (EMG) signal amplitude is typically used to compare the neural drive to muscles. We experimentally investigated this association by studying the motor unit (MU) behavior and action potentials in the vastus medialis (VM) and vastus lateralis (VL) muscles. Eighteen participants performed isometric knee extensions at four target torques [10, 30, 50, and 70% of the maximum torque (MVC)] while high-density EMG signals were recorded from the VM and VL. The absolute EMG amplitude was greater for VM than VL ( P < 0.001), whereas the EMG amplitude normalized with respect to MVC was greater for VL than VM ( P < 0.04). Because differences in EMG amplitude can be due to both differences in the neural drive and in the size of the MU action potentials, we indirectly inferred the neural drives received by the two muscles by estimating the synaptic inputs received by the corresponding motor neuron pools. For this purpose, we analyzed the increase in discharge rate from recruitment to target torque for motor units matched by recruitment threshold in the two muscles. This analysis indicated that the two muscles received similar levels of neural drive. Nonetheless, the size of the MU action potentials was greater for VM than VL ( P < 0.001), and this difference explained most of the differences in EMG amplitude between the two muscles (~63% of explained variance). These results indicate that EMG amplitude, even following normalization, does not reflect the neural drive to synergistic muscles. Moreover, absolute EMG amplitude is mainly explained by the size of MU action potentials. NEW & NOTEWORTHY Electromyographic (EMG) amplitude is widely used to compare indirectly the strength of neural drive received by synergistic muscles. However, there are no studies validating this approach with motor unit data. Here, we compared between-muscles differences in surface EMG amplitude and motor unit behavior. The results clarify the limitations of surface EMG to interpret differences in neural drive between muscles.

Neural network post-processing of grayscale optical correlator

NASA Technical Reports Server (NTRS)

Lu, Thomas T; Hughlett, Casey L.; Zhoua, Hanying; Chao, Tien-Hsin; Hanan, Jay C.

2005-01-01

In this paper we present the use of a radial basis function neural network (RBFNN) as a post-processor to assist the optical correlator to identify the objects and to reject false alarms. Image plane features near the correlation peaks are extracted and fed to the neural network for analysis. The approach is capable of handling large number of object variations and filter sets. Preliminary experimental results are presented and the performance is analyzed.

Test of Neural Network Techniques using Simulated Dual-Band Data of LEO Satellites

DTIC Science & Technology

2010-09-01

resolved images of satellites are unavailable[1]. Neural networks have been evaluated as a potential automated technique for identifying satellites in...neural network, multiple photometric measurements must be made for each satellite under similar observational conditions. At the same time , this set...are compared to values posted in a real- time satellite tracking website[6]. Agreement to within 0.01 degrees in latitude and longitude and ~100 meters

Why Study Music?

ERIC Educational Resources Information Center

Hodges, Donald A.

2005-01-01

Cognitive neuroscience is identifying neural networks in the brain that support multiple ways of knowing. This notion is also supported by evidence from psychology, anthropology, sociology, and other related disciplines. These human knowledge systems provide a means for sharing, expressing, understanding, knowing, and gaining insights into one's…

Neural Correlates of Impaired Reward-Effort Integration in Remitted Bulimia Nervosa.

PubMed

Mueller, Stefanie Verena; Morishima, Yosuke; Schwab, Simon; Wiest, Roland; Federspiel, Andrea; Hasler, Gregor

2018-03-01

The integration of reward magnitudes and effort costs is required for an effective behavioral guidance. This reward-effort integration was reported to be dependent on dopaminergic neurotransmission. As bulimia nervosa has been associated with a dysregulated dopamine system and catecholamine depletion led to reward-processing deficits in remitted bulimia nervosa, the purpose of this study was to identify the role of catecholamine dysfunction and its relation to behavioral and neural reward-effort integration in bulimia nervosa. To investigate the interaction between catecholamine functioning and behavioral, and neural responses directly, 17 remitted bulimic (rBN) and 21 healthy individuals (HC) received alpha-methyl-paratyrosine (AMPT) over 24 h to achieve catecholamine depletion in a randomized, crossover study design. We used functional magnetic resonance imaging (fMRI) and the monetary incentive delay (MID) task to assess reward-effort integration in relation to catecholaminergic neurotransmission at the behavioral and neural level. AMPT reduced the ability to integrate rewards and efforts effectively in HC participants. In contrast, in rBN participants, the reduced reward-effort integration was associated with illness duration in the sham condition and unrelated to catecholamine depletion. Regarding neural activation, AMPT decreased the reward anticipation-related neural activation in the anteroventral striatum. This decrease was associated with the AMPT-induced reduction of monetary earning in HC in contrast to rBN participants. Our findings contributed to the theory of a desensitized dopaminergic system in bulimia nervosa. A disrupted processing of reward magnitudes and effort costs might increase the probability of maintenance of bulimic symptoms.

From the Cover: Exposing Imidacloprid Interferes With Neurogenesis Through Impacting on Chick Neural Tube Cell Survival.

PubMed

Liu, Meng; Wang, Guang; Zhang, Shi-Yao; Zhong, Shan; Qi, Guo-Long; Wang, Chao-Jie; Chuai, Manli; Lee, Kenneth Ka Ho; Lu, Da-Xiang; Yang, Xuesong

2016-09-01

As a neonicotinoid pesticide, imidacloprid is widely used to control insects in agriculture and fleas on domestic animals. However, it is not known whether imidacloprid exposure negatively affects neurogenesis during embryonic development. In this study, using a chick embryo model, we investigated the effects of imidacloprid exposure on neurogenesis at the earliest stage and during late-stage embryo development. Exposing HH0 chick embryos to imidacloprid in EC culture caused neural tube defects (NTDs) and neuronal differentiation dysplasia as determined by NF/Tuj1 labeling. Furthermore, we found that F-actin accumulation on the apical side of the neural tube was suppressed by exposure to imidacloprid, and the expression of BMP4 and Shh on the dorsal and ventral sides of the neural tubes, respectively, were also reduced, which in turn affects the dorsolateral hinge points during bending of the neural plate. In addition, exposure to imidacloprid reduced cell proliferation and increased cell apoptosis, as determined by pHIS3 labeling and TUNEL staining, respectively, also contributing to the malformation. We obtained similar results in late-stage embryos exposed to imidacloprid. Finally, a bioinformatics analysis was employed to determine which genes identified in this study were involved in NTDs. The experimental evidence and bioinformatics analysis suggested that imidacloprid exposure during chick embryo development could increase the risk of NTDs and neural dysplasia. © The Author 2016. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

KCNQ5/Kv7.5 potassium channel expression and subcellular localization in primate retinal pigment epithelium and neural retina

PubMed Central

Zhang, Xiaoming; Yang, Dongli

2011-01-01

Previous studies identified in retinal pigment epithelial (RPE) cells an M-type K+ current, which in many other cell types is mediated by channels encoded by KCNQ genes. The aim of this study was to assess the expression of KCNQ genes in the monkey RPE and neural retina. Application of the specific KCNQ channel blocker XE991 eliminated the M-type current in freshly isolated monkey RPE cells, indicating that KCNQ subunits contribute to the underlying channels. RT-PCR analysis revealed the expression of KCNQ1, KCNQ4, and KCNQ5 transcripts in the RPE and all five KCNQ transcripts in the neural retina. At the protein level, KCNQ5 was detected in the RPE, whereas both KCNQ4 and KCNQ5 were found in neural retina. In situ hybridization in frozen monkey retinal sections revealed KCNQ5 gene expression in the ganglion cell layer and the inner and outer nuclear layers of the neural retina, but results in the RPE were inconclusive due to the presence of melanin. Immunohistochemistry revealed KCNQ5 in the inner and outer plexiform layers, in cone and rod photoreceptor inner segments, and near the basal membrane of the RPE. The data suggest that KCNQ5 channels contribute to the RPE basal membrane K+ conductance and, thus, likely play an important role in active K+ absorption. The distribution of KCNQ5 in neural retina suggests that these channels may function in the shaping of the photoresponses of cone and rod photoreceptors and the processing of visual information by retinal neurons. PMID:21795522

A functional genomics screen in planarians reveals regulators of whole-brain regeneration.

PubMed

Roberts-Galbraith, Rachel H; Brubacher, John L; Newmark, Phillip A

2016-09-09

Planarians regenerate all body parts after injury, including the central nervous system (CNS). We capitalized on this distinctive trait and completed a gene expression-guided functional screen to identify factors that regulate diverse aspects of neural regeneration in Schmidtea mediterranea . Our screen revealed molecules that influence neural cell fates, support the formation of a major connective hub, and promote reestablishment of chemosensory behavior. We also identified genes that encode signaling molecules with roles in head regeneration, including some that are produced in a previously uncharacterized parenchymal population of cells. Finally, we explored genes downregulated during planarian regeneration and characterized, for the first time, glial cells in the planarian CNS that respond to injury by repressing several transcripts. Collectively, our studies revealed diverse molecules and cell types that underlie an animal's ability to regenerate its brain.

Dynamic decomposition of spatiotemporal neural signals

PubMed Central

2017-01-01

Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals. PMID:28558039

Cluster analysis reveals subclinical subgroups with shared autistic and schizotypal traits.

PubMed

Ford, Talitha C; Apputhurai, Pragalathan; Meyer, Denny; Crewther, David P

2018-07-01

Autism and schizophrenia spectrum research is typically based on coarse diagnostic classification, which overlooks individual variation within clinical groups. This method limits the identification of underlying cognitive, genetic and neural correlates of specific symptom dimensions. This study, therefore, aimed to identify homogenous subclinical subgroups of specific autistic and schizotypal traits dimensions, that may be utilised to establish more effective diagnostic and treatment practices. Latent profile analysis of subscale scores derived from an autism-schizotypy questionnaire, completed by 1678 subclinical adults aged 18-40 years (1250 females), identified a local optimum of eight population clusters: High, Moderate and Low Psychosocial Difficulties; High, Moderate and Low Autism-Schizotypy; High Psychosis-Proneness; and Moderate Schizotypy. These subgroups represent the convergent and discriminant dimensions of autism and schizotypy in the subclinical population, and highlight the importance of examining subgroups of specific symptom characteristics across these spectra in order to identify the underlying genetic and neural correlates that can be utilised to advance diagnostic and treatment practices. Copyright © 2018 Elsevier B.V. All rights reserved.

Lineage analysis of quiescent regenerative stem cells in the adult brain by genetic labelling reveals spatially restricted neurogenic niches in the olfactory bulb.

PubMed

Giachino, Claudio; Taylor, Verdon

2009-07-01

The subventricular zone (SVZ) of the lateral ventricles is the major neurogenic region in the adult mammalian brain, harbouring neural stem cells within defined niches. The identity of these stem cells and the factors regulating their fate are poorly understood. We have genetically mapped a population of Nestin-expressing cells during postnatal development to study their potential and fate in vivo. Taking advantage of the recombination characteristics of a nestin::CreER(T2) allele, we followed a subpopulation of neural stem cells and traced their fate in a largely unrecombined neurogenic niche. Perinatal nestin::CreER(T2)-expressing cells give rise to multiple glial cell types and neurons, as well as to stem cells of the adult SVZ. In the adult SVZ nestin::CreER(T2)-expressing neural stem cells give rise to several neuronal subtypes in the olfactory bulb (OB). We addressed whether the same population of neural stem cells play a role in SVZ regeneration. Following anti-mitotic treatment to eliminate rapidly dividing progenitors, relatively quiescent nestin::CreER(T2)-targeted cells are spared and contribute to SVZ regeneration, generating new proliferating precursors and neuroblasts. Finally, we have identified neurogenic progenitors clustered in ependymal-like niches within the rostral migratory stream (RMS) of the OB. These OB-RMS progenitors generate neuroblasts that, upon transplantation, graft, migrate and differentiate into granule and glomerular neurons. In summary, using conditional lineage tracing we have identified neonatal cells that are the source of neurogenic and regenerative neural stem cells in the adult SVZ and occupy a novel neurogenic niche in the OB.

Sf3b4-depleted Xenopus embryos: a model to study the pathogenesis of craniofacial defects in Nager syndrome

PubMed Central

Devotta, Arun; Juraver-Geslin, Hugo; Gonzalez, Jose Antonio; Hong, Chang-Soo; Saint-Jeannet, Jean-Pierre

2016-01-01

Mandibulofacial dysostosis (MFD) is a human developmental disorder characterized by defects of the facial bones. It is the second most frequent craniofacial malformation after cleft lip and palate. Nager syndrome combines many features of MFD with a variety of limb defects. Mutations in SF3B4 (splicing factor 3b, subunit 4) gene, which encodes a component of the pre-mRNA spliceosomal complex, were recently identified as a cause for Nager syndrome, accounting for 60% of affected individuals. Nothing is known about the cellular pathogenesis underlying Nager type MFD. Here we describe the first animal model for Nager syndrome, generated by knocking down Sf3b4 function in Xenopus laevis embryos, using morpholino antisense oligonucleotides. Our results indicate that Sf3b4-depleted embryos show reduced expression of the neural crest genes sox10, snail2 and twist at the neural plate border, associated with a broadening of the neural plate. This phenotype can be rescued by injection of wild-type human SF3B4 mRNA but not by mRNAs carrying mutations that cause Nager syndrome. At the tailbud stage, morphant embryos had decreased sox10 and tfap2a expression in the pharyngeal arches, indicative of a reduced number of neural crest cells. Later in development, Sf3b4-depleted tadpoles exhibited hypoplasia of neural crest-derived craniofacial cartilages, phenocopying aspects of the craniofacial skeletal defects seen in Nager syndrome patients. With this animal model we are now poised to gain important insights into the etiology and pathogenesis of Nager type MFD, and to identify the molecular targets of Sf3b4. PMID:26874011

The iso-response method: measuring neuronal stimulus integration with closed-loop experiments

PubMed Central

Gollisch, Tim; Herz, Andreas V. M.

2012-01-01

Throughout the nervous system, neurons integrate high-dimensional input streams and transform them into an output of their own. This integration of incoming signals involves filtering processes and complex non-linear operations. The shapes of these filters and non-linearities determine the computational features of single neurons and their functional roles within larger networks. A detailed characterization of signal integration is thus a central ingredient to understanding information processing in neural circuits. Conventional methods for measuring single-neuron response properties, such as reverse correlation, however, are often limited by the implicit assumption that stimulus integration occurs in a linear fashion. Here, we review a conceptual and experimental alternative that is based on exploring the space of those sensory stimuli that result in the same neural output. As demonstrated by recent results in the auditory and visual system, such iso-response stimuli can be used to identify the non-linearities relevant for stimulus integration, disentangle consecutive neural processing steps, and determine their characteristics with unprecedented precision. Automated closed-loop experiments are crucial for this advance, allowing rapid search strategies for identifying iso-response stimuli during experiments. Prime targets for the method are feed-forward neural signaling chains in sensory systems, but the method has also been successfully applied to feedback systems. Depending on the specific question, “iso-response” may refer to a predefined firing rate, single-spike probability, first-spike latency, or other output measures. Examples from different studies show that substantial progress in understanding neural dynamics and coding can be achieved once rapid online data analysis and stimulus generation, adaptive sampling, and computational modeling are tightly integrated into experiments. PMID:23267315

Infrared neural stimulation of human spinal nerve roots in vivo

PubMed Central

Cayce, Jonathan M.; Wells, Jonathon D.; Malphrus, Jonathan D.; Kao, Chris; Thomsen, Sharon; Tulipan, Noel B.; Konrad, Peter E.; Jansen, E. Duco; Mahadevan-Jansen, Anita

2015-01-01

Abstract. Infrared neural stimulation (INS) is a neurostimulation modality that uses pulsed infrared light to evoke artifact-free, spatially precise neural activity with a noncontact interface; however, the technique has not been demonstrated in humans. The objective of this study is to demonstrate the safety and efficacy of INS in humans in vivo. The feasibility of INS in humans was assessed in patients (n=7) undergoing selective dorsal root rhizotomy, where hyperactive dorsal roots, identified for transection, were stimulated in vivo with INS on two to three sites per nerve with electromyogram recordings acquired throughout the stimulation. The stimulated dorsal root was removed and histology was performed to determine thermal damage thresholds of INS. Threshold activation of human dorsal rootlets occurred in 63% of nerves for radiant exposures between 0.53 and 1.23  J/cm2. In all cases, only one or two monitored muscle groups were activated from INS stimulation of a hyperactive spinal root identified by electrical stimulation. Thermal damage was first noted at 1.09  J/cm2 and a 2∶1 safety ratio was identified. These findings demonstrate the success of INS as a fresh approach for activating human nerves in vivo and providing the necessary safety data needed to pursue clinically driven therapeutic and diagnostic applications of INS in humans. PMID:26157986

Neural and receptor cochlear potentials obtained by transtympanic electrocochleography in auditory neuropathy.

PubMed

Santarelli, Rosamaria; Starr, Arnold; Michalewski, Henry J; Arslan, Edoardo

2008-05-01

Transtympanic electrocochleography (ECochG) was recorded bilaterally in children and adults with auditory neuropathy (AN) to evaluate receptor and neural generators. Test stimuli were clicks from 60 to 120dB p.e. SPL. Measures obtained from eight AN subjects were compared to 16 normally hearing children. Receptor cochlear microphonics (CMs) in AN were of normal or enhanced amplitude. Neural compound action potentials (CAPs) and receptor summating potentials (SPs) were identified in five AN ears. ECochG potentials in those ears without CAPs were of negative polarity and of normal or prolonged duration. We used adaptation to rapid stimulus rates to distinguish whether the generators of the negative potentials were of neural or receptor origin. Adaptation in controls resulted in amplitude reduction of CAP twice that of SP without affecting the duration of ECochG potentials. In seven AN ears without CAP and with prolonged negative potential, adaptation was accompanied by reduction of both amplitude and duration of the negative potential to control values consistent with neural generation. In four ears without CAP and with normal duration potentials, adaptation was without effect consistent with receptor generation. In five AN ears with CAP, there was reduction in amplitude of CAP and SP as controls but with a significant decrease in response duration. Three patterns of cochlear potentials were identified in AN: (1) presence of receptor SP without CAP consistent with pre-synaptic disorder of inner hair cells; (2) presence of both SP and CAP consistent with post-synaptic disorder of proximal auditory nerve; (3) presence of prolonged neural potentials without a CAP consistent with post-synaptic disorder of nerve terminals. Cochlear potential measures may identify pre- and post-synaptic disorders of inner hair cells and auditory nerves in AN.

Identification of the subthalamic nucleus in deep brain stimulation surgery with a novel wavelet-derived measure of neural background activity.

PubMed

Snellings, André; Sagher, Oren; Anderson, David J; Aldridge, J Wayne

2009-10-01

The authors developed a wavelet-based measure for quantitative assessment of neural background activity during intraoperative neurophysiological recordings so that the boundaries of the subthalamic nucleus (STN) can be more easily localized for electrode implantation. Neural electrophysiological data were recorded in 14 patients (20 tracks and 275 individual recording sites) with dopamine-sensitive idiopathic Parkinson disease during the target localization portion of deep brain stimulator implantation surgery. During intraoperative recording, the STN was identified based on audio and visual monitoring of neural firing patterns, kinesthetic tests, and comparisons between neural behavior and the known characteristics of the target nucleus. The quantitative wavelet-based measure was applied offline using commercially available software to measure the magnitude of the neural background activity, and the results of this analysis were compared with the intraoperative conclusions. Wavelet-derived estimates were also compared with power spectral density measurements. The wavelet-derived background levels were significantly higher in regions encompassed by the clinically estimated boundaries of the STN than in the surrounding regions (STN, 225 +/- 61 microV; ventral to the STN, 112 +/- 32 microV; and dorsal to the STN, 136 +/- 66 microV). In every track, the absolute maximum magnitude was found within the clinically identified STN. The wavelet-derived background levels provided a more consistent index with less variability than measurements with power spectral density. Wavelet-derived background activity can be calculated quickly, does not require spike sorting, and can be used to identify the STN reliably with very little subjective interpretation required. This method may facilitate the rapid intraoperative identification of STN borders.

Identification of the subthalamic nucleus in deep brain stimulation surgery with a novel wavelet-derived measure of neural background activity

PubMed Central

Snellings, André; Sagher, Oren; Anderson, David J.; Aldridge, J. Wayne

2016-01-01

Object A wavelet-based measure was developed to quantitatively assess neural background activity taken during surgical neurophysiological recordings to localize the boundaries of the subthalamic nucleus during target localization for deep brain stimulator implant surgery. Methods Neural electrophysiological data was recorded from 14 patients (20 tracks, n = 275 individual recording sites) with dopamine-sensitive idiopathic Parkinson’s disease during the target localization portion of deep brain stimulator implant surgery. During intraoperative recording the STN was identified based upon audio and visual monitoring of neural firing patterns, kinesthetic tests, and comparisons between neural behavior and known characteristics of the target nucleus. The quantitative wavelet-based measure was applied off-line using MATLAB software to measure the magnitude of the neural background activity, and the results of this analysis were compared to the intraoperative conclusions. Wavelet-derived estimates were compared to power spectral density measures. Results The wavelet-derived background levels were significantly higher in regions encompassed by the clinically estimated boundaries of the STN than in surrounding regions (STN: 225 ± 61 μV vs. ventral to STN: 112 ± 32 μV, and dorsal to STN: 136 ± 66 μV). In every track, the absolute maximum magnitude was found within the clinically identified STN. The wavelet-derived background levels provided a more consistent index with less variability than power spectral density. Conclusions The wavelet-derived background activity assessor can be calculated quickly, requires no spike sorting, and can be reliably used to identify the STN with very little subjective interpretation required. This method may facilitate rapid intraoperative identification of subthalamic nucleus borders. PMID:19344225

An Improved Cloud Classification Algorithm for China’s FY-2C Multi-Channel Images Using Artificial Neural Network

PubMed Central

Liu, Yu; Xia, Jun; Shi, Chun-Xiang; Hong, Yang

2009-01-01

The crowning objective of this research was to identify a better cloud classification method to upgrade the current window-based clustering algorithm used operationally for China’s first operational geostationary meteorological satellite FengYun-2C (FY-2C) data. First, the capabilities of six widely-used Artificial Neural Network (ANN) methods are analyzed, together with the comparison of two other methods: Principal Component Analysis (PCA) and a Support Vector Machine (SVM), using 2864 cloud samples manually collected by meteorologists in June, July, and August in 2007 from three FY-2C channel (IR1, 10.3–11.3 μm; IR2, 11.5–12.5 μm and WV 6.3–7.6 μm) imagery. The result shows that: (1) ANN approaches, in general, outperformed the PCA and the SVM given sufficient training samples and (2) among the six ANN networks, higher cloud classification accuracy was obtained with the Self-Organizing Map (SOM) and Probabilistic Neural Network (PNN). Second, to compare the ANN methods to the present FY-2C operational algorithm, this study implemented SOM, one of the best ANN network identified from this study, as an automated cloud classification system for the FY-2C multi-channel data. It shows that SOM method has improved the results greatly not only in pixel-level accuracy but also in cloud patch-level classification by more accurately identifying cloud types such as cumulonimbus, cirrus and clouds in high latitude. Findings of this study suggest that the ANN-based classifiers, in particular the SOM, can be potentially used as an improved Automated Cloud Classification Algorithm to upgrade the current window-based clustering method for the FY-2C operational products. PMID:22346714

An Improved Cloud Classification Algorithm for China's FY-2C Multi-Channel Images Using Artificial Neural Network.

PubMed

Liu, Yu; Xia, Jun; Shi, Chun-Xiang; Hong, Yang

2009-01-01

The crowning objective of this research was to identify a better cloud classification method to upgrade the current window-based clustering algorithm used operationally for China's first operational geostationary meteorological satellite FengYun-2C (FY-2C) data. First, the capabilities of six widely-used Artificial Neural Network (ANN) methods are analyzed, together with the comparison of two other methods: Principal Component Analysis (PCA) and a Support Vector Machine (SVM), using 2864 cloud samples manually collected by meteorologists in June, July, and August in 2007 from three FY-2C channel (IR1, 10.3-11.3 μm; IR2, 11.5-12.5 μm and WV 6.3-7.6 μm) imagery. The result shows that: (1) ANN approaches, in general, outperformed the PCA and the SVM given sufficient training samples and (2) among the six ANN networks, higher cloud classification accuracy was obtained with the Self-Organizing Map (SOM) and Probabilistic Neural Network (PNN). Second, to compare the ANN methods to the present FY-2C operational algorithm, this study implemented SOM, one of the best ANN network identified from this study, as an automated cloud classification system for the FY-2C multi-channel data. It shows that SOM method has improved the results greatly not only in pixel-level accuracy but also in cloud patch-level classification by more accurately identifying cloud types such as cumulonimbus, cirrus and clouds in high latitude. Findings of this study suggest that the ANN-based classifiers, in particular the SOM, can be potentially used as an improved Automated Cloud Classification Algorithm to upgrade the current window-based clustering method for the FY-2C operational products.

Neuronal Substrates of Relapse to Cocaine-Seeking Behavior: Role of Prefrontal Cortex

ERIC Educational Resources Information Center

Rebec, George V.; Sun, WenLin

2005-01-01

The return to drug seeking, even after prolonged periods of abstinence, is a defining feature of cocaine addiction. The neural circuitry underlying relapse has been identified in neuropharmacological studies of experimental animals, typically rats, and supported in brain imaging studies of human addicts. Although the nucleus accumbens (NAcc),…

Integration and segregation in auditory streaming

NASA Astrophysics Data System (ADS)

Almonte, Felix; Jirsa, Viktor K.; Large, Edward W.; Tuller, Betty

2005-12-01

We aim to capture the perceptual dynamics of auditory streaming using a neurally inspired model of auditory processing. Traditional approaches view streaming as a competition of streams, realized within a tonotopically organized neural network. In contrast, we view streaming to be a dynamic integration process which resides at locations other than the sensory specific neural subsystems. This process finds its realization in the synchronization of neural ensembles or in the existence of informational convergence zones. Our approach uses two interacting dynamical systems, in which the first system responds to incoming acoustic stimuli and transforms them into a spatiotemporal neural field dynamics. The second system is a classification system coupled to the neural field and evolves to a stationary state. These states are identified with a single perceptual stream or multiple streams. Several results in human perception are modelled including temporal coherence and fission boundaries [L.P.A.S. van Noorden, Temporal coherence in the perception of tone sequences, Ph.D. Thesis, Eindhoven University of Technology, The Netherlands, 1975], and crossing of motions [A.S. Bregman, Auditory Scene Analysis: The Perceptual Organization of Sound, MIT Press, 1990]. Our model predicts phenomena such as the existence of two streams with the same pitch, which cannot be explained by the traditional stream competition models. An experimental study is performed to provide proof of existence of this phenomenon. The model elucidates possible mechanisms that may underlie perceptual phenomena.

Molecular stages of rapid and uniform neuralization of human embryonic stem cells.

