Pattern of distribution of serotonergic fibers to the amygdala and extended amygdala in the rat.

PubMed

Linley, Stephanie B; Olucha-Bordonau, Francisco; Vertes, Robert P

2017-01-01

As is well recognized, serotonergic (5-HT) fibers distribute widely throughout the forebrain, including the amygdala. Although a few reports have examined the 5-HT innervation of select nuclei of the amygdala in the rat, no previous report has described overall 5-HT projections to the amygdala in the rat. Using immunostaining for the serotonin transporter, SERT, we describe the complete pattern of distribution of 5-HT fibers to the amygdala (proper) and to the extended amygdala in the rat. Based on its ontogenetic origins, the amygdala was subdivided into two major parts, pallial and subpallial components, with the pallial component further divided into superficial and deep nuclei (Olucha-Bordonau et al. 2015). SERT + fibers were shown to distributed moderately to densely to the deep and cortical pallial nuclei, but, by contrast, lightly to the subpallial nuclei. Specifically, 1) of the deep pallial nuclei, the lateral, basolateral, and basomedial nuclei contained a very dense concentration of 5-HT fibers; 2) of the cortical pallial nuclei, the anterior cortical and amygdala-cortical transition zone rostrally and the posteromedial and posterolateral nuclei caudally contained a moderate concentration of 5-HT fibers; and 3) of the subpallial nuclei, the anterior nuclei and the rostral part of the medial (Me) nuclei contained a moderate concentration of 5-HT fibers, whereas caudal regions of Me as well as the central nuclei and the intercalated nuclei contained a sparse/light concentration of 5-HT fibers. With regard to the extended amygdala (primarily the bed nucleus of stria terminalis; BST), on the whole, the BST contained moderate numbers of 5-HT fibers, spread fairly uniformly throughout BST. The findings are discussed with respect to a critical serotonergic influence on the amygdala, particularly on the basal complex, and on the extended amygdala in the control of states of fear and anxiety. J. Comp. Neurol. 525:116-139, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Sparsely-synchronized brain rhythm in a small-world neural network

NASA Astrophysics Data System (ADS)

Kim, Sang-Yoon; Lim, Woochang

2013-07-01

Sparsely-synchronized cortical rhythms, associated with diverse cognitive functions, have been observed in electric recordings of brain activity. At the population level, cortical rhythms exhibit small-amplitude fast oscillations while at the cellular level, individual neurons show stochastic firings sparsely at a much lower rate than the population rate. We study the effect of network architecture on sparse synchronization in an inhibitory population of subthreshold Morris-Lecar neurons (which cannot fire spontaneously without noise). Previously, sparse synchronization was found to occur for cases of both global coupling ( i.e., regular all-to-all coupling) and random coupling. However, a real neural network is known to be non-regular and non-random. Here, we consider sparse Watts-Strogatz small-world networks which interpolate between a regular lattice and a random graph via rewiring. We start from a regular lattice with only short-range connections and then investigate the emergence of sparse synchronization by increasing the rewiring probability p for the short-range connections. For p = 0, the average synaptic path length between pairs of neurons becomes long; hence, only an unsynchronized population state exists because the global efficiency of information transfer is low. However, as p is increased, long-range connections begin to appear, and global effective communication between distant neurons may be available via shorter synaptic paths. Consequently, as p passes a threshold p th (}~ 0.044), sparsely-synchronized population rhythms emerge. However, with increasing p, longer axon wirings become expensive because of their material and energy costs. At an optimal value p* DE (}~ 0.24) of the rewiring probability, the ratio of the synchrony degree to the wiring cost is found to become maximal. In this way, an optimal sparse synchronization is found to occur at a minimal wiring cost in an economic small-world network through trade-off between synchrony and wiring cost.

Regional brain activity that determines successful and unsuccessful working memory formation.

PubMed

Teramoto, Shohei; Inaoka, Tsubasa; Ono, Yumie

2016-08-01

Using EEG source reconstruction with Multiple Sparse Priors (MSP), we investigated the regional brain activity that determines successful memory encoding in two participant groups of high and low accuracy rates. Eighteen healthy young adults performed a sequential fashion of visual Sternberg memory task. The 32-channel EEG was continuously measured during participants performed two 70 trials of memory task. The regional brain activity corresponding to the oscillatory EEG activity in the alpha band (8-13 Hz) during encoding period was analyzed by MSP implemented in SPM8. We divided the data of all participants into 2 groups (low- and highperformance group) and analyzed differences in regional brain activity between trials in which participants answered correctly and incorrectly within each of the group. Participants in low-performance group showed significant activity increase in the visual cortices in their successful trials compared to unsuccessful ones. On the other hand, those in high-performance group showed a significant activity increase in widely distributed cortical regions in the frontal, temporal, and parietal areas including those suggested as Baddeley's working memory model. Further comparison of activated cortical volumes and mean current source intensities within the cortical regions of Baddeley's model during memory encoding demonstrated that participants in high-performance group showed enhanced activity in the right premotor cortex, which plays an important role in maintaining visuospatial attention, compared to those in low performance group. Our results suggest that better ability in memory encoding is associated with distributed and stronger regional brain activities including the premotor cortex, possibly indicating efficient allocation of cognitive load and maintenance of attention.

Simultaneous EEG and MEG source reconstruction in sparse electromagnetic source imaging.

PubMed

Ding, Lei; Yuan, Han

2013-04-01

Electroencephalography (EEG) and magnetoencephalography (MEG) have different sensitivities to differently configured brain activations, making them complimentary in providing independent information for better detection and inverse reconstruction of brain sources. In the present study, we developed an integrative approach, which integrates a novel sparse electromagnetic source imaging method, i.e., variation-based cortical current density (VB-SCCD), together with the combined use of EEG and MEG data in reconstructing complex brain activity. To perform simultaneous analysis of multimodal data, we proposed to normalize EEG and MEG signals according to their individual noise levels to create unit-free measures. Our Monte Carlo simulations demonstrated that this integrative approach is capable of reconstructing complex cortical brain activations (up to 10 simultaneously activated and randomly located sources). Results from experimental data showed that complex brain activations evoked in a face recognition task were successfully reconstructed using the integrative approach, which were consistent with other research findings and validated by independent data from functional magnetic resonance imaging using the same stimulus protocol. Reconstructed cortical brain activations from both simulations and experimental data provided precise source localizations as well as accurate spatial extents of localized sources. In comparison with studies using EEG or MEG alone, the performance of cortical source reconstructions using combined EEG and MEG was significantly improved. We demonstrated that this new sparse ESI methodology with integrated analysis of EEG and MEG data could accurately probe spatiotemporal processes of complex human brain activations. This is promising for noninvasively studying large-scale brain networks of high clinical and scientific significance. Copyright © 2011 Wiley Periodicals, Inc.

A physiologically motivated sparse, compact, and smooth (SCS) approach to EEG source localization.

PubMed

Cao, Cheng; Akalin Acar, Zeynep; Kreutz-Delgado, Kenneth; Makeig, Scott

2012-01-01

Here, we introduce a novel approach to the EEG inverse problem based on the assumption that principal cortical sources of multi-channel EEG recordings may be assumed to be spatially sparse, compact, and smooth (SCS). To enforce these characteristics of solutions to the EEG inverse problem, we propose a correlation-variance model which factors a cortical source space covariance matrix into the multiplication of a pre-given correlation coefficient matrix and the square root of the diagonal variance matrix learned from the data under a Bayesian learning framework. We tested the SCS method using simulated EEG data with various SNR and applied it to a real ECOG data set. We compare the results of SCS to those of an established SBL algorithm.

Mandibular bone changes in 24 years and skeletal fracture prediction.

PubMed

Jonasson, G; Sundh, V; Hakeberg, M; Hassani-Nejad, A; Lissner, L; Ahlqwist, M

2013-03-01

The objectives of the investigation were to describe changes in mandibular bone structure with aging and to compare the usefulness of cortical and trabecular bone for fracture prediction. From 1968 to 1993, 1,003 women were examined. With the help of panoramic radiographs, cortex thickness was measured and cortex was categorized as: normal, moderately, or severely eroded. The trabeculation was assessed as sparse, mixed, or dense. Visually, the mandibular compact and trabecular bone transformed gradually during the 24 years. The compact bone became more porous, the intertrabecular spaces increased, and the radiographic image of the trabeculae seemed less mineralized. Cortex thickness increased up to the age of 50 and decreased significantly thereafter. At all examinations, the sparse trabeculation group had more fractures (71-78 %) than the non-sparse group (27-31 %), whereas the severely eroded compact group showed more fractures than the less eroded groups only in 1992/1993, 24 years later. Sparse trabecular pattern was associated with future fractures both in perimenopausal and older women (relative risk (RR), 1.47-4.37) and cortical erosion in older women (RR, 1.35-1.55). RR for future fracture associated with a severely eroded cortex increased to 4.98 for cohort 1930 in 1992/1993. RR for future fracture associated with sparse trabeculation increased to 11.43 for cohort 1922 in 1992/1993. Dental radiographs contain enough information to identify women most at risk of future fracture. When observing sparse mandibular trabeculation, dentists can identify 40-69 % of women at risk for future fractures, depending on participant age at examination.

Distribution of Vesicular Glutamate Transporter 2 (VGluT2) in the Primary Visual Cortex of the Macaque and Human

PubMed Central

Garcia-Marin, Virginia; Ahmed, Tunazzina H.; Afzal, Yasmeen C.; Hawken, Michael J.

2014-01-01

The majority of thalamic terminals in V1 arise from lateral geniculate nucleus (LGN) afferents. Thalamic afferent terminals are preferentially labeled by an isoform of the vesicular glutamate transporter, VGluT2. The goal of our study was to determine the distribution of VGluT2-ir puncta in macaque and human visual cortex. First, we investigated the distribution of VGluT2-ir puncta in all layers of macaque monkey primary visual cortex (V1), and found a very close correspondence between the known distribution of LGN afferents from previous studies and the distribution of VGluT2-immunoreactive (-ir) puncta. There was also a close correspondence between cytochrome oxidase density and VGluT2-ir puncta distribution. After validating the correspondence in macaque, we made a comparative study in human V1. In many aspects, the distribution of VGluT2-ir puncta in human was qualitatively similar to that of the macaque: high densities in layer 4C, patches of VGluT2-ir puncta in the supragranular layer (2/3), lower but clear distribution in layers 1 and 6, and very few puncta in layers 5 and 4B. However, there were also important differences between macaques and humans. In layer 4A of human, there was a sparse distribution of VGluT2-ir puncta, whereas in macaque, there was a dense distribution with the characteristic honeycomb organization. The results suggest important changes in the pattern of cortical VGluT2 immunostaining that may be related to evolutionary differences in the cortical organization of LGN afferents between Old World monkeys and humans. PMID:22684983

Kanerva's sparse distributed memory: An associative memory algorithm well-suited to the Connection Machine

NASA Technical Reports Server (NTRS)

Rogers, David

1988-01-01

The advent of the Connection Machine profoundly changes the world of supercomputers. The highly nontraditional architecture makes possible the exploration of algorithms that were impractical for standard Von Neumann architectures. Sparse distributed memory (SDM) is an example of such an algorithm. Sparse distributed memory is a particularly simple and elegant formulation for an associative memory. The foundations for sparse distributed memory are described, and some simple examples of using the memory are presented. The relationship of sparse distributed memory to three important computational systems is shown: random-access memory, neural networks, and the cerebellum of the brain. Finally, the implementation of the algorithm for sparse distributed memory on the Connection Machine is discussed.

Statistical regularities of art images and natural scenes: spectra, sparseness and nonlinearities.

PubMed

Graham, Daniel J; Field, David J

2007-01-01

Paintings are the product of a process that begins with ordinary vision in the natural world and ends with manipulation of pigments on canvas. Because artists must produce images that can be seen by a visual system that is thought to take advantage of statistical regularities in natural scenes, artists are likely to replicate many of these regularities in their painted art. We have tested this notion by computing basic statistical properties and modeled cell response properties for a large set of digitized paintings and natural scenes. We find that both representational and non-representational (abstract) paintings from our sample (124 images) show basic similarities to a sample of natural scenes in terms of their spatial frequency amplitude spectra, but the paintings and natural scenes show significantly different mean amplitude spectrum slopes. We also find that the intensity distributions of paintings show a lower skewness and sparseness than natural scenes. We account for this by considering the range of luminances found in the environment compared to the range available in the medium of paint. A painting's range is limited by the reflective properties of its materials. We argue that artists do not simply scale the intensity range down but use a compressive nonlinearity. In our studies, modeled retinal and cortical filter responses to the images were less sparse for the paintings than for the natural scenes. But when a compressive nonlinearity was applied to the images, both the paintings' sparseness and the modeled responses to the paintings showed the same or greater sparseness compared to the natural scenes. This suggests that artists achieve some degree of nonlinear compression in their paintings. Because paintings have captivated humans for millennia, finding basic statistical regularities in paintings' spatial structure could grant insights into the range of spatial patterns that humans find compelling.

Cortical rewiring and information storage

NASA Astrophysics Data System (ADS)

Chklovskii, D. B.; Mel, B. W.; Svoboda, K.

2004-10-01

Current thinking about long-term memory in the cortex is focused on changes in the strengths of connections between neurons. But ongoing structural plasticity in the adult brain, including synapse formation/elimination and remodelling of axons and dendrites, suggests that memory could also depend on learning-induced changes in the cortical `wiring diagram'. Given that the cortex is sparsely connected, wiring plasticity could provide a substantial boost in storage capacity, although at a cost of more elaborate biological machinery and slower learning.

Synaptic and Network Mechanisms of Sparse and Reliable Visual Cortical Activity during Nonclassical Receptive Field Stimulation

PubMed Central

Haider, Bilal; Krause, Matthew R.; Duque, Alvaro; Yu, Yuguo; Touryan, Jonathan; Mazer, James A.; McCormick, David A.

2011-01-01

SUMMARY During natural vision, the entire visual field is stimulated by images rich in spatiotemporal structure. Although many visual system studies restrict stimuli to the classical receptive field (CRF), it is known that costimulation of the CRF and the surrounding nonclassical receptive field (nCRF) increases neuronal response sparseness. The cellular and network mechanisms underlying increased response sparseness remain largely unexplored. Here we show that combined CRF + nCRF stimulation increases the sparseness, reliability, and precision of spiking and membrane potential responses in classical regular spiking (RSC) pyramidal neurons of cat primary visual cortex. Conversely, fast-spiking interneurons exhibit increased activity and decreased selectivity during CRF + nCRF stimulation. The increased sparseness and reliability of RSC neuron spiking is associated with increased inhibitory barrages and narrower visually evoked synaptic potentials. Our experimental observations were replicated with a simple computational model, suggesting that network interactions among neuronal subtypes ultimately sharpen recurrent excitation, producing specific and reliable visual responses. PMID:20152117

The Theory of Localist Representation and of a Purely Abstract Cognitive System: The Evidence from Cortical Columns, Category Cells, and Multisensory Neurons.

PubMed

Roy, Asim

2017-01-01

The debate about representation in the brain and the nature of the cognitive system has been going on for decades now. This paper examines the neurophysiological evidence, primarily from single cell recordings, to get a better perspective on both the issues. After an initial review of some basic concepts, the paper reviews the data from single cell recordings - in cortical columns and of category-selective and multisensory neurons. In neuroscience, columns in the neocortex (cortical columns) are understood to be a basic functional/computational unit. The paper reviews the fundamental discoveries about the columnar organization and finds that it reveals a massively parallel search mechanism. This columnar organization could be the most extensive neurophysiological evidence for the widespread use of localist representation in the brain. The paper also reviews studies of category-selective cells. The evidence for category-selective cells reveals that localist representation is also used to encode complex abstract concepts at the highest levels of processing in the brain. A third major issue is the nature of the cognitive system in the brain and whether there is a form that is purely abstract and encoded by single cells. To provide evidence for a single-cell based purely abstract cognitive system, the paper reviews some of the findings related to multisensory cells. It appears that there is widespread usage of multisensory cells in the brain in the same areas where sensory processing takes place. Plus there is evidence for abstract modality invariant cells at higher levels of cortical processing. Overall, that reveals the existence of a purely abstract cognitive system in the brain. The paper also argues that since there is no evidence for dense distributed representation and since sparse representation is actually used to encode memories, there is actually no evidence for distributed representation in the brain. Overall, it appears that, at an abstract level, the brain is a massively parallel, distributed computing system that is symbolic. The paper also explains how grounded cognition and other theories of the brain are fully compatible with localist representation and a purely abstract cognitive system.

The Theory of Localist Representation and of a Purely Abstract Cognitive System: The Evidence from Cortical Columns, Category Cells, and Multisensory Neurons

PubMed Central

Roy, Asim

2017-01-01

The debate about representation in the brain and the nature of the cognitive system has been going on for decades now. This paper examines the neurophysiological evidence, primarily from single cell recordings, to get a better perspective on both the issues. After an initial review of some basic concepts, the paper reviews the data from single cell recordings – in cortical columns and of category-selective and multisensory neurons. In neuroscience, columns in the neocortex (cortical columns) are understood to be a basic functional/computational unit. The paper reviews the fundamental discoveries about the columnar organization and finds that it reveals a massively parallel search mechanism. This columnar organization could be the most extensive neurophysiological evidence for the widespread use of localist representation in the brain. The paper also reviews studies of category-selective cells. The evidence for category-selective cells reveals that localist representation is also used to encode complex abstract concepts at the highest levels of processing in the brain. A third major issue is the nature of the cognitive system in the brain and whether there is a form that is purely abstract and encoded by single cells. To provide evidence for a single-cell based purely abstract cognitive system, the paper reviews some of the findings related to multisensory cells. It appears that there is widespread usage of multisensory cells in the brain in the same areas where sensory processing takes place. Plus there is evidence for abstract modality invariant cells at higher levels of cortical processing. Overall, that reveals the existence of a purely abstract cognitive system in the brain. The paper also argues that since there is no evidence for dense distributed representation and since sparse representation is actually used to encode memories, there is actually no evidence for distributed representation in the brain. Overall, it appears that, at an abstract level, the brain is a massively parallel, distributed computing system that is symbolic. The paper also explains how grounded cognition and other theories of the brain are fully compatible with localist representation and a purely abstract cognitive system. PMID:28261127

The Influence of Synaptic Weight Distribution on Neuronal Population Dynamics

PubMed Central

Buice, Michael; Koch, Christof; Mihalas, Stefan

2013-01-01

The manner in which different distributions of synaptic weights onto cortical neurons shape their spiking activity remains open. To characterize a homogeneous neuronal population, we use the master equation for generalized leaky integrate-and-fire neurons with shot-noise synapses. We develop fast semi-analytic numerical methods to solve this equation for either current or conductance synapses, with and without synaptic depression. We show that its solutions match simulations of equivalent neuronal networks better than those of the Fokker-Planck equation and we compute bounds on the network response to non-instantaneous synapses. We apply these methods to study different synaptic weight distributions in feed-forward networks. We characterize the synaptic amplitude distributions using a set of measures, called tail weight numbers, designed to quantify the preponderance of very strong synapses. Even if synaptic amplitude distributions are equated for both the total current and average synaptic weight, distributions with sparse but strong synapses produce higher responses for small inputs, leading to a larger operating range. Furthermore, despite their small number, such synapses enable the network to respond faster and with more stability in the face of external fluctuations. PMID:24204219

Feedback Inhibition Shapes Emergent Computational Properties of Cortical Microcircuit Motifs.

PubMed

Jonke, Zeno; Legenstein, Robert; Habenschuss, Stefan; Maass, Wolfgang

2017-08-30

Cortical microcircuits are very complex networks, but they are composed of a relatively small number of stereotypical motifs. Hence, one strategy for throwing light on the computational function of cortical microcircuits is to analyze emergent computational properties of these stereotypical microcircuit motifs. We are addressing here the question how spike timing-dependent plasticity shapes the computational properties of one motif that has frequently been studied experimentally: interconnected populations of pyramidal cells and parvalbumin-positive inhibitory cells in layer 2/3. Experimental studies suggest that these inhibitory neurons exert some form of divisive inhibition on the pyramidal cells. We show that this data-based form of feedback inhibition, which is softer than that of winner-take-all models that are commonly considered in theoretical analyses, contributes to the emergence of an important computational function through spike timing-dependent plasticity: The capability to disentangle superimposed firing patterns in upstream networks, and to represent their information content through a sparse assembly code. SIGNIFICANCE STATEMENT We analyze emergent computational properties of a ubiquitous cortical microcircuit motif: populations of pyramidal cells that are densely interconnected with inhibitory neurons. Simulations of this model predict that sparse assembly codes emerge in this microcircuit motif under spike timing-dependent plasticity. Furthermore, we show that different assemblies will represent different hidden sources of upstream firing activity. Hence, we propose that spike timing-dependent plasticity enables this microcircuit motif to perform a fundamental computational operation on neural activity patterns. Copyright © 2017 the authors 0270-6474/17/378511-13$15.00/0.

Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons.

PubMed

Burbank, Kendra S

2015-12-01

The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field's Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks.

Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons

PubMed Central

Burbank, Kendra S.

2015-01-01

The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field’s Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks. PMID:26633645

How do neurons work together? Lessons from auditory cortex.

PubMed

Harris, Kenneth D; Bartho, Peter; Chadderton, Paul; Curto, Carina; de la Rocha, Jaime; Hollender, Liad; Itskov, Vladimir; Luczak, Artur; Marguet, Stephan L; Renart, Alfonso; Sakata, Shuzo

2011-01-01

Recordings of single neurons have yielded great insights into the way acoustic stimuli are represented in auditory cortex. However, any one neuron functions as part of a population whose combined activity underlies cortical information processing. Here we review some results obtained by recording simultaneously from auditory cortical populations and individual morphologically identified neurons, in urethane-anesthetized and unanesthetized passively listening rats. Auditory cortical populations produced structured activity patterns both in response to acoustic stimuli, and spontaneously without sensory input. Population spike time patterns were broadly conserved across multiple sensory stimuli and spontaneous events, exhibiting a generally conserved sequential organization lasting approximately 100 ms. Both spontaneous and evoked events exhibited sparse, spatially localized activity in layer 2/3 pyramidal cells, and densely distributed activity in larger layer 5 pyramidal cells and putative interneurons. Laminar propagation differed however, with spontaneous activity spreading upward from deep layers and slowly across columns, but sensory responses initiating in presumptive thalamorecipient layers, spreading rapidly across columns. In both unanesthetized and urethanized rats, global activity fluctuated between "desynchronized" state characterized by low amplitude, high-frequency local field potentials and a "synchronized" state of larger, lower-frequency waves. Computational studies suggested that responses could be predicted by a simple dynamical system model fitted to the spontaneous activity immediately preceding stimulus presentation. Fitting this model to the data yielded a nonlinear self-exciting system model in synchronized states and an approximately linear system in desynchronized states. We comment on the significance of these results for auditory cortical processing of acoustic and non-acoustic information. © 2010 Elsevier B.V. All rights reserved.

Self-organization of globally continuous and locally distributed information representation.

PubMed

Wada, Koji; Kurata, Koji; Okada, Masato

2004-01-01

A number of findings suggest that the preferences of neighboring neurons in the inferior temporal (IT) cortex of macaque monkeys tend to be similar. However, a recent study reports convincingly that the preferences of neighboring neurons actually differ. These findings seem contradictory. To explain this conflict, we propose a new view of information representation in the IT cortex. This view takes into account sparse and local neuronal excitation. Since the excitation is sparse, information regarding visual objects seems to be encoded in a distributed manner. The local excitation of neurons coincides with the classical notion of a column structure. Our model consists of input layer and output layer. The main difference from conventional models is that the output layer has local and random intra-layer connections. In this paper, we adopt two rings embedded in three-dimensional space as an input signal space, and examine how resultant information representation depends on the distance between two rings that is denoted as D. We show that there exists critical value for the distance Dc. When D > Dc the output layer becomes able to form the column structure, this model can obtain the distributed representation within the column. While the output layer acquires the conventional information representation observed in the V1 cortex when D < Dc. Moreover, we consider the origin of the difference between information representation of the V1 cortex and that of the IT cortex. Our finding suggests that the difference in the information representations between the V1 and the IT cortices could be caused by difference between the input space structures.

Rapid and highly resolving associative affective learning: convergent electro- and magnetoencephalographic evidence from vision and audition.

PubMed

Steinberg, Christian; Bröckelmann, Ann-Kathrin; Rehbein, Maimu; Dobel, Christian; Junghöfer, Markus

2013-03-01

Various pathway models for emotional processing suggest early prefrontal contributions to affective stimulus evaluation. Yet, electrophysiological evidence for such rapid modulations is still sparse. In a series of four MEG/EEG studies which investigated associative learning in vision and audition using a novel MultiCS Conditioning paradigm, many different neutral stimuli (faces, tones) were paired with aversive and appetitive events in only two to three learning instances. Electrophysiological correlates of neural activity revealed highly significant amplified processing for conditioned stimuli within distributed prefrontal and sensory cortical networks. In both, vision and audition, affect-specific responses occurred in two successive waves of rapid (vision: 50-80 ms, audition: 25-65 ms) and mid-latency (vision: >130 ms, audition: >100 ms) processing. Interestingly, behavioral measures indicated that MultiCS Conditioning successfully prevented contingency awareness. We conclude that affective processing rapidly recruits highly elaborate and widely distributed networks with substantial capacity for fast learning and excellent resolving power. Copyright © 2012 Elsevier B.V. All rights reserved.

Sparse distributed memory overview

NASA Technical Reports Server (NTRS)

Raugh, Mike

1990-01-01

The Sparse Distributed Memory (SDM) project is investigating the theory and applications of massively parallel computing architecture, called sparse distributed memory, that will support the storage and retrieval of sensory and motor patterns characteristic of autonomous systems. The immediate objectives of the project are centered in studies of the memory itself and in the use of the memory to solve problems in speech, vision, and robotics. Investigation of methods for encoding sensory data is an important part of the research. Examples of NASA missions that may benefit from this work are Space Station, planetary rovers, and solar exploration. Sparse distributed memory offers promising technology for systems that must learn through experience and be capable of adapting to new circumstances, and for operating any large complex system requiring automatic monitoring and control. Sparse distributed memory is a massively parallel architecture motivated by efforts to understand how the human brain works. Sparse distributed memory is an associative memory, able to retrieve information from cues that only partially match patterns stored in the memory. It is able to store long temporal sequences derived from the behavior of a complex system, such as progressive records of the system's sensory data and correlated records of the system's motor controls.

Using Copula Distributions to Support More Accurate Imaging-Based Diagnostic Classifiers for Neuropsychiatric Disorders

PubMed Central

Bansal, Ravi; Hao, Xuejun; Liu, Jun; Peterson, Bradley S.

2014-01-01

Many investigators have tried to apply machine learning techniques to magnetic resonance images (MRIs) of the brain in order to diagnose neuropsychiatric disorders. Usually the number of brain imaging measures (such as measures of cortical thickness and measures of local surface morphology) derived from the MRIs (i.e., their dimensionality) has been large (e.g. >10) relative to the number of participants who provide the MRI data (<100). Sparse data in a high dimensional space increases the variability of the classification rules that machine learning algorithms generate, thereby limiting the validity, reproducibility, and generalizability of those classifiers. The accuracy and stability of the classifiers can improve significantly if the multivariate distributions of the imaging measures can be estimated accurately. To accurately estimate the multivariate distributions using sparse data, we propose to estimate first the univariate distributions of imaging data and then combine them using a Copula to generate more accurate estimates of their multivariate distributions. We then sample the estimated Copula distributions to generate dense sets of imaging measures and use those measures to train classifiers. We hypothesize that the dense sets of brain imaging measures will generate classifiers that are stable to variations in brain imaging measures, thereby improving the reproducibility, validity, and generalizability of diagnostic classification algorithms in imaging datasets from clinical populations. In our experiments, we used both computer-generated and real-world brain imaging datasets to assess the accuracy of multivariate Copula distributions in estimating the corresponding multivariate distributions of real-world imaging data. Our experiments showed that diagnostic classifiers generated using imaging measures sampled from the Copula were significantly more accurate and more reproducible than were the classifiers generated using either the real-world imaging measures or their multivariate Gaussian distributions. Thus, our findings demonstrate that estimated multivariate Copula distributions can generate dense sets of brain imaging measures that can in turn be used to train classifiers, and those classifiers are significantly more accurate and more reproducible than are those generated using real-world imaging measures alone. PMID:25093634

Spatial Learning and Action Planning in a Prefrontal Cortical Network Model

PubMed Central

Martinet, Louis-Emmanuel; Sheynikhovich, Denis; Benchenane, Karim; Arleo, Angelo

2011-01-01

The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive “insight” capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates. PMID:21625569

Active learning of cortical connectivity from two-photon imaging data.

PubMed

Bertrán, Martín A; Martínez, Natalia L; Wang, Ye; Dunson, David; Sapiro, Guillermo; Ringach, Dario

2018-01-01

Understanding how groups of neurons interact within a network is a fundamental question in system neuroscience. Instead of passively observing the ongoing activity of a network, we can typically perturb its activity, either by external sensory stimulation or directly via techniques such as two-photon optogenetics. A natural question is how to use such perturbations to identify the connectivity of the network efficiently. Here we introduce a method to infer sparse connectivity graphs from in-vivo, two-photon imaging of population activity in response to external stimuli. A novel aspect of the work is the introduction of a recommended distribution, incrementally learned from the data, to optimally refine the inferred network. Unlike existing system identification techniques, this "active learning" method automatically focuses its attention on key undiscovered areas of the network, instead of targeting global uncertainty indicators like parameter variance. We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters. We present simulations on artificial small-world networks to validate the methods and apply the method to real data. Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model.

Active learning of cortical connectivity from two-photon imaging data

PubMed Central

Wang, Ye; Dunson, David; Sapiro, Guillermo; Ringach, Dario

2018-01-01

Understanding how groups of neurons interact within a network is a fundamental question in system neuroscience. Instead of passively observing the ongoing activity of a network, we can typically perturb its activity, either by external sensory stimulation or directly via techniques such as two-photon optogenetics. A natural question is how to use such perturbations to identify the connectivity of the network efficiently. Here we introduce a method to infer sparse connectivity graphs from in-vivo, two-photon imaging of population activity in response to external stimuli. A novel aspect of the work is the introduction of a recommended distribution, incrementally learned from the data, to optimally refine the inferred network. Unlike existing system identification techniques, this “active learning” method automatically focuses its attention on key undiscovered areas of the network, instead of targeting global uncertainty indicators like parameter variance. We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters. We present simulations on artificial small-world networks to validate the methods and apply the method to real data. Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model. PMID:29718955

Scenario generation for stochastic optimization problems via the sparse grid method

DOE PAGES

Chen, Michael; Mehrotra, Sanjay; Papp, David

2015-04-19

We study the use of sparse grids in the scenario generation (or discretization) problem in stochastic programming problems where the uncertainty is modeled using a continuous multivariate distribution. We show that, under a regularity assumption on the random function involved, the sequence of optimal objective function values of the sparse grid approximations converges to the true optimal objective function values as the number of scenarios increases. The rate of convergence is also established. We treat separately the special case when the underlying distribution is an affine transform of a product of univariate distributions, and show how the sparse grid methodmore » can be adapted to the distribution by the use of quadrature formulas tailored to the distribution. We numerically compare the performance of the sparse grid method using different quadrature rules with classic quasi-Monte Carlo (QMC) methods, optimal rank-one lattice rules, and Monte Carlo (MC) scenario generation, using a series of utility maximization problems with up to 160 random variables. The results show that the sparse grid method is very efficient, especially if the integrand is sufficiently smooth. In such problems the sparse grid scenario generation method is found to need several orders of magnitude fewer scenarios than MC and QMC scenario generation to achieve the same accuracy. As a result, it is indicated that the method scales well with the dimension of the distribution--especially when the underlying distribution is an affine transform of a product of univariate distributions, in which case the method appears scalable to thousands of random variables.« less

Theoretical Limitations on Functional Imaging Resolution in Auditory Cortex

PubMed Central

Chen, Thomas L.; Watkins, Paul V.; Barbour, Dennis L.

2010-01-01

Functional imaging can reveal detailed organizational structure in cerebral cortical areas, but neuronal response features and local neural interconnectivity can influence the resulting images, possibly limiting the inferences that can be drawn about neural function. Discerning the fundamental principles of organizational structure in the auditory cortex of multiple species has been somewhat challenging historically both with functional imaging and with electrophysiology. A possible limitation affecting any methodology using pooled neuronal measures may be the relative distribution of response selectivity throughout the population of auditory cortex neurons. One neuronal response type inherited from the cochlea, for example, exhibits a receptive field that increases in size (i.e., decreases in selectivity) at higher stimulus intensities. Even though these neurons appear to represent a minority of auditory cortex neurons, they are likely to contribute disproportionately to the activity detected in functional images, especially if intense sounds are used for stimulation. To evaluate the potential influence of neuronal subpopulations upon functional images of primary auditory cortex, a model array representing cortical neurons was probed with virtual imaging experiments under various assumptions about the local circuit organization. As expected, different neuronal subpopulations were activated preferentially under different stimulus conditions. In fact, stimulus protocols that can preferentially excite selective neurons, resulting in a relatively sparse activation map, have the potential to improve the effective resolution of functional auditory cortical images. These experimental results also make predictions about auditory cortex organization that can be tested with refined functional imaging experiments. PMID:20079343

Representation of memories in the cortical-hippocampal system: Results from the application of population similarity analyses

PubMed Central

McKenzie, Sam; Keene, Chris; Farovik, Anja; Blandon, John; Place, Ryan; Komorowski, Robert; Eichenbaum, Howard

2016-01-01

Here we consider the value of neural population analysis as an approach to understanding how information is represented in the hippocampus and cortical areas and how these areas might interact as a brain system to support memory. We argue that models based on sparse coding of different individual features by single neurons in these areas (e.g., place cells, grid cells) are inadequate to capture the complexity of experience represented within this system. By contrast, population analyses of neurons with denser coding and mixed selectivity reveal new and important insights into the organization of memories. Furthermore, comparisons of the organization of information in interconnected areas suggest a model of hippocampal-cortical interactions that mediates the fundamental features of memory. PMID:26748022

In vivo wide-field calcium imaging of mouse thalamocortical synapses with an 8 K ultra-high-definition camera.

PubMed

Yoshida, Eriko; Terada, Shin-Ichiro; Tanaka, Yasuyo H; Kobayashi, Kenta; Ohkura, Masamichi; Nakai, Junichi; Matsuzaki, Masanori

2018-05-29

In vivo wide-field imaging of neural activity with a high spatio-temporal resolution is a challenge in modern neuroscience. Although two-photon imaging is very powerful, high-speed imaging of the activity of individual synapses is mostly limited to a field of approximately 200 µm on a side. Wide-field one-photon epifluorescence imaging can reveal neuronal activity over a field of ≥1 mm 2 at a high speed, but is not able to resolve a single synapse. Here, to achieve a high spatio-temporal resolution, we combine an 8 K ultra-high-definition camera with spinning-disk one-photon confocal microscopy. This combination allowed us to image a 1 mm 2 field with a pixel resolution of 0.21 µm at 60 fps. When we imaged motor cortical layer 1 in a behaving head-restrained mouse, calcium transients were detected in presynaptic boutons of thalamocortical axons sparsely labeled with GCaMP6s, although their density was lower than when two-photon imaging was used. The effects of out-of-focus fluorescence changes on calcium transients in individual boutons appeared minimal. Axonal boutons with highly correlated activity were detected over the 1 mm 2 field, and were probably distributed on multiple axonal arbors originating from the same thalamic neuron. This new microscopy with an 8 K ultra-high-definition camera should serve to clarify the activity and plasticity of widely distributed cortical synapses.

Correlated activity supports efficient cortical processing

PubMed Central

Hung, Chou P.; Cui, Ding; Chen, Yueh-peng; Lin, Chia-pei; Levine, Matthew R.

2015-01-01

Visual recognition is a computational challenge that is thought to occur via efficient coding. An important concept is sparseness, a measure of coding efficiency. The prevailing view is that sparseness supports efficiency by minimizing redundancy and correlations in spiking populations. Yet, we recently reported that “choristers”, neurons that behave more similarly (have correlated stimulus preferences and spontaneous coincident spiking), carry more generalizable object information than uncorrelated neurons (“soloists”) in macaque inferior temporal (IT) cortex. The rarity of choristers (as low as 6% of IT neurons) indicates that they were likely missed in previous studies. Here, we report that correlation strength is distinct from sparseness (choristers are not simply broadly tuned neurons), that choristers are located in non-granular output layers, and that correlated activity predicts human visual search efficiency. These counterintuitive results suggest that a redundant correlational structure supports efficient processing and behavior. PMID:25610392

The Impact of Structural Heterogeneity on Excitation-Inhibition Balance in Cortical Networks.

PubMed

Landau, Itamar D; Egger, Robert; Dercksen, Vincent J; Oberlaender, Marcel; Sompolinsky, Haim

2016-12-07

Models of cortical dynamics often assume a homogeneous connectivity structure. However, we show that heterogeneous input connectivity can prevent the dynamic balance between excitation and inhibition, a hallmark of cortical dynamics, and yield unrealistically sparse and temporally regular firing. Anatomically based estimates of the connectivity of layer 4 (L4) rat barrel cortex and numerical simulations of this circuit indicate that the local network possesses substantial heterogeneity in input connectivity, sufficient to disrupt excitation-inhibition balance. We show that homeostatic plasticity in inhibitory synapses can align the functional connectivity to compensate for structural heterogeneity. Alternatively, spike-frequency adaptation can give rise to a novel state in which local firing rates adjust dynamically so that adaptation currents and synaptic inputs are balanced. This theory is supported by simulations of L4 barrel cortex during spontaneous and stimulus-evoked conditions. Our study shows how synaptic and cellular mechanisms yield fluctuation-driven dynamics despite structural heterogeneity in cortical circuits. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

A performance study of sparse Cholesky factorization on INTEL iPSC/860

NASA Technical Reports Server (NTRS)

Zubair, M.; Ghose, M.

1992-01-01

The problem of Cholesky factorization of a sparse matrix has been very well investigated on sequential machines. A number of efficient codes exist for factorizing large unstructured sparse matrices. However, there is a lack of such efficient codes on parallel machines in general, and distributed machines in particular. Some of the issues that are critical to the implementation of sparse Cholesky factorization on a distributed memory parallel machine are ordering, partitioning and mapping, load balancing, and ordering of various tasks within a processor. Here, we focus on the effect of various partitioning schemes on the performance of sparse Cholesky factorization on the Intel iPSC/860. Also, a new partitioning heuristic for structured as well as unstructured sparse matrices is proposed, and its performance is compared with other schemes.

Synapse-Centric Mapping of Cortical Models to the SpiNNaker Neuromorphic Architecture

PubMed Central

Knight, James C.; Furber, Steve B.

2016-01-01

While the adult human brain has approximately 8.8 × 1010 neurons, this number is dwarfed by its 1 × 1015 synapses. From the point of view of neuromorphic engineering and neural simulation in general this makes the simulation of these synapses a particularly complex problem. SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Current solutions for simulating spiking neural networks on SpiNNaker are heavily inspired by work on distributed high-performance computing. However, while SpiNNaker shares many characteristics with such distributed systems, its component nodes have much more limited resources and, as the system lacks global synchronization, the computation performed on each node must complete within a fixed time step. We first analyze the performance of the current SpiNNaker neural simulation software and identify several problems that occur when it is used to simulate networks of the type often used to model the cortex which contain large numbers of sparsely connected synapses. We then present a new, more flexible approach for mapping the simulation of such networks to SpiNNaker which solves many of these problems. Finally we analyze the performance of our new approach using both benchmarks, designed to represent cortical connectivity, and larger, functional cortical models. In a benchmark network where neurons receive input from 8000 STDP synapses, our new approach allows 4× more neurons to be simulated on each SpiNNaker core than has been previously possible. We also demonstrate that the largest plastic neural network previously simulated on neuromorphic hardware can be run in real time using our new approach: double the speed that was previously achieved. Additionally this network contains two types of plastic synapse which previously had to be trained separately but, using our new approach, can be trained simultaneously. PMID:27683540

Interpretation of the Precision Matrix and Its Application in Estimating Sparse Brain Connectivity during Sleep Spindles from Human Electrocorticography Recordings

PubMed Central

Das, Anup; Sampson, Aaron L.; Lainscsek, Claudia; Muller, Lyle; Lin, Wutu; Doyle, John C.; Cash, Sydney S.; Halgren, Eric; Sejnowski, Terrence J.

2017-01-01

The correlation method from brain imaging has been used to estimate functional connectivity in the human brain. However, brain regions might show very high correlation even when the two regions are not directly connected due to the strong interaction of the two regions with common input from a third region. One previously proposed solution to this problem is to use a sparse regularized inverse covariance matrix or precision matrix (SRPM) assuming that the connectivity structure is sparse. This method yields partial correlations to measure strong direct interactions between pairs of regions while simultaneously removing the influence of the rest of the regions, thus identifying regions that are conditionally independent. To test our methods, we first demonstrated conditions under which the SRPM method could indeed find the true physical connection between a pair of nodes for a spring-mass example and an RC circuit example. The recovery of the connectivity structure using the SRPM method can be explained by energy models using the Boltzmann distribution. We then demonstrated the application of the SRPM method for estimating brain connectivity during stage 2 sleep spindles from human electrocorticography (ECoG) recordings using an 8 × 8 electrode array. The ECoG recordings that we analyzed were from a 32-year-old male patient with long-standing pharmaco-resistant left temporal lobe complex partial epilepsy. Sleep spindles were automatically detected using delay differential analysis and then analyzed with SRPM and the Louvain method for community detection. We found spatially localized brain networks within and between neighboring cortical areas during spindles, in contrast to the case when sleep spindles were not present. PMID:28095202

MO-FG-204-08: Optimization-Based Image Reconstruction From Unevenly Distributed Sparse Projection Views

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

Xie, Huiqiao; Yang, Yi; Tang, Xiangyang

2015-06-15

Purpose: Optimization-based reconstruction has been proposed and investigated for reconstructing CT images from sparse views, as such the radiation dose can be substantially reduced while maintaining acceptable image quality. The investigation has so far focused on reconstruction from evenly distributed sparse views. Recognizing the clinical situations wherein only unevenly sparse views are available, e.g., image guided radiation therapy, CT perfusion and multi-cycle cardiovascular imaging, we investigate the performance of optimization-based image reconstruction from unevenly sparse projection views in this work. Methods: The investigation is carried out using the FORBILD and an anthropomorphic head phantoms. In the study, 82 views, whichmore » are evenly sorted out from a full (360°) axial CT scan consisting of 984 views, form sub-scan I. Another 82 views are sorted out in a similar manner to form sub-scan II. As such, a CT scan with sparse (164) views at 1:6 ratio are formed. By shifting the two sub-scans relatively in view angulation, a CT scan with unevenly distributed sparse (164) views at 1:6 ratio are formed. An optimization-based method is implemented to reconstruct images from the unevenly distributed views. By taking the FBP reconstruction from the full scan (984 views) as the reference, the root mean square (RMS) between the reference and the optimization-based reconstruction is used to evaluate the performance quantitatively. Results: In visual inspection, the optimization-based method outperforms the FBP substantially in the reconstruction from unevenly distributed, which are quantitatively verified by the RMS gauged globally and in ROIs in both the FORBILD and anthropomorphic head phantoms. The RMS increases with increasing severity in the uneven angular distribution, especially in the case of anthropomorphic head phantom. Conclusion: The optimization-based image reconstruction can save radiation dose up to 12-fold while providing acceptable image quality for advanced clinical applications wherein only unevenly distributed sparse views are available. Research Grants: W81XWH-12-1-0138 (DoD), Sinovision Technologies.« less

Development of cortical orientation selectivity in the absence of visual experience with contour

PubMed Central

Hussain, Shaista; Weliky, Michael

2011-01-01

Visual cortical neurons are selective for the orientation of lines, and the full development of this selectivity requires natural visual experience after eye opening. Here we examined whether this selectivity develops without seeing lines and contours. Juvenile ferrets were reared in a dark room and visually trained by being shown a movie of flickering, sparse spots. We found that despite the lack of contour visual experience, the cortical neurons of these ferrets developed strong orientation selectivity and exhibited simple-cell receptive fields. This finding suggests that overt contour visual experience is unnecessary for the maturation of orientation selectivity and is inconsistent with the computational models that crucially require the visual inputs of lines and contours for the development of orientation selectivity. We propose that a correlation-based model supplemented with a constraint on synaptic strength dynamics is able to account for our experimental result. PMID:21753023

Sparse Bayesian Learning for Identifying Imaging Biomarkers in AD Prediction

PubMed Central

Shen, Li; Qi, Yuan; Kim, Sungeun; Nho, Kwangsik; Wan, Jing; Risacher, Shannon L.; Saykin, Andrew J.

2010-01-01

We apply sparse Bayesian learning methods, automatic relevance determination (ARD) and predictive ARD (PARD), to Alzheimer’s disease (AD) classification to make accurate prediction and identify critical imaging markers relevant to AD at the same time. ARD is one of the most successful Bayesian feature selection methods. PARD is a powerful Bayesian feature selection method, and provides sparse models that is easy to interpret. PARD selects the model with the best estimate of the predictive performance instead of choosing the one with the largest marginal model likelihood. Comparative study with support vector machine (SVM) shows that ARD/PARD in general outperform SVM in terms of prediction accuracy. Additional comparison with surface-based general linear model (GLM) analysis shows that regions with strongest signals are identified by both GLM and ARD/PARD. While GLM P-map returns significant regions all over the cortex, ARD/PARD provide a small number of relevant and meaningful imaging markers with predictive power, including both cortical and subcortical measures. PMID:20879451

Evaluating Environmental Impact of Traffic Congestion in Real Time Based on Sparse Mobile Crowd-sourced Data

DOT National Transportation Integrated Search

2018-02-02

Traffic congestion at arterial intersections and freeway bottlenecks degrades the air quality and threatens the public health. Conventionally, air pollutants are monitored by sparsely-distributed Quality Assurance Air Monitoring Sites. Sparse mobile ...

A novel craniotomy simulation system for evaluation of stereo-pair reconstruction fidelity and tracking

NASA Astrophysics Data System (ADS)

Yang, Xiaochen; Clements, Logan W.; Conley, Rebekah H.; Thompson, Reid C.; Dawant, Benoit M.; Miga, Michael I.

2016-03-01

Brain shift compensation using computer modeling strategies is an important research area in the field of image-guided neurosurgery (IGNS). One important source of available sparse data during surgery to drive these frameworks is deformation tracking of the visible cortical surface. Possible methods to measure intra-operative cortical displacement include laser range scanners (LRS), which typically complicate the clinical workflow, and reconstruction of cortical surfaces from stereo pairs acquired with the operating microscopes. In this work, we propose and demonstrate a craniotomy simulation device that permits simulating realistic cortical displacements designed to measure and validate the proposed intra-operative cortical shift measurement systems. The device permits 3D deformations of a mock cortical surface which consists of a membrane made of a Dragon Skin® high performance silicone rubber on which vascular patterns are drawn. We then use this device to validate our stereo pair-based surface reconstruction system by comparing landmark positions and displacements measured with our systems to those positions and displacements as measured by a stylus tracked by a commercial optical system. Our results show a 1mm average difference in localization error and a 1.2mm average difference in displacement measurement. These results suggest that our stereo-pair technique is accurate enough for estimating intra-operative displacements in near real-time without affecting the surgical workflow.

Advancing Underwater Acoustic Communication for Autonomous Distributed Networks via Sparse Channel Sensing, Coding, and Navigation Support

DTIC Science & Technology

2014-09-30

underwater acoustic communication technologies for autonomous distributed underwater networks , through innovative signal processing, coding, and...4. TITLE AND SUBTITLE Advancing Underwater Acoustic Communication for Autonomous Distributed Networks via Sparse Channel Sensing, Coding, and...coding: 3) OFDM modulated dynamic coded cooperation in underwater acoustic channels; 3 Localization, Networking , and Testbed: 4) On-demand

BIRD: A general interface for sparse distributed memory simulators

NASA Technical Reports Server (NTRS)

Rogers, David

1990-01-01

Kanerva's sparse distributed memory (SDM) has now been implemented for at least six different computers, including SUN3 workstations, the Apple Macintosh, and the Connection Machine. A common interface for input of commands would both aid testing of programs on a broad range of computer architectures and assist users in transferring results from research environments to applications. A common interface also allows secondary programs to generate command sequences for a sparse distributed memory, which may then be executed on the appropriate hardware. The BIRD program is an attempt to create such an interface. Simplifying access to different simulators should assist developers in finding appropriate uses for SDM.

Joint representation of consistent structural and functional profiles for identification of common cortical landmarks.

PubMed

Zhang, Shu; Zhao, Yu; Jiang, Xi; Shen, Dinggang; Liu, Tianming

2018-06-01

In the brain mapping field, there have been significant interests in representation of structural/functional profiles to establish structural/functional landmark correspondences across individuals and populations. For example, from the structural perspective, our previous studies have identified hundreds of consistent DICCCOL (dense individualized and common connectivity-based cortical landmarks) landmarks across individuals and populations, each of which possess consistent DTI-derived fiber connection patterns. From the functional perspective, a large collection of well-characterized HAFNI (holistic atlases of functional networks and interactions) networks based on sparse representation of whole-brain fMRI signals have been identified in our prior studies. However, due to the remarkable variability of structural and functional architectures in the human brain, it is challenging for earlier studies to jointly represent the connectome-scale structural and functional profiles for establishing a common cortical architecture which can comprehensively encode both structural and functional characteristics across individuals. To address this challenge, we propose an effective computational framework to jointly represent the structural and functional profiles for identification of consistent and common cortical landmarks with both structural and functional correspondences across different brains based on DTI and fMRI data. Experimental results demonstrate that 55 structurally and functionally common cortical landmarks can be successfully identified.

Hierarchical Bayesian sparse image reconstruction with application to MRFM.

PubMed

Dobigeon, Nicolas; Hero, Alfred O; Tourneret, Jean-Yves

2009-09-01

This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally sparse image applications as it seamlessly accounts for properties such as sparsity and positivity of the image via appropriate Bayes priors. We propose a prior that is based on a weighted mixture of a positive exponential distribution and a mass at zero. The prior has hyperparameters that are tuned automatically by marginalization over the hierarchical Bayesian model. To overcome the complexity of the posterior distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be used to estimate the image to be recovered, e.g., by maximizing the estimated posterior distribution. In our fully Bayesian approach, the posteriors of all the parameters are available. Thus, our algorithm provides more information than other previously proposed sparse reconstruction methods that only give a point estimate. The performance of the proposed hierarchical Bayesian sparse reconstruction method is illustrated on synthetic data and real data collected from a tobacco virus sample using a prototype MRFM instrument.

Sparse modeling of spatial environmental variables associated with asthma

PubMed Central

Chang, Timothy S.; Gangnon, Ronald E.; Page, C. David; Buckingham, William R.; Tandias, Aman; Cowan, Kelly J.; Tomasallo, Carrie D.; Arndt, Brian G.; Hanrahan, Lawrence P.; Guilbert, Theresa W.

2014-01-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin’s Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5–50 years over a three-year period. Each patient’s home address was geocoded to one of 3,456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin’s geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. PMID:25533437

Sparse modeling of spatial environmental variables associated with asthma.

PubMed

Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W

2015-02-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

Simulations inform design of regional occupancy-based monitoring for a sparsely distributed, territorial species

Treesearch

Quresh S. Latif; Martha M. Ellis; Victoria A. Saab; Kim Mellen-McLean

2017-01-01

Sparsely distributed species attract conservation concern, but insufficient information on population trends challenges conservation and funding prioritization. Occupancy-based monitoring is attractive for these species, but appropriate sampling design and inference depend on particulars of the study system. We employed spatially explicit simulations to identify...

Sparsely sampling the sky: Regular vs. random sampling

NASA Astrophysics Data System (ADS)

Paykari, P.; Pires, S.; Starck, J.-L.; Jaffe, A. H.