PubMed

Bajpai, R; Coppola, G; Kaul, M; Talantova, M; Cimadamore, F; Nilbratt, M; Geschwind, D H; Lipton, S A; Terskikh, A V

2009-06-01

Insights into early human development are fundamental for our understanding of human biology. Efficient differentiation of human embryonic stem cells (hESCs) into neural precursor cells is critical for future cell-based therapies. Here, using defined conditions, we characterized a new method for rapid and uniform differentiation of hESCs into committed neural precursor cells (designated C-NPCs). Dynamic gene expression analysis identified several distinct stages of ESC neuralization and revealed functional modules of coregulated genes and pathways. The first wave of gene expression changes, likely corresponding to the transition through primitive ectoderm, started at day 3, preceding the formation of columnar neuroepithelial rosettes. The second wave started at day 5, coinciding with the formation of rosettes. The majority of C-NPCs were positive for both anterior and posterior markers of developing neuroepithelium. In culture, C-NPCs became electrophysiologically functional neurons; on transplantation into neonatal mouse brains, C-NPCs integrated into the cortex and olfactory bulb, acquiring appropriate neuronal morphologies and markers. Compared to rosette-NPCs,(1) C-NPCs exhibited limited in vitro expansion capacity and did not express potent oncogenes such as PLAG1 or RSPO3. Concordantly, we never detected tumors or excessive neural proliferation after transplantation of C-NPCs into mouse brains. In conclusion, our study provides a framework for future analysis of molecular signaling during ESC neuralization.

Mechanics of neurulation: From classical to current perspectives on the physical mechanics that shape, fold, and form the neural tube.

PubMed

Vijayraghavan, Deepthi S; Davidson, Lance A

2017-01-30

Neural tube defects arise from mechanical failures in the process of neurulation. At the most fundamental level, formation of the neural tube relies on coordinated, complex tissue movements that mechanically transform the flat neural epithelium into a lumenized epithelial tube (Davidson, 2012). The nature of this mechanical transformation has mystified embryologists, geneticists, and clinicians for more than 100 years. Early embryologists pondered the physical mechanisms that guide this transformation. Detailed observations of cell and tissue movements as well as experimental embryological manipulations allowed researchers to generate and test elementary hypotheses of the intrinsic and extrinsic forces acting on the neural tissue. Current research has turned toward understanding the molecular mechanisms underlying neurulation. Genetic and molecular perturbation have identified a multitude of subcellular components that correlate with cell behaviors and tissue movements during neural tube formation. In this review, we focus on methods and conceptual frameworks that have been applied to the study of amphibian neurulation that can be used to determine how molecular and physical mechanisms are integrated and responsible for neurulation. We will describe how qualitative descriptions and quantitative measurements of strain, force generation, and tissue material properties as well as simulations can be used to understand how embryos use morphogenetic programs to drive neurulation. Birth Defects Research 109:153-168, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Discrete Neural Correlates for the Recognition of Negative Emotions: Insights from Frontotemporal Dementia

PubMed Central

Kumfor, Fiona; Irish, Muireann; Hodges, John R.; Piguet, Olivier

2013-01-01

Patients with frontotemporal dementia have pervasive changes in emotion recognition and social cognition, yet the neural changes underlying these emotion processing deficits remain unclear. The multimodal system model of emotion proposes that basic emotions are dependent on distinct brain regions, which undergo significant pathological changes in frontotemporal dementia. As such, this syndrome may provide important insight into the impact of neural network degeneration upon the innate ability to recognise emotions. This study used voxel-based morphometry to identify discrete neural correlates involved in the recognition of basic emotions (anger, disgust, fear, sadness, surprise and happiness) in frontotemporal dementia. Forty frontotemporal dementia patients (18 behavioural-variant, 11 semantic dementia, 11 progressive nonfluent aphasia) and 27 healthy controls were tested on two facial emotion recognition tasks: The Ekman 60 and Ekman Caricatures. Although each frontotemporal dementia group showed impaired recognition of negative emotions, distinct associations between emotion-specific task performance and changes in grey matter intensity emerged. Fear recognition was associated with the right amygdala; disgust recognition with the left insula; anger recognition with the left middle and superior temporal gyrus; and sadness recognition with the left subcallosal cingulate, indicating that discrete neural substrates are necessary for emotion recognition in frontotemporal dementia. The erosion of emotion-specific neural networks in neurodegenerative disorders may produce distinct profiles of performance that are relevant to understanding the neurobiological basis of emotion processing. PMID:23805313

Identifying Broadband Rotational Spectra with Neural Networks

NASA Astrophysics Data System (ADS)

Zaleski, Daniel P.; Prozument, Kirill

2017-06-01

A typical broadband rotational spectrum may contain several thousand observable transitions, spanning many species. Identifying the individual spectra, particularly when the dynamic range reaches 1,000:1 or even 10,000:1, can be challenging. One approach is to apply automated fitting routines. In this approach, combinations of 3 transitions can be created to form a "triple", which allows fitting of the A, B, and C rotational constants in a Watson-type Hamiltonian. On a standard desktop computer, with a target molecule of interest, a typical AUTOFIT routine takes 2-12 hours depending on the spectral density. A new approach is to utilize machine learning to train a computer to recognize the patterns (frequency spacing and relative intensities) inherit in rotational spectra and to identify the individual spectra in a raw broadband rotational spectrum. Here, recurrent neural networks have been trained to identify different types of rotational spectra and classify them accordingly. Furthermore, early results in applying convolutional neural networks for spectral object recognition in broadband rotational spectra appear promising. Perez et al. "Broadband Fourier transform rotational spectroscopy for structure determination: The water heptamer." Chem. Phys. Lett., 2013, 571, 1-15. Seifert et al. "AUTOFIT, an Automated Fitting Tool for Broadband Rotational Spectra, and Applications to 1-Hexanal." J. Mol. Spectrosc., 2015, 312, 13-21. Bishop. "Neural networks for pattern recognition." Oxford university press, 1995.

Does context matter in evaluations of stigmatized individuals? An fMRI study.

PubMed

Krendl, Anne C; Moran, Joseph M; Ambady, Nalini

2013-06-01

The manner in which disparate affective responses shape attitudes toward other individuals has received a great deal of attention in neuroscience research. However, the malleability of these affective responses remains largely unexplored. The perceived controllability of a stigma (whether or not the bearer of the stigma is perceived as being responsible for his or her condition) has been found to polarize behavioral affective responses to that stigma. The current study uses functional magnetic resonance imaging to identify the neural correlates underlying the evaluation of stigmatized individuals (people who are homeless) when perceptions of the controllability of their condition are altered. Results demonstrated that perceivers engaged neural networks implicated in inferring intentionality (e.g. the medial prefrontal cortex) when they evaluated a homeless individual who was described as being responsible for becoming homeless. Conversely, neural networks associated with resolving strong affective responses (e.g. insula) were engaged when evaluating a homeless individual who was described as not being responsible for becoming homeless.

When language meets action: the neural integration of gesture and speech.

PubMed

Willems, Roel M; Ozyürek, Asli; Hagoort, Peter

2007-10-01

Although generally studied in isolation, language and action often co-occur in everyday life. Here we investigated one particular form of simultaneous language and action, namely speech and gestures that speakers use in everyday communication. In a functional magnetic resonance imaging study, we identified the neural networks involved in the integration of semantic information from speech and gestures. Verbal and/or gestural content could be integrated easily or less easily with the content of the preceding part of speech. Premotor areas involved in action observation (Brodmann area [BA] 6) were found to be specifically modulated by action information "mismatching" to a language context. Importantly, an increase in integration load of both verbal and gestural information into prior speech context activated Broca's area and adjacent cortex (BA 45/47). A classical language area, Broca's area, is not only recruited for language-internal processing but also when action observation is integrated with speech. These findings provide direct evidence that action and language processing share a high-level neural integration system.

Indian hedgehog signaling from endothelial cells is required for sclera and retinal pigment epithelium development in the mouse eye.

PubMed

Dakubo, Gabriel D; Mazerolle, Chantal; Furimsky, Marosh; Yu, Chuan; St-Jacques, Benoit; McMahon, Andrew P; Wallace, Valerie A

2008-08-01

The development of extraocular orbital structures, in particular the choroid and sclera, is regulated by a complex series of interactions between neuroectoderm, neural crest and mesoderm derivatives, although in many instances the signals that mediate these interactions are not known. In this study we have investigated the function of Indian hedgehog (Ihh) in the developing mammalian eye. We show that Ihh is expressed in a population of non-pigmented cells located in the developing choroid adjacent to the RPE. The analysis of Hh mutant mice demonstrates that the RPE and developing scleral mesenchyme are direct targets of Ihh signaling and that Ihh is required for the normal pigmentation pattern of the RPE and the condensation of mesenchymal cells to form the sclera. Our findings also indicate that Ihh signals indirectly to promote proliferation and photoreceptor specification in the neural retina. This study identifies Ihh as a novel choroid-derived signal that regulates RPE, sclera and neural retina development.

Neural network classification of clinical neurophysiological data for acute care monitoring

NASA Technical Reports Server (NTRS)

Sgro, Joseph

1994-01-01

The purpose of neurophysiological monitoring of the 'acute care' patient is to allow the accurate recognition of changing or deteriorating neurological function as close to the moment of occurrence as possible, thus permitting immediate intervention. Results confirm that: (1) neural networks are able to accurately identify electroencephalogram (EEG) patterns and evoked potential (EP) wave components, and measuring EP waveform latencies and amplitudes; (2) neural networks are able to accurately detect EP and EEG recordings that have been contaminated by noise; (3) the best performance was obtained consistently with the back propagation network for EP and the HONN for EEG's; (4) neural network performed consistently better than other methods evaluated; and (5) neural network EEG and EP analyses are readily performed on multichannel data.

A Two-Factor Model of Relapse/Recurrence Vulnerability in Unipolar Depression

PubMed Central

Farb, Norman A. S.; Irving, Julie A.; Anderson, Adam K.; Segal, Zindel V.

2015-01-01

The substantial health burden associated with Major Depressive Disorder is a product of both its high prevalence and the significant risk of relapse, recurrence and chronicity. Establishing recurrence vulnerability factors (VFs) could improve the long-term management of MDD by identifying the need for further intervention in seemingly recovered patients. We present a model of sensitization in depression vulnerability, with an emphasis on the integration of behavioral and neural systems accounts. Evidence suggests that VFs fall into two categories: dysphoric attention and dysphoric elaboration. Dysphoric attention is driven by fixation on negative life events, and is characterized behaviorally by reduced executive control, and neurally by elevated activity in the brain’s salience network. Dysphoric elaboration is driven by rumination that promotes over-general self and contextual appraisals, and is characterized behaviorally by dysfunctional attitudes, and neurally by elevated connectivity within normally-distinct prefrontal brain networks. While, at present, few prospective VF studies exist from which to catalogue a definitive neurobehavioral account, extant data support the value of the proposed two-factor model. Measuring the continued presence of these two VFs during recovery may more accurately identify remitted patients who would benefit from targeted prophylactic intervention. PMID:25688431

Large deep neural networks for MS lesion segmentation

NASA Astrophysics Data System (ADS)

Prieto, Juan C.; Cavallari, Michele; Palotai, Miklos; Morales Pinzon, Alfredo; Egorova, Svetlana; Styner, Martin; Guttmann, Charles R. G.

2017-02-01

Multiple sclerosis (MS) is a multi-factorial autoimmune disorder, characterized by spatial and temporal dissemination of brain lesions that are visible in T2-weighted and Proton Density (PD) MRI. Assessment of lesion burden and is useful for monitoring the course of the disease, and assessing correlates of clinical outcomes. Although there are established semi-automated methods to measure lesion volume, most of them require human interaction and editing, which are time consuming and limits the ability to analyze large sets of data with high accuracy. The primary objective of this work is to improve existing segmentation algorithms and accelerate the time consuming operation of identifying and validating MS lesions. In this paper, a Deep Neural Network for MS Lesion Segmentation is implemented. The MS lesion samples are extracted from the Partners Comprehensive Longitudinal Investigation of Multiple Sclerosis (CLIMB) study. A set of 900 subjects with T2, PD and a manually corrected label map images were used to train a Deep Neural Network and identify MS lesions. Initial tests using this network achieved a 90% accuracy rate. A secondary goal was to enable this data repository for big data analysis by using this algorithm to segment the remaining cases available in the CLIMB repository.

Distributed Patterns of Reactivation Predict Vividness of Recollection.

PubMed

St-Laurent, Marie; Abdi, Hervé; Buchsbaum, Bradley R

2015-10-01

According to the principle of reactivation, memory retrieval evokes patterns of brain activity that resemble those instantiated when an event was first experienced. Intuitively, one would expect neural reactivation to contribute to recollection (i.e., the vivid impression of reliving past events), but evidence of a direct relationship between the subjective quality of recollection and multiregional reactivation of item-specific neural patterns is lacking. The current study assessed this relationship using fMRI to measure brain activity as participants viewed and mentally replayed a set of short videos. We used multivoxel pattern analysis to train a classifier to identify individual videos based on brain activity evoked during perception and tested how accurately the classifier could distinguish among videos during mental replay. Classification accuracy correlated positively with memory vividness, indicating that the specificity of multivariate brain patterns observed during memory retrieval was related to the subjective quality of a memory. In addition, we identified a set of brain regions whose univariate activity during retrieval predicted both memory vividness and the strength of the classifier's prediction irrespective of the particular video that was retrieved. Our results establish distributed patterns of neural reactivation as a valid and objective marker of the quality of recollection.

The association between brain activity and motor imagery during motor illusion induction by vibratory stimulation

PubMed Central

Kodama, Takayuki; Nakano, Hideki; Katayama, Osamu; Murata, Shin

2017-01-01

Background: The association between motor imagery ability and brain neural activity that leads to the manifestation of a motor illusion remains unclear. Objective: In this study, we examined the association between the ability to generate motor imagery and brain neural activity leading to the induction of a motor illusion by vibratory stimulation. Methods: The sample consisted of 20 healthy individuals who did not have movement or sensory disorders. We measured the time between the starting and ending points of a motor illusion (the time to illusion induction, TII) and performed electroencephalography (EEG). We conducted a temporo-spatial analysis on brain activity leading to the induction of motor illusions using the EEG microstate segmentation method. Additionally, we assessed the ability to generate motor imagery using the Japanese version of the Movement Imagery Questionnaire-Revised (JMIQ-R) prior to performing the task and examined the associations among brain neural activity levels as identified by microstate segmentation method, TII, and the JMIQ-R scores. Results: The results showed four typical microstates during TII and significantly higher neural activity in the ventrolateral prefrontal cortex, primary sensorimotor area, supplementary motor area (SMA), and inferior parietal lobule (IPL). Moreover, there were significant negative correlations between the neural activity of the primary motor cortex (MI), SMA, IPL, and TII, and a significant positive correlation between the neural activity of the SMA and the JMIQ-R scores. Conclusion: These findings suggest the possibility that a neural network primarily comprised of the neural activity of SMA and M1, which are involved in generating motor imagery, may be the neural basis for inducing motor illusions. This may aid in creating a new approach to neurorehabilitation that enables a more robust reorganization of the neural base for patients with brain dysfunction with a motor function disorder. PMID:29172013

Black Holes as Brains: Neural Networks with Area Law Entropy

NASA Astrophysics Data System (ADS)

Dvali, Gia

2018-04-01

Motivated by the potential similarities between the underlying mechanisms of the enhanced memory storage capacity in black holes and in brain networks, we construct an artificial quantum neural network based on gravity-like synaptic connections and a symmetry structure that allows to describe the network in terms of geometry of a d-dimensional space. We show that the network possesses a critical state in which the gapless neurons emerge that appear to inhabit a (d-1)-dimensional surface, with their number given by the surface area. In the excitations of these neurons, the network can store and retrieve an exponentially large number of patterns within an arbitrarily narrow energy gap. The corresponding micro-state entropy of the brain network exhibits an area law. The neural network can be described in terms of a quantum field, via identifying the different neurons with the different momentum modes of the field, while identifying the synaptic connections among the neurons with the interactions among the corresponding momentum modes. Such a mapping allows to attribute a well-defined sense of geometry to an intrinsically non-local system, such as the neural network, and vice versa, it allows to represent the quantum field model as a neural network.

The hierarchical brain network for face recognition.

PubMed

Zhen, Zonglei; Fang, Huizhen; Liu, Jia

2013-01-01

Numerous functional magnetic resonance imaging (fMRI) studies have identified multiple cortical regions that are involved in face processing in the human brain. However, few studies have characterized the face-processing network as a functioning whole. In this study, we used fMRI to identify face-selective regions in the entire brain and then explore the hierarchical structure of the face-processing network by analyzing functional connectivity among these regions. We identified twenty-five regions mainly in the occipital, temporal and frontal cortex that showed a reliable response selective to faces (versus objects) across participants and across scan sessions. Furthermore, these regions were clustered into three relatively independent sub-networks in a face-recognition task on the basis of the strength of functional connectivity among them. The functionality of the sub-networks likely corresponds to the recognition of individual identity, retrieval of semantic knowledge and representation of emotional information. Interestingly, when the task was switched to object recognition from face recognition, the functional connectivity between the inferior occipital gyrus and the rest of the face-selective regions were significantly reduced, suggesting that this region may serve as an entry node in the face-processing network. In sum, our study provides empirical evidence for cognitive and neural models of face recognition and helps elucidate the neural mechanisms underlying face recognition at the network level.

Asymmetric segregation of the double-stranded RNA binding protein Staufen2 during mammalian neural stem cell divisions promotes lineage progression.

PubMed

Kusek, Gretchen; Campbell, Melissa; Doyle, Frank; Tenenbaum, Scott A; Kiebler, Michael; Temple, Sally

2012-10-05

Asymmetric cell divisions are a fundamental feature of neural development, and misregulation can lead to brain abnormalities or tumor formation. During an asymmetric cell division, molecular determinants are segregated preferentially into one daughter cell to specify its fate. An important goal is to identify the asymmetric determinants in neural progenitor cells, which could be tumor suppressors or inducers of specific neural fates. Here, we show that the double-stranded RNA-binding protein Stau2 is distributed asymmetrically during progenitor divisions in the developing mouse cortex, preferentially segregating into the Tbr2(+) neuroblast daughter, taking with it a subset of RNAs. Knockdown of Stau2 stimulates differentiation and overexpression produces periventricular neuronal masses, demonstrating its functional importance for normal cortical development. We immunoprecipitated Stau2 to examine its cargo mRNAs, and found enrichment for known asymmetric and basal cell determinants, such as Trim32, and identified candidates, including a subset involved in primary cilium function. Copyright © 2012 Elsevier Inc. All rights reserved.

Asymmetric Segregation of the Double-Stranded RNA Binding Protein Staufen2 during Mammalian Neural Stem Cell Divisions Promotes Lineage Progression

PubMed Central

Kusek, Gretchen; Campbell, Melissa; Doyle, Frank; Tenenbaum, Scott A.; Kiebler, Michael; Temple, Sally

2012-01-01

Summary Asymmetric cell divisions are a fundamental feature of neural development, and misregulation can lead to brain abnormalities or tumor formation. During an asymmetric cell division, molecular determinants are segregated preferentially into one daughter cell to specify its fate. An important goal is to identify the asymmetric determinants in neural progenitor cells, which could be tumor suppressors or inducers of specific neural fates. Here we show that the double-stranded RNA-binding protein Stau2 is distributed asymmetrically during progenitor divisions in the developing mouse cortex, preferentially segregating into the Tbr2+ neuroblast daughter, taking with it a sub-set of RNAs. Knockdown of Stau2 stimulates differentiation and over-expression produces periventricular neuronal masses, demonstrating its functional importance for normal cortical development. We immunoprecipitated Stau2 to examine its cargo mRNAs, and found enrichment for known asymmetric and basal cell determinants, such as Trim32, and identified novel candidates, including a subset involved in primary cilium function. PMID:22902295

Cognitive and Neural Correlates of Mathematical Giftedness in Adults and Children: A Review

PubMed Central

Myers, Timothy; Carey, Emma; Szűcs, Dénes

2017-01-01

Most mathematical cognition research has focused on understanding normal adult function and child development as well as mildly and moderately impaired mathematical skill, often labeled developmental dyscalculia and/or mathematical learning disability. In contrast, much less research is available on cognitive and neural correlates of gifted/excellent mathematical knowledge in adults and children. In order to facilitate further inquiry into this area, here we review 40 available studies, which examine the cognitive and neural basis of gifted mathematics. Studies associated a large number of cognitive factors with gifted mathematics, with spatial processing and working memory being the most frequently identified contributors. However, the current literature suffers from low statistical power, which most probably contributes to variability across findings. Other major shortcomings include failing to establish domain and stimulus specificity of findings, suggesting causation without sufficient evidence and the frequent use of invalid backward inference in neuro-imaging studies. Future studies must increase statistical power and neuro-imaging studies must rely on supporting behavioral data when interpreting findings. Studies should investigate the factors shown to correlate with math giftedness in a more specific manner and determine exactly how individual factors may contribute to gifted math ability. PMID:29118725

Characterization of essential proteins based on network topology in proteins interaction networks

NASA Astrophysics Data System (ADS)

Bakar, Sakhinah Abu; Taheri, Javid; Zomaya, Albert Y.

2014-06-01

The identification of essential proteins is theoretically and practically important as (1) it is essential to understand the minimal surviving requirements for cellular lives, and (2) it provides fundamental for development of drug. As conducting experimental studies to identify essential proteins are both time and resource consuming, here we present a computational approach in predicting them based on network topology properties from protein-protein interaction networks of Saccharomyces cerevisiae. The proposed method, namely EP3NN (Essential Proteins Prediction using Probabilistic Neural Network) employed a machine learning algorithm called Probabilistic Neural Network as a classifier to identify essential proteins of the organism of interest; it uses degree centrality, closeness centrality, local assortativity and local clustering coefficient of each protein in the network for such predictions. Results show that EP3NN managed to successfully predict essential proteins with an accuracy of 95% for our studied organism. Results also show that most of the essential proteins are close to other proteins, have assortativity behavior and form clusters/sub-graph in the network.

Development of the disable software reporting system on the basis of the neural network

NASA Astrophysics Data System (ADS)

Gavrylenko, S.; Babenko, O.; Ignatova, E.

2018-04-01

The PE structure of malicious and secure software is analyzed, features are highlighted, binary sign vectors are obtained and used as inputs for training the neural network. A software model for detecting malware based on the ART-1 neural network was developed, optimal similarity coefficients were found, and testing was performed. The obtained research results showed the possibility of using the developed system of identifying malicious software in computer systems protection systems

“We make choices we think are going to save us”: Debate and stance identification for online breast cancer CAM discussions

PubMed Central

Zhang, Shaodian; Qiu, Lin; Chen, Frank; Zhang, Weinan; Yu, Yong; Elhadad, Noémie

2017-01-01

Patients discuss complementary and alternative medicine (CAM) in online health communities. Sometimes, patients’ conflicting opinions toward CAM-related issues trigger debates in the community. The objectives of this paper are to identify such debates, identify controversial CAM therapies in a popular online breast cancer community, as well as patients’ stances towards them. To scale our analysis, we trained a set of classifiers. We first constructed a supervised classifier based on a long short-term memory neural network (LSTM) stacked over a convolutional neural network (CNN) to detect automatically CAM-related debates from a popular breast cancer forum. Members’ stances in these debates were also identified by a CNN-based classifier. Finally, posts automatically flagged as debates by the classifier were analyzed to explore which specific CAM therapies trigger debates more often than others. Our methods are able to detect CAM debates with F score of 77%, and identify stances with F score of 70%. The debate classifier identified about 1/6 of all CAM-related posts as debate. About 60% of CAM-related debate posts represent the supportive stance toward CAM usage. Qualitative analysis shows that some specific therapies, such as Gerson therapy and usage of laetrile, trigger debates frequently among members of the breast cancer community. This study demonstrates that neural networks can effectively locate debates on usage and effectiveness of controversial CAM therapies, and can help make sense of patients’ opinions on such issues under dispute. As to CAM for breast cancer, perceptions of their effectiveness vary among patients. Many of the specific therapies trigger debates frequently and are worth more exploration in future work. PMID:28967000

"We make choices we think are going to save us": Debate and stance identification for online breast cancer CAM discussions.

PubMed

Zhang, Shaodian; Qiu, Lin; Chen, Frank; Zhang, Weinan; Yu, Yong; Elhadad, Noémie

2017-04-01

Patients discuss complementary and alternative medicine (CAM) in online health communities. Sometimes, patients' conflicting opinions toward CAM-related issues trigger debates in the community. The objectives of this paper are to identify such debates, identify controversial CAM therapies in a popular online breast cancer community, as well as patients' stances towards them. To scale our analysis, we trained a set of classifiers. We first constructed a supervised classifier based on a long short-term memory neural network (LSTM) stacked over a convolutional neural network (CNN) to detect automatically CAM-related debates from a popular breast cancer forum. Members' stances in these debates were also identified by a CNN-based classifier. Finally, posts automatically flagged as debates by the classifier were analyzed to explore which specific CAM therapies trigger debates more often than others. Our methods are able to detect CAM debates with F score of 77%, and identify stances with F score of 70%. The debate classifier identified about 1/6 of all CAM-related posts as debate. About 60% of CAM-related debate posts represent the supportive stance toward CAM usage. Qualitative analysis shows that some specific therapies, such as Gerson therapy and usage of laetrile, trigger debates frequently among members of the breast cancer community. This study demonstrates that neural networks can effectively locate debates on usage and effectiveness of controversial CAM therapies, and can help make sense of patients' opinions on such issues under dispute. As to CAM for breast cancer, perceptions of their effectiveness vary among patients. Many of the specific therapies trigger debates frequently and are worth more exploration in future work.

Similar patterns of neural activity predict memory function during encoding and retrieval.

PubMed

Kragel, James E; Ezzyat, Youssef; Sperling, Michael R; Gorniak, Richard; Worrell, Gregory A; Berry, Brent M; Inman, Cory; Lin, Jui-Jui; Davis, Kathryn A; Das, Sandhitsu R; Stein, Joel M; Jobst, Barbara C; Zaghloul, Kareem A; Sheth, Sameer A; Rizzuto, Daniel S; Kahana, Michael J

2017-07-15

Neural networks that span the medial temporal lobe (MTL), prefrontal cortex, and posterior cortical regions are essential to episodic memory function in humans. Encoding and retrieval are supported by the engagement of both distinct neural pathways across the cortex and common structures within the medial temporal lobes. However, the degree to which memory performance can be determined by neural processing that is common to encoding and retrieval remains to be determined. To identify neural signatures of successful memory function, we administered a delayed free-recall task to 187 neurosurgical patients implanted with subdural or intraparenchymal depth electrodes. We developed multivariate classifiers to identify patterns of spectral power across the brain that independently predicted successful episodic encoding and retrieval. During encoding and retrieval, patterns of increased high frequency activity in prefrontal, MTL, and inferior parietal cortices, accompanied by widespread decreases in low frequency power across the brain predicted successful memory function. Using a cross-decoding approach, we demonstrate the ability to predict memory function across distinct phases of the free-recall task. Furthermore, we demonstrate that classifiers that combine information from both encoding and retrieval states can outperform task-independent models. These findings suggest that the engagement of a core memory network during either encoding or retrieval shapes the ability to remember the past, despite distinct neural interactions that facilitate encoding and retrieval. Copyright © 2017 Elsevier Inc. All rights reserved.

Diprosopia revisited in light of the recognized role of neural crest cells in facial development.