2015-09-01

Aims: The next generation of galaxy surveys, aiming to observe millions of galaxies, are expensive both in time and money. This raises questions regarding the optimal investment of this time and money for future surveys. In a previous work, we have shown that a sparse sampling strategy could be a powerful substitute for the - usually favoured - contiguous observation of the sky. In our previous paper, regular sparse sampling was investigated, where the sparse observed patches were regularly distributed on the sky. The regularity of the mask introduces a periodic pattern in the window function, which induces periodic correlations at specific scales. Methods: In this paper, we use a Bayesian experimental design to investigate a "random" sparse sampling approach, where the observed patches are randomly distributed over the total sparsely sampled area. Results: We find that in this setting, the induced correlation is evenly distributed amongst all scales as there is no preferred scale in the window function. Conclusions: This is desirable when we are interested in any specific scale in the galaxy power spectrum, such as the matter-radiation equality scale. As the figure of merit shows, however, there is no preference between regular and random sampling to constrain the overall galaxy power spectrum and the cosmological parameters.

Statistical prediction with Kanerva's sparse distributed memory

NASA Technical Reports Server (NTRS)

Rogers, David

1989-01-01

A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is presented. In conditions of near- or over-capacity, where the associative-memory behavior of the model breaks down, the processing performed by the model can be interpreted as that of a statistical predictor. Mathematical results are presented which serve as the framework for a new statistical viewpoint of sparse distributed memory and for which the standard formulation of SDM is a special case. This viewpoint suggests possible enhancements to the SDM model, including a procedure for improving the predictiveness of the system based on Holland's work with genetic algorithms, and a method for improving the capacity of SDM even when used as an associative memory.

Immunological memory is associative

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

Smith, D.J.; Forrest, S.; Perelson, A.S.

1996-12-31

The purpose of this paper is to show that immunological memory is an associative and robust memory that belongs to the class of sparse distributed memories. This class of memories derives its associative and robust nature by sparsely sampling the input space and distributing the data among many independent agents. Other members of this class include a model of the cerebellar cortex and Sparse Distributed Memory (SDM). First we present a simplified account of the immune response and immunological memory. Next we present SDM, and then we show the correlations between immunological memory and SDM. Finally, we show how associativemore » recall in the immune response can be both beneficial and detrimental to the fitness of an individual.« less

Noise in Attractor Networks in the Brain Produced by Graded Firing Rate Representations

PubMed Central

Webb, Tristan J.; Rolls, Edmund T.; Deco, Gustavo; Feng, Jianfeng

2011-01-01

Representations in the cortex are often distributed with graded firing rates in the neuronal populations. The firing rate probability distribution of each neuron to a set of stimuli is often exponential or gamma. In processes in the brain, such as decision-making, that are influenced by the noise produced by the close to random spike timings of each neuron for a given mean rate, the noise with this graded type of representation may be larger than with the binary firing rate distribution that is usually investigated. In integrate-and-fire simulations of an attractor decision-making network, we show that the noise is indeed greater for a given sparseness of the representation for graded, exponential, than for binary firing rate distributions. The greater noise was measured by faster escaping times from the spontaneous firing rate state when the decision cues are applied, and this corresponds to faster decision or reaction times. The greater noise was also evident as less stability of the spontaneous firing state before the decision cues are applied. The implication is that spiking-related noise will continue to be a factor that influences processes such as decision-making, signal detection, short-term memory, and memory recall even with the quite large networks found in the cerebral cortex. In these networks there are several thousand recurrent collateral synapses onto each neuron. The greater noise with graded firing rate distributions has the advantage that it can increase the speed of operation of cortical circuitry. PMID:21931607

Learning to read aloud: A neural network approach using sparse distributed memory

NASA Technical Reports Server (NTRS)

Joglekar, Umesh Dwarkanath

1989-01-01

An attempt to solve a problem of text-to-phoneme mapping is described which does not appear amenable to solution by use of standard algorithmic procedures. Experiments based on a model of distributed processing are also described. This model (sparse distributed memory (SDM)) can be used in an iterative supervised learning mode to solve the problem. Additional improvements aimed at obtaining better performance are suggested.

Effects of Habitual Physical Activity and Fitness on Tibial Cortical Bone Mass, Structure and Mass Distribution in Pre-pubertal Boys and Girls: The Look Study.

PubMed

Duckham, Rachel L; Rantalainen, Timo; Ducher, Gaele; Hill, Briony; Telford, Richard D; Telford, Rohan M; Daly, Robin M

2016-07-01

Targeted weight-bearing activities during the pre-pubertal years can improve cortical bone mass, structure and distribution, but less is known about the influence of habitual physical activity (PA) and fitness. This study examined the effects of contrasting habitual PA and fitness levels on cortical bone density, geometry and mass distribution in pre-pubertal children. Boys (n = 241) and girls (n = 245) aged 7-9 years had a pQCT scan to measure tibial mid-shaft total, cortical and medullary area, cortical thickness, density, polar strength strain index (SSIpolar) and the mass/density distribution through the bone cortex (radial distribution divided into endo-, mid- and pericortical regions) and around the centre of mass (polar distribution). Four contrasting PA and fitness groups (inactive-unfit, inactive-fit, active-unfit, active-fit) were generated based on daily step counts (pedometer, 7-days) and fitness levels (20-m shuttle test and vertical jump) for boys and girls separately. Active-fit boys had 7.3-7.7 % greater cortical area and thickness compared to inactive-unfit boys (P < 0.05), which was largely due to a 6.4-7.8 % (P < 0.05) greater cortical mass in the posterior-lateral, medial and posterior-medial 66 % tibial regions. Cortical area was not significantly different across PA-fitness categories in girls, but active-fit girls had 6.1 % (P < 0.05) greater SSIpolar compared to inactive-fit girls, which was likely due to their 6.7 % (P < 0.05) greater total bone area. There was also a small region-specific cortical mass benefit in the posterior-medial 66 % tibia cortex in active-fit girls. Higher levels of habitual PA-fitness were associated with small regional-specific gains in 66 % tibial cortical bone mass in pre-pubertal children, particularly boys.

Zygomatic osteoma with atypical heterogeneity in a dog.

PubMed

Johnson, K A; Cooley, A J; Darien, D L

1996-02-01

An osteoma of the zygomatic bone in a young dog is described. It had large, centralized radiolucent regions consisting of fatty bone marrow and sparse trabeculae. A discrete, proliferative nodule within the osteoma consisted of closely-packed woven bone trabeculae and pleomorphic osteoblasts associated with haphazard osteoid deposits, resembling osteosarcoma-like change. These heterogeneous structural features were at variance with more classic reports of osteoma, which usually describe a uniform cancellous or cortical bone architecture.

Sparse Matrix Software Catalog, Sparse Matrix Symposium 1982, Fairfield Glade, Tennessee, October 24-27, 1982,

DTIC Science & Technology

1982-10-27

are buried within * a much larger, special purpose package. We regret such omissions, but to have reached the practi- tioners in each of the diverse...sparse matrix (form PAQ ) 4. Method of solution: Distribution count sort 5. Programming language: FORTRAN g Precision: Single and double precision 7

Investigation of wall-bounded turbulence over sparsely distributed roughness

NASA Astrophysics Data System (ADS)

Placidi, Marco; Ganapathisubramani, Bharath

2011-11-01

The effects of sparsely distributed roughness elements on the structure of a turbulent boundary layer are examined by performing a series of Particle Image Velocimetry (PIV) experiments in a wind tunnel. From the literature, the best way to characterise a rough wall, especially one where the density of roughness elements is sparse, is unclear. In this study, rough surfaces consisting of sparsely and uniformly distributed LEGO® blocks are used. Five different patterns are adopted in order to examine the effects of frontal solidity (λf, frontal area of the roughness elements per unit wall-parallel area), plan solidity (λp, plan area of roughness elements per unit wall-parallel area) and the geometry of the roughness element (square and cylindrical elements), on the turbulence structure. The Karman number, Reτ , has been matched, at the value of approximately 2300, in order to compare across the different cases. In the talk, we will present detailed analysis of mean and rms velocity profiles, Reynolds stresses and quadrant decomposition.

Two demonstrators and a simulator for a sparse, distributed memory

NASA Technical Reports Server (NTRS)

Brown, Robert L.

1987-01-01

Described are two programs demonstrating different aspects of Kanerva's Sparse, Distributed Memory (SDM). These programs run on Sun 3 workstations, one using color, and have straightforward graphically oriented user interfaces and graphical output. Presented are descriptions of the programs, how to use them, and what they show. Additionally, this paper describes the software simulator behind each program.

An empirical investigation of sparse distributed memory using discrete speech recognition

NASA Technical Reports Server (NTRS)

Danforth, Douglas G.

1990-01-01

Presented here is a step by step analysis of how the basic Sparse Distributed Memory (SDM) model can be modified to enhance its generalization capabilities for classification tasks. Data is taken from speech generated by a single talker. Experiments are used to investigate the theory of associative memories and the question of generalization from specific instances.

Communication requirements of sparse Cholesky factorization with nested dissection ordering

NASA Technical Reports Server (NTRS)

Naik, Vijay K.; Patrick, Merrell L.

1989-01-01

Load distribution schemes for minimizing the communication requirements of the Cholesky factorization of dense and sparse, symmetric, positive definite matrices on multiprocessor systems are presented. The total data traffic in factoring an n x n sparse symmetric positive definite matrix representing an n-vertex regular two-dimensional grid graph using n exp alpha, alpha not greater than 1, processors are shown to be O(n exp 1 + alpha/2). It is O(n), when n exp alpha, alpha not smaller than 1, processors are used. Under the conditions of uniform load distribution, these results are shown to be asymptotically optimal.

Distributed memory compiler design for sparse problems

NASA Technical Reports Server (NTRS)

Wu, Janet; Saltz, Joel; Berryman, Harry; Hiranandani, Seema

1991-01-01

A compiler and runtime support mechanism is described and demonstrated. The methods presented are capable of solving a wide range of sparse and unstructured problems in scientific computing. The compiler takes as input a FORTRAN 77 program enhanced with specifications for distributing data, and the compiler outputs a message passing program that runs on a distributed memory computer. The runtime support for this compiler is a library of primitives designed to efficiently support irregular patterns of distributed array accesses and irregular distributed array partitions. A variety of Intel iPSC/860 performance results obtained through the use of this compiler are presented.

The application of a sparse, distributed memory to the detection, identification and manipulation of physical objects

NASA Technical Reports Server (NTRS)

Kanerva, P.

1986-01-01

To determine the relation of the sparse, distributed memory to other architectures, a broad review of the literature was made. The memory is called a pattern memory because they work with large patterns of features (high-dimensional vectors). A pattern is stored in a pattern memory by distributing it over a large number of storage elements and by superimposing it over other stored patterns. A pattern is retrieved by mathematical or statistical reconstruction from the distributed elements. Three pattern memories are discussed.

Towards a Holistic Cortical Thickness Descriptor: Heat Kernel-Based Grey Matter Morphology Signatures.

PubMed

Wang, Gang; Wang, Yalin

2017-02-15

In this paper, we propose a heat kernel based regional shape descriptor that may be capable of better exploiting volumetric morphological information than other available methods, thereby improving statistical power on brain magnetic resonance imaging (MRI) analysis. The mechanism of our analysis is driven by the graph spectrum and the heat kernel theory, to capture the volumetric geometry information in the constructed tetrahedral meshes. In order to capture profound brain grey matter shape changes, we first use the volumetric Laplace-Beltrami operator to determine the point pair correspondence between white-grey matter and CSF-grey matter boundary surfaces by computing the streamlines in a tetrahedral mesh. Secondly, we propose multi-scale grey matter morphology signatures to describe the transition probability by random walk between the point pairs, which reflects the inherent geometric characteristics. Thirdly, a point distribution model is applied to reduce the dimensionality of the grey matter morphology signatures and generate the internal structure features. With the sparse linear discriminant analysis, we select a concise morphology feature set with improved classification accuracies. In our experiments, the proposed work outperformed the cortical thickness features computed by FreeSurfer software in the classification of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment, on publicly available data from the Alzheimer's Disease Neuroimaging Initiative. The multi-scale and physics based volumetric structure feature may bring stronger statistical power than some traditional methods for MRI-based grey matter morphology analysis. Copyright © 2016 Elsevier Inc. All rights reserved.

Is the Sensorimotor Cortex Relevant for Speech Perception and Understanding? An Integrative Review

PubMed Central

Schomers, Malte R.; Pulvermüller, Friedemann

2016-01-01

In the neuroscience of language, phonemes are frequently described as multimodal units whose neuronal representations are distributed across perisylvian cortical regions, including auditory and sensorimotor areas. A different position views phonemes primarily as acoustic entities with posterior temporal localization, which are functionally independent from frontoparietal articulatory programs. To address this current controversy, we here discuss experimental results from functional magnetic resonance imaging (fMRI) as well as transcranial magnetic stimulation (TMS) studies. On first glance, a mixed picture emerges, with earlier research documenting neurofunctional distinctions between phonemes in both temporal and frontoparietal sensorimotor systems, but some recent work seemingly failing to replicate the latter. Detailed analysis of methodological differences between studies reveals that the way experiments are set up explains whether sensorimotor cortex maps phonological information during speech perception or not. In particular, acoustic noise during the experiment and ‘motor noise’ caused by button press tasks work against the frontoparietal manifestation of phonemes. We highlight recent studies using sparse imaging and passive speech perception tasks along with multivariate pattern analysis (MVPA) and especially representational similarity analysis (RSA), which succeeded in separating acoustic-phonological from general-acoustic processes and in mapping specific phonological information on temporal and frontoparietal regions. The question about a causal role of sensorimotor cortex on speech perception and understanding is addressed by reviewing recent TMS studies. We conclude that frontoparietal cortices, including ventral motor and somatosensory areas, reflect phonological information during speech perception and exert a causal influence on language understanding. PMID:27708566

Vomeronasal inputs to the rodent ventral striatum.

PubMed

Ubeda-Bañon, I; Novejarque, A; Mohedano-Moriano, A; Pro-Sistiaga, P; Insausti, R; Martinez-Garcia, F; Lanuza, E; Martinez-Marcos, A

2008-03-18

Vertebrates sense chemical signals through the olfactory and vomeronasal systems. In squamate reptiles, which possess the largest vomeronasal system of all vertebrates, the accessory olfactory bulb projects to the nucleus sphericus, which in turn projects to a portion of the ventral striatum known as olfactostriatum. Characteristically, the olfactostriatum is innervated by neuropeptide Y, tyrosine hydroxylase and serotonin immunoreactive fibers. In this study, the possibility that a structure similar to the reptilian olfactostriatum might be present in the mammalian brain has been investigated. Injections of dextran-amines have been aimed at the posteromedial cortical amygdaloid nucleus (the putative mammalian homologue of the reptilian nucleus sphericus) of rats and mice. The resulting anterograde labeling includes the olfactory tubercle, the islands of Calleja and sparse terminal fields in the shell of the nucleus accumbens and ventral pallidum. This projection has been confirmed by injections of retrograde tracers into the ventral striato-pallidum that render retrograde labeling in the posteromedial cortical amygdaloid nucleus. The analysis of the distribution of neuropeptide Y, tyrosine hydroxylase, serotonin and substance P in the ventral striato-pallidum of rats, and the anterograde tracing of the vomeronasal amygdaloid input in the same material confirm that, similar to reptiles, the ventral striatum of mammals includes a specialized vomeronasal structure (olfactory tubercle and islands of Calleja) displaying dense neuropeptide Y-, tyrosine hydroxylase- and serotonin-immunoreactive innervations. The possibility that parts of the accumbens shell and/or ventral pallidum could be included in the mammalian olfactostriatum cannot be discarded.

Balanced increases in selectivity and tolerance produce constant sparseness along the ventral visual stream

PubMed Central

Rust, Nicole C.; DiCarlo, James J.

2012-01-01

While popular accounts suggest that neurons along the ventral visual processing stream become increasingly selective for particular objects, this appears at odds with the fact that inferior temporal cortical (IT) neurons are broadly tuned. To explore this apparent contradiction, we compared processing in two ventral stream stages (V4 and IT) in the rhesus macaque monkey. We confirmed that IT neurons are indeed more selective for conjunctions of visual features than V4 neurons, and that this increase in feature conjunction selectivity is accompanied by an increase in tolerance (“invariance”) to identity-preserving transformations (e.g. shifting, scaling) of those features. We report here that V4 and IT neurons are, on average, tightly matched in their tuning breadth for natural images (“sparseness”), and that the average V4 or IT neuron will produce a robust firing rate response (over 50% of its peak observed firing rate) to ~10% of all natural images. We also observed that sparseness was positively correlated with conjunction selectivity and negatively correlated with tolerance within both V4 and IT, consistent with selectivity-building and invariance-building computations that offset one another to produce sparseness. Our results imply that the conjunction-selectivity-building and invariance-building computations necessary to support object recognition are implemented in a balanced fashion to maintain sparseness at each stage of processing. PMID:22836252

There's more than one way to scan a cat: imaging cat auditory cortex with high-field fMRI using continuous or sparse sampling.

PubMed

Hall, Amee J; Brown, Trecia A; Grahn, Jessica A; Gati, Joseph S; Nixon, Pam L; Hughes, Sarah M; Menon, Ravi S; Lomber, Stephen G

2014-03-15

When conducting auditory investigations using functional magnetic resonance imaging (fMRI), there are inherent potential confounds that need to be considered. Traditional continuous fMRI acquisition methods produce sounds >90 dB which compete with stimuli or produce neural activation masking evoked activity. Sparse scanning methods insert a period of reduced MRI-related noise, between image acquisitions, in which a stimulus can be presented without competition. In this study, we compared sparse and continuous scanning methods to identify the optimal approach to investigate acoustically evoked cortical, thalamic and midbrain activity in the cat. Using a 7 T magnet, we presented broadband noise, 10 kHz tones, or 0.5 kHz tones in a block design, interleaved with blocks in which no stimulus was presented. Continuous scanning resulted in larger clusters of activation and more peak voxels within the auditory cortex. However, no significant activation was observed within the thalamus. Also, there was no significant difference found, between continuous or sparse scanning, in activations of midbrain structures. Higher magnitude activations were identified in auditory cortex compared to the midbrain using both continuous and sparse scanning. These results indicate that continuous scanning is the preferred method for investigations of auditory cortex in the cat using fMRI. Also, choice of method for future investigations of midbrain activity should be driven by other experimental factors, such as stimulus intensity and task performance during scanning. Copyright © 2014 Elsevier B.V. All rights reserved.

Multivariate quadrature for representing cloud condensation nuclei activity of aerosol populations

DOE PAGES

Fierce, Laura; McGraw, Robert L.

2017-07-26

Here, sparse representations of atmospheric aerosols are needed for efficient regional- and global-scale chemical transport models. Here we introduce a new framework for representing aerosol distributions, based on the quadrature method of moments. Given a set of moment constraints, we show how linear programming, combined with an entropy-inspired cost function, can be used to construct optimized quadrature representations of aerosol distributions. The sparse representations derived from this approach accurately reproduce cloud condensation nuclei (CCN) activity for realistically complex distributions simulated by a particleresolved model. Additionally, the linear programming techniques described in this study can be used to bound key aerosolmore » properties, such as the number concentration of CCN. Unlike the commonly used sparse representations, such as modal and sectional schemes, the maximum-entropy approach described here is not constrained to pre-determined size bins or assumed distribution shapes. This study is a first step toward a particle-based aerosol scheme that will track multivariate aerosol distributions with sufficient computational efficiency for large-scale simulations.« less

Multivariate quadrature for representing cloud condensation nuclei activity of aerosol populations

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

Fierce, Laura; McGraw, Robert L.

Here, sparse representations of atmospheric aerosols are needed for efficient regional- and global-scale chemical transport models. Here we introduce a new framework for representing aerosol distributions, based on the quadrature method of moments. Given a set of moment constraints, we show how linear programming, combined with an entropy-inspired cost function, can be used to construct optimized quadrature representations of aerosol distributions. The sparse representations derived from this approach accurately reproduce cloud condensation nuclei (CCN) activity for realistically complex distributions simulated by a particleresolved model. Additionally, the linear programming techniques described in this study can be used to bound key aerosolmore » properties, such as the number concentration of CCN. Unlike the commonly used sparse representations, such as modal and sectional schemes, the maximum-entropy approach described here is not constrained to pre-determined size bins or assumed distribution shapes. This study is a first step toward a particle-based aerosol scheme that will track multivariate aerosol distributions with sufficient computational efficiency for large-scale simulations.« less

New shape models of asteroids reconstructed from sparse-in-time photometry

NASA Astrophysics Data System (ADS)

Durech, Josef; Hanus, Josef; Vanco, Radim; Oszkiewicz, Dagmara Anna

2015-08-01

Asteroid physical parameters - the shape, the sidereal rotation period, and the spin axis orientation - can be reconstructed from the disk-integrated photometry either dense (classical lightcurves) or sparse in time by the lightcurve inversion method. We will review our recent progress in asteroid shape reconstruction from sparse photometry. The problem of finding a unique solution of the inverse problem is time consuming because the sidereal rotation period has to be found by scanning a wide interval of possible periods. This can be efficiently solved by splitting the period parameter space into small parts that are sent to computers of volunteers and processed in parallel. We will show how this approach of distributed computing works with currently available sparse photometry processed in the framework of project Asteroids@home. In particular, we will show the results based on the Lowell Photometric Database. The method produce reliable asteroid models with very low rate of false solutions and the pipelines and codes can be directly used also to other sources of sparse photometry - Gaia data, for example. We will present the distribution of spin axis of hundreds of asteroids, discuss the dependence of the spin obliquity on the size of an asteroid,and show examples of spin-axis distribution in asteroid families that confirm the Yarkovsky/YORP evolution scenario.

Kanerva's sparse distributed memory with multiple hamming thresholds

NASA Technical Reports Server (NTRS)

Pohja, Seppo; Kaski, Kimmo

1992-01-01

If the stored input patterns of Kanerva's Sparse Distributed Memory (SDM) are highly correlated, utilization of the storage capacity is very low compared to the case of uniformly distributed random input patterns. We consider a variation of SDM that has a better storage capacity utilization for correlated input patterns. This approach uses a separate selection threshold for each physical storage address or hard location. The selection of the hard locations for reading or writing can be done in parallel of which SDM implementations can benefit.

Functional reorganization during cognitive function tasks in patients with amyotrophic lateral sclerosis.

PubMed

Keller, Jürgen; Böhm, Sarah; Aho-Özhan, Helena E A; Loose, Markus; Gorges, Martin; Kassubek, Jan; Uttner, Ingo; Abrahams, Sharon; Ludolph, Albert C; Lulé, Dorothée

2018-06-01

Cognitive deficits, especially in the domains of social cognition and executive function including verbal fluency, are common in amyotrophic lateral sclerosis (ALS) patients. There is yet sparse understanding of pathogenesis of the underlying, possibly adaptive, cortical patterns. To address this issue, 65 patients with ALS and 33 age-, gender- and education-matched healthy controls were tested on cognitive and behavioral deficits with the Edinburgh Cognitive and Behavioural ALS Screen (ECAS). Using functional magnetic resonance imaging (fMRI), cortical activity during social cognition and executive function tasks (theory of mind, verbal fluency, alternation) adapted from the ECAS was determined in a 3 Tesla scanner. Compared to healthy controls, ALS patients performed worse in the ECAS overall (p < 0.001) and in all of its subdomains (p < 0.02), except memory. Imaging revealed altered cortical activation during all tasks, with patients consistently showing a hyperactivation in relevant brain areas compared to healthy controls. Additionally, cognitively high performing ALS patients consistently exhibited more activation in frontal brain areas than low performing patients and behaviorally unimpaired patients presented with more neuronal activity in orbitofrontal areas than behaviorally impaired patients. In conclusion, hyperactivation in fMRI cognitive tasks seems to represent an early adaptive process to overcome neuronal cell loss in relevant brain areas. The hereby presented cortical pattern change might suggest that, once this loss passes a critical threshold and no cortical buffering is possible, clinical representation of cognitive and behavioral impairment evolves. Future studies might shed light on the pattern of cortical pattern change in the course of ALS.

Evaluation of feature selection algorithms for classification in temporal lobe epilepsy based on MR images

NASA Astrophysics Data System (ADS)

Lai, Chunren; Guo, Shengwen; Cheng, Lina; Wang, Wensheng; Wu, Kai

2017-02-01

It's very important to differentiate the temporal lobe epilepsy (TLE) patients from healthy people and localize the abnormal brain regions of the TLE patients. The cortical features and changes can reveal the unique anatomical patterns of brain regions from the structural MR images. In this study, structural MR images from 28 normal controls (NC), 18 left TLE (LTLE), and 21 right TLE (RTLE) were acquired, and four types of cortical feature, namely cortical thickness (CTh), cortical surface area (CSA), gray matter volume (GMV), and mean curvature (MCu), were explored for discriminative analysis. Three feature selection methods, the independent sample t-test filtering, the sparse-constrained dimensionality reduction model (SCDRM), and the support vector machine-recursive feature elimination (SVM-RFE), were investigated to extract dominant regions with significant differences among the compared groups for classification using the SVM classifier. The results showed that the SVM-REF achieved the highest performance (most classifications with more than 92% accuracy), followed by the SCDRM, and the t-test. Especially, the surface area and gray volume matter exhibited prominent discriminative ability, and the performance of the SVM was improved significantly when the four cortical features were combined. Additionally, the dominant regions with higher classification weights were mainly located in temporal and frontal lobe, including the inferior temporal, entorhinal cortex, fusiform, parahippocampal cortex, middle frontal and frontal pole. It was demonstrated that the cortical features provided effective information to determine the abnormal anatomical pattern and the proposed method has the potential to improve the clinical diagnosis of the TLE.

Parallel prefrontal pathways reach distinct excitatory and inhibitory systems in memory-related rhinal cortices.

PubMed

Bunce, Jamie G; Zikopoulos, Basilis; Feinberg, Marcia; Barbas, Helen

2013-12-15

To investigate how prefrontal cortices impinge on medial temporal cortices we labeled pathways from the anterior cingulate cortex (ACC) and posterior orbitofrontal cortex (pOFC) in rhesus monkeys to compare their relationship with excitatory and inhibitory systems in rhinal cortices. The ACC pathway terminated mostly in areas 28 and 35 with a high proportion of large terminals, whereas the pOFC pathway terminated mostly through small terminals in area 36 and sparsely in areas 28 and 35. Both pathways terminated in all layers. Simultaneous labeling of pathways and distinct neurochemical classes of inhibitory neurons, followed by analyses of appositions of presynaptic and postsynaptic fluorescent signal, or synapses, showed overall predominant association with spines of putative excitatory neurons, but also significant interactions with presumed inhibitory neurons labeled for calretinin, calbindin, or parvalbumin. In the upper layers of areas 28 and 35 the ACC pathway was associated with dendrites of neurons labeled with calretinin, which are thought to disinhibit neighboring excitatory neurons, suggesting facilitated hippocampal access. In contrast, in area 36 pOFC axons were associated with dendrites of calbindin neurons, which are poised to reduce noise and enhance signal. In the deep layers, both pathways innervated mostly dendrites of parvalbumin neurons, which strongly inhibit neighboring excitatory neurons, suggesting gating of hippocampal output to other cortices. These findings suggest that the ACC, associated with attention and context, and the pOFC, associated with emotional valuation, have distinct contributions to memory in rhinal cortices, in processes that are disrupted in psychiatric diseases. Copyright © 2013 Wiley Periodicals, Inc.

Reconstructing for joint angles on the shoulder and elbow from non-invasive electroencephalographic signals through electromyography

PubMed Central

Choi, Kyuwan

2013-01-01

In this study, first the cortical activities over 2240 vertexes on the brain were estimated from 64 channels electroencephalography (EEG) signals using the Hierarchical Bayesian estimation while 5 subjects did continuous arm reaching movements. From the estimated cortical activities, a sparse linear regression method selected only useful features in reconstructing the electromyography (EMG) signals and estimated the EMG signals of 9 arm muscles. Then, a modular artificial neural network was used to estimate four joint angles from the estimated EMG signals of 9 muscles: one for movement control and the other for posture control. The estimated joint angles using this method have the correlation coefficient (CC) of 0.807 (±0.10) and the normalized root-mean-square error (nRMSE) of 0.176 (±0.29) with the actual joint angles. PMID:24167469

Altered structural brain changes and neurocognitive performance in pediatric HIV.

PubMed

Yadav, Santosh K; Gupta, Rakesh K; Garg, Ravindra K; Venkatesh, Vimala; Gupta, Pradeep K; Singh, Alok K; Hashem, Sheema; Al-Sulaiti, Asma; Kaura, Deepak; Wang, Ena; Marincola, Francesco M; Haris, Mohammad

2017-01-01

Pediatric HIV patients often suffer with neurodevelopmental delay and subsequently cognitive impairment. While tissue injury in cortical and subcortical regions in the brain of adult HIV patients has been well reported there is sparse knowledge about these changes in perinatally HIV infected pediatric patients. We analyzed cortical thickness, subcortical volume, structural connectivity, and neurocognitive functions in pediatric HIV patients and compared with those of pediatric healthy controls. With informed consent, 34 perinatally infected pediatric HIV patients and 32 age and gender matched pediatric healthy controls underwent neurocognitive assessment and brain magnetic resonance imaging (MRI) on a 3 T clinical scanner. Altered cortical thickness, subcortical volumes, and abnormal neuropsychological test scores were observed in pediatric HIV patients. The structural network connectivity analysis depicted lower connection strengths, lower clustering coefficients, and higher path length in pediatric HIV patients than healthy controls. The network betweenness and network hubs in cortico-limbic regions were distorted in pediatric HIV patients. The findings suggest that altered cortical and subcortical structures and regional brain connectivity in pediatric HIV patients may contribute to deficits in their neurocognitive functions. Further, longitudinal studies are required for better understanding of the effect of HIV pathogenesis on brain structural changes throughout the brain development process under standard ART treatment.

Representation-Independent Iteration of Sparse Data Arrays

NASA Technical Reports Server (NTRS)

James, Mark

2007-01-01

An approach is defined that describes a method of iterating over massively large arrays containing sparse data using an approach that is implementation independent of how the contents of the sparse arrays are laid out in memory. What is unique and important here is the decoupling of the iteration over the sparse set of array elements from how they are internally represented in memory. This enables this approach to be backward compatible with existing schemes for representing sparse arrays as well as new approaches. What is novel here is a new approach for efficiently iterating over sparse arrays that is independent of the underlying memory layout representation of the array. A functional interface is defined for implementing sparse arrays in any modern programming language with a particular focus for the Chapel programming language. Examples are provided that show the translation of a loop that computes a matrix vector product into this representation for both the distributed and not-distributed cases. This work is directly applicable to NASA and its High Productivity Computing Systems (HPCS) program that JPL and our current program are engaged in. The goal of this program is to create powerful, scalable, and economically viable high-powered computer systems suitable for use in national security and industry by 2010. This is important to NASA for its computationally intensive requirements for analyzing and understanding the volumes of science data from our returned missions.

Sparsey™: event recognition via deep hierarchical sparse distributed codes

PubMed Central

Rinkus, Gerard J.

2014-01-01

The visual cortex's hierarchical, multi-level organization is captured in many biologically inspired computational vision models, the general idea being that progressively larger scale (spatially/temporally) and more complex visual features are represented in progressively higher areas. However, most earlier models use localist representations (codes) in each representational field (which we equate with the cortical macrocolumn, “mac”), at each level. In localism, each represented feature/concept/event (hereinafter “item”) is coded by a single unit. The model we describe, Sparsey, is hierarchical as well but crucially, it uses sparse distributed coding (SDC) in every mac in all levels. In SDC, each represented item is coded by a small subset of the mac's units. The SDCs of different items can overlap and the size of overlap between items can be used to represent their similarity. The difference between localism and SDC is crucial because SDC allows the two essential operations of associative memory, storing a new item and retrieving the best-matching stored item, to be done in fixed time for the life of the model. Since the model's core algorithm, which does both storage and retrieval (inference), makes a single pass over all macs on each time step, the overall model's storage/retrieval operation is also fixed-time, a criterion we consider essential for scalability to the huge (“Big Data”) problems. A 2010 paper described a nonhierarchical version of this model in the context of purely spatial pattern processing. Here, we elaborate a fully hierarchical model (arbitrary numbers of levels and macs per level), describing novel model principles like progressive critical periods, dynamic modulation of principal cells' activation functions based on a mac-level familiarity measure, representation of multiple simultaneously active hypotheses, a novel method of time warp invariant recognition, and we report results showing learning/recognition of spatiotemporal patterns. PMID:25566046

Return probabilities and hitting times of random walks on sparse Erdös-Rényi graphs.

PubMed

Martin, O C; Sulc, P

2010-03-01

We consider random walks on random graphs, focusing on return probabilities and hitting times for sparse Erdös-Rényi graphs. Using the tree approach, which is expected to be exact in the large graph limit, we show how to solve for the distribution of these quantities and we find that these distributions exhibit a form of self-similarity.

Distributed Compressive Sensing

DTIC Science & Technology

2009-01-01

example, smooth signals are sparse in the Fourier basis, and piecewise smooth signals are sparse in a wavelet basis [8]; the commercial coding standards MP3...including wavelets [8], Gabor bases [8], curvelets [35], etc., are widely used for representation and compression of natural signals, images, and...spikes and the sine waves of a Fourier basis, or the Fourier basis and wavelets . Signals that are sparsely represented in frames or unions of bases can

Small Modifications to Network Topology Can Induce Stochastic Bistable Spiking Dynamics in a Balanced Cortical Model

PubMed Central

McDonnell, Mark D.; Ward, Lawrence M.

2014-01-01

Abstract Directed random graph models frequently are used successfully in modeling the population dynamics of networks of cortical neurons connected by chemical synapses. Experimental results consistently reveal that neuronal network topology is complex, however, in the sense that it differs statistically from a random network, and differs for classes of neurons that are physiologically different. This suggests that complex network models whose subnetworks have distinct topological structure may be a useful, and more biologically realistic, alternative to random networks. Here we demonstrate that the balanced excitation and inhibition frequently observed in small cortical regions can transiently disappear in otherwise standard neuronal-scale models of fluctuation-driven dynamics, solely because the random network topology was replaced by a complex clustered one, whilst not changing the in-degree of any neurons. In this network, a small subset of cells whose inhibition comes only from outside their local cluster are the cause of bistable population dynamics, where different clusters of these cells irregularly switch back and forth from a sparsely firing state to a highly active state. Transitions to the highly active state occur when a cluster of these cells spikes sufficiently often to cause strong unbalanced positive feedback to each other. Transitions back to the sparsely firing state rely on occasional large fluctuations in the amount of non-local inhibition received. Neurons in the model are homogeneous in their intrinsic dynamics and in-degrees, but differ in the abundance of various directed feedback motifs in which they participate. Our findings suggest that (i) models and simulations should take into account complex structure that varies for neuron and synapse classes; (ii) differences in the dynamics of neurons with similar intrinsic properties may be caused by their membership in distinctive local networks; (iii) it is important to identify neurons that share physiological properties and location, but differ in their connectivity. PMID:24743633

The human cerebral cortex is neither one nor many: neuronal distribution reveals two quantitatively different zones in the gray matter, three in the white matter, and explains local variations in cortical folding

PubMed Central

Ribeiro, Pedro F. M.; Ventura-Antunes, Lissa; Gabi, Mariana; Mota, Bruno; Grinberg, Lea T.; Farfel, José M.; Ferretti-Rebustini, Renata E. L.; Leite, Renata E. P.; Filho, Wilson J.; Herculano-Houzel, Suzana

2013-01-01

The human prefrontal cortex has been considered different in several aspects and relatively enlarged compared to the rest of the cortical areas. Here we determine whether the white and gray matter of the prefrontal portion of the human cerebral cortex have similar or different cellular compositions relative to the rest of the cortical regions by applying the Isotropic Fractionator to analyze the distribution of neurons along the entire anteroposterior axis of the cortex, and its relationship with the degree of gyrification, number of neurons under the cortical surface, and other parameters. The prefrontal region shares with the remainder of the cerebral cortex (except for occipital cortex) the same relationship between cortical volume and number of neurons. In contrast, both occipital and prefrontal areas vary from other cortical areas in their connectivity through the white matter, with a systematic reduction of cortical connectivity through the white matter and an increase of the mean axon caliber along the anteroposterior axis. These two parameters explain local differences in the distribution of neurons underneath the cortical surface. We also show that local variations in cortical folding are neither a function of local numbers of neurons nor of cortical thickness, but correlate with properties of the white matter, and are best explained by the folding of the white matter surface. Our results suggest that the human cerebral cortex is divided in two zones (occipital and non-occipital) that differ in how neurons are distributed across their gray matter volume and in three zones (prefrontal, occipital, and non-occipital) that differ in how neurons are connected through the white matter. Thus, the human prefrontal cortex has the largest fraction of neuronal connectivity through the white matter and the smallest average axonal caliber in the white matter within the cortex, although its neuronal composition fits the pattern found for other, non-occipital areas. PMID:24032005

Integrated system for point cloud reconstruction and simulated brain shift validation using tracked surgical microscope

NASA Astrophysics Data System (ADS)

Yang, Xiaochen; Clements, Logan W.; Luo, Ma; Narasimhan, Saramati; Thompson, Reid C.; Dawant, Benoit M.; Miga, Michael I.

2017-03-01

Intra-operative soft tissue deformation, referred to as brain shift, compromises the application of current imageguided surgery (IGS) navigation systems in neurosurgery. A computational model driven by sparse data has been used as a cost effective method to compensate for cortical surface and volumetric displacements. Stereoscopic microscopes and laser range scanners (LRS) are the two most investigated sparse intra-operative imaging modalities for driving these systems. However, integrating these devices in the clinical workflow to facilitate development and evaluation requires developing systems that easily permit data acquisition and processing. In this work we present a mock environment developed to acquire stereo images from a tracked operating microscope and to reconstruct 3D point clouds from these images. A reconstruction error of 1 mm is estimated by using a phantom with a known geometry and independently measured deformation extent. The microscope is tracked via an attached tracking rigid body that facilitates the recording of the position of the microscope via a commercial optical tracking system as it moves during the procedure. Point clouds, reconstructed under different microscope positions, are registered into the same space in order to compute the feature displacements. Using our mock craniotomy device, realistic cortical deformations are generated. Our experimental results report approximately 2mm average displacement error compared with the optical tracking system. These results demonstrate the practicality of using tracked stereoscopic microscope as an alternative to LRS to collect sufficient intraoperative information for brain shift correction.

Two alternate proofs of Wang's lune formula for sparse distributed memory and an integral approximation

NASA Technical Reports Server (NTRS)

Jaeckel, Louis A.

1988-01-01

In Kanerva's Sparse Distributed Memory, writing to and reading from the memory are done in relation to spheres in an n-dimensional binary vector space. Thus it is important to know how many points are in the intersection of two spheres in this space. Two proofs are given of Wang's formula for spheres of unequal radii, and an integral approximation for the intersection in this case.

Learning multiple variable-speed sequences in striatum via cortical tutoring.

PubMed

Murray, James M; Escola, G Sean

2017-05-08

Sparse, sequential patterns of neural activity have been observed in numerous brain areas during timekeeping and motor sequence tasks. Inspired by such observations, we construct a model of the striatum, an all-inhibitory circuit where sequential activity patterns are prominent, addressing the following key challenges: (i) obtaining control over temporal rescaling of the sequence speed, with the ability to generalize to new speeds; (ii) facilitating flexible expression of distinct sequences via selective activation, concatenation, and recycling of specific subsequences; and (iii) enabling the biologically plausible learning of sequences, consistent with the decoupling of learning and execution suggested by lesion studies showing that cortical circuits are necessary for learning, but that subcortical circuits are sufficient to drive learned behaviors. The same mechanisms that we describe can also be applied to circuits with both excitatory and inhibitory populations, and hence may underlie general features of sequential neural activity pattern generation in the brain.

Approximate method of variational Bayesian matrix factorization/completion with sparse prior

NASA Astrophysics Data System (ADS)

Kawasumi, Ryota; Takeda, Koujin

2018-05-01

We derive the analytical expression of a matrix factorization/completion solution by the variational Bayes method, under the assumption that the observed matrix is originally the product of low-rank, dense and sparse matrices with additive noise. We assume the prior of a sparse matrix is a Laplace distribution by taking matrix sparsity into consideration. Then we use several approximations for the derivation of a matrix factorization/completion solution. By our solution, we also numerically evaluate the performance of a sparse matrix reconstruction in matrix factorization, and completion of a missing matrix element in matrix completion.

Massive formation of square array junctions dramatically alters cell shape but does not cause lens opacity in the cav1-KO mice.

PubMed

Biswas, Sondip K; Brako, Lawrence; Lo, Woo-Kuen

2014-08-01

The wavy square array junctions are composed of truncated aquaporin-0 (AQP0) proteins typically distributed in the deep cortical and nuclear fibers in wild-type lenses. These junctions may help maintain the narrowed extracellular spaces between fiber cells to minimize light scattering. Herein, we investigate the impact of the cell shape changes, due to abnormal formation of extensive square array junctions, on the lens opacification in the caveolin-1 knockout mice. The cav1-KO and wild-type mice at age 1-22 months were used. By light microscopy examinations, cav1-KO lenses at age 1-18 months were transparent in both cortical and nuclear regions, whereas some lenses older than 18 months old exhibited nuclear cataracts. Scanning EM consistently observed the massive formation of ridge-and-valley membrane surfaces in young fibers at approximately 150 μm deep in all cav1-KO lenses studied. In contrast, the typical ridge-and-valleys were only seen in mature fibers deeper than 400 μm in wild-type lenses. The resulting extensive ridge-and-valleys dramatically altered the overall cell shape in cav1-KO lenses. Remarkably, despite dramatic shape changes, these deformed fiber cells remained intact and made close contact with their neighboring cells. By freeze-fracture TEM, ridge-and-valleys exhibited the typical orthogonal arrangement of 6.6 nm square array intramembrane particles and displayed the narrowed extracellular spaces. Immunofluorescence analysis showed that AQP0 C-terminus labeling was significantly decreased in outer cortical fibers in cav1-KO lenses. However, freeze-fracture immunogold labeling showed that the AQP0 C-terminus antibody was sparsely distributed on the wavy square array junctions, suggesting that the cleavage of AQP0 C-termini might not yet be complete. The cav1-KO lenses with nuclear cataracts showed complete cellular breakdown and large globule formation in the lens nucleus. This study suggests that despite dramatic cell shape changes, the massive formation of wavy square array junctions in intact fibers may provide additional adhesive support for maintaining the narrowed extracellular spaces that are crucial for the transparency of cav1-KO lenses. Copyright © 2014 Elsevier Ltd. All rights reserved.

Advancing Cost-Effective Readiness by Improving the Supply Chain Management of Sparse, Intermittently-Demanded Parts

DTIC Science & Technology

2015-03-26

DEMANDED PARTS DISSERTATION Gregory H. Gehret AFIT-ENS-DS-15-M- 256 DEPARTMENT OF THE AIR FORCE AIR UNIVERSITY AIR FORCE INSTITUTE...protection in the United States. AFIT-ENS-DS-15-M- 256 ADVANCING COST-EFFECTIVE READINESS BY IMPROVING THE SUPPLY CHAIN MANAGEMENT OF SPARSE...RELEASE; DISTRIBUTION UNLIMITED. AFIT-ENS-DS-15-M- 256 ADVANCING COST-EFFECTIVE READINESS BY IMPROVING THE SUPPLY CHAIN MANAGEMENT OF SPARSE

Distribution of Longitudinal Wave Velocities in Bovine Cortical Bone in vitro

NASA Astrophysics Data System (ADS)

Yamato, Yu; Kataoka, Hideo; Matsukawa, Mami; Yamazaki, Kaoru; Otani, Takahiko; Nagano, Akira

2005-06-01

The distribution of longitudinal wave velocities and longitudinal moduli in a bovine femoral cortical bone was experimentally investigated. In all parts of the long cylindrical bone, the velocities and longitudinal moduli in the axial direction were the highest. In the anterior (A) part, the velocities in the axial direction were high and almost constant, whereas the velocities in the proximal postero medial (PM) and distal postero lateral (PL) parts markedly decreased. Classifying the cortical bone into three structures (plexiform, Haversian, and porotic), we clarify the velocity distributions in the bone with discussion from an anatomical point of view.

Network activity influences the subthreshold and spiking visual responses of pyramidal neurons in the three-layer turtle cortex.

PubMed

Wright, Nathaniel C; Wessel, Ralf

2017-10-01

A primary goal of systems neuroscience is to understand cortical function, typically by studying spontaneous and stimulus-modulated cortical activity. Mounting evidence suggests a strong and complex relationship exists between the ongoing and stimulus-modulated cortical state. To date, most work in this area has been based on spiking in populations of neurons. While advantageous in many respects, this approach is limited in scope: it records the activity of a minority of neurons and gives no direct indication of the underlying subthreshold dynamics. Membrane potential recordings can fill these gaps in our understanding, but stable recordings are difficult to obtain in vivo. Here, we recorded subthreshold cortical visual responses in the ex vivo turtle eye-attached whole brain preparation, which is ideally suited for such a study. We found that, in the absence of visual stimulation, the network was "synchronous"; neurons displayed network-mediated transitions between hyperpolarized (Down) and depolarized (Up) membrane potential states. The prevalence of these slow-wave transitions varied across turtles and recording sessions. Visual stimulation evoked similar Up states, which were on average larger and less reliable when the ongoing state was more synchronous. Responses were muted when immediately preceded by large, spontaneous Up states. Evoked spiking was sparse, highly variable across trials, and mediated by concerted synaptic inputs that were, in general, only very weakly correlated with inputs to nearby neurons. Together, these results highlight the multiplexed influence of the cortical network on the spontaneous and sensory-evoked activity of individual cortical neurons. NEW & NOTEWORTHY Most studies of cortical activity focus on spikes. Subthreshold membrane potential recordings can provide complementary insight, but stable recordings are difficult to obtain in vivo. Here, we recorded the membrane potentials of cortical neurons during ongoing and visually evoked activity. We observed a strong relationship between network and single-neuron evoked activity spanning multiple temporal scales. The membrane potential perspective of cortical dynamics thus highlights the influence of intrinsic network properties on visual processing. Copyright © 2017 the American Physiological Society.

Automatic correction of intensity nonuniformity from sparseness of gradient distribution in medical images.

PubMed

Zheng, Yuanjie; Grossman, Murray; Awate, Suyash P; Gee, James C

2009-01-01

We propose to use the sparseness property of the gradient probability distribution to estimate the intensity nonuniformity in medical images, resulting in two novel automatic methods: a non-parametric method and a parametric method. Our methods are easy to implement because they both solve an iteratively re-weighted least squares problem. They are remarkably accurate as shown by our experiments on images of different imaged objects and from different imaging modalities.

Automatic Correction of Intensity Nonuniformity from Sparseness of Gradient Distribution in Medical Images

PubMed Central

Zheng, Yuanjie; Grossman, Murray; Awate, Suyash P.; Gee, James C.

2013-01-01

We propose to use the sparseness property of the gradient probability distribution to estimate the intensity nonuniformity in medical images, resulting in two novel automatic methods: a non-parametric method and a parametric method. Our methods are easy to implement because they both solve an iteratively re-weighted least squares problem. They are remarkably accurate as shown by our experiments on images of different imaged objects and from different imaging modalities. PMID:20426191

Generative models for discovering sparse distributed representations.

PubMed Central

Hinton, G E; Ghahramani, Z

1997-01-01

We describe a hierarchical, generative model that can be viewed as a nonlinear generalization of factor analysis and can be implemented in a neural network. The model uses bottom-up, top-down and lateral connections to perform Bayesian perceptual inference correctly. Once perceptual inference has been performed the connection strengths can be updated using a very simple learning rule that only requires locally available information. We demonstrate that the network learns to extract sparse, distributed, hierarchical representations. PMID:9304685

Ridit Analysis for Cooper-Harper and Other Ordinal Ratings for Sparse Data - A Distance-based Approach

DTIC Science & Technology

2016-09-01

is to fit empirical Beta distributions to observed data, and then to use a randomization approach to make inferences on the difference between...a Ridit analysis on the often sparse data sets in many Flying Qualities applicationsi. The method of this paper is to fit empirical Beta ...One such measure is the discrete- probability-distribution version of the (squared) ‘Hellinger Distance’ (Yang & Le Cam , 2000) 2(, ) = 1

A Sparse Bayesian Approach for Forward-Looking Superresolution Radar Imaging

PubMed Central

Zhang, Yin; Zhang, Yongchao; Huang, Yulin; Yang, Jianyu

2017-01-01

This paper presents a sparse superresolution approach for high cross-range resolution imaging of forward-looking scanning radar based on the Bayesian criterion. First, a novel forward-looking signal model is established as the product of the measurement matrix and the cross-range target distribution, which is more accurate than the conventional convolution model. Then, based on the Bayesian criterion, the widely-used sparse regularization is considered as the penalty term to recover the target distribution. The derivation of the cost function is described, and finally, an iterative expression for minimizing this function is presented. Alternatively, this paper discusses how to estimate the single parameter of Gaussian noise. With the advantage of a more accurate model, the proposed sparse Bayesian approach enjoys a lower model error. Meanwhile, when compared with the conventional superresolution methods, the proposed approach shows high cross-range resolution and small location error. The superresolution results for the simulated point target, scene data, and real measured data are presented to demonstrate the superior performance of the proposed approach. PMID:28604583

Dimension-Factorized Range Migration Algorithm for Regularly Distributed Array Imaging

PubMed Central

Guo, Qijia; Wang, Jie; Chang, Tianying

2017-01-01

The two-dimensional planar MIMO array is a popular approach for millimeter wave imaging applications. As a promising practical alternative, sparse MIMO arrays have been devised to reduce the number of antenna elements and transmitting/receiving channels with predictable and acceptable loss in image quality. In this paper, a high precision three-dimensional imaging algorithm is proposed for MIMO arrays of the regularly distributed type, especially the sparse varieties. Termed the Dimension-Factorized Range Migration Algorithm, the new imaging approach factorizes the conventional MIMO Range Migration Algorithm into multiple operations across the sparse dimensions. The thinner the sparse dimensions of the array, the more efficient the new algorithm will be. Advantages of the proposed approach are demonstrated by comparison with the conventional MIMO Range Migration Algorithm and its non-uniform fast Fourier transform based variant in terms of all the important characteristics of the approaches, especially the anti-noise capability. The computation cost is analyzed as well to evaluate the efficiency quantitatively. PMID:29113083

Cross-domain expression recognition based on sparse coding and transfer learning

NASA Astrophysics Data System (ADS)

Yang, Yong; Zhang, Weiyi; Huang, Yong

2017-05-01

Traditional facial expression recognition methods usually assume that the training set and the test set are independent and identically distributed. However, in actual expression recognition applications, the conditions of independent and identical distribution are hardly satisfied for the training set and test set because of the difference of light, shade, race and so on. In order to solve this problem and improve the performance of expression recognition in the actual applications, a novel method based on transfer learning and sparse coding is applied to facial expression recognition. First of all, a common primitive model, that is, the dictionary is learnt. Then, based on the idea of transfer learning, the learned primitive pattern is transferred to facial expression and the corresponding feature representation is obtained by sparse coding. The experimental results in CK +, JAFFE and NVIE database shows that the transfer learning based on sparse coding method can effectively improve the expression recognition rate in the cross-domain expression recognition task and is suitable for the practical facial expression recognition applications.