PubMed

Carles, D; Weichhold, W; Alberti, E M; Léger, F; Pigeau, F; Horovitz, J

1995-01-01

The aim of this study is to compare the theory of embryogenesis of the face with human diprosopia. This peculiar form of conjoined twinning is of great interest because 1) only the facial structures are duplicated and 2) almost all cases have a rather monomorphic pattern. The hypothesis is that an initial duplication of the notochord leads to two neural plates and subsequently duplicated neural crests. In those conditions, derivatives of the neural crests will be partially or totally duplicated; therefore, in diprosopia, the duplicated facial structures would be considered to be neural crest derivatives. If these structures are identical to those that are experimentally demonstrated to be neural crest derivatives in animals, these findings are an argument to apply this theory of facial embryogenesis in man. Serial horizontal sections of the face of two diprosopic fetuses (11 and 21 weeks gestation) were studied macro- and microscopically to determine the external and internal structures that are duplicated. Complete postmortem examination was performed in search for additional malformations. The face of both fetuses showed a very similar morphologic pattern with duplication of ocular, nasal, and buccal structures. The nasal fossae and the anterior part of the tongue were also duplicated, albeit the posterior part and the pharyngolaryngeal structures were unique. Additional facial clefts were present in both fetuses. Extrafacial anomalies were represented by a craniorachischisis, two fused vertebral columns and, in the older fetus, by a complex cardiac malformation morphologically identical to malformations induced by removal or grafting of additional cardiac neural crest cells in animals. These pathological findings could identify the facial structures that are neural crest derivatives in man. They are similar to those experimentally demonstrated to be neural crest derivatives in animals. In this respect, diprosopia could be considered as the end of a spectrum, whereas the other end is agnathia-holoprosencephaly complex. This assumption has to be discussed, but we want to draw attention to the fact that diprosopia must not be considered as a curious form of conjoined twinning, but as a major means of bringing us a better knowledge of the facial embryogenesis in man.

A novel material screening platform for nanoporous gold-based neural electrodes

NASA Astrophysics Data System (ADS)

Chapman, Christopher Abbott Reece

Neural-electrical interfaces have emerged in the past decades as a promising modality to facilitate the understanding of the electropathophysiology of neurological disorders as well as the normal functioning of the central nervous system, and enable the treatment of neurological defects through electrical stimulation or electrically-controlled drug delivery. However, chronically implanted electrodes face a myriad of design challenges, including their coupling to neural tissue (biocompatibility), small form factor requirement, and their electrical properties (maintaining a low electrical impedance). Planar electrode materials such as planar platinum and gold experience a large increase in electrical impedance when electrode dimensions are reduced to increase spatial resolution of neural recordings. A decrease in electrode surface area reduces the total capacitance of the electrode double layer resulting in an increase in electrode impedance. This high impedance can reduce the signal amplitude and increase the thermal noise, resulting in degradation of signal-to-noise ratio. Conventionally, this increase in electrical impedance at small electrode dimensions has been mitigated by coatings with rough morphologies such as platinum black, conducting polymers, and titanium nitride. Porous surfaces have high effective surface area enabling low impedance at small electrode dimensions. However, achieving long-term stability of cellular coupling to the electrode surface has remained difficult. Designing electrodes that can physically couple with neurons successfully and maintain low impedance at small electrode dimensions necessitates consideration of novel electrode coatings, such as carbon nanotubes and gold nanopillars. Another promising material, and focus of this proposal, is thin film nanoporous gold (np-Au). Nanoporous gold is a promising material for addressing these limitations because of its inherently large effective surface area allows for lower impedances at small form factors, and its modifiable surface morphology can be used to control cell-electrode coupling. Additionally, thin film nanoporous gold is fabricated by traditional microfabrication methods, and thus can be directly adopted by the current state-of-the-art neural electrode fabrication processes. All these properties make thin film nanoporous gold a promising candidate for use in neural electrode surfaces. This dissertation seeks to characterize both the morphological and the electrical response of neural cells to thin film nanoporous gold morphologies using an in vitro electrode morphology screening platform. The specific aims for this proposal are to: (i) develop a electrode morphology library that displays varying topographies to study structure-property relationships of thin film nanoporous gold and cellular response, (ii) characterize neural cell response to identified nanoporous gold topographies that reduce adverse tissue response in vitro, and (iii) develop an electrophysiology platform to characterize neural coupling to each identified nanoporous gold topography.

Intergroup relationships do not reduce racial bias in empathic neural responses to pain.

PubMed

Contreras-Huerta, Luis Sebastian; Hielscher, Emily; Sherwell, Chase S; Rens, Natalie; Cunnington, Ross

2014-11-01

Perceiving the pain of others activates similar neural structures to those involved in the direct experience of pain, including sensory and affective-motivational areas. Empathic responses can be modulated by race, such that stronger neural activation is elicited by the perception of pain in people of the same race compared with another race. In the present study, we aimed to identify when racial bias occurs in the time course of neural empathic responses to pain. We also investigated whether group affiliation could modulate the race effect. Using the minimal group paradigm, we assigned participants to one of two mixed-race teams. We examined event-related potentials from participants when viewing members of their own and the other team receiving painful or non-painful touch. We identified a significant racial bias in early ERP components at N1 over frontal electrodes, where Painful stimuli elicited a greater negative shift relative to Non-Painful stimuli in response to own race faces only. A long latency empathic response was also found at P3, where there was significant differentiation between Painful and Non-Painful stimuli regardless of Race or Group. There was no evidence that empathy-related brain activity was modulated by minimal group manipulation. These results support a model of empathy for pain that consists of early, automatic bias towards own-race empathic responses and a later top-down cognitive evaluation that does not differentiate between races and may ultimately lead to unbiased behaviour. Copyright © 2014 Elsevier Ltd. All rights reserved.

Study on recognition algorithm for paper currency numbers based on neural network

NASA Astrophysics Data System (ADS)

Li, Xiuyan; Liu, Tiegen; Li, Yuanyao; Zhang, Zhongchuan; Deng, Shichao

2008-12-01

Based on the unique characteristic, the paper currency numbers can be put into record and the automatic identification equipment for paper currency numbers is supplied to currency circulation market in order to provide convenience for financial sectors to trace the fiduciary circulation socially and provide effective supervision on paper currency. Simultaneously it is favorable for identifying forged notes, blacklisting the forged notes numbers and solving the major social problems, such as armor cash carrier robbery, money laundering. For the purpose of recognizing the paper currency numbers, a recognition algorithm based on neural network is presented in the paper. Number lines in original paper currency images can be draw out through image processing, such as image de-noising, skew correction, segmentation, and image normalization. According to the different characteristics between digits and letters in serial number, two kinds of classifiers are designed. With the characteristics of associative memory, optimization-compute and rapid convergence, the Discrete Hopfield Neural Network (DHNN) is utilized to recognize the letters; with the characteristics of simple structure, quick learning and global optimum, the Radial-Basis Function Neural Network (RBFNN) is adopted to identify the digits. Then the final recognition results are obtained by combining the two kinds of recognition results in regular sequence. Through the simulation tests, it is confirmed by simulation results that the recognition algorithm of combination of two kinds of recognition methods has such advantages as high recognition rate and faster recognition simultaneously, which is worthy of broad application prospect.

The behavioural patterns and neural correlates of concrete and abstract verb processing in aphasia: A novel verb semantic battery.

PubMed

Alyahya, Reem S W; Halai, Ajay D; Conroy, Paul; Lambon Ralph, Matthew A

2018-01-01

Typically, processing is more accurate and efficient for concrete than abstract concepts in both healthy adults and individuals with aphasia. While, concreteness effects have been thoroughly documented with respect to noun processing, other words classes have received little attention despite tending to be less concrete than nouns. The aim of the current study was to explore concrete-abstract differences in verbs and identify their neural correlates in post-stroke aphasia. Given the dearth of comprehension tests for verbs, a battery of neuropsychological tests was developed in this study to assess the comprehension of concrete and abstract verbs. Specifically, a sensitive verb synonym judgment test was generated that varied both the items' imageability and frequency, and a picture-to-word matching test with numerous concrete verbs. Normative data were then collected and the tests were administered to a cohort of 48 individuals with chronic post-stroke aphasia to explore the behavioural patterns and neural correlates of verb processing. The results revealed significantly better comprehension of concrete than abstract verbs, aligning with the existing aphasiological literature on noun processing. In addition, the patients performed better during verb comprehension than verb production. Lesion-symptom correlational analyses revealed common areas that support processing of concrete and abstract verbs, including the left anterior temporal lobe, posterior supramarginal gyrus and superior lateral occipital cortex. A direct contrast between them revealed additional regions with graded differences. Specifically, the left frontal regions were associated with processing abstract verbs; whereas, the left posterior temporal and occipital regions were associated with processing concrete verbs. Moreover, overlapping and distinct neural correlates were identified in association with the comprehension and production of concrete verbs. These patient findings align with data from functional neuroimaging and neuro-stimulation, and existing models of language organisation.

Association between the oxytocin receptor (OXTR) gene and mesolimbic responses to rewards.

PubMed

Damiano, Cara R; Aloi, Joseph; Dunlap, Kaitlyn; Burrus, Caley J; Mosner, Maya G; Kozink, Rachel V; McLaurin, Ralph Edward; Mullette-Gillman, O'Dhaniel A; Carter, Ronald McKell; Huettel, Scott A; McClernon, Francis Joseph; Ashley-Koch, Allison; Dichter, Gabriel S

2014-01-31

There has been significant progress in identifying genes that confer risk for autism spectrum disorders (ASDs). However, the heterogeneity of symptom presentation in ASDs impedes the detection of ASD risk genes. One approach to understanding genetic influences on ASD symptom expression is to evaluate relations between variants of ASD candidate genes and neural endophenotypes in unaffected samples. Allelic variations in the oxytocin receptor (OXTR) gene confer small but significant risk for ASDs for which the underlying mechanisms may involve associations between variability in oxytocin signaling pathways and neural response to rewards. The purpose of this preliminary study was to investigate the influence of allelic variability in the OXTR gene on neural responses to monetary rewards in healthy adults using functional magnetic resonance imaging (fMRI). The moderating effects of three single nucleotide polymorphisms (SNPs) (rs1042778, rs2268493 and rs237887) of the OXTR gene on mesolimbic responses to rewards were evaluated using a monetary incentive delay fMRI task. T homozygotes of the rs2268493 SNP demonstrated relatively decreased activation in mesolimbic reward circuitry (including the nucleus accumbens, amygdala, insula, thalamus and prefrontal cortical regions) during the anticipation of rewards but not during the outcome phase of the task. Allelic variation of the rs1042778 and rs237887 SNPs did not moderate mesolimbic activation during either reward anticipation or outcomes. This preliminary study suggests that the OXTR SNP rs2268493, which has been previously identified as an ASD risk gene, moderates mesolimbic responses during reward anticipation. Given previous findings of decreased mesolimbic activation during reward anticipation in ASD, the present results suggest that OXTR may confer ASD risk via influences on the neural systems that support reward anticipation.

Neuron’s eye view: Inferring features of complex stimuli from neural responses

PubMed Central

Chen, Xin; Beck, Jeffrey M.

2017-01-01

Experiments that study neural encoding of stimuli at the level of individual neurons typically choose a small set of features present in the world—contrast and luminance for vision, pitch and intensity for sound—and assemble a stimulus set that systematically varies along these dimensions. Subsequent analysis of neural responses to these stimuli typically focuses on regression models, with experimenter-controlled features as predictors and spike counts or firing rates as responses. Unfortunately, this approach requires knowledge in advance about the relevant features coded by a given population of neurons. For domains as complex as social interaction or natural movement, however, the relevant feature space is poorly understood, and an arbitrary a priori choice of features may give rise to confirmation bias. Here, we present a Bayesian model for exploratory data analysis that is capable of automatically identifying the features present in unstructured stimuli based solely on neuronal responses. Our approach is unique within the class of latent state space models of neural activity in that it assumes that firing rates of neurons are sensitive to multiple discrete time-varying features tied to the stimulus, each of which has Markov (or semi-Markov) dynamics. That is, we are modeling neural activity as driven by multiple simultaneous stimulus features rather than intrinsic neural dynamics. We derive a fast variational Bayesian inference algorithm and show that it correctly recovers hidden features in synthetic data, as well as ground-truth stimulus features in a prototypical neural dataset. To demonstrate the utility of the algorithm, we also apply it to cluster neural responses and demonstrate successful recovery of features corresponding to monkeys and faces in the image set. PMID:28827790

A New Measure for Neural Compensation Is Positively Correlated With Working Memory and Gait Speed.

PubMed

Ji, Lanxin; Pearlson, Godfrey D; Hawkins, Keith A; Steffens, David C; Guo, Hua; Wang, Lihong

2018-01-01

Neuroimaging studies suggest that older adults may compensate for declines in brain function and cognition through reorganization of neural resources. A limitation of prior research is reliance on between-group comparisons of neural activation (e.g., younger vs. older), which cannot be used to assess compensatory ability quantitatively. It is also unclear about the relationship between compensatory ability with cognitive function or how other factors such as physical exercise modulates compensatory ability. Here, we proposed a data-driven method to semi-quantitatively measure neural compensation under a challenging cognitive task, and we then explored connections between neural compensation to cognitive engagement and cognitive reserve (CR). Functional and structural magnetic resonance imaging scans were acquired for 26 healthy older adults during a face-name memory task. Spatial independent component analysis (ICA) identified visual, attentional and left executive as core networks. Results show that the smaller the volumes of the gray matter (GM) structures within core networks, the more networks were needed to conduct the task ( r = -0.408, p = 0.035). Therefore, the number of task-activated networks controlling for the GM volume within core networks was defined as a measure of neural compensatory ability. We found that compensatory ability correlated with working memory performance ( r = 0.528, p = 0.035). Among subjects with good memory task performance, those with higher CR used fewer networks than subjects with lower CR. Among poor-performance subjects, those using more networks had higher CR. Our results indicated that using a high cognitive-demanding task to measure the number of activated neural networks could be a useful and sensitive measure of neural compensation in older adults.

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

PubMed

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

2018-01-01

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

Behavioral and Physiological Effects of Hindlimb Unloading in Rats

NASA Technical Reports Server (NTRS)

Fox, Robert A.

1998-01-01

The overarching objective of this project was to identify changes in neural and biochemical systems of the central and peripheral nervous systems (the CNS and PNS) that are related to disruptions of functional motor responses, or motor control. The identification of neural and biochemical changes that are related to sensory-motor adaptation elicited as animals react to changes in the gravitational field was of particular interest. Thus, the major objective of this work was to study disruptions of motor responses that arise after (sic. due to) chronic exposure to altered gravity (G). To do this, parallel studies investigating changes in neural, sensory, and neuromuscular systems were conducted after animals (rats) experienced chronic exposure to conditions of altered-G. Conditions of altered-G included hyper-G produced by centrifugation, micro-G produced by orbital flight, and simulated micro-G produced by hind limb suspension. A second major interest was to examine the contribution of putative changes in sensory systems to disruptions of motor responses. To do this, motor responses and reflexes of rats were studied following chronic treatment with streptomycin sulfate (STP, an ototoxic chemical) to damage the vestibular hair cells.

Concentration dependent survival and neural differentiation of murine embryonic stem cells cultured on polyethylene glycol dimethacrylate hydrogels possessing a continuous concentration gradient of n-cadherin derived peptide His-Ala-Val-Asp-Lle.

PubMed

Lim, Hyun Ju; Mosley, Matthew C; Kurosu, Yuki; Smith Callahan, Laura A

2017-07-01

N-cadherin cell-cell signaling plays a key role in the structure and function of the nervous system. However, few studies have incorporated bioactive signaling from n-cadherin into tissue engineering matrices. The present study uses a continuous gradient approach in polyethylene glycol dimethacrylate hydrogels to identify concentration dependent effects of n-cadherin peptide, His-Ala-Val-Asp-Lle (HAVDI), on murine embryonic stem cell survival and neural differentiation. The n-cadherin peptide was found to affect the expression of pluripotency marker, alkaline phosphatase, in murine embryonic stem cells cultured on n-cadherin peptide containing hydrogels in a concentration dependent manner. Increasing n-cadherin peptide concentrations in the hydrogels elicited a biphasic response in neurite extension length and mRNA expression of neural differentiation marker, neuron-specific class III β-tubulin, in murine embryonic stem cells cultured on the hydrogels. High concentrations of n-cadherin peptide in the hydrogels were found to increase the expression of apoptotic marker, caspase 3/7, in murine embryonic stem cells compared to that of murine embryonic stem cell cultures on hydrogels containing lower concentrations of n-cadherin peptide. Increasing the n-cadherin peptide concentration in the hydrogels facilitated greater survival of murine embryonic stem cells exposed to increasing oxidative stress caused by hydrogen peroxide exposure. The combinatorial approach presented in this work demonstrates concentration dependent effects of n-cadherin signaling on mouse embryonic stem cell behavior, underscoring the need for the greater use of systematic approaches in tissue engineering matrix design in order to understand and optimize bioactive signaling in the matrix for tissue formation. Single cell encapsulation is common in tissue engineering matrices. This eliminates cellular access to cell-cell signaling. N-cadherin, a cell-cell signaling molecule, plays a vital role in the development of neural tissues, but has not been well studied as a bioactive signaling element in neural tissue engineering matrices. The present study uses a systematic continuous gradient approach to identify concentration dependent effects of n-cadherin derived peptide, HAVDI, on the survival and neural differentiation of murine embryonic stem cells. This work underscores the need for greater use to combinatorial strategies to understand the effect complex bioactive signaling, such as n-cadherin, and the need to optimize the concentration of such bioactive signaling within tissue engineering matrices for maximal cellular response. Copyright © 2016 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

The relevance of central command for the neural cardiovascular control of exercise.

PubMed

Williamson, J W

2010-11-01

This paper briefly reviews the role of central command in the neural control of the circulation during exercise. While defined as a feedforward component of the cardiovascular control system, central command is also associated with perception of effort or effort sense. The specific factors influencing perception of effort and their effect on autonomic regulation of cardiovascular function during exercise can vary according to condition. Centrally mediated integration of multiple signals occurring during exercise certainly involves feedback mechanisms, but it is unclear whether or how these signals modify central command via their influence on perception of effort. As our understanding of central neural control systems continues to develop, it will be important to examine more closely how multiple sensory signals are prioritized and processed centrally to modulate cardiovascular responses during exercise. The purpose of this article is briefly to review the concepts underlying central command and its assessment via perception of effort, and to identify potential areas for future studies towards determining the role and relevance of central command for neural control of exercise.

Mittag-Leffler stability of fractional-order neural networks in the presence of generalized piecewise constant arguments.

PubMed

Wu, Ailong; Liu, Ling; Huang, Tingwen; Zeng, Zhigang

2017-01-01

Neurodynamic system is an emerging research field. To understand the essential motivational representations of neural activity, neurodynamics is an important question in cognitive system research. This paper is to investigate Mittag-Leffler stability of a class of fractional-order neural networks in the presence of generalized piecewise constant arguments. To identify neural types of computational principles in mathematical and computational analysis, the existence and uniqueness of the solution of neurodynamic system is the first prerequisite. We prove that the existence and uniqueness of the solution of the network holds when some conditions are satisfied. In addition, self-active neurodynamic system demands stable internal dynamical states (equilibria). The main emphasis will be then on several sufficient conditions to guarantee a unique equilibrium point. Furthermore, to provide deeper explanations of neurodynamic process, Mittag-Leffler stability is studied in detail. The established results are based on the theories of fractional differential equation and differential equation with generalized piecewise constant arguments. The derived criteria improve and extend the existing related results. Copyright © 2016 Elsevier Ltd. All rights reserved.

Neural circuitry and plasticity mechanisms underlying delay eyeblink conditioning

PubMed Central

Freeman, John H.; Steinmetz, Adam B.

2011-01-01

Pavlovian eyeblink conditioning has been used extensively as a model system for examining the neural mechanisms underlying associative learning. Delay eyeblink conditioning depends on the intermediate cerebellum ipsilateral to the conditioned eye. Evidence favors a two-site plasticity model within the cerebellum with long-term depression of parallel fiber synapses on Purkinje cells and long-term potentiation of mossy fiber synapses on neurons in the anterior interpositus nucleus. Conditioned stimulus and unconditioned stimulus inputs arise from the pontine nuclei and inferior olive, respectively, converging in the cerebellar cortex and deep nuclei. Projections from subcortical sensory nuclei to the pontine nuclei that are necessary for eyeblink conditioning are beginning to be identified, and recent studies indicate that there are dynamic interactions between sensory thalamic nuclei and the cerebellum during eyeblink conditioning. Cerebellar output is projected to the magnocellular red nucleus and then to the motor nuclei that generate the blink response(s). Tremendous progress has been made toward determining the neural mechanisms of delay eyeblink conditioning but there are still significant gaps in our understanding of the necessary neural circuitry and plasticity mechanisms underlying cerebellar learning. PMID:21969489

Can beneficial ends justify lying? Neural responses to the passive reception of lies and truth-telling with beneficial and harmful monetary outcomes.

PubMed

Yin, Lijun; Weber, Bernd

2016-03-01

Can beneficial ends justify morally questionable means? To investigate how monetary outcomes influence the neural responses to lying, we used a modified, cheap talk sender-receiver game in which participants were the direct recipients of lies and truthful statements resulting in either beneficial or harmful monetary outcomes. Both truth-telling (vs lying) as well as beneficial (vs harmful) outcomes elicited higher activity in the nucleus accumbens. Lying (vs truth-telling) elicited higher activity in the supplementary motor area, right inferior frontal gyrus, superior temporal sulcus and left anterior insula. Moreover, the significant interaction effect was found in the left amygdala, which showed that the monetary outcomes modulated the neural activity in the left amygdala only when truth-telling rather than lying. Our study identified a neural network associated with the reception of lies and truth, including the regions linked to the reward process, recognition and emotional experiences of being treated (dis)honestly. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

The relevance of central command for the neural cardiovascular control of exercise

PubMed Central

Williamson, J W

2010-01-01

This paper briefly reviews the role of central command in the neural control of the circulation during exercise. While defined as a feedfoward component of the cardiovascular control system, central command is also associated with perception of effort or effort sense. The specific factors influencing perception of effort and their effect on autonomic regulation of cardiovascular function during exercise can vary according to condition. Centrally mediated integration of multiple signals occurring during exercise certainly involves feedback mechanisms, but it is unclear whether or how these signals modify central command via their influence on perception of effort. As our understanding of central neural control systems continues to develop, it will be important to examine more closely how multiple sensory signals are prioritized and processed centrally to modulate cardiovascular responses during exercise. The purpose of this article is briefly to review the concepts underlying central command and its assessment via perception of effort, and to identify potential areas for future studies towards determining the role and relevance of central command for neural control of exercise. PMID:20696787

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

PubMed

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 Correlates of the Perception for Novel Objects

PubMed Central

Zhang, Hao; Liu, Jia; Zhang, Qinglin

2013-01-01

Perception of novel objects is of enormous importance in our lives. People have to perceive or understand novel objects when seeing an original painting, admiring an unconventional construction, and using an inventive device. However, very little is known about neural mechanisms underlying the perception for novel objects. Perception of novel objects relies on the integration of unusual features of novel objects in order to identify what such objects are. In the present study, functional Magnetic Resonance Imaging (MRI) was employed to investigate neural correlates of perception of novel objects. The neuroimaging data on participants engaged in novel object viewing versus ordinary object viewing revealed that perception of novel objects involves significant activation in the left precuneus (Brodmann area 7) and the right visual cortex. The results suggest that the left precuneus is associated with the integration of unusual features of novel objects, while the right visual cortex is sensitive to the detection of such features. Our findings highlight the left precuneus as a crucial component of the neural circuitry underlying perception of novel objects. PMID:23646167

Neural bases of ingroup altruistic motivation in soccer fans.

PubMed

Bortolini, Tiago; Bado, Patrícia; Hoefle, Sebastian; Engel, Annerose; Zahn, Roland; de Oliveira Souza, Ricardo; Dreher, Jean-Claude; Moll, Jorge

2017-11-23

Humans have a strong need to belong to social groups and a natural inclination to benefit ingroup members. Although the psychological mechanisms behind human prosociality have extensively been studied, the specific neural systems bridging group belongingness and altruistic motivation remain to be identified. Here, we used soccer fandom as an ecological framing of group membership to investigate the neural mechanisms underlying ingroup altruistic behaviour in male fans using event-related functional magnetic resonance. We designed an effort measure based on handgrip strength to assess the motivation to earn money (i) for oneself, (ii) for anonymous ingroup fans, or (iii) for a neutral group of anonymous non-fans. While overlapping valuation signals in the medial orbitofrontal cortex (mOFC) were observed for the three conditions, the subgenual cingulate cortex (SCC) exhibited increased functional connectivity with the mOFC as well as stronger hemodynamic responses for ingroup versus outgroup decisions. These findings indicate a key role for the SCC, a region previously implicated in altruistic decisions and group affiliation, in dovetailing altruistic motivations with neural valuation systems in real-life ingroup behaviour.

A Systematic Review of fMRI Reward Paradigms in Adolescents versus Adults: The Impact of Task Design and Implications for Understanding Neurodevelopment

PubMed Central

Richards, Jessica M.; Plate, Rista C.; Ernst, Monique

2013-01-01

The neural systems underlying reward-related behaviors across development have recently generated a great amount of interest. Yet, the neurodevelopmental literature on reward processing is marked by inconsistencies due to the heterogeneity of the reward paradigms used, the complexity of the behaviors being studied, and the developing brain itself as a moving target. The present review will examine task design as one source of variability across findings by compiling this literature along three dimensions: (1) task structures, (2) cognitive processes, and (3) neural systems. We start with the presentation of a heuristic neural systems model, the Triadic Model, as a way to provide a theoretical framework for the neuroscience research on motivated behaviors. We then discuss the principles guiding reward task development. Finally, we review the extant developmental neuroimaging literature on reward-related processing, organized by reward task type. We hope that this approach will help to clarify the literature on the functional neurodevelopment of reward-related neural systems, and to identify the role of the experimental parameters that significantly influence these findings. PMID:23518270

Evaluation of Sex-Specific Movement Patterns in Judo Using Probabilistic Neural Networks.

PubMed

Miarka, Bianca; Sterkowicz-Przybycien, Katarzyna; Fukuda, David H

2017-10-01

The purpose of the present study was to create a probabilistic neural network to clarify the understanding of movement patterns in international judo competitions by gender. Analysis of 773 male and 638 female bouts was utilized to identify movements during the approach, gripping, attack (including biomechanical designations), groundwork, defense, and pause phases. Probabilistic neural network and chi-square (χ 2 ) tests modeled and compared frequencies (p ≤ .05). Women (mean [interquartile range]: 9.9 [4; 14]) attacked more than men (7.0 [3; 10]) while attempting a greater number of arm/leg lever (women: 2.7 [1; 6]; men: 4.0 [0; 4]) and trunk/leg lever (women: 0.8 [0; 1]; men: 2.4 [0; 4]) techniques but fewer maximal length-moment arm techniques (women: 0.7 [0; 1]; men: 1.0 [0; 2]). Male athletes displayed one-handed gripping of the back and sleeve, whereas female athletes executed a greater number of groundwork techniques. An optimized probabilistic neural network model, using patterns from the gripping, attack, groundwork, and pause phases, produced an overall prediction accuracy of 76% for discrimination between men and women.

Distinct Neural Circuits Subserve Interpersonal and Non-interpersonal Emotions

PubMed Central

Landa, Alla; Wang, Zhishun; Russell, James A.; Posner, Jonathan; Duan, Yunsuo; Kangarlu, Alayar; Huo, Yuankai; Fallon, Brian A.; Peterson, Bradley S.

2013-01-01

Emotions elicited by interpersonal versus non-interpersonal experiences have different effects on neurobiological functioning in both animals and humans. However, the extent to which the brain circuits underlying interpersonal and non-interpersonal emotions are distinct still remains unclear. The goal of our study was to assess whether different neural circuits are implicated in the processing of arousal and valence of interpersonal versus non-interpersonal emotions. During functional magnetic resonance imaging, participants imagined themselves in emotion-eliciting interpersonal or non-interpersonal situations and then rated the arousal and valence of emotions they experienced. We identified (a) separate neural circuits that are implicated in the arousal and valence dimensions of interpersonal versus non-interpersonal emotions, (b) circuits that are implicated in arousal and valence for both types of emotion, and (c) circuits that are responsive to the type of emotion, regardless of the valence or arousal level of the emotion. We found extensive recruitment of limbic (for arousal) and temporal-parietal (for valence) systems associated with processing of specifically interpersonal emotions compared to non-interpersonal ones. The neural bases of interpersonal and non-interpersonal emotions may, therefore, be largely distinct. PMID:24028312

Neural Interfaces for Intracortical Recording: Requirements, Fabrication Methods, and Characteristics

PubMed Central

Szostak, Katarzyna M.; Grand, Laszlo; Constandinou, Timothy G.

2017-01-01

Implantable neural interfaces for central nervous system research have been designed with wire, polymer, or micromachining technologies over the past 70 years. Research on biocompatible materials, ideal probe shapes, and insertion methods has resulted in building more and more capable neural interfaces. Although the trend is promising, the long-term reliability of such devices has not yet met the required criteria for chronic human application. The performance of neural interfaces in chronic settings often degrades due to foreign body response to the implant that is initiated by the surgical procedure, and related to the probe structure, and material properties used in fabricating the neural interface. In this review, we identify the key requirements for neural interfaces for intracortical recording, describe the three different types of probes—microwire, micromachined, and polymer-based probes; their materials, fabrication methods, and discuss their characteristics and related challenges. PMID:29270103

Neural Interfaces for Intracortical Recording: Requirements, Fabrication Methods, and Characteristics.

PubMed

Szostak, Katarzyna M; Grand, Laszlo; Constandinou, Timothy G

2017-01-01

Implantable neural interfaces for central nervous system research have been designed with wire, polymer, or micromachining technologies over the past 70 years. Research on biocompatible materials, ideal probe shapes, and insertion methods has resulted in building more and more capable neural interfaces. Although the trend is promising, the long-term reliability of such devices has not yet met the required criteria for chronic human application. The performance of neural interfaces in chronic settings often degrades due to foreign body response to the implant that is initiated by the surgical procedure, and related to the probe structure, and material properties used in fabricating the neural interface. In this review, we identify the key requirements for neural interfaces for intracortical recording, describe the three different types of probes-microwire, micromachined, and polymer-based probes; their materials, fabrication methods, and discuss their characteristics and related challenges.

Neural Responses to Visual Food Cues According to Weight Status: A Systematic Review of Functional Magnetic Resonance Imaging Studies

PubMed Central

Pursey, Kirrilly M.; Stanwell, Peter; Callister, Robert J.; Brain, Katherine; Collins, Clare E.; Burrows, Tracy L.

2014-01-01

Emerging evidence from recent neuroimaging studies suggests that specific food-related behaviors contribute to the development of obesity. The aim of this review was to report the neural responses to visual food cues, as assessed by functional magnetic resonance imaging (fMRI), in humans of differing weight status. Published studies to 2014 were retrieved and included if they used visual food cues, studied humans >18 years old, reported weight status, and included fMRI outcomes. Sixty studies were identified that investigated the neural responses of healthy weight participants (n = 26), healthy weight compared to obese participants (n = 17), and weight-loss interventions (n = 12). High-calorie food images were used in the majority of studies (n = 36), however, image selection justification was only provided in 19 studies. Obese individuals had increased activation of reward-related brain areas including the insula and orbitofrontal cortex in response to visual food cues compared to healthy weight individuals, and this was particularly evident in response to energy dense cues. Additionally, obese individuals were more responsive to food images when satiated. Meta-analysis of changes in neural activation post-weight loss revealed small areas of convergence across studies in brain areas related to emotion, memory, and learning, including the cingulate gyrus, lentiform nucleus, and precuneus. Differential activation patterns to visual food cues were observed between obese, healthy weight, and weight-loss populations. Future studies require standardization of nutrition variables and fMRI outcomes to enable more direct comparisons between studies. PMID:25988110

Neural responses to visual food cues according to weight status: a systematic review of functional magnetic resonance imaging studies.

PubMed

Pursey, Kirrilly M; Stanwell, Peter; Callister, Robert J; Brain, Katherine; Collins, Clare E; Burrows, Tracy L

2014-01-01

Emerging evidence from recent neuroimaging studies suggests that specific food-related behaviors contribute to the development of obesity. The aim of this review was to report the neural responses to visual food cues, as assessed by functional magnetic resonance imaging (fMRI), in humans of differing weight status. Published studies to 2014 were retrieved and included if they used visual food cues, studied humans >18 years old, reported weight status, and included fMRI outcomes. Sixty studies were identified that investigated the neural responses of healthy weight participants (n = 26), healthy weight compared to obese participants (n = 17), and weight-loss interventions (n = 12). High-calorie food images were used in the majority of studies (n = 36), however, image selection justification was only provided in 19 studies. Obese individuals had increased activation of reward-related brain areas including the insula and orbitofrontal cortex in response to visual food cues compared to healthy weight individuals, and this was particularly evident in response to energy dense cues. Additionally, obese individuals were more responsive to food images when satiated. Meta-analysis of changes in neural activation post-weight loss revealed small areas of convergence across studies in brain areas related to emotion, memory, and learning, including the cingulate gyrus, lentiform nucleus, and precuneus. Differential activation patterns to visual food cues were observed between obese, healthy weight, and weight-loss populations. Future studies require standardization of nutrition variables and fMRI outcomes to enable more direct comparisons between studies.

Muscle synergies evoked by microstimulation are preferentially encoded during behavior

PubMed Central

Overduin, Simon A.; d'Avella, Andrea; Carmena, Jose M.; Bizzi, Emilio

2014-01-01

Electrical microstimulation studies provide some of the most direct evidence for the neural representation of muscle synergies. These synergies, i.e., coordinated activations of groups of muscles, have been proposed as building blocks for the construction of motor behaviors by the nervous system. Intraspinal or intracortical microstimulation (ICMS) has been shown to evoke muscle patterns that can be resolved into a small set of synergies similar to those seen in natural behavior. However, questions remain about the validity of microstimulation as a probe of neural function, particularly given the relatively long trains of supratheshold stimuli used in these studies. Here, we examined whether muscle synergies evoked during ICMS in two rhesus macaques were similarly encoded by nearby motor cortical units during a purely voluntary behavior involving object reach, grasp, and carry movements. At each microstimulation site we identified the synergy most strongly evoked among those extracted from muscle patterns evoked over all microstimulation sites. For each cortical unit recorded at the same microstimulation site, we then identified the synergy most strongly encoded among those extracted from muscle patterns recorded during the voluntary behavior. We found that the synergy most strongly evoked at an ICMS site matched the synergy most strongly encoded by proximal units more often than expected by chance. These results suggest a common neural substrate for microstimulation-evoked motor responses and for the generation of muscle patterns during natural behaviors. PMID:24634652

Getting the cocktail party started: masking effects in speech perception

PubMed Central

Evans, S; McGettigan, C; Agnew, ZK; Rosen, S; Scott, SK

2016-01-01

Spoken conversations typically take place in noisy environments and different kinds of masking sounds place differing demands on cognitive resources. Previous studies, examining the modulation of neural activity associated with the properties of competing sounds, have shown that additional speech streams engage the superior temporal gyrus. However, the absence of a condition in which target speech was heard without additional masking made it difficult to identify brain networks specific to masking and to ascertain the extent to which competing speech was processed equivalently to target speech. In this study, we scanned young healthy adults with continuous functional Magnetic Resonance Imaging (fMRI), whilst they listened to stories masked by sounds that differed in their similarity to speech. We show that auditory attention and control networks are activated during attentive listening to masked speech in the absence of an overt behavioural task. We demonstrate that competing speech is processed predominantly in the left hemisphere within the same pathway as target speech but is not treated equivalently within that stream, and that individuals who perform better in speech in noise tasks activate the left mid-posterior superior temporal gyrus more. Finally, we identify neural responses associated with the onset of sounds in the auditory environment, activity was found within right lateralised frontal regions consistent with a phasic alerting response. Taken together, these results provide a comprehensive account of the neural processes involved in listening in noise. PMID:26696297

Effects of Transcranial Direct Current Stimulation on Neural Networks in Young and Older Adults

PubMed

Martin, Andrew K; Meinzer, Marcus; Lindenberg, Robert; Sieg, Mira M; Nachtigall, Laura; Flöel, Agnes

2017-11-01

Transcranial direct current stimulation (tDCS) may be a viable tool to improve motor and cognitive function in advanced age. However, although a number of studies have demonstrated improved cognitive performance in older adults, other studies have failed to show restorative effects. The neural effects of beneficial stimulation response in both age groups is lacking. In the current study, tDCS was administered during simultaneous fMRI in 42 healthy young and older participants. Semantic word generation and motor speech baseline tasks were used to investigate behavioral and neural effects of uni- and bihemispheric motor cortex tDCS in a three-way, crossover, sham tDCS controlled design. Independent components analysis assessed differences in task-related activity between the two age groups and tDCS effects at the network level. We also explored whether laterality of language network organization was effected by tDCS. Behaviorally, both active tDCS conditions significantly improved semantic word retrieval performance in young and older adults and were comparable between groups and stimulation conditions. Network-level tDCS effects were identified in the ventral and dorsal anterior cingulate networks in the combined sample during semantic fluency and motor speech tasks. In addition, a shift toward enhanced left laterality was identified in the older adults for both active stimulation conditions. Thus, tDCS results in common network-level modulations and behavioral improvements for both age groups, with an additional effect of increasing left laterality in older adults.

Improved discriminability of spatiotemporal neural patterns in rat motor cortical areas as directional choice learning progresses

PubMed Central

Mao, Hongwei; Yuan, Yuan; Si, Jennie

2015-01-01

Animals learn to choose a proper action among alternatives to improve their odds of success in food foraging and other activities critical for survival. Through trial-and-error, they learn correct associations between their choices and external stimuli. While a neural network that underlies such learning process has been identified at a high level, it is still unclear how individual neurons and a neural ensemble adapt as learning progresses. In this study, we monitored the activity of single units in the rat medial and lateral agranular (AGm and AGl, respectively) areas as rats learned to make a left or right side lever press in response to a left or right side light cue. We noticed that rat movement parameters during the performance of the directional choice task quickly became stereotyped during the first 2–3 days or sessions. But learning the directional choice problem took weeks to occur. Accompanying rats' behavioral performance adaptation, we observed neural modulation by directional choice in recorded single units. Our analysis shows that ensemble mean firing rates in the cue-on period did not change significantly as learning progressed, and the ensemble mean rate difference between left and right side choices did not show a clear trend of change either. However, the spatiotemporal firing patterns of the neural ensemble exhibited improved discriminability between the two directional choices through learning. These results suggest a spatiotemporal neural coding scheme in a motor cortical neural ensemble that may be responsible for and contributing to learning the directional choice task. PMID:25798093

Astrocyte-Secreted Factors Selectively Alter Neural Stem and Progenitor Cell Proliferation in the Fragile X Mouse

PubMed Central

Sourial, Mary; Doering, Laurie C.

2016-01-01

An increasing body of evidence indicates that astrocytes contribute to the governance and fine tuning of stem and progenitor cell production during brain development. The effect of astrocyte function in cell production in neurodevelopmental disorders is unknown. We used the Neural Colony Forming Cell assay to determine the effect of astrocyte conditioned media (ACM) on the generation of neurospheres originating from either progenitor cells or functional stem cells in the knock out (KO) Fragile X mouse model. ACM from both normal and Fmr1-KO mice generated higher percentages of smaller neurospheres indicative of restricted proliferation of the progenitor cell population in Fmr1-KO brains. Wild type (WT) neurospheres, but not KO neurospheres, showed enhanced responses to ACM from the Fmr1-KO mice. In particular, Fmr1-KO ACM increased the percentage of large neurospheres generated, representative of spheres produced from neural stem cells. We also used 2D DIGE to initiate identification of the astrocyte-secreted proteins with differential expression between Fmr1-KO and WT cortices and hippocampi. The results further support the critical role of astrocytes in governing neural cell production in brain development and point to significant alterations in neural cell proliferation due to astrocyte secreted factors from the Fragile X brain. Highlights: • We studied the proliferation of neural stem and progenitor cells in Fragile X. • We examined the role of astrocyte-secreted factors in neural precursor cell biology. • Astrocyte-secreted factors with differential expression in Fragile X identified. PMID:27242437

Neural evidence that three dimensions organize mental state representation: Rationality, social impact, and valence

PubMed Central

Tamir, Diana I.; Thornton, Mark A.; Contreras, Juan Manuel; Mitchell, Jason P.

2016-01-01

How do people understand the minds of others? Existing psychological theories have suggested a number of dimensions that perceivers could use to make sense of others’ internal mental states. However, it remains unclear which of these dimensions, if any, the brain spontaneously uses when we think about others. The present study used multivoxel pattern analysis (MVPA) of neuroimaging data to identify the primary organizing principles of social cognition. We derived four unique dimensions of mental state representation from existing psychological theories and used functional magnetic resonance imaging to test whether these dimensions organize the neural encoding of others’ mental states. MVPA revealed that three such dimensions could predict neural patterns within the medial prefrontal and parietal cortices, temporoparietal junction, and anterior temporal lobes during social thought: rationality, social impact, and valence. These results suggest that these dimensions serve as organizing principles for our understanding of other people. PMID:26621704

Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security.

PubMed

Kang, Min-Joo; Kang, Je-Won

2016-01-01

A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus.

Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security

PubMed Central

Kang, Min-Joo

2016-01-01

A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus. PMID:27271802

Long Noncoding RNA-1604 Orchestrates Neural Differentiation through the miR-200c/ZEB Axis.

PubMed

Weng, Rong; Lu, Chenqi; Liu, Xiaoqin; Li, Guoping; Lan, Yuanyuan; Qiao, Jing; Bai, Mingliang; Wang, Zhaojie; Guo, Xudong; Ye, Dan; Jiapaer, Zeyidan; Yang, Yiwei; Xia, Chenliang; Wang, Guiying; Kang, Jiuhong

2018-03-01

Clarifying the regulatory mechanisms of embryonic stem cell (ESC) neural differentiation is helpful not only for understanding neural development but also for obtaining high-quality neural progenitor cells required by stem cell therapy of neurodegenerative diseases. Here, we found that long noncoding RNA 1604 (lncRNA-1604) was highly expressed in cytoplasm during neural differentiation, and knockdown of lncRNA-1604 significantly repressed neural differentiation of mouse ESCs both in vitro and in vivo. Bioinformatics prediction and mechanistic analysis revealed that lncRNA-1604 functioned as a novel competing endogenous RNA of miR-200c and regulated the core transcription factors ZEB1 and ZEB2 during neural differentiation. Furthermore, we also demonstrated the critical role of miR-200c and ZEB1/2 in mouse neural differentiation. Either introduction of miR-200c sponge or overexpression of ZEB1/2 significantly reversed the lncRNA-1604 knockdown-induced repression of mouse ESC neural differentiation. Collectively, these findings not only identified a previously unknown role of lncRNA-1604 and ZEB1/2 but also elucidated a new regulatory lncRNA-1604/miR-200c/ZEB axis in neural differentiation. Stem Cells 2018;36:325-336. © 2017 AlphaMed Press.

Looking at the Brains behind Figurative Language--A Quantitative Meta-Analysis of Neuroimaging Studies on Metaphor, Idiom, and Irony Processing

ERIC Educational Resources Information Center

Bohrn, Isabel C.; Altmann, Ulrike; Jacobs, Arthur M.

2012-01-01

A quantitative, coordinate-based meta-analysis combined data from 354 participants across 22 fMRI studies and one positron emission tomography (PET) study to identify the differences in neural correlates of figurative and literal language processing, and to investigate the role of the right hemisphere (RH) in figurative language processing.…

Neuroimaging Studies of Normal Brain Development and Their Relevance for Understanding Childhood Neuropsychiatric Disorders

ERIC Educational Resources Information Center

Marsh, Rachel; Gerber, Andrew J.; Peterson, Bradley S.

2008-01-01

Neuroimaging findings which identify normal brain development trajectories are presented. Results show that early brain development begins with the neural tube formation and ends with myelintation. How disturbances in brain development patterns are related to childhood psychiatric disorders is examined.

Trait-level temporal lobe hypoactivation to social exclusion in unaffected siblings of children and adolescents with autism spectrum disorders

PubMed Central

Bolling, Danielle Z.; Pelphrey, Kevin A.; Vander Wyk, Brent C.

2015-01-01

Social exclusion elicits powerful feelings of negative affect associated with rejection. Additionally, experiencing social exclusion reliably recruits neural circuitry associated with emotion processing. Recent work has demonstrated abnormal neural responses to social exclusion in children and adolescents with autism spectrum disorders (ASD). However, it remains unknown to what extent these abnormalities are due to atypical social experiences versus genetic predispositions to atypical neural processing. To address this question, the current study investigated brain responses to social exclusion compared to a baseline condition of fair play in unaffected siblings of youth with ASD using functional magnetic resonance imaging. We identified common deviations between unaffected siblings and ASD probands that might represent trait-level abnormalities in processing social exclusion versus fair play, specifically in the right anterior temporoparietal junction extending into posterior superior temporal sulcus. Thus, hypoactivation to social exclusion versus fair play in this region may represent a shared genetic vulnerability to developing autism. In addition, we present evidence supporting the idea that one’s status as an unaffected sibling moderates the relationship between IQ and neural activation to social exclusion versus fair play in anterior cingulate cortex. These results are discussed in the context of previous literature on neural endophenotypes of autism. PMID:26011751

Influence of auditory attention on sentence recognition captured by the neural phase.

PubMed

Müller, Jana Annina; Kollmeier, Birger; Debener, Stefan; Brand, Thomas

2018-03-07

The aim of this study was to investigate whether attentional influences on speech recognition are reflected in the neural phase entrained by an external modulator. Sentences were presented in 7 Hz sinusoidally modulated noise while the neural response to that modulation frequency was monitored by electroencephalogram (EEG) recordings in 21 participants. We implemented a selective attention paradigm including three different attention conditions while keeping physical stimulus parameters constant. The participants' task was either to repeat the sentence as accurately as possible (speech recognition task), to count the number of decrements implemented in modulated noise (decrement detection task), or to do both (dual task), while the EEG was recorded. Behavioural analysis revealed reduced performance in the dual task condition for decrement detection, possibly reflecting limited cognitive resources. EEG analysis revealed no significant differences in power for the 7 Hz modulation frequency, but an attention-dependent phase difference between tasks. Further phase analysis revealed a significant difference 500 ms after sentence onset between trials with correct and incorrect responses for speech recognition, indicating that speech recognition performance and the neural phase are linked via selective attention mechanisms, at least shortly after sentence onset. However, the neural phase effects identified were small and await further investigation. © 2018 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Neural network identification of aircraft nonlinear aerodynamic characteristics

NASA Astrophysics Data System (ADS)

Egorchev, M. V.; Tiumentsev, Yu V.

2018-02-01

The simulation problem for the controlled aircraft motion is considered in the case of imperfect knowledge of the modeling object and its operating conditions. The work aims to develop a class of modular semi-empirical dynamic models that combine the capabilities of theoretical and neural network modeling. We consider the use of semi-empirical neural network models for solving the problem of identifying aerodynamic characteristics of an aircraft. We also discuss the formation problem for a representative set of data characterizing the behavior of a simulated dynamic system, which is one of the critical tasks in the synthesis of ANN-models. The effectiveness of the proposed approach is demonstrated using a simulation example of the aircraft angular motion and identifying the corresponding coefficients of aerodynamic forces and moments.

Insights into Metabolic Mechanisms Underlying Folate-Responsive Neural Tube Defects: A Minireview

PubMed Central

Beaudin, Anna E.; Stover, Patrick J.

2015-01-01

Neural tube defects (NTDs), including anencephaly and spina bifida, arise from the failure of neurulation during early embryonic development. Neural tube defects are common birth defects with a heterogenous and multifactorial etiology with interacting genetic and environmental risk factors. Although the mechanisms resulting in failure of neural tube closure are unknown, up to 70% of NTDs can be prevented by maternal folic acid supplementation. However, the metabolic mechanisms underlying the association between folic acid and NTD pathogenesis have not been identified. This review summarizes our current understanding of the mechanisms by which impairments in folate metabolism might ultimately lead to failure of neural tube closure, with an emphasis on untangling the relative contributions of nutritional deficiency and genetic risk factors to NTD pathogenesis. PMID:19180567

Neural networks for self-learning control systems

NASA Technical Reports Server (NTRS)

Nguyen, Derrick H.; Widrow, Bernard

1990-01-01

It is shown how a neural network can learn of its own accord to control a nonlinear dynamic system. An emulator, a multilayered neural network, learns to identify the system's dynamic characteristics. The controller, another multilayered neural network, next learns to control the emulator. The self-trained controller is then used to control the actual dynamic system. The learning process continues as the emulator and controller improve and track the physical process. An example is given to illustrate these ideas. The 'truck backer-upper,' a neural network controller that steers a trailer truck while the truck is backing up to a loading dock, is demonstrated. The controller is able to guide the truck to the dock from almost any initial position. The technique explored should be applicable to a wide variety of nonlinear control problems.

Melatonin receptors: Current status, facts, and hypothesis

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

Stankov, B.; Reiter, R.J.

Great progress has been made in the identification of melatonin binding sites, commonly identified as melatonin receptors by many authors, in recent years. The bulk of these studies have investigated the sites using either autoradiographic and biochemical techniques with the majority of the experiments being done on the rat, Djungarian and Syrian hamster, and sheep, although human tissue has also been employed. Many of the studies have identified melatonin binding in the central nervous system with either tritium- or iodine-labelled ligands. The latter ligand seems to provide the most reproducible and consistent data. Of the central neural tissues examined, themore » suprachiasmatic nuclei are most frequently mentioned as a location for melatonin binding sites although binding seems to be widespread in the brain. The other tissue that has been prominently mentioned as a site for melatonin binding is the pars tuberalis of the anterior pituitary gland. There may be time-dependent variations in melatonin binding densities in both neural and pituitary gland tissue. Very few attempts have been made to identify melatonin binding outside of the central nervous system despite the widespread actions of melatonin. Preliminary experiments have been carried out on the intracellular second messengers which mediate the actions of melatonin.« less

Genome-wide association study identifies 74 loci associated with educational attainment

PubMed Central

Okbay, Aysu; Beauchamp, Jonathan P.; Fontana, Mark A.; Lee, James J.; Pers, Tune H.; Rietveld, Cornelius A.; Turley, Patrick; Chen, Guo-Bo; Emilsson, Valur; Meddens, S. Fleur W.; Oskarsson, Sven; Pickrell, Joseph K.; Thom, Kevin; Timshel, Pascal; de Vlaming, Ronald; Abdellaoui, Abdel; Ahluwalia, Tarunveer S.; Bacelis, Jonas; Baumbach, Clemens; Bjornsdottir, Gyda; Brandsma, Johannes H.; Concas, Maria Pina; Derringer, Jaime; Furlotte, Nicholas A.; Galesloot, Tessel E.; Girotto, Giorgia; Gupta, Richa; Hall, Leanne M.; Harris, Sarah E.; Hofer, Edith; Horikoshi, Momoko; Huffman, Jennifer E.; Kaasik, Kadri; Kalafati, Ioanna P.; Karlsson, Robert; Kong, Augustine; Lahti, Jari; van der Lee, Sven J.; de Leeuw, Christiaan; Lind, Penelope A.; Lindgren, Karl-Oskar; Liu, Tian; Mangino, Massimo; Marten, Jonathan; Mihailov, Evelin; Miller, Michael B.; van der Most, Peter J.; Oldmeadow, Christopher; Payton, Antony; Pervjakova, Natalia; Peyrot, Wouter J.; Qian, Yong; Raitakari, Olli; Rueedi, Rico; Salvi, Erika; Schmidt, Börge; Schraut, Katharina E.; Shi, Jianxin; Smith, Albert V.; Poot, Raymond A.; Pourcain, Beate; Teumer, Alexander; Thorleifsson, Gudmar; Verweij, Niek; Vuckovic, Dragana; Wellmann, Juergen; Westra, Harm-Jan; Yang, Jingyun; Zhao, Wei; Zhu, Zhihong; Alizadeh, Behrooz Z.; Amin, Najaf; Bakshi, Andrew; Baumeister, Sebastian E.; Biino, Ginevra; Bønnelykke, Klaus; Boyle, Patricia A.; Campbell, Harry; Cappuccio, Francesco P.; Davies, Gail; De Neve, Jan-Emmanuel; Deloukas, Panos; Demuth, Ilja; Ding, Jun; Eibich, Peter; Eisele, Lewin; Eklund, Niina; Evans68, David M.; Faul, Jessica D.; Feitosa, Mary F.; Forstner, Andreas J.; Gandin, Ilaria; Gunnarsson, Bjarni; Halldórsson, Bjarni V.; Harris, Tamara B.; Heath, Andrew C.; Hocking, Lynne J.; Holliday, Elizabeth G.; Homuth, Georg; Horan, Michael A.; Hottenga, Jouke-Jan; de Jager, Philip L.; Joshi, Peter K.; Jugessur, Astanand; Kaakinen, Marika A.; Kähönen, Mika; Kanoni, Stavroula; Keltigangas-Järvinen, Liisa; Kiemeney, Lambertus A.L.M.; Kolcic, Ivana; Koskinen, Seppo; Kraja, Aldi T.; Kroh, Martin; Kutalik, Zoltan; Latvala, Antti; Launer, Lenore J.; Lebreton, Maël P.; Levinson, Douglas F.; Lichtenstein, Paul; Lichtner, Peter; Liewald, David C.M.; Loukola, Anu; Madden, Pamela A.; Mägi, Reedik; Mäki-Opas, Tomi; Marioni, Riccardo E.; Marques-Vidal, Pedro; Meddens, Gerardus A.; McMahon, George; Meisinger, Christa; Meitinger, Thomas; Milaneschi, Yusplitri; Milani, Lili; Montgomery, Grant W.; Myhre, Ronny; Nelson, Christopher P.; Nyholt, Dale R.; Ollier, William E.R.; Palotie, Aarno; Paternoster, Lavinia; Pedersen, Nancy L.; Petrovic, Katja E.; Porteous, David J.; Räikkönen, Katri; Ring, Susan M.; Robino, Antonietta; Rostapshova, Olga; Rudan, Igor; Rustichini, Aldo; Salomaa, Veikko; Sanders, Alan R.; Sarin, Antti-Pekka; Schmidt, Helena; Scott, Rodney J.; Smith, Blair H.; Smith, Jennifer A.; Staessen, Jan A.; Steinhagen-Thiessen, Elisabeth; Strauch, Konstantin; Terracciano, Antonio; Tobin, Martin D.; Ulivi, Sheila; Vaccargiu, Simona; Quaye, Lydia; van Rooij, Frank J.A.; Venturini, Cristina; Vinkhuyzen, Anna A.E.; Völker, Uwe; Völzke, Henry; Vonk, Judith M.; Vozzi, Diego; Waage, Johannes; Ware, Erin B.; Willemsen, Gonneke; Attia, John R.; Bennett, David A.; Berger, Klaus; Bertram, Lars; Bisgaard, Hans; Boomsma, Dorret I.; Borecki, Ingrid B.; Bultmann, Ute; Chabris, Christopher F.; Cucca, Francesco; Cusi, Daniele; Deary, Ian J.; Dedoussis, George V.; van Duijn, Cornelia M.; Eriksson, Johan G.; Franke, Barbara; Franke, Lude; Gasparini, Paolo; Gejman, Pablo V.; Gieger, Christian; Grabe, Hans-Jörgen; Gratten, Jacob; Groenen, Patrick J.F.; Gudnason, Vilmundur; van der Harst, Pim; Hayward, Caroline; Hinds, David A.; Hoffmann, Wolfgang; Hyppönen, Elina; Iacono, William G.; Jacobsson, Bo; Järvelin, Marjo-Riitta; Jöckel, Karl-Heinz; Kaprio, Jaakko; Kardia, Sharon L.R.; Lehtimäki, Terho; Lehrer, Steven F.; Magnusson, Patrik K.E.; Martin, Nicholas G.; McGue, Matt; Metspalu, Andres; Pendleton, Neil; Penninx, Brenda W.J.H.; Perola, Markus; Pirastu, Nicola; Pirastu, Mario; Polasek, Ozren; Posthuma, Danielle; Power, Christine; Province, Michael A.; Samani, Nilesh J.; Schlessinger, David; Schmidt, Reinhold; Sørensen, Thorkild I.A.; Spector, Tim D.; Stefansson, Kari; Thorsteinsdottir, Unnur; Thurik, A. Roy; Timpson, Nicholas J.; Tiemeier, Henning; Tung, Joyce Y.; Uitterlinden, André G.; Vitart, Veronique; Vollenweider, Peter; Weir, David R.; Wilson, James F.; Wright, Alan F.; Conley, Dalton C.; Krueger, Robert F.; Smith, George Davey; Hofman, Albert; Laibson, David I.; Medland, Sarah E.; Meyer, Michelle N.; Yang, Jian; Johannesson, Magnus; Visscher, Peter M.; Esko, Tõnu; Koellinger, Philipp D.; Cesarini, David; Benjamin, Daniel J.