Distribution of neurons in functional areas of the mouse cerebral cortex reveals quantitatively different cortical zones

PubMed Central

Herculano-Houzel, Suzana; Watson, Charles; Paxinos, George

2013-01-01

How are neurons distributed along the cortical surface and across functional areas? Here we use the isotropic fractionator (Herculano-Houzel and Lent, 2005) to analyze the distribution of neurons across the entire isocortex of the mouse, divided into 18 functional areas defined anatomically. We find that the number of neurons underneath a surface area (the N/A ratio) varies 4.5-fold across functional areas and neuronal density varies 3.2-fold. The face area of S1 contains the most neurons, followed by motor cortex and the primary visual cortex. Remarkably, while the distribution of neurons across functional areas does not accompany the distribution of surface area, it mirrors closely the distribution of cortical volumes—with the exception of the visual areas, which hold more neurons than expected for their volume. Across the non-visual cortex, the volume of individual functional areas is a shared linear function of their number of neurons, while in the visual areas, neuronal densities are much higher than in all other areas. In contrast, the 18 functional areas cluster into three different zones according to the relationship between the N/A ratio and cortical thickness and neuronal density: these three clusters can be called visual, sensory, and, possibly, associative. These findings are remarkably similar to those in the human cerebral cortex (Ribeiro et al., 2013) and suggest that, like the human cerebral cortex, the mouse cerebral cortex comprises two zones that differ in how neurons form the cortical volume, and three zones that differ in how neurons are distributed underneath the cortical surface, possibly in relation to local differences in connectivity through the white matter. Our results suggest that beyond the developmental divide into visual and non-visual cortex, functional areas initially share a common distribution of neurons along the parenchyma that become delimited into functional areas according to the pattern of connectivity established later. PMID:24155697

Greedy Sparse Approaches for Homological Coverage in Location Unaware Sensor Networks

DTIC Science & Technology

2017-12-08

GlobalSIP); 2013 Dec; Austin , TX . p. 595– 598. 33. Farah C, Schwaner F, Abedi A, Worboys M. Distributed homology algorithm to detect topological events...ARL-TR-8235•DEC 2017 US Army Research Laboratory Greedy Sparse Approaches for Homological Coverage in Location-Unaware Sensor Net- works by Terrence...8235•DEC 2017 US Army Research Laboratory Greedy Sparse Approaches for Homological Coverage in Location-Unaware Sensor Net- works by Terrence J Moore

Compilation of 1985 Annual Reports of the Navy ELF (Extremely Low Frequency) Communications System Ecological Monitoring Program. Volume 1. Tabs A-C.

DTIC Science & Technology

1986-07-01

and straight when short, but spindly and often crooked when long, ususally somewhat constricted at the base. Microscopic -- Surface hyphae sparse, 2-3... hyphae with conspicuous interlocking, "jig-saw puzzle-like" pattern; cortical cells red-brown except over apex where they are colorless; Hartig net hyphae ...Type S Macroscopic -- Black, sometimes with lighter apex; usually fuzzy, with abundant attached, coarse hyphae ; 1-3 mm long X 0.5-1.0 mm diameter; mono

Prediction of brain maturity based on cortical thickness at different spatial resolutions.

PubMed

Khundrakpam, Budhachandra S; Tohka, Jussi; Evans, Alan C

2015-05-01

Several studies using magnetic resonance imaging (MRI) scans have shown developmental trajectories of cortical thickness. Cognitive milestones happen concurrently with these structural changes, and a delay in such changes has been implicated in developmental disorders such as attention-deficit/hyperactivity disorder (ADHD). Accurate estimation of individuals' brain maturity, therefore, is critical in establishing a baseline for normal brain development against which neurodevelopmental disorders can be assessed. In this study, cortical thickness derived from structural magnetic resonance imaging (MRI) scans of a large longitudinal dataset of normally growing children and adolescents (n=308), were used to build a highly accurate predictive model for estimating chronological age (cross-validated correlation up to R=0.84). Unlike previous studies which used kernelized approach in building prediction models, we used an elastic net penalized linear regression model capable of producing a spatially sparse, yet accurate predictive model of chronological age. Upon investigating different scales of cortical parcellation from 78 to 10,240 brain parcels, we observed that the accuracy in estimated age improved with increased spatial scale of brain parcellation, with the best estimations obtained for spatial resolutions consisting of 2560 and 10,240 brain parcels. The top predictors of brain maturity were found in highly localized sensorimotor and association areas. The results of our study demonstrate that cortical thickness can be used to estimate individuals' brain maturity with high accuracy, and the estimated ages relate to functional and behavioural measures, underscoring the relevance and scope of the study in the understanding of biological maturity. Copyright © 2015 Elsevier Inc. All rights reserved.

Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition

PubMed Central

Bill, Johannes; Buesing, Lars; Habenschuss, Stefan; Nessler, Bernhard; Maass, Wolfgang; Legenstein, Robert

2015-01-01

During the last decade, Bayesian probability theory has emerged as a framework in cognitive science and neuroscience for describing perception, reasoning and learning of mammals. However, our understanding of how probabilistic computations could be organized in the brain, and how the observed connectivity structure of cortical microcircuits supports these calculations, is rudimentary at best. In this study, we investigate statistical inference and self-organized learning in a spatially extended spiking network model, that accommodates both local competitive and large-scale associative aspects of neural information processing, under a unified Bayesian account. Specifically, we show how the spiking dynamics of a recurrent network with lateral excitation and local inhibition in response to distributed spiking input, can be understood as sampling from a variational posterior distribution of a well-defined implicit probabilistic model. This interpretation further permits a rigorous analytical treatment of experience-dependent plasticity on the network level. Using machine learning theory, we derive update rules for neuron and synapse parameters which equate with Hebbian synaptic and homeostatic intrinsic plasticity rules in a neural implementation. In computer simulations, we demonstrate that the interplay of these plasticity rules leads to the emergence of probabilistic local experts that form distributed assemblies of similarly tuned cells communicating through lateral excitatory connections. The resulting sparse distributed spike code of a well-adapted network carries compressed information on salient input features combined with prior experience on correlations among them. Our theory predicts that the emergence of such efficient representations benefits from network architectures in which the range of local inhibition matches the spatial extent of pyramidal cells that share common afferent input. PMID:26284370

Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images.

PubMed

Guo, Shengwen; Lai, Chunren; Wu, Congling; Cen, Guiyin

2017-01-01

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI-cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI-NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI-NC comparison. The best performances obtained by the SVM classifier using the essential features were 5-40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease.

A view of Kanerva's sparse distributed memory

NASA Technical Reports Server (NTRS)

Denning, P. J.

1986-01-01

Pentti Kanerva is working on a new class of computers, which are called pattern computers. Pattern computers may close the gap between capabilities of biological organisms to recognize and act on patterns (visual, auditory, tactile, or olfactory) and capabilities of modern computers. Combinations of numeric, symbolic, and pattern computers may one day be capable of sustaining robots. The overview of the requirements for a pattern computer, a summary of Kanerva's Sparse Distributed Memory (SDM), and examples of tasks this computer can be expected to perform well are given.

Augmented l1 and Nuclear-Norm Models with a Globally Linearly Convergent Algorithm. Revision 1

DTIC Science & Technology

2012-10-17

nonzero and sampled from the standard Gaussian distribution (for Figure 2) or the Bernoulli distribution (for Figure 3). Both tests had the same sensing...dual variable y(k) Figure 3: Convergence of primal and dual variables of three algorithms on Bernoulli sparse x0 was the slowest. Besides the obvious...slower convergence than the final stage. Comparing the results of two tests, the convergence was faster on the Bernoulli sparse signal than the

Notes on implementation of sparsely distributed memory

NASA Technical Reports Server (NTRS)

Keeler, J. D.; Denning, P. J.

1986-01-01

The Sparsely Distributed Memory (SDM) developed by Kanerva is an unconventional memory design with very interesting and desirable properties. The memory works in a manner that is closely related to modern theories of human memory. The SDM model is discussed in terms of its implementation in hardware. Two appendices discuss the unconventional approaches of the SDM: Appendix A treats a resistive circuit for fast, parallel address decoding; and Appendix B treats a systolic array for high throughput read and write operations.

Distribution Analysis of Anthocyanins, Sugars, and Organic Acids in Strawberry Fruits Using Matrix-Assisted Laser Desorption/Ionization-Imaging Mass Spectrometry.

PubMed

Enomoto, Hirofumi; Sato, Kei; Miyamoto, Koji; Ohtsuka, Akira; Yamane, Hisakazu

2018-05-16

Anthocyanins, sugars, and organic acids contribute to the appearance, health benefits, and taste of strawberries. However, their spatial distribution in the ripe fruit has been fully unrevealed. Therefore, we performed matrix-assisted laser desorption/ionization, MALDI-IMS, analysis to investigate their spatial distribution in ripe strawberries. The detection sensitivity was improved by using the TM-Sprayer for matrix application. In the receptacle, pelargonidins were distributed in the skin, cortical, and pith tissues, whereas cyanidins and delphinidins were slightly localized in the skin. In the achene, mainly cyanidins were localized in the outside of the skin. Citric acid was mainly distributed in the upper and bottom side of cortical tissue. Although hexose was distributed almost equally throughout the fruits, sucrose was mainly distributed in the upper side of cortical and pith tissues. These results suggest that using the TM-Sprayer in MALDI-IMS was useful for microscopic distribution analysis of anthocyanins, sugars, and organic acids in strawberries.

Low-Rank Correction Methods for Algebraic Domain Decomposition Preconditioners

DOE PAGES

Li, Ruipeng; Saad, Yousef

2017-08-01

This study presents a parallel preconditioning method for distributed sparse linear systems, based on an approximate inverse of the original matrix, that adopts a general framework of distributed sparse matrices and exploits domain decomposition (DD) and low-rank corrections. The DD approach decouples the matrix and, once inverted, a low-rank approximation is applied by exploiting the Sherman--Morrison--Woodbury formula, which yields two variants of the preconditioning methods. The low-rank expansion is computed by the Lanczos procedure with reorthogonalizations. Numerical experiments indicate that, when combined with Krylov subspace accelerators, this preconditioner can be efficient and robust for solving symmetric sparse linear systems. Comparisonsmore » with pARMS, a DD-based parallel incomplete LU (ILU) preconditioning method, are presented for solving Poisson's equation and linear elasticity problems.« less

Low-Rank Correction Methods for Algebraic Domain Decomposition Preconditioners

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

Li, Ruipeng; Saad, Yousef

This study presents a parallel preconditioning method for distributed sparse linear systems, based on an approximate inverse of the original matrix, that adopts a general framework of distributed sparse matrices and exploits domain decomposition (DD) and low-rank corrections. The DD approach decouples the matrix and, once inverted, a low-rank approximation is applied by exploiting the Sherman--Morrison--Woodbury formula, which yields two variants of the preconditioning methods. The low-rank expansion is computed by the Lanczos procedure with reorthogonalizations. Numerical experiments indicate that, when combined with Krylov subspace accelerators, this preconditioner can be efficient and robust for solving symmetric sparse linear systems. Comparisonsmore » with pARMS, a DD-based parallel incomplete LU (ILU) preconditioning method, are presented for solving Poisson's equation and linear elasticity problems.« less

Discovering sparse transcription factor codes for cell states and state transitions during development

PubMed Central

Furchtgott, Leon A; Melton, Samuel; Menon, Vilas; Ramanathan, Sharad

2017-01-01

Computational analysis of gene expression to determine both the sequence of lineage choices made by multipotent cells and to identify the genes influencing these decisions is challenging. Here we discover a pattern in the expression levels of a sparse subset of genes among cell types in B- and T-cell developmental lineages that correlates with developmental topologies. We develop a statistical framework using this pattern to simultaneously infer lineage transitions and the genes that determine these relationships. We use this technique to reconstruct the early hematopoietic and intestinal developmental trees. We extend this framework to analyze single-cell RNA-seq data from early human cortical development, inferring a neocortical-hindbrain split in early progenitor cells and the key genes that could control this lineage decision. Our work allows us to simultaneously infer both the identity and lineage of cell types as well as a small set of key genes whose expression patterns reflect these relationships. DOI: http://dx.doi.org/10.7554/eLife.20488.001 PMID:28296636

Short-term memory capacity in networks via the restricted isometry property.

PubMed

Charles, Adam S; Yap, Han Lun; Rozell, Christopher J

2014-06-01

Cortical networks are hypothesized to rely on transient network activity to support short-term memory (STM). In this letter, we study the capacity of randomly connected recurrent linear networks for performing STM when the input signals are approximately sparse in some basis. We leverage results from compressed sensing to provide rigorous nonasymptotic recovery guarantees, quantifying the impact of the input sparsity level, the input sparsity basis, and the network characteristics on the system capacity. Our analysis demonstrates that network memory capacities can scale superlinearly with the number of nodes and in some situations can achieve STM capacities that are much larger than the network size. We provide perfect recovery guarantees for finite sequences and recovery bounds for infinite sequences. The latter analysis predicts that network STM systems may have an optimal recovery length that balances errors due to omission and recall mistakes. Furthermore, we show that the conditions yielding optimal STM capacity can be embodied in several network topologies, including networks with sparse or dense connectivities.

Objective sea level pressure analysis for sparse data areas

NASA Technical Reports Server (NTRS)

Druyan, L. M.

1972-01-01

A computer procedure was used to analyze the pressure distribution over the North Pacific Ocean for eleven synoptic times in February, 1967. Independent knowledge of the central pressures of lows is shown to reduce the analysis errors for very sparse data coverage. The application of planned remote sensing of sea-level wind speeds is shown to make a significant contribution to the quality of the analysis especially in the high gradient mid-latitudes and for sparse coverage of conventional observations (such as over Southern Hemisphere oceans). Uniform distribution of the available observations of sea-level pressure and wind velocity yields results far superior to those derived from a random distribution. A generalization of the results indicates that the average lower limit for analysis errors is between 2 and 2.5 mb based on the perfect specification of the magnitude of the sea-level pressure gradient from a known verification analysis. A less than perfect specification will derive from wind-pressure relationships applied to satellite observed wind speeds.

The opponent channel population code of sound location is an efficient representation of natural binaural sounds.

PubMed

Młynarski, Wiktor

2015-05-01

In mammalian auditory cortex, sound source position is represented by a population of broadly tuned neurons whose firing is modulated by sounds located at all positions surrounding the animal. Peaks of their tuning curves are concentrated at lateral position, while their slopes are steepest at the interaural midline, allowing for the maximum localization accuracy in that area. These experimental observations contradict initial assumptions that the auditory space is represented as a topographic cortical map. It has been suggested that a "panoramic" code has evolved to match specific demands of the sound localization task. This work provides evidence suggesting that properties of spatial auditory neurons identified experimentally follow from a general design principle- learning a sparse, efficient representation of natural stimuli. Natural binaural sounds were recorded and served as input to a hierarchical sparse-coding model. In the first layer, left and right ear sounds were separately encoded by a population of complex-valued basis functions which separated phase and amplitude. Both parameters are known to carry information relevant for spatial hearing. Monaural input converged in the second layer, which learned a joint representation of amplitude and interaural phase difference. Spatial selectivity of each second-layer unit was measured by exposing the model to natural sound sources recorded at different positions. Obtained tuning curves match well tuning characteristics of neurons in the mammalian auditory cortex. This study connects neuronal coding of the auditory space with natural stimulus statistics and generates new experimental predictions. Moreover, results presented here suggest that cortical regions with seemingly different functions may implement the same computational strategy-efficient coding.

Mouse auditory cortex differs from visual and somatosensory cortices in the laminar distribution of cytochrome oxidase and acetylcholinesterase.

PubMed

Anderson, L A; Christianson, G B; Linden, J F

2009-02-03

Cytochrome oxidase (CYO) and acetylcholinesterase (AChE) staining density varies across the cortical layers in many sensory areas. The laminar variations likely reflect differences between the layers in levels of metabolic activity and cholinergic modulation. The question of whether these laminar variations differ between primary sensory cortices has never been systematically addressed in the same set of animals, since most studies of sensory cortex focus on a single sensory modality. Here, we compared the laminar distribution of CYO and AChE activity in the primary auditory, visual, and somatosensory cortices of the mouse, using Nissl-stained sections to define laminar boundaries. Interestingly, for both CYO and AChE, laminar patterns of enzyme activity were similar in the visual and somatosensory cortices, but differed in the auditory cortex. In the visual and somatosensory areas, staining densities for both enzymes were highest in layers III/IV or IV and in lower layer V. In the auditory cortex, CYO activity showed a reliable peak only at the layer III/IV border, while AChE distribution was relatively homogeneous across layers. These results suggest that laminar patterns of metabolic activity and cholinergic influence are similar in the mouse visual and somatosensory cortices, but differ in the auditory cortex.

BI-sparsity pursuit for robust subspace recovery

DOE PAGES

Bian, Xiao; Krim, Hamid

2015-09-01

Here, the success of sparse models in computer vision and machine learning in many real-world applications, may be attributed in large part, to the fact that many high dimensional data are distributed in a union of low dimensional subspaces. The underlying structure may, however, be adversely affected by sparse errors, thus inducing additional complexity in recovering it. In this paper, we propose a bi-sparse model as a framework to investigate and analyze this problem, and provide as a result , a novel algorithm to recover the union of subspaces in presence of sparse corruptions. We additionally demonstrate the effectiveness ofmore » our method by experiments on real-world vision data.« less

Application of a sparseness constraint in multivariate curve resolution - Alternating least squares.

PubMed

Hugelier, Siewert; Piqueras, Sara; Bedia, Carmen; de Juan, Anna; Ruckebusch, Cyril

2018-02-13

The use of sparseness in chemometrics is a concept that has increased in popularity. The advantage is, above all, a better interpretability of the results obtained. In this work, sparseness is implemented as a constraint in multivariate curve resolution - alternating least squares (MCR-ALS), which aims at reproducing raw (mixed) data by a bilinear model of chemically meaningful profiles. In many cases, the mixed raw data analyzed are not sparse by nature, but their decomposition profiles can be, as it is the case in some instrumental responses, such as mass spectra, or in concentration profiles linked to scattered distribution maps of powdered samples in hyperspectral images. To induce sparseness in the constrained profiles, one-dimensional and/or two-dimensional numerical arrays can be fitted using a basis of Gaussian functions with a penalty on the coefficients. In this work, a least squares regression framework with L 0 -norm penalty is applied. This L 0 -norm penalty constrains the number of non-null coefficients in the fit of the array constrained without having an a priori on the number and their positions. It has been shown that the sparseness constraint induces the suppression of values linked to uninformative channels and noise in MS spectra and improves the location of scattered compounds in distribution maps, resulting in a better interpretability of the constrained profiles. An additional benefit of the sparseness constraint is a lower ambiguity in the bilinear model, since the major presence of null coefficients in the constrained profiles also helps to limit the solutions for the profiles in the counterpart matrix of the MCR bilinear model. Copyright © 2017 Elsevier B.V. All rights reserved.

A structured sparse regression method for estimating isoform expression level from multi-sample RNA-seq data.

PubMed

Zhang, L; Liu, X J

2016-06-03

With the rapid development of next-generation high-throughput sequencing technology, RNA-seq has become a standard and important technique for transcriptome analysis. For multi-sample RNA-seq data, the existing expression estimation methods usually deal with each single-RNA-seq sample, and ignore that the read distributions are consistent across multiple samples. In the current study, we propose a structured sparse regression method, SSRSeq, to estimate isoform expression using multi-sample RNA-seq data. SSRSeq uses a non-parameter model to capture the general tendency of non-uniformity read distribution for all genes across multiple samples. Additionally, our method adds a structured sparse regularization, which not only incorporates the sparse specificity between a gene and its corresponding isoform expression levels, but also reduces the effects of noisy reads, especially for lowly expressed genes and isoforms. Four real datasets were used to evaluate our method on isoform expression estimation. Compared with other popular methods, SSRSeq reduced the variance between multiple samples, and produced more accurate isoform expression estimations, and thus more meaningful biological interpretations.

Perceptually controlled doping for audio source separation

NASA Astrophysics Data System (ADS)

Mahé, Gaël; Nadalin, Everton Z.; Suyama, Ricardo; Romano, João MT

2014-12-01

The separation of an underdetermined audio mixture can be performed through sparse component analysis (SCA) that relies however on the strong hypothesis that source signals are sparse in some domain. To overcome this difficulty in the case where the original sources are available before the mixing process, the informed source separation (ISS) embeds in the mixture a watermark, which information can help a further separation. Though powerful, this technique is generally specific to a particular mixing setup and may be compromised by an additional bitrate compression stage. Thus, instead of watermarking, we propose a `doping' method that makes the time-frequency representation of each source more sparse, while preserving its audio quality. This method is based on an iterative decrease of the distance between the distribution of the signal and a target sparse distribution, under a perceptual constraint. We aim to show that the proposed approach is robust to audio coding and that the use of the sparsified signals improves the source separation, in comparison with the original sources. In this work, the analysis is made only in instantaneous mixtures and focused on voice sources.

EIT Imaging Regularization Based on Spectral Graph Wavelets.

PubMed

Gong, Bo; Schullcke, Benjamin; Krueger-Ziolek, Sabine; Vauhkonen, Marko; Wolf, Gerhard; Mueller-Lisse, Ullrich; Moeller, Knut

2017-09-01

The objective of electrical impedance tomographic reconstruction is to identify the distribution of tissue conductivity from electrical boundary conditions. This is an ill-posed inverse problem usually solved under the finite-element method framework. In previous studies, standard sparse regularization was used for difference electrical impedance tomography to achieve a sparse solution. However, regarding elementwise sparsity, standard sparse regularization interferes with the smoothness of conductivity distribution between neighboring elements and is sensitive to noise. As an effect, the reconstructed images are spiky and depict a lack of smoothness. Such unexpected artifacts are not realistic and may lead to misinterpretation in clinical applications. To eliminate such artifacts, we present a novel sparse regularization method that uses spectral graph wavelet transforms. Single-scale or multiscale graph wavelet transforms are employed to introduce local smoothness on different scales into the reconstructed images. The proposed approach relies on viewing finite-element meshes as undirected graphs and applying wavelet transforms derived from spectral graph theory. Reconstruction results from simulations, a phantom experiment, and patient data suggest that our algorithm is more robust to noise and produces more reliable images.

The HTM Spatial Pooler-A Neocortical Algorithm for Online Sparse Distributed Coding.

PubMed

Cui, Yuwei; Ahmad, Subutai; Hawkins, Jeff

2017-01-01

Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper, we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the input. It converts arbitrary binary input patterns into sparse distributed representations (SDRs) using a combination of competitive Hebbian learning rules and homeostatic excitability control. We describe a number of key properties of the SP, including fast adaptation to changing input statistics, improved noise robustness through learning, efficient use of cells, and robustness to cell death. In order to quantify these properties we develop a set of metrics that can be directly computed from the SP outputs. We show how the properties are met using these metrics and targeted artificial simulations. We then demonstrate the value of the SP in a complete end-to-end real-world HTM system. We discuss the relationship with neuroscience and previous studies of sparse coding. The HTM spatial pooler represents a neurally inspired algorithm for learning sparse representations from noisy data streams in an online fashion.

EPR oximetry in three spatial dimensions using sparse spin distribution

NASA Astrophysics Data System (ADS)

Som, Subhojit; Potter, Lee C.; Ahmad, Rizwan; Vikram, Deepti S.; Kuppusamy, Periannan

2008-08-01

A method is presented to use continuous wave electron paramagnetic resonance imaging for rapid measurement of oxygen partial pressure in three spatial dimensions. A particulate paramagnetic probe is employed to create a sparse distribution of spins in a volume of interest. Information encoding location and spectral linewidth is collected by varying the spatial orientation and strength of an applied magnetic gradient field. Data processing exploits the spatial sparseness of spins to detect voxels with nonzero spin and to estimate the spectral linewidth for those voxels. The parsimonious representation of spin locations and linewidths permits an order of magnitude reduction in data acquisition time, compared to four-dimensional tomographic reconstruction using traditional spectral-spatial imaging. The proposed oximetry method is experimentally demonstrated for a lithium octa- n-butoxy naphthalocyanine (LiNc-BuO) probe using an L-band EPR spectrometer.

Retrieval of high-fidelity memory arises from distributed cortical networks.

PubMed

Wais, Peter E; Jahanikia, Sahar; Steiner, Daniel; Stark, Craig E L; Gazzaley, Adam

2017-04-01

Medial temporal lobe (MTL) function is well established as necessary for memory of facts and events. It is likely that lateral cortical regions critically guide cognitive control processes to tune in high-fidelity details that are most relevant for memory retrieval. Here, convergent results from functional and structural MRI show that retrieval of detailed episodic memory arises from lateral cortical-MTL networks, including regions of inferior frontal and angular gyrii. Results also suggest that recognition of items based on low-fidelity, generalized information, rather than memory arising from retrieval of relevant episodic details, is not associated with functional connectivity between MTL and lateral cortical regions. Additionally, individual differences in microstructural properties in white matter pathways, associated with distributed MTL-cortical networks, are positively correlated with better performance on a mnemonic discrimination task. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Large Scale Density Estimation of Blue and Fin Whales: Utilizing Sparse Array Data to Develop and Implement a New Method for Estimating Blue and Fin Whale Density

DTIC Science & Technology

2015-09-30

1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Large Scale Density Estimation of Blue and Fin Whales ...Utilizing Sparse Array Data to Develop and Implement a New Method for Estimating Blue and Fin Whale Density Len Thomas & Danielle Harris Centre...to develop and implement a new method for estimating blue and fin whale density that is effective over large spatial scales and is designed to cope

A new scheduling algorithm for parallel sparse LU factorization with static pivoting

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

Grigori, Laura; Li, Xiaoye S.

2002-08-20

In this paper we present a static scheduling algorithm for parallel sparse LU factorization with static pivoting. The algorithm is divided into mapping and scheduling phases, using the symmetric pruned graphs of L' and U to represent dependencies. The scheduling algorithm is designed for driving the parallel execution of the factorization on a distributed-memory architecture. Experimental results and comparisons with SuperLU{_}DIST are reported after applying this algorithm on real world application matrices on an IBM SP RS/6000 distributed memory machine.

Corticostriatal circuit mechanisms of value-based action selection: Implementation of reinforcement learning algorithms and beyond.

PubMed

Morita, Kenji; Jitsev, Jenia; Morrison, Abigail

2016-09-15

Value-based action selection has been suggested to be realized in the corticostriatal local circuits through competition among neural populations. In this article, we review theoretical and experimental studies that have constructed and verified this notion, and provide new perspectives on how the local-circuit selection mechanisms implement reinforcement learning (RL) algorithms and computations beyond them. The striatal neurons are mostly inhibitory, and lateral inhibition among them has been classically proposed to realize "Winner-Take-All (WTA)" selection of the maximum-valued action (i.e., 'max' operation). Although this view has been challenged by the revealed weakness, sparseness, and asymmetry of lateral inhibition, which suggest more complex dynamics, WTA-like competition could still occur on short time scales. Unlike the striatal circuit, the cortical circuit contains recurrent excitation, which may enable retention or temporal integration of information and probabilistic "soft-max" selection. The striatal "max" circuit and the cortical "soft-max" circuit might co-implement an RL algorithm called Q-learning; the cortical circuit might also similarly serve for other algorithms such as SARSA. In these implementations, the cortical circuit presumably sustains activity representing the executed action, which negatively impacts dopamine neurons so that they can calculate reward-prediction-error. Regarding the suggested more complex dynamics of striatal, as well as cortical, circuits on long time scales, which could be viewed as a sequence of short WTA fragments, computational roles remain open: such a sequence might represent (1) sequential state-action-state transitions, constituting replay or simulation of the internal model, (2) a single state/action by the whole trajectory, or (3) probabilistic sampling of state/action. Copyright © 2016. Published by Elsevier B.V.

Cognition and brain development in children with benign epilepsy with centrotemporal spikes.

PubMed

Garcia-Ramos, Camille; Jackson, Daren C; Lin, Jack J; Dabbs, Kevin; Jones, Jana E; Hsu, David A; Stafstrom, Carl E; Zawadzki, Lucy; Seidenberg, Michael; Prabhakaran, Vivek; Hermann, Bruce P

2015-10-01

Benign epilepsy with centrotemporal spikes (BECTS), the most common focal childhood epilepsy, is associated with subtle abnormalities in cognition and possible developmental alterations in brain structure when compared to healthy participants, as indicated by previous cross-sectional studies. To examine the natural history of BECTS, we investigated cognition, cortical thickness, and subcortical volumes in children with new/recent onset BECTS and healthy controls (HC). Participants were 8-15 years of age, including 24 children with new-onset BECTS and 41 age- and gender-matched HC. At baseline and 2 years later, all participants completed a cognitive assessment, and a subset (13 BECTS, 24 HC) underwent T1 volumetric magnetic resonance imaging (MRI) scans focusing on cortical thickness and subcortical volumes. Baseline cognitive abnormalities associated with BECTS (object naming, verbal learning, arithmetic computation, and psychomotor speed/dexterity) persisted over 2 years, with the rate of cognitive development paralleling that of HC. Baseline neuroimaging revealed thinner cortex in BECTS compared to controls in frontal, temporal, and occipital regions. Longitudinally, HC showed widespread cortical thinning in both hemispheres, whereas BECTS participants showed sparse regions of both cortical thinning and thickening. Analyses of subcortical volumes showed larger left and right putamens persisting over 2 years in BECTS compared to HC. Cognitive and structural brain abnormalities associated with BECTS are present at onset and persist (cognition) and/or evolve (brain structure) over time. Atypical maturation of cortical thickness antecedent to BECTS onset results in early identified abnormalities that continue to develop abnormally over time. However, compared to anatomic development, cognition appears more resistant to further change over time. Wiley Periodicals, Inc. © 2015 International League Against Epilepsy.

Sustained Activity in Hierarchical Modular Neural Networks: Self-Organized Criticality and Oscillations

PubMed Central

Wang, Sheng-Jun; Hilgetag, Claus C.; Zhou, Changsong

2010-01-01

Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. In particular, they are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality (SOC). We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. Previously, it was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We found that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and SOC, which are not present in the respective random networks. The mechanism underlying the sustained activity is that each dense module cannot sustain activity on its own, but displays SOC in the presence of weak perturbations. Therefore, the hierarchical modular networks provide the coupling among subsystems with SOC. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivity of critical states and the predictability and timing of oscillations for efficient information processing. PMID:21852971

The dark matter of galaxy voids

NASA Astrophysics Data System (ADS)

Sutter, P. M.; Lavaux, Guilhem; Wandelt, Benjamin D.; Weinberg, David H.; Warren, Michael S.

2014-03-01

How do observed voids relate to the underlying dark matter distribution? To examine the spatial distribution of dark matter contained within voids identified in galaxy surveys, we apply Halo Occupation Distribution models representing sparsely and densely sampled galaxy surveys to a high-resolution N-body simulation. We compare these galaxy voids to voids found in the halo distribution, low-resolution dark matter and high-resolution dark matter. We find that voids at all scales in densely sampled surveys - and medium- to large-scale voids in sparse surveys - trace the same underdensities as dark matter, but they are larger in radius by ˜20 per cent, they have somewhat shallower density profiles and they have centres offset by ˜ 0.4Rv rms. However, in void-to-void comparison we find that shape estimators are less robust to sampling, and the largest voids in sparsely sampled surveys suffer fragmentation at their edges. We find that voids in galaxy surveys always correspond to underdensities in the dark matter, though the centres may be offset. When this offset is taken into account, we recover almost identical radial density profiles between galaxies and dark matter. All mock catalogues used in this work are available at http://www.cosmicvoids.net.

Distinct Corticostriatal and Intracortical Pathways Mediate Bilateral Sensory Responses in the Striatum.

PubMed

Reig, Ramon; Silberberg, Gilad

2016-12-01

Individual striatal neurons integrate somatosensory information from both sides of the body, however, the afferent pathways mediating these bilateral responses are unclear. Whereas ipsilateral corticostriatal projections are prevalent throughout the neocortex, contralateral projections provide sparse input from primary sensory cortices, in contrast to the dense innervation from motor and frontal regions. There is, therefore, an apparent discrepancy between the observed anatomical pathways and the recorded striatal responses. We used simultaneous in vivo whole-cell and extracellular recordings combined with focal cortical silencing, to dissect the afferent pathways underlying bilateral sensory integration in the mouse striatum. We show that unlike direct corticostriatal projections mediating responses to contralateral whisker deflection, responses to ipsilateral stimuli are mediated mainly by intracortical projections from the contralateral somatosensory cortex (S1). The dominant pathway is the callosal projection from contralateral to ipsilateral S1. Our results suggest a functional difference between the cortico-basal ganglia pathways underlying bilateral sensory and motor processes. © The Author 2016. Published by Oxford University Press.

Complementary codes for odor identity and intensity in olfactory cortex

PubMed Central

Bolding, Kevin A; Franks, Kevin M

2017-01-01

The ability to represent both stimulus identity and intensity is fundamental for perception. Using large-scale population recordings in awake mice, we find distinct coding strategies facilitate non-interfering representations of odor identity and intensity in piriform cortex. Simply knowing which neurons were activated is sufficient to accurately represent odor identity, with no additional information about identity provided by spike time or spike count. Decoding analyses indicate that cortical odor representations are not sparse. Odorant concentration had no systematic effect on spike counts, indicating that rate cannot encode intensity. Instead, odor intensity can be encoded by temporal features of the population response. We found a subpopulation of rapid, largely concentration-invariant responses was followed by another population of responses whose latencies systematically decreased at higher concentrations. Cortical inhibition transforms olfactory bulb output to sharpen these dynamics. Our data therefore reveal complementary coding strategies that can selectively represent distinct features of a stimulus. DOI: http://dx.doi.org/10.7554/eLife.22630.001 PMID:28379135

Uncertainty Analysis Based on Sparse Grid Collocation and Quasi-Monte Carlo Sampling with Application in Groundwater Modeling

NASA Astrophysics Data System (ADS)

Zhang, G.; Lu, D.; Ye, M.; Gunzburger, M.

2011-12-01

Markov Chain Monte Carlo (MCMC) methods have been widely used in many fields of uncertainty analysis to estimate the posterior distributions of parameters and credible intervals of predictions in the Bayesian framework. However, in practice, MCMC may be computationally unaffordable due to slow convergence and the excessive number of forward model executions required, especially when the forward model is expensive to compute. Both disadvantages arise from the curse of dimensionality, i.e., the posterior distribution is usually a multivariate function of parameters. Recently, sparse grid method has been demonstrated to be an effective technique for coping with high-dimensional interpolation or integration problems. Thus, in order to accelerate the forward model and avoid the slow convergence of MCMC, we propose a new method for uncertainty analysis based on sparse grid interpolation and quasi-Monte Carlo sampling. First, we construct a polynomial approximation of the forward model in the parameter space by using the sparse grid interpolation. This approximation then defines an accurate surrogate posterior distribution that can be evaluated repeatedly at minimal computational cost. Second, instead of using MCMC, a quasi-Monte Carlo method is applied to draw samples in the parameter space. Then, the desired probability density function of each prediction is approximated by accumulating the posterior density values of all the samples according to the prediction values. Our method has the following advantages: (1) the polynomial approximation of the forward model on the sparse grid provides a very efficient evaluation of the surrogate posterior distribution; (2) the quasi-Monte Carlo method retains the same accuracy in approximating the PDF of predictions but avoids all disadvantages of MCMC. The proposed method is applied to a controlled numerical experiment of groundwater flow modeling. The results show that our method attains the same accuracy much more efficiently than traditional MCMC.

Color opponent receptive fields self-organize in a biophysical model of visual cortex via spike-timing dependent plasticity

PubMed Central

Eguchi, Akihiro; Neymotin, Samuel A.; Stringer, Simon M.

2014-01-01

Although many computational models have been proposed to explain orientation maps in primary visual cortex (V1), it is not yet known how similar clusters of color-selective neurons in macaque V1/V2 are connected and develop. In this work, we address the problem of understanding the cortical processing of color information with a possible mechanism of the development of the patchy distribution of color selectivity via computational modeling. Each color input is decomposed into a red, green, and blue representation and transmitted to the visual cortex via a simulated optic nerve in a luminance channel and red–green and blue–yellow opponent color channels. Our model of the early visual system consists of multiple topographically-arranged layers of excitatory and inhibitory neurons, with sparse intra-layer connectivity and feed-forward connectivity between layers. Layers are arranged based on anatomy of early visual pathways, and include a retina, lateral geniculate nucleus, and layered neocortex. Each neuron in the V1 output layer makes synaptic connections to neighboring neurons and receives the three types of signals in the different channels from the corresponding photoreceptor position. Synaptic weights are randomized and learned using spike-timing-dependent plasticity (STDP). After training with natural images, the neurons display heightened sensitivity to specific colors. Information-theoretic analysis reveals mutual information between particular stimuli and responses, and that the information reaches a maximum with fewer neurons in the higher layers, indicating that estimations of the input colors can be done using the output of fewer cells in the later stages of cortical processing. In addition, cells with similar color receptive fields form clusters. Analysis of spiking activity reveals increased firing synchrony between neurons when particular color inputs are presented or removed (ON-cell/OFF-cell). PMID:24659956

Changes in interoceptive processes following brain stimulation

PubMed Central

Mai, Sandra

2016-01-01

The processing and perception of individual internal bodily signals (interoception) has been differentiated to comprise different levels and processes involved. The so-called heartbeat-evoked potential (HEP) offers an additional possibility to examine automatic processing of cardiac signals. Knowledge on neural structures potentially supporting different facets of interoception is still sparse. One way to get insights into neuroanatomical function is to manipulate the activity of different brain structures. In this study, we used repetitive transcranial magnetic stimulation (rTMS) and a continuous theta-burst protocol to inhibit specific central locations of the interoceptive network including the right anterior insula and the right somatosensory cortices and assessed effects on interoceptive facets and the HEP in 18 male participants. Main results were that inhibiting anterior insula resulted in a significant decline in cardiac and respiratory interoceptive accuracy (IAc) and in a consistent decrease in perception confidence. Continuous theta-burst stimulation (cTBS) over somatosensory cortices reduced only cardiac IAc and affected perception confidence. Inhibiting right anterior insula and right somatosensory cortices increased interoceptive sensibility and reduced the HEP amplitude over frontocentral locations. Our findings strongly suggest that cTBS is an effective tool to investigate the neural network supporting interoceptive processes. This article is part of the themed issue ‘Interoception beyond homeostasis: affect, cognition and mental health’. PMID:28080973

Neonatal Atlas Construction Using Sparse Representation

PubMed Central

Shi, Feng; Wang, Li; Wu, Guorong; Li, Gang; Gilmore, John H.; Lin, Weili; Shen, Dinggang

2014-01-01

Atlas construction generally includes first an image registration step to normalize all images into a common space and then an atlas building step to fuse the information from all the aligned images. Although numerous atlas construction studies have been performed to improve the accuracy of the image registration step, unweighted or simply weighted average is often used in the atlas building step. In this article, we propose a novel patch-based sparse representation method for atlas construction after all images have been registered into the common space. By taking advantage of local sparse representation, more anatomical details can be recovered in the built atlas. To make the anatomical structures spatially smooth in the atlas, the anatomical feature constraints on group structure of representations and also the overlapping of neighboring patches are imposed to ensure the anatomical consistency between neighboring patches. The proposed method has been applied to 73 neonatal MR images with poor spatial resolution and low tissue contrast, for constructing a neonatal brain atlas with sharp anatomical details. Experimental results demonstrate that the proposed method can significantly enhance the quality of the constructed atlas by discovering more anatomical details especially in the highly convoluted cortical regions. The resulting atlas demonstrates superior performance of our atlas when applied to spatially normalizing three different neonatal datasets, compared with other start-of-the-art neonatal brain atlases. PMID:24638883

Spatial information outflow from the hippocampal circuit: distributed spatial coding and phase precession in the subiculum.

PubMed

Kim, Steve M; Ganguli, Surya; Frank, Loren M

2012-08-22

Hippocampal place cells convey spatial information through a combination of spatially selective firing and theta phase precession. The way in which this information influences regions like the subiculum that receive input from the hippocampus remains unclear. The subiculum receives direct inputs from area CA1 of the hippocampus and sends divergent output projections to many other parts of the brain, so we examined the firing patterns of rat subicular neurons. We found a substantial transformation in the subicular code for space from sparse to dense firing rate representations along a proximal-distal anatomical gradient: neurons in the proximal subiculum are more similar to canonical, sparsely firing hippocampal place cells, whereas neurons in the distal subiculum have higher firing rates and more distributed spatial firing patterns. Using information theory, we found that the more distributed spatial representation in the subiculum carries, on average, more information about spatial location and context than the sparse spatial representation in CA1. Remarkably, despite the disparate firing rate properties of subicular neurons, we found that neurons at all proximal-distal locations exhibit robust theta phase precession, with similar spiking oscillation frequencies as neurons in area CA1. Our findings suggest that the subiculum is specialized to compress sparse hippocampal spatial codes into highly informative distributed codes suitable for efficient communication to other brain regions. Moreover, despite this substantial compression, the subiculum maintains finer scale temporal properties that may allow it to participate in oscillatory phase coding and spike timing-dependent plasticity in coordination with other regions of the hippocampal circuit.

Analysis, tuning and comparison of two general sparse solvers for distributed memory computers

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

Amestoy, P.R.; Duff, I.S.; L'Excellent, J.-Y.

2000-06-30

We describe the work performed in the context of a Franco-Berkeley funded project between NERSC-LBNL located in Berkeley (USA) and CERFACS-ENSEEIHT located in Toulouse (France). We discuss both the tuning and performance analysis of two distributed memory sparse solvers (superlu from Berkeley and mumps from Toulouse) on the 512 processor Cray T3E from NERSC (Lawrence Berkeley National Laboratory). This project gave us the opportunity to improve the algorithms and add new features to the codes. We then quite extensively analyze and compare the two approaches on a set of large problems from real applications. We further explain the main differencesmore » in the behavior of the approaches on artificial regular grid problems. As a conclusion to this activity report, we mention a set of parallel sparse solvers on which this type of study should be extended.« less

Statistical aspects of genetic association testing in small samples, based on selective DNA pooling data in the arctic fox.

PubMed

Szyda, Joanna; Liu, Zengting; Zatoń-Dobrowolska, Magdalena; Wierzbicki, Heliodor; Rzasa, Anna

2008-01-01

We analysed data from a selective DNA pooling experiment with 130 individuals of the arctic fox (Alopex lagopus), which originated from 2 different types regarding body size. The association between alleles of 6 selected unlinked molecular markers and body size was tested by using univariate and multinomial logistic regression models, applying odds ratio and test statistics from the power divergence family. Due to the small sample size and the resulting sparseness of the data table, in hypothesis testing we could not rely on the asymptotic distributions of the tests. Instead, we tried to account for data sparseness by (i) modifying confidence intervals of odds ratio; (ii) using a normal approximation of the asymptotic distribution of the power divergence tests with different approaches for calculating moments of the statistics; and (iii) assessing P values empirically, based on bootstrap samples. As a result, a significant association was observed for 3 markers. Furthermore, we used simulations to assess the validity of the normal approximation of the asymptotic distribution of the test statistics under the conditions of small and sparse samples.

Discriminative Bayesian Dictionary Learning for Classification.

PubMed

Akhtar, Naveed; Shafait, Faisal; Mian, Ajmal

2016-12-01

We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a finite approximation of Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforementioned Bernoulli distributions in separately learning a linear classifier. The classifier uses the same hierarchical Bayesian model as the dictionary, which we present along the analytical inference solution for Gibbs sampling. For classification, a test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier. We performed experiments for face and action recognition; and object and scene-category classification using five public datasets and compared the results with state-of-the-art discriminative sparse representation approaches. Experiments show that the proposed Bayesian approach consistently outperforms the existing approaches.

Transmitter receptors reveal segregation of cortical areas in the human superior parietal cortex: relations to visual and somatosensory regions.

PubMed

Scheperjans, Filip; Palomero-Gallagher, Nicola; Grefkes, Christian; Schleicher, Axel; Zilles, Karl

2005-11-01

Regional distributions of ligand binding sites of 12 different neurotransmitter receptors (glutamatergic: AMPA, kainate, NMDA; GABAergic: GABA(A), GABA(B); cholinergic: muscarinic M2, nicotinic; adrenergic: alpha1, alpha2; serotonergic: 5-HT1A, 5-HT2; dopaminergic: D1) were studied in human postmortem brains by means of quantitative receptor autoradiography. Binding site densities were measured in the superior parietal lobule (SPL) (areas 5L, 5M, 5Ci, and different locations within Brodmann's area (BA) 7), somatosensory (BA 2), and visual cortical areas (BA 17, and different locations within BAs 18 and 19). Similarities of receptor distribution between cortical areas were analyzed by cluster analysis, uni- and multivariate statistics of mean receptor densities (averaged over all cortical layers), and profiles representing the laminar distribution patterns of receptors. A considerable heterogeneity of regional receptor densities and laminar patterns between the sites was found in the SPL and the visual cortex. The most prominent regional differences were found for M2 receptors. In the SPL, rostrocaudally oriented changes of receptor densities were more pronounced than those in mediolateral direction. The receptor distribution in the rostral SPL was more similar to that of the somatosensory cortex, whereas caudal SPL resembled the receptor patterns of the dorsolateral extrastriate visual areas. These results suggest a segregation of the different SPL areas based on receptor distribution features typical for somatosensory or visual areas, which fits to the dual functional role of this cortical region, i.e., the involvement of the human SPL in visuomotor and somatosensory motor transformations.

Computer Sciences and Data Systems, volume 1

NASA Technical Reports Server (NTRS)

1987-01-01

Topics addressed include: software engineering; university grants; institutes; concurrent processing; sparse distributed memory; distributed operating systems; intelligent data management processes; expert system for image analysis; fault tolerant software; and architecture research.

Diagnostic Significance of Cortical Superficial Siderosis for Alzheimer Disease in Patients with Cognitive Impairment.

PubMed

Inoue, Y; Nakajima, M; Uetani, H; Hirai, T; Ueda, M; Kitajima, M; Utsunomiya, D; Watanabe, M; Hashimoto, M; Ikeda, M; Yamashita, Y; Ando, Y

2016-02-01

Because the diagnostic significance of cortical superficial siderosis for Alzheimer disease and the association between cortical superficial siderosis and the topographic distribution of cerebral microbleeds have been unclear, we investigated the association between cortical superficial siderosis and clinicoradiologic characteristics of patients with cognitive impairment. We studied 347 patients (217 women, 130 men; mean age, 74 ± 9 years) who visited our memory clinic and underwent MR imaging (3T SWI). We analyzed the association between cortical superficial siderosis and the topographic distribution of cerebral microbleeds plus clinical characteristics including types of dementia. We used multivariate logistic regression analysis to determine the diagnostic significance of cortical superficial siderosis for Alzheimer disease. Twelve patients (3.5%) manifested cortical superficial siderosis. They were older (P = .026) and had strictly lobar cerebral microbleeds significantly more often than did patients without cortical superficial siderosis (50.0% versus 19.4%, P = .02); the occurrence of strictly deep and mixed cerebral microbleeds, however, did not differ in the 2 groups. Alzheimer disease was diagnosed in 162 (46.7%) patients. Of these, 8 patients (4.9%) had cortical superficial siderosis. In the multivariate logistic regression analysis for the diagnosis of Alzheimer disease, lacunar infarcts were negatively and independently associated with Alzheimer disease (P = .007). Although cortical superficial siderosis was associated with a strictly lobar cerebral microbleed location, it was not independently associated with Alzheimer disease in a memory clinic setting. Additional studies are required to investigate the temporal changes of these cerebral amyloid angiopathy-related MR imaging findings. © 2016 by American Journal of Neuroradiology.

The Opponent Channel Population Code of Sound Location Is an Efficient Representation of Natural Binaural Sounds

PubMed Central

Młynarski, Wiktor

2015-01-01

In mammalian auditory cortex, sound source position is represented by a population of broadly tuned neurons whose firing is modulated by sounds located at all positions surrounding the animal. Peaks of their tuning curves are concentrated at lateral position, while their slopes are steepest at the interaural midline, allowing for the maximum localization accuracy in that area. These experimental observations contradict initial assumptions that the auditory space is represented as a topographic cortical map. It has been suggested that a “panoramic” code has evolved to match specific demands of the sound localization task. This work provides evidence suggesting that properties of spatial auditory neurons identified experimentally follow from a general design principle- learning a sparse, efficient representation of natural stimuli. Natural binaural sounds were recorded and served as input to a hierarchical sparse-coding model. In the first layer, left and right ear sounds were separately encoded by a population of complex-valued basis functions which separated phase and amplitude. Both parameters are known to carry information relevant for spatial hearing. Monaural input converged in the second layer, which learned a joint representation of amplitude and interaural phase difference. Spatial selectivity of each second-layer unit was measured by exposing the model to natural sound sources recorded at different positions. Obtained tuning curves match well tuning characteristics of neurons in the mammalian auditory cortex. This study connects neuronal coding of the auditory space with natural stimulus statistics and generates new experimental predictions. Moreover, results presented here suggest that cortical regions with seemingly different functions may implement the same computational strategy-efficient coding. PMID:25996373

Discovering Cortical Folding Patterns in Neonatal Cortical Surfaces Using Large-Scale Dataset

PubMed Central

Meng, Yu; Li, Gang; Wang, Li; Lin, Weili; Gilmore, John H.

2017-01-01

The cortical folding of the human brain is highly complex and variable across individuals. Mining the major patterns of cortical folding from modern large-scale neuroimaging datasets is of great importance in advancing techniques for neuroimaging analysis and understanding the inter-individual variations of cortical folding and its relationship with cognitive function and disorders. As the primary cortical folding is genetically influenced and has been established at term birth, neonates with the minimal exposure to the complicated postnatal environmental influence are the ideal candidates for understanding the major patterns of cortical folding. In this paper, for the first time, we propose a novel method for discovering the major patterns of cortical folding in a large-scale dataset of neonatal brain MR images (N = 677). In our method, first, cortical folding is characterized by the distribution of sulcal pits, which are the locally deepest points in cortical sulci. Because deep sulcal pits are genetically related, relatively consistent across individuals, and also stable during brain development, they are well suitable for representing and characterizing cortical folding. Then, the similarities between sulcal pit distributions of any two subjects are measured from spatial, geometrical, and topological points of view. Next, these different measurements are adaptively fused together using a similarity network fusion technique, to preserve their common information and also catch their complementary information. Finally, leveraging the fused similarity measurements, a hierarchical affinity propagation algorithm is used to group similar sulcal folding patterns together. The proposed method has been applied to 677 neonatal brains (the largest neonatal dataset to our knowledge) in the central sulcus, superior temporal sulcus, and cingulate sulcus, and revealed multiple distinct and meaningful folding patterns in each region. PMID:28229131

Effects of partitioning and scheduling sparse matrix factorization on communication and load balance

NASA Technical Reports Server (NTRS)

Venugopal, Sesh; Naik, Vijay K.

1991-01-01

A block based, automatic partitioning and scheduling methodology is presented for sparse matrix factorization on distributed memory systems. Using experimental results, this technique is analyzed for communication and load imbalance overhead. To study the performance effects, these overheads were compared with those obtained from a straightforward 'wrap mapped' column assignment scheme. All experimental results were obtained using test sparse matrices from the Harwell-Boeing data set. The results show that there is a communication and load balance tradeoff. The block based method results in lower communication cost whereas the wrap mapped scheme gives better load balance.

Power Enhancement in High Dimensional Cross-Sectional Tests

PubMed Central

Fan, Jianqing; Liao, Yuan; Yao, Jiawei

2016-01-01

We propose a novel technique to boost the power of testing a high-dimensional vector H : θ = 0 against sparse alternatives where the null hypothesis is violated only by a couple of components. Existing tests based on quadratic forms such as the Wald statistic often suffer from low powers due to the accumulation of errors in estimating high-dimensional parameters. More powerful tests for sparse alternatives such as thresholding and extreme-value tests, on the other hand, require either stringent conditions or bootstrap to derive the null distribution and often suffer from size distortions due to the slow convergence. Based on a screening technique, we introduce a “power enhancement component”, which is zero under the null hypothesis with high probability, but diverges quickly under sparse alternatives. The proposed test statistic combines the power enhancement component with an asymptotically pivotal statistic, and strengthens the power under sparse alternatives. The null distribution does not require stringent regularity conditions, and is completely determined by that of the pivotal statistic. As specific applications, the proposed methods are applied to testing the factor pricing models and validating the cross-sectional independence in panel data models. PMID:26778846

Sensory-evoked perturbations of locomotor activity by sparse sensory input: a computational study

PubMed Central

Brownstone, Robert M.