2016-01-01

Summary Educational attainment (EA) is strongly influenced by social and other environmental factors, but genetic factors are also estimated to account for at least 20% of the variation across individuals1. We report the results of a genome-wide association study (GWAS) for EA that extends our earlier discovery sample1,2 of 101,069 individuals to 293,723 individuals, and a replication in an independent sample of 111,349 individuals from the UK Biobank. We now identify 74 genome-wide significant loci associated with number of years of schooling completed. Single-nucleotide polymorphisms (SNPs) associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioral phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because EA is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric disease. PMID:27225129

Abnormal fronto-limbic engagement in incarcerated stimulant users during moral processing.

PubMed

Fede, Samantha J; Harenski, Carla L; Schaich Borg, Jana; Sinnott-Armstrong, Walter; Rao, Vikram; Caldwell, Brendan M; Nyalakanti, Prashanth K; Koenigs, Michael R; Decety, Jean; Calhoun, Vince D; Kiehl, Kent A

2016-09-01

Stimulant use is a significant and prevalent problem, particularly in criminal populations. Previous studies found that cocaine and methamphetamine use is related to impairment in identifying emotions and empathy. Stimulant users also have abnormal neural structure and function of the ventromedial prefrontal cortex (vmPFC), amygdala, and anterior (ACC) and posterior cingulate (PCC), regions implicated in moral decision-making. However, no research has studied the neural correlates of stimulant use and explicit moral processing in an incarcerated population. Here, we examine how stimulant use affects sociomoral processing that might contribute to antisocial behavior. We predicted that vmPFC, amygdala, PCC, and ACC would show abnormal neural response during a moral processing task in incarcerated methamphetamine and cocaine users. Incarcerated adult males (N = 211) were scanned with a mobile MRI system while completing a moral decision-making task. Lifetime drug use was assessed. Neural responses during moral processing were compared between users and non-users. The relationship between duration of use and neural function was also examined. Incarcerated stimulant users showed less amygdala engagement than non-users during moral processing. Duration of stimulant use was negatively associated with activity in ACC and positively associated with vmPFC response during moral processing. These results suggest a dynamic pattern of fronto-limbic moral processing related to stimulant use with deficits in both central motive and cognitive integration elements of biological moral processes theory. This increases our understanding of how drug use relates to moral processing in the brain in an ultra-high-risk population.

Molecular Features of Neural Stem Cells Enable their Enrichment Using Pharmacological Inhibitors of Survival-Promoting Kinases

PubMed Central

Brazel, Christine Y.; Alaythan, Abdulaziz A.; Felling, Ryan J.; Calderon, Frances; Levison, Steven W.

2013-01-01

Isolating a pure population of neural stem cells (NSCs) has been difficult since no exclusive surface markers have been identified for panning or FACS purification. Moreover, additional refinements for maintaining NSCs in culture are required, since NSCs generate a variety of neural precursors (NPs) as they proliferate. Here, we demonstrate that postnatal rat NPs express low levels of pro-apoptotic molecules and resist PI3K and ERK1/2 inhibition as compared to late oligodendrocyte progenitors. Furthermore, maintaining SVZ precursors in LY294002 and PD98059, inhibitors of PI3K and ERK1/2 signaling, eliminated lineage-restricted precursors as revealed by enrichment for Nestin+/SOX-2+ cells. The cells that survived formed neurospheres and 89% of these neurospheres were tripotential, generating neurons, astrocytes and oligodendrocytes. Without this enrichment step, less than 50% of the NPs were Nestin+/SOX-2+ and 42% of the neurospheres were tripotential. Additionally, neurospheres enriched using this procedure produced 3-times more secondary neurospheres, supporting the conclusion that this procedure enriches for NSCs. A number of genes that enhance survival were more highly expressed in neurospheres compared to late oligodendrocyte progenitors. Altogether, these studies demonstrate that primitive neural precursors can be enriched using a relatively simple and inexpensive means that will facilitate cell replacement strategies using stem cells as well as other studies whose goal is to reveal the fundamental properties of primitive neural precursors. PMID:24032666

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

PubMed Central

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

2012-01-01

Background 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. Methods 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.” Results 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. Limitations Studies that did not include control tasks or a comparator group were included in this review. Conclusion 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 underlying higher-order social cognitive processes, including empathy, remain unexplored in patients with mood disorders. PMID:22297065

Collaborative identification method for sea battlefield target based on deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Zheng, Guangdi; Pan, Mingbo; Liu, Wei; Wu, Xuetong

2018-03-01

The target identification of the sea battlefield is the prerequisite for the judgment of the enemy in the modern naval battle. In this paper, a collaborative identification method based on convolution neural network is proposed to identify the typical targets of sea battlefields. Different from the traditional single-input/single-output identification method, the proposed method constructs a multi-input/single-output co-identification architecture based on optimized convolution neural network and weighted D-S evidence theory. The simulation results show that

Brain Representations of Basic Physics Concepts

NASA Astrophysics Data System (ADS)

Just, Marcel Adam

2017-09-01

The findings concerning physics concepts build on the remarkable new ability to determine the neural signature (or activation pattern) corresponding to an individual concept using fMRI brain imaging. Moreover, the neural signatures can be decomposed into meaningful underlying dimensions, identifying the individual, interpretable components of the neural representation of a concept. The investigation of physics concepts representations reveals how relatively recent physics concepts (formalized only in the last few centuries) are stored in the millenia-old information system of the human brain.

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

PubMed

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.

Identification and interpretation of patterns in rocket engine data: Artificial intelligence and neural network approaches

NASA Technical Reports Server (NTRS)

Ali, Moonis; Whitehead, Bruce; Gupta, Uday K.; Ferber, Harry

1995-01-01

This paper describes an expert system which is designed to perform automatic data analysis, identify anomalous events and determine the characteristic features of these events. We have employed both artificial intelligence and neural net approaches in the design of this expert system.

Cadherin genes and evolutionary novelties in the octopus.

PubMed

Wang, Z Yan; Ragsdale, Clifton W

2017-09-01

All animals with large brains must have molecular mechanisms to regulate neuronal process outgrowth and prevent neurite self-entanglement. In vertebrates, two major gene families implicated in these mechanisms are the clustered protocadherins and the atypical cadherins. However, the molecular mechanisms utilized in complex invertebrate brains, such as those of the cephalopods, remain largely unknown. Recently, we identified protocadherins and atypical cadherins in the octopus. The octopus protocadherin expansion shares features with the mammalian clustered protocadherins, including enrichment in neural tissues, clustered head-to-tail orientations in the genome, and a large first exon encoding all cadherin domains. Other octopus cadherins, including a newly-identified cadherin with 77 extracellular cadherin domains, are elevated in the suckers, a striking cephalopod novelty. Future study of these octopus genes may yield insights into the general functions of protocadherins in neural wiring and cadherin-related proteins in complex morphogenesis. Copyright © 2017 Elsevier Ltd. All rights reserved.

Extracellular space preservation aids the connectomic analysis of neural circuits.

PubMed

Pallotto, Marta; Watkins, Paul V; Fubara, Boma; Singer, Joshua H; Briggman, Kevin L

2015-12-09

Dense connectomic mapping of neuronal circuits is limited by the time and effort required to analyze 3D electron microscopy (EM) datasets. Algorithms designed to automate image segmentation suffer from substantial error rates and require significant manual error correction. Any improvement in segmentation error rates would therefore directly reduce the time required to analyze 3D EM data. We explored preserving extracellular space (ECS) during chemical tissue fixation to improve the ability to segment neurites and to identify synaptic contacts. ECS preserved tissue is easier to segment using machine learning algorithms, leading to significantly reduced error rates. In addition, we observed that electrical synapses are readily identified in ECS preserved tissue. Finally, we determined that antibodies penetrate deep into ECS preserved tissue with only minimal permeabilization, thereby enabling correlated light microscopy (LM) and EM studies. We conclude that preservation of ECS benefits multiple aspects of the connectomic analysis of neural circuits.

New approaches to investigating social gestures in autism spectrum disorder

PubMed Central

2012-01-01

The combination of economic games and human neuroimaging presents the possibility of using economic probes to identify biomarkers for quantitative features of healthy and diseased cognition. These probes span a range of important cognitive functions, but one new use is in the domain of reciprocating social exchange with other humans - a capacity perturbed in a number of psychopathologies. We summarize the use of a reciprocating exchange game to elicit neural and behavioral signatures for subjects diagnosed with autism spectrum disorder (ASD). Furthermore, we outline early efforts to capture features of social exchange in computational models and use these to identify quantitative behavioral differences between subjects with ASD and matched controls. Lastly, we summarize a number of subsequent studies inspired by the modeling results, which suggest new neural and behavioral signatures that could be used to characterize subtle deficits in information processing during interactions with other humans. PMID:22958572

Two algorithms for neural-network design and training with application to channel equalization.

PubMed

Sweatman, C Z; Mulgrew, B; Gibson, G J

1998-01-01

We describe two algorithms for designing and training neural-network classifiers. The first, the linear programming slab algorithm (LPSA), is motivated by the problem of reconstructing digital signals corrupted by passage through a dispersive channel and by additive noise. It constructs a multilayer perceptron (MLP) to separate two disjoint sets by using linear programming methods to identify network parameters. The second, the perceptron learning slab algorithm (PLSA), avoids the computational costs of linear programming by using an error-correction approach to identify parameters. Both algorithms operate in highly constrained parameter spaces and are able to exploit symmetry in the classification problem. Using these algorithms, we develop a number of procedures for the adaptive equalization of a complex linear 4-quadrature amplitude modulation (QAM) channel, and compare their performance in a simulation study. Results are given for both stationary and time-varying channels, the latter based on the COST 207 GSM propagation model.

A functional genomics screen in planarians reveals regulators of whole-brain regeneration

PubMed Central

Roberts-Galbraith, Rachel H; Brubacher, John L; Newmark, Phillip A

2016-01-01

Planarians regenerate all body parts after injury, including the central nervous system (CNS). We capitalized on this distinctive trait and completed a gene expression-guided functional screen to identify factors that regulate diverse aspects of neural regeneration in Schmidtea mediterranea. Our screen revealed molecules that influence neural cell fates, support the formation of a major connective hub, and promote reestablishment of chemosensory behavior. We also identified genes that encode signaling molecules with roles in head regeneration, including some that are produced in a previously uncharacterized parenchymal population of cells. Finally, we explored genes downregulated during planarian regeneration and characterized, for the first time, glial cells in the planarian CNS that respond to injury by repressing several transcripts. Collectively, our studies revealed diverse molecules and cell types that underlie an animal’s ability to regenerate its brain. DOI: http://dx.doi.org/10.7554/eLife.17002.001 PMID:27612384

Neural Specificity for Grammatical Operations is Revealed by Content-Independent fMR Adaptation

PubMed Central

Shapiro, Kevin A.; Moo, Lauren R.; Caramazza, Alfonso

2012-01-01

The ability to generate novel sentences depends on cognitive operations that specify the syntactic function of nouns, verbs, and other words retrieved from the mental lexicon. Although neuropsychological studies suggest that such operations rely on neural circuits distinct from those encoding word form and meaning, it has not been possible to characterize this distinction definitively with neuroimaging. We used functional magnetic resonance imaging (fMRI) to show that a brain area engaged in a given grammatical operation can be identified uniquely by a monotonic decrease in activation as that operation is repeated. We applied this methodology to identify areas involved selectively in the operation of inflection of nouns or verbs. By contrast, areas involved in processing word meaning do not show this monotonic adaptation across stimuli. These results are the first to demonstrate adaptation in the fMR signal evoked not by specific stimuli, but by well-defined cognitive linguistic operations. PMID:22347206

A stereo-compound hybrid microscope for combined intracellular and optical recording of invertebrate neural network activity.

PubMed

Frost, William N; Wang, Jean; Brandon, Christopher J

2007-05-15

Optical recording studies of invertebrate neural networks with voltage-sensitive dyes seldom employ conventional intracellular electrodes. This may in part be due to the traditional reliance on compound microscopes for such work. While such microscopes have high light-gathering power, they do not provide depth of field, making working with sharp electrodes difficult. Here we describe a hybrid microscope design, with switchable compound and stereo objectives, that eases the use of conventional intracellular electrodes in optical recording experiments. We use it, in combination with a voltage-sensitive dye and photodiode array, to identify neurons participating in the swim motor program of the marine mollusk Tritonia. This microscope design should be applicable to optical recording studies in many preparations.

Transcriptional Profiling of Hypoxic Neural Stem Cells Identifies Calcineurin-NFATc4 Signaling as a Major Regulator of Neural Stem Cell Biology

PubMed Central

Moreno, Marta; Fernández, Virginia; Monllau, Josep M.; Borrell, Víctor; Lerin, Carles; de la Iglesia, Núria

2015-01-01

Summary Neural stem cells (NSCs) reside in a hypoxic microenvironment within the brain. However, the crucial transcription factors (TFs) that regulate NSC biology under physiologic hypoxia are poorly understood. Here we have performed gene set enrichment analysis (GSEA) of microarray datasets from hypoxic versus normoxic NSCs with the aim of identifying pathways and TFs that are activated under oxygen concentrations mimicking normal brain tissue microenvironment. Integration of TF target (TFT) and pathway enrichment analysis identified the calcium-regulated TF NFATc4 as a major candidate to regulate hypoxic NSC functions. Nfatc4 expression was coordinately upregulated by top hypoxia-activated TFs, while NFATc4 target genes were enriched in hypoxic NSCs. Loss-of-function analyses further revealed that the calcineurin-NFATc4 signaling axis acts as a major regulator of NSC self-renewal and proliferation in vitro and in vivo by promoting the expression of TFs, including Id2, that contribute to the maintenance of the NSC state. PMID:26235896

Elements of person knowledge: Episodic recollection helps us to identify people but not to recognize their faces.

PubMed

MacKenzie, Graham; Donaldson, David I

2016-12-01

Faces automatically draw attention, allowing rapid assessments of personality and likely behaviour. How we respond to people is, however, highly dependent on whether we know who they are. According to face processing models person knowledge comes from an extended neural system that includes structures linked to episodic memory. Here we use scalp recorded brain signals to demonstrate the specific role of episodic memory processes during face processing. In two experiments we recorded Event-Related Potentials (ERPs) while participants made identify, familiar or unknown responses to famous faces. ERPs revealed neural signals previously associated with episodic recollection for identify but not familiar faces. These findings provide novel evidence suggesting that recollection is central to face processing, providing one source of person knowledge that can be used to moderate the initial impressions gleaned from the core neural system that supports face recognition. Crown Copyright © 2016. Published by Elsevier Ltd. All rights reserved.

Understanding the dynamical control of animal movement

NASA Astrophysics Data System (ADS)

Edwards, Donald

2008-03-01

Over the last 50 years, neurophysiologists have described many neural circuits that transform sensory input into motor commands, while biomechanicians and behavioral biologists have described many patterns of animal movement that occur in response to sensory input. Attempts to link these two have been frustrated by our technical inability to record from the necessary neurons in a freely behaving animal. As a result, we don't know how these neural circuits function in the closed loop context of free behavior, where the sensory and motor context changes on a millisecond time-scale. To address this problem, we have developed a software package, AnimatLab (www.AnimatLab.com), that enables users to reconstruct an animal's body and its relevant neural circuits, to link them at the sensory and motor ends, and through simulation, to test their ability to reproduce appropriate patterns of the animal's movements in a simulated Newtonian world. A Windows-based program, AnimatLab consists of a neural editor, a body editor, a world editor, stimulus and recording facilities, neural and physics engines, and an interactive 3-D graphical display. We have used AnimatLab to study three patterns of behavior: the grasshopper jump, crayfish escape, and crayfish leg movements used in postural control, walking, reaching and grasping. In each instance, the simulation helped identify constraints on both nervous function and biomechanical performance that have provided the basis for new experiments. Colleagues elsewhere have begun to use AnimatLab to study control of paw movements in cats and postural control in humans. We have also used AnimatLab simulations to guide the development of an autonomous hexapod robot in which the neural control circuitry is downloaded to the robot from the test computer.

The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding.

PubMed

Testolin, Alberto; De Filippo De Grazia, Michele; Zorzi, Marco

2017-01-01

The recent "deep learning revolution" in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems.

The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding

PubMed Central

Testolin, Alberto; De Filippo De Grazia, Michele; Zorzi, Marco

2017-01-01

The recent “deep learning revolution” in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems. PMID:28377709

A recurrent neural-network-based sensor and actuator fault detection and isolation for nonlinear systems with application to the satellite's attitude control subsystem.

PubMed

Talebi, H A; Khorasani, K; Tafazoli, S

2009-01-01

This paper presents a robust fault detection and isolation (FDI) scheme for a general class of nonlinear systems using a neural-network-based observer strategy. Both actuator and sensor faults are considered. The nonlinear system considered is subject to both state and sensor uncertainties and disturbances. Two recurrent neural networks are employed to identify general unknown actuator and sensor faults, respectively. The neural network weights are updated according to a modified backpropagation scheme. Unlike many previous methods developed in the literature, our proposed FDI scheme does not rely on availability of full state measurements. The stability of the overall FDI scheme in presence of unknown sensor and actuator faults as well as plant and sensor noise and uncertainties is shown by using the Lyapunov's direct method. The stability analysis developed requires no restrictive assumptions on the system and/or the FDI algorithm. Magnetorquer-type actuators and magnetometer-type sensors that are commonly employed in the attitude control subsystem (ACS) of low-Earth orbit (LEO) satellites for attitude determination and control are considered in our case studies. The effectiveness and capabilities of our proposed fault diagnosis strategy are demonstrated and validated through extensive simulation studies.

Estimating spatio-temporal dynamics of stream total phosphate concentration by soft computing techniques.

PubMed

Chang, Fi-John; Chen, Pin-An; Chang, Li-Chiu; Tsai, Yu-Hsuan

2016-08-15

This study attempts to model the spatio-temporal dynamics of total phosphate (TP) concentrations along a river for effective hydro-environmental management. We propose a systematical modeling scheme (SMS), which is an ingenious modeling process equipped with a dynamic neural network and three refined statistical methods, for reliably predicting the TP concentrations along a river simultaneously. Two different types of artificial neural network (BPNN-static neural network; NARX network-dynamic neural network) are constructed in modeling the dynamic system. The Dahan River in Taiwan is used as a study case, where ten-year seasonal water quality data collected at seven monitoring stations along the river are used for model training and validation. Results demonstrate that the NARX network can suitably capture the important dynamic features and remarkably outperforms the BPNN model, and the SMS can effectively identify key input factors, suitably overcome data scarcity, significantly increase model reliability, satisfactorily estimate site-specific TP concentration at seven monitoring stations simultaneously, and adequately reconstruct seasonal TP data into a monthly scale. The proposed SMS can reliably model the dynamic spatio-temporal water pollution variation in a river system for missing, hazardous or costly data of interest. Copyright © 2016 Elsevier B.V. All rights reserved.

Role of vitamin D in regulating the neural stem cells of mouse model with multiple sclerosis.

PubMed

Gu, S-G; Wang, C-J; Zhao, G; Li, G-Y

2015-11-01

Multiple sclerosis (MS) is an autoimmune disease that results with a damaged myelin sheath as a result, there is an impairment of nerve impulse conduction. The medication for MS is able to delay its progression, but complete recovery is impossible. Recent studies with neural stem cells have promising results in treating as well as to recover the damaged nerves, but research on in vivo model system is limited in this aspect. Here we are able to successfully establish an MS mice model by injecting with myelin basic protein and we studied the neural stem cell response in supplement with vitamin D. Through histology we provide strong evidence that the MS pathogenesis is reverted on response to vitamin D. We also identified through immunohistochemistry and western blotting that the vitamin D has the ability to trigger neural stem cells, and thereby it assist in recovery from MS. Further, their roles in preventing as well as delaying the MS development are also proven. The role of vitamin D has also cross checked with the help of tunnel assay. Overall, our results conclude that the lesion associated apoptotic signals are reduced on administrated with vitamin D. The present data help to design a new therapeutic intervention to cure MS.

Expression profiles of the Gα subunits during Xenopus tropicalis embryonic development.

PubMed

Fuentealba, Jaime; Toro-Tapia, Gabriela; Rodriguez, Marion; Arriagada, Cecilia; Maureira, Alejandro; Beyer, Andrea; Villaseca, Soraya; Leal, Juan I; Hinrichs, Maria V; Olate, Juan; Caprile, Teresa; Torrejón, Marcela

2016-09-01

Heterotrimeric G protein signaling plays major roles during different cellular events. However, there is a limited understanding of the molecular mechanisms underlying G protein control during embryogenesis. G proteins are highly conserved and can be grouped into four subfamilies according to sequence homology and function. To further studies on G protein function during embryogenesis, the present analysis identified four Gα subunits representative of the different subfamilies and determined their spatiotemporal expression patterns during Xenopus tropicalis embryogenesis. Each of the Gα subunit transcripts was maternally and zygotically expressed, and, as development progressed, dynamic expression patterns were observed. In the early developmental stages, the Gα subunits were expressed in the animal hemisphere and dorsal marginal zone. While expression was observed at the somite boundaries, in vascular structures, in the eye, and in the otic vesicle during the later stages, expression was mainly found in neural tissues, such as the neural tube and, especially, in the cephalic vesicles, neural crest region, and neural crest-derived structures. Together, these results support the pleiotropism and complexity of G protein subfamily functions in different cellular events. The present study constitutes the most comprehensive description to date of the spatiotemporal expression patterns of Gα subunits during vertebrate development. Copyright © 2016 Elsevier B.V. All rights reserved.

Social cognition in members of conflict groups: behavioural and neural responses in Arabs, Israelis and South Americans to each other's misfortunes.

PubMed

Bruneau, Emile G; Dufour, Nicholas; Saxe, Rebecca

2012-03-05

In contexts of cultural conflict, people delegitimize the other group's perspective and lose compassion for the other group's suffering. These psychological biases have been empirically characterized in intergroup settings, but rarely in groups involved in active conflict. Similarly, the basic brain networks involved in recognizing others' narratives and misfortunes have been identified, but how these brain networks are modulated by intergroup conflict is largely untested. In the present study, we examined behavioural and neural responses in Arab, Israeli and South American participants while they considered the pain and suffering of individuals from each group. Arabs and Israelis reported feeling significantly less compassion for each other's pain and suffering (the 'conflict outgroup'), but did not show an ingroup bias relative to South Americans (the 'distant outgroup'). In contrast, the brain regions that respond to others' tragedies showed an ingroup bias relative to the distant outgroup but not the conflict outgroup, particularly for descriptions of emotional suffering. Over all, neural responses to conflict group members were qualitatively different from neural responses to distant group members. This is the first neuroimaging study to examine brain responses to others' suffering across both distant and conflict groups, and provides a first step towards building a foundation for the biological basis of conflict.

Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors.

PubMed

Ali, Hany S M; Blagden, Nicholas; York, Peter; Amani, Amir; Brook, Toni

2009-06-28

This study employs artificial neural networks (ANNs) to create a model to identify relationships between variables affecting drug nanoprecipitation using microfluidic reactors. The input variables examined were saturation levels of prednisolone, solvent and antisolvent flow rates, microreactor inlet angles and internal diameters, while particle size was the single output. ANNs software was used to analyse a set of data obtained by random selection of the variables. The developed model was then assessed using a separate set of validation data and provided good agreement with the observed results. The antisolvent flow rate was found to have the dominant role on determining final particle size.

Ossicles of lumbar articular facets: normal variant or spondylolytic variant?