2015-01-01

Sensory inputs from muscle, cutaneous, and joint afferents project to the spinal cord, where they are able to affect ongoing locomotor activity. Activation of sensory input can initiate or prolong bouts of locomotor activity depending on the identity of the sensory afferent activated and the timing of the activation within the locomotor cycle. However, the mechanisms by which afferent activity modifies locomotor rhythm and the distribution of sensory afferents to the spinal locomotor networks have not been determined. Considering the many sources of sensory inputs to the spinal cord, determining this distribution would provide insights into how sensory inputs are integrated to adjust ongoing locomotor activity. We asked whether a sparsely distributed set of sensory inputs could modify ongoing locomotor activity. To address this question, several computational models of locomotor central pattern generators (CPGs) that were mechanistically diverse and generated locomotor-like rhythmic activity were developed. We show that sensory inputs restricted to a small subset of the network neurons can perturb locomotor activity in the same manner as seen experimentally. Furthermore, we show that an architecture with sparse sensory input improves the capacity to gate sensory information by selectively modulating sensory channels. These data demonstrate that sensory input to rhythm-generating networks need not be extensively distributed. PMID:25673740

Three-Dimensional Finite Element Analysis of Varying Diameter and Connection Type in Implants with High Crown-Implant Ratio.

PubMed

Moraes, Sandra Lúcia Dantas de; Verri, Fellippo Ramos; Santiago, Joel Ferreira; Almeida, Daniel Augusto de Faria; Lemos, Cleidiel Aparecido Araujo; Gomes, Jéssica Marcela de Luna; Pellizzer, Eduardo Piza

2018-01-01

The aim of this study was to evaluate the effect of varying the diameter, connection type and loading on stress distribution in the cortical bone for implants with a high crown-implant ratio. Six 3D models were simulated with the InVesalius, Rhinoceros 3D 4.0 and SolidWorks 2011 software programs. Models were composed of bone from the posterior mandibular region; they included an implant of 8.5 mm length, diameter Ø 3.75 mm or Ø 5.00 mm and connection types such as external hexagon (EH), internal hexagon (IH) and Morse taper (MT). Models were processed using the Femap 11.2 and NeiNastran 11.0 programs and by using an axial force of 200 N and oblique force of 100 N. Results were recorded in terms of the maximum principal stress. Oblique loading showed high stress in the cortical bone compared to that shown by axial loading. The results showed that implants with a wide diameter showed more favorable stress distribution in the cortical bone region than regular diameter, regardless of the connection type. Morse taper implants showed better stress distribution compared to other connection types, especially in the oblique loading. Thus, oblique loading showed higher stress concentration in cortical bone tissue when compared with axial loading. Wide diameter implant was favorable for improved stress distribution in the cortical bone region, while Morse taper implants showed lower stress concentration than other connections.

Cortical Dynamics in Presence of Assemblies of Densely Connected Weight-Hub Neurons

PubMed Central

Setareh, Hesam; Deger, Moritz; Petersen, Carl C. H.; Gerstner, Wulfram

2017-01-01

Experimental measurements of pairwise connection probability of pyramidal neurons together with the distribution of synaptic weights have been used to construct randomly connected model networks. However, several experimental studies suggest that both wiring and synaptic weight structure between neurons show statistics that differ from random networks. Here we study a network containing a subset of neurons which we call weight-hub neurons, that are characterized by strong inward synapses. We propose a connectivity structure for excitatory neurons that contain assemblies of densely connected weight-hub neurons, while the pairwise connection probability and synaptic weight distribution remain consistent with experimental data. Simulations of such a network with generalized integrate-and-fire neurons display regular and irregular slow oscillations akin to experimentally observed up/down state transitions in the activity of cortical neurons with a broad distribution of pairwise spike correlations. Moreover, stimulation of a model network in the presence or absence of assembly structure exhibits responses similar to light-evoked responses of cortical layers in optogenetically modified animals. We conclude that a high connection probability into and within assemblies of excitatory weight-hub neurons, as it likely is present in some but not all cortical layers, changes the dynamics of a layer of cortical microcircuitry significantly. PMID:28690508

Two Distinct Synchronization Processes in the Transition to Sleep: A High-Density Electroencephalographic Study

PubMed Central

Siclari, Francesca; Bernardi, Giulio; Riedner, Brady A.; LaRocque, Joshua J.; Benca, Ruth M.; Tononi, Giulio

2014-01-01

Objectives: To assess how the characteristics of slow waves and spindles change in the falling-asleep process. Design: Participants undergoing overnight high-density electroencephalographic recordings were awakened at 15- to 30-min intervals. One hundred forty-one falling-asleep periods were analyzed at the scalp and source level. Setting: Sleep laboratory. Participants: Six healthy participants. Interventions: Serial awakenings. Results: The number and amplitude of slow waves followed two dissociated, intersecting courses during the transition to sleep: slow wave number increased slowly at the beginning and rapidly at the end of the falling-asleep period, whereas amplitude at first increased rapidly and then decreased linearly. Most slow waves occurring early in the transition to sleep had a large amplitude, a steep slope, involved broad regions of the cortex, predominated over frontomedial regions, and preferentially originated from the sensorimotor and the posteromedial parietal cortex. Most slow waves occurring later had a smaller amplitude and slope, involved more circumscribed parts of the cortex, and had more evenly distributed origins. Spindles were initially sparse, fast, and involved few cortical regions, then became more numerous and slower, and involved more areas. Conclusions: Our results provide evidence for two types of slow waves, which follow dissociated temporal courses in the transition to sleep and have distinct cortical origins and distributions. We hypothesize that these two types of slow waves result from two distinct synchronization processes: (1) a “bottom-up,” subcorticocortical, arousal system-dependent process that predominates in the early phase and leads to type I slow waves, and (2) a “horizontal,” corticocortical synchronization process that predominates in the late phase and leads to type II slow waves. The dissociation between these two synchronization processes in time and space suggests that they may be differentially affected by experimental manipulations and sleep disorders. Citation: Siclari F, Bernardi G, Riedner BA, LaRocque JJ, Benca RM, Tononi G. Two distinct synchronization processes in the transition to sleep: a high-density electroencephalographic study. SLEEP 2014;37(10):1621-1637. PMID:25197810

Effect of blood vessels on light distribution in optogenetic stimulation of cortex.

PubMed

Azimipour, Mehdi; Atry, Farid; Pashaie, Ramin

2015-05-15

In this Letter, the impact of blood vessels on light distribution during photostimulation of cortical tissue in small rodents is investigated. Brain optical properties were extracted using a double-integrating sphere setup, and optical coherence tomography was used to image cortical vessels and capillaries to generate a three-dimensional angiogram of the cortex. By combining these two datasets, a complete volumetric structure of the cortical tissue was developed and linked to a Monte Carlo code which simulates light propagation in this inhomogeneous structure and illustrates the effect of blood vessels on the penetration depth and pattern preservation in optogenetic stimulation.

INTRINSIC CURVATURE: A MARKER OF MILLIMETER-SCALE TANGENTIAL CORTICO-CORTICAL CONNECTIVITY?

PubMed Central

RONAN, LISA; PIENAAR, RUDOLPH; WILLIAMS, GUY; BULLMORE, ED; CROW, TIM J.; ROBERTS, NEIL; JONES, PETER B.; SUCKLING, JOHN; FLETCHER, PAUL C.

2012-01-01

In this paper, we draw a link between cortical intrinsic curvature and the distributions of tangential connection lengths. We suggest that differential rates of surface expansion not only lead to intrinsic curvature of the cortical sheet, but also to differential inter-neuronal spacing. We propose that there follows a consequential change in the profile of neuronal connections: specifically an enhancement of the tendency towards proportionately more short connections. Thus, the degree of cortical intrinsic curvature may have implications for short-range connectivity. PMID:21956929

Nonlinear spike-and-slab sparse coding for interpretable image encoding.

PubMed

Shelton, Jacquelyn A; Sheikh, Abdul-Saboor; Bornschein, Jörg; Sterne, Philip; Lücke, Jörg

2015-01-01

Sparse coding is a popular approach to model natural images but has faced two main challenges: modelling low-level image components (such as edge-like structures and their occlusions) and modelling varying pixel intensities. Traditionally, images are modelled as a sparse linear superposition of dictionary elements, where the probabilistic view of this problem is that the coefficients follow a Laplace or Cauchy prior distribution. We propose a novel model that instead uses a spike-and-slab prior and nonlinear combination of components. With the prior, our model can easily represent exact zeros for e.g. the absence of an image component, such as an edge, and a distribution over non-zero pixel intensities. With the nonlinearity (the nonlinear max combination rule), the idea is to target occlusions; dictionary elements correspond to image components that can occlude each other. There are major consequences of the model assumptions made by both (non)linear approaches, thus the main goal of this paper is to isolate and highlight differences between them. Parameter optimization is analytically and computationally intractable in our model, thus as a main contribution we design an exact Gibbs sampler for efficient inference which we can apply to higher dimensional data using latent variable preselection. Results on natural and artificial occlusion-rich data with controlled forms of sparse structure show that our model can extract a sparse set of edge-like components that closely match the generating process, which we refer to as interpretable components. Furthermore, the sparseness of the solution closely follows the ground-truth number of components/edges in the images. The linear model did not learn such edge-like components with any level of sparsity. This suggests that our model can adaptively well-approximate and characterize the meaningful generation process.

Nonlinear Spike-And-Slab Sparse Coding for Interpretable Image Encoding

PubMed Central

Shelton, Jacquelyn A.; Sheikh, Abdul-Saboor; Bornschein, Jörg; Sterne, Philip; Lücke, Jörg

2015-01-01

Sparse coding is a popular approach to model natural images but has faced two main challenges: modelling low-level image components (such as edge-like structures and their occlusions) and modelling varying pixel intensities. Traditionally, images are modelled as a sparse linear superposition of dictionary elements, where the probabilistic view of this problem is that the coefficients follow a Laplace or Cauchy prior distribution. We propose a novel model that instead uses a spike-and-slab prior and nonlinear combination of components. With the prior, our model can easily represent exact zeros for e.g. the absence of an image component, such as an edge, and a distribution over non-zero pixel intensities. With the nonlinearity (the nonlinear max combination rule), the idea is to target occlusions; dictionary elements correspond to image components that can occlude each other. There are major consequences of the model assumptions made by both (non)linear approaches, thus the main goal of this paper is to isolate and highlight differences between them. Parameter optimization is analytically and computationally intractable in our model, thus as a main contribution we design an exact Gibbs sampler for efficient inference which we can apply to higher dimensional data using latent variable preselection. Results on natural and artificial occlusion-rich data with controlled forms of sparse structure show that our model can extract a sparse set of edge-like components that closely match the generating process, which we refer to as interpretable components. Furthermore, the sparseness of the solution closely follows the ground-truth number of components/edges in the images. The linear model did not learn such edge-like components with any level of sparsity. This suggests that our model can adaptively well-approximate and characterize the meaningful generation process. PMID:25954947

DISTRIBUTIONAL CHANGES AND POPULATION STATUS FOR AMPHIBIANS IN THE EASTERN MOJAVE DESERT

EPA Science Inventory

A number of amphibian species historically inhabited sparsely distributed wetlands in the Mojave Desert of western North America, habitats that have been dramatically altered or eliminated as a result of human activities. The population status and distributional changes for amphi...

Branching angles of pyramidal cell dendrites follow common geometrical design principles in different cortical areas.

PubMed

Bielza, Concha; Benavides-Piccione, Ruth; López-Cruz, Pedro; Larrañaga, Pedro; DeFelipe, Javier

2014-08-01

Unraveling pyramidal cell structure is crucial to understanding cortical circuit computations. Although it is well known that pyramidal cell branching structure differs in the various cortical areas, the principles that determine the geometric shapes of these cells are not fully understood. Here we analyzed and modeled with a von Mises distribution the branching angles in 3D reconstructed basal dendritic arbors of hundreds of intracellularly injected cortical pyramidal cells in seven different cortical regions of the frontal, parietal, and occipital cortex of the mouse. We found that, despite the differences in the structure of the pyramidal cells in these distinct functional and cytoarchitectonic cortical areas, there are common design principles that govern the geometry of dendritic branching angles of pyramidal cells in all cortical areas.

Branching angles of pyramidal cell dendrites follow common geometrical design principles in different cortical areas

PubMed Central

Bielza, Concha; Benavides-Piccione, Ruth; López-Cruz, Pedro; Larrañaga, Pedro; DeFelipe, Javier

2014-01-01

Unraveling pyramidal cell structure is crucial to understanding cortical circuit computations. Although it is well known that pyramidal cell branching structure differs in the various cortical areas, the principles that determine the geometric shapes of these cells are not fully understood. Here we analyzed and modeled with a von Mises distribution the branching angles in 3D reconstructed basal dendritic arbors of hundreds of intracellularly injected cortical pyramidal cells in seven different cortical regions of the frontal, parietal, and occipital cortex of the mouse. We found that, despite the differences in the structure of the pyramidal cells in these distinct functional and cytoarchitectonic cortical areas, there are common design principles that govern the geometry of dendritic branching angles of pyramidal cells in all cortical areas. PMID:25081193

Postmortem 3-D brain hemisphere cortical tau and amyloid-β pathology mapping and quantification as a validation method of neuropathology imaging.

PubMed

Smid, Lojze M; Kepe, Vladimir; Vinters, Harry V; Bresjanac, Mara; Toyokuni, Tatsushi; Satyamurthy, Nagichettiar; Wong, Koon-Pong; Huang, Sung-Cheng; Silverman, Daniel H S; Miller, Karen; Small, Gary W; Barrio, Jorge R

2013-01-01

This work is aimed at correlating pre-mortem [18F]FDDNP positron emission tomography (PET) scan results in a patient with dementia with Lewy bodies (DLB), with cortical neuropathology distribution determined postmortem in three physical dimensions in whole brain coronal sections. Analysis of total amyloid-β (Aβ) distribution in frontal cortex and posterior cingulate gyrus confirmed its statistically significant correlation with cortical [18F]FDDNP PET binding values (distribution volume ratios, DVR) (p < 0.001, R = 0.97, R2 = 0.94). Neurofibrillary tangle (NFT) distribution correlated significantly with cortical [18F]FDDNP PET DVR in the temporal lobe (p < 0.001, R = 0.87, R2 = 0.76). Linear combination of Aβ and NFT densities was highly predictive of [18F]FDDNP PET DVR through all analyzed regions of interest (p < 0.0001, R = 0.92, R2 = 0.85), and both densities contributed significantly to the model. Lewy bodies were present at a much lower level than either Aβ or NFTs and did not significantly contribute to the in vivo signal. [18F]FDG PET scan results in this patient were consistent with the distinctive DLB pattern of hypometabolism. This work offers a mapping brain model applicable to all imaging probes for verification of imaging results with Aβ and/or tau neuropathology brain distribution using immunohistochemistry, fluorescence microscopy, and autoradiography.

Associations between cortical thickness and general intelligence in children, adolescents and young adults

PubMed Central

Menary, Kyle; Collins, Paul F.; Porter, James N.; Muetzel, Ryan; Olson, Elizabeth A.; Kumar, Vipin; Steinbach, Michael; Lim, Kelvin O.; Luciana, Monica

2013-01-01

Neuroimaging research indicates that human intellectual ability is related to brain structure including the thickness of the cerebral cortex. Most studies indicate that general intelligence is positively associated with cortical thickness in areas of association cortex distributed throughout both brain hemispheres. In this study, we performed a cortical thickness mapping analysis on data from 182 healthy typically developing males and females ages 9 to 24 years to identify correlates of general intelligence (g) scores. To determine if these correlates also mediate associations of specific cognitive abilities with cortical thickness, we regressed specific cognitive test scores on g scores and analyzed the residuals with respect to cortical thickness. The effect of age on the association between cortical thickness and intelligence was examined. We found a widely distributed pattern of positive associations between cortical thickness and g scores, as derived from the first unrotated principal factor of a factor analysis of Wechsler Abbreviated Scale of Intelligence (WASI) subtest scores. After WASI specific cognitive subtest scores were regressed on g factor scores, the residual score variances did not correlate significantly with cortical thickness in the full sample with age covaried. When participants were grouped at the age median, significant positive associations of cortical thickness were obtained in the older group for g-residualized scores on Block Design (a measure of visual-motor integrative processing) while significant negative associations of cortical thickness were observed in the younger group for g-residualized Vocabulary scores. These results regarding correlates of general intelligence are concordant with the existing literature, while the findings from younger versus older subgroups have implications for future research on brain structural correlates of specific cognitive abilities, as well as the cognitive domain specificity of behavioral performance correlates of normative gray matter thinning during adolescence. PMID:24744452

Disconnection syndromes of basal ganglia, thalamus, and cerebrocerebellar systems.

PubMed

Schmahmann, Jeremy D; Pandya, Deepak N

2008-09-01

Disconnection syndromes were originally conceptualized as a disruption of communication between different cerebral cortical areas. Two developments mandate a re-evaluation of this notion. First, we present a synopsis of our anatomical studies in monkey elucidating principles of organization of cerebral cortex. Efferent fibers emanate from every cortical area, and are directed with topographic precision via association fibers to ipsilateral cortical areas, commissural fibers to contralateral cerebral regions, striatal fibers to basal ganglia, and projection subcortical bundles to thalamus, brainstem and/or pontocerebellar system. We note that cortical areas can be defined by their patterns of subcortical and cortical connections. Second, we consider motor, cognitive and neuropsychiatric disorders in patients with lesions restricted to basal ganglia, thalamus, or cerebellum, and recognize that these lesions mimic deficits resulting from cortical lesions, with qualitative differences between the manifestations of lesions in functionally related areas of cortical and subcortical nodes. We consider these findings on the basis of anatomical observations from tract tracing studies in monkey, viewing them as disconnection syndromes reflecting loss of the contribution of subcortical nodes to the distributed neural circuits. We introduce a new theoretical framework for the distributed neural circuits, based on general, and specific, principles of anatomical organization, and on the architecture of the nodes that comprise these systems. We propose that neural architecture determines function, i.e., each architectonically distinct cortical and subcortical area contributes a unique transform, or computation, to information processing; anatomically precise and segregated connections between nodes define behavior; and association fiber tracts that link cerebral cortical areas with each other enable the cross-modal integration required for evolved complex behaviors. This model enables the formulation and testing of future hypotheses in investigations using evolving magnetic resonance imaging techniques in humans, and in clinical studies in patients with cortical and subcortical lesions.

Effect of distribution of striated laser hardening tracks on dry sliding wear resistance of biomimetic surface

NASA Astrophysics Data System (ADS)

Su, Wei; Zhou, Ti; Zhang, Peng; Zhou, Hong; Li, Hui

2018-01-01

Some biological surfaces were proved to have excellent anti-wear performance. Being inspired, Nd:YAG pulsed laser was used to create striated biomimetic laser hardening tracks on medium carbon steel samples. Dry sliding wear tests biomimetic samples were performed to investigate specific influence of distribution of laser hardening tracks on sliding wear resistance of biomimetic samples. After comparing wear weight loss of biomimetic samples, quenched sample and untreated sample, it can be suggested that the sample covered with dense laser tracks (3.5 mm spacing) has lower wear weight loss than the one covered with sparse laser tracks (4.5 mm spacing); samples distributed with only dense laser tracks or sparse laser tracks (even distribution) were proved to have better wear resistance than samples distributed with both dense and sparse tracks (uneven distribution). Wear mechanisms indicate that laser track and exposed substrate of biomimetic sample can be regarded as hard zone and soft zone respectively. Inconsecutive striated hard regions, on the one hand, can disperse load into small branches, on the other hand, will hinder sliding abrasives during wear. Soft regions with small range are beneficial in consuming mechanical energy and storing lubricative oxides, however, soft zone with large width (>0.5 mm) will be harmful to abrasion resistance of biomimetic sample because damages and material loss are more obvious on surface of soft phase. As for the reason why samples with even distributed bionic laser tracks have better wear resistance, it can be explained by the fact that even distributed laser hardening tracks can inhibit severe worn of local regions, thus sliding process can be more stable and wear extent can be alleviated as well.

Bone Area Histomorphometry.

PubMed

Andronowski, Janna M; Crowder, Christian

2018-05-21

Quantifying the amount of cortical bone loss is one variable used in histological methods of adult age estimation. Measurements of cortical area tend to be subjective and additional information regarding bone loss is not captured considering cancellous bone is disregarded. We describe whether measuring bone area (cancellous + cortical area) rather than cortical area may improve histological age estimation for the sixth rib. Mid-shaft rib cross-sections (n = 114) with a skewed sex distribution were analyzed. Ages range from 16 to 87 years. Variables included: total cross-sectional area, cortical area, bone area, relative bone area, relative cortical area, and endosteal area. Males have larger mean total cross-sectional area, bone area, and cortical area than females. Females display a larger mean endosteal area and greater mean relative measure values. Relative bone area significantly correlates with age. The relative bone area variable will provide researchers with a less subjective and more accurate measure than cortical area. © 2018 American Academy of Forensic Sciences.

AZTEC. Parallel Iterative method Software for Solving Linear Systems

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

Hutchinson, S.; Shadid, J.; Tuminaro, R.

1995-07-01

AZTEC is an interactive library that greatly simplifies the parrallelization process when solving the linear systems of equations Ax=b where A is a user supplied n X n sparse matrix, b is a user supplied vector of length n and x is a vector of length n to be computed. AZTEC is intended as a software tool for users who want to avoid cumbersome parallel programming details but who have large sparse linear systems which require an efficiently utilized parallel processing system. A collection of data transformation tools are provided that allow for easy creation of distributed sparse unstructured matricesmore » for parallel solutions.« less

On Edge Exchangeable Random Graphs

NASA Astrophysics Data System (ADS)

Janson, Svante

2017-06-01

We study a recent model for edge exchangeable random graphs introduced by Crane and Dempsey; in particular we study asymptotic properties of the random simple graph obtained by merging multiple edges. We study a number of examples, and show that the model can produce dense, sparse and extremely sparse random graphs. One example yields a power-law degree distribution. We give some examples where the random graph is dense and converges a.s. in the sense of graph limit theory, but also an example where a.s. every graph limit is the limit of some subsequence. Another example is sparse and yields convergence to a non-integrable generalized graphon defined on (0,∞).

Efficient diagonalization of the sparse matrices produced within the framework of the UK R-matrix molecular codes

NASA Astrophysics Data System (ADS)

Galiatsatos, P. G.; Tennyson, J.

2012-11-01

The most time consuming step within the framework of the UK R-matrix molecular codes is that of the diagonalization of the inner region Hamiltonian matrix (IRHM). Here we present the method that we follow to speed up this step. We use shared memory machines (SMM), distributed memory machines (DMM), the OpenMP directive based parallel language, the MPI function based parallel language, the sparse matrix diagonalizers ARPACK and PARPACK, a variation for real symmetric matrices of the official coordinate sparse matrix format and finally a parallel sparse matrix-vector product (PSMV). The efficient application of the previous techniques rely on two important facts: the sparsity of the matrix is large enough (more than 98%) and in order to get back converged results we need a small only part of the matrix spectrum.

Multi-Source Cooperative Data Collection with a Mobile Sink for the Wireless Sensor Network.

PubMed

Han, Changcai; Yang, Jinsheng

2017-10-30

The multi-source cooperation integrating distributed low-density parity-check codes is investigated to jointly collect data from multiple sensor nodes to the mobile sink in the wireless sensor network. The one-round and two-round cooperative data collection schemes are proposed according to the moving trajectories of the sink node. Specifically, two sparse cooperation models are firstly formed based on geographical locations of sensor source nodes, the impairment of inter-node wireless channels and moving trajectories of the mobile sink. Then, distributed low-density parity-check codes are devised to match the directed graphs and cooperation matrices related with the cooperation models. In the proposed schemes, each source node has quite low complexity attributed to the sparse cooperation and the distributed processing. Simulation results reveal that the proposed cooperative data collection schemes obtain significant bit error rate performance and the two-round cooperation exhibits better performance compared with the one-round scheme. The performance can be further improved when more source nodes participate in the sparse cooperation. For the two-round data collection schemes, the performance is evaluated for the wireless sensor networks with different moving trajectories and the variant data sizes.

Multi-Source Cooperative Data Collection with a Mobile Sink for the Wireless Sensor Network

PubMed Central

Han, Changcai; Yang, Jinsheng

2017-01-01

The multi-source cooperation integrating distributed low-density parity-check codes is investigated to jointly collect data from multiple sensor nodes to the mobile sink in the wireless sensor network. The one-round and two-round cooperative data collection schemes are proposed according to the moving trajectories of the sink node. Specifically, two sparse cooperation models are firstly formed based on geographical locations of sensor source nodes, the impairment of inter-node wireless channels and moving trajectories of the mobile sink. Then, distributed low-density parity-check codes are devised to match the directed graphs and cooperation matrices related with the cooperation models. In the proposed schemes, each source node has quite low complexity attributed to the sparse cooperation and the distributed processing. Simulation results reveal that the proposed cooperative data collection schemes obtain significant bit error rate performance and the two-round cooperation exhibits better performance compared with the one-round scheme. The performance can be further improved when more source nodes participate in the sparse cooperation. For the two-round data collection schemes, the performance is evaluated for the wireless sensor networks with different moving trajectories and the variant data sizes. PMID:29084155

The effects of missing data on global ozone estimates

NASA Technical Reports Server (NTRS)

Drewry, J. W.; Robbins, J. L.

1981-01-01

The effects of missing data and model truncation on estimates of the global mean, zonal distribution, and global distribution of ozone are considered. It is shown that missing data can introduce biased estimates with errors that are not accounted for in the accuracy calculations of empirical modeling techniques. Data-fill techniques are introduced and used for evaluating error bounds and constraining the estimate in areas of sparse and missing data. It is found that the accuracy of the global mean estimate is more dependent on data distribution than model size. Zonal features can be accurately described by 7th order models over regions of adequate data distribution. Data variance accounted for by higher order models appears to represent climatological features of columnar ozone rather than pure error. Data-fill techniques can prevent artificial feature generation in regions of sparse or missing data without degrading high order estimates over dense data regions.

Pole-Like Road Furniture Detection in Sparse and Unevenly Distributed Mobile Laser Scanning Data

NASA Astrophysics Data System (ADS)

Li, F.; Lehtomäki, M.; Oude Elberink, S.; Vosselman, G.; Puttonen, E.; Kukko, A.; Hyyppä, J.

2018-05-01

Pole-like road furniture detection received much attention due to its traffic functionality in recent years. In this paper, we develop a framework to detect pole-like road furniture from sparse mobile laser scanning data. The framework is carried out in four steps. The unorganised point cloud is first partitioned. Then above ground points are clustered and roughly classified after removing ground points. A slicing check in combination with cylinder masking is proposed to extract pole-like road furniture candidates. Pole-like road furniture are obtained after occlusion analysis in the last stage. The average completeness and correctness of pole-like road furniture in sparse and unevenly distributed mobile laser scanning data was above 0.83. It is comparable to the state of art in the field of pole-like road furniture detection in mobile laser scanning data of good quality and is potentially of practical use in the processing of point clouds collected by autonomous driving platforms.

Data traffic reduction schemes for sparse Cholesky factorizations

NASA Technical Reports Server (NTRS)

Naik, Vijay K.; Patrick, Merrell L.

1988-01-01

Load distribution schemes are presented which minimize the total data traffic in the Cholesky factorization of dense and sparse, symmetric, positive definite matrices on multiprocessor systems with local and shared memory. The total data traffic in factoring an n x n sparse, symmetric, positive definite matrix representing an n-vertex regular 2-D grid graph using n (sup alpha), alpha is equal to or less than 1, processors are shown to be O(n(sup 1 + alpha/2)). It is O(n(sup 3/2)), when n (sup alpha), alpha is equal to or greater than 1, processors are used. Under the conditions of uniform load distribution, these results are shown to be asymptotically optimal. The schemes allow efficient use of up to O(n) processors before the total data traffic reaches the maximum value of O(n(sup 3/2)). The partitioning employed within the scheme, allows a better utilization of the data accessed from shared memory than those of previously published methods.

Actin kinetics shapes cortical network structure and mechanics

PubMed Central

Fritzsche, Marco; Erlenkämper, Christoph; Moeendarbary, Emad; Charras, Guillaume; Kruse, Karsten

2016-01-01

The actin cortex of animal cells is the main determinant of cellular mechanics. The continuous turnover of cortical actin filaments enables cells to quickly respond to stimuli. Recent work has shown that most of the cortical actin is generated by only two actin nucleators, the Arp2/3 complex and the formin Diaph1. However, our understanding of their interplay, their kinetics, and the length distribution of the filaments that they nucleate within living cells is poor. Such knowledge is necessary for a thorough comprehension of cellular processes and cell mechanics from basic polymer physics principles. We determined cortical assembly rates in living cells by using single-molecule fluorescence imaging in combination with stochastic simulations. We find that formin-nucleated filaments are, on average, 10 times longer than Arp2/3-nucleated filaments. Although formin-generated filaments represent less than 10% of all actin filaments, mechanical measurements indicate that they are important determinants of cortical elasticity. Tuning the activity of actin nucleators to alter filament length distribution may thus be a mechanism allowing cells to adjust their macroscopic mechanical properties to their physiological needs. PMID:27152338

Actin kinetics shapes cortical network structure and mechanics.

PubMed

Fritzsche, Marco; Erlenkämper, Christoph; Moeendarbary, Emad; Charras, Guillaume; Kruse, Karsten

2016-04-01

The actin cortex of animal cells is the main determinant of cellular mechanics. The continuous turnover of cortical actin filaments enables cells to quickly respond to stimuli. Recent work has shown that most of the cortical actin is generated by only two actin nucleators, the Arp2/3 complex and the formin Diaph1. However, our understanding of their interplay, their kinetics, and the length distribution of the filaments that they nucleate within living cells is poor. Such knowledge is necessary for a thorough comprehension of cellular processes and cell mechanics from basic polymer physics principles. We determined cortical assembly rates in living cells by using single-molecule fluorescence imaging in combination with stochastic simulations. We find that formin-nucleated filaments are, on average, 10 times longer than Arp2/3-nucleated filaments. Although formin-generated filaments represent less than 10% of all actin filaments, mechanical measurements indicate that they are important determinants of cortical elasticity. Tuning the activity of actin nucleators to alter filament length distribution may thus be a mechanism allowing cells to adjust their macroscopic mechanical properties to their physiological needs.

Sparsity-Based Representation for Classification Algorithms and Comparison Results for Transient Acoustic Signals

DTIC Science & Technology

2016-05-01

large but correlated noise and signal interference (i.e., low -rank interference). Another contribution is the implementation of deep learning...representation, low rank, deep learning 52 Tung-Duong Tran-Luu 301-394-3082Unclassified Unclassified Unclassified UU ii Approved for public release; distribution...Classification of Acoustic Transients 6 3.2 Joint Sparse Representation with Low -Rank Interference 7 3.3 Simultaneous Group-and-Joint Sparse Representation

Effect of rotopositioning on the growth and maturation of mandibular bone in immobilized Rhesus monkeys

NASA Technical Reports Server (NTRS)

Simmons, D. J.; Parvin, C.; Smith, K. C.; France, P.; Kazarian, L.

1986-01-01

The rates of bone formation and mineralization in the mandibular cortex of juvenile Rhesus monkeys exposed to immobilization/rotopositioning are evaluated. The monkeys were restrained in a supine position and rotated 90 deg every 30 minutes through a full 360 deg for 14 days. The microscopic distribution of mineral densities in osteonal bone and the porosity of cortical bone are studied using microradiographs, and osteon closure rates are assessed using tetracycline labeling; normal distributions of osteons of different mineral density and cortical bone porosity values are observed. It is concluded that 14 days of immobilization/rotopositioning did not cause abnormal changes in osteon mineralization, cortical porosity, and osteon closure rates.

Brief communication: Paleobiological inferences on the locomotor repertoire of extinct hominoids based on femoral neck cortical thickness: The fossil great ape hispanopithecus laietanus as a test-case study.

PubMed

Pina, Marta; Alba, David M; Almécija, Sergio; Fortuny, Josep; Moyà-Solà, Salvador

2012-09-01

The relationship between femoral neck superior and inferior cortical thickness in primates is related to locomotor behavior. This relationship has been employed to infer bipedalism in fossil hominins, although bipeds share the same pattern of generalized quadrupeds, where the superior cortex is thinner than the inferior one. In contrast, knuckle-walkers and specialized suspensory taxa display a more homogeneous distribution of cortical bone. These different patterns, probably related to the range of movement at the hip joint and concomitant differences in the load stresses at the femoral neck, are very promising for making locomotor inferences in extinct primates. To evaluate the utility of this feature in the fossil record, we relied on computed tomography applied to the femur of the Late Miocene hominoid Hispanopithecus laietanus as a test-case study. Both an orthograde body plan and orang-like suspensory adaptations had been previously documented for this taxon on different anatomical grounds, leading to the hypothesis that this fossil ape should display a modern ape-like distribution of femoral neck cortical thickness. This is confirmed by the results of this study, leading to the conclusion that Hispanopithecus represents the oldest evidence of a homogeneous cortical bone distribution in the hominoid fossil record. Our results therefore strengthen the utility of femoral neck cortical thickness for making paleobiological inferences on the locomotor repertoire of fossil primates. This feature would be particularly useful for assessing the degree of orthograde arboreal locomotor behaviors vs. terrestrial bipedalism in putative early hominins. Copyright © 2012 Wiley Periodicals, Inc.

Application distribution model and related security attacks in VANET

NASA Astrophysics Data System (ADS)

Nikaein, Navid; Kanti Datta, Soumya; Marecar, Irshad; Bonnet, Christian

2013-03-01

In this paper, we present a model for application distribution and related security attacks in dense vehicular ad hoc networks (VANET) and sparse VANET which forms a delay tolerant network (DTN). We study the vulnerabilities of VANET to evaluate the attack scenarios and introduce a new attacker`s model as an extension to the work done in [6]. Then a VANET model has been proposed that supports the application distribution through proxy app stores on top of mobile platforms installed in vehicles. The steps of application distribution have been studied in detail. We have identified key attacks (e.g. malware, spamming and phishing, software attack and threat to location privacy) for dense VANET and two attack scenarios for sparse VANET. It has been shown that attacks can be launched by distributing malicious applications and injecting malicious codes to On Board Unit (OBU) by exploiting OBU software security holes. Consequences of such security attacks have been described. Finally, countermeasures including the concepts of sandbox have also been presented in depth.

Atypical form of Alzheimer's disease with prominent posterior cortical atrophy: a review of lesion distribution and circuit disconnection in cortical visual pathways

NASA Technical Reports Server (NTRS)

Hof, P. R.; Vogt, B. A.; Bouras, C.; Morrison, J. H.; Bloom, F. E. (Principal Investigator)

1997-01-01

In recent years, the existence of visual variants of Alzheimer's disease characterized by atypical clinical presentation at onset has been increasingly recognized. In many of these cases post-mortem neuropathological assessment revealed that correlations could be established between clinical symptoms and the distribution of neurodegenerative lesions. We have analyzed a series of Alzheimer's disease patients presenting with prominent visual symptomatology as a cardinal sign of the disease. In these cases, a shift in the distribution of pathological lesions was observed such that the primary visual areas and certain visual association areas within the occipito-parieto-temporal junction and posterior cingulate cortex had very high densities of lesions, whereas the prefrontal cortex had fewer lesions than usually observed in Alzheimer's disease. Previous quantitative analyses have demonstrated that in Alzheimer's disease, primary sensory and motor cortical areas are less damaged than the multimodal association areas of the frontal and temporal lobes, as indicated by the laminar and regional distribution patterns of neurofibrillary tangles and senile plaques. The distribution of pathological lesions in the cerebral cortex of Alzheimer's disease cases with visual symptomatology revealed that specific visual association pathways were disrupted, whereas these particular connections are likely to be affected to a less severe degree in the more common form of Alzheimer's disease. These data suggest that in some cases with visual variants of Alzheimer's disease, the neurological symptomatology may be related to the loss of certain components of the cortical visual pathways, as reflected by the particular distribution of the neuropathological markers of the disease.

Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

NASA Astrophysics Data System (ADS)

Hyman, J. D.; Aldrich, G.; Viswanathan, H.; Makedonska, N.; Karra, S.

2016-08-01

We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semicorrelation, and noncorrelation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected so that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same. We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. These observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.

Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

NASA Astrophysics Data System (ADS)

Hyman, J.; Aldrich, G. A.; Viswanathan, H. S.; Makedonska, N.; Karra, S.

2016-12-01

We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semi-correlation, and non-correlation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected so that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same.We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. These observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.

Differential impact of partial cortical blindness on gaze strategies when sitting and walking - an immersive virtual reality study

PubMed Central

Iorizzo, Dana B.; Riley, Meghan E.; Hayhoe, Mary; Huxlin, Krystel R.

2011-01-01

The present experiments aimed to characterize the visual performance of subjects with long-standing, unilateral cortical blindness when walking in a naturalistic, virtual environment. Under static, seated testing conditions, cortically blind subjects are known to exhibit compensatory eye movement strategies. However, they still complain of significant impairment in visual detection during navigation. To assess whether this is due to a change in compensatory eye movement strategy between sitting and walking, we measured eye and head movements in subjects asked to detect peripherally-presented, moving basketballs. When seated, cortically blind subjects detected ~80% of balls, while controls detected almost all balls. Seated blind subjects did not make larger head movements than controls, but they consistently biased their fixation distribution towards their blind hemifield. When walking, head movements were similar in the two groups, but the fixation bias decreased to the point that fixation distribution in cortically blind subjects became similar to that in controls - with one major exception: at the time of basketball appearance, walking controls looked primarily at the far ground, in upper quadrants of the virtual field of view; cortically blind subjects looked significantly more at the near ground, in lower quadrants of the virtual field. Cortically blind subjects detected only 58% of the balls when walking while controls detected ~90%. Thus, the adaptive gaze strategies adopted by cortically blind individuals as a compensation for their visual loss are strongest and most effective when seated and stationary. Walking significantly alters these gaze strategies in a way that seems to favor walking performance, but impairs peripheral target detection. It is possible that this impairment underlies the experienced difficulty of those with cortical blindness when navigating in real life. PMID:21414339

Differential impact of partial cortical blindness on gaze strategies when sitting and walking - an immersive virtual reality study.

PubMed

Iorizzo, Dana B; Riley, Meghan E; Hayhoe, Mary; Huxlin, Krystel R

2011-05-25

The present experiments aimed to characterize the visual performance of subjects with long-standing, unilateral cortical blindness when walking in a naturalistic, virtual environment. Under static, seated testing conditions, cortically blind subjects are known to exhibit compensatory eye movement strategies. However, they still complain of significant impairment in visual detection during navigation. To assess whether this is due to a change in compensatory eye movement strategy between sitting and walking, we measured eye and head movements in subjects asked to detect peripherally-presented, moving basketballs. When seated, cortically blind subjects detected ∼80% of balls, while controls detected almost all balls. Seated blind subjects did not make larger head movements than controls, but they consistently biased their fixation distribution towards their blind hemifield. When walking, head movements were similar in the two groups, but the fixation bias decreased to the point that fixation distribution in cortically blind subjects became similar to that in controls - with one major exception: at the time of basketball appearance, walking controls looked primarily at the far ground, in upper quadrants of the virtual field of view; cortically blind subjects looked significantly more at the near ground, in lower quadrants of the virtual field. Cortically blind subjects detected only 58% of the balls when walking while controls detected ∼90%. Thus, the adaptive gaze strategies adopted by cortically blind individuals as a compensation for their visual loss are strongest and most effective when seated and stationary. Walking significantly alters these gaze strategies in a way that seems to favor walking performance, but impairs peripheral target detection. It is possible that this impairment underlies the experienced difficulty of those with cortical blindness when navigating in real life. Copyright © 2011 Elsevier Ltd. All rights reserved.

Massively parallel sparse matrix function calculations with NTPoly

NASA Astrophysics Data System (ADS)

Dawson, William; Nakajima, Takahito

2018-04-01

We present NTPoly, a massively parallel library for computing the functions of sparse, symmetric matrices. The theory of matrix functions is a well developed framework with a wide range of applications including differential equations, graph theory, and electronic structure calculations. One particularly important application area is diagonalization free methods in quantum chemistry. When the input and output of the matrix function are sparse, methods based on polynomial expansions can be used to compute matrix functions in linear time. We present a library based on these methods that can compute a variety of matrix functions. Distributed memory parallelization is based on a communication avoiding sparse matrix multiplication algorithm. OpenMP task parallellization is utilized to implement hybrid parallelization. We describe NTPoly's interface and show how it can be integrated with programs written in many different programming languages. We demonstrate the merits of NTPoly by performing large scale calculations on the K computer.

Doubly Nonparametric Sparse Nonnegative Matrix Factorization Based on Dependent Indian Buffet Processes.

PubMed

Xuan, Junyu; Lu, Jie; Zhang, Guangquan; Xu, Richard Yi Da; Luo, Xiangfeng

2018-05-01

Sparse nonnegative matrix factorization (SNMF) aims to factorize a data matrix into two optimized nonnegative sparse factor matrices, which could benefit many tasks, such as document-word co-clustering. However, the traditional SNMF typically assumes the number of latent factors (i.e., dimensionality of the factor matrices) to be fixed. This assumption makes it inflexible in practice. In this paper, we propose a doubly sparse nonparametric NMF framework to mitigate this issue by using dependent Indian buffet processes (dIBP). We apply a correlation function for the generation of two stick weights associated with each column pair of factor matrices while still maintaining their respective marginal distribution specified by IBP. As a consequence, the generation of two factor matrices will be columnwise correlated. Under this framework, two classes of correlation function are proposed: 1) using bivariate Beta distribution and 2) using Copula function. Compared with the single IBP-based NMF, this paper jointly makes two factor matrices nonparametric and sparse, which could be applied to broader scenarios, such as co-clustering. This paper is seen to be much more flexible than Gaussian process-based and hierarchial Beta process-based dIBPs in terms of allowing the two corresponding binary matrix columns to have greater variations in their nonzero entries. Our experiments on synthetic data show the merits of this paper compared with the state-of-the-art models in respect of factorization efficiency, sparsity, and flexibility. Experiments on real-world data sets demonstrate the efficiency of this paper in document-word co-clustering tasks.

Lipid-laden cells differentially distributed in the aging brain are functionally active and correspond to distinct phenotypes

PubMed Central

Shimabukuro, Marilia Kimie; Langhi, Larissa Gutman Paranhos; Cordeiro, Ingrid; Brito, José M.; Batista, Claudia Maria de Castro; Mattson, Mark P.; de Mello Coelho, Valeria

2016-01-01

We characterized cerebral Oil Red O-positive lipid-laden cells (LLC) of aging mice evaluating their distribution, morphology, density, functional activities and inflammatory phenotype. We identified LLC in meningeal, cortical and neurogenic brain regions. The density of cerebral LLC increased with age. LLC presenting small lipid droplets were visualized adjacent to blood vessels or deeper in the brain cortical and striatal parenchyma of aging mice. LLC with larger droplets were asymmetrically distributed in the cerebral ventricle walls, mainly located in the lateral wall. We also found that LLC in the subventricular region co-expressed beclin-1 or LC3, markers for autophagosome or autophagolysosome formation, and perilipin (PLIN), a lipid droplet-associated protein, suggesting lipophagic activity. Some cerebral LLC exhibited β galactosidase activity indicating a senescence phenotype. Moreover, we detected production of the pro-inflammatory cytokine TNF-α in cortical PLIN+ LLC. Some cortical NeuN+ neurons, GFAP+ glia limitans astrocytes, Iba-1+ microglia and S100β+ ependymal cells expressed PLIN in the aging brain. Our findings suggest that cerebral LLC exhibit distinct cellular phenotypes and may participate in the age-associated neuroinflammatory processes. PMID:27029648

Lipid-laden cells differentially distributed in the aging brain are functionally active and correspond to distinct phenotypes.

PubMed

Shimabukuro, Marilia Kimie; Langhi, Larissa Gutman Paranhos; Cordeiro, Ingrid; Brito, José M; Batista, Claudia Maria de Castro; Mattson, Mark P; Mello Coelho, Valeria de

2016-03-31

We characterized cerebral Oil Red O-positive lipid-laden cells (LLC) of aging mice evaluating their distribution, morphology, density, functional activities and inflammatory phenotype. We identified LLC in meningeal, cortical and neurogenic brain regions. The density of cerebral LLC increased with age. LLC presenting small lipid droplets were visualized adjacent to blood vessels or deeper in the brain cortical and striatal parenchyma of aging mice. LLC with larger droplets were asymmetrically distributed in the cerebral ventricle walls, mainly located in the lateral wall. We also found that LLC in the subventricular region co-expressed beclin-1 or LC3, markers for autophagosome or autophagolysosome formation, and perilipin (PLIN), a lipid droplet-associated protein, suggesting lipophagic activity. Some cerebral LLC exhibited β galactosidase activity indicating a senescence phenotype. Moreover, we detected production of the pro-inflammatory cytokine TNF-α in cortical PLIN(+) LLC. Some cortical NeuN(+) neurons, GFAP(+) glia limitans astrocytes, Iba-1(+) microglia and S100β(+) ependymal cells expressed PLIN in the aging brain. Our findings suggest that cerebral LLC exhibit distinct cellular phenotypes and may participate in the age-associated neuroinflammatory processes.

Spatial distribution and longitudinal development of deep cortical sulcal landmarks in infants.

PubMed

Meng, Yu; Li, Gang; Lin, Weili; Gilmore, John H; Shen, Dinggang

2014-10-15

Sulcal pits, the locally deepest points in sulci of the highly convoluted and variable cerebral cortex, are found to be spatially consistent across human adult individuals. It is suggested that sulcal pits are genetically controlled and have close relationships with functional areas. To date, the existing imaging studies of sulcal pits are mainly focused on adult brains, yet little is known about the spatial distribution and temporal development of sulcal pits in the first 2 years of life, which is the most dynamic and critical period of postnatal brain development. Studying sulcal pits during this period would greatly enrich our limited understandings of the origins and developmental trajectories of sulcal pits, and would also provide important insights into many neurodevelopmental disorders associated with abnormal cortical foldings. In this paper, by using surface-based morphometry, for the first time, we systemically investigated the spatial distribution and temporal development of sulcal pits in major cortical sulci from 73 healthy infants, each with three longitudinal 3T MR scans at term birth, 1 year, and 2 years of age. Our results suggest that the spatially consistent distributions of sulcal pits in major sulci across individuals have already existed at term birth and this spatial distribution pattern keeps relatively stable in the first 2 years of life, despite that the cerebral cortex expands dramatically and the sulcal depth increases considerably during this period. Specially, the depth of sulcal pits increases regionally heterogeneously, with more rapid growth in the high-order association cortex, including the prefrontal and temporal cortices, than the sensorimotor cortex in the first 2 years of life. Meanwhile, our results also suggest that there exist hemispheric asymmetries of the spatial distributions of sulcal pits in several cortical regions, such as the central, superior temporal and postcentral sulci, consistently from birth to 2 years of age, which likely has close relationships with the lateralization of brain functions of these regions. This study provides detailed insights into the spatial distribution and temporal development of deep sulcal landmarks in infants. Copyright © 2014 Elsevier Inc. All rights reserved.

Margin based ontology sparse vector learning algorithm and applied in biology science.

PubMed

Gao, Wei; Qudair Baig, Abdul; Ali, Haidar; Sajjad, Wasim; Reza Farahani, Mohammad

2017-01-01

In biology field, the ontology application relates to a large amount of genetic information and chemical information of molecular structure, which makes knowledge of ontology concepts convey much information. Therefore, in mathematical notation, the dimension of vector which corresponds to the ontology concept is often very large, and thus improves the higher requirements of ontology algorithm. Under this background, we consider the designing of ontology sparse vector algorithm and application in biology. In this paper, using knowledge of marginal likelihood and marginal distribution, the optimized strategy of marginal based ontology sparse vector learning algorithm is presented. Finally, the new algorithm is applied to gene ontology and plant ontology to verify its efficiency.

Bi Sparsity Pursuit: A Paradigm for Robust Subspace Recovery

DTIC Science & Technology

2016-09-27

16. SECURITY CLASSIFICATION OF: The success of sparse models in computer vision and machine learning is due to the fact that, high dimensional data...Office P.O. Box 12211 Research Triangle Park, NC 27709-2211 Signal recovery, Sparse learning , Subspace modeling REPORT DOCUMENTATION PAGE 11...vision and machine learning is due to the fact that, high dimensional data is distributed in a union of low dimensional subspaces in many real-world

Compressive sensing for sparse time-frequency representation of nonstationary signals in the presence of impulsive noise

NASA Astrophysics Data System (ADS)

Orović, Irena; Stanković, Srdjan; Amin, Moeness

2013-05-01

A modified robust two-dimensional compressive sensing algorithm for reconstruction of sparse time-frequency representation (TFR) is proposed. The ambiguity function domain is assumed to be the domain of observations. The two-dimensional Fourier bases are used to linearly relate the observations to the sparse TFR, in lieu of the Wigner distribution. We assume that a set of available samples in the ambiguity domain is heavily corrupted by an impulsive type of noise. Consequently, the problem of sparse TFR reconstruction cannot be tackled using standard compressive sensing optimization algorithms. We introduce a two-dimensional L-statistics based modification into the transform domain representation. It provides suitable initial conditions that will produce efficient convergence of the reconstruction algorithm. This approach applies sorting and weighting operations to discard an expected amount of samples corrupted by noise. The remaining samples serve as observations used in sparse reconstruction of the time-frequency signal representation. The efficiency of the proposed approach is demonstrated on numerical examples that comprise both cases of monocomponent and multicomponent signals.

The Cortex Transform as an image preprocessor for sparse distributed memory: An initial study

NASA Technical Reports Server (NTRS)

Olshausen, Bruno; Watson, Andrew

1990-01-01

An experiment is described which was designed to evaluate the use of the Cortex Transform as an image processor for Sparse Distributed Memory (SDM). In the experiment, a set of images were injected with Gaussian noise, preprocessed with the Cortex Transform, and then encoded into bit patterns. The various spatial frequency bands of the Cortex Transform were encoded separately so that they could be evaluated based on their ability to properly cluster patterns belonging to the same class. The results of this study indicate that by simply encoding the low pass band of the Cortex Transform, a very suitable input representation for the SDM can be achieved.

ROPE: Recoverable Order-Preserving Embedding of Natural Language

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

Widemann, David P.; Wang, Eric X.; Thiagarajan, Jayaraman J.

We present a novel Recoverable Order-Preserving Embedding (ROPE) of natural language. ROPE maps natural language passages from sparse concatenated one-hot representations to distributed vector representations of predetermined fixed length. We use Euclidean distance to return search results that are both grammatically and semantically similar. ROPE is based on a series of random projections of distributed word embeddings. We show that our technique typically forms a dictionary with sufficient incoherence such that sparse recovery of the original text is possible. We then show how our embedding allows for efficient and meaningful natural search and retrieval on Microsoft’s COCO dataset and themore » IMDB Movie Review dataset.« less

Stochastic Computations in Cortical Microcircuit Models

PubMed Central

Maass, Wolfgang

2013-01-01

Experimental data from neuroscience suggest that a substantial amount of knowledge is stored in the brain in the form of probability distributions over network states and trajectories of network states. We provide a theoretical foundation for this hypothesis by showing that even very detailed models for cortical microcircuits, with data-based diverse nonlinear neurons and synapses, have a stationary distribution of network states and trajectories of network states to which they converge exponentially fast from any initial state. We demonstrate that this convergence holds in spite of the non-reversibility of the stochastic dynamics of cortical microcircuits. We further show that, in the presence of background network oscillations, separate stationary distributions emerge for different phases of the oscillation, in accordance with experimentally reported phase-specific codes. We complement these theoretical results by computer simulations that investigate resulting computation times for typical probabilistic inference tasks on these internally stored distributions, such as marginalization or marginal maximum-a-posteriori estimation. Furthermore, we show that the inherent stochastic dynamics of generic cortical microcircuits enables them to quickly generate approximate solutions to difficult constraint satisfaction problems, where stored knowledge and current inputs jointly constrain possible solutions. This provides a powerful new computing paradigm for networks of spiking neurons, that also throws new light on how networks of neurons in the brain could carry out complex computational tasks such as prediction, imagination, memory recall and problem solving. PMID:24244126

Plasticity-Driven Self-Organization under Topological Constraints Accounts for Non-random Features of Cortical Synaptic Wiring

PubMed Central

Miner, Daniel; Triesch, Jochen

2016-01-01

Understanding the structure and dynamics of cortical connectivity is vital to understanding cortical function. Experimental data strongly suggest that local recurrent connectivity in the cortex is significantly non-random, exhibiting, for example, above-chance bidirectionality and an overrepresentation of certain triangular motifs. Additional evidence suggests a significant distance dependency to connectivity over a local scale of a few hundred microns, and particular patterns of synaptic turnover dynamics, including a heavy-tailed distribution of synaptic efficacies, a power law distribution of synaptic lifetimes, and a tendency for stronger synapses to be more stable over time. Understanding how many of these non-random features simultaneously arise would provide valuable insights into the development and function of the cortex. While previous work has modeled some of the individual features of local cortical wiring, there is no model that begins to comprehensively account for all of them. We present a spiking network model of a rodent Layer 5 cortical slice which, via the interactions of a few simple biologically motivated intrinsic, synaptic, and structural plasticity mechanisms, qualitatively reproduces these non-random effects when combined with simple topological constraints. Our model suggests that mechanisms of self-organization arising from a small number of plasticity rules provide a parsimonious explanation for numerous experimentally observed non-random features of recurrent cortical wiring. Interestingly, similar mechanisms have been shown to endow recurrent networks with powerful learning abilities, suggesting that these mechanism are central to understanding both structure and function of cortical synaptic wiring. PMID:26866369

Automatic Management of Parallel and Distributed System Resources

NASA Technical Reports Server (NTRS)

Yan, Jerry; Ngai, Tin Fook; Lundstrom, Stephen F.