PubMed

Kumar, Dalavaye S; Fotiadou, Anastasia; Lalam, Radhesh; Ginder, Laurence M; Eisenstein, Stephen M; Tins, Bernard J; Tyrrell, Prudencia N M; Delieu, John M; McCall, Iain W; Rees, Dai A; Cassar-Pullicino, Victor N

2012-12-01

The objective of this study was to establish the prevalence and significance of ossicles of lumbar articular facets (OLAF) in young athletes with backache diagnosed by multi-detector computed tomography (MDCT). The MDCT examinations of the lumbar spine carried out for suspected spondylolysis on 46 consecutive symptomatic young athletes presenting to a sports injury clinic over a 1-year period were retrospectively reviewed. OLAF study included detailed correlation with the structural and morphological stress features of the posterior neural arches. This was then compared with a control group composed of 39 patients. Twenty-three OLAF were identified in 15 patients. Eleven of the 15 patients with ossicles had posterior element stress changes (PEST)/pars defects. In the control group, two OLAF were identified in two patients, one demonstrating PEST changes. The high prevalence of OLAF in young symptomatic athletes compared with the asymptomatic control group is indicative of stress fractures. The non-united articular process fractures should be regarded as part of the spectrum of stress-induced changes in the posterior neural arch in the same way as spondylolysis. MDCT with volumetric acquisition and multi-planar reformation is the most reliable investigation in the diagnosis of OLAF. 1) This CT study supports a traumatic aetiology for lumbar articular facets ossicles. 2) OLAF represent part of a spectrum of stress-induced changes in the posterior neural arch. 3) OLAF are associated with typical spondylolysis. 4) OLAF can be overlooked on reverse gantry angle computed tomography (RG-CT). 5) OLAF may account for some of the discrepancy between radionuclide and RG-CT studies.

The fungicide benomyl inhibits differentiation of neural tissue in the Xenopus embryo and animal cap explants.

PubMed

Yoon, Chun-Sik; Jin, Jung-Hyo; Park, Joo-Hung; Youn, Hyun-Joo; Cheong, Seon-Woo

2003-10-01

The toxic effect of benomyl on the embryogenesis of Xenopus laevis was investigated, and the tissues most affected by benomyl were identified. The toxicity of benomyl at various concentrations (5-20 microM) was tested with the Xenopus frog embryo teratogenesis assay (FETAX), used with slight modification. All test embryos subjected to 20 microM of benomyl died, and exposure to 10 and 15 microM benomyl produced growth inhibition and 11 types of severe external malformations. Histological examination of the test embryos showed dysplasia of the brain, eyes, intestine, otic vesicle, and muscle and swelling of the pronephric ducts and integuments. Among the tissues and organs affected, malformation of neural tissue was the most severe. The presumptive ectoderm isolated from st. 9 embryo was cultured in 10 ng/mL of activin A to induce neural tissue and mesoderm. When it was cultured with 10 ng/mL of activin A in the presence of 1 and 10 microM of benomyl, neural tissue induction was inhibited more severely than that of any other tissue. The gene expression of cultivated explants was investigated by reverse transcription-polymerase chain reaction (RT-PCR) assay in order to study the inhibition of neural tissue by benomyl. The results showed that with increasing benomyl concentration, the expression of the neural-specific marker NCAM (neural cell adhesion molecule), was more strongly inhibited than the muscle-specific marker muscle actin. Electron micrographs of test explants showed many residual yolk platelets and mitochondrial degeneration. In the present investigation the most severe toxic effects of benomyl were seen in the nerve tissues of the Xenopus embryo. This inhibition of neural development may have been caused by the inhibition of the assembly of neural microtubules and by the effect of benomyl on neuronal proliferation and migration. Copyright 2003 Wiley Periodicals, Inc.

Regional differences in the expression of laminin isoforms during mouse neural tube development

PubMed Central

Copp, Andrew J.; Carvalho, Rita; Wallace, Adam; Sorokin, Lydia; Sasaki, Takako; Greene, Nicholas D.E.; Ybot-Gonzalez, Patricia

2013-01-01

Many significant human birth defects originate around the time of neural tube closure or early during post-closure nervous system development. For example, failure of the neural tube to close generates anencephaly and spina bifida, faulty cell cycle progression is implicated in primary microcephaly, while defective migration of neuroblasts can lead to neuronal migration disorders such as lissencephaly. At the stage of neural tube closure, basement membranes are becoming organised around the neuroepithelium, and beneath the adjacent non-neural surface ectoderm. While there is circumstantial evidence to implicate basement membrane dynamics in neural tube and surface ectodermal development, we have an incomplete understanding of the molecular composition of basement membranes at this stage. In the present study, we examined the developing basement membranes of the mouse embryo at mid-gestation (embryonic day 9.5), with particular reference to laminin composition. We performed in situ hybridization to detect the mRNAs of all eleven individual laminin chains, and immunohistochemistry to identify which laminin chains are present in the basement membranes. From this information, we inferred the likely laminin variants and their tissues of origin: that is, whether a given basement membrane laminin is contributed by epithelium, mesenchyme, or both. Our findings reveal major differences in basement composition along the body axis, with the rostral neural tube (at mandibular arch and heart levels) exhibiting many distinct laminin variants, while the lumbar level where the neural tube is just closing shows a much simpler laminin profile. Moreover, there appears to be a marked difference in the extent to which the mesenchyme contributes laminin variants to the basement membrane, with potential contribution of several laminins rostrally, but no contribution caudally. This information paves the way towards a mechanistic analysis of basement membrane laminin function during early neural tube development in mammals. PMID:21524702

Alcohol-Induced Molecular Dysregulation in Human Embryonic Stem Cell-Derived Neural Precursor Cells

PubMed Central

Kim, Yi Young; Roubal, Ivan; Lee, Youn Soo; Kim, Jin Seok; Hoang, Michael; Mathiyakom, Nathan; Kim, Yong

2016-01-01

Adverse effect of alcohol on neural function has been well documented. Especially, the teratogenic effect of alcohol on neurodevelopment during embryogenesis has been demonstrated in various models, which could be a pathologic basis for fetal alcohol spectrum disorders (FASDs). While the developmental defects from alcohol abuse during gestation have been described, the specific mechanisms by which alcohol mediates these injuries have yet to be determined. Recent studies have shown that alcohol has significant effect on molecular and cellular regulatory mechanisms in embryonic stem cell (ESC) differentiation including genes involved in neural development. To test our hypothesis that alcohol induces molecular alterations during neural differentiation we have derived neural precursor cells from pluripotent human ESCs in the presence or absence of ethanol treatment. Genome-wide transcriptomic profiling identified molecular alterations induced by ethanol exposure during neural differentiation of hESCs into neural rosettes and neural precursor cell populations. The Database for Annotation, Visualization and Integrated Discovery (DAVID) functional analysis on significantly altered genes showed potential ethanol’s effect on JAK-STAT signaling pathway, neuroactive ligand-receptor interaction, Toll-like receptor (TLR) signaling pathway, cytokine-cytokine receptor interaction and regulation of autophagy. We have further quantitatively verified ethanol-induced alterations of selected candidate genes. Among verified genes we further examined the expression of P2RX3, which is associated with nociception, a peripheral pain response. We found ethanol significantly reduced the level of P2RX3 in undifferentiated hESCs, but induced the level of P2RX3 mRNA and protein in hESC-derived NPCs. Our result suggests ethanol-induced dysregulation of P2RX3 along with alterations in molecules involved in neural activity such as neuroactive ligand-receptor interaction may be a molecular event associated with alcohol-related peripheral neuropathy of an enhanced nociceptive response. PMID:27682028

Neural Representation. A Survey-Based Analysis of the Notion

PubMed Central

Vilarroya, Oscar

2017-01-01

The word representation (as in “neural representation”), and many of its related terms, such as to represent, representational and the like, play a central explanatory role in neuroscience literature. For instance, in “place cell” literature, place cells are extensively associated with their role in “the representation of space.” In spite of its extended use, we still lack a clear, universal and widely accepted view on what it means for a nervous system to represent something, on what makes a neural activity a representation, and on what is re-presented. The lack of a theoretical foundation and definition of the notion has not hindered actual research. My aim here is to identify how active scientists use the notion of neural representation, and eventually to list a set of criteria, based on actual use, that can help in distinguishing between genuine or non-genuine neural-representation candidates. In order to attain this objective, I present first the results of a survey of authors within two domains, place-cell and multivariate pattern analysis (MVPA) research. Based on the authors’ replies, and on a review of neuroscientific research, I outline a set of common properties that an account of neural representation seems to require. I then apply these properties to assess the use of the notion in two domains of the survey, place-cell and MVPA studies. I conclude by exploring a shift in the notion of representation suggested by recent literature. PMID:28900406

Ca(2+) coding and decoding strategies for the specification of neural and renal precursor cells during development.

PubMed

Moreau, Marc; Néant, Isabelle; Webb, Sarah E; Miller, Andrew L; Riou, Jean-François; Leclerc, Catherine

2016-03-01

During embryogenesis, a rise in intracellular Ca(2+) is known to be a widespread trigger for directing stem cells towards a specific tissue fate, but the precise Ca(2+) signalling mechanisms involved in achieving these pleiotropic effects are still poorly understood. In this review, we compare the Ca(2+) signalling events that appear to be one of the first steps in initiating and regulating both neural determination (neural induction) and kidney development (nephrogenesis). We have highlighted the necessary and sufficient role played by Ca(2+) influx and by Ca(2+) transients in the determination and differentiation of pools of neural or renal precursors. We have identified new Ca(2+) target genes involved in neural induction and we showed that the same Ca(2+) early target genes studied are not restricted to neural tissue but are also present in other tissues, principally in the pronephros. In this review, we also described a mechanism whereby the transcriptional control of gene expression during neurogenesis and nephrogenesis might be directly controlled by Ca(2+) signalling. This mechanism involves members of the Kcnip family such that a change in their binding properties to specific DNA sites is a result of Ca(2+) binding to EF-hand motifs. The different functions of Ca(2+) signalling during these two events illustrate the versatility of Ca(2+) as a second messenger. Copyright © 2015 Elsevier Ltd. All rights reserved.

Spinning in the Scanner: Neural Correlates of Virtual Reorientation

ERIC Educational Resources Information Center

Sutton, Jennifer E.; Joanisse, Marc F.; Newcombe, Nora S.

2010-01-01

Recent studies have used spatial reorientation task paradigms to identify underlying cognitive mechanisms of navigation in children, adults, and a range of animal species. Despite broad interest in this task across disciplines, little is known about the brain bases of reorientation. We used functional magnetic resonance imaging to examine neural…

A Markov Model Analysis of Problem-Solving Progress.

ERIC Educational Resources Information Center

Vendlinski, Terry

This study used a computerized simulation and problem-solving tool along with artificial neural networks (ANN) as pattern recognizers to identify the common types of strategies high school and college undergraduate chemistry students would use to solve qualitative chemistry problems. Participants were 134 high school chemistry students who used…

Cognitive Inflexibility and Frontal-Cortical Activation in Pediatric Obsessive-Compulsive Disorder

ERIC Educational Resources Information Center

Britton, Jennifer C.; Rauch, Scott L.; Rosso, Isabelle M.; Killgore, William D. S.; Price, Lauren M.; Ragan, Jennifer; Chosak, Anne; Hezel, Dianne M.; Pine, Daniel S.; Leibenluft, Ellen; Pauls, David L.; Jenike, Michael A.; Stewart, S. Evelyn

2010-01-01

Objective: Deficits in cognitive flexibility and response inhibition have been linked to perturbations in cortico-striatal-thalamic circuitry in adult obsessive-compulsive disorder (OCD). Although similar cognitive deficits have been identified in pediatric OCD, few neuroimaging studies have been conducted to examine its neural correlates in the…

Learning from Examples versus Verbal Directions in Mathematical Problem Solving

ERIC Educational Resources Information Center

Lee, Hee Seung; Fincham, Jon M.; Anderson, John R.

2015-01-01

This event-related fMRI study investigated the differences between learning from examples and learning from verbal directions in mathematical problem solving and how these instruction types affect the activity of relevant brain regions during instruction and solution periods within problem-solving trials. We identified distinct neural signatures…

Masked Repetition Priming Using Magnetoencephalography

ERIC Educational Resources Information Center

Monahan, Philip J.; Fiorentino, Robert; Poeppel, David

2008-01-01

Masked priming is used in psycholinguistic studies to assess questions about lexical access and representation. We present two masked priming experiments using MEG. If the MEG signal elicited by words reflects specific aspects of lexical retrieval, then one expects to identify specific neural correlates of retrieval that are sensitive to priming.…

Soluble Factors from Human Olfactory Neural Stem/Progenitor Cells Influence the Fate Decisions of Hippocampal Neural Precursor Cells.

PubMed

Gómez-Virgilio, Laura; Ramírez-Rodríguez, Gerardo Bernabé; Sánchez-Torres, Carmen; Ortiz-López, Leonardo; Meraz-Ríos, Marco Antonio

2018-03-01

Neurogenesis plays a significant role during adulthood, and the observation that neural stem cells reside in the central nervous system and the olfactory epithelium has attracted attention due to their importance in neuronal regeneration. In addition, soluble factors (SFs) release by neural stem cells may modulate the neurogenic process. Thus, in this study, we identified the SFs released by olfactory human neural stem/progenitor cells (hNS/PCs-OE). These cells express Ki67, nestin, and βIII-tubulin, indicating their neural lineage. The hNS/PCs-OE also express PSD95 and tau proteins during proliferation, but increased levels are observed after differentiation. Thus, we evaluated the effects of SFs from hNS/PCs-OE on the viability, proliferation, and differentiation potential of adult murine hippocampal neural precursor cells (AHPCs). SFs from hNS/PCs-OE maintain cells in the precursor and proliferative stages and mainly promote the astrocytic differentiation of AHPCs. These effects involved the activation, as measured by phosphorylation, of several proteins (Erk1/2; Akt/PRAS40/GSK3β and JAK/STAT) involved in key events of the neurogenic process. Moreover, according to the results from the antibody-based microarray approach, among the soluble factors, hNS/PCs-OE produce interleukin-6 (IL-6) and neurotrophin 4 (NT4). However, residual epidermal growth factor (EGF) was also detected. These proteins partially reproduced the effects of SFs from hNS/PCs-OE on AHPCs, and the mechanism underlying these effects is mediated by Src proteins, which have been implicated in EGF-induced transactivation of TrkB receptor. The results of the present study suggest the potential use of SFs from hNS/PCs-OE in controlling the differentiation potential of AHPCs. Thus, the potential clinical relevance of hNS/PCs-OE is worth pursuing.

miR-146b-5p promotes the neural conversion of pluripotent stem cells by targeting Smad4

PubMed Central

Zhang, Nianping; Lyu, Ying; Pan, Xuebing; Xu, Liping; Xuan, Aiguo; He, Xiaosong; Huang, Wandan; Long, Dahong

2017-01-01

Pluripotent stem cells (PSCs) are regarded as potential sources that provide specific neural cells for cell therapy in some nervous system diseases. However, the mechanisms underlying the neural differentiation of PSCs remain largely unknown. MicroRNAs (miRNAs or miRs) are a class of small non-protein-coding RNAs that act as critical regulatory molecules in many cellular processes. In this study, we found that miR-146b-5p expression was markedly increased following the neural induction of mouse embryonic stem cells (ESCs) or induced PSCs (iPSCs). In this study, to further identify the role of miR-146b-5p, we generated stable miR-146b-5p- overexpressing ESC and iPSC cell lines, and induced the differentiation of these cells by the adherent monolayer culture method. In the miR-146b-5p-overexpressing ESC- or iPSC- derived cultures, RT-qPCR analysis revealed that the mRNA expression levels of neuroectoderm markers, such as Sox1, Nestin and Pax6, were markedly increased, and flow cytometric analysis verified that the number of Nestin-positive cells was higher in the miR-146b-5p-overexpressing compared with the control cells. Mechanistically, the miR-146b-5p-overexpressing ESCs or iPSCs exhibited a significant reduction in Oct4 expression, which may be an explanation for these cells having a tendency to differentiate towards the neural lineage. Moreover, we confirmed that miR-146b-5p directly targeted Smad4 and negatively regulated the transforming growth factor (TGF)-β signaling pathway, which contributed to the neural commitment of PSCs. Collectively, our findings uncover the essential role of miR-146b-5p in the neural conversion of PSCs. PMID:28713933

A Common Neural Code for Perceived and Inferred Emotion

PubMed Central

Saxe, Rebecca

2014-01-01

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. PMID:25429141

A common neural code for perceived and inferred emotion.

PubMed

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.

Inhibitory Control and Emotional Stress Regulation: Neuroimaging Evidence for Frontal-Limbic Dysfunction in Psycho-stimulant Addiction

PubMed Central

Ray Li, Chiang-shan; Sinha, Rajita

2008-01-01

This review focuses on neuroimaging studies that examined stress processing and regulation and cognitive inhibitory control in patients with psycho-stimulant addiction. We provide an overview of these studies, summarizing converging evidence and discrepancies as they occur in the literature. We also adopt an analytic perspective and dissect these psychological processes into their sub-components, to identify the neural pathways specific to each component process and those that are more specifically involved in psycho-stimulant addiction. To this aim we refer frequently to studies conducted in healthy individuals. Despite the separate treatment of stress/affect regulation, stress-related craving or compulsive drug seeking, and inhibitory control, neural underpinnings of these processes overlap significantly. In particular, the ventromedial prefrontal regions including the anterior cingulate cortex, amygdala and the striatum are implicated in psychostimulant dependence. Our overarching thesis is that prefrontal activity ensures intact emotional stress regulation and inhibitory control. Altered prefrontal activity along with heightened striatal responses to addicted drug and drug-related salient stimuli perpetuates habitual drug seeking. Further studies that examine the functional relationships of these neural systems will likely provide the key to understanding the mechanisms underlying compulsive drug use behaviors in psycho-stimulant dependence. PMID:18164058

Identifying the Neural Substrates of Procrastination: a Resting-State fMRI Study.

PubMed

Zhang, Wenwen; Wang, Xiangpeng; Feng, Tingyong

2016-09-12

Procrastination is a prevalent problematic behavior that brings serious consequences to individuals who suffer from it. Although this phenomenon has received increasing attention from researchers, the underpinning neural substrates of it is poorly studied. To examine the neural bases subserving procrastination, the present study employed resting-state fMRI. The main results were as follows: (1) the behavioral procrastination was positively correlated with the regional activity of the ventromedial prefrontal cortex (vmPFC) and the parahippocampal cortex (PHC), while negatively correlated with that of the anterior prefrontal cortex (aPFC). (2) The aPFC-seed connectivity with the anterior medial prefrontal cortex and the posterior cingulate cortex was positively associated with procrastination. (3) The connectivity between vmPFC and several other regions, such as the dorsomedial prefrontal cortex, the bilateral inferior prefrontal cortex showed a negative association with procrastination. These results suggested that procrastination could be attributed to, on the one hand, hyper-activity of the default mode network (DMN) that overrides the prefrontal control signal; while on the other hand, the failure of top-down control exerted by the aPFC on the DMN. Therefore, the present study unravels the biomarkers of procrastination and provides treatment targets for procrastination prevention.

Identifying the Neural Substrates of Procrastination: a Resting-State fMRI Study

PubMed Central

Zhang, Wenwen; Wang, Xiangpeng; Feng, Tingyong

2016-01-01

Procrastination is a prevalent problematic behavior that brings serious consequences to individuals who suffer from it. Although this phenomenon has received increasing attention from researchers, the underpinning neural substrates of it is poorly studied. To examine the neural bases subserving procrastination, the present study employed resting-state fMRI. The main results were as follows: (1) the behavioral procrastination was positively correlated with the regional activity of the ventromedial prefrontal cortex (vmPFC) and the parahippocampal cortex (PHC), while negatively correlated with that of the anterior prefrontal cortex (aPFC). (2) The aPFC-seed connectivity with the anterior medial prefrontal cortex and the posterior cingulate cortex was positively associated with procrastination. (3) The connectivity between vmPFC and several other regions, such as the dorsomedial prefrontal cortex, the bilateral inferior prefrontal cortex showed a negative association with procrastination. These results suggested that procrastination could be attributed to, on the one hand, hyper-activity of the default mode network (DMN) that overrides the prefrontal control signal; while on the other hand, the failure of top-down control exerted by the aPFC on the DMN. Therefore, the present study unravels the biomarkers of procrastination and provides treatment targets for procrastination prevention. PMID:27616687

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

PubMed Central

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

2016-01-01

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

"Neural overlap of L1 and L2 semantic representations across visual and auditory modalities: a decoding approach".

PubMed

Van de Putte, Eowyn; De Baene, Wouter; Price, Cathy J; Duyck, Wouter

2018-05-01

This study investigated whether brain activity in Dutch-French bilinguals during semantic access to concepts from one language could be used to predict neural activation during access to the same concepts from another language, in different language modalities/tasks. This was tested using multi-voxel pattern analysis (MVPA), within and across language comprehension (word listening and word reading) and production (picture naming). It was possible to identify the picture or word named, read or heard in one language (e.g. maan, meaning moon) based on the brain activity in a distributed bilateral brain network while, respectively, naming, reading or listening to the picture or word in the other language (e.g. lune). The brain regions identified differed across tasks. During picture naming, brain activation in the occipital and temporal regions allowed concepts to be predicted across languages. During word listening and word reading, across-language predictions were observed in the rolandic operculum and several motor-related areas (pre- and postcentral, the cerebellum). In addition, across-language predictions during reading were identified in regions typically associated with semantic processing (left inferior frontal, middle temporal cortex, right cerebellum and precuneus) and visual processing (inferior and middle occipital regions and calcarine sulcus). Furthermore, across modalities and languages, the left lingual gyrus showed semantic overlap across production and word reading. These findings support the idea of at least partially language- and modality-independent semantic neural representations. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Shallow Transits—Deep Learning. I. Feasibility Study of Deep Learning to Detect Periodic Transits of Exoplanets

NASA Astrophysics Data System (ADS)

Zucker, Shay; Giryes, Raja

2018-04-01

Transits of habitable planets around solar-like stars are expected to be shallow, and to have long periods, which means low information content. The current bottleneck in the detection of such transits is caused in large part by the presence of red (correlated) noise in the light curves obtained from the dedicated space telescopes. Based on the groundbreaking results deep learning achieves in many signal and image processing applications, we propose to use deep neural networks to solve this problem. We present a feasibility study, in which we applied a convolutional neural network on a simulated training set. The training set comprised light curves received from a hypothetical high-cadence space-based telescope. We simulated the red noise by using Gaussian Processes with a wide variety of hyper-parameters. We then tested the network on a completely different test set simulated in the same way. Our study proves that very difficult cases can indeed be detected. Furthermore, we show how detection trends can be studied and detection biases quantified. We have also checked the robustness of the neural-network performance against practical artifacts such as outliers and discontinuities, which are known to affect space-based high-cadence light curves. Future work will allow us to use the neural networks to characterize the transit model and identify individual transits. This new approach will certainly be an indispensable tool for the detection of habitable planets in the future planet-detection space missions such as PLATO.

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

PubMed

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

2017-11-01

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

The impact of oxytocin administration on brain activity: a systematic review and meta-analysis protocol.

PubMed

Quintana, Daniel S; Outhred, Tim; Westlye, Lars T; Malhi, Gin S; Andreassen, Ole A

2016-11-29

Converging evidence demonstrates the important role of the neuropeptide hormone oxytocin (OT) in human behaviour and cognition. Intranasal OT administration has been shown to improve several aspects of social communication, such as the theory of mind performance and gaze to the eye region, and reduce anxiety and related negative cognitive appraisals. While this early research has demonstrated the potential for intranasal OT to treat psychiatric illnesses characterized by social impairments, the neurobiological mechanisms are not well known. Researchers have used functional magnetic resonance imaging (fMRI) to examine the neural correlates of OT response; however, results have been variable and moderating factors are poorly understood. The aim of this meta-analysis is to synthesize data examining the impact of intranasal OT administration on neural activity. Studies that report fMRI data after intranasal OT administration will be identified. PubMed, Embase, PsycINFO, and Google Scholar databases will be searched as well as the citation lists of retrieved articles. Eligible articles written in English from 2005 onwards will be included in the meta-analysis, and corresponding authors of these papers will be invited to contribute t-maps. Data will be collected from eligible studies for synthesis using Seed-based d Mapping (SDM) or Multi-Level Kernel Density Analysis (MKDA), depending on the number of usable t-maps received. Additionally, publication bias and risk of bias will be assessed. This systematic review and meta-analysis will be the first pre-registered synthesis of data to identify the neural correlates of OT nasal spray response. The identification of brain regions underlying OT's observed effects will help guide future research and better identify treatment targets. PROSPERO CRD42016038781.

Automated selection of computed tomography display parameters using neural networks

NASA Astrophysics Data System (ADS)

Zhang, Di; Neu, Scott; Valentino, Daniel J.

2001-07-01

A collection of artificial neural networks (ANN's) was trained to identify simple anatomical structures in a set of x-ray computed tomography (CT) images. These neural networks learned to associate a point in an image with the anatomical structure containing the point by using the image pixels located on the horizontal and vertical lines that ran through the point. The neural networks were integrated into a computer software tool whose function is to select an index into a list of CT window/level values from the location of the user's mouse cursor. Based upon the anatomical structure selected by the user, the software tool automatically adjusts the image display to optimally view the structure.

Cascade process modeling with mechanism-based hierarchical neural networks.

PubMed

Cong, Qiumei; Yu, Wen; Chai, Tianyou

2010-02-01

Cascade process, such as wastewater treatment plant, includes many nonlinear sub-systems and many variables. When the number of sub-systems is big, the input-output relation in the first block and the last block cannot represent the whole process. In this paper we use two techniques to overcome the above problem. Firstly we propose a new neural model: hierarchical neural networks to identify the cascade process; then we use serial structural mechanism model based on the physical equations to connect with neural model. A stable learning algorithm and theoretical analysis are given. Finally, this method is used to model a wastewater treatment plant. Real operational data of wastewater treatment plant is applied to illustrate the modeling approach.

Identification and characterization of secondary neural tube-derived embryonic neural stem cells in vitro.

PubMed

Shaker, Mohammed R; Kim, Joo Yeon; Kim, Hyun; Sun, Woong

2015-05-15

Secondary neurulation is an embryonic progress that gives rise to the secondary neural tube, the precursor of the lower spinal cord region. The secondary neural tube is derived from aggregated Sox2-expressing neural cells at the dorsal region of the tail bud, which eventually forms rosette or tube-like structures to give rise to neural tissues in the tail bud. We addressed whether the embryonic tail contains neural stem cells (NSCs), namely secondary NSCs (sNSCs), with the potential for self-renewal in vitro. Using in vitro neurosphere assays, neurospheres readily formed at the rosette and neural-tube levels, but less frequently at the tail bud tip level. Furthermore, we identified that sNSC-generated neurospheres were significantly smaller in size compared with cortical neurospheres. Interestingly, various cell cycle analyses revealed that this difference was not due to a reduction in the proliferation rate of NSCs, but rather the neuronal commitment of sNSCs, as sNSC-derived neurospheres contain more committed neuronal progenitor cells, even in the presence of epidermal growth factor (EGF) and basic fibroblast growth factor (bFGF). These results suggest that the higher tendency for sNSCs to spontaneously differentiate into progenitor cells may explain the limited expansion of the secondary neural tube during embryonic development.

A Tensor-Product-Kernel Framework for Multiscale Neural Activity Decoding and Control

PubMed Central

Li, Lin; Brockmeier, Austin J.; Choi, John S.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2014-01-01

Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain's motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field potentials (LFPs) brings potential opportunities to enhance computational modeling by enriching the characterization of the neural system state. However, heterogeneity on data type (spike timing versus continuous amplitude signals) and spatiotemporal scale complicates the model integration of multiscale neural activity. In this paper, we propose a tensor-product-kernel-based framework to integrate the multiscale activity and exploit the complementary information available in multiscale neural activity. This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control. For neural decoding, the framework is able to identify the nonlinear functional relationship between the multiscale neural responses and the stimuli using general purpose kernel adaptive filtering. In a sensory stimulation experiment, the tensor-product-kernel decoder outperforms decoders that use only a single neural data type. In addition, an adaptive inverse controller for delivering electrical microstimulation patterns that utilizes the tensor-product kernel achieves promising results in emulating the responses to natural stimulation. PMID:24829569

Decreased Connectivity and Cerebellar Activity in Autism during Motor Task Performance

ERIC Educational Resources Information Center

Mostofsky, Stewart H.; Powell, Stephanie K.; Simmonds, Daniel J.; Goldberg, Melissa C.; Caffo, Brian; Pekar, James J.

2009-01-01

Although motor deficits are common in autism, the neural correlates underlying the disruption of even basic motor execution are unknown. Motor deficits may be some of the earliest identifiable signs of abnormal development and increased understanding of their neural underpinnings may provide insight into autism-associated differences in parallel…

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

Jason L. Wright; Milos Manic

Finding and identifying cryptography is a growing concern in the malware analysis community. In this paper, artificial neural networks are used to classify functional blocks from a disassembled program as being either cryptography related or not. The resulting system, referred to as NNLC (Neural Net for Locating Cryptography) is presented and results of applying this system to various libraries are described.

Reading for Meaning in Dyslexic and Young Children: Distinct Neural Pathways but Common Endpoints

ERIC Educational Resources Information Center

Schulz, Enrico; Maurer, Urs; van der Mark, Sanne; Bucher, Kerstin; Brem, Silvia; Martin, Ernst; Brandeis, Daniel

2009-01-01

Developmental dyslexia is a highly prevalent and specific disorder of reading acquisition characterised by impaired reading fluency and comprehension. We have previously identified fMRI- and ERP-based neural markers of impaired sentence reading in dyslexia that indicated both deviant basic word processing and deviant semantic incongruency…

Neural Correlates of Appetitive-Aversive Interactions in Pavlovian Fear Conditioning

ERIC Educational Resources Information Center

Nasser, Helen M.; McNally, Gavan P.

2013-01-01

We used Pavlovian counterconditioning in rats to identify the neural mechanisms for appetitive-aversive motivational interactions. In Stage I, rats were trained on conditioned stimulus (CS)-food (unconditioned stimulus [US]) pairings. In Stage II, this appetitive CS was transformed into a fear CS via pairings with footshock. The development of…

Deep learning in the small sample size setting: cascaded feed forward neural networks for medical image segmentation

NASA Astrophysics Data System (ADS)

Gaonkar, Bilwaj; Hovda, David; Martin, Neil; Macyszyn, Luke

2016-03-01

Deep Learning, refers to large set of neural network based algorithms, have emerged as promising machine- learning tools in the general imaging and computer vision domains. Convolutional neural networks (CNNs), a specific class of deep learning algorithms, have been extremely effective in object recognition and localization in natural images. A characteristic feature of CNNs, is the use of a locally connected multi layer topology that is inspired by the animal visual cortex (the most powerful vision system in existence). While CNNs, perform admirably in object identification and localization tasks, typically require training on extremely large datasets. Unfortunately, in medical image analysis, large datasets are either unavailable or are extremely expensive to obtain. Further, the primary tasks in medical imaging are organ identification and segmentation from 3D scans, which are different from the standard computer vision tasks of object recognition. Thus, in order to translate the advantages of deep learning to medical image analysis, there is a need to develop deep network topologies and training methodologies, that are geared towards medical imaging related tasks and can work in a setting where dataset sizes are relatively small. In this paper, we present a technique for stacked supervised training of deep feed forward neural networks for segmenting organs from medical scans. Each `neural network layer' in the stack is trained to identify a sub region of the original image, that contains the organ of interest. By layering several such stacks together a very deep neural network is constructed. Such a network can be used to identify extremely small regions of interest in extremely large images, inspite of a lack of clear contrast in the signal or easily identifiable shape characteristics. What is even more intriguing is that the network stack achieves accurate segmentation even when it is trained on a single image with manually labelled ground truth. We validate this approach,using a publicly available head and neck CT dataset. We also show that a deep neural network of similar depth, if trained directly using backpropagation, cannot acheive the tasks achieved using our layer wise training paradigm.

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

PubMed

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

2011-01-01

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

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

PubMed Central

Goto, Takahiro; Aoyagi, Toshio

2018-01-01

Synchronization of neural oscillations as a mechanism of brain function is attracting increasing attention. Neural oscillation is a rhythmic neural activity that can be easily observed by noninvasive electroencephalography (EEG). Neural oscillations show the same frequency and cross-frequency synchronization for various cognitive and perceptual functions. However, it is unclear how this neural synchronization is achieved by a dynamical system. If neural oscillations are weakly coupled oscillators, the dynamics of neural synchronization can be described theoretically using a phase oscillator model. We propose an estimation method to identify the phase oscillator model from real data of cross-frequency synchronized activities. The proposed method can estimate the coupling function governing the properties of synchronization. Furthermore, we examine the reliability of the proposed method using time-series data obtained from numerical simulation and an electronic circuit experiment, and show that our method can estimate the coupling function correctly. Finally, we estimate the coupling function between EEG oscillation and the speech sound envelope, and discuss the validity of these results. PMID:29337999

Msx1-Positive Progenitors in the Retinal Ciliary Margin Give Rise to Both Neural and Non-neural Progenies in Mammals.

PubMed

Bélanger, Marie-Claude; Robert, Benoit; Cayouette, Michel

2017-01-23

In lower vertebrates, stem/progenitor cells located in a peripheral domain of the retina, called the ciliary margin zone (CMZ), cooperate with retinal domain progenitors to build the mature neural retina. In mammals, it is believed that the CMZ lacks neurogenic potential and that the retina develops from one pool of multipotent retinal progenitor cells (RPCs). Here we identify a population of Msx1-expressing progenitors in the mouse CMZ that is both molecularly and functionally distinct from RPCs. Using genetic lineage tracing, we report that Msx1 progenitors have unique developmental properties compared with RPCs. Msx1 lineages contain both neural retina and non-neural ciliary epithelial progenies and overall generate fewer photoreceptors than classical RPC lineages. Furthermore, we show that the endocytic adaptor protein Numb regulates the balance between neural and non-neural fates in Msx1 progenitors. These results uncover a population of CMZ progenitors, distinct from classical RPCs, that also contributes to mammalian retinogenesis. Copyright © 2017 Elsevier Inc. All rights reserved.

Stable and Dynamic Coding for Working Memory in Primate Prefrontal Cortex

PubMed Central

Watanabe, Kei; Funahashi, Shintaro; Stokes, Mark G.

2017-01-01

Working memory (WM) provides the stability necessary for high-level cognition. Influential theories typically assume that WM depends on the persistence of stable neural representations, yet increasing evidence suggests that neural states are highly dynamic. Here we apply multivariate pattern analysis to explore the population dynamics in primate lateral prefrontal cortex (PFC) during three variants of the classic memory-guided saccade task (recorded in four animals). We observed the hallmark of dynamic population coding across key phases of a working memory task: sensory processing, memory encoding, and response execution. Throughout both these dynamic epochs and the memory delay period, however, the neural representational geometry remained stable. We identified two characteristics that jointly explain these dynamics: (1) time-varying changes in the subpopulation of neurons coding for task variables (i.e., dynamic subpopulations); and (2) time-varying selectivity within neurons (i.e., dynamic selectivity). These results indicate that even in a very simple memory-guided saccade task, PFC neurons display complex dynamics to support stable representations for WM. SIGNIFICANCE STATEMENT Flexible, intelligent behavior requires the maintenance and manipulation of incoming information over various time spans. For short time spans, this faculty is labeled “working memory” (WM). Dominant models propose that WM is maintained by stable, persistent patterns of neural activity in prefrontal cortex (PFC). However, recent evidence suggests that neural activity in PFC is dynamic, even while the contents of WM remain stably represented. Here, we explored the neural dynamics in PFC during a memory-guided saccade task. We found evidence for dynamic population coding in various task epochs, despite striking stability in the neural representational geometry of WM. Furthermore, we identified two distinct cellular mechanisms that contribute to dynamic population coding. PMID:28559375

You Can't Think and Hit at the Same Time: Neural Correlates of Baseball Pitch Classification.

PubMed

Sherwin, Jason; Muraskin, Jordan; Sajda, Paul

2012-01-01

Hitting a baseball is often described as the most difficult thing to do in sports. A key aptitude of a good hitter is the ability to determine which pitch is coming. This rapid decision requires the batter to make a judgment in a fraction of a second based largely on the trajectory and spin of the ball. When does this decision occur relative to the ball's trajectory and is it possible to identify neural correlates that represent how the decision evolves over a split second? Using single-trial analysis of electroencephalography (EEG) we address this question within the context of subjects discriminating three types of pitches (fastball, curveball, slider) based on pitch trajectories. We find clear neural signatures of pitch classification and, using signal detection theory, we identify the times of discrimination on a trial-to-trial basis. Based on these neural signatures we estimate neural discrimination distributions as a function of the distance the ball is from the plate. We find all three pitches yield unique distributions, namely the timing of the discriminating neural signatures relative to the position of the ball in its trajectory. For instance, fastballs are discriminated at the earliest points in their trajectory, relative to the two other pitches, which is consistent with the need for some constant time to generate and execute the motor plan for the swing (or inhibition of the swing). We also find incorrect discrimination of a pitch (errors) yields neural sources in Brodmann Area 10, which has been implicated in prospective memory, recall, and task difficulty. In summary, we show that single-trial analysis of EEG yields informative distributions of the relative point in a baseball's trajectory when the batter makes a decision on which pitch is coming.

Transgenic analysis of a SoxB gene reveals neural progenitor cells in the cnidarian Nematostella vectensis.

PubMed

Richards, Gemma Sian; Rentzsch, Fabian

2014-12-01

Bilaterian neurogenesis is characterized by the generation of diverse neural cell types from dedicated neural stem/progenitor cells (NPCs). However, the evolutionary origin of NPCs is unclear, as neurogenesis in representatives of the bilaterian sister group, the Cnidaria, occurs via interstitial stem cells that also possess broader, non-neural, developmental potential. We address this question by analysing neurogenesis in an anthozoan cnidarian, Nematostella vectensis. Using a transgenic reporter line, we show that NvSoxB(2) - an orthologue of bilaterian SoxB genes that have conserved roles in neurogenesis - is expressed in a cell population that gives rise to sensory neurons, ganglion neurons and nematocytes: the three primary neural cell types of cnidarians. EdU labelling together with in situ hybridization, and within the NvSoxB(2)::mOrange transgenic line, demonstrates that cells express NvSoxB(2) before mitosis and identifies asymmetric behaviours of sibling cells within NvSoxB(2)(+) lineages. Morpholino-mediated gene knockdown of NvSoxB(2) blocks the formation of all three neural cell types, thereby identifying NvSoxB(2) as an essential positive regulator of nervous system development. Our results demonstrate that diverse neural cell types derive from an NvSoxB(2)-expressing population of mitotic cells in Nematostella and that SoxB genes are ancient components of a neurogenic program. To our knowledge this is the first description of a lineage-restricted, multipotent cell population outside the Bilateria and we propose that neurogenesis via dedicated, SoxB-expressing NPCs predates the split between cnidarians and bilaterians. © 2014. Published by The Company of Biologists Ltd.

Particle identification with neural networks using a rotational invariant moment representation

NASA Astrophysics Data System (ADS)

Sinkus, Ralph; Voss, Thomas

1997-02-01

A feed-forward neural network is used to identify electromagnetic particles based upon their showering properties within a segmented calorimeter. A preprocessing procedure is applied to the spatial energy distribution of the particle shower in order to account for the varying geometry of the calorimeter. The novel feature is the expansion of the energy distribution in terms of moments of the so-called Zernike functions which are invariant under rotation. The distributions of moments exhibit very different scales, thus the multidimensional input distribution for the neural network is transformed via a principal component analysis and rescaled by its respective variances to ensure input values of the order of one. This increases the sensitivity of the network and thus results in better performance in identifying and separating electromagnetic from hadronic particles, especially at low energies.

Gamma Spectroscopy by Artificial Neural Network Coupled with MCNP

NASA Astrophysics Data System (ADS)

Sahiner, Huseyin

While neutron activation analysis is widely used in many areas, sensitivity of the analysis depends on how the analysis is conducted. Even though the sensitivity of the techniques carries error, compared to chemical analysis, its range is in parts per million or sometimes billion. Due to this sensitivity, the use of neutron activation analysis becomes important when analyzing bio-samples. Artificial neural network is an attractive technique for complex systems. Although there are neural network applications on spectral analysis, training by simulated data to analyze experimental data has not been made. This study offers an improvement on spectral analysis and optimization on neural network for the purpose. The work considers five elements that are considered as trace elements for bio-samples. However, the system is not limited to five elements. The only limitation of the study comes from data library availability on MCNP. A perceptron network was employed to identify five elements from gamma spectra. In quantitative analysis, better results were obtained when the neural fitting tool in MATLAB was used. As a training function, Levenberg-Marquardt algorithm was used with 23 neurons in the hidden layer with 259 gamma spectra in the input. Because the interest of the study deals with five elements, five neurons representing peak counts of five isotopes in the input layer were used. Five output neurons revealed mass information of these elements from irradiated kidney stones. Results showing max error of 17.9% in APA, 24.9% in UA, 28.2% in COM, 27.9% in STRU type showed the success of neural network approach in analyzing gamma spectra. This high error was attributed to Zn that has a very long decay half-life compared to the other elements. The simulation and experiments were made under certain experimental setup (3 hours irradiation, 96 hours decay time, 8 hours counting time). Nevertheless, the approach is subject to be generalized for different setups.

Computing with Neural Synchrony

PubMed Central

Brette, Romain

2012-01-01

Neurons communicate primarily with spikes, but most theories of neural computation are based on firing rates. Yet, many experimental observations suggest that the temporal coordination of spikes plays a role in sensory processing. Among potential spike-based codes, synchrony appears as a good candidate because neural firing and plasticity are sensitive to fine input correlations. However, it is unclear what role synchrony may play in neural computation, and what functional advantage it may provide. With a theoretical approach, I show that the computational interest of neural synchrony appears when neurons have heterogeneous properties. In this context, the relationship between stimuli and neural synchrony is captured by the concept of synchrony receptive field, the set of stimuli which induce synchronous responses in a group of neurons. In a heterogeneous neural population, it appears that synchrony patterns represent structure or sensory invariants in stimuli, which can then be detected by postsynaptic neurons. The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity. Using examples in different sensory modalities, I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli, for example to identify a fluctuating odor in the presence of distractors. This theory of synchrony-based computation shows that relative spike timing may indeed have computational relevance, and suggests new types of neural network models for sensory processing with appealing computational properties. PMID:22719243

4-dimensional functional profiling in the convulsant-treated larval zebrafish brain.

PubMed

Winter, Matthew J; Windell, Dylan; Metz, Jeremy; Matthews, Peter; Pinion, Joe; Brown, Jonathan T; Hetheridge, Malcolm J; Ball, Jonathan S; Owen, Stewart F; Redfern, Will S; Moger, Julian; Randall, Andrew D; Tyler, Charles R

2017-07-26

Functional neuroimaging, using genetically-encoded Ca 2+ sensors in larval zebrafish, offers a powerful combination of high spatiotemporal resolution and higher vertebrate relevance for quantitative neuropharmacological profiling. Here we use zebrafish larvae with pan-neuronal expression of GCaMP6s, combined with light sheet microscopy and a novel image processing pipeline, for the 4D profiling of chemoconvulsant action in multiple brain regions. In untreated larvae, regions associated with autonomic functionality, sensory processing and stress-responsiveness, consistently exhibited elevated spontaneous activity. The application of drugs targeting different convulsant mechanisms (4-Aminopyridine, Pentylenetetrazole, Pilocarpine and Strychnine) resulted in distinct spatiotemporal patterns of activity. These activity patterns showed some interesting parallels with what is known of the distribution of their respective molecular targets, but crucially also revealed system-wide neural circuit responses to stimulation or suppression. Drug concentration-response curves of neural activity were identified in a number of anatomically-defined zebrafish brain regions, and in vivo larval electrophysiology, also conducted in 4dpf larvae, provided additional measures of neural activity. Our quantification of network-wide chemoconvulsant drug activity in the whole zebrafish brain illustrates the power of this approach for neuropharmacological profiling in applications ranging from accelerating studies of drug safety and efficacy, to identifying pharmacologically-altered networks in zebrafish models of human neurological disorders.

Study on Practical Application of Turboprop Engine Condition Monitoring and Fault Diagnostic System Using Fuzzy-Neuro Algorithms

NASA Astrophysics Data System (ADS)

Kong, Changduk; Lim, Semyeong; Kim, Keunwoo

2013-03-01

The Neural Networks is mostly used to engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measuring performance data, and proposes a fault diagnostic system using the base performance model and artificial intelligent methods such as Fuzzy and Neural Networks. Each real engine performance model, which is named as the base performance model that can simulate a new engine performance, is inversely made using its performance test data. Therefore the condition monitoring of each engine can be more precisely carried out through comparison with measuring performance data. The proposed diagnostic system identifies firstly the faulted components using Fuzzy Logic, and then quantifies faults of the identified components using Neural Networks leaned by fault learning data base obtained from the developed base performance model. In leaning the measuring performance data of the faulted components, the FFBP (Feed Forward Back Propagation) is used. In order to user's friendly purpose, the proposed diagnostic program is coded by the GUI type using MATLAB.

Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN.

PubMed

Bascil, M Serdar; Tesneli, Ahmet Y; Temurtas, Feyzullah

2016-09-01

Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine control commands. The main goal of BCI is to make available assistive environmental devices for paralyzed people such as computers and makes their life easier. This study deals with feature extraction and mental task pattern recognition on 2-D cursor control from EEG as offline analysis approach. The hemispherical power density changes are computed and compared on alpha-beta frequency bands with only mental imagination of cursor movements. First of all, power spectral density (PSD) features of EEG signals are extracted and high dimensional data reduced by principle component analysis (PCA) and independent component analysis (ICA) which are statistical algorithms. In the last stage, all features are classified with two types of support vector machine (SVM) which are linear and least squares (LS-SVM) and three different artificial neural network (ANN) structures which are learning vector quantization (LVQ), multilayer neural network (MLNN) and probabilistic neural network (PNN) and mental task patterns are successfully identified via k-fold cross validation technique.

A large-scale perspective on stress-induced alterations in resting-state networks

NASA Astrophysics Data System (ADS)

Maron-Katz, Adi; Vaisvaser, Sharon; Lin, Tamar; Hendler, Talma; Shamir, Ron

2016-02-01

Stress is known to induce large-scale neural modulations. However, its neural effect once the stressor is removed and how it relates to subjective experience are not fully understood. Here we used a statistically sound data-driven approach to investigate alterations in large-scale resting-state functional connectivity (rsFC) induced by acute social stress. We compared rsfMRI profiles of 57 healthy male subjects before and after stress induction. Using a parcellation-based univariate statistical analysis, we identified a large-scale rsFC change, involving 490 parcel-pairs. Aiming to characterize this change, we employed statistical enrichment analysis, identifying anatomic structures that were significantly interconnected by these pairs. This analysis revealed strengthening of thalamo-cortical connectivity and weakening of cross-hemispheral parieto-temporal connectivity. These alterations were further found to be associated with change in subjective stress reports. Integrating report-based information on stress sustainment 20 minutes post induction, revealed a single significant rsFC change between the right amygdala and the precuneus, which inversely correlated with the level of subjective recovery. Our study demonstrates the value of enrichment analysis for exploring large-scale network reorganization patterns, and provides new insight on stress-induced neural modulations and their relation to subjective experience.

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

PubMed

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.

Selective attention to temporal features on nested time scales.

PubMed

Henry, Molly J; Herrmann, Björn; Obleser, Jonas

2015-02-01

Meaningful auditory stimuli such as speech and music often vary simultaneously along multiple time scales. Thus, listeners must selectively attend to, and selectively ignore, separate but intertwined temporal features. The current study aimed to identify and characterize the neural network specifically involved in this feature-selective attention to time. We used a novel paradigm where listeners judged either the duration or modulation rate of auditory stimuli, and in which the stimulation, working memory demands, response requirements, and task difficulty were held constant. A first analysis identified all brain regions where individual brain activation patterns were correlated with individual behavioral performance patterns, which thus supported temporal judgments generically. A second analysis then isolated those brain regions that specifically regulated selective attention to temporal features: Neural responses in a bilateral fronto-parietal network including insular cortex and basal ganglia decreased with degree of change of the attended temporal feature. Critically, response patterns in these regions were inverted when the task required selectively ignoring this feature. The results demonstrate how the neural analysis of complex acoustic stimuli with multiple temporal features depends on a fronto-parietal network that simultaneously regulates the selective gain for attended and ignored temporal features. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Differential display cloning of a novel rat cDNA (RNB6) that shows high expression in the neonatal brain revealed a member of Ena/VASP family.

PubMed

Ohta, S; Mineta, T; Kimoto, M; Tabuchi, K

1997-08-18

We have used the differential display method to identify genes that control the neural cell development in CNS. Screening of the differential display bands that showed higher expression at neonate than at adult age enabled us to identify a novel rat cDNA (RNB6) coding for a protein of 393 amino acid residues. Database search revealed this gene as a rat homologue of the murine EVL, a member of Ena/VASP protein family that is implicated to be involved in the control of cell motility through actin filament assembly by their GP5 motifs. Although the precise characterization of EVL was not reported, our Northern blot and immunoblot analyses demonstrated that RNB6 expression in the brain gradually increases during embryonic development, reaches maximum at postnatal day 1 and decreases thereafter. Studies of tissue distribution revealed the expression of RNB6 not only in the brain but also in the spleen, thymus and testis. Histochemical analyses showed that RNB6 protein is mainly expressed in neurons and may be expressed in neural fibers. Our analyses suggest that RNB6 is critically involved in the development of CNS probably through the control of neural cell motility and/or including neuronal fiber extension.

Distinct Neural Activities in Premotor Cortex during Natural Vocal Behaviors in a New World Primate, the Common Marmoset (Callithrix jacchus).

PubMed

Roy, Sabyasachi; Zhao, Lingyun; Wang, Xiaoqin

2016-11-30

Although evidence from human studies has long indicated the crucial role of the frontal cortex in speech production, it has remained uncertain whether the frontal cortex in nonhuman primates plays a similar role in vocal communication. Previous studies of prefrontal and premotor cortices of macaque monkeys have found neural signals associated with cue- and reward-conditioned vocal production, but not with self-initiated or spontaneous vocalizations (Coudé et al., 2011; Hage and Nieder, 2013), which casts doubt on the role of the frontal cortex of the Old World monkeys in vocal communication. A recent study of marmoset frontal cortex observed modulated neural activities associated with self-initiated vocal production (Miller et al., 2015), but it did not delineate whether these neural activities were specifically attributed to vocal production or if they may result from other nonvocal motor activity such as orofacial motor movement. In the present study, we attempted to resolve these issues and examined single neuron activities in premotor cortex during natural vocal exchanges in the common marmoset (Callithrix jacchus), a highly vocal New World primate. Neural activation and suppression were observed both before and during self-initiated vocal production. Furthermore, by comparing neural activities between self-initiated vocal production and nonvocal orofacial motor movement, we identified a subpopulation of neurons in marmoset premotor cortex that was activated or suppressed by vocal production, but not by orofacial movement. These findings provide clear evidence of the premotor cortex's involvement in self-initiated vocal production in natural vocal behaviors of a New World primate. Human frontal cortex plays a crucial role in speech production. However, it has remained unclear whether the frontal cortex of nonhuman primates is involved in the production of self-initiated vocalizations during natural vocal communication. Using a wireless multichannel neural recording technique, we observed in the premotor cortex neural activation and suppression both before and during self-initiated vocalizations when marmosets, a highly vocal New World primate species, engaged in vocal exchanges with conspecifics. A novel finding of the present study is the discovery of a subpopulation of premotor cortex neurons that was activated by vocal production, but not by orofacial movement. These observations provide clear evidence of the premotor cortex's involvement in vocal production in a New World primate species. Copyright © 2016 the authors 0270-6474/16/3612168-12$15.00/0.

The Hierarchical Brain Network for Face Recognition

PubMed Central

Zhen, Zonglei; Fang, Huizhen; Liu, Jia

2013-01-01

Numerous functional magnetic resonance imaging (fMRI) studies have identified multiple cortical regions that are involved in face processing in the human brain. However, few studies have characterized the face-processing network as a functioning whole. In this study, we used fMRI to identify face-selective regions in the entire brain and then explore the hierarchical structure of the face-processing network by analyzing functional connectivity among these regions. We identified twenty-five regions mainly in the occipital, temporal and frontal cortex that showed a reliable response selective to faces (versus objects) across participants and across scan sessions. Furthermore, these regions were clustered into three relatively independent sub-networks in a face-recognition task on the basis of the strength of functional connectivity among them. The functionality of the sub-networks likely corresponds to the recognition of individual identity, retrieval of semantic knowledge and representation of emotional information. Interestingly, when the task was switched to object recognition from face recognition, the functional connectivity between the inferior occipital gyrus and the rest of the face-selective regions were significantly reduced, suggesting that this region may serve as an entry node in the face-processing network. In sum, our study provides empirical evidence for cognitive and neural models of face recognition and helps elucidate the neural mechanisms underlying face recognition at the network level. PMID:23527282

Identification and Evolutionary Analysis of Potential Candidate Genes in a Human Eating Disorder.

PubMed

Sabbagh, Ubadah; Mullegama, Saman; Wyckoff, Gerald J

2016-01-01

The purpose of this study was to find genes linked with eating disorders and associated with both metabolic and neural systems. Our operating hypothesis was that there are genetic factors underlying some eating disorders resting in both those pathways. Specifically, we are interested in disorders that may rest in both sleep and metabolic function, generally called Night Eating Syndrome (NES). A meta-analysis of the Gene Expression Omnibus targeting the mammalian nervous system, sleep, and obesity studies was performed, yielding numerous genes of interest. Through a text-based analysis of the results, a number of potential candidate genes were identified. VGF, in particular, appeared to be relevant both to obesity and, broadly, to brain or neural development. VGF is a highly connected protein that interacts with numerous targets via proteolytically digested peptides. We examined VGF from an evolutionary perspective to determine whether other available evidence supported a role for the gene in human disease. We conclude that some of the already identified variants in VGF from human polymorphism studies may contribute to eating disorders and obesity. Our data suggest that there is enough evidence to warrant eGWAS and GWAS analysis of these genes in NES patients in a case-control study.

Human V4 Activity Patterns Predict Behavioral Performance in Imagery of Object Color.