1990-01-01

Viewgraphs on automatic management of parallel and distributed system resources are presented. Topics covered include: parallel applications; intelligent management of multiprocessing systems; performance evaluation of parallel architecture; dynamic concurrent programs; compiler-directed system approach; lattice gaseous cellular automata; and sparse matrix Cholesky factorization.

AMPHIBIAN DECLINES AND ENVIRONMENTAL CHANGE IN THE EASTERN "MOJAVE DESERT"

EPA Science Inventory

A number of amphibian species historically inhabited sparsely distributed wetlands in the Mojave Desert, USA, habitats that have been dramatically altered or eliminated as a result of human activities. The population status and distribution of amphibians were investigated in a 20...

A Distributed Learning Method for ℓ1-Regularized Kernel Machine over Wireless Sensor Networks

PubMed Central

Ji, Xinrong; Hou, Cuiqin; Hou, Yibin; Gao, Fang; Wang, Shulong

2016-01-01

In wireless sensor networks, centralized learning methods have very high communication costs and energy consumption. These are caused by the need to transmit scattered training examples from various sensor nodes to the central fusion center where a classifier or a regression machine is trained. To reduce the communication cost, a distributed learning method for a kernel machine that incorporates ℓ1 norm regularization (ℓ1-regularized) is investigated, and a novel distributed learning algorithm for the ℓ1-regularized kernel minimum mean squared error (KMSE) machine is proposed. The proposed algorithm relies on in-network processing and a collaboration that transmits the sparse model only between single-hop neighboring nodes. This paper evaluates the proposed algorithm with respect to the prediction accuracy, the sparse rate of model, the communication cost and the number of iterations on synthetic and real datasets. The simulation results show that the proposed algorithm can obtain approximately the same prediction accuracy as that obtained by the batch learning method. Moreover, it is significantly superior in terms of the sparse rate of model and communication cost, and it can converge with fewer iterations. Finally, an experiment conducted on a wireless sensor network (WSN) test platform further shows the advantages of the proposed algorithm with respect to communication cost. PMID:27376298

Differential distribution of neurons in the gyral white matter of the human cerebral cortex.

PubMed

García-Marín, V; Blazquez-Llorca, L; Rodriguez, J R; Gonzalez-Soriano, J; DeFelipe, J

2010-12-01

The neurons in the cortical white matter (WM neurons) originate from the first set of postmitotic neurons that migrates from the ventricular zone. In particular, they arise in the subplate that contains the earliest cells generated in the telencephalon, prior to the appearance of neurons in gray matter cortical layers. These cortical WM neurons are very numerous during development, when they are thought to participate in transient synaptic networks, although many of these cells later die, and relatively few cells survive as WM neurons in the adult. We used light and electron microscopy to analyze the distribution and density of WM neurons in various areas of the adult human cerebral cortex. Furthermore, we examined the perisomatic innervation of these neurons and estimated the density of synapses in the white matter. Finally, we examined the distribution and neurochemical nature of interneurons that putatively innervate the somata of WM neurons. From the data obtained, we can draw three main conclusions: first, the density of WM neurons varies depending on the cortical areas; second, calretinin-immunoreactive neurons represent the major subpopulation of GABAergic WM neurons; and, third, the somata of WM neurons are surrounded by both glutamatergic and GABAergic axon terminals, although only symmetric axosomatic synapses were found. By contrast, both symmetric and asymmetric axodendritic synapses were observed in the neuropil. We discuss the possible functional implications of these findings in terms of cortical circuits. © 2010 Wiley-Liss, Inc.

Current dipole orientation and distribution of epileptiform activity correlates with cortical thinning in left mesiotemporal epilepsy

PubMed Central

Reinsberger, Claus; Tanaka, Naoaki; Cole, Andrew J.; Woo Lee, Jong; Dworetzky, Barbara A.; Bromfield, Edward B.; Hamiwka, Lorie; Bourgeois, Blaise F.; Golby, Alexandra J.; Madsen, Joseph R.; Stufflebeam, Steven M.

2011-01-01

To evaluate cortical architecture in mesial temporal lobe epilepsy (MTLE) with respect to electrophysiology, we analyze both magnetic resonance imaging (MRI) and magnetoencephalography (MEG) in 19 patients with left MTLE. We divide the patients into two groups: 9 patients (Group A) had vertically oriented antero-medial equivalent current dipoles (ECDs). 10 patients (Group B) had ECDs that were diversely oriented and widely distributed. Group analysis of MRI data showed widespread cortical thinning in Group B compared with Group A, in the left hemisphere involving the cingulate, supramarginal, occipito-temporal and parahippocampal gyri, precuneus and parietal lobule, and in the right hemisphere involving the fronto-medial, -central and -basal gyri and the precuneus. These results suggest that regardless of the presence of hippocampal sclerosis, in a subgroup of patients with MTLE a large cortical network is affected. This finding may, in part, explain the unfavorable outcome in some MTLE patients after epilepsy surgery. PMID:20472073

Feature-Based Visual Short-Term Memory Is Widely Distributed and Hierarchically Organized.

PubMed

Dotson, Nicholas M; Hoffman, Steven J; Goodell, Baldwin; Gray, Charles M

2018-06-15

Feature-based visual short-term memory is known to engage both sensory and association cortices. However, the extent of the participating circuit and the neural mechanisms underlying memory maintenance is still a matter of vigorous debate. To address these questions, we recorded neuronal activity from 42 cortical areas in monkeys performing a feature-based visual short-term memory task and an interleaved fixation task. We find that task-dependent differences in firing rates are widely distributed throughout the cortex, while stimulus-specific changes in firing rates are more restricted and hierarchically organized. We also show that microsaccades during the memory delay encode the stimuli held in memory and that units modulated by microsaccades are more likely to exhibit stimulus specificity, suggesting that eye movements contribute to visual short-term memory processes. These results support a framework in which most cortical areas, within a modality, contribute to mnemonic representations at timescales that increase along the cortical hierarchy. Copyright © 2018 Elsevier Inc. All rights reserved.

Ontogenetic pattern of gyrification in fetuses of cynomolgus monkeys.

PubMed

Sawada, K; Sun, X-Z; Fukunishi, K; Kashima, M; Saito, S; Sakata-Haga, H; Sukamoto, T; Aoki, I; Fukui, Y

2010-05-19

The ontogenetic pattern of gyrification and its relationship with cerebral cortical volume were examined in cynomolgus monkey fetuses. T(1)-weighted coronal magnetic resonance (MR) images at 7 T were acquired from the fixed cerebra of three male fetuses, each at embryonic days (EDs) 70 to 150, and the gyrification index (GI) of each slice was estimated. The mean GI was low (1.1-1.2) during EDs 70 to 90, and then increased dramatically on ED 100. The developmental profiles of the rostrocaudal GI distribution revealed that cortical convolution was more frequent in the parietooccipital region than in other regions during EDs 100 to 150, forming an adult-like pattern by ED 150. The mean GI was closely correlated with the volume of cortical gray matter (r=0.9877), and also with the volume of white matter/intermediate zone (r=0.8961). These findings suggest that cortical convolution is correlated with either the maturation of cortical gray matter or the development of white matter bundles. The characteristic GI distribution pattern of catarrhines was formed by ED 150 in correlation with the progressive sulcal infolding in the parietooccipital region of the cerebrum. Copyright 2010 IBRO. Published by Elsevier Ltd. All rights reserved.

A robust holographic autofocusing criterion based on edge sparsity: comparison of Gini index and Tamura coefficient for holographic autofocusing based on the edge sparsity of the complex optical wavefront

NASA Astrophysics Data System (ADS)

Tamamitsu, Miu; Zhang, Yibo; Wang, Hongda; Wu, Yichen; Ozcan, Aydogan

2018-02-01

The Sparsity of the Gradient (SoG) is a robust autofocusing criterion for holography, where the gradient modulus of the complex refocused hologram is calculated, on which a sparsity metric is applied. Here, we compare two different choices of sparsity metrics used in SoG, specifically, the Gini index (GI) and the Tamura coefficient (TC), for holographic autofocusing on dense/connected or sparse samples. We provide a theoretical analysis predicting that for uniformly distributed image data, TC and GI exhibit similar behavior, while for naturally sparse images containing few high-valued signal entries and many low-valued noisy background pixels, TC is more sensitive to distribution changes in the signal and more resistive to background noise. These predictions are also confirmed by experimental results using SoG-based holographic autofocusing on dense and connected samples (such as stained breast tissue sections) as well as highly sparse samples (such as isolated Giardia lamblia cysts). Through these experiments, we found that ToG and GoG offer almost identical autofocusing performance on dense and connected samples, whereas for naturally sparse samples, GoG should be calculated on a relatively small region of interest (ROI) closely surrounding the object, while ToG offers more flexibility in choosing a larger ROI containing more background pixels.

Toward a theory of the general-anesthetic-induced phase transition of the cerebral cortex. I. A thermodynamics analogy

NASA Astrophysics Data System (ADS)

Steyn-Ross, Moira L.; Steyn-Ross, D. A.; Sleigh, J. W.; Wilcocks, Lara C.

2001-07-01

In a recent paper the authors developed a stochastic model for the response of the cerebral cortex to a general anesthetic agent. The model predicted that there would be an anesthetic-induced phase change at the point of transition into unconsciousness, manifested as a divergence in the electroencephalogram spectral power, and a change in spectral energy distribution from being relatively broadband in the conscious state to being strongly biased towards much lower frequencies in the unconscious state. Both predictions have been verified in recent clinical measurements. In the present paper we extend the model by calculating the equilibrium distribution function for the cortex, allowing us to establish a correspondence between the cortical phase transition and the more familiar thermodynamic phase transitions. This correspondence is achieved by first identifying a cortical free energy function, then by postulating that there exists an inverse relationship between an anesthetic effect and a quantity we define as cortical excitability, which plays a role analogous to temperature in thermodynamic phase transitions. We follow standard thermodynamic theory to compute a cortical entropy and a cortical ``heat capacity,'' and we investigate how these will vary with anesthetic concentration. The significant result is the prediction that the entropy will decrease discontinuously at the moment of induction into unconsciousness, concomitant with a release of ``latent heat'' which should manifest as a divergence in the analogous heat capacity. There is clear clinical evidence of heat capacity divergence in historical anesthetic-effect measurements performed in 1977 by Stullken et al. [Anesthesiology 46, 28 (1977)]. The discontinuous step change in cortical entropy suggests that the cortical phase transition is analogous to a first-order thermodynamic transition in which the comatose-quiescent state is strongly ordered, while the active cortical state is relatively disordered.

Internal rib structure can be predicted using mathematical models: An anatomic study comparing the chest to a shell dome with application to understanding fractures.

PubMed

Casha, Aaron R; Camilleri, Liberato; Manché, Alexander; Gatt, Ruben; Attard, Daphne; Gauci, Marilyn; Camilleri-Podesta, Marie-Therese; Mcdonald, Stuart; Grima, Joseph N

2015-11-01

The human rib cage resembles a masonry dome in shape. Masonry domes have a particular construction that mimics stress distribution. Rib cortical thickness and bone density were analyzed to determine whether the morphology of the rib cage is sufficiently similar to a shell dome for internal rib structure to be predicted mathematically. A finite element analysis (FEA) simulation was used to measure stresses on the internal and external surfaces of a chest-shaped dome. Inner and outer rib cortical thickness and bone density were measured in the mid-axillary lines of seven cadaveric rib cages using computerized tomography scanning. Paired t tests and Pearson correlation were used to relate cortical thickness and bone density to stress. FEA modeling showed that the stress was 82% higher on the internal than the external surface, with a gradual decrease in internal and external wall stresses from the base to the apex. The inner cortex was more radio-dense, P < 0.001, and thicker, P < 0.001, than the outer cortex. Inner cortical thickness was related to internal stress, r = 0.94, P < 0.001, inner cortical bone density to internal stress, r = 0.87, P = 0.003, and outer cortical thickness to external stress, r = 0.65, P = 0.035. Mathematical models were developed relating internal and external cortical thicknesses and bone densities to rib level. The internal anatomical features of ribs, including the inner and outer cortical thicknesses and bone densities, are similar to the stress distribution in dome-shaped structures modeled using FEA computer simulations of a thick-walled dome pressure vessel. Fixation of rib fractures should include the stronger internal cortex. © 2015 Wiley Periodicals, Inc.

Measurement of strain distribution in cortical bone around miniscrew implants used for orthodontic anchorage using digital speckle pattern interferometry

NASA Astrophysics Data System (ADS)

Kumar, Manoj; Agarwal, Rupali; Bhutani, Ravi; Shakher, Chandra

2016-05-01

An application of digital speckle pattern interferometry (DSPI) for the measurement of deformations and strain-field distributions developed in cortical bone around orthodontic miniscrew implants inserted into the human maxilla is presented. The purpose of this study is to measure and compare the strain distribution in cortical bone/miniscrew interface of human maxilla around miniscrew implants of different diameters, different implant lengths, and implants of different commercially available companies. The technique is also used to measure tilt/rotation of canine caused due to the application of retraction springs. The proposed technique has high sensitivity and enables the observation of deformation/strain distribution. In DSPI, two specklegrams are recorded corresponding to pre- and postloading of the retraction spring. The DSPI fringe pattern is observed by subtracting these two specklegrams. Optical phase was extracted using Riesz transform and the monogenic signal from a single DSPI fringe pattern. The obtained phase is used to calculate the parameters of interest such as displacement/deformation and strain/stress. The experiment was conducted on a dry human skull fulfilling the criteria of intact dental arches and all teeth present. Eight different miniscrew implants were loaded with an insertion angulation of 45 deg in the inter-radicular region of the maxillary second premolar and molar region. The loading of miniscrew implants was done with force level (150 gf) by nickel-titanium closed-coil springs (9 mm). The obtained results from DSPI reveal that implant diameter and implant length affect the displacement and strain distribution in cortical bone layer surrounding the miniscrew implant.

Statistics of single unit responses in the human medial temporal lobe: A sparse and overdispersed code

NASA Astrophysics Data System (ADS)

Magyar, Andrew

The recent discovery of cells that respond to purely conceptual features of the environment (particular people, landmarks, objects, etc) in the human medial temporal lobe (MTL), has raised many questions about the nature of the neural code in humans. The goal of this dissertation is to develop a novel statistical method based upon maximum likelihood regression which will then be applied to these experiments in order to produce a quantitative description of the coding properties of the human MTL. In general, the method is applicable to any experiments in which a sequence of stimuli are presented to an organism while the binary responses of a large number of cells are recorded in parallel. The central concept underlying the approach is the total probability that a neuron responds to a random stimulus, called the neuronal sparsity. The model then estimates the distribution of response probabilities across the population of cells. Applying the method to single-unit recordings from the human medial temporal lobe, estimates of the sparsity distributions are acquired in four regions: the hippocampus, the entorhinal cortex, the amygdala, and the parahippocampal cortex. The resulting distributions are found to be sparse (large fraction of cells with a low response probability) and highly non-uniform, with a large proportion of ultra-sparse neurons that possess a very low response probability, and a smaller population of cells which respond much more frequently. Rammifications of the results are discussed in relation to the sparse coding hypothesis, and comparisons are made between the statistics of the human medial temporal lobe cells and place cells observed in the rodent hippocampus.

Multi-linear sparse reconstruction for SAR imaging based on higher-order SVD

NASA Astrophysics Data System (ADS)

Gao, Yu-Fei; Gui, Guan; Cong, Xun-Chao; Yang, Yue; Zou, Yan-Bin; Wan, Qun

2017-12-01

This paper focuses on the spotlight synthetic aperture radar (SAR) imaging for point scattering targets based on tensor modeling. In a real-world scenario, scatterers usually distribute in the block sparse pattern. Such a distribution feature has been scarcely utilized by the previous studies of SAR imaging. Our work takes advantage of this structure property of the target scene, constructing a multi-linear sparse reconstruction algorithm for SAR imaging. The multi-linear block sparsity is introduced into higher-order singular value decomposition (SVD) with a dictionary constructing procedure by this research. The simulation experiments for ideal point targets show the robustness of the proposed algorithm to the noise and sidelobe disturbance which always influence the imaging quality of the conventional methods. The computational resources requirement is further investigated in this paper. As a consequence of the algorithm complexity analysis, the present method possesses the superiority on resource consumption compared with the classic matching pursuit method. The imaging implementations for practical measured data also demonstrate the effectiveness of the algorithm developed in this paper.

Neural networks and MIMD-multiprocessors

NASA Technical Reports Server (NTRS)

Vanhala, Jukka; Kaski, Kimmo

1990-01-01

Two artificial neural network models are compared. They are the Hopfield Neural Network Model and the Sparse Distributed Memory model. Distributed algorithms for both of them are designed and implemented. The run time characteristics of the algorithms are analyzed theoretically and tested in practice. The storage capacities of the networks are compared. Implementations are done using a distributed multiprocessor system.

Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

DOE PAGES

Hyman, Jeffrey De'Haven; Aldrich, Garrett Allen; Viswanathan, Hari S.; ...

2016-08-01

We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semicorrelation, and noncorrelation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected somore » that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same. We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. Lastly, these observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.« less

Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

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

Hyman, Jeffrey De'Haven; Aldrich, Garrett Allen; Viswanathan, Hari S.

We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semicorrelation, and noncorrelation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected somore » that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same. We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. Lastly, these observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.« less

Eye coding mechanisms in early human face event-related potentials.

PubMed

Rousselet, Guillaume A; Ince, Robin A A; van Rijsbergen, Nicola J; Schyns, Philippe G

2014-11-10

In humans, the N170 event-related potential (ERP) is an integrated measure of cortical activity that varies in amplitude and latency across trials. Researchers often conjecture that N170 variations reflect cortical mechanisms of stimulus coding for recognition. Here, to settle the conjecture and understand cortical information processing mechanisms, we unraveled the coding function of N170 latency and amplitude variations in possibly the simplest socially important natural visual task: face detection. On each experimental trial, 16 observers saw face and noise pictures sparsely sampled with small Gaussian apertures. Reverse-correlation methods coupled with information theory revealed that the presence of the eye specifically covaries with behavioral and neural measurements: the left eye strongly modulates reaction times and lateral electrodes represent mainly the presence of the contralateral eye during the rising part of the N170, with maximum sensitivity before the N170 peak. Furthermore, single-trial N170 latencies code more about the presence of the contralateral eye than N170 amplitudes and early latencies are associated with faster reaction times. The absence of these effects in control images that did not contain a face refutes alternative accounts based on retinal biases or allocation of attention to the eye location on the face. We conclude that the rising part of the N170, roughly 120-170 ms post-stimulus, is a critical time-window in human face processing mechanisms, reflecting predominantly, in a face detection task, the encoding of a single feature: the contralateral eye. © 2014 ARVO.

Dissecting local circuits in vivo: integrated optogenetic and electrophysiology approaches for exploring inhibitory regulation of cortical activity.

PubMed

Cardin, Jessica A

2012-01-01

Local cortical circuit activity in vivo comprises a complex and flexible series of interactions between excitatory and inhibitory neurons. Our understanding of the functional interactions between these different neural populations has been limited by the difficulty of identifying and selectively manipulating the diverse and sparsely represented inhibitory interneuron classes in the intact brain. The integration of recently developed optical tools with traditional electrophysiological techniques provides a powerful window into the role of inhibition in regulating the activity of excitatory neurons. In particular, optogenetic targeting of specific cell classes reveals the distinct impacts of local inhibitory populations on other neurons in the surrounding local network. In addition to providing the ability to activate or suppress spiking in target cells, optogenetic activation identifies extracellularly recorded neurons by class, even when naturally occurring spike rates are extremely low. However, there are several important limitations on the use of these tools and the interpretation of resulting data. The purpose of this article is to outline the uses and limitations of optogenetic tools, along with current methods for achieving cell type-specific expression, and to highlight the advantages of an experimental approach combining optogenetics and electrophysiology to explore the role of inhibition in active networks. To illustrate the efficacy of these combined approaches, I present data comparing targeted manipulations of cortical fast-spiking, parvalbumin-expressing and low threshold-spiking, somatostatin-expressing interneurons in vivo. Copyright © 2011 Elsevier Ltd. All rights reserved.

Comparing the influence of crestal cortical bone and sinus floor cortical bone in posterior maxilla bi-cortical dental implantation: a three-dimensional finite element analysis.

PubMed

Yan, Xu; Zhang, Xinwen; Chi, Weichao; Ai, Hongjun; Wu, Lin

2015-05-01

This study aimed to compare the influence of alveolar ridge cortical bone and sinus floor cortical bone in sinus areabi-cortical dental implantation by means of 3D finite element analysis. Three-dimensional finite element (FE) models in a posterior maxillary region with sinus membrane and the same height of alveolar ridge of 10 mm were generated according to the anatomical data of the sinus area. They were either with fixed thickness of crestal cortical bone and variable thickness of sinus floor cortical bone or vice versa. Ten models were assumed to be under immediate loading or conventional loading. The standard implant model based on the Nobel Biocare implant system was created via computer-aided design software. All materials were assumed to be isotropic and linearly elastic. An inclined force of 129 N was applied. Von Mises stress mainly concentrated on the surface of crestal cortical bone around the implant neck. For all the models, both the axial and buccolingual resonance frequencies of conventional loading were higher than those of immediate loading; however, the difference is less than 5%. The results showed that bi-cortical implant in sinus area increased the stability of the implant, especially for immediately loading implantation. The thickness of both crestal cortical bone and sinus floor cortical bone influenced implant micromotion and stress distribution; however, crestal cortical bone may be more important than sinus floor cortical bone.

Estimating network parameters from combined dynamics of firing rate and irregularity of single neurons.

PubMed

Hamaguchi, Kosuke; Riehle, Alexa; Brunel, Nicolas

2011-01-01

High firing irregularity is a hallmark of cortical neurons in vivo, and modeling studies suggest a balance of excitation and inhibition is necessary to explain this high irregularity. Such a balance must be generated, at least partly, from local interconnected networks of excitatory and inhibitory neurons, but the details of the local network structure are largely unknown. The dynamics of the neural activity depends on the local network structure; this in turn suggests the possibility of estimating network structure from the dynamics of the firing statistics. Here we report a new method to estimate properties of the local cortical network from the instantaneous firing rate and irregularity (CV(2)) under the assumption that recorded neurons are a part of a randomly connected sparse network. The firing irregularity, measured in monkey motor cortex, exhibits two features; many neurons show relatively stable firing irregularity in time and across different task conditions; the time-averaged CV(2) is widely distributed from quasi-regular to irregular (CV(2) = 0.3-1.0). For each recorded neuron, we estimate the three parameters of a local network [balance of local excitation-inhibition, number of recurrent connections per neuron, and excitatory postsynaptic potential (EPSP) size] that best describe the dynamics of the measured firing rates and irregularities. Our analysis shows that optimal parameter sets form a two-dimensional manifold in the three-dimensional parameter space that is confined for most of the neurons to the inhibition-dominated region. High irregularity neurons tend to be more strongly connected to the local network, either in terms of larger EPSP and inhibitory PSP size or larger number of recurrent connections, compared with the low irregularity neurons, for a given excitatory/inhibitory balance. Incorporating either synaptic short-term depression or conductance-based synapses leads many low CV(2) neurons to move to the excitation-dominated region as well as to an increase of EPSP size.

Decoding of Ankle Flexion and Extension from Cortical Current Sources Estimated from Non-invasive Brain Activity Recording Methods.

PubMed

Mejia Tobar, Alejandra; Hyoudou, Rikiya; Kita, Kahori; Nakamura, Tatsuhiro; Kambara, Hiroyuki; Ogata, Yousuke; Hanakawa, Takashi; Koike, Yasuharu; Yoshimura, Natsue

2017-01-01

The classification of ankle movements from non-invasive brain recordings can be applied to a brain-computer interface (BCI) to control exoskeletons, prosthesis, and functional electrical stimulators for the benefit of patients with walking impairments. In this research, ankle flexion and extension tasks at two force levels in both legs, were classified from cortical current sources estimated by a hierarchical variational Bayesian method, using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. The hierarchical prior for the current source estimation from EEG was obtained from activated brain areas and their intensities from an fMRI group (second-level) analysis. The fMRI group analysis was performed on regions of interest defined over the primary motor cortex, the supplementary motor area, and the somatosensory area, which are well-known to contribute to movement control. A sparse logistic regression method was applied for a nine-class classification (eight active tasks and a resting control task) obtaining a mean accuracy of 65.64% for time series of current sources, estimated from the EEG and the fMRI signals using a variational Bayesian method, and a mean accuracy of 22.19% for the classification of the pre-processed of EEG sensor signals, with a chance level of 11.11%. The higher classification accuracy of current sources, when compared to EEG classification accuracy, was attributed to the high number of sources and the different signal patterns obtained in the same vertex for different motor tasks. Since the inverse filter estimation for current sources can be done offline with the present method, the present method is applicable to real-time BCIs. Finally, due to the highly enhanced spatial distribution of current sources over the brain cortex, this method has the potential to identify activation patterns to design BCIs for the control of an affected limb in patients with stroke, or BCIs from motor imagery in patients with spinal cord injury.

Capacity for patterns and sequences in Kanerva's SDM as compared to other associative memory models. [Sparse, Distributed Memory

NASA Technical Reports Server (NTRS)

Keeler, James D.

1988-01-01

The information capacity of Kanerva's Sparse Distributed Memory (SDM) and Hopfield-type neural networks is investigated. Under the approximations used here, it is shown that the total information stored in these systems is proportional to the number connections in the network. The proportionality constant is the same for the SDM and Hopfield-type models independent of the particular model, or the order of the model. The approximations are checked numerically. This same analysis can be used to show that the SDM can store sequences of spatiotemporal patterns, and the addition of time-delayed connections allows the retrieval of context dependent temporal patterns. A minor modification of the SDM can be used to store correlated patterns.

Dissecting the actin cortex density and membrane-cortex distance in living cells by super-resolution microscopy

NASA Astrophysics Data System (ADS)

Clausen, M. P.; Colin-York, H.; Schneider, F.; Eggeling, C.; Fritzsche, M.

2017-02-01

Nanoscale spacing between the plasma membrane and the underlying cortical actin cytoskeleton profoundly modulates cellular morphology, mechanics, and function. Measuring this distance has been a key challenge in cell biology. Current methods for dissecting the nanoscale spacing either limit themselves to complex survey design using fixed samples or rely on diffraction-limited fluorescence imaging whose spatial resolution is insufficient to quantify distances on the nanoscale. Using dual-color super-resolution STED (stimulated-emission-depletion) microscopy, we here overcome this challenge and accurately measure the density distribution of the cortical actin cytoskeleton and the distance between the actin cortex and the membrane in live Jurkat T-cells. We found an asymmetric cortical actin density distribution with a mean width of 230 (+105/-125) nm. The spatial distances measured between the maximum density peaks of the cortex and the membrane were bi-modally distributed with mean values of 50  ±  15 nm and 120  ±  40 nm, respectively. Taken together with the finite width of the cortex, our results suggest that in some regions the cortical actin is closer than 10 nm to the membrane and a maximum of 20 nm in others.

Gravity-induced asymmetric distribution of a plant growth hormone

NASA Technical Reports Server (NTRS)

Bandurski, R. S.; Schulze, A.; Momonoki, Y.

1984-01-01

Dolk (1936) demonstrated that gravistimulation induced an asymmetric distribution of auxin in a horizontally-placed shoot. An attempt is made to determine where and how that asymmetry arises, and to demonstrate that the endogenous auxin, indole-3-acetic acid, becomes asymmetrically distributed in the cortical cells of the Zea mays mesocotyl during 3 min of geostimulation. Further, indole-3-acetic acid derived by hydrolysis of an applied transport form of the hormone, indole-3-acetyl-myo-inositol, becomes asymmetrically distributed within 15 min of geostimulus time. From these and prior data is developed a working theory that the gravitational stimulus induces a selective leakage, or secretion, of the hormone from the vascular tissue to the cortical cells of the mesocotyl.

Distribution of neurons expressing tyrosine hydroxylase in the human cerebral cortex

PubMed Central

Benavides-Piccione, Ruth; DeFelipe, Javier

2007-01-01

Since the very first detailed description of the different types of cortical interneurons by Cajal, the tremendous variation in the morphology, physiology and neurochemical properties of these cells has become apparent. However, it still remains unclear whether all types of interneurons are present in all cortical areas and species. Here we have focused on tyrosine hydroxylase (TH)-immunoreactive cortical interneurons, which although only present in certain species, are particularly abundant in the human neocortex. We argue that this type of interneuron is more widespread in the human neocortex than in any other species examined so far and that, therefore, it is probably involved in a larger variety of cortical circuits. In addition, notable regional variation can be seen in relation to these interneurons. These differences further emphasize the variability in the design of microcircuits between cortical areas and species, and they probably reflect an evolutionary adaptation of cortical circuits to particular functions. PMID:17593221

Patterns of cytochrome oxidase activity in the visual cortex of a South American opossum (Didelphis marsupialis aurita).

PubMed

Martinich, S; Rosa, M G; Rocha-Miranda, C E

1990-01-01

The normal pattern of cytochrome oxidase (CO) activity in the posterior cortical areas of the South American opossum (Didelphis marsupialis aurita) was assessed both in horizontal sections of flattened cortices and in transversal cortical sections. The tangential distribution of CO activity was uniformly high in the striate cortex. In the peristriate region alternating bands of dense and weak staining occupied all the cortical layers with the exception of layer I. This observation suggests the existence of a functional segregation of visual processing in the peristriate cortex of the opossum similar to that present in phylogenetically more recent groups.

Intra-operative multi-site stimulation: Expanding methodology for cortical brain mapping of language functions

PubMed Central

Korn, Akiva; Kirschner, Adi; Perry, Daniella; Hendler, Talma; Ram, Zvi

2017-01-01

Direct cortical stimulation (DCS) is considered the gold-standard for functional cortical mapping during awake surgery for brain tumor resection. DCS is performed by stimulating one local cortical area at a time. We present a feasibility study using an intra-operative technique aimed at improving our ability to map brain functions which rely on activity in distributed cortical regions. Following standard DCS, Multi-Site Stimulation (MSS) was performed in 15 patients by applying simultaneous cortical stimulations at multiple locations. Language functioning was chosen as a case-cognitive domain due to its relatively well-known cortical organization. MSS, performed at sites that did not produce disruption when applied in a single stimulation point, revealed additional language dysfunction in 73% of the patients. Functional regions identified by this technique were presumed to be significant to language circuitry and were spared during surgery. No new neurological deficits were observed in any of the patients following surgery. Though the neuro-electrical effects of MSS need further investigation, this feasibility study may provide a first step towards sophistication of intra-operative cortical mapping. PMID:28700619

Intra-operative multi-site stimulation: Expanding methodology for cortical brain mapping of language functions.

PubMed

Gonen, Tal; Gazit, Tomer; Korn, Akiva; Kirschner, Adi; Perry, Daniella; Hendler, Talma; Ram, Zvi

2017-01-01

Direct cortical stimulation (DCS) is considered the gold-standard for functional cortical mapping during awake surgery for brain tumor resection. DCS is performed by stimulating one local cortical area at a time. We present a feasibility study using an intra-operative technique aimed at improving our ability to map brain functions which rely on activity in distributed cortical regions. Following standard DCS, Multi-Site Stimulation (MSS) was performed in 15 patients by applying simultaneous cortical stimulations at multiple locations. Language functioning was chosen as a case-cognitive domain due to its relatively well-known cortical organization. MSS, performed at sites that did not produce disruption when applied in a single stimulation point, revealed additional language dysfunction in 73% of the patients. Functional regions identified by this technique were presumed to be significant to language circuitry and were spared during surgery. No new neurological deficits were observed in any of the patients following surgery. Though the neuro-electrical effects of MSS need further investigation, this feasibility study may provide a first step towards sophistication of intra-operative cortical mapping.

Real-Time Characterization of Aerospace Structures Using Onboard Strain Measurement Technologies and Inverse Finite Element Method

DTIC Science & Technology

2011-09-01

strain data provided by in-situ strain sensors. The application focus is on the stain data obtained from FBG (Fiber Bragg Grating) sensor arrays...sparsely distributed lines to simulate strain data from FBG (Fiber Bragg Grating) arrays that provide either single-core (axial) or rosette (tri...when the measured strain data are sparse, as it is often the case when FBG sensors are used. For an inverse element without strain-sensor data, the

DEM generation from contours and a low-resolution DEM

NASA Astrophysics Data System (ADS)

Li, Xinghua; Shen, Huanfeng; Feng, Ruitao; Li, Jie; Zhang, Liangpei

2017-12-01

A digital elevation model (DEM) is a virtual representation of topography, where the terrain is established by the three-dimensional co-ordinates. In the framework of sparse representation, this paper investigates DEM generation from contours. Since contours are usually sparsely distributed and closely related in space, sparse spatial regularization (SSR) is enforced on them. In order to make up for the lack of spatial information, another lower spatial resolution DEM from the same geographical area is introduced. In this way, the sparse representation implements the spatial constraints in the contours and extracts the complementary information from the auxiliary DEM. Furthermore, the proposed method integrates the advantage of the unbiased estimation of kriging. For brevity, the proposed method is called the kriging and sparse spatial regularization (KSSR) method. The performance of the proposed KSSR method is demonstrated by experiments in Shuttle Radar Topography Mission (SRTM) 30 m DEM and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m global digital elevation model (GDEM) generation from the corresponding contours and a 90 m DEM. The experiments confirm that the proposed KSSR method outperforms the traditional kriging and SSR methods, and it can be successfully used for DEM generation from contours.

Failure to pop out: Feature singletons do not capture attention under low signal-to-noise ratio conditions.

PubMed

Rangelov, Dragan; Müller, Hermann J; Zehetleitner, Michael

2017-05-01

Pop-out search implies that the target is always the first item selected, no matter how many distractors are presented. However, increasing evidence indicates that search is not entirely independent of display density even for pop-out targets: search is slower with sparse (few distractors) than with dense displays (many distractors). Despite its significance, the cause of this anomaly remains unclear. We investigated several mechanisms that could slow down search for pop-out targets. Consistent with the assumption that pop-out targets frequently fail to pop out in sparse displays, we observed greater variability of search duration for sparse displays relative to dense. Computational modeling of the response time distributions also supported the view that pop-out targets fail to pop out in sparse displays. Our findings strongly question the classical assumption that early processing of pop-out targets is independent of the distractors. Rather, the density of distractors critically influences whether or not a stimulus pops out. These results call for new, more reliable measures of pop-out search and potentially a reinterpretation of studies that used relatively sparse displays. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Elastic-Waveform Inversion with Compressive Sensing for Sparse Seismic Data

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

Lin, Youzuo; Huang, Lianjie

2015-01-28

Accurate velocity models of compressional- and shear-waves are essential for geothermal reservoir characterization and microseismic imaging. Elastic-waveform inversion of multi-component seismic data can provide high-resolution inversion results of subsurface geophysical properties. However, the method requires seismic data acquired using dense source and receiver arrays. In practice, seismic sources and/or geophones are often sparsely distributed on the surface and/or in a borehole, such as 3D vertical seismic profiling (VSP) surveys. We develop a novel elastic-waveform inversion method with compressive sensing for inversion of sparse seismic data. We employ an alternating-minimization algorithm to solve the optimization problem of our new waveform inversionmore » method. We validate our new method using synthetic VSP data for a geophysical model built using geologic features found at the Raft River enhanced-geothermal-system (EGS) field. We apply our method to synthetic VSP data with a sparse source array and compare the results with those obtained with a dense source array. Our numerical results demonstrate that the velocity models produced with our new method using a sparse source array are almost as accurate as those obtained using a dense source array.« less

Target Transformation Constrained Sparse Unmixing (ttcsu) Algorithm for Retrieving Hydrous Minerals on Mars: Application to Southwest Melas Chasma

NASA Astrophysics Data System (ADS)

Lin, H.; Zhang, X.; Wu, X.; Tarnas, J. D.; Mustard, J. F.

2018-04-01

Quantitative analysis of hydrated minerals from hyperspectral remote sensing data is fundamental for understanding Martian geologic process. Because of the difficulties for selecting endmembers from hyperspectral images, a sparse unmixing algorithm has been proposed to be applied to CRISM data on Mars. However, it's challenge when the endmember library increases dramatically. Here, we proposed a new methodology termed Target Transformation Constrained Sparse Unmixing (TTCSU) to accurately detect hydrous minerals on Mars. A new version of target transformation technique proposed in our recent work was used to obtain the potential detections from CRISM data. Sparse unmixing constrained with these detections as prior information was applied to CRISM single-scattering albedo images, which were calculated using a Hapke radiative transfer model. This methodology increases success rate of the automatic endmember selection of sparse unmixing and could get more accurate abundances. CRISM images with well analyzed in Southwest Melas Chasma was used to validate our methodology in this study. The sulfates jarosite was detected from Southwest Melas Chasma, the distribution is consistent with previous work and the abundance is comparable. More validations will be done in our future work.

Medical Image Fusion Based on Feature Extraction and Sparse Representation

PubMed Central

Wei, Gao; Zongxi, Song

2017-01-01

As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM) and energy information map (EM) as well as structure and energy map (SEM) to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG) and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods. PMID:28321246

Highly parallel sparse Cholesky factorization

NASA Technical Reports Server (NTRS)

Gilbert, John R.; Schreiber, Robert

1990-01-01

Several fine grained parallel algorithms were developed and compared to compute the Cholesky factorization of a sparse matrix. The experimental implementations are on the Connection Machine, a distributed memory SIMD machine whose programming model conceptually supplies one processor per data element. In contrast to special purpose algorithms in which the matrix structure conforms to the connection structure of the machine, the focus is on matrices with arbitrary sparsity structure. The most promising algorithm is one whose inner loop performs several dense factorizations simultaneously on a 2-D grid of processors. Virtually any massively parallel dense factorization algorithm can be used as the key subroutine. The sparse code attains execution rates comparable to those of the dense subroutine. Although at present architectural limitations prevent the dense factorization from realizing its potential efficiency, it is concluded that a regular data parallel architecture can be used efficiently to solve arbitrarily structured sparse problems. A performance model is also presented and it is used to analyze the algorithms.

Advancing Underwater Acoustic Communication for Autonomous Distributed Networks via Sparse Channel Sensing, Coding, and Navigation Support

DTIC Science & Technology

2012-09-30

Estimation Methods for Underwater OFDM 5) Two Iterative Receivers for Distributed MIMO - OFDM with Large Doppler Deviations. 6) Asynchronous Multiuser...multi-input multi-output ( MIMO ) OFDM is also pursued, where it is shown that the proposed hybrid initialization enables drastically improved receiver...are investigated. 5) Two Iterative Receivers for Distributed MIMO - OFDM with Large Doppler Deviations. This work studies a distributed system with

Mapping visual stimuli to perceptual decisions via sparse decoding of mesoscopic neural activity.

PubMed

Sajda, Paul

2010-01-01

In this talk I will describe our work investigating sparse decoding of neural activity, given a realistic mapping of the visual scene to neuronal spike trains generated by a model of primary visual cortex (V1). We use a linear decoder which imposes sparsity via an L1 norm. The decoder can be viewed as a decoding neuron (linear summation followed by a sigmoidal nonlinearity) in which there are relatively few non-zero synaptic weights. We find: (1) the best decoding performance is for a representation that is sparse in both space and time, (2) decoding of a temporal code results in better performance than a rate code and is also a better fit to the psychophysical data, (3) the number of neurons required for decoding increases monotonically as signal-to-noise in the stimulus decreases, with as little as 1% of the neurons required for decoding at the highest signal-to-noise levels, and (4) sparse decoding results in a more accurate decoding of the stimulus and is a better fit to psychophysical performance than a distributed decoding, for example one imposed by an L2 norm. We conclude that sparse coding is well-justified from a decoding perspective in that it results in a minimum number of neurons and maximum accuracy when sparse representations can be decoded from the neural dynamics.

Layer-Specific fMRI Reflects Different Neuronal Computations at Different Depths in Human V1

PubMed Central

Olman, Cheryl A.; Harel, Noam; Feinberg, David A.; He, Sheng; Zhang, Peng; Ugurbil, Kamil; Yacoub, Essa

2012-01-01

Recent work has established that cerebral blood flow is regulated at a spatial scale that can be resolved by high field fMRI to show cortical columns in humans. While cortical columns represent a cluster of neurons with similar response properties (spanning from the pial surface to the white matter), important information regarding neuronal interactions and computational processes is also contained within a single column, distributed across the six cortical lamina. A basic understanding of underlying neuronal circuitry or computations may be revealed through investigations of the distribution of neural responses at different cortical depths. In this study, we used T2-weighted imaging with 0.7 mm (isotropic) resolution to measure fMRI responses at different depths in the gray matter while human subjects observed images with either recognizable or scrambled (physically impossible) objects. Intact and scrambled images were partially occluded, resulting in clusters of activity distributed across primary visual cortex. A subset of the identified clusters of voxels showed a preference for scrambled objects over intact; in these clusters, the fMRI response in middle layers was stronger during the presentation of scrambled objects than during the presentation of intact objects. A second experiment, using stimuli targeted at either the magnocellular or the parvocellular visual pathway, shows that laminar profiles in response to parvocellular-targeted stimuli peak in more superficial layers. These findings provide new evidence for the differential sensitivity of high-field fMRI to modulations of the neural responses at different cortical depths. PMID:22448223

Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain

PubMed Central

Krienen, Fenna M.; Yeo, B. T. Thomas; Ge, Tian; Buckner, Randy L.; Sherwood, Chet C.

2016-01-01

The human brain is patterned with disproportionately large, distributed cerebral networks that connect multiple association zones in the frontal, temporal, and parietal lobes. The expansion of the cortical surface, along with the emergence of long-range connectivity networks, may be reflected in changes to the underlying molecular architecture. Using the Allen Institute’s human brain transcriptional atlas, we demonstrate that genes particularly enriched in supragranular layers of the human cerebral cortex relative to mouse distinguish major cortical classes. The topography of transcriptional expression reflects large-scale brain network organization consistent with estimates from functional connectivity MRI and anatomical tracing in nonhuman primates. Microarray expression data for genes preferentially expressed in human upper layers (II/III), but enriched only in lower layers (V/VI) of mouse, were cross-correlated to identify molecular profiles across the cerebral cortex of postmortem human brains (n = 6). Unimodal sensory and motor zones have similar molecular profiles, despite being distributed across the cortical mantle. Sensory/motor profiles were anticorrelated with paralimbic and certain distributed association network profiles. Tests of alternative gene sets did not consistently distinguish sensory and motor regions from paralimbic and association regions: (i) genes enriched in supragranular layers in both humans and mice, (ii) genes cortically enriched in humans relative to nonhuman primates, (iii) genes related to connectivity in rodents, (iv) genes associated with human and mouse connectivity, and (v) 1,454 gene sets curated from known gene ontologies. Molecular innovations of upper cortical layers may be an important component in the evolution of long-range corticocortical projections. PMID:26739559

Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain.

PubMed

Krienen, Fenna M; Yeo, B T Thomas; Ge, Tian; Buckner, Randy L; Sherwood, Chet C

2016-01-26

The human brain is patterned with disproportionately large, distributed cerebral networks that connect multiple association zones in the frontal, temporal, and parietal lobes. The expansion of the cortical surface, along with the emergence of long-range connectivity networks, may be reflected in changes to the underlying molecular architecture. Using the Allen Institute's human brain transcriptional atlas, we demonstrate that genes particularly enriched in supragranular layers of the human cerebral cortex relative to mouse distinguish major cortical classes. The topography of transcriptional expression reflects large-scale brain network organization consistent with estimates from functional connectivity MRI and anatomical tracing in nonhuman primates. Microarray expression data for genes preferentially expressed in human upper layers (II/III), but enriched only in lower layers (V/VI) of mouse, were cross-correlated to identify molecular profiles across the cerebral cortex of postmortem human brains (n = 6). Unimodal sensory and motor zones have similar molecular profiles, despite being distributed across the cortical mantle. Sensory/motor profiles were anticorrelated with paralimbic and certain distributed association network profiles. Tests of alternative gene sets did not consistently distinguish sensory and motor regions from paralimbic and association regions: (i) genes enriched in supragranular layers in both humans and mice, (ii) genes cortically enriched in humans relative to nonhuman primates, (iii) genes related to connectivity in rodents, (iv) genes associated with human and mouse connectivity, and (v) 1,454 gene sets curated from known gene ontologies. Molecular innovations of upper cortical layers may be an important component in the evolution of long-range corticocortical projections.

Measuring Sparseness in the Brain: Comment on Bowers (2009)

ERIC Educational Resources Information Center

Quian Quiroga, Rodrigo; Kreiman, Gabriel

2010-01-01

Bowers challenged the common view in favor of distributed representations in psychological modeling and the main arguments given against localist and grandmother cell coding schemes. He revisited the results of several single-cell studies, arguing that they do not support distributed representations. We praise the contribution of Bowers (2009) for…

High-order graph matching based feature selection for Alzheimer's disease identification.

PubMed

Liu, Feng; Suk, Heung-Il; Wee, Chong-Yaw; Chen, Huafu; Shen, Dinggang

2013-01-01

One of the main limitations of l1-norm feature selection is that it focuses on estimating the target vector for each sample individually without considering relations with other samples. However, it's believed that the geometrical relation among target vectors in the training set may provide useful information, and it would be natural to expect that the predicted vectors have similar geometric relations as the target vectors. To overcome these limitations, we formulate this as a graph-matching feature selection problem between a predicted graph and a target graph. In the predicted graph a node is represented by predicted vector that may describe regional gray matter volume or cortical thickness features, and in the target graph a node is represented by target vector that include class label and clinical scores. In particular, we devise new regularization terms in sparse representation to impose high-order graph matching between the target vectors and the predicted ones. Finally, the selected regional gray matter volume and cortical thickness features are fused in kernel space for classification. Using the ADNI dataset, we evaluate the effectiveness of the proposed method and obtain the accuracies of 92.17% and 81.57% in AD and MCI classification, respectively.

Wavelet-based localization of oscillatory sources from magnetoencephalography data.

PubMed

Lina, J M; Chowdhury, R; Lemay, E; Kobayashi, E; Grova, C

2014-08-01

Transient brain oscillatory activities recorded with Eelectroencephalography (EEG) or magnetoencephalography (MEG) are characteristic features in physiological and pathological processes. This study is aimed at describing, evaluating, and illustrating with clinical data a new method for localizing the sources of oscillatory cortical activity recorded by MEG. The method combines time-frequency representation and an entropic regularization technique in a common framework, assuming that brain activity is sparse in time and space. Spatial sparsity relies on the assumption that brain activity is organized among cortical parcels. Sparsity in time is achieved by transposing the inverse problem in the wavelet representation, for both data and sources. We propose an estimator of the wavelet coefficients of the sources based on the maximum entropy on the mean (MEM) principle. The full dynamics of the sources is obtained from the inverse wavelet transform, and principal component analysis of the reconstructed time courses is applied to extract oscillatory components. This methodology is evaluated using realistic simulations of single-trial signals, combining fast and sudden discharges (spike) along with bursts of oscillating activity. The method is finally illustrated with a clinical application using MEG data acquired on a patient with a right orbitofrontal epilepsy.

The brain dynamics of rapid perceptual adaptation to adverse listening conditions.

PubMed

Erb, Julia; Henry, Molly J; Eisner, Frank; Obleser, Jonas

2013-06-26

Listeners show a remarkable ability to quickly adjust to degraded speech input. Here, we aimed to identify the neural mechanisms of such short-term perceptual adaptation. In a sparse-sampling, cardiac-gated functional magnetic resonance imaging (fMRI) acquisition, human listeners heard and repeated back 4-band-vocoded sentences (in which the temporal envelope of the acoustic signal is preserved, while spectral information is highly degraded). Clear-speech trials were included as baseline. An additional fMRI experiment on amplitude modulation rate discrimination quantified the convergence of neural mechanisms that subserve coping with challenging listening conditions for speech and non-speech. First, the degraded speech task revealed an "executive" network (comprising the anterior insula and anterior cingulate cortex), parts of which were also activated in the non-speech discrimination task. Second, trial-by-trial fluctuations in successful comprehension of degraded speech drove hemodynamic signal change in classic "language" areas (bilateral temporal cortices). Third, as listeners perceptually adapted to degraded speech, downregulation in a cortico-striato-thalamo-cortical circuit was observable. The present data highlight differential upregulation and downregulation in auditory-language and executive networks, respectively, with important subcortical contributions when successfully adapting to a challenging listening situation.

Sparsely-distributed organization of face and limb activations in human ventral temporal cortex

PubMed Central

Weiner, Kevin S.; Grill-Spector, Kalanit

2011-01-01

Functional magnetic resonance imaging (fMRI) has identified face- and body part-selective regions, as well as distributed activation patterns for object categories across human ventral temporal cortex (VTC), eliciting a debate regarding functional organization in VTC and neural coding of object categories. Using high-resolution fMRI, we illustrate that face- and limb-selective activations alternate in a series of largely nonoverlapping clusters in lateral VTC along the inferior occipital gyrus (IOG), fusiform gyrus (FG), and occipitotemporal sulcus (OTS). Both general linear model (GLM) and multivoxel pattern (MVP) analyses show that face- and limb-selective activations minimally overlap and that this organization is consistent across experiments and days. We provide a reliable method to separate two face-selective clusters on the middle and posterior FG (mFus and pFus), and another on the IOG using their spatial relation to limb-selective activations and retinotopic areas hV4, VO-1/2, and hMT+. Furthermore, these activations show a gradient of increasing face selectivity and decreasing limb selectivity from the IOG to the mFus. Finally, MVP analyses indicate that there is differential information for faces in lateral VTC (containing weakly- and highly-selective voxels) relative to non-selective voxels in medial VTC. These findings suggest a sparsely-distributed organization where sparseness refers to the presence of several face- and limb-selective clusters in VTC, and distributed refers to the presence of different amounts of information in highly-, weakly-, and non-selective voxels. Consequently, theories of object recognition should consider the functional and spatial constraints of neural coding across a series of nonoverlapping category-selective clusters that are themselves distributed. PMID:20457261

Sparsity-weighted outlier FLOODing (OFLOOD) method: Efficient rare event sampling method using sparsity of distribution.

PubMed

Harada, Ryuhei; Nakamura, Tomotake; Shigeta, Yasuteru

2016-03-30

As an extension of the Outlier FLOODing (OFLOOD) method [Harada et al., J. Comput. Chem. 2015, 36, 763], the sparsity of the outliers defined by a hierarchical clustering algorithm, FlexDice, was considered to achieve an efficient conformational search as sparsity-weighted "OFLOOD." In OFLOOD, FlexDice detects areas of sparse distribution as outliers. The outliers are regarded as candidates that have high potential to promote conformational transitions and are employed as initial structures for conformational resampling by restarting molecular dynamics simulations. When detecting outliers, FlexDice defines a rank in the hierarchy for each outlier, which relates to sparsity in the distribution. In this study, we define a lower rank (first ranked), a medium rank (second ranked), and the highest rank (third ranked) outliers, respectively. For instance, the first-ranked outliers are located in a given conformational space away from the clusters (highly sparse distribution), whereas those with the third-ranked outliers are nearby the clusters (a moderately sparse distribution). To achieve the conformational search efficiently, resampling from the outliers with a given rank is performed. As demonstrations, this method was applied to several model systems: Alanine dipeptide, Met-enkephalin, Trp-cage, T4 lysozyme, and glutamine binding protein. In each demonstration, the present method successfully reproduced transitions among metastable states. In particular, the first-ranked OFLOOD highly accelerated the exploration of conformational space by expanding the edges. In contrast, the third-ranked OFLOOD reproduced local transitions among neighboring metastable states intensively. For quantitatively evaluations of sampled snapshots, free energy calculations were performed with a combination of umbrella samplings, providing rigorous landscapes of the biomolecules. © 2015 Wiley Periodicals, Inc.

The Bat as a New Model of Cortical Development.

PubMed

Martínez-Cerdeño, Verónica; Camacho, Jasmin; Ariza, Jeanelle; Rogers, Hailee; Horton-Sparks, Kayla; Kreutz, Anna; Behringer, Richard; Rasweiler, John J; Noctor, Stephen C

2017-11-09

The organization of the mammalian cerebral cortex shares fundamental features across species. However, while the radial thickness of grey matter varies within one order of magnitude, the tangential spread of the cortical sheet varies by orders of magnitude across species. A broader sample of model species may provide additional clues for understanding mechanisms that drive cortical expansion. Here, we introduce the bat Carollia perspicillata as a new model species. The brain of C. perspicillata is similar in size to that of mouse but has a cortical neurogenic period at least 5 times longer than mouse, and nearly as long as that of the rhesus macaque, whose brain is 100 times larger. We describe the development of laminar and regional structures, neural precursor cell identity and distribution, immune cell distribution, and a novel population of Tbr2+ cells in the caudal ganglionic eminence of the developing neocortex of C. perspicillata. Our data indicate that unique mechanisms guide bat cortical development, particularly concerning cell cycle length. The bat model provides new perspective on the evolution of developmental programs that regulate neurogenesis in mammalian cerebral cortex, and offers insight into mechanisms that contribute to tangential expansion and gyri formation in the cerebral cortex. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

IPR 1.0: an efficient method for calculating solar radiation absorbed by individual plants in sparse heterogeneous woody plant communities

NASA Astrophysics Data System (ADS)

Zhang, Y.; Chen, W.; Li, J.

2013-12-01

Climate change may alter the spatial distribution, composition, structure, and functions of plant communities. Transitional zones between biomes, or ecotones, are particularly sensitive to climate change. Ecotones are usually heterogeneous with sparse trees. The dynamics of ecotones are mainly determined by the growth and competition of individual plants in the communities. Therefore it is necessary to calculate solar radiation absorbed by individual plants for understanding and predicting their responses to climate change. In this study, we developed an individual plant radiation model, IPR (version 1.0), to calculate solar radiation absorbed by individual plants in sparse heterogeneous woody plant communities. The model is developed based on geometrical optical relationships assuming crowns of woody plants are rectangular boxes with uniform leaf area density. The model calculates the fractions of sunlit and shaded leaf classes and the solar radiation absorbed by each class, including direct radiation from the sun, diffuse radiation from the sky, and scattered radiation from the plant community. The solar radiation received on the ground is also calculated. We tested the model by comparing with the analytical solutions of random distributions of plants. The tests show that the model results are very close to the averages of the random distributions. This model is efficient in computation, and is suitable for ecological models to simulate long-term transient responses of plant communities to climate change.

Performance analysis of distributed symmetric sparse matrix vector multiplication algorithm for multi-core architectures

DOE PAGES

Oryspayev, Dossay; Aktulga, Hasan Metin; Sosonkina, Masha; ...

2015-07-14

In this article, sparse matrix vector multiply (SpMVM) is an important kernel that frequently arises in high performance computing applications. Due to its low arithmetic intensity, several approaches have been proposed in literature to improve its scalability and efficiency in large scale computations. In this paper, our target systems are high end multi-core architectures and we use messaging passing interface + open multiprocessing hybrid programming model for parallelism. We analyze the performance of recently proposed implementation of the distributed symmetric SpMVM, originally developed for large sparse symmetric matrices arising in ab initio nuclear structure calculations. We also study important featuresmore » of this implementation and compare with previously reported implementations that do not exploit underlying symmetry. Our SpMVM implementations leverage the hybrid paradigm to efficiently overlap expensive communications with computations. Our main comparison criterion is the "CPU core hours" metric, which is the main measure of resource usage on supercomputers. We analyze the effects of topology-aware mapping heuristic using simplified network load model. Furthermore, we have tested the different SpMVM implementations on two large clusters with 3D Torus and Dragonfly topology. Our results show that the distributed SpMVM implementation that exploits matrix symmetry and hides communication yields the best value for the "CPU core hours" metric and significantly reduces data movement overheads.« less

FleCSI: Connection to Legion

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

Bergen, Benjamin Karl

2016-08-03

These are slides which are part of the ASC L2 Milestone Review. The following topics are covered: Legion Backend, Distributed-Memory Partitioning, Sparse Data Representations, and MPI-Legion Interoperability.

A Sparse Bayesian Learning Algorithm for White Matter Parameter Estimation from Compressed Multi-shell Diffusion MRI.

PubMed

Pisharady, Pramod Kumar; Sotiropoulos, Stamatios N; Sapiro, Guillermo; Lenglet, Christophe

2017-09-01

We propose a sparse Bayesian learning algorithm for improved estimation of white matter fiber parameters from compressed (under-sampled q-space) multi-shell diffusion MRI data. The multi-shell data is represented in a dictionary form using a non-monoexponential decay model of diffusion, based on continuous gamma distribution of diffusivities. The fiber volume fractions with predefined orientations, which are the unknown parameters, form the dictionary weights. These unknown parameters are estimated with a linear un-mixing framework, using a sparse Bayesian learning algorithm. A localized learning of hyperparameters at each voxel and for each possible fiber orientations improves the parameter estimation. Our experiments using synthetic data from the ISBI 2012 HARDI reconstruction challenge and in-vivo data from the Human Connectome Project demonstrate the improvements.

Short-Term Variations in Response Distribution to Cortical Stimulation

ERIC Educational Resources Information Center

Lesser, Ronald P.; Lee, Hyang Woon; Webber, W. R. S.; Prince, Barry; Crone, Nathan E.; Miglioretti, Diana L.

2008-01-01

Patterns of responses in the cerebral cortex can vary, and are influenced by pre-existing cortical function, but it is not known how rapidly these variations can occur in humans. We investigated how rapidly response patterns to electrical stimulation can vary in intact human brain. We also investigated whether the type of functional change…

Using data tagging to improve the performance of Kanerva's sparse distributed memory

NASA Technical Reports Server (NTRS)

Rogers, David

1988-01-01

The standard formulation of Kanerva's sparse distributed memory (SDM) involves the selection of a large number of data storage locations, followed by averaging the data contained in those locations to reconstruct the stored data. A variant of this model is discussed, in which the predominant pattern is the focus of reconstruction. First, one architecture is proposed which returns the predominant pattern rather than the average pattern. However, this model will require too much storage for most uses. Next, a hybrid model is proposed, called tagged SDM, which approximates the results of the predominant pattern machine, but is nearly as efficient as Kanerva's original formulation. Finally, some experimental results are shown which confirm that significant improvements in the recall capability of SDM can be achieved using the tagged architecture.

Detecting Shielded Special Nuclear Materials Using Multi-Dimensional Neutron Source and Detector Geometries

NASA Astrophysics Data System (ADS)

Santarius, John; Navarro, Marcos; Michalak, Matthew; Fancher, Aaron; Kulcinski, Gerald; Bonomo, Richard

2016-10-01

A newly initiated research project will be described that investigates methods for detecting shielded special nuclear materials by combining multi-dimensional neutron sources, forward/adjoint calculations modeling neutron and gamma transport, and sparse data analysis of detector signals. The key tasks for this project are: (1) developing a radiation transport capability for use in optimizing adaptive-geometry, inertial-electrostatic confinement (IEC) neutron source/detector configurations for neutron pulses distributed in space and/or phased in time; (2) creating distributed-geometry, gas-target, IEC fusion neutron sources; (3) applying sparse data and noise reduction algorithms, such as principal component analysis (PCA) and wavelet transform analysis, to enhance detection fidelity; and (4) educating graduate and undergraduate students. Funded by DHS DNDO Project 2015-DN-077-ARI095.

Sleep affects cortical source modularity in temporal lobe epilepsy: A high-density EEG study.

PubMed

Del Felice, Alessandra; Storti, Silvia Francesca; Manganotti, Paolo

2015-09-01

Interictal epileptiform discharges (IEDs) constitute a perturbation of ongoing cerebral rhythms, usually more frequent during sleep. The aim of the study was to determine whether sleep influences the spread of IEDs over the scalp and whether their distribution depends on vigilance-related modifications in cortical interactions. Wake and sleep 256-channel electroencephalography (EEG) data were recorded in 12 subjects with right temporal lobe epilepsy (TLE) differentiated by whether they had mesial or neocortical TLE. Spikes were selected during wake and sleep. The averaged waking signal was subtracted from the sleep signal and projected on a bidimensional scalp map; sleep and wake spike distributions were compared by using a t-test. The superimposed signal of sleep and wake traces was obtained; the rising phase of the spike, the peak, and the deflections following the spike were identified, and their cortical generator was calculated using low-resolution brain electromagnetic tomography (LORETA) for each group. A mean of 21 IEDs in wake and 39 in sleep per subject were selected. As compared to wake, a larger IED scalp projection was detected during sleep in both mesial and neocortical TLE (p<0.05). A series of EEG deflections followed the spike, the cortical sources of which displayed alternating activations of different cortical areas in wake, substituted by isolated, stationary activations in sleep in mesial TLE and a silencing in neocortical TLE. During sleep, the IED scalp region increases, while cortical interaction decreases. The interaction of cortical modules in sleep and wake in TLE may influence the appearance of IEDs on scalp EEG; in addition, IEDs could be proxies for cerebral oscillation perturbation. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Effect of micromorphology of cortical bone tissue on crack propagation under dynamic loading

NASA Astrophysics Data System (ADS)

Wang, Mayao; Gao, Xing; Abdel-Wahab, Adel; Li, Simin; Zimmermann, Elizabeth A.; Riedel, Christoph; Busse, Björn; Silberschmidt, Vadim V.

2015-09-01

Structural integrity of bone tissue plays an important role in daily activities of humans. However, traumatic incidents such as sports injuries, collisions and falls can cause bone fracture, servere pain and mobility loss. In addition, ageing and degenerative bone diseases such as osteoporosis can increase the risk of fracture [1]. As a composite-like material, a cortical bone tissue is capable of tolerating moderate fracture/cracks without complete failure. The key to this is its heterogeneously distributed microstructural constituents providing both intrinsic and extrinsic toughening mechanisms. At micro-scale level, cortical bone can be considered as a four-phase composite material consisting of osteons, Haversian canals, cement lines and interstitial matrix. These microstructural constituents can directly affect local distributions of stresses and strains, and, hence, crack initiation and propagation. Therefore, understanding the effect of micromorphology of cortical bone on crack initiation and propagation, especially under dynamic loading regimes is of great importance for fracture risk evaluation. In this study, random microstructures of a cortical bone tissue were modelled with finite elements for four groups: healthy (control), young age, osteoporosis and bisphosphonate-treated, based on osteonal morphometric parameters measured from microscopic images for these groups. The developed models were loaded under the same dynamic loading conditions, representing a direct impact incident, resulting in progressive crack propagation. An extended finite-element method (X-FEM) was implemented to realize solution-dependent crack propagation within the microstructured cortical bone tissues. The obtained simulation results demonstrate significant differences due to micromorphology of cortical bone, in terms of crack propagation characteristics for different groups, with the young group showing highest fracture resistance and the senior group the lowest.

[Alteration of mitochondrial distribution and gene expression of fission 1 protein in cortical neurons of rats with chronic fluorosis].

PubMed

Lou, Di-dong; Zhang, Kai-lin; Qin, Shuang-li; Liu, Yan-fei; Yu, Yan-ni; Guan, Zhi-zhong

2012-04-01

To investigate the changes of mitochondrial distribution in axon/soma and the expression of mitochondrial fission 1 (Fis1) protein in the cortical neurons of rats with chronic fluorosis. Sixty SD rats were divided into 3 groups (20 each) according to weight hierarchy and fed with different concentrations of fluoride in drinking water (0, 10 and 50 mg/L, respectively) for 6 months. Images of mitochondria and tubulin labeled by immunofluorescence COXIV and tubulin-α were captured with fluorescence microscope. Fis1 protein expression in cortical neurons was analyzed with immunohistochemistry and Western blot. The expression of Fis1 mRNA was detected with real-time PCR. Varying degrees of dental fluorosis and increased fluoride contents in urine were observed in the rats receiving additional fluoride in drinking water. In the cortical neurons of rats fed with 10 mg/L and 50 mg/L fluoride, the numbers of neuronal soma stained with COXIV(34.8 ± 4.7 and 39.3 ± 3.0, respectively), and the expression of Fis1 protein (immunohistochemistry: 54.0 ± 3.6 and 51.3 ± 4.1, respectively; Western blot: 2.9 ± 0.4 and 2.6 ± 0.6, respectively) and mRNA (3773 ± 1292 and 1274 ± 162, respectively) was markedly increased as compared with controls (4.4 ± 2.3, 25.2 ± 2.5, 1.8 ± 0.2 and 277 ± 73) over the experimental period of 6 months. Excessive intake of fluoride results in an altered mitochondrial distribution in axon and soma in cortical neurons (i.e., the increase in soma and the decrease in axon), increased expression of Fis1 gene and enhanced mitochondrial fission. The altered mitochondrial distribution may be related to the high expression level of Fis1 and a functional disorder of mitochondria.

Rab3A, a possible marker of cortical granules, participates in cortical granule exocytosis in mouse eggs

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

Bello, Oscar Daniel; Cappa, Andrea Isabel; Paola, Matilde de

Fusion of cortical granules with the oocyte plasma membrane is the most significant event to prevent polyspermy. This particular exocytosis, also known as cortical reaction, is regulated by calcium and its molecular mechanism is still not known. Rab3A, a member of the small GTP-binding protein superfamily, has been implicated in calcium-dependent exocytosis and is not yet clear whether Rab3A participates in cortical granules exocytosis. Here, we examine the involvement of Rab3A in the physiology of cortical granules, particularly, in their distribution during oocyte maturation and activation, and their participation in membrane fusion during cortical granule exocytosis. Immunofluorescence and Western blotmore » analysis showed that Rab3A and cortical granules have a similar migration pattern during oocyte maturation, and that Rab3A is no longer detected after cortical granule exocytosis. These results suggested that Rab3A might be a marker of cortical granules. Overexpression of EGFP-Rab3A colocalized with cortical granules with a Pearson correlation coefficient of +0.967, indicating that Rab3A and cortical granules have almost a perfect colocalization in the egg cortical region. Using a functional assay, we demonstrated that microinjection of recombinant, prenylated and active GST-Rab3A triggered cortical granule exocytosis, indicating that Rab3A has an active role in this secretory pathway. To confirm this active role, we inhibited the function of endogenous Rab3A by microinjecting a polyclonal antibody raised against Rab3A prior to parthenogenetic activation. Our results showed that Rab3A antibody microinjection abolished cortical granule exocytosis in parthenogenetically activated oocytes. Altogether, our findings confirm that Rab3A might function as a marker of cortical granules and participates in cortical granule exocytosis in mouse eggs. - Highlights: • Rab3A has a similar migration pattern to cortical granules in mouse oocytes. • Rab3A can be a marker of cortical granules. • Active Rab3A triggered cortical granule exocytosis. • Blocking endogenous Rab3A inhibits cortical granule exocytosis. • Rab3A participates in cortical reaction in mouse oocytes.« less

Determination of spatial distribution of increase in bone temperature during drilling by infrared thermography: preliminary report.

PubMed

Augustin, Goran; Davila, Slavko; Udiljak, Toma; Vedrina, Denis Stjepan; Bagatin, Dinko

2009-05-01

During the drilling of the bone, the temperature could increase above 47 degrees C and cause irreversible osteonecrosis. The spatial distribution of increase in bone temperature could only be presumed using several thermocouples around the drilling site. The aim of this study was to use infrared thermographic camera for determination of spatial distribution of increase in bone temperature during drilling. One combination of drill parameters was used (drill diameter 4.5 mm; drill speed 1,820 rpm; feed-rate 84 mm/min; drill point angle 100 degrees) without external irrigation on room temperature of 26 degrees C. The increase in bone temperature during drilling was analyzed with infrared thermographic camera in two perpendicular planes. Thermographic pictures were taken before drilling, during drilling with measurement of maximal temperature values and after extraction of the drill from the bone. The thermographic picture shows that the increase in bone temperature has irregular shape with maximal increase along cortical bone, which is the most compact component of the bone. The width of this area with the temperature above critical level is three times broader than the width of cortical bone. From the front, the distribution of increase in bone temperature follows the form of the cortical bone (segment of a ring), which is the most compact part and causes the highest resistance to drilling and subsequent friction. Thermography showed that increase in bone temperature spreads through cortical bone, which is the most compact and dense part, and generates highest frictional heat during drilling. The medullar cavity, because of its gelatinous structure, contributes only to thermal dissipation.

Total recall in distributive associative memories

NASA Technical Reports Server (NTRS)

Danforth, Douglas G.

1991-01-01

Iterative error correction of asymptotically large associative memories is equivalent to a one-step learning rule. This rule is the inverse of the activation function of the memory. Spectral representations of nonlinear activation functions are used to obtain the inverse in closed form for Sparse Distributed Memory, Selected-Coordinate Design, and Radial Basis Functions.

Population rate dynamics and multineuron firing patterns in sensory cortex

PubMed Central

Okun, Michael; Yger, Pierre; Marguet, Stephan; Gerard-Mercier, Florian; Benucci, Andrea; Katzner, Steffen; Busse, Laura; Carandini, Matteo; Harris, Kenneth D.

2012-01-01

Cortical circuits encode sensory stimuli through the firing of neuronal ensembles, and also produce spontaneous population patterns in the absence of sensory drive. This population activity is often characterized experimentally by the distribution of multineuron “words” (binary firing vectors), and a match between spontaneous and evoked word distributions has been suggested to reflect learning of a probabilistic model of the sensory world. We analyzed multineuron word distributions in sensory cortex of anesthetized rats and cats, and found that they are dominated by fluctuations in population firing rate rather than precise interactions between individual units. Furthermore, cortical word distributions change when brain state shifts, and similar behavior is seen in simulated networks with fixed, random connectivity. Our results suggest that similarity or dissimilarity in multineuron word distributions could primarily reflect similarity or dissimilarity in population firing rate dynamics, and not necessarily the precise interactions between neurons that would indicate learning of sensory features. PMID:23197704

Prefrontal cortical minicolumn: from executive control to disrupted cognitive processing

PubMed Central

Casanova, Manuel F.

2014-01-01

The prefrontal cortex of the primate brain has a modular architecture based on the aggregation of neurons in minicolumnar arrangements having afferent and efferent connections distributed across many brain regions to represent, select and/or maintain behavioural goals and executive commands. Prefrontal cortical microcircuits are assumed to play a key role in the perception to action cycle that integrates relevant information about environment, and then selects and enacts behavioural responses. Thus, neurons within the interlaminar microcircuits participate in various functional states requiring the integration of signals across cortical layers and the selection of executive variables. Recent research suggests that executive abilities emerge from cortico-cortical interactions between interlaminar prefrontal cortical microcircuits, whereas their disruption is involved in a broad spectrum of neurologic and psychiatric disorders such as autism, schizophrenia, Alzheimer’s and drug addiction. The focus of this review is on the structural, functional and pathological approaches involving cortical minicolumns. Based on recent technological progress it has been demonstrated that microstimulation of infragranular cortical layers with patterns of microcurrents derived from supragranular layers led to an increase in cognitive performance. This suggests that interlaminar prefrontal cortical microcircuits are playing a causal role in improving cognitive performance. An important reason for the new interest in cortical modularity comes from both the impressive progress in understanding anatomical, physiological and pathological facets of cortical microcircuits and the promise of neural prosthetics for patients with neurological and psychiatric disorders. PMID:24531625

Neuroimaging paradigms for tonotopic mapping (II): the influence of acquisition protocol.

PubMed

Langers, Dave R M; Sanchez-Panchuelo, Rosa M; Francis, Susan T; Krumbholz, Katrin; Hall, Deborah A

2014-10-15

Numerous studies on the tonotopic organisation of auditory cortex in humans have employed a wide range of neuroimaging protocols to assess cortical frequency tuning. In the present functional magnetic resonance imaging (fMRI) study, we made a systematic comparison between acquisition protocols with variable levels of interference from acoustic scanner noise. Using sweep stimuli to evoke travelling waves of activation, we measured sound-evoked response signals using sparse, clustered, and continuous imaging protocols that were characterised by inter-scan intervals of 8.8, 2.2, or 0.0 s, respectively. With regard to sensitivity to sound-evoked activation, the sparse and clustered protocols performed similarly, and both detected more activation than the continuous method. Qualitatively, tonotopic maps in activated areas proved highly similar, in the sense that the overall pattern of tonotopic gradients was reproducible across all three protocols. However, quantitatively, we observed substantial reductions in response amplitudes to moderately low stimulus frequencies that coincided with regions of strong energy in the scanner noise spectrum for the clustered and continuous protocols compared to the sparse protocol. At the same time, extreme frequencies became over-represented for these two protocols, and high best frequencies became relatively more abundant. Our results indicate that although all three scanning protocols are suitable to determine the layout of tonotopic fields, an exact quantitative assessment of the representation of various sound frequencies is substantially confounded by the presence of scanner noise. In addition, we noticed anomalous signal dynamics in response to our travelling wave paradigm that suggest that the assessment of frequency-dependent tuning is non-trivially influenced by time-dependent (hemo)dynamics when using sweep stimuli. Copyright © 2014. Published by Elsevier Inc.

Real-time prediction of hand trajectory by ensembles of cortical neurons in primates

NASA Astrophysics Data System (ADS)

Wessberg, Johan; Stambaugh, Christopher R.; Kralik, Jerald D.; Beck, Pamela D.; Laubach, Mark; Chapin, John K.; Kim, Jung; Biggs, S. James; Srinivasan, Mandayam A.; Nicolelis, Miguel A. L.

2000-11-01

Signals derived from the rat motor cortex can be used for controlling one-dimensional movements of a robot arm. It remains unknown, however, whether real-time processing of cortical signals can be employed to reproduce, in a robotic device, the kind of complex arm movements used by primates to reach objects in space. Here we recorded the simultaneous activity of large populations of neurons, distributed in the premotor, primary motor and posterior parietal cortical areas, as non-human primates performed two distinct motor tasks. Accurate real-time predictions of one- and three-dimensional arm movement trajectories were obtained by applying both linear and nonlinear algorithms to cortical neuronal ensemble activity recorded from each animal. In addition, cortically derived signals were successfully used for real-time control of robotic devices, both locally and through the Internet. These results suggest that long-term control of complex prosthetic robot arm movements can be achieved by simple real-time transformations of neuronal population signals derived from multiple cortical areas in primates.

Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization.

PubMed

Wen, Haiguang; Shi, Junxing; Chen, Wei; Liu, Zhongming

2018-02-28

The brain represents visual objects with topographic cortical patterns. To address how distributed visual representations enable object categorization, we established predictive encoding models based on a deep residual network, and trained them to predict cortical responses to natural movies. Using this predictive model, we mapped human cortical representations to 64,000 visual objects from 80 categories with high throughput and accuracy. Such representations covered both the ventral and dorsal pathways, reflected multiple levels of object features, and preserved semantic relationships between categories. In the entire visual cortex, object representations were organized into three clusters of categories: biological objects, non-biological objects, and background scenes. In a finer scale specific to each cluster, object representations revealed sub-clusters for further categorization. Such hierarchical clustering of category representations was mostly contributed by cortical representations of object features from middle to high levels. In summary, this study demonstrates a useful computational strategy to characterize the cortical organization and representations of visual features for rapid categorization.

Self-referenced processing, neurodevelopment and joint attention in autism.

PubMed

Mundy, Peter; Gwaltney, Mary; Henderson, Heather

2010-09-01

This article describes a parallel and distributed processing model (PDPM) of joint attention, self-referenced processing and autism. According to this model, autism involves early impairments in the capacity for rapid, integrated processing of self-referenced (proprioceptive and interoceptive) and other-referenced (exteroceptive) information. Measures of joint attention have proven useful in research on autism because they are sensitive to the early development of the 'parallel' and integrated processing of self- and other-referenced stimuli. Moreover, joint attention behaviors are a consequence, but also an organizer of the functional development of a distal distributed cortical system involving anterior networks including the prefrontal and insula cortices, as well as posterior neural networks including the temporal and parietal cortices. Measures of joint attention provide early behavioral indicators of atypical development in this parallel and distributed processing system in autism. In addition it is proposed that an early, chronic disturbance in the capacity for integrating self- and other-referenced information may have cascading effects on the development of self awareness in autism. The assumptions, empirical support and future research implications of this model are discussed.

Parallel pivoting combined with parallel reduction

NASA Technical Reports Server (NTRS)

Alaghband, Gita

1987-01-01

Parallel algorithms for triangularization of large, sparse, and unsymmetric matrices are presented. The method combines the parallel reduction with a new parallel pivoting technique, control over generations of fill-ins and a check for numerical stability, all done in parallel with the work being distributed over the active processes. The parallel technique uses the compatibility relation between pivots to identify parallel pivot candidates and uses the Markowitz number of pivots to minimize fill-in. This technique is not a preordering of the sparse matrix and is applied dynamically as the decomposition proceeds.

Mechanical Network in Titin Immunoglobulin from Force Distribution Analysis

PubMed Central

Wilmanns, Matthias; Gräter, Frauke

2009-01-01

The role of mechanical force in cellular processes is increasingly revealed by single molecule experiments and simulations of force-induced transitions in proteins. How the applied force propagates within proteins determines their mechanical behavior yet remains largely unknown. We present a new method based on molecular dynamics simulations to disclose the distribution of strain in protein structures, here for the newly determined high-resolution crystal structure of I27, a titin immunoglobulin (IG) domain. We obtain a sparse, spatially connected, and highly anisotropic mechanical network. This allows us to detect load-bearing motifs composed of interstrand hydrogen bonds and hydrophobic core interactions, including parts distal to the site to which force was applied. The role of the force distribution pattern for mechanical stability is tested by in silico unfolding of I27 mutants. We then compare the observed force pattern to the sparse network of coevolved residues found in this family. We find a remarkable overlap, suggesting the force distribution to reflect constraints for the evolutionary design of mechanical resistance in the IG family. The force distribution analysis provides a molecular interpretation of coevolution and opens the road to the study of the mechanism of signal propagation in proteins in general. PMID:19282960

Optimal Couple Projections for Domain Adaptive Sparse Representation-based Classification.

PubMed

Zhang, Guoqing; Sun, Huaijiang; Porikli, Fatih; Liu, Yazhou; Sun, Quansen

2017-08-29

In recent years, sparse representation based classification (SRC) is one of the most successful methods and has been shown impressive performance in various classification tasks. However, when the training data has a different distribution than the testing data, the learned sparse representation may not be optimal, and the performance of SRC will be degraded significantly. To address this problem, in this paper, we propose an optimal couple projections for domain-adaptive sparse representation-based classification (OCPD-SRC) method, in which the discriminative features of data in the two domains are simultaneously learned with the dictionary that can succinctly represent the training and testing data in the projected space. OCPD-SRC is designed based on the decision rule of SRC, with the objective to learn coupled projection matrices and a common discriminative dictionary such that the between-class sparse reconstruction residuals of data from both domains are maximized, and the within-class sparse reconstruction residuals of data are minimized in the projected low-dimensional space. Thus, the resulting representations can well fit SRC and simultaneously have a better discriminant ability. In addition, our method can be easily extended to multiple domains and can be kernelized to deal with the nonlinear structure of data. The optimal solution for the proposed method can be efficiently obtained following the alternative optimization method. Extensive experimental results on a series of benchmark databases show that our method is better or comparable to many state-of-the-art methods.

Simulation study of axial ultrasound transmission in heterogeneous cortical bone model

NASA Astrophysics Data System (ADS)

Takano, Koki; Nagatani, Yoshiki; Matsukawa, Mami

2017-07-01

Ultrasound propagation in a heterogeneous cortical bone was studied. Using a bovine radius, the longitudinal wave velocity distribution in the axial direction was experimentally measured in the MHz range. The bilinear interpolation and piecewise cubic Hermite interpolation methods were applied to create a three-dimensional (3D) precise velocity model of the bone using experimental data. By assuming the uniaxial anisotropy of the bone, the distributions of all elastic moduli of a 3D heterogeneous model were estimated. The elastic finite-difference time-domain method was used to simulate axial ultrasonic wave propagation. The wave propagation in the initial model was compared with that in the thinner model, where the inner part of the cortical bone model was removed. The wave front of the first arriving signal (FAS) slightly depended on the heterogeneity in each model. Owing to the decrease in bone thickness, the propagation behavior also changed and the FAS velocity clearly decreased.

Principles for the dynamic maintenance of cortical polarity

PubMed Central

Marco, Eugenio; Wedlich-Soldner, Roland; Li, Rong; Altschuler, Steven J.; Wu, Lani F.

2007-01-01

Summary Diverse cell types require the ability to dynamically maintain polarized membrane protein distributions through balancing transport and diffusion. However, design principles underlying dynamically maintained cortical polarity are not well understood. Here we constructed a mathematical model for characterizing the morphology of dynamically polarized protein distributions. We developed analytical approaches for measuring all model parameters from single-cell experiments. We applied our methods to a well-characterized system for studying polarized membrane proteins: budding yeast cells expressing activated Cdc42. We found that balanced diffusion and colocalized transport to and from the plasma membrane were sufficient for accurately describing polarization morphologies. Surprisingly, the model predicts that polarized regions are defined with a precision that is nearly optimal for measured transport rates, and that polarity can be dynamically stabilized through positive feedback with directed transport. Our approach provides a step towards understanding how biological systems shape spatially precise, unambiguous cortical polarity domains using dynamic processes. PMID:17448998

Visualizing the engram: learning stabilizes odor representations in the olfactory network.

PubMed

Shakhawat, Amin M D; Gheidi, Ali; Hou, Qinlong; Dhillon, Sandeep K; Marrone, Diano F; Harley, Carolyn W; Yuan, Qi

2014-11-12

The nature of memory is a central issue in neuroscience. How does our representation of the world change with learning and experience? Here we use the transcription of Arc mRNA, which permits probing the neural representations of temporally separated events, to address this in a well characterized odor learning model. Rat pups readily associate odor with maternal care. In pups, the lateralized olfactory networks are independent, permitting separate training and within-subject control. We use multiday training to create an enduring memory of peppermint odor. Training stabilized rewarded, but not nonrewarded, odor representations in both mitral cells and associated granule cells of the olfactory bulb and in the pyramidal cells of the anterior piriform cortex. An enlarged core of stable, likely highly active neurons represent rewarded odor at both stages of the olfactory network. Odor representations in anterior piriform cortex were sparser than typical in adult rat and did not enlarge with learning. This sparser representation of odor is congruent with the maturation of lateral olfactory tract input in rat pups. Cortical representations elsewhere have been shown to be highly variable in electrophysiological experiments, suggesting brains operate normally using dynamic and network-modulated representations. The olfactory cortical representations here are consistent with the generalized associative model of sparse variable cortical representation, as normal responses to repeated odors were highly variable (∼70% of the cells change as indexed by Arc). Learning and memory modified rewarded odor ensembles to increase stability in a core representational component. Copyright © 2014 the authors 0270-6474/14/3415394-08$15.00/0.

A range-based predictive localization algorithm for WSID networks

NASA Astrophysics Data System (ADS)

Liu, Yuan; Chen, Junjie; Li, Gang

2017-11-01

Most studies on localization algorithms are conducted on the sensor networks with densely distributed nodes. However, the non-localizable problems are prone to occur in the network with sparsely distributed sensor nodes. To solve this problem, a range-based predictive localization algorithm (RPLA) is proposed in this paper for the wireless sensor networks syncretizing the RFID (WSID) networks. The Gaussian mixture model is established to predict the trajectory of a mobile target. Then, the received signal strength indication is used to reduce the residence area of the target location based on the approximate point-in-triangulation test algorithm. In addition, collaborative localization schemes are introduced to locate the target in the non-localizable situations. Simulation results verify that the RPLA achieves accurate localization for the network with sparsely distributed sensor nodes. The localization accuracy of the RPLA is 48.7% higher than that of the APIT algorithm, 16.8% higher than that of the single Gaussian model-based algorithm and 10.5% higher than that of the Kalman filtering-based algorithm.

Non-convex Statistical Optimization for Sparse Tensor Graphical Model

PubMed Central

Sun, Wei; Wang, Zhaoran; Liu, Han; Cheng, Guang

2016-01-01

We consider the estimation of sparse graphical models that characterize the dependency structure of high-dimensional tensor-valued data. To facilitate the estimation of the precision matrix corresponding to each way of the tensor, we assume the data follow a tensor normal distribution whose covariance has a Kronecker product structure. The penalized maximum likelihood estimation of this model involves minimizing a non-convex objective function. In spite of the non-convexity of this estimation problem, we prove that an alternating minimization algorithm, which iteratively estimates each sparse precision matrix while fixing the others, attains an estimator with the optimal statistical rate of convergence as well as consistent graph recovery. Notably, such an estimator achieves estimation consistency with only one tensor sample, which is unobserved in previous work. Our theoretical results are backed by thorough numerical studies. PMID:28316459

Particle Size Distributions in Atmospheric Clouds

NASA Technical Reports Server (NTRS)

Paoli, Roberto; Shariff, Karim

2003-01-01

In this note, we derive a transport equation for a spatially integrated distribution function of particles size that is suitable for sparse particle systems, such as in atmospheric clouds. This is done by integrating a Boltzmann equation for a (local) distribution function over an arbitrary but finite volume. A methodology for evolving the moments of the integrated distribution is presented. These moments can be either tracked for a finite number of discrete populations ('clusters') or treated as continuum variables.

Stress distributions of a bracket type orthodontic miniscrew and the surrounding bone under moment loadings: Three-dimensional finite element analysis

PubMed Central

Ajami, Shabnam; Mina, Ahmad; Nabavizadeh, Seyed Amin

2016-01-01

Objectives: To evaluate the effect of moments and the combination of forces and moments on the mechanical properties of a bracket type miniscrew, resembling engagement of a rectangular wire by three-dimensional (3D) finite element study. Materials and Methods: By solid work software (Dassaunlt systems solid works, concord, Mass), a 3D miniscrew model of 6, 8, 10 mm lengths was designed and inserted in the osseous block, consisted of the cortical, and cancellous bones. The stress distributions, maximum stresses, and deflections of the miniscrew were evaluated for all parts using ANSYS (Work Bench, 2014). Results: As the magnitudes of the load increased from 100 to 200, 400 and 800 grf-mm, the peak of stresses in the 6 mm long miniscrew were increased from 7.7 to 61.5 Mpa. The maximum values of Von Mises in the cancellous bone were tremendously lower in comparison to the cortical bone by one hundredth. As the length of the miniscrew in contact with the bone was increased, the amounts and patterns of stress distribution in the cortical bone and the miniscrew did not change significantly. Conclusions: As the moment magnitude increased, the pick stresses increased linearly. The existence of cancellous bone was not significantly responsible for the stress distribution. The pattern of stress distribution did not change by the length of the miniscrew. PMID:27127753

Summer Proceedings 2016: The Center for Computing Research at Sandia National Laboratories

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

Carleton, James Brian; Parks, Michael L.

Solving sparse linear systems from the discretization of elliptic partial differential equations (PDEs) is an important building block in many engineering applications. Sparse direct solvers can solve general linear systems, but are usually slower and use much more memory than effective iterative solvers. To overcome these two disadvantages, a hierarchical solver (LoRaSp) based on H2-matrices was introduced in [22]. Here, we have developed a parallel version of the algorithm in LoRaSp to solve large sparse matrices on distributed memory machines. On a single processor, the factorization time of our parallel solver scales almost linearly with the problem size for three-dimensionalmore » problems, as opposed to the quadratic scalability of many existing sparse direct solvers. Moreover, our solver leads to almost constant numbers of iterations, when used as a preconditioner for Poisson problems. On more than one processor, our algorithm has significant speedups compared to sequential runs. With this parallel algorithm, we are able to solve large problems much faster than many existing packages as demonstrated by the numerical experiments.« less

Optical fringe-reflection deflectometry with sparse representation

NASA Astrophysics Data System (ADS)

Xiao, Yong-Liang; Li, Sikun; Zhang, Qican; Zhong, Jianxin; Su, Xianyu; You, Zhisheng

2018-05-01

Optical fringe-reflection deflectometry is a surprisingly attractive scratch detection technique for specular surfaces owing to its unparalleled local sensibility. Full-field surface topography is obtained from a measured normal field using gradient integration. However, there may not be an ideal measured gradient field for deflectometry reconstruction in practice. Both the non-integrability condition and various kinds of image noise distributions, which are present in the indirect measured gradient field, may lead to ambiguity about the scratches on specular surfaces. In order to reduce misjudgment of scratches, sparse representation is introduced into the Southwell curl equation for deflectometry. The curl can be represented as a linear combination of the given redundant dictionary for curl and the sparsest solution for gradient refinement. The non-integrability condition and noise permutation can be overcome with sparse representation for gradient refinement. Numerical simulations demonstrate that the accuracy rate of judgment of scratches can be enhanced with sparse representation compared to the standard least-squares integration. Preliminary experiments are performed with the application of practical measured deflectometric data to verify the validity of the algorithm.

Cortical Iron Reflects Severity of Alzheimer’s Disease

PubMed Central

van Duijn, Sara; Bulk, Marjolein; van Duinen, Sjoerd G.; Nabuurs, Rob J.A.; van Buchem, Mark A.; van der Weerd, Louise; Natté, Remco

2017-01-01

Abnormal iron distribution in the isocortex is increasingly recognized as an in vivo marker for Alzheimer’s disease (AD). However, the contribution of iron accumulation to the AD pathology is still poorly understood. In this study, we investigated: 1) frontal cortical iron distribution in AD and normal aging and 2) the relation between iron distribution and degree of AD pathology. We used formalin fixed paraffin embedded frontal cortex from 10 AD patients, 10 elder, 10 middle aged, and 10 young controls and visualized iron with a modified Perl’s histochemical procedure. AD and elderly subjects were not different with respect to age and sex distribution. Iron distribution in the frontal cortex was not affected by normal aging but was clearly different between AD and controls. AD showed accumulation of iron in plaques, activated microglia, and, in the most severe cases, in the mid-cortical layers along myelinated fibers. The degree of altered iron accumulations was correlated to the amount of amyloid-β plaques and tau pathology in the same block, as well as to Braak stage (p < 0.001). AD and normal aging show different iron and myelin distribution in frontal cortex. These changes appear to occur after the development of the AD pathological hallmarks. These findings may help the interpretation of high resolution in vivo MRI and suggest the potential of using changes in iron-based MRI contrast to indirectly determine the degree of AD pathology in the frontal cortex. PMID:29081415

Quantitative neuropathological study of Alzheimer-type pathology in the hippocampus: comparison of senile dementia of Alzheimer type, senile dementia of Lewy body type, Parkinson's disease and non-demented elderly control patients.

PubMed

Ince, P; Irving, D; MacArthur, F; Perry, R H

1991-12-01

A Lewy body dementing syndrome in the elderly has been recently described and designated senile dementia of Lewy body type (SDLT) on the basis of a distinct clinicopathological profile. The pathological changes seen in SDLT include the presence of cortical Lewy bodies (LB) frequently, but not invariably, associated with senile plaque (SP) formation. Whilst neocortical neurofibrillary tangles (NFT) are sparse or absent, a proportion of these cases show involvement of the temporal archicortex by lesions comprising Alzheimer-type pathology (ATP, i.e. NFT, SP and granulovacuolar degeneration [GVD]). Thus the relationship between SDLT and senile dementia of Alzheimer type (SDAT) is complex and controversial. In this study quantitative neuropathology was used to compare the intensity and distribution of ATP in the hippocampus and entorhinal cortex of 53 patients from 3 disease groups (SDLT, SDAT, Parkinson's disease (PD)) and a group of neurologically and mentally normal elderly control patients. For most brain areas examined the extent of ATP between the patient groups followed the trend SDAT greater than SDLT greater than PD greater than control. Statistical comparison of these groups revealed significant differences between the mean densities of NFT, SP and GVD although individual cases showed considerable variability. These results confirm additional pathological differences between SDAT and SDLT regarding the intensity of involvement of the temporal archicortex by ATP. Many patients with Lewy body disorders (LBdis) show a predisposition to develop ATP albeit in a more restricted distribution (e.g. low or absent neocortical NFT) and at lower densities than is found in SDAT. Some cases of SDLT show minimal SP and NFT formation in both neocortex and archicortex supporting previously published data distinguishing this group from Alzheimer's disease.

The convergence of maturational change and structural covariance in human cortical networks.

PubMed

Alexander-Bloch, Aaron; Raznahan, Armin; Bullmore, Ed; Giedd, Jay

2013-02-13

Large-scale covariance of cortical thickness or volume in distributed brain regions has been consistently reported by human neuroimaging studies. The mechanism of this population covariance of regional cortical anatomy has been hypothetically related to synchronized maturational changes in anatomically connected neuronal populations. Brain regions that grow together, i.e., increase or decrease in volume at the same rate over the course of years in the same individual, are thus expected to demonstrate strong structural covariance or anatomical connectivity across individuals. To test this prediction, we used a structural MRI dataset on healthy young people (N = 108; aged 9-22 years at enrollment), comprising 3-6 longitudinal scans on each participant over 6-12 years of follow-up. At each of 360 regional nodes, and for each participant, we estimated the following: (1) the cortical thickness in the median scan and (2) the linear rate of change in cortical thickness over years of serial scanning. We constructed structural and maturational association matrices and networks from these measurements. Both structural and maturational networks shared similar global and nodal topological properties, as well as mesoscopic features including a modular community structure, a relatively small number of highly connected hub regions, and a bias toward short distance connections. Using resting-state functional magnetic resonance imaging data on a subset of the sample (N = 32), we also demonstrated that functional connectivity and network organization was somewhat predictable by structural/maturational networks but demonstrated a stronger bias toward short distance connections and greater topological segregation. Brain structural covariance networks are likely to reflect synchronized developmental change in distributed cortical regions.

Altered Cortical Swallowing Processing in Patients with Functional Dysphagia: A Preliminary Study

PubMed Central

Wollbrink, Andreas; Warnecke, Tobias; Winkels, Martin; Pantev, Christo; Dziewas, Rainer

2014-01-01

Objective Current neuroimaging research on functional disturbances provides growing evidence for objective neuronal correlates of allegedly psychogenic symptoms, thereby shifting the disease concept from a psychological towards a neurobiological model. Functional dysphagia is such a rare condition, whose pathogenetic mechanism is largely unknown. In the absence of any organic reason for a patient's persistent swallowing complaints, sensorimotor processing abnormalities involving central neural pathways constitute a potential etiology. Methods In this pilot study we measured cortical swallow-related activation in 5 patients diagnosed with functional dysphagia and a matched group of healthy subjects applying magnetoencephalography. Source localization of cortical activation was done with synthetic aperture magnetometry. To test for significant differences in cortical swallowing processing between groups, a non-parametric permutation test was afterwards performed on individual source localization maps. Results Swallowing task performance was comparable between groups. In relation to control subjects, in whom activation was symmetrically distributed in rostro-medial parts of the sensorimotor cortices of both hemispheres, patients showed prominent activation of the right insula, dorsolateral prefrontal cortex and lateral premotor, motor as well as inferolateral parietal cortex. Furthermore, activation was markedly reduced in the left medial primary sensory cortex as well as right medial sensorimotor cortex and adjacent supplementary motor area (p<0.01). Conclusions Functional dysphagia - a condition with assumed normal brain function - seems to be associated with distinctive changes of the swallow-related cortical activation pattern. Alterations may reflect exaggerated activation of a widely distributed vigilance, self-monitoring and salience rating network that interferes with down-stream deglutition sensorimotor control. PMID:24586948

Aggrecan-based extracellular matrix shows unique cortical features and conserved subcortical principles of mammalian brain organization in the Madagascan lesser hedgehog tenrec (Echinops telfairi Martin, 1838).

PubMed

Morawski, M; Brückner, G; Jäger, C; Seeger, G; Künzle, H; Arendt, T

2010-02-03

The Madagascan tenrecs (Afrotheria), an ancient mammalian clade, are characterized by unique brain anatomy. Striking features are an expanded paleocortex but a small and poorly differentiated neocortex devoid of a distinct granular layer IV. To investigate the organization of cortical areas we analyzed extracellular matrix components in perineuronal nets (PNs) using antibodies to aggrecan, lectin staining and hyaluronan-binding protein. Selected subcortical regions were studied to correlate the cortical patterns with features in evolutionary conserved systems. In the neocortex, paleocortex and hippocampus PNs were associated with nonpyramidal neurons. Quantitative analysis in the cerebral cortex revealed area-specific proportions and laminar distribution patterns of neurons ensheathed by PNs. Cortical PNs showed divergent structural phenotypes. Diffuse PNs forming a cotton wool-like perisomatic rim were characteristic of the paleocortex. These PNs were associated with a dense pericellular plexus of calretinin-immunoreactive fibres. Clearly contoured PNs were devoid of a calretinin-positive plexus and predominated in the neocortex and hippocampus. The organization of the extracellular matrix in subcortical nuclei followed the widely distributed mammalian type. We conclude that molecular properties of the aggrecan-based extracellular matrix are conserved during evolution of mammals; however, the matrix scaffold is adapted to specific wiring patterns of cortical and subcortical neuronal networks. Copyright 2010 IBRO. Published by Elsevier Ltd. All rights reserved.

Catechol-o-methyl transferase (COMT) val158met polymorphism and adolescent cortical development in patients with childhood-onset schizophrenia, their non-psychotic siblings, and healthy controls

PubMed Central

Raznahan, Armin; Greenstein, Deanna; Lee, Yohan; Long, Robert; Clasen, Liv; Gochman, Pete; Addington, Anjene; Giedd, Jay N.; Rapoport, Judith L.; Gogtay, Nitin

2012-01-01

Non-psychotic individuals at increased risk for schizophrenia show alterations in fronto-striatal dopamine signaling and cortical gray matter maturation reminiscent of those seen in schizophrenia. It remains unclear however if variations in dopamine signaling influence rates of structural cortical maturation in typically developing individuals, and whether such influences are disrupted in patients with schizophrenia and their non-psychotic siblings. We sought to address these issues by relating a functional Val→Met polymorphism within the gene encoding catechol-o-methyltransferase (COMT)—a key enzymatic regulator of cortical dopamine levels—to longitudinal structural neuroimaging measures of cortical gray matter thickness. We included a total of 792 magnetic resonance imaging brain scans, acquired between ages 9 and 22 years from patients with childhood-onset schizophrenia (COS), their non-psychotic full siblings, and matched healthy controls. Whereas greater Val allele dose (which confers enhanced dopamine catabolism and is proposed to aggravate cortical deficits in schizophrenia) accelerated adolescent cortical thinning in both schizophrenia probands and their siblings, it attenuated cortical thinning in healthy controls. This similarity between COS patients and their siblings was accompanied by differences between the two groups in the timing and spatial distribution of disrupted COMT influences on cortical maturation. Consequently, whereas greater Val “dose” conferred persistent dorsolateral prefrontal cortical deficits amongst affected probands by adulthood, cortical thickness differences associated with varying Val dose in non-psychotic siblings resolved over the age-range studied. These findings suggest that cortical abnormalities in pedigrees affected by schizophrenia may be contributed to by a disruption of dopaminergic infleunces on cortical maturation. PMID:21620981

Combining Different Tools for EEG Analysis to Study the Distributed Character of Language Processing

PubMed Central

da Rocha, Armando Freitas; Foz, Flávia Benevides; Pereira, Alfredo

2015-01-01

Recent studies on language processing indicate that language cognition is better understood if assumed to be supported by a distributed intelligent processing system enrolling neurons located all over the cortex, in contrast to reductionism that proposes to localize cognitive functions to specific cortical structures. Here, brain activity was recorded using electroencephalogram while volunteers were listening or reading small texts and had to select pictures that translate meaning of these texts. Several techniques for EEG analysis were used to show this distributed character of neuronal enrollment associated with the comprehension of oral and written descriptive texts. Low Resolution Tomography identified the many different sets (s i) of neurons activated in several distinct cortical areas by text understanding. Linear correlation was used to calculate the information H(e i) provided by each electrode of the 10/20 system about the identified s i. H(e i) Principal Component Analysis (PCA) was used to study the temporal and spatial activation of these sources s i. This analysis evidenced 4 different patterns of H(e i) covariation that are generated by neurons located at different cortical locations. These results clearly show that the distributed character of language processing is clearly evidenced by combining available EEG technologies. PMID:26713089

Combining Different Tools for EEG Analysis to Study the Distributed Character of Language Processing.

PubMed

Rocha, Armando Freitas da; Foz, Flávia Benevides; Pereira, Alfredo

2015-01-01

Recent studies on language processing indicate that language cognition is better understood if assumed to be supported by a distributed intelligent processing system enrolling neurons located all over the cortex, in contrast to reductionism that proposes to localize cognitive functions to specific cortical structures. Here, brain activity was recorded using electroencephalogram while volunteers were listening or reading small texts and had to select pictures that translate meaning of these texts. Several techniques for EEG analysis were used to show this distributed character of neuronal enrollment associated with the comprehension of oral and written descriptive texts. Low Resolution Tomography identified the many different sets (s i ) of neurons activated in several distinct cortical areas by text understanding. Linear correlation was used to calculate the information H(e i ) provided by each electrode of the 10/20 system about the identified s i . H(e i ) Principal Component Analysis (PCA) was used to study the temporal and spatial activation of these sources s i . This analysis evidenced 4 different patterns of H(e i ) covariation that are generated by neurons located at different cortical locations. These results clearly show that the distributed character of language processing is clearly evidenced by combining available EEG technologies.

Comparison between sparsely distributed memory and Hopfield-type neural network models

NASA Technical Reports Server (NTRS)

Keeler, James D.

1986-01-01

The Sparsely Distributed Memory (SDM) model (Kanerva, 1984) is compared to Hopfield-type neural-network models. A mathematical framework for comparing the two is developed, and the capacity of each model is investigated. The capacity of the SDM can be increased independently of the dimension of the stored vectors, whereas the Hopfield capacity is limited to a fraction of this dimension. However, the total number of stored bits per matrix element is the same in the two models, as well as for extended models with higher order interactions. The models are also compared in their ability to store sequences of patterns. The SDM is extended to include time delays so that contextual information can be used to cover sequences. Finally, it is shown how a generalization of the SDM allows storage of correlated input pattern vectors.

Sparse distributed memory: understanding the speed and robustness of expert memory

PubMed Central

Brogliato, Marcelo S.; Chada, Daniel M.; Linhares, Alexandre

2014-01-01

How can experts, sometimes in exacting detail, almost immediately and very precisely recall memory items from a vast repertoire? The problem in which we will be interested concerns models of theoretical neuroscience that could explain the speed and robustness of an expert's recollection. The approach is based on Sparse Distributed Memory, which has been shown to be plausible, both in a neuroscientific and in a psychological manner, in a number of ways. A crucial characteristic concerns the limits of human recollection, the “tip-of-tongue” memory event—which is found at a non-linearity in the model. We expand the theoretical framework, deriving an optimization formula to solve this non-linearity. Numerical results demonstrate how the higher frequency of rehearsal, through work or study, immediately increases the robustness and speed associated with expert memory. PMID:24808842

Hadza Color Terms Are Sparse, Diverse, and Distributed, and Presage the Universal Color Categories Found in Other World Languages

PubMed Central

Lindsey, Delwin T.; Brainard, David H.; Apicella, Coren L.