PubMed

Bannert, Michael M; Bartels, Andreas

2018-04-11

Color is special among basic visual features in that it can form a defining part of objects that are engrained in our memory. Whereas most neuroimaging research on human color vision has focused on responses related to external stimulation, the present study investigated how sensory-driven color vision is linked to subjective color perception induced by object imagery. We recorded fMRI activity in male and female volunteers during viewing of abstract color stimuli that were red, green, or yellow in half of the runs. In the other half we asked them to produce mental images of colored, meaningful objects (such as tomato, grapes, banana) corresponding to the same three color categories. Although physically presented color could be decoded from all retinotopically mapped visual areas, only hV4 allowed predicting colors of imagined objects when classifiers were trained on responses to physical colors. Importantly, only neural signal in hV4 was predictive of behavioral performance in the color judgment task on a trial-by-trial basis. The commonality between neural representations of sensory-driven and imagined object color and the behavioral link to neural representations in hV4 identifies area hV4 as a perceptual hub linking externally triggered color vision with color in self-generated object imagery. SIGNIFICANCE STATEMENT Humans experience color not only when visually exploring the outside world, but also in the absence of visual input, for example when remembering, dreaming, and during imagery. It is not known where neural codes for sensory-driven and internally generated hue converge. In the current study we evoked matching subjective color percepts, one driven by physically presented color stimuli, the other by internally generated color imagery. This allowed us to identify area hV4 as the only site where neural codes of corresponding subjective color perception converged regardless of its origin. Color codes in hV4 also predicted behavioral performance in an imagery task, suggesting it forms a perceptual hub for color perception. Copyright © 2018 the authors 0270-6474/18/383657-12$15.00/0.

The brain as a dynamic physical system.

PubMed

McKenna, T M; McMullen, T A; Shlesinger, M F

1994-06-01

The brain is a dynamic system that is non-linear at multiple levels of analysis. Characterization of its non-linear dynamics is fundamental to our understanding of brain function. Identifying families of attractors in phase space analysis, an approach which has proven valuable in describing non-linear mechanical and electrical systems, can prove valuable in describing a range of behaviors and associated neural activity including sensory and motor repertoires. Additionally, transitions between attractors may serve as useful descriptors for analysing state changes in neurons and neural ensembles. Recent observations of synchronous neural activity, and the emerging capability to record the spatiotemporal dynamics of neural activity by voltage-sensitive dyes and electrode arrays, provide opportunities for observing the population dynamics of neural ensembles within a dynamic systems context. New developments in the experimental physics of complex systems, such as the control of chaotic systems, selection of attractors, attractor switching and transient states, can be a source of powerful new analytical tools and insights into the dynamics of neural systems.

A neural net based architecture for the segmentation of mixed gray-level and binary pictures

NASA Technical Reports Server (NTRS)

Tabatabai, Ali; Troudet, Terry P.

1991-01-01

A neural-net-based architecture is proposed to perform segmentation in real time for mixed gray-level and binary pictures. In this approach, the composite picture is divided into 16 x 16 pixel blocks, which are identified as character blocks or image blocks on the basis of a dichotomy measure computed by an adaptive 16 x 16 neural net. For compression purposes, each image block is further divided into 4 x 4 subblocks; a one-bit nonparametric quantizer is used to encode 16 x 16 character and 4 x 4 image blocks; and the binary map and quantizer levels are obtained through a neural net segmentor over each block. The efficiency of the neural segmentation in terms of computational speed, data compression, and quality of the compressed picture is demonstrated. The effect of weight quantization is also discussed. VLSI implementations of such adaptive neural nets in CMOS technology are described and simulated in real time for a maximum block size of 256 pixels.

Encoding sensory and motor patterns as time-invariant trajectories in recurrent neural networks

PubMed Central

2018-01-01

Much of the information the brain processes and stores is temporal in nature—a spoken word or a handwritten signature, for example, is defined by how it unfolds in time. However, it remains unclear how neural circuits encode complex time-varying patterns. We show that by tuning the weights of a recurrent neural network (RNN), it can recognize and then transcribe spoken digits. The model elucidates how neural dynamics in cortical networks may resolve three fundamental challenges: first, encode multiple time-varying sensory and motor patterns as stable neural trajectories; second, generalize across relevant spatial features; third, identify the same stimuli played at different speeds—we show that this temporal invariance emerges because the recurrent dynamics generate neural trajectories with appropriately modulated angular velocities. Together our results generate testable predictions as to how recurrent networks may use different mechanisms to generalize across the relevant spatial and temporal features of complex time-varying stimuli. PMID:29537963

Encoding sensory and motor patterns as time-invariant trajectories in recurrent neural networks.

PubMed

Goudar, Vishwa; Buonomano, Dean V

2018-03-14

Much of the information the brain processes and stores is temporal in nature-a spoken word or a handwritten signature, for example, is defined by how it unfolds in time. However, it remains unclear how neural circuits encode complex time-varying patterns. We show that by tuning the weights of a recurrent neural network (RNN), it can recognize and then transcribe spoken digits. The model elucidates how neural dynamics in cortical networks may resolve three fundamental challenges: first, encode multiple time-varying sensory and motor patterns as stable neural trajectories; second, generalize across relevant spatial features; third, identify the same stimuli played at different speeds-we show that this temporal invariance emerges because the recurrent dynamics generate neural trajectories with appropriately modulated angular velocities. Together our results generate testable predictions as to how recurrent networks may use different mechanisms to generalize across the relevant spatial and temporal features of complex time-varying stimuli. © 2018, Goudar et al.

Electronic bypass of spinal lesions: activation of lower motor neurons directly driven by cortical neural signals.

PubMed

Li, Yan; Alam, Monzurul; Guo, Shanshan; Ting, K H; He, Jufang

2014-07-03

Lower motor neurons in the spinal cord lose supraspinal inputs after complete spinal cord injury, leading to a loss of volitional control below the injury site. Extensive locomotor training with spinal cord stimulation can restore locomotion function after spinal cord injury in humans and animals. However, this locomotion is non-voluntary, meaning that subjects cannot control stimulation via their natural "intent". A recent study demonstrated an advanced system that triggers a stimulator using forelimb stepping electromyographic patterns to restore quadrupedal walking in rats with spinal cord transection. However, this indirect source of "intent" may mean that other non-stepping forelimb activities may false-trigger the spinal stimulator and thus produce unwanted hindlimb movements. We hypothesized that there are distinguishable neural activities in the primary motor cortex during treadmill walking, even after low-thoracic spinal transection in adult guinea pigs. We developed an electronic spinal bridge, called "Motolink", which detects these neural patterns and triggers a "spinal" stimulator for hindlimb movement. This hardware can be head-mounted or carried in a backpack. Neural data were processed in real-time and transmitted to a computer for analysis by an embedded processor. Off-line neural spike analysis was conducted to calculate and preset the spike threshold for "Motolink" hardware. We identified correlated activities of primary motor cortex neurons during treadmill walking of guinea pigs with spinal cord transection. These neural activities were used to predict the kinematic states of the animals. The appropriate selection of spike threshold value enabled the "Motolink" system to detect the neural "intent" of walking, which triggered electrical stimulation of the spinal cord and induced stepping-like hindlimb movements. We present a direct cortical "intent"-driven electronic spinal bridge to restore hindlimb locomotion after complete spinal cord injury.

The impact of natural aging on computational and neural indices of perceptual decision making: A review.

PubMed

Dully, Jessica; McGovern, David P; O'Connell, Redmond G

2018-02-10

It is well established that natural aging negatively impacts on a wide variety of cognitive functions and research has sought to identify core neural mechanisms that may account for these disparate changes. A central feature of any cognitive task is the requirement to translate sensory information into an appropriate action - a process commonly known as perceptual decision making. While computational, psychophysical, and neurophysiological research has made substantial progress in establishing the key computations and neural mechanisms underpinning decision making, it is only relatively recently that this knowledge has begun to be applied to research on aging. The purpose of this review is to provide an overview of this work which is beginning to offer new insights into the core psychological processes that mediate age-related cognitive decline in adults aged 65 years and over. Mathematical modelling studies have consistently reported that older adults display longer non-decisional processing times and implement more conservative decision policies than their younger counterparts. However, there are limits on what we can learn from behavioural modeling alone and neurophysiological analyses can play an essential role in empirically validating model predictions and in pinpointing the precise neural mechanisms that are impacted by aging. Although few studies to date have explicitly examined correspondences between computational models and neural data with respect to cognitive aging, neurophysiological studies have already highlighted age-related changes at multiple levels of the sensorimotor hierarchy that are likely to be consequential for decision making behaviour. Here, we provide an overview of this literature and suggest some future directions for the field. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

The Neural Substrates of Self-Evaluation of Mental Fatigue: A Magnetoencephalography Study

PubMed Central

Ishii, Akira; Tanaka, Masaaki; Watanabe, Yasuyoshi

2014-01-01

There have been several studies of the neural mechanisms underlying sensation of fatigue. However, little is known about the neural mechanisms underlying self-evaluation of the level of fatigue. The aim of this study was to identify the neural substrates involved in self-evaluation of the level of mental fatigue. We used magnetoencephalography (MEG) with high temporal resolution on 14 healthy participants. During MEG recordings, participants were asked to evaluate their level of mental fatigue in time with execution cues (evaluation trials) or to do nothing in time with execution cues (control trials). The MEG data were analyzed with equivalent current dipole (ECD) and spatial filtering methods to localize the neural activity related to the evaluation of mental fatigue. The daily level of fatigue sensation was assessed using the Checklist Individual Strength questionnaire. In evaluation trials, ECDs were observed in the posterior cingulate cortex (PCC) in seven of 14 participants, with a mean latency of 366.0 ms. The proportion of the participants with ECDs in the PCC was higher in evaluation trials than in control trials (P<0.05, McNemar test). The extent of the decreased delta band power in the PCC (Brodmann’s area 31) 600–700 ms after the onset of the execution cue and that in the dorsolateral prefrontal cortex (DLPFC; Brodmann’s area 9) 800–900 ms after the onset of the execution cue were greater in the evaluation trials than in the control trials. The decrease in delta band power in the DLPFC was positively related to that in the PCC and to the daily level of fatigue sensation. These data suggest that the PCC and DLPFC are involved in the self-evaluation of mental fatigue. PMID:24752677

Neurological outcomes by mode of delivery for fetuses with open neural tube defects: A systematic review and meta-analysis.

PubMed

Tolcher, Mary C; Shazly, Sherif A; Shamshirsaz, Alireza A; Whitehead, William E; Espinoza, Jimmy; Vidaeff, Alex C; Belfort, Michael A; Nassr, Ahmed A

2018-06-20

Controversy exists regarding the optimal mode of delivery for fetuses with open neural tube defects. To compare neurological outcomes among infants with open neural tube defects who underwent vaginal compared to caesarean delivery. Electronic databases MEDLINE, EMBASE, Scopus, and Clinicaltrials. gov were searched from inception to November 2017. Eligible studies included observational or randomised studies comparing vaginal and caesarean delivery in pregnancies with fetal open neural tube defects who did not undergo prenatal repair. Two reviewers independently reviewed abstracts and full text articles. Outcomes were compared between vaginal and caesarean delivery and prelabour caesarean versus labour. The primary outcome was motor-anatomic level difference. Secondary outcomes included shunt requirement, sac disruption, meningitis, and ambulation at 2 years. Meta-analysis was performed and mean difference or odds ratios with 95% confidence interval calculated. Of 201 abstracts identified in the primary search, 9 studies (672 women) met eligibility criteria. Comparing vaginal and caesarean delivery, there was no significant difference in motor-anatomic level difference (mean difference -0.10, 95% CI -0.58-0.38; I 2 =57%). The vaginal delivery group was less likely to require a shunt or have sac disruption (OR 0.37, 95% CI 0.14-0.95 and OR 0.46, 95% CI 0.23-0.90, respectively). Comparisons by prelabour caesarean versus labour showed no significant difference in motor-anatomic level difference (OR 1.29, 95% CI -0.63-3.21) or ambulation at 2 years (OR 2.13, 95% CI 0.35-13.12). Caesarean delivery was not associated with improved neurological outcomes among fetuses with open neural tube defects. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Machine learning approaches to the social determinants of health in the health and retirement study.

PubMed

Seligman, Benjamin; Tuljapurkar, Shripad; Rehkopf, David

2018-04-01

Social and economic factors are important predictors of health and of recognized importance for health systems. However, machine learning, used elsewhere in the biomedical literature, has not been extensively applied to study relationships between society and health. We investigate how machine learning may add to our understanding of social determinants of health using data from the Health and Retirement Study. A linear regression of age and gender, and a parsimonious theory-based regression additionally incorporating income, wealth, and education, were used to predict systolic blood pressure, body mass index, waist circumference, and telomere length. Prediction, fit, and interpretability were compared across four machine learning methods: linear regression, penalized regressions, random forests, and neural networks. All models had poor out-of-sample prediction. Most machine learning models performed similarly to the simpler models. However, neural networks greatly outperformed the three other methods. Neural networks also had good fit to the data ( R 2 between 0.4-0.6, versus <0.3 for all others). Across machine learning models, nine variables were frequently selected or highly weighted as predictors: dental visits, current smoking, self-rated health, serial-seven subtractions, probability of receiving an inheritance, probability of leaving an inheritance of at least $10,000, number of children ever born, African-American race, and gender. Some of the machine learning methods do not improve prediction or fit beyond simpler models, however, neural networks performed well. The predictors identified across models suggest underlying social factors that are important predictors of biological indicators of chronic disease, and that the non-linear and interactive relationships between variables fundamental to the neural network approach may be important to consider.

A Comparison of Synoptic Classification Methods for Application to Wind Power Prediction

NASA Astrophysics Data System (ADS)

Fowler, P.; Basu, S.

2008-12-01

Wind energy is a highly variable resource. To make it competitive with other sources of energy for integration on the power grid, at the very least, a day-ahead forecast of power output must be available. In many grid operations worldwide, next-day power output is scheduled in 30 minute intervals and grid management routinely occurs at real time. Maintenance and repairs require costly time to complete and must be scheduled along with normal operations. Revenue is dependent on the reliability of the entire system. In other words, there is financial and managerial benefit to short-term prediction of wind power. One approach to short-term forecasting is to combine a data centric method such as an artificial neural network with a physically based approach like numerical weather prediction (NWP). The key is in associating high-dimensional NWP model output with the most appropriately trained neural network. Because neural networks perform the best in the situations they are designed for, one can hypothesize that if one can identify similar recurring states in historical weather data, this data can be used to train multiple custom designed neural networks to be used when called upon by numerical prediction. Identifying similar recurring states may offer insight to how a neural network forecast can be improved, but amassing the knowledge and utilizing it efficiently in the time required for power prediction would be difficult for a human to master, thus showing the advantage of classification. Classification methods are important tools for short-term forecasting because they can be unsupervised, objective, and computationally quick. They primarily involve categorizing data sets in to dominant weather classes, but there are numerous ways to define a class and a great variety in interpretation of the results. In the present study a collection of classification methods are used on a sampling of atmospheric variables from the North American Regional Reanalysis data set. The results will be discussed in relation to their use for short-term wind power forecasting by neural networks.

Human cortical activity evoked by contextual processing in attentional orienting.

PubMed

Zhao, Shuo; Li, Chunlin; Uono, Shota; Yoshimura, Sayaka; Toichi, Motomi

2017-06-07

The ability to assess another person's direction of attention is paramount in social communication, many studies have reported a similar pattern between gaze and arrow cues in attention orienting. Neuroimaging research has also demonstrated no qualitative differences in attention to gaze and arrow cues. However, these studies were implemented under simple experiment conditions. Researchers have highlighted the importance of contextual processing (i.e., the semantic congruence between cue and target) in attentional orienting, showing that attentional orienting by social gaze or arrow cues could be modulated through contextual processing. Here, we examine the neural activity of attentional orienting by gaze and arrow cues in response to contextual processing using functional magnetic resonance imaging. The results demonstrated that the influence of neural activity through contextual processing to attentional orienting occurred under invalid conditions (when the cue and target were incongruent versus congruent) in the ventral frontoparietal network, although we did not identify any differences in the neural substrates of attentional orienting in contextual processing between gaze and arrow cues. These results support behavioural data of attentional orienting modulated by contextual processing based on the neurocognitive architecture.

The neural substrates of in-group bias: a functional magnetic resonance imaging investigation.

PubMed

Van Bavel, Jay J; Packer, Dominic J; Cunningham, William A

2008-11-01

Classic minimal-group studies found that people arbitrarily assigned to a novel group quickly display a range of perceptual, affective, and behavioral in-group biases. We randomly assigned participants to a mixed-race team and used functional magnetic resonance imaging to identify brain regions involved in processing novel in-group and out-group members independently of preexisting attitudes, stereotypes, or familiarity. Whereas previous research on intergroup perception found amygdala activity--typically interpreted as negativity--in response to stigmatized social groups, we found greater activity in the amygdala, fusiform gyri, orbitofrontal cortex, and dorsal striatum when participants viewed novel in-group faces than when they viewed novel out-group faces. Moreover, activity in orbitofrontal cortex mediated the in-group bias in self-reported liking for the faces. These in-group biases in neural activity were not moderated by race or by whether participants explicitly attended to team membership or race, a finding suggesting that they may occur automatically. This study helps clarify the role of neural substrates involved in perceptual and affective in-group biases.

Motivational Deficits in Schizophrenia and the Representation of Expected Value

PubMed Central

Waltz, James A.; Gold, James M.

2016-01-01

Motivational deficits (avolition and anhedonia) have historically been considered important negative symptoms of schizophrenia. Numerous studies have attempted to identify the neural substrates of avolition and anhedonia in schizophrenia, but these studies have not produced much agreement. Deficits in various aspects of reinforcement processing have been observed in individuals with schizophrenia, but it is not exactly clear which of these deficits actually engender motivational impairments in SZ. The purpose of this chapter is to examine how various reinforcement-related behavioral and neural signals could contribute to motivational impairments in both schizophrenia, and psychiatric illness, in general. In particular, we describe different aspects of the concept of expected value (EV), such as the distinction between the EV of stimuli and the expected value of actions, the acquisition of value vs. the estimation of value, and the discounting of value as a consequence of time or effort required. We conclude that avolition and anhedonia in SZ are most commonly tied to aberrant signals for expected value, in the context of learning. We discuss implications for further research on the neural substrates of motivational impairments in psychiatric illness. PMID:26370946

Neural pathways in processing of sexual arousal: a dynamic causal modeling study.

PubMed

Seok, J-W; Park, M-S; Sohn, J-H

2016-09-01

Three decades of research have investigated brain processing of visual sexual stimuli with neuroimaging methods. These researchers have found that sexual arousal stimuli elicit activity in a broad neural network of cortical and subcortical brain areas that are known to be associated with cognitive, emotional, motivational and physiological components. However, it is not completely understood how these neural systems integrate and modulated incoming information. Therefore, we identify cerebral areas whose activations were correlated with sexual arousal using event-related functional magnetic resonance imaging and used the dynamic causal modeling method for searching the effective connectivity about the sexual arousal processing network. Thirteen heterosexual males were scanned while they passively viewed alternating short trials of erotic and neutral pictures on a monitor. We created a subset of seven models based on our results and previous studies and selected a dominant connectivity model. Consequently, we suggest a dynamic causal model of the brain processes mediating the cognitive, emotional, motivational and physiological factors of human male sexual arousal. These findings are significant implications for the neuropsychology of male sexuality.

A neural network approach to lung nodule segmentation

NASA Astrophysics Data System (ADS)

Hu, Yaoxiu; Menon, Prahlad G.

2016-03-01

Computed tomography (CT) imaging is a sensitive and specific lung cancer screening tool for the high-risk population and shown to be promising for detection of lung cancer. This study proposes an automatic methodology for detecting and segmenting lung nodules from CT images. The proposed methods begin with thorax segmentation, lung extraction and reconstruction of the original shape of the parenchyma using morphology operations. Next, a multi-scale hessian-based vesselness filter is applied to extract lung vasculature in lung. The lung vasculature mask is subtracted from the lung region segmentation mask to extract 3D regions representing candidate pulmonary nodules. Finally, the remaining structures are classified as nodules through shape and intensity features which are together used to train an artificial neural network. Up to 75% sensitivity and 98% specificity was achieved for detection of lung nodules in our testing dataset, with an overall accuracy of 97.62%+/-0.72% using 11 selected features as input to the neural network classifier, based on 4-fold cross-validation studies. Receiver operator characteristics for identifying nodules revealed an area under curve of 0.9476.

Triangular Quantum Loop Topography for Machine Learning

NASA Astrophysics Data System (ADS)

Zhang, Yi; Kim, Eun-Ah

Despite rapidly growing interest in harnessing machine learning in the study of quantum many-body systems there has been little success in training neural networks to identify topological phases. The key challenge is in efficiently extracting essential information from the many-body Hamiltonian or wave function and turning the information into an image that can be fed into a neural network. When targeting topological phases, this task becomes particularly challenging as topological phases are defined in terms of non-local properties. Here we introduce triangular quantum loop (TQL) topography: a procedure of constructing a multi-dimensional image from the ''sample'' Hamiltonian or wave function using two-point functions that form triangles. Feeding the TQL topography to a fully-connected neural network with a single hidden layer, we demonstrate that the architecture can be effectively trained to distinguish Chern insulator and fractional Chern insulator from trivial insulators with high fidelity. Given the versatility of the TQL topography procedure that can handle different lattice geometries, disorder, interaction and even degeneracy our work paves the route towards powerful applications of machine learning in the study of topological quantum matters.

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

PubMed

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

2018-06-21

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

Neural loss aversion differences between depression patients and healthy individuals: A functional MRI investigation.

PubMed

Chandrasekhar Pammi, V S; Pillai Geethabhavan Rajesh, Purushothaman; Kesavadas, Chandrasekharan; Rappai Mary, Paramban; Seema, Satish; Radhakrishnan, Ashalatha; Sitaram, Ranganatha

2015-04-01

Neuroeconomics employs neuroscience techniques to explain decision-making behaviours. Prospect theory, a prominent model of decision-making, features a value function with parameters for risk and loss aversion. Recent work with normal participants identified activation related to loss aversion in brain regions including the amygdala, ventral striatum, and ventromedial prefrontal cortex. However, the brain network for loss aversion in pathologies such as depression has yet to be identified. The aim of the current study is to employ the value function from prospect theory to examine behavioural and neural manifestations of loss aversion in depressed and healthy individuals to identify the neurobiological markers of loss aversion in economic behaviour. We acquired behavioural data and fMRI scans while healthy controls and patients with depression performed an economic decision-making task. Behavioural loss aversion was higher in patients with depression than in healthy controls. fMRI results revealed that the two groups shared a brain network for value function including right ventral striatum, ventromedial prefrontal cortex, and right amygdala. However, the neural loss aversion results revealed greater activations in the right dorsal striatum and the right anterior insula for controls compared with patients with depression, and higher activations in the midbrain region ventral tegmental area for patients with depression compared with controls. These results suggest that while the brain network for loss aversion is shared between depressed and healthy individuals, some differences exist with respect to differential activation of additional areas. Our findings are relevant to identifying neurobiological markers for altered decision-making in the depressed. © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Experimental Design and Interpretation of Functional Neuroimaging Studies of Cognitive Processes

PubMed Central

Caplan, David

2008-01-01

This article discusses how the relation between experimental and baseline conditions in functional neuroimaging studies affects the conclusions that can be drawn from a study about the neural correlates of components of the cognitive system and about the nature and organization of those components. I argue that certain designs in common use—in particular the contrast of qualitatively different representations that are processed at parallel stages of a functional architecture—can never identify the neural basis of a cognitive operation and have limited use in providing information about the nature of cognitive systems. Other types of designs—such as ones that contrast representations that are computed in immediately sequential processing steps and ones that contrast qualitatively similar representations that are parametrically related within a single processing stage—are more easily interpreted. PMID:17979122

High-Throughput Screening to Identify Compounds That Increase Fragile X Mental Retardation Protein Expression in Neural Stem Cells Differentiated From Fragile X Syndrome Patient-Derived Induced Pluripotent Stem Cells.

PubMed

Kumari, Daman; Swaroop, Manju; Southall, Noel; Huang, Wenwei; Zheng, Wei; Usdin, Karen

2015-07-01

: Fragile X syndrome (FXS), the most common form of inherited cognitive disability, is caused by a deficiency of the fragile X mental retardation protein (FMRP). In most patients, the absence of FMRP is due to an aberrant transcriptional silencing of the fragile X mental retardation 1 (FMR1) gene. FXS has no cure, and the available treatments only provide symptomatic relief. Given that FMR1 gene silencing in FXS patient cells can be partially reversed by treatment with compounds that target repressive epigenetic marks, restoring FMRP expression could be one approach for the treatment of FXS. We describe a homogeneous and highly sensitive time-resolved fluorescence resonance energy transfer assay for FMRP detection in a 1,536-well plate format. Using neural stem cells differentiated from an FXS patient-derived induced pluripotent stem cell (iPSC) line that does not express any FMRP, we screened a collection of approximately 5,000 known tool compounds and approved drugs using this FMRP assay and identified 6 compounds that modestly increase FMR1 gene expression in FXS patient cells. Although none of these compounds resulted in clinically relevant levels of FMR1 mRNA, our data provide proof of principle that this assay combined with FXS patient-derived neural stem cells can be used in a high-throughput format to identify better lead compounds for FXS drug development. In this study, a specific and sensitive fluorescence resonance energy transfer-based assay for fragile X mental retardation protein detection was developed and optimized for high-throughput screening (HTS) of compound libraries using fragile X syndrome (FXS) patient-derived neural stem cells. The data suggest that this HTS format will be useful for the identification of better lead compounds for developing new therapeutics for FXS. This assay can also be adapted for FMRP detection in clinical and research settings. ©AlphaMed Press.

Multiple Resting-State Networks Are Associated With Tremors and Cognitive Features in Essential Tremor.

PubMed

Fang, Weidong; Chen, Huiyue; Wang, Hansheng; Zhang, Han; Liu, Mengqi; Puneet, Munankami; Lv, Fajin; Cheng, Oumei; Wang, Xuefeng; Lu, Xiurong; Luo, Tianyou

2015-12-01

The heterogeneous clinical features of essential tremor indicate that the dysfunctions of this syndrome are not confined to motor networks, but extend to nonmotor networks. Currently, these neural network dysfunctions in essential tremor remain unclear. In this study, independent component analysis of resting-state functional MRI was used to study these neural network mechanisms. Thirty-five essential tremor patients and 35 matched healthy controls with clinical and neuropsychological tests were included, and eight resting-state networks were identified. After considering the structure and head-motion factors and testing the reliability of the selected resting-state networks, we assessed the functional connectivity changes within or between resting-state networks. Finally, image-behavior correlation analysis was performed. Compared to healthy controls, essential tremor patients displayed increased functional connectivity in the sensorimotor and salience networks and decreased functional connectivity in the cerebellum network. Additionally, increased functional network connectivity was observed between anterior and posterior default mode networks, and a decreased functional network connectivity was noted between the cerebellum network and the sensorimotor and posterior default mode networks. Importantly, the functional connectivity changes within and between these resting-state networks were correlated with the tremor severity and total cognitive scores of essential tremor patients. The findings of this study provide the first evidence that functional connectivity changes within and between multiple resting-state networks are associated with tremors and cognitive features of essential tremor, and this work demonstrates a potential approach for identifying the underlying neural network mechanisms of this syndrome. © 2015 International Parkinson and Movement Disorder Society.