2016-01-01

In our empirical and theoretical study of color naming among the Hadza, a Tanzanian hunter-gatherer group, we show that Hadza color naming is sparse (the color appearance of many stimulus tiles was not named), diverse (there was little consensus in the terms for the color appearance of most tiles), and distributed (the universal color categories of world languages are revealed in nascent form within the Hadza language community, when we analyze the patterns of how individual Hadza deploy color terms). Using our Hadza data set, Witzel shows an association between two measures of color naming performance and the chroma of the stimuli. His prediction of which colored tiles will be named with what level of consensus, while interesting, does not alter the validity of our conclusions. PMID:28781734

Sparse distributed memory

NASA Technical Reports Server (NTRS)

Denning, Peter J.

1989-01-01

Sparse distributed memory was proposed be Pentti Kanerva as a realizable architecture that could store large patterns and retrieve them based on partial matches with patterns representing current sensory inputs. This memory exhibits behaviors, both in theory and in experiment, that resemble those previously unapproached by machines - e.g., rapid recognition of faces or odors, discovery of new connections between seemingly unrelated ideas, continuation of a sequence of events when given a cue from the middle, knowing that one doesn't know, or getting stuck with an answer on the tip of one's tongue. These behaviors are now within reach of machines that can be incorporated into the computing systems of robots capable of seeing, talking, and manipulating. Kanerva's theory is a break with the Western rationalistic tradition, allowing a new interpretation of learning and cognition that respects biology and the mysteries of individual human beings.

IPR 1.0: an efficient method for calculating solar radiation absorbed by individual plants in sparse heterogeneous woody plant communities

NASA Astrophysics Data System (ADS)

Zhang, Y.; Chen, W.; Li, J.

2014-07-01

Climate change may alter the spatial distribution, composition, structure and functions of plant communities. Transitional zones between biomes, or ecotones, are particularly sensitive to climate change. Ecotones are usually heterogeneous with sparse trees. The dynamics of ecotones are mainly determined by the growth and competition of individual plants in the communities. Therefore it is necessary to calculate the solar radiation absorbed by individual plants in order to understand and predict their responses to climate change. In this study, we developed an individual plant radiation model, IPR (version 1.0), to calculate solar radiation absorbed by individual plants in sparse heterogeneous woody plant communities. The model is developed based on geometrical optical relationships assuming that crowns of woody plants are rectangular boxes with uniform leaf area density. The model calculates the fractions of sunlit and shaded leaf classes and the solar radiation absorbed by each class, including direct radiation from the sun, diffuse radiation from the sky, and scattered radiation from the plant community. The solar radiation received on the ground is also calculated. We tested the model by comparing with the results of random distribution of plants. The tests show that the model results are very close to the averages of the random distributions. This model is efficient in computation, and can be included in vegetation models to simulate long-term transient responses of plant communities to climate change. The code and a user's manual are provided as Supplement of the paper.

Signal-Preserving Erratic Noise Attenuation via Iterative Robust Sparsity-Promoting Filter

DOE PAGES

Zhao, Qiang; Du, Qizhen; Gong, Xufei; ...

2018-04-06

Sparse domain thresholding filters operating in a sparse domain are highly effective in removing Gaussian random noise under Gaussian distribution assumption. Erratic noise, which designates non-Gaussian noise that consists of large isolated events with known or unknown distribution, also needs to be explicitly taken into account. However, conventional sparse domain thresholding filters based on the least-squares (LS) criterion are severely sensitive to data with high-amplitude and non-Gaussian noise, i.e., the erratic noise, which makes the suppression of this type of noise extremely challenging. Here, in this paper, we present a robust sparsity-promoting denoising model, in which the LS criterion ismore » replaced by the Huber criterion to weaken the effects of erratic noise. The random and erratic noise is distinguished by using a data-adaptive parameter in the presented method, where random noise is described by mean square, while the erratic noise is downweighted through a damped weight. Different from conventional sparse domain thresholding filters, definition of the misfit between noisy data and recovered signal via the Huber criterion results in a nonlinear optimization problem. With the help of theoretical pseudoseismic data, an iterative robust sparsity-promoting filter is proposed to transform the nonlinear optimization problem into a linear LS problem through an iterative procedure. The main advantage of this transformation is that the nonlinear denoising filter can be solved by conventional LS solvers. Lastly, tests with several data sets demonstrate that the proposed denoising filter can successfully attenuate the erratic noise without damaging useful signal when compared with conventional denoising approaches based on the LS criterion.« less

Signal-Preserving Erratic Noise Attenuation via Iterative Robust Sparsity-Promoting Filter

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

Zhao, Qiang; Du, Qizhen; Gong, Xufei

Sparse domain thresholding filters operating in a sparse domain are highly effective in removing Gaussian random noise under Gaussian distribution assumption. Erratic noise, which designates non-Gaussian noise that consists of large isolated events with known or unknown distribution, also needs to be explicitly taken into account. However, conventional sparse domain thresholding filters based on the least-squares (LS) criterion are severely sensitive to data with high-amplitude and non-Gaussian noise, i.e., the erratic noise, which makes the suppression of this type of noise extremely challenging. Here, in this paper, we present a robust sparsity-promoting denoising model, in which the LS criterion ismore » replaced by the Huber criterion to weaken the effects of erratic noise. The random and erratic noise is distinguished by using a data-adaptive parameter in the presented method, where random noise is described by mean square, while the erratic noise is downweighted through a damped weight. Different from conventional sparse domain thresholding filters, definition of the misfit between noisy data and recovered signal via the Huber criterion results in a nonlinear optimization problem. With the help of theoretical pseudoseismic data, an iterative robust sparsity-promoting filter is proposed to transform the nonlinear optimization problem into a linear LS problem through an iterative procedure. The main advantage of this transformation is that the nonlinear denoising filter can be solved by conventional LS solvers. Lastly, tests with several data sets demonstrate that the proposed denoising filter can successfully attenuate the erratic noise without damaging useful signal when compared with conventional denoising approaches based on the LS criterion.« less

The Use of Compressive Sensing to Reconstruct Radiation Characteristics of Wide-Band Antennas from Sparse Measurements

DTIC Science & Technology

2015-06-01

of uniform- versus nonuniform -pattern reconstruction, of transform function used, and of minimum randomly distributed measurements needed to...the radiation-frequency pattern’s reconstruction using uniform and nonuniform randomly distributed samples even though the pattern error manifests...5 Fig. 3 The nonuniform compressive-sensing reconstruction of the radiation

A manual for PARTI runtime primitives

NASA Technical Reports Server (NTRS)

Berryman, Harry; Saltz, Joel

1990-01-01

Primitives are presented that are designed to help users efficiently program irregular problems (e.g., unstructured mesh sweeps, sparse matrix codes, adaptive mesh partial differential equations solvers) on distributed memory machines. These primitives are also designed for use in compilers for distributed memory multiprocessors. Communications patterns are captured at runtime, and the appropriate send and receive messages are automatically generated.

Alveolar bone stress around implants with different abutment angulation: an FE-analysis of anterior maxilla.

PubMed

Sadrimanesh, Roozbeh; Siadat, Hakimeh; Sadr-Eshkevari, Pooyan; Monzavi, Abbas; Maurer, Peter; Rashad, Ashkan

2012-06-01

To comparatively assess the masticatory stress distribution in bone around implants placed in the anterior maxilla with three different labial inclinations. Three-dimensional finite element models were fabricated for three situations in anterior maxilla: (1) a fixture in contact with buccal cortical plate restored by straight abutment, (2) a fixture inclined at 15 degrees, and (3) 20 degrees labially restored with corresponding angled abutment. A palatal bite force of 146 N was applied to a point 3 mm below the incisal edge. Stress distribution around the bone-fixture interface was determined using ANSYS software. The maximum compressive stress, concentrated in the labial crestal cortical bone, was measured to be 62, 108, and 122 MPa for 0-, 15-, and 20-degree labially inclined fixtures, respectively. The maximum tensile stress, concentrated in the palatal crestal cortical bone, was measured to be 60, 108, and 120 MPa for 0-, 15-, and 20-degree labially inclined fixtures, respectively. While all compressive stress values were under the cortical yield strength of 169 MPa, tensile stress values partially surpassed the yield strength (104 MPa) especially when a 20-degree inclination was followed for fixture placement.

Cortical language lateralization in right handed normal subjects using functional magnetic resonance imaging.

PubMed

Vikingstad, E M; George, K P; Johnson, A F; Cao, Y

2000-04-01

In 95% of right handed individuals the left hemisphere is dominant for speech and language function. The evidence for this is accumulated primarily from clinical populations. We investigated cortical topography of language function and lateralization in a sample of the right handed population using functional magnetic resonance imaging and two lexical-semantic paradigms. Activated cortical language networks were assessed topographically and quantitatively by using a lateralization index. As a group, we observed left hemispheric language dominance. Individually, the lateralization index varied continuously from left hemisphere dominant to bilateral representation. In males, language primarily lateralized to left, and in females, approximately half had left lateralization and the other half had bilateral representation. Our data indicate that a previous view of female bilateral hemispheric dominance for language (McGlone, 1980. Sex differences in human brain asymmetry: a critical survey. Behav Brain Sci 3:215-263; Shaywitz et al., 1995. Sex differences in the functional organization of the brain for language. Nature 373:607-609) simplifies the complexity of cortical language distribution in this population. Analysis of the distribution of the lateralization index in our study allowed us to make this difference in females apparent.

Anatomically constrained dipole adjustment (ANACONDA) for accurate MEG/EEG focal source localizations

NASA Astrophysics Data System (ADS)

Im, Chang-Hwan; Jung, Hyun-Kyo; Fujimaki, Norio

2005-10-01

This paper proposes an alternative approach to enhance localization accuracy of MEG and EEG focal sources. The proposed approach assumes anatomically constrained spatio-temporal dipoles, initial positions of which are estimated from local peak positions of distributed sources obtained from a pre-execution of distributed source reconstruction. The positions of the dipoles are then adjusted on the cortical surface using a novel updating scheme named cortical surface scanning. The proposed approach has many advantages over the conventional ones: (1) as the cortical surface scanning algorithm uses spatio-temporal dipoles, it is robust with respect to noise; (2) it requires no a priori information on the numbers and initial locations of the activations; (3) as the locations of dipoles are restricted only on a tessellated cortical surface, it is physiologically more plausible than the conventional ECD model. To verify the proposed approach, it was applied to several realistic MEG/EEG simulations and practical experiments. From the several case studies, it is concluded that the anatomically constrained dipole adjustment (ANACONDA) approach will be a very promising technique to enhance accuracy of focal source localization which is essential in many clinical and neurological applications of MEG and EEG.

Evaluation of a Class of Simple and Effective Uncertainty Methods for Sparse Samples of Random Variables and Functions

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

Romero, Vicente; Bonney, Matthew; Schroeder, Benjamin

When very few samples of a random quantity are available from a source distribution of unknown shape, it is usually not possible to accurately infer the exact distribution from which the data samples come. Under-estimation of important quantities such as response variance and failure probabilities can result. For many engineering purposes, including design and risk analysis, we attempt to avoid under-estimation with a strategy to conservatively estimate (bound) these types of quantities -- without being overly conservative -- when only a few samples of a random quantity are available from model predictions or replicate experiments. This report examines a classmore » of related sparse-data uncertainty representation and inference approaches that are relatively simple, inexpensive, and effective. Tradeoffs between the methods' conservatism, reliability, and risk versus number of data samples (cost) are quantified with multi-attribute metrics use d to assess method performance for conservative estimation of two representative quantities: central 95% of response; and 10 -4 probability of exceeding a response threshold in a tail of the distribution. Each method's performance is characterized with 10,000 random trials on a large number of diverse and challenging distributions. The best method and number of samples to use in a given circumstance depends on the uncertainty quantity to be estimated, the PDF character, and the desired reliability of bounding the true value. On the basis of this large data base and study, a strategy is proposed for selecting the method and number of samples for attaining reasonable credibility levels in bounding these types of quantities when sparse samples of random variables or functions are available from experiments or simulations.« less

Cortical networks dynamically emerge with the interplay of slow and fast oscillations for memory of a natural scene.

PubMed

Mizuhara, Hiroaki; Sato, Naoyuki; Yamaguchi, Yoko

2015-05-01

Neural oscillations are crucial for revealing dynamic cortical networks and for serving as a possible mechanism of inter-cortical communication, especially in association with mnemonic function. The interplay of the slow and fast oscillations might dynamically coordinate the mnemonic cortical circuits to rehearse stored items during working memory retention. We recorded simultaneous EEG-fMRI during a working memory task involving a natural scene to verify whether the cortical networks emerge with the neural oscillations for memory of the natural scene. The slow EEG power was enhanced in association with the better accuracy of working memory retention, and accompanied cortical activities in the mnemonic circuits for the natural scene. Fast oscillation showed a phase-amplitude coupling to the slow oscillation, and its power was tightly coupled with the cortical activities for representing the visual images of natural scenes. The mnemonic cortical circuit with the slow neural oscillations would rehearse the distributed natural scene representations with the fast oscillation for working memory retention. The coincidence of the natural scene representations could be obtained by the slow oscillation phase to create a coherent whole of the natural scene in the working memory. Copyright © 2015 Elsevier Inc. All rights reserved.

Stress distribution of various designs of prostheses on short implants or standard implants in posterior maxilla: a three dimensional finite element analysis

PubMed Central

JOMJUNYONG, K.; RUNGSIYAKULL, P.; RUNGSIYAKULL, C.; AUNMEUNGTONG, W.; CHANTARAMUNGKORN, M.; KHONGKHUNTHIAN, P.

2017-01-01

SUMMARY Introduction. Although many previous studies have reported on the high success rate of short dental implants, prosthetic design still plays an important role in the long-term implant treatment results. This study aims to evaluate stress distribution characteristics involved with various prosthetic designs on standard implants or short implants in the posterior maxilla. Materials and methods. Six finite element models were simulated representing the missing first and second maxillary molars. A standard implant (PW+ implant: 5.0×10 mm) and a short implant (PW+ implant: 5.0×6.0 mm) were applied under the various prosthetic conditions. The peri-implant maximum bone stress (V on mises stress) was evaluated when 200 N 30° oblique load was applied. A type III bone was approximated and complete osseous integration was assumed. Results. Maximum Von mises stress was numerically located at the cortical bone around the implant neck in all models. In every standard implant model shows better stress distribution. Stress values and concentration area decreased in the cortical and cancellous bone when implants were splinted in both the standard and short implant models. With regard to the non-replacing second molar models found that the area of stress at the cortical bone around the first molar implant to be more intensive. Moreover, in the non-replacing second molar models, the stress also spread to the second pre-molar in both the standard and short implant models. Conclusions. The length of the implant and prosthetics designs both affect the stress value and distribution of stress to the cortical and cancellous bones around the implant. PMID:29682254

Change in cortical bone density and its distribution differs between boys and girls during puberty.

PubMed

Kontulainen, Saija A; Macdonald, Heather M; McKay, Heather A

2006-07-01

Postmenarchal girls and premenopausal women have 3-4% higher cortical bone density (CoD, milligrams per cubic centimeter), compared with postpubertal boys and men, respectively. Females' denser cortical bone is thought to serve as a calcium reservoir for reproductive needs. However, prospective data are lacking that describe CoD development and bone mineral density distribution during puberty in both sexes. Thus, our objectives were to assess maturity and sex differences in the 20-month change of CoD and radial distribution of bone mineral density (RDBMD, milligrams per cubic centimeter) in early-, peri-, and postpubertal girls and boys. Maturity groups were based on change in menarcheal status (girls, n = 68) and pubic hair stage (Tanner) (boys, n = 59). Peripheral quantitative computed tomography was used to measure CoD and RDBMD at the tibial middiaphysis. The increase in average CoD was 1.9% [22.8 mg/cm(3); 95% confidence interval (CI), 10-36], 2.8% (33.8 mg/cm(3); 95% CI, 21-47), and 1.5% (55.0 mg/cm(3); 95% CI, 17-93) greater in early, peri-, and postpubertal girls, compared with boys, respectively. Analysis of RDBMD revealed that the change in density distribution varied across pubertal groups in girls. Across puberty, all girls showed an increase in the high density midcortical region, whereas only peripubertal girls showed an increase in the lower density subcortical region. A sex-difference in RDBMD change was noted within early and peripubertal groups. Our findings of sexual dimorphism in CoD development give support to the hypothesis that female bone deposits calcium for reproductive needs by consolidation of cortical bone during puberty.

A cortical integrate-and-fire neural network model for blind decoding of visual prosthetic stimulation.

PubMed

Eiber, Calvin D; Morley, John W; Lovell, Nigel H; Suaning, Gregg J

2014-01-01

We present a computational model of the optic pathway which has been adapted to simulate cortical responses to visual-prosthetic stimulation. This model reproduces the statistically observed distributions of spikes for cortical recordings of sham and maximum-intensity stimuli, while simultaneously generating cellular receptive fields consistent with those observed using traditional visual neuroscience methods. By inverting this model to generate candidate phosphenes which could generate the responses observed to novel stimulation strategies, we hope to aid the development of said strategies in-vivo before being deployed in clinical settings.

Beamforming approaches for untethered, ultrasonic neural dust motes for cortical recording: a simulation study.

PubMed

Bertrand, Alexander; Seo, Dongjin; Maksimovic, Filip; Carmena, Jose M; Maharbiz, Michel M; Alon, Elad; Rabaey, Jan M

2014-01-01

In this paper, we examine the use of beamforming techniques to interrogate a multitude of neural implants in a distributed, ultrasound-based intra-cortical recording platform known as Neural Dust. We propose a general framework to analyze system design tradeoffs in the ultrasonic beamformer that extracts neural signals from modulated ultrasound waves that are backscattered by free-floating neural dust (ND) motes. Simulations indicate that high-resolution linearly-constrained minimum variance beamforming sufficiently suppresses interference from unselected ND motes and can be incorporated into the ND-based cortical recording system.

Cortical Entropy, Mutual Information and Scale-Free Dynamics in Waking Mice.

PubMed

Fagerholm, Erik D; Scott, Gregory; Shew, Woodrow L; Song, Chenchen; Leech, Robert; Knöpfel, Thomas; Sharp, David J

2016-10-01

Some neural circuits operate with simple dynamics characterized by one or a few well-defined spatiotemporal scales (e.g. central pattern generators). In contrast, cortical neuronal networks often exhibit richer activity patterns in which all spatiotemporal scales are represented. Such "scale-free" cortical dynamics manifest as cascades of activity with cascade sizes that are distributed according to a power-law. Theory and in vitro experiments suggest that information transmission among cortical circuits is optimized by scale-free dynamics. In vivo tests of this hypothesis have been limited by experimental techniques with insufficient spatial coverage and resolution, i.e., restricted access to a wide range of scales. We overcame these limitations by using genetically encoded voltage imaging to track neural activity in layer 2/3 pyramidal cells across the cortex in mice. As mice recovered from anesthesia, we observed three changes: (a) cortical information capacity increased, (b) information transmission among cortical regions increased and (c) neural activity became scale-free. Our results demonstrate that both information capacity and information transmission are maximized in the awake state in cortical regions with scale-free network dynamics. © The Author 2016. Published by Oxford University Press.

White matter stimulation for the treatment of epilepsy.

PubMed

Girgis, Fady; Miller, Jonathan P

2016-04-01

Electrical stimulation in the treatment of epilepsy has been tried in numerous forms and with a variety of targets. Some of these, such as anterior thalamic stimulation, responsive cortical stimulation, and vagal nerve stimulation, have shown promise. A relatively novel concept, that of white matter stimulation, offers a different mechanism in that a small population of stimulated axons can transmit current to a large population of epileptogenic neurons. In theory, this allows for the modulation of seizure circuits and neural networks using lower stimulation volumes. Although clinical data is currently sparse, we review the relevant studies pertaining to white matter stimulation in epilepsy thus far, and offer explanations as to its effects, potential advantages, and utility. Copyright © 2016 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Sparse matrix methods research using the CSM testbed software system

NASA Technical Reports Server (NTRS)

Chu, Eleanor; George, J. Alan

1989-01-01

Research is described on sparse matrix techniques for the Computational Structural Mechanics (CSM) Testbed. The primary objective was to compare the performance of state-of-the-art techniques for solving sparse systems with those that are currently available in the CSM Testbed. Thus, one of the first tasks was to become familiar with the structure of the testbed, and to install some or all of the SPARSPAK package in the testbed. A suite of subroutines to extract from the data base the relevant structural and numerical information about the matrix equations was written, and all the demonstration problems distributed with the testbed were successfully solved. These codes were documented, and performance studies comparing the SPARSPAK technology to the methods currently in the testbed were completed. In addition, some preliminary studies were done comparing some recently developed out-of-core techniques with the performance of the testbed processor INV.

Distribution of model uncertainty across multiple data streams

NASA Astrophysics Data System (ADS)

Wutzler, Thomas

2014-05-01

When confronting biogeochemical models with a diversity of observational data streams, we are faced with the problem of weighing the data streams. Without weighing or multiple blocked cost functions, model uncertainty is allocated to the sparse data streams and possible bias in processes that are strongly constraint is exported to processes that are constrained by sparse data streams only. In this study we propose an approach that aims at making model uncertainty a factor of observations uncertainty, that is constant over all data streams. Further we propose an implementation based on Monte-Carlo Markov chain sampling combined with simulated annealing that is able to determine this variance factor. The method is exemplified both with very simple models, artificial data and with an inversion of the DALEC ecosystem carbon model against multiple observations of Howland forest. We argue that the presented approach is able to help and maybe resolve the problem of bias export to sparse data streams.

Age of language acquisition and cortical language organization in multilingual patients undergoing awake brain mapping.

PubMed

Fernández-Coello, Alejandro; Havas, Viktória; Juncadella, Montserrat; Sierpowska, Joanna; Rodríguez-Fornells, Antoni; Gabarrós, Andreu

2017-06-01

OBJECTIVE Most knowledge regarding the anatomical organization of multilingualism is based on aphasiology and functional imaging studies. However, the results have still to be validated by the gold standard approach, namely electrical stimulation mapping (ESM) during awake neurosurgical procedures. In this ESM study the authors describe language representation in a highly specific group of 13 multilingual individuals, focusing on how age of acquisition may influence the cortical organization of language. METHODS Thirteen patients who had a high degree of proficiency in multiple languages and were harboring lesions within the dominant, left hemisphere underwent ESM while being operated on under awake conditions. Demographic and language data were recorded in relation to age of language acquisition (for native languages and early- and late-acquired languages), neuropsychological pre- and postoperative language testing, the number and location of language sites, and overlapping distribution in terms of language acquisition time. Lesion growth patterns and histopathological characteristics, location, and size were also recorded. The distribution of language sites was analyzed with respect to age of acquisition and overlap. RESULTS The functional language-related sites were distributed in the frontal (55%), temporal (29%), and parietal lobes (16%). The total number of native language sites was 47. Early-acquired languages (including native languages) were represented in 97 sites (55 overlapped) and late-acquired languages in 70 sites (45 overlapped). The overlapping distribution was 20% for early-early, 71% for early-late, and 9% for late-late. The average lesion size (maximum diameter) was 3.3 cm. There were 5 fast-growing and 7 slow-growing lesions. CONCLUSIONS Cortical language distribution in multilingual patients is not homogeneous, and it is influenced by age of acquisition. Early-acquired languages have a greater cortical representation than languages acquired later. The prevalent native and early-acquired languages are largely represented within the perisylvian left hemisphere frontoparietotemporal areas, and the less prevalent late-acquired languages are mostly overlapped with them.

A transition in brain state during propofol-induced unconsciousness.

PubMed

Mukamel, Eran A; Pirondini, Elvira; Babadi, Behtash; Wong, Kin Foon Kevin; Pierce, Eric T; Harrell, P Grace; Walsh, John L; Salazar-Gomez, Andres F; Cash, Sydney S; Eskandar, Emad N; Weiner, Veronica S; Brown, Emery N; Purdon, Patrick L

2014-01-15

Rhythmic oscillations shape cortical dynamics during active behavior, sleep, and general anesthesia. Cross-frequency phase-amplitude coupling is a prominent feature of cortical oscillations, but its role in organizing conscious and unconscious brain states is poorly understood. Using high-density EEG and intracranial electrocorticography during gradual induction of propofol general anesthesia in humans, we discovered a rapid drug-induced transition between distinct states with opposite phase-amplitude coupling and different cortical source distributions. One state occurs during unconsciousness and may be similar to sleep slow oscillations. A second state occurs at the loss or recovery of consciousness and resembles an enhanced slow cortical potential. These results provide objective electrophysiological landmarks of distinct unconscious brain states, and could be used to help improve EEG-based monitoring for general anesthesia.

[A study of complexity and power spectrum of cortical EEG and hippocampal potential in rats under different behavioral states].

PubMed

Feng, Zhou-yan; Zheng, Xiao-xiang

2002-08-01

Objective. To study the complexity and the power spectrum of cortical EEG and hippocampal potential in rats under waking and sleep states. Method. Cortical EEG and hippocampal potential were collected by implanted electrodes in freely moving rats. Algorithmic complexity (Kc), approximate entropy (ApEn), power spectral density (PSD) and gravity frequency of PSD of the potential waves were calculated. Result. The complexity of hippocampal potential was higher than that of cortical EEG under every state. The complexity of cortical EEG was lowest under the state of non rapid eye movement (NREM) sleep. The complexity of hippocampal potential was highest under waking state. The total power of both potentials in 0.5- 30 Hz frequency band showed their highest values under NREM state. Conclusion. The values of Kc and ApEn are closely related to the distributions of PSD. When there are evident peaks in PSD, the complexities of signals will decrease. The complexities may be used to distinguish the difference between cortical EEG and hippocampal potential, or large differences between the same kind of potentials under different behavioral states.

Stereovision-based integrated system for point cloud reconstruction and simulated brain shift validation.

PubMed

Yang, Xiaochen; Clements, Logan W; Luo, Ma; Narasimhan, Saramati; Thompson, Reid C; Dawant, Benoit M; Miga, Michael I

2017-07-01

Intraoperative soft tissue deformation, referred to as brain shift, compromises the application of current image-guided surgery navigation systems in neurosurgery. A computational model driven by sparse data has been proposed as a cost-effective method to compensate for cortical surface and volumetric displacements. We present a mock environment developed to acquire stereoimages from a tracked operating microscope and to reconstruct three-dimensional point clouds from these images. A reconstruction error of 1 mm is estimated by using a phantom with a known geometry and independently measured deformation extent. The microscope is tracked via an attached tracking rigid body that facilitates the recording of the position of the microscope via a commercial optical tracking system as it moves during the procedure. Point clouds, reconstructed under different microscope positions, are registered into the same space to compute the feature displacements. Using our mock craniotomy device, realistic cortical deformations are generated. When comparing our tracked microscope stereo-pair measure of mock vessel displacements to that of the measurement determined by the independent optically tracked stylus marking, the displacement error was [Formula: see text] on average. These results demonstrate the practicality of using tracked stereoscopic microscope as an alternative to laser range scanners to collect sufficient intraoperative information for brain shift correction.

Inference of the sparse kinetic Ising model using the decimation method

NASA Astrophysics Data System (ADS)

Decelle, Aurélien; Zhang, Pan

2015-05-01

In this paper we study the inference of the kinetic Ising model on sparse graphs by the decimation method. The decimation method, which was first proposed in Decelle and Ricci-Tersenghi [Phys. Rev. Lett. 112, 070603 (2014), 10.1103/PhysRevLett.112.070603] for the static inverse Ising problem, tries to recover the topology of the inferred system by setting the weakest couplings to zero iteratively. During the decimation process the likelihood function is maximized over the remaining couplings. Unlike the ℓ1-optimization-based methods, the decimation method does not use the Laplace distribution as a heuristic choice of prior to select a sparse solution. In our case, the whole process can be done auto-matically without fixing any parameters by hand. We show that in the dynamical inference problem, where the task is to reconstruct the couplings of an Ising model given the data, the decimation process can be applied naturally into a maximum-likelihood optimization algorithm, as opposed to the static case where pseudolikelihood method needs to be adopted. We also use extensive numerical studies to validate the accuracy of our methods in dynamical inference problems. Our results illustrate that, on various topologies and with different distribution of couplings, the decimation method outperforms the widely used ℓ1-optimization-based methods.

Microbleeds in postmortem brains of patients with Alzheimer disease: a T2*-weighted gradient-echo 7.0 T magnetic resonance imaging study.

PubMed

De Reuck, Jacques L; Cordonnier, Charlotte; Deramecourt, Vincent; Auger, Florent; Durieux, Nicolas; Bordet, Regis; Maurage, Claude-Alain; Leys, Didier; Pasquier, Florence

2013-01-01

This study aims to determine the distribution and to quantify microbleeds (MBs) in postmortem brains of patients with Alzheimer disease (AD) on T2*-weighted gradient-echo 7.0 T magnetic resonance imaging. Twenty-eight AD brains were compared with 5 controls. The AD brains were subdivided further: 18 without and 10 with additional severe cerebral amyloid angiopathy (AD-CAA). The distribution and the number of cortical focal signal intensity losses, representing MBs, were assessed on coronal sections at the frontal, the central, and the occipital level of a cerebral hemisphere. MBs prevailed in the central sections (P=0.005) of AD brains without CAA, whereas in AD-CAA brains, they were more frequent in all coronal sections (P≤0.002). They prevailed in the deep cortical layers of the AD brains and of the controls (P≤0.03). They were significantly increased in all cortical layers of the AD-CAA brains (P≤0.04), compared with the controls. MBs prevalence in brains of AD patients had a different topographic distribution according to the absence or presence of severe CAA.

Effects of methylmercury on muscarinic receptors in the mouse brain: A quantitative autoradiographic study

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

Lee, Haesung; Yee, S.; Geddes, J.

1991-03-11

Methylmercury (MeHg) is reported to inhibit several stages of cholinergic neurotransmission in brain tissue in-vitro and in-vivo. To examine whether or not behavioral disturbances and/or selective vulnerability of specific neuronal groups in MeHg poisoning may be related to MeHg effects on cholinergic receptors in specific regions of the brain, the density and distribution of muscarinic receptors in the brains of C57BL/6J mice were determined following repeated injections of 5 mg/kg of methylmercuric chloride (MMC). The receptor densities in six cortical laminae of seven cerebral cortical regions, hippocampus and striatum were quantitated by computer-assisted imaging system following in-vitro labeling with ({supmore » 3}H)-pirenzepine (M1) and ({sup 3}H)N-methyl scopolamine (M2). The results showed heterogeneous distribution of M1 and M2 sites in different regions of the brain, and significant reduction in the density of both receptor subtypes following MeHg poisoning in many cortical and subcortical regions. However, the changes in the density were variable in different laminae even in the same cortical regions. Prominent reductions in M1 densities were noted in the temporal and entorhinal cortices, CA3 and hilar regions of the hippocampus as compared to control, whereas the reduction in M2 receptor density was most prominently noted in the frontal, perirhinal and entorhinal cortices, and CA1 and hilar regions of the hippocampus. Thus, it is apparent that MeHg significantly affects muscarinic receptors in the mouse brain, and that these data when used in conjunction with immunocytochemical and other morphological studies would provide further insights into the mechanisms of neurotoxic effects of MeHg.« less

Effect of Integration Patterns Around Implant Neck on Stress Distribution in Peri-Implant Bone: A Finite Element Analysis.

PubMed

Han, Jingyun; Sun, Yuchun; Wang, Chao

2017-08-01

To investigate the biomechanical performance of different osseointegration patterns between cortical bone and implants using finite element analysis. Fifteen finite element models were constructed of the mandibular fixed prosthesis supported by implants. Masticatory loads (200 N axial, 100 N oblique, 40 N horizontal) were applied. The cortical bone/implant interface was divided equally into four layers: upper, upper-middle, lower-middle, and lower. The bone stress and implant displacement were calculated for 5 degrees of uniform integration (0, 20%, 40%, 60%, and 100%) and 10 integration patterns. The stress was concentrated in the bone margin and gradually decreased as osseointegration progressed, when the integrated and nonintegrated areas were alternated on the bone-implant surface. Compared with full integration, the integration of only the lower-middle layer or lower half layers significantly decreased von Mises, tensile, and compressive stresses in cortical bone under oblique and horizontal loads, and these patterns did not induce higher stress in the cancellous bone. For the integration of only the upper or upper-middle layer, stress in the cortical and cancellous bones significantly increased and was considerably higher than in the case of nonintegration. In addition, the maximum stress in the cortical bone was sensitive to the quantity of integrated nodes at the bone margin; lower quantity was associated with higher stress. There was no significant difference in the displacement of implants among 15 models. Integration patterns of cortical bone significantly affect stress distribution in peri-implant bone. The integration of only the lower-middle or lower half layers helps to increase the load-bearing capacity of peri-implant bone and decrease the risk of overloading, while upper integration may further increase the risk of bone resorption. © 2016 by the American College of Prosthodontists.

A manual for PARTI runtime primitives, revision 1

NASA Technical Reports Server (NTRS)

Das, Raja; Saltz, Joel; Berryman, Harry

1991-01-01

Primitives are presented that are designed to help users efficiently program irregular problems (e.g., unstructured mesh sweeps, sparse matrix codes, adaptive mesh partial differential equations solvers) on distributed memory machines. These primitives are also designed for use in compilers for distributed memory multiprocessors. Communications patterns are captured at runtime, and the appropriate send and receive messages are automatically generated.

Soil carbon distribution in Alaska in relation to soil-forming factors

Treesearch

Kristofer D. Johnson; Jennifer Harden; A. David McGuire; Norman B. Bliss; James G. Bockheim; Mark Clark; Teresa Nettleton-Hollingsworth; M. Torre Jorgenson; Evan S. Kane; Michelle Mack; Johathan ODonnell; Chien-Lu Ping; Edward A.G. Schuur; Merritt R. Turetsky; David W. Valentine

2011-01-01

The direction and magnitude of soil organic carbon (SOC) changes in response to climate change remain unclear and depend on the spatial distribution of SOC across landscapes. Uncertainties regarding the fate of SOC are greater in high-latitude systems where data are sparse and the soils are affected by sub-zero temperatures. To address these issues in Alaska, a first-...

Neural network classifications and correlation analysis of EEG and MEG activity accompanying spontaneous reversals of the Necker cube.

PubMed

Gaetz, M; Weinberg, H; Rzempoluck, E; Jantzen, K J

1998-04-01

It has recently been suggested that reentrant connections are essential in systems that process complex information [A. Damasio, H. Damasio, Cortical systems for the retrieval of concrete knowledge: the convergence zone framework, in: C. Koch, J.L. Davis (Eds.), Large Scale Neuronal Theories of the Brain, The MIT Press, Cambridge, 1995, pp. 61-74; G. Edelman, The Remembered Present, Basic Books, New York, 1989; M.I. Posner, M. Rothbart, Constructing neuronal theories of mind, in: C. Koch, J.L. Davis (Eds.), Large Scale Neuronal Theories of the Brain, The MIT Press, Cambridge, 1995, pp. 183-199; C. von der Malsburg, W. Schneider, A neuronal cocktail party processor, Biol. Cybem., 54 (1986) 29-40]. Reentry is not feedback, but parallel signalling in the time domain between spatially distributed maps, similar to a process of correlation between distributed systems. Accordingly, it was expected that during spontaneous reversals of the Necker cube, complex patterns of correlations between distributed systems would be present in the cortex. The present study included EEG (n=4) and MEG recordings (n=5). Two experimental questions were posed: (1) Can distributed cortical patterns present during perceptual reversals be classified differently using a generalised regression neural network (GRNN) compared to processing of a two-dimensional figure? (2) Does correlated cortical activity increase significantly during perception of a Necker cube reversal? One-second duration single trials of EEG and MEG data were analysed using the GRNN. Electrode/sensor pairings based on cortico-cortical connections were selected to assess correlated activity in each condition. The GRNN significantly classified single trials recorded during Necker cube reversals as different from single trials recorded during perception of a two-dimensional figure for both EEG and MEG. In addition, correlated cortical activity increased significantly in the Necker cube reversal condition for EEG and MEG compared to the perception of a non-reversing stimulus. Coherent MEG activity observed over occipital, parietal and temporal regions is believed to represent neural systems related to the perception of Necker cube reversals. Copyright 1998 Elsevier Science B.V.

Laminar-specific distribution of zinc: evidence for presence of layer IV in forelimb motor cortex in the rat.

PubMed

Alaverdashvili, Mariam; Hackett, Mark J; Pickering, Ingrid J; Paterson, Phyllis G

2014-12-01

The rat is the most widely studied pre-clinical model system of various neurological and neurodegenerative disorders affecting hand function. Although brain injury to the forelimb region of the motor cortex in rats mostly induces behavioral abnormalities in motor control of hand movements, behavioral deficits in the sensory-motor domain are also observed. This questions the prevailing view that cortical layer IV, a recipient of sensory information from the thalamus, is absent in rat motor cortex. Because zinc-containing neurons are generally not found in pathways that run from the thalamus, an absence of zinc (Zn) in a cortical layer would be suggestive of sensory input from the thalamus. To test this hypothesis, we used synchrotron micro X-ray fluorescence imaging to measure Zn distribution across cortical layers. Zn maps revealed a heterogeneous layered Zn distribution in primary and secondary motor cortices of the forelimb region in the adult rat. Two wider bands with elevated Zn content were separated by a narrow band having reduced Zn content, and this was evident in two rat strains. The Zn distribution pattern was comparable to that in sensorimotor cortex, which is known to contain a well demarcated layer IV. Juxtaposition of Zn maps and the images of brain stained for Nissl bodies revealed a "Zn valley" in primary motor cortex, apparently starting at the ventral border of pyramidal layer III and ending at the close vicinity of layer V. This finding indicates the presence of a conspicuous cortical layer between layers III and V, i.e. layer IV, the presence of which previously has been disputed. The results have implications for the use of rat models to investigate human brain function and neuropathology, such as after stroke. The presence of layer IV in the forelimb region of the motor cortex suggests that therapeutic interventions used in rat models of motor cortex injury should target functional abnormalities in both motor and sensory domains. The finding is also critical for future investigation of the biochemical mechanisms through which therapeutic interventions can enhance neural plasticity, particularly through Zn dependent pathways. Copyright © 2014 Elsevier Inc. All rights reserved.

Laminar-specific distribution of zinc: Evidence for presence of layer IV in forelimb motor cortex in the rat

PubMed Central

Alaverdashvili, Mariam; Hackett, Mark J.; Pickering, Ingrid J.; Paterson, Phyllis G.

2015-01-01

The rat is the most widely studied pre-clinical model system of various neurological and neurodegenerative disorders affecting hand function. Although brain injury to the forelimb region of the motor cortex in rats mostly induces behavioral abnormalities in motor control of hand movements, behavioral deficits in the sensory-motor domain are also observed. This questions the prevailing view that cortical layer IV, a recipient of sensory information from the thalamus, is absent in rat motor cortex. Because zinc-containing neurons are generally not found in pathways that run from the thalamus, an absence of zinc (Zn) in a cortical layer would be suggestive of sensory input from the thalamus. To test this hypothesis, we used synchrotron micro X-ray fluorescence imaging to measure Zn distribution across cortical layers. Zn maps revealed a heterogeneous layered Zn distribution in primary and secondary motor cortices of the forelimb region in the adult rat. Two wider bands with elevated Zn content were separated by a narrow band having reduced Zn content, and this was evident in two rat strains. The Zn distribution pattern was comparable to that in sensorimotor cortex, which is known to contain a well demarcated layer IV. Juxtaposition of Zn maps and the images of brain stained for Nissl bodies revealed a “Zn valley” in primary motor cortex, apparently starting at the ventral border of pyramidal layer III and ending at the close vicinity of layer V. This finding indicates the presence of a conspicuous cortical layer between layers III and V, i.e. layer IV, the presence of which previously has been disputed. The results have implications for the use of rat models to investigate human brain function and neuropathology, such as after stroke. The presence of layer IV in the forelimb region of the motor cortex suggests that therapeutic interventions used in rat models of motor cortex injury should target functional abnormalities in both motor and sensory domains. The finding is also critical for future investigation of the biochemical mechanisms through which therapeutic interventions can enhance neural plasticity, particularly through Zn dependent pathways. PMID:25192655

Validation of cortical bone mineral density distribution using micro-computed tomography.

PubMed

Mashiatulla, Maleeha; Ross, Ryan D; Sumner, D Rick

2017-06-01

Changes in the bone mineral density distribution (BMDD), due to disease or drugs, can alter whole bone mechanical properties such as strength, stiffness and toughness. The methods currently available for assessing BMDD are destructive and two-dimensional. Micro-computed tomography (μCT) has been used extensively to quantify the three-dimensional geometry of bone and to measure the mean degree of mineralization, commonly called the tissue mineral density (TMD). The TMD measurement has been validated to ash density; however parameters describing the frequency distribution of TMD have not yet been validated. In the current study we tested the ability of μCT to estimate six BMDD parameters: mean, heterogeneity (assessed by the full-width-at-half-maximum (FWHM) and the coefficient of variation (CoV)), the upper and lower 5% cutoffs of the frequency distribution, and peak mineralization) in rat sized femoral cortical bone samples. We used backscatter scanning electron microscopy (bSEM) as the standard. Aluminum and hydroxyapatite phantoms were used to identify optimal scanner settings (70kVp, and 57μA, with a 1500ms integration time). When using hydroxyapatite samples that spanned a broad range of mineralization levels, high correlations were found between μCT and bSEM for all BMDD parameters (R 2 ≥0.92, p<0.010). When using cortical bone samples from rats and various species machined to mimic rat cortical bone geometry, significant correlations between μCT and bSEM were found for mean mineralization (R 2 =0.65, p<0.001), peak mineralization (R 2 =0.61, p<0.001) the lower 5% cutoff (R 2 =0.62, p<0.001) and the upper 5% cutoff (R 2 =0.33, p=0.021), but not for heterogeneity, measured by FWHM (R 2 =0.05, p=0.412) and CoV (R 2 =0.04, p=0.469). Thus, while mean mineralization and most parameters used to characterize the BMDD can be assessed with μCT in rat sized cortical bone samples, caution should be used when reporting the heterogeneity. Copyright © 2017 Elsevier Inc. All rights reserved.

Characterization of Femoral Component Initial Stability and Cortical Strain in a Reduced Stem-Length Design.

PubMed

Small, Scott R; Hensley, Sarah E; Cook, Paige L; Stevens, Rebecca A; Rogge, Renee D; Meding, John B; Berend, Michael E

2017-02-01

Short-stemmed femoral components facilitate reduced exposure surgical techniques while preserving native bone. A clinically successful stem should ideally reduce risk for stress shielding while maintaining adequate primary stability for biological fixation. We asked (1) how stem-length changes cortical strain distribution in the proximal femur in a fit-and-fill geometry and (2) if short-stemmed components exhibit primary stability on par with clinically successful designs. Cortical strain was assessed via digital image correlation in composite femurs implanted with long, medium, and short metaphyseal fit-and-fill stem designs in a single-leg stance loading model. Strain was compared to a loaded, unimplanted femur. Bone-implant micromotion was then compared with reduced lateral shoulder short stem and short tapered-wedge designs in cyclic axial and torsional testing. Femurs implanted with short-stemmed components exhibited cortical strain response most closely matching that of the intact femur model, theoretically reducing the potential for proximal stress shielding. In micromotion testing, no difference in primary stability was observed as a function of reduced stem length within the same component design. Our findings demonstrate that within this fit-and-fill stem design, reduction in stem length improved proximal cortical strain distribution and maintained axial and torsional stability on par with other stem designs in a composite femur model. Short-stemmed implants may accommodate less invasive surgical techniques while facilitating more physiological femoral loading without sacrificing primary implant stability. Copyright © 2016 Elsevier Inc. All rights reserved.

Predicting Cortical Dark/Bright Asymmetries from Natural Image Statistics and Early Visual Transforms

PubMed Central

Cooper, Emily A.; Norcia, Anthony M.

2015-01-01

The nervous system has evolved in an environment with structure and predictability. One of the ubiquitous principles of sensory systems is the creation of circuits that capitalize on this predictability. Previous work has identified predictable non-uniformities in the distributions of basic visual features in natural images that are relevant to the encoding tasks of the visual system. Here, we report that the well-established statistical distributions of visual features -- such as visual contrast, spatial scale, and depth -- differ between bright and dark image components. Following this analysis, we go on to trace how these differences in natural images translate into different patterns of cortical input that arise from the separate bright (ON) and dark (OFF) pathways originating in the retina. We use models of these early visual pathways to transform natural images into statistical patterns of cortical input. The models include the receptive fields and non-linear response properties of the magnocellular (M) and parvocellular (P) pathways, with their ON and OFF pathway divisions. The results indicate that there are regularities in visual cortical input beyond those that have previously been appreciated from the direct analysis of natural images. In particular, several dark/bright asymmetries provide a potential account for recently discovered asymmetries in how the brain processes visual features, such as violations of classic energy-type models. On the basis of our analysis, we expect that the dark/bright dichotomy in natural images plays a key role in the generation of both cortical and perceptual asymmetries. PMID:26020624

An alternative design for a sparse distributed memory

NASA Technical Reports Server (NTRS)

Jaeckel, Louis A.

1989-01-01

A new design for a Sparse Distributed Memory, called the selected-coordinate design, is described. As in the original design, there are a large number of memory locations, each of which may be activated by many different addresses (binary vectors) in a very large address space. Each memory location is defined by specifying ten selected coordinates (bit positions in the address vectors) and a set of corresponding assigned values, consisting of one bit for each selected coordinate. A memory location is activated by an address if, for all ten of the locations's selected coordinates, the corresponding bits in the address vector match the respective assigned value bits, regardless of the other bits in the address vector. Some comparative memory capacity and signal-to-noise ratio estimates for the both the new and original designs are given. A few possible hardware embodiments of the new design are described.

Distribution of millipedes along an altitudinal gradient in the south of Lake Teletskoye, Altai Mts, Russia (Diplopoda)

PubMed Central

Nefedieva, Julia S.; Nefediev, Pavel S.; Sakhnevich, Miroslava B.; Dyachkov, Yuri V.

2015-01-01

Abstract The distribution of millipedes along an altitudinal gradient in the south of Lake Teletskoye, Altai, Russia based on new samples from the Kyga Profile sites, as well as on partly published and freshly revised material (Mikhaljova et al. 2007, 2008, 2014, Nefedieva and Nefediev 2008, Nefediev and Nefedieva 2013, Nefedieva et al. 2014), is established. The millipede diversity is estimated to be at least 15 species and subspecies from 10 genera, 6 families and three orders. The bulk of species diversity is confined both to low- and mid-mountain chern taiga forests and high-mountain shrub tundras, whereas the highest numbers, reaching up to 130 ind./m², is shown in subalpine Pinus sibirica sparse growths. Based on clustering studied localities on species diversity similarity two groups of sites are defined: low-mountain sites and subalpine sparse growths of Pinus sibirica ones. PMID:26257540

Sparse Bayesian Inference and the Temperature Structure of the Solar Corona

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

Warren, Harry P.; Byers, Jeff M.; Crump, Nicholas A.

Measuring the temperature structure of the solar atmosphere is critical to understanding how it is heated to high temperatures. Unfortunately, the temperature of the upper atmosphere cannot be observed directly, but must be inferred from spectrally resolved observations of individual emission lines that span a wide range of temperatures. Such observations are “inverted” to determine the distribution of plasma temperatures along the line of sight. This inversion is ill posed and, in the absence of regularization, tends to produce wildly oscillatory solutions. We introduce the application of sparse Bayesian inference to the problem of inferring the temperature structure of themore » solar corona. Within a Bayesian framework a preference for solutions that utilize a minimum number of basis functions can be encoded into the prior and many ad hoc assumptions can be avoided. We demonstrate the efficacy of the Bayesian approach by considering a test library of 40 assumed temperature distributions.« less

Sparse distributed memory and related models

NASA Technical Reports Server (NTRS)

Kanerva, Pentti

1992-01-01

Described here is sparse distributed memory (SDM) as a neural-net associative memory. It is characterized by two weight matrices and by a large internal dimension - the number of hidden units is much larger than the number of input or output units. The first matrix, A, is fixed and possibly random, and the second matrix, C, is modifiable. The SDM is compared and contrasted to (1) computer memory, (2) correlation-matrix memory, (3) feet-forward artificial neural network, (4) cortex of the cerebellum, (5) Marr and Albus models of the cerebellum, and (6) Albus' cerebellar model arithmetic computer (CMAC). Several variations of the basic SDM design are discussed: the selected-coordinate and hyperplane designs of Jaeckel, the pseudorandom associative neural memory of Hassoun, and SDM with real-valued input variables by Prager and Fallside. SDM research conducted mainly at the Research Institute for Advanced Computer Science (RIACS) in 1986-1991 is highlighted.

Label-free optical imaging of membrane patches for atomic force microscopy

PubMed Central

Churnside, Allison B.; King, Gavin M.; Perkins, Thomas T.

2010-01-01

In atomic force microscopy (AFM), finding sparsely distributed regions of interest can be difficult and time-consuming. Typically, the tip is scanned until the desired object is located. This process can mechanically or chemically degrade the tip, as well as damage fragile biological samples. Protein assemblies can be detected using the back-scattered light from a focused laser beam. We previously used back-scattered light from a pair of laser foci to stabilize an AFM. In the present work, we integrate these techniques to optically image patches of purple membranes prior to AFM investigation. These rapidly acquired optical images were aligned to the subsequent AFM images to ~40 nm, since the tip position was aligned to the optical axis of the imaging laser. Thus, this label-free imaging efficiently locates sparsely distributed protein assemblies for subsequent AFM study while simultaneously minimizing degradation of the tip and the sample. PMID:21164738

Specification of Cortical Parenchyma and Stele of Maize Primary Roots by Asymmetric Levels of Auxin, Cytokinin, and Cytokinin-Regulated Proteins1[C][W][OA

PubMed Central

Saleem, Muhammad; Lamkemeyer, Tobias; Schützenmeister, André; Madlung, Johannes; Sakai, Hajime; Piepho, Hans-Peter; Nordheim, Alfred; Hochholdinger, Frank

2010-01-01

In transverse orientation, maize (Zea mays) roots are composed of a central stele that is embedded in multiple layers of cortical parenchyma. The stele functions in the transport of water, nutrients, and photosynthates, while the cortical parenchyma fulfills metabolic functions that are not very well characterized. To better understand the molecular functions of these root tissues, protein- and phytohormone-profiling experiments were conducted. Two-dimensional gel electrophoresis combined with electrospray ionization tandem mass spectrometry identified 59 proteins that were preferentially accumulated in the cortical parenchyma and 11 stele-specific proteins. Hormone profiling revealed preferential accumulation of indole acetic acid and its conjugate indole acetic acid-aspartate in the stele and predominant localization of the cytokinin cis-zeatin, its precursor cis-zeatin riboside, and its conjugate cis-zeatin O-glucoside in the cortical parenchyma. A root-specific β-glucosidase that functions in the hydrolysis of cis-zeatin O-glucoside was preferentially accumulated in the cortical parenchyma. Similarly, four enzymes involved in ammonium assimilation that are regulated by cytokinin were preferentially accumulated in the cortical parenchyma. The antagonistic distribution of auxin and cytokinin in the stele and cortical parenchyma, together with the cortical parenchyma-specific accumulation of cytokinin-regulated proteins, suggest a molecular framework that specifies the function of these root tissues that also play a role in the formation of lateral roots from pericycle and endodermis cells. PMID:19933382

The Mouse Cortical Connectome, Characterized by an Ultra-Dense Cortical Graph, Maintains Specificity by Distinct Connectivity Profiles.

PubMed

Gămănuţ, Răzvan; Kennedy, Henry; Toroczkai, Zoltán; Ercsey-Ravasz, Mária; Van Essen, David C; Knoblauch, Kenneth; Burkhalter, Andreas

2018-02-07

The inter-areal wiring pattern of the mouse cerebral cortex was analyzed in relation to a refined parcellation of cortical areas. Twenty-seven retrograde tracer injections were made in 19 areas of a 47-area parcellation of the mouse neocortex. Flat mounts of the cortex and multiple histological markers enabled detailed counts of labeled neurons in individual areas. The observed log-normal distribution of connection weights to each cortical area spans 5 orders of magnitude and reveals a distinct connectivity profile for each area, analogous to that observed in macaques. The cortical network has a density of 97%, considerably higher than the 66% density reported in macaques. A weighted graph analysis reveals a similar global efficiency but weaker spatial clustering compared with that reported in macaques. The consistency, precision of the connectivity profile, density, and weighted graph analysis of the present data differ significantly from those obtained in earlier studies in the mouse. Copyright © 2017 Elsevier Inc. All rights reserved.

Opioid and orexin hedonic hotspots in rat orbitofrontal cortex and insula

PubMed Central

Castro, Daniel C.; Berridge, Kent C.

2017-01-01

Hedonic hotspots are brain sites where particular neurochemical stimulations causally amplify the hedonic impact of sensory rewards, such as “liking” for sweetness. Here, we report the mapping of two hedonic hotspots in cortex, where mu opioid or orexin stimulations enhance the hedonic impact of sucrose taste. One hedonic hotspot was found in anterior orbitofrontal cortex (OFC), and another was found in posterior insula. A suppressive hedonic coldspot was also found in the form of an intervening strip stretching from the posterior OFC through the anterior and middle insula, bracketed by the two cortical hotspots. Opioid/orexin stimulations in either cortical hotspot activated Fos throughout a distributed “hedonic circuit” involving cortical and subcortical structures. Conversely, cortical coldspot stimulation activated circuitry for “hedonic suppression.” Finally, food intake was increased by stimulations at several prefrontal cortical sites, indicating that the anatomical substrates in cortex for enhancing the motivation to eat are discriminable from those for hedonic impact. PMID:29073109

Cortical network architecture for context processing in primate brain

PubMed Central

Chao, Zenas C; Nagasaka, Yasuo; Fujii, Naotaka

2015-01-01

Context is information linked to a situation that can guide behavior. In the brain, context is encoded by sensory processing and can later be retrieved from memory. How context is communicated within the cortical network in sensory and mnemonic forms is unknown due to the lack of methods for high-resolution, brain-wide neuronal recording and analysis. Here, we report the comprehensive architecture of a cortical network for context processing. Using hemisphere-wide, high-density electrocorticography, we measured large-scale neuronal activity from monkeys observing videos of agents interacting in situations with different contexts. We extracted five context-related network structures including a bottom-up network during encoding and, seconds later, cue-dependent retrieval of the same network with the opposite top-down connectivity. These findings show that context is represented in the cortical network as distributed communication structures with dynamic information flows. This study provides a general methodology for recording and analyzing cortical network neuronal communication during cognition. DOI: http://dx.doi.org/10.7554/eLife.06121.001 PMID:26416139

Three Types of Cortical L5 Neurons that Differ in Brain-Wide Connectivity and Function

PubMed Central

Kim, Euiseok J.; Juavinett, Ashley L.; Kyubwa, Espoir M.; Jacobs, Matthew W.; Callaway, Edward M.

2015-01-01

SUMMARY Cortical layer 5 (L5) pyramidal neurons integrate inputs from many sources and distribute outputs to cortical and subcortical structures. Previous studies demonstrate two L5 pyramid types: cortico-cortical (CC) and cortico-subcortical (CS). We characterize connectivity and function of these cell types in mouse primary visual cortex and reveal a new subtype. Unlike previously described L5 CC and CS neurons, this new subtype does not project to striatum [cortico-cortical, non-striatal (CC-NS)] and has distinct morphology, physiology and visual responses. Monosynaptic rabies tracing reveals that CC neurons preferentially receive input from higher visual areas, while CS neurons receive more input from structures implicated in top-down modulation of brain states. CS neurons are also more direction-selective and prefer faster stimuli than CC neurons. These differences suggest distinct roles as specialized output channels, with CS neurons integrating information and generating responses more relevant to movement control and CC neurons being more important in visual perception. PMID:26671462

Three Types of Cortical Layer 5 Neurons That Differ in Brain-wide Connectivity and Function.

PubMed

Kim, Euiseok J; Juavinett, Ashley L; Kyubwa, Espoir M; Jacobs, Matthew W; Callaway, Edward M

2015-12-16

Cortical layer 5 (L5) pyramidal neurons integrate inputs from many sources and distribute outputs to cortical and subcortical structures. Previous studies demonstrate two L5 pyramid types: cortico-cortical (CC) and cortico-subcortical (CS). We characterize connectivity and function of these cell types in mouse primary visual cortex and reveal a new subtype. Unlike previously described L5 CC and CS neurons, this new subtype does not project to striatum [cortico-cortical, non-striatal (CC-NS)] and has distinct morphology, physiology, and visual responses. Monosynaptic rabies tracing reveals that CC neurons preferentially receive input from higher visual areas, while CS neurons receive more input from structures implicated in top-down modulation of brain states. CS neurons are also more direction-selective and prefer faster stimuli than CC neurons. These differences suggest distinct roles as specialized output channels, with CS neurons integrating information and generating responses more relevant to movement control and CC neurons being more important in visual perception. Copyright © 2015 Elsevier Inc. All rights reserved.

Sparse orthogonal population representation of spatial context in the retrosplenial cortex.

PubMed

Mao, Dun; Kandler, Steffen; McNaughton, Bruce L; Bonin, Vincent

2017-08-15

Sparse orthogonal coding is a key feature of hippocampal neural activity, which is believed to increase episodic memory capacity and to assist in navigation. Some retrosplenial cortex (RSC) neurons convey distributed spatial and navigational signals, but place-field representations such as observed in the hippocampus have not been reported. Combining cellular Ca 2+ imaging in RSC of mice with a head-fixed locomotion assay, we identified a population of RSC neurons, located predominantly in superficial layers, whose ensemble activity closely resembles that of hippocampal CA1 place cells during the same task. Like CA1 place cells, these RSC neurons fire in sequences during movement, and show narrowly tuned firing fields that form a sparse, orthogonal code correlated with location. RSC 'place' cell activity is robust to environmental manipulations, showing partial remapping similar to that observed in CA1. This population code for spatial context may assist the RSC in its role in memory and/or navigation.Neurons in the retrosplenial cortex (RSC) encode spatial and navigational signals. Here the authors use calcium imaging to show that, similar to the hippocampus, RSC neurons also encode place cell-like activity in a sparse orthogonal representation, partially anchored to the allocentric cues on the linear track.

Using an Android application to assess registration strategies in open hepatic procedures: a planning and simulation tool

NASA Astrophysics Data System (ADS)

Doss, Derek J.; Heiselman, Jon S.; Collins, Jarrod A.; Weis, Jared A.; Clements, Logan W.; Geevarghese, Sunil K.; Miga, Michael I.

2017-03-01

Sparse surface digitization with an optically tracked stylus for use in an organ surface-based image-to-physical registration is an established approach for image-guided open liver surgery procedures. However, variability in sparse data collections during open hepatic procedures can produce disparity in registration alignments. In part, this variability arises from inconsistencies with the patterns and fidelity of collected intraoperative data. The liver lacks distinct landmarks and experiences considerable soft tissue deformation. Furthermore, data coverage of the organ is often incomplete or unevenly distributed. While more robust feature-based registration methodologies have been developed for image-guided liver surgery, it is still unclear how variation in sparse intraoperative data affects registration. In this work, we have developed an application to allow surgeons to study the performance of surface digitization patterns on registration. Given the intrinsic nature of soft-tissue, we incorporate realistic organ deformation when assessing fidelity of a rigid registration methodology. We report the construction of our application and preliminary registration results using four participants. Our preliminary results indicate that registration quality improves as users acquire more experience selecting patterns of sparse intraoperative surface data.

A coarse-to-fine approach for medical hyperspectral image classification with sparse representation

NASA Astrophysics Data System (ADS)

Chang, Lan; Zhang, Mengmeng; Li, Wei

2017-10-01

A coarse-to-fine approach with sparse representation is proposed for medical hyperspectral image classification in this work. Segmentation technique with different scales is employed to exploit edges of the input image, where coarse super-pixel patches provide global classification information while fine ones further provide detail information. Different from common RGB image, hyperspectral image has multi bands to adjust the cluster center with more high precision. After segmentation, each super pixel is classified by recently-developed sparse representation-based classification (SRC), which assigns label for testing samples in one local patch by means of sparse linear combination of all the training samples. Furthermore, segmentation with multiple scales is employed because single scale is not suitable for complicate distribution of medical hyperspectral imagery. Finally, classification results for different sizes of super pixel are fused by some fusion strategy, offering at least two benefits: (1) the final result is obviously superior to that of segmentation with single scale, and (2) the fusion process significantly simplifies the choice of scales. Experimental results using real medical hyperspectral images demonstrate that the proposed method outperforms the state-of-the-art SRC.

Cloud-In-Cell modeling of shocked particle-laden flows at a ``SPARSE'' cost

NASA Astrophysics Data System (ADS)

Taverniers, Soren; Jacobs, Gustaaf; Sen, Oishik; Udaykumar, H. S.

2017-11-01

A common tool for enabling process-scale simulations of shocked particle-laden flows is Eulerian-Lagrangian Particle-Source-In-Cell (PSIC) modeling where each particle is traced in its Lagrangian frame and treated as a mathematical point. Its dynamics are governed by Stokes drag corrected for high Reynolds and Mach numbers. The computational burden is often reduced further through a ``Cloud-In-Cell'' (CIC) approach which amalgamates groups of physical particles into computational ``macro-particles''. CIC does not account for subgrid particle fluctuations, leading to erroneous predictions of cloud dynamics. A Subgrid Particle-Averaged Reynolds-Stress Equivalent (SPARSE) model is proposed that incorporates subgrid interphase velocity and temperature perturbations. A bivariate Gaussian source distribution, whose covariance captures the cloud's deformation to first order, accounts for the particles' momentum and energy influence on the carrier gas. SPARSE is validated by conducting tests on the interaction of a particle cloud with the accelerated flow behind a shock. The cloud's average dynamics and its deformation over time predicted with SPARSE converge to their counterparts computed with reference PSIC models as the number of Gaussians is increased from 1 to 16. This work was supported by AFOSR Grant No. FA9550-16-1-0008.

Large tree diameter distribution modelling using sparse airborne laser scanning data in a subtropical forest in Nepal

NASA Astrophysics Data System (ADS)

Rana, Parvez; Vauhkonen, Jari; Junttila, Virpi; Hou, Zhengyang; Gautam, Basanta; Cawkwell, Fiona; Tokola, Timo

2017-12-01

Large-diameter trees (taking DBH > 30 cm to define large trees) dominate the dynamics, function and structure of a forest ecosystem. The aim here was to employ sparse airborne laser scanning (ALS) data with a mean point density of 0.8 m-2 and the non-parametric k-most similar neighbour (k-MSN) to predict tree diameter at breast height (DBH) distributions in a subtropical forest in southern Nepal. The specific objectives were: (1) to evaluate the accuracy of the large-tree fraction of the diameter distribution; and (2) to assess the effect of the number of training areas (sample size, n) on the accuracy of the predicted tree diameter distribution. Comparison of the predicted distributions with empirical ones indicated that the large tree diameter distribution can be derived in a mixed species forest with a RMSE% of 66% and a bias% of -1.33%. It was also feasible to downsize the sample size without losing the interpretability capacity of the model. For large-diameter trees, even a reduction of half of the training plots (n = 250), giving a marginal increase in the RMSE% (1.12-1.97%) was reported compared with the original training plots (n = 500). To be consistent with these outcomes, the sample areas should capture the entire range of spatial and feature variability in order to reduce the occurrence of error.

Mapping of cortical language function by functional magnetic resonance imaging and repetitive navigated transcranial magnetic stimulation in 40 healthy subjects.

PubMed

Sollmann, Nico; Ille, Sebastian; Boeckh-Behrens, Tobias; Ringel, Florian; Meyer, Bernhard; Krieg, Sandro M

2016-07-01

Functional magnetic resonance imaging (fMRI) is considered to be the standard method regarding non-invasive language mapping. However, repetitive navigated transcranial magnetic stimulation (rTMS) gains increasing importance with respect to that purpose. However, comparisons between both methods are sparse. We performed fMRI and rTMS language mapping of the left hemisphere in 40 healthy, right-handed subjects in combination with the tasks that are most commonly used in the neurosurgical context (fMRI: word-generation = WGEN task; rTMS: object-naming = ON task). Different rTMS error rate thresholds (ERTs) were calculated, and Cohen's kappa coefficient and the cortical parcellation system (CPS) were used for systematic comparison of the two techniques. Overall, mean kappa coefficients were low, revealing no distinct agreement. We found the highest agreement for both techniques when using the 2-out-of-3 rule (CPS region defined as language positive in terms of rTMS if at least 2 out of 3 stimulations led to a naming error). However, kappa for this threshold was only 0.24 (kappa of <0, 0.01-0.20, 0.21-0.40, 0.41-0.60, 0.61-0.80 and 0.81-0.99 indicate less than chance, slight, fair, moderate, substantial and almost perfect agreement, respectively). Because of the inherent differences in the underlying physiology of fMRI and rTMS, the different tasks used and the impossibility of verifying the results via direct cortical stimulation (DCS) in the population of healthy volunteers, one must exercise caution in drawing conclusions about the relative usefulness of each technique for language mapping. Nevertheless, this study yields valuable insights into these two mapping techniques for the most common language tasks currently used in neurosurgical practice.

Assessing attention and cognitive function in completely locked-in state with event-related brain potentials and epidural electrocorticography

NASA Astrophysics Data System (ADS)

Bensch, Michael; Martens, Suzanne; Halder, Sebastian; Hill, Jeremy; Nijboer, Femke; Ramos, Ander; Birbaumer, Niels; Bogdan, Martin; Kotchoubey, Boris; Rosenstiel, Wolfgang; Schölkopf, Bernhard; Gharabaghi, Alireza

2014-04-01

Objective. Patients in the completely locked-in state (CLIS), due to, for example, amyotrophic lateral sclerosis (ALS), no longer possess voluntary muscle control. Assessing attention and cognitive function in these patients during the course of the disease is a challenging but essential task for both nursing staff and physicians. Approach. An electrophysiological cognition test battery, including auditory and semantic stimuli, was applied in a late-stage ALS patient at four different time points during a six-month epidural electrocorticography (ECoG) recording period. Event-related cortical potentials (ERP), together with changes in the ECoG signal spectrum, were recorded via 128 channels that partially covered the left frontal, temporal and parietal cortex. Main results. Auditory but not semantic stimuli induced significant and reproducible ERP projecting to specific temporal and parietal cortical areas. N1/P2 responses could be detected throughout the whole study period. The highest P3 ERP was measured immediately after the patient's last communication through voluntary muscle control, which was paralleled by low theta and high gamma spectral power. Three months after the patient's last communication, i.e., in the CLIS, P3 responses could no longer be detected. At the same time, increased activity in low-frequency bands and a sharp drop of gamma spectral power were recorded. Significance. Cortical electrophysiological measures indicate at least partially intact attention and cognitive function during sparse volitional motor control for communication. Although the P3 ERP and frequency-specific changes in the ECoG spectrum may serve as indicators for CLIS, a close-meshed monitoring will be required to define the exact time point of the transition.

Galaxy redshift surveys with sparse sampling

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

Chiang, Chi-Ting; Wullstein, Philipp; Komatsu, Eiichiro

2013-12-01

Survey observations of the three-dimensional locations of galaxies are a powerful approach to measure the distribution of matter in the universe, which can be used to learn about the nature of dark energy, physics of inflation, neutrino masses, etc. A competitive survey, however, requires a large volume (e.g., V{sub survey} ∼ 10Gpc{sup 3}) to be covered, and thus tends to be expensive. A ''sparse sampling'' method offers a more affordable solution to this problem: within a survey footprint covering a given survey volume, V{sub survey}, we observe only a fraction of the volume. The distribution of observed regions should bemore » chosen such that their separation is smaller than the length scale corresponding to the wavenumber of interest. Then one can recover the power spectrum of galaxies with precision expected for a survey covering a volume of V{sub survey} (rather than the volume of the sum of observed regions) with the number density of galaxies given by the total number of observed galaxies divided by V{sub survey} (rather than the number density of galaxies within an observed region). We find that regularly-spaced sampling yields an unbiased power spectrum with no window function effect, and deviations from regularly-spaced sampling, which are unavoidable in realistic surveys, introduce calculable window function effects and increase the uncertainties of the recovered power spectrum. On the other hand, we show that the two-point correlation function (pair counting) is not affected by sparse sampling. While we discuss the sparse sampling method within the context of the forthcoming Hobby-Eberly Telescope Dark Energy Experiment, the method is general and can be applied to other galaxy surveys.« less

The Two-Dimensional Gabor Function Adapted to Natural Image Statistics: A Model of Simple-Cell Receptive Fields and Sparse Structure in Images.

PubMed

Loxley, P N

2017-10-01

The two-dimensional Gabor function is adapted to natural image statistics, leading to a tractable probabilistic generative model that can be used to model simple cell receptive field profiles, or generate basis functions for sparse coding applications. Learning is found to be most pronounced in three Gabor function parameters representing the size and spatial frequency of the two-dimensional Gabor function and characterized by a nonuniform probability distribution with heavy tails. All three parameters are found to be strongly correlated, resulting in a basis of multiscale Gabor functions with similar aspect ratios and size-dependent spatial frequencies. A key finding is that the distribution of receptive-field sizes is scale invariant over a wide range of values, so there is no characteristic receptive field size selected by natural image statistics. The Gabor function aspect ratio is found to be approximately conserved by the learning rules and is therefore not well determined by natural image statistics. This allows for three distinct solutions: a basis of Gabor functions with sharp orientation resolution at the expense of spatial-frequency resolution, a basis of Gabor functions with sharp spatial-frequency resolution at the expense of orientation resolution, or a basis with unit aspect ratio. Arbitrary mixtures of all three cases are also possible. Two parameters controlling the shape of the marginal distributions in a probabilistic generative model fully account for all three solutions. The best-performing probabilistic generative model for sparse coding applications is found to be a gaussian copula with Pareto marginal probability density functions.

Shape models of asteroids reconstructed from WISE data and sparse photometry

NASA Astrophysics Data System (ADS)

Durech, Josef; Hanus, Josef; Ali-Lagoa, Victor

2017-10-01

By combining sparse-in-time photometry from the Lowell Observatory photometry database with WISE observations, we reconstructed convex shape models for about 700 new asteroids and for other ~850 we derived 'partial' models with unconstrained ecliptic longitude of the spin axis direction. In our approach, the WISE data were treated as reflected light, which enabled us to directly join them with sparse photometry into one dataset that was processed by the lightcurve inversion method. This simplified treatment of thermal infrared data turned out to provide correct results, because in most cases the phase offset between optical and thermal lightcurves was small and the correct sidereal rotation period was determined. The spin and shape parameters derived from only optical data and from a combination of optical and WISE data were very similar. The new models together with those already available in the Database of Asteroid Models from Inversion Techniques (DAMIT) represent a sample of ~1650 asteroids. When including also partial models, the total sample is about 2500 asteroids, which significantly increases the number of models with respect to those that have been available so far. We will show the distribution of spin axes for different size groups and also for several collisional families. These observed distributions in general agree with theoretical expectations proving that smaller asteroids are more affected by YORP/Yarkovsky evolution. In asteroid families, we see a clear bimodal distribution of prograde/retrograde rotation that correlates with the position to the right/left from the center of the family measured by the semimajor axis.

Vertical distribution of the soil microbiota along a successional gradient in a glacier forefield.

PubMed

Rime, Thomas; Hartmann, Martin; Brunner, Ivano; Widmer, Franco; Zeyer, Josef; Frey, Beat

2015-03-01

Spatial patterns of microbial communities have been extensively surveyed in well-developed soils, but few studies investigated the vertical distribution of micro-organisms in newly developed soils after glacier retreat. We used 454-pyrosequencing to assess whether bacterial and fungal community structures differed between stages of soil development (SSD) characterized by an increasing vegetation cover from barren (vegetation cover: 0%/age: 10 years), sparsely vegetated (13%/60 years), transient (60%/80 years) to vegetated (95%/110 years) and depths (surface, 5 and 20 cm) along the Damma glacier forefield (Switzerland). The SSD significantly influenced the bacterial and fungal communities. Based on indicator species analyses, metabolically versatile bacteria (e.g. Geobacter) and psychrophilic yeasts (e.g. Mrakia) characterized the barren soils. Vegetated soils with higher C, N and root biomass consisted of bacteria able to degrade complex organic compounds (e.g. Candidatus Solibacter), lignocellulolytic Ascomycota (e.g. Geoglossum) and ectomycorrhizal Basidiomycota (e.g. Laccaria). Soil depth only influenced bacterial and fungal communities in barren and sparsely vegetated soils. These changes were partly due to more silt and higher soil moisture in the surface. In both soil ages, the surface was characterized by OTUs affiliated to Phormidium and Sphingobacteriales. In lower depths, however, bacterial and fungal communities differed between SSD. Lower depths of sparsely vegetated soils consisted of OTUs affiliated to Acidobacteria and Geoglossum, whereas depths of barren soils were characterized by OTUs related to Gemmatimonadetes. Overall, plant establishment drives the soil microbiota along the successional gradient but does not influence the vertical distribution of microbiota in recently deglaciated soils. © 2014 John Wiley & Sons Ltd.

Effects of Ordering Strategies and Programming Paradigms on Sparse Matrix Computations

NASA Technical Reports Server (NTRS)

Oliker, Leonid; Li, Xiaoye; Husbands, Parry; Biswas, Rupak; Biegel, Bryan (Technical Monitor)

2002-01-01

The Conjugate Gradient (CG) algorithm is perhaps the best-known iterative technique to solve sparse linear systems that are symmetric and positive definite. For systems that are ill-conditioned, it is often necessary to use a preconditioning technique. In this paper, we investigate the effects of various ordering and partitioning strategies on the performance of parallel CG and ILU(O) preconditioned CG (PCG) using different programming paradigms and architectures. Results show that for this class of applications: ordering significantly improves overall performance on both distributed and distributed shared-memory systems, that cache reuse may be more important than reducing communication, that it is possible to achieve message-passing performance using shared-memory constructs through careful data ordering and distribution, and that a hybrid MPI+OpenMP paradigm increases programming complexity with little performance gains. A implementation of CG on the Cray MTA does not require special ordering or partitioning to obtain high efficiency and scalability, giving it a distinct advantage for adaptive applications; however, it shows limited scalability for PCG due to a lack of thread level parallelism.

Sparse Reconstruction for Temperature Distribution Using DTS Fiber Optic Sensors with Applications in Electrical Generator Stator Monitoring.

PubMed

Bazzo, João Paulo; Pipa, Daniel Rodrigues; da Silva, Erlon Vagner; Martelli, Cicero; Cardozo da Silva, Jean Carlos

2016-09-07

This paper presents an image reconstruction method to monitor the temperature distribution of electric generator stators. The main objective is to identify insulation failures that may arise as hotspots in the structure. The method is based on temperature readings of fiber optic distributed sensors (DTS) and a sparse reconstruction algorithm. Thermal images of the structure are formed by appropriately combining atoms of a dictionary of hotspots, which was constructed by finite element simulation with a multi-physical model. Due to difficulties for reproducing insulation faults in real stator structure, experimental tests were performed using a prototype similar to the real structure. The results demonstrate the ability of the proposed method to reconstruct images of hotspots with dimensions down to 15 cm, representing a resolution gain of up to six times when compared to the DTS spatial resolution. In addition, satisfactory results were also obtained to detect hotspots with only 5 cm. The application of the proposed algorithm for thermal imaging of generator stators can contribute to the identification of insulation faults in early stages, thereby avoiding catastrophic damage to the structure.

Distributed Processing and Cortical Specialization for Speech and Environmental Sounds in Human Temporal Cortex

ERIC Educational Resources Information Center

Leech, Robert; Saygin, Ayse Pinar

2011-01-01

Using functional MRI, we investigated whether auditory processing of both speech and meaningful non-linguistic environmental sounds in superior and middle temporal cortex relies on a complex and spatially distributed neural system. We found that evidence for spatially distributed processing of speech and environmental sounds in a substantial…

High-expanding cortical regions in human development and evolution are related to higher intellectual abilities.

PubMed

Fjell, Anders M; Westlye, Lars T; Amlien, Inge; Tamnes, Christian K; Grydeland, Håkon; Engvig, Andreas; Espeseth, Thomas; Reinvang, Ivar; Lundervold, Astri J; Lundervold, Arvid; Walhovd, Kristine B

2015-01-01

Cortical surface area has tremendously expanded during human evolution, and similar patterns of cortical expansion have been observed during childhood development. An intriguing hypothesis is that the high-expanding cortical regions also show the strongest correlations with intellectual function in humans. However, we do not know how the regional distribution of correlations between intellectual function and cortical area maps onto expansion in development and evolution. Here, in a sample of 1048 participants, we show that regions in which cortical area correlates with visuospatial reasoning abilities are generally high expanding in both development and evolution. Several regions in the frontal cortex, especially the anterior cingulate, showed high expansion in both development and evolution. The area of these regions was related to intellectual functions in humans. Low-expanding areas were not related to cognitive scores. These findings suggest that cortical regions involved in higher intellectual functions have expanded the most during development and evolution. The radial unit hypothesis provides a common framework for interpretation of the findings in the context of evolution and prenatal development, while additional cellular mechanisms, such as synaptogenesis, gliogenesis, dendritic arborization, and intracortical myelination, likely impact area expansion in later childhood. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Locally induced neuronal synchrony precisely propagates to specific cortical areas without rhythm distortion.

PubMed

Toda, Haruo; Kawasaki, Keisuke; Sato, Sho; Horie, Masao; Nakahara, Kiyoshi; Bepari, Asim K; Sawahata, Hirohito; Suzuki, Takafumi; Okado, Haruo; Takebayashi, Hirohide; Hasegawa, Isao

2018-05-16

Propagation of oscillatory spike firing activity at specific frequencies plays an important role in distributed cortical networks. However, there is limited evidence for how such frequency-specific signals are induced or how the signal spectra of the propagating signals are modulated during across-layer (radial) and inter-areal (tangential) neuronal interactions. To directly evaluate the direction specificity of spectral changes in a spiking cortical network, we selectively photostimulated infragranular excitatory neurons in the rat primary visual cortex (V1) at a supra-threshold level with various frequencies, and recorded local field potentials (LFPs) at the infragranular stimulation site, the cortical surface site immediately above the stimulation site in V1, and cortical surface sites outside V1. We found a significant reduction of LFP powers during radial propagation, especially at high-frequency stimulation conditions. Moreover, low-gamma-band dominant rhythms were transiently induced during radial propagation. Contrastingly, inter-areal LFP propagation, directed to specific cortical sites, accompanied no significant signal reduction nor gamma-band power induction. We propose an anisotropic mechanism for signal processing in the spiking cortical network, in which the neuronal rhythms are locally induced/modulated along the radial direction, and then propagate without distortion via intrinsic horizontal connections for spatiotemporally precise, inter-areal communication.

The concentration of light in the human lens.

PubMed Central

Merriam, J C

1996-01-01

PURPOSE: This thesis explores the idea that light energy, especially ultraviolet light, contributes to the unequal distribution of cataract around the world and to the development of cortical opacities. METHODS: In the first section, the thesis reviews historical concepts of the function of the lens and the nature of cataract, epidemiologic data on the global distribution of cataract, and clinical observations of the predominant location of cortical opacification. Second, computer ray tracings and geometric optics demonstrate the passage of light of varying angle of incidence within the lens. Third, two models of the human eye are used to study the refraction of light by the cornea and lens and illustrate the concentration of energy at the equatorial plane of the lens. RESULTS: Cataract prevalence increases with proximity to the earth's equator, and cortical cataract is most common in the inferior and inferonasal lens. Theoretical studies and the eye models both demonstrate that the concentration of light within the lens increases with angle of incidence, and the eye models suggest that the inferior and inferonasal lens receives significantly more energy than other sections of the lens. CONCLUSION: The prevalence of cataract and exposure to ultraviolet energy both increase with decreasing latitude. The most common location of cortical cataract in the inferonasal lens is consistent with the greater dose of light energy received by this portion of the lens. These studies suggest that the global distribution of cataract and the development of cortical cataract are at least in part dependent on the dose of ultraviolet light received by the lens. Images FIGURE 1 FIGURE 2 FIGURE 27 FIGURE 28 FIGURE 29 FIGURE 31 FIGURE 32 FIGURE 33 FIGURE 34 FIGURE 36 FIGURE 37 FIGURE 38 FIGURE 50 FIGURE 51 FIGURE 52 FIGURE 53 FIGURE 54 FIGURE 56 FIGURE 60 FIGURE 61 FIGURE 63 FIGURE 64 FIGURE 65 FIGURE 68 FIGURE 69 FIGURE 70 FIGURE 71 PMID:8981716

Discrete Sparse Coding.

PubMed

Exarchakis, Georgios; Lücke, Jörg

2017-11-01

Sparse coding algorithms with continuous latent variables have been the subject of a large number of studies. However, discrete latent spaces for sparse coding have been largely ignored. In this work, we study sparse coding with latents described by discrete instead of continuous prior distributions. We consider the general case in which the latents (while being sparse) can take on any value of a finite set of possible values and in which we learn the prior probability of any value from data. This approach can be applied to any data generated by discrete causes, and it can be applied as an approximation of continuous causes. As the prior probabilities are learned, the approach then allows for estimating the prior shape without assuming specific functional forms. To efficiently train the parameters of our probabilistic generative model, we apply a truncated expectation-maximization approach (expectation truncation) that we modify to work with a general discrete prior. We evaluate the performance of the algorithm by applying it to a variety of tasks: (1) we use artificial data to verify that the algorithm can recover the generating parameters from a random initialization, (2) use image patches of natural images and discuss the role of the prior for the extraction of image components, (3) use extracellular recordings of neurons to present a novel method of analysis for spiking neurons that includes an intuitive discretization strategy, and (4) apply the algorithm on the task of encoding audio waveforms of human speech. The diverse set of numerical experiments presented in this letter suggests that discrete sparse coding algorithms can scale efficiently to work with realistic data sets and provide novel statistical quantities to describe the structure of the data.

FloCon 2011 Proceedings

DTIC Science & Technology

2011-01-01

and G. Armitage. Dening and evaluating greynets (sparse darknets ). In LCN󈧉: Proceedings of the IEEE Conference on Local Computer Networks 30th...analysis of distributed darknet trac. In IMC󈧉: Proceedings of the USENIX/ACM Internet Measurement Conference, 2005. Indexing Full Packet Capture Data

Particle Filter Based Tracking in a Detection Sparse Discrete Event Simulation Environment

DTIC Science & Technology

2007-03-01

obtained by disqualifying a large number of particles. 52 (a) (b) ( c ) Figure 31. Particle Disqualification via Sanitization b...1 B. RESEARCH APPROACH..............................................................................5 C . THESIS ORGANIZATION...38 b. Detection Distribution Sampling............................................43 c . Estimated Position Calculation

Using modern human cortical bone distribution to test the systemic robusticity hypothesis.

PubMed

Baab, Karen L; Copes, Lynn E; Ward, Devin L; Wells, Nora; Grine, Frederick E

2018-06-01

The systemic robusticity hypothesis links the thickness of cortical bone in both the cranium and limb bones. This hypothesis posits that thick cortical bone is in part a systemic response to circulating hormones, such as growth hormone and thyroid hormone, possibly related to physical activity or cold climates. Although this hypothesis has gained popular traction, only rarely has robusticity of the cranium and postcranial skeleton been considered jointly. We acquired computed tomographic scans from associated crania, femora and humeri from single individuals representing 11 populations in Africa and North America (n = 228). Cortical thickness in the parietal, frontal and occipital bones and cortical bone area in limb bone diaphyses were analyzed using correlation, multiple regression and general linear models to test the hypothesis. Absolute thickness values from the crania were not correlated with cortical bone area of the femur or humerus, which is at odds with the systemic robusticity hypothesis. However, measures of cortical bone scaled by total vault thickness and limb cross-sectional area were positively correlated between the cranium and postcranium. When accounting for a range of potential confounding variables, including sex, age and body mass, variation in relative postcranial cortical bone area explained ∼20% of variation in the proportion of cortical cranial bone thickness. While these findings provide limited support for the systemic robusticity hypothesis, cranial cortical thickness did not track climate or physical activity across populations. Thus, some of the variation in cranial cortical bone thickness in modern humans is attributable to systemic effects, but the driving force behind this effect remains obscure. Moreover, neither absolute nor proportional measures of cranial cortical bone thickness are positively correlated with total cranial bone thickness, complicating the extrapolation of these findings to extinct species where only cranial vault thickness has been measured. Copyright © 2018 Elsevier Ltd. All rights reserved.

Removal of nuisance signals from limited and sparse 1H MRSI data using a union-of-subspaces model.

PubMed

Ma, Chao; Lam, Fan; Johnson, Curtis L; Liang, Zhi-Pei

2016-02-01

To remove nuisance signals (e.g., water and lipid signals) for (1) H MRSI data collected from the brain with limited and/or sparse (k, t)-space coverage. A union-of-subspace model is proposed for removing nuisance signals. The model exploits the partial separability of both the nuisance signals and the metabolite signal, and decomposes an MRSI dataset into several sets of generalized voxels that share the same spectral distributions. This model enables the estimation of the nuisance signals from an MRSI dataset that has limited and/or sparse (k, t)-space coverage. The proposed method has been evaluated using in vivo MRSI data. For conventional chemical shift imaging data with limited k-space coverage, the proposed method produced "lipid-free" spectra without lipid suppression during data acquisition at 130 ms echo time. For sparse (k, t)-space data acquired with conventional pulses for water and lipid suppression, the proposed method was also able to remove the remaining water and lipid signals with negligible residuals. Nuisance signals in (1) H MRSI data reside in low-dimensional subspaces. This property can be utilized for estimation and removal of nuisance signals from (1) H MRSI data even when they have limited and/or sparse coverage of (k, t)-space. The proposed method should prove useful especially for accelerated high-resolution (1) H MRSI of the brain. © 2015 Wiley Periodicals, Inc.

Association of In Vivo [18F]AV-1451 Tau PET Imaging Results With Cortical Atrophy and Symptoms in Typical and Atypical Alzheimer Disease.

PubMed

Xia, Chenjie; Makaretz, Sara J; Caso, Christina; McGinnis, Scott; Gomperts, Stephen N; Sepulcre, Jorge; Gomez-Isla, Teresa; Hyman, Bradley T; Schultz, Aaron; Vasdev, Neil; Johnson, Keith A; Dickerson, Bradford C

2017-04-01

Previous postmortem studies have long demonstrated that neurofibrillary tangles made of hyperphosphorylated tau proteins are closely associated with Alzheimer disease clinical phenotype and neurodegeneration pattern. Validating these associations in vivo will lead to new diagnostic tools for Alzheimer disease and better understanding of its neurobiology. To examine whether topographical distribution and severity of hyperphosphorylated tau pathologic findings measured by fluorine 18-labeled AV-1451 ([18F]AV-1451) positron emission tomographic (PET) imaging are linked with clinical phenotype and cortical atrophy in patients with Alzheimer disease. This observational case series, conducted from July 1, 2012, to July 30, 2015, in an outpatient referral center for patients with neurodegenerative diseases, included 6 patients: 3 with typical amnesic Alzheimer disease and 3 with atypical variants (posterior cortical atrophy, logopenic variant primary progressive aphasia, and corticobasal syndrome). Patients underwent [18F]AV-1451 PET imaging to measure tau burden, carbon 11-labeled Pittsburgh Compound B ([11C]PiB) PET imaging to measure amyloid burden, and structural magnetic resonance imaging to measure cortical thickness. Seventy-seven age-matched controls with normal cognitive function also underwent structural magnetic resonance imaging but not tau or amyloid PET imaging. Tau burden, amyloid burden, and cortical thickness. In all 6 patients (3 women and 3 men; mean age 61.8 years), the underlying clinical phenotype was associated with the regional distribution of the [18F]AV-1451 signal. Furthermore, within 68 cortical regions of interest measured from each patient, the magnitude of cortical atrophy was strongly correlated with the magnitude of [18F]AV-1451 binding (3 patients with amnesic Alzheimer disease, r = -0.82; P < .001; r = -0.70; P < .001; r = -0.58; P < .001; and 3 patients with nonamnesic Alzheimer disease, r = -0.51; P < .001; r = -0.63; P < .001; r = -0.70; P < .001), but not of [11C]PiB binding. These findings provide further in vivo evidence that distribution of the [18F]AV-1451 signal as seen on results of PET imaging is a valid marker of clinical symptoms and neurodegeneration. By localizing and quantifying hyperphosphorylated tau in vivo, results of tau PET imaging will likely serve as a key biomarker that links a specific type of molecular Alzheimer disease neuropathologic condition with clinically significant neurodegeneration, which will likely catalyze additional efforts to develop disease-modifying therapeutics.

Spatial integration and cortical dynamics.

PubMed

Gilbert, C D; Das, A; Ito, M; Kapadia, M; Westheimer, G

1996-01-23

Cells in adult primary visual cortex are capable of integrating information over much larger portions of the visual field than was originally thought. Moreover, their receptive field properties can be altered by the context within which local features are presented and by changes in visual experience. The substrate for both spatial integration and cortical plasticity is likely to be found in a plexus of long-range horizontal connections, formed by cortical pyramidal cells, which link cells within each cortical area over distances of 6-8 mm. The relationship between horizontal connections and cortical functional architecture suggests a role in visual segmentation and spatial integration. The distribution of lateral interactions within striate cortex was visualized with optical recording, and their functional consequences were explored by using comparable stimuli in human psychophysical experiments and in recordings from alert monkeys. They may represent the substrate for perceptual phenomena such as illusory contours, surface fill-in, and contour saliency. The dynamic nature of receptive field properties and cortical architecture has been seen over time scales ranging from seconds to months. One can induce a remapping of the topography of visual cortex by making focal binocular retinal lesions. Shorter-term plasticity of cortical receptive fields was observed following brief periods of visual stimulation. The mechanisms involved entailed, for the short-term changes, altering the effectiveness of existing cortical connections, and for the long-term changes, sprouting of axon collaterals and synaptogenesis. The mutability of cortical function implies a continual process of calibration and normalization of the perception of visual attributes that is dependent on sensory experience throughout adulthood and might further represent the mechanism of perceptual learning.

Perfusion characteristics of Moyamoya disease: an anatomically and clinically oriented analysis and comparison.

PubMed

Schubert, Gerrit Alexander; Czabanka, Marcus; Seiz, Marcel; Horn, Peter; Vajkoczy, Peter; Thomé, Claudius

2014-01-01

Moyamoya disease (MMD) is characterized by unique angiographic features of collateralization. However, a detailed quantification as well as comparative analysis with cerebrovascular atherosclerotic disease (CAD) and healthy controls have not been performed to date. We reviewed 67 patients with MMD undergoing Xenon-enhanced computed tomography, as well as 108 patients with CAD and 5 controls. In addition to cortical, central, and infratentorial regions of interest, particular emphasis was put on regions that are typically involved in MMD (pericallosal territory, basal ganglia). Cerebral blood flow (CBF), cerebrovascular reserve capacity (CVRC), and hemodynamic stress distribution were calculated. MMD is characterized by a significant, ubiquitous decrease in CVRC and a cortical but not pericallosal decrease in CBF when compared with controls. Baseline perfusion is maintained within the basal ganglia, and hemodynamic stress distribution confirmed a relative preservation of central regions of interest in MMD, indicative for its characteristic proximal collateralization pattern. In MMD and CAD, cortical and central CBF decreased significantly with age, whereas CVRC and hemodynamic stress distribution are relatively unaffected by age. No difference in CVRC of comparable regions of interest was seen between MMD and CAD, but stress distribution was significantly higher in MMD, illustrating the functionality of the characteristic rete mirabilis. Our data provide quantitative support for a territory-specific perfusion pattern that is unique for MMD, including central preservation of CBF compared with controls and patients with CAD. This correlates well with its characteristic feature of proximal collateralization. CVRC and hemodynamic stress distribution seem to be more robust parameters than CBF alone for assessment of disease severity.

The role of the first postmitotic cortical cells in the development of thalamocortical innervation in the reeler mouse.

PubMed

Molnár, Z; Adams, R; Goffinet, A M; Blakemore, C

1998-08-01

In the mutant mouse reeler, the tangential distribution of thalamocortical fibers is essentially normal, even though neurons of the cortical plate accumulate below the entire early-born preplate population (Caviness et al., 1998). This seems incompatible with the hypothesis that cells of the subplate (the lower component of the preplate in normal mammals) form an axonal scaffold that guides thalamic fibers and act as temporary targets for them (Blakemore and Molnár, 1990, Shatz et al., 1990). We used carbocyanine dyes to trace projections in wild-type and reeler mice between embryonic day 13 and postnatal day 3. Preplate formation and early extension of corticofugal fibers to form a topographic array are indistinguishable in the two phenotypes. So too are the emergence of thalamic axons in topographic order through the primitive internal capsule, their meeting with preplate axons, and their distribution over the preplate scaffold. Distinctive differences appear after the cortical plate begins to accumulate below the preplate of reeler, causing the preplate axons to form oblique fascicles, running through the cortical plate. Thalamic axons then pass through the plate within the same fascicles and accumulate in the "superplate" layer for approximately 2-3 d, before defasciculating and plunging down to terminate deep in the cortical plate, creating the curious "looping" pattern seen in the adult. Thus, thalamocortical innervation in reeler follows the same algorithm of development but in relation to the misplaced population of early-born neurons. Far from challenging the theory that preplate fibers guide thalamic axons, reeler provides strong evidence for it.

Effects of hyperglycemia and effects of ketosis on cerebral perfusion, cerebral water distribution, and cerebral metabolism.

PubMed

Glaser, Nicole; Ngo, Catherine; Anderson, Steven; Yuen, Natalie; Trifu, Alexandra; O'Donnell, Martha

2012-07-01

Diabetic ketoacidosis (DKA) may cause brain injuries in children. The mechanisms responsible are difficult to elucidate because DKA involves multiple metabolic derangements. We aimed to determine the independent effects of hyperglycemia and ketosis on cerebral metabolism, blood flow, and water distribution. We used magnetic resonance spectroscopy to measure ratios of cerebral metabolites (ATP to inorganic phosphate [Pi], phosphocreatine [PCr] to Pi, N-acetyl aspartate [NAA] to creatine [Cr], and lactate to Cr) and diffusion-weighted imaging and perfusion-weighted imaging to assess cerebral water distribution (apparent diffusion coefficient [ADC] values) and cerebral blood flow (CBF) in three groups of juvenile rats (hyperglycemic, ketotic, and normal control). ATP-to-Pi ratio was reduced in both hyperglycemic and ketotic rats in comparison with controls. PCr-to-Pi ratio was reduced in the ketotic group, and there was a trend toward reduction in the hyperglycemic group. No significant differences were observed in NAA-to-Cr or lactate-to-Cr ratio. Cortical ADC was reduced in both groups (indicating brain cell swelling). Cortical CBF was also reduced in both groups. We conclude that both hyperglycemia and ketosis independently cause reductions in cerebral high-energy phosphates, CBF, and cortical ADC values. These effects may play a role in the pathophysiology of DKA-related brain injury.

Brain-wide Maps Reveal Stereotyped Cell-Type-Based Cortical Architecture and Subcortical Sexual Dimorphism.

PubMed

Kim, Yongsoo; Yang, Guangyu Robert; Pradhan, Kith; Venkataraju, Kannan Umadevi; Bota, Mihail; García Del Molino, Luis Carlos; Fitzgerald, Greg; Ram, Keerthi; He, Miao; Levine, Jesse Maurica; Mitra, Partha; Huang, Z Josh; Wang, Xiao-Jing; Osten, Pavel

2017-10-05

The stereotyped features of neuronal circuits are those most likely to explain the remarkable capacity of the brain to process information and govern behaviors, yet it has not been possible to comprehensively quantify neuronal distributions across animals or genders due to the size and complexity of the mammalian brain. Here we apply our quantitative brain-wide (qBrain) mapping platform to document the stereotyped distributions of mainly inhibitory cell types. We discover an unexpected cortical organizing principle: sensory-motor areas are dominated by output-modulating parvalbumin-positive interneurons, whereas association, including frontal, areas are dominated by input-modulating somatostatin-positive interneurons. Furthermore, we identify local cell type distributions with more cells in the female brain in 10 out of 11 sexually dimorphic subcortical areas, in contrast to the overall larger brains in males. The qBrain resource can be further mined to link stereotyped aspects of neuronal distributions to known and unknown functions of diverse brain regions. Copyright © 2017 Elsevier Inc. All rights reserved.

Cortical Silent Period Reveals Differences Between Adductor Spasmodic Dysphonia and Muscle Tension Dysphonia.

PubMed

Samargia, Sharyl; Schmidt, Rebekah; Kimberley, Teresa Jacobson

2016-03-01

The pathophysiology of adductor spasmodic dysphonia (AdSD), like other focal dystonias, is largely unknown. The purposes of this study were to determine (a) cortical excitability differences between AdSD, muscle tension dysphonia (MTD), and healthy controls; (b) distribution of potential differences in cranial or skeletal muscle; and (c) if cortical excitability measures assist in the differential diagnosis of AdSD and MTD. Ten participants with adductor spasmodic dysphonia, 8 with muscle tension dysphonia, and 10 healthy controls received single and paired pulse transcranial magnetic stimulation (TMS) to the primary motor cortex contralateral to tested muscles, first dorsal interosseus (FDI), and masseter. We tested the hypothesis that cortical excitability measures in AdSD would be significantly different from those in MTD and healthy controls. In addition, we hypothesized that there would be a correlation between cortical excitability measures and clinical voice severity in AdSD. Cortical silent period duration in masseter and FDI was significantly shorter in AdSD than MTD and healthy controls. Other measures failed to demonstrate differences. There are differences in cortical excitability between AdSD, MTD, and healthy controls. These differences in the cortical measure of both the FDI and masseter muscles in AdSD suggest widespread dysfunction of the GABAB mechanism may be a pathophysiologic feature of AdSD, similar to other forms of focal dystonia. Further exploration of the use of TMS to assist in the differential diagnosis of AdSD and MTD is warranted. © The Author(s) 2015.

Cortical silent period reveals differences between adductor spasmodic dysphonia and muscle tension dysphonia

PubMed Central

Samargia, Sharyl; Schmidt, Rebekah; Kimberley, Teresa Jacobson

2015-01-01

Background The pathophysiology of adductor spasmodic dysphonia (AdSD), like other focal dystonias, is largely unknown. Objective The purposes of this study were to determine 1) cortical excitability differences between AdSD, muscle tension dysphonia (MTD) and healthy controls 2) distribution of potential differences in cranial or skeletal muscle, and 3) if cortical excitability measures assist in the differential diagnosis of AdSD and MTD. Methods 10 participants with adductor spasmodic dysphonia, 8 with muscle tension dysphonia and 10 healthy controls received single and paired pulse transcranial magnetic stimulation (TMS) to the primary motor cortex contralateral to tested muscles, first dorsal interosseus (FDI) and masseter. We tested the hypothesis that cortical excitability measures in AdSD would be significantly different than in MTD and healthy. In addition, we hypothesized there would be a correlation between cortical excitability measures and clinical voice severity in AdSD. Results Cortical silent period (CSP) duration in masseter and FDI was significantly shorter in AdSD than MTD and healthy controls. Other measures failed to demonstrate differences. Conclusion There are differences in cortical excitability between AdSD, MTD and healthy controls. These differences in the cortical measure of both the FDI and masseter muscles in AdSD suggest widespread dysfunction of the GABAB mechanism may be a pathophysiologic feature of AdSD, similar to other forms of focal dystonia. Further exploration of the use of TMS to assist in the differential diagnosis of AdSD and MTD is warranted. PMID:26089309

Cerebral Amyloid Burden and Hoehn and Yahr Stage 3 Scoring in Parkinson Disease.

PubMed

Kotagal, Vikas; Bohnen, Nicolaas I; Müller, Martijn L T M; Frey, Kirk A; Albin, Roger L

2017-01-01

Progression to Hoehn and Yahr (HY) stage 3 marks the transition to advanced disease staging and disability in Parkinson disease (PD). We conducted a case-control study of 36 PD subjects at HY stage 2.5 or 3, with groups matched for gender, age, and disease duration. Positron Emission tomography (PET) imaging included dihydrotetrabenazine [11C]DTBZ and Pittsburgh Compound B [11C]PiB. Subjects with HY 2.5 differed from HY 3.0 in mean cortical PiB distribution volume ratio (1.14 vs. 1.23; Wilcoxon two-sample Z = 2.36, p = 0.024) but not striatal DTBZ PET. Cortical amyloid burden differentiates subjects below and at HY stage 3. These results suggest that cortical amyloid accumulation influences the transition from HY2.5 to HY3 and that cortical amyloidopathy may be a therapeutic target in PD.

Altered cortical anatomical networks in temporal lobe epilepsy

NASA Astrophysics Data System (ADS)

Lv, Bin; He, Huiguang; Lu, Jingjing; Li, Wenjing; Dai, Dai; Li, Meng; Jin, Zhengyu

2011-03-01

Temporal lobe epilepsy (TLE) is one of the most common epilepsy syndromes with focal seizures generated in the left or right temporal lobes. With the magnetic resonance imaging (MRI), many evidences have demonstrated that the abnormalities in hippocampal volume and the distributed atrophies in cortical cortex. However, few studies have investigated if TLE patients have the alternation in the structural networks. In the present study, we used the cortical thickness to establish the morphological connectivity networks, and investigated the network properties using the graph theoretical methods. We found that all the morphological networks exhibited the small-world efficiency in left TLE, right TLE and normal groups. And the betweenness centrality analysis revealed that there were statistical inter-group differences in the right uncus region. Since the right uncus located at the right temporal lobe, these preliminary evidences may suggest that there are topological alternations of the cortical anatomical networks in TLE, especially for the right TLE.

Regional distribution of acid mucopolysaccharides in the kidney

PubMed Central

Castor, C. W.; Greene, J. A.

1968-01-01

Kidneys from 20 dogs were dissected into cortical and medullary components and analysed for acid mucopolysaccharide content. Heparitin sulfate accounted for approximately 80% of cortical acid mucopolysaccharide, 10% was chondroitin sulfate B, and 10% was low molecular weight hyaluronic acid. Medullary tissue exhibited a 4- to 5-fold higher concentration of acid mucopolysaccharide than did cortical tissue, and the dominant compound was moderately highly polymerized hyaluronic acid. While chondroitin sulfates A and (or) C were not detected in this study, the presence of minor amounts of these substances could not be excluded. A model experiment indicated that hyaluronic acid retards sodium diffusion, apparently due to its viscous properties rather than its electronegativity. PMID:4233982

Functional linear models for zero-inflated count data with application to modeling hospitalizations in patients on dialysis.

PubMed

Sentürk, Damla; Dalrymple, Lorien S; Nguyen, Danh V

2014-11-30

We propose functional linear models for zero-inflated count data with a focus on the functional hurdle and functional zero-inflated Poisson (ZIP) models. Although the hurdle model assumes the counts come from a mixture of a degenerate distribution at zero and a zero-truncated Poisson distribution, the ZIP model considers a mixture of a degenerate distribution at zero and a standard Poisson distribution. We extend the generalized functional linear model framework with a functional predictor and multiple cross-sectional predictors to model counts generated by a mixture distribution. We propose an estimation procedure for functional hurdle and ZIP models, called penalized reconstruction, geared towards error-prone and sparsely observed longitudinal functional predictors. The approach relies on dimension reduction and pooling of information across subjects involving basis expansions and penalized maximum likelihood techniques. The developed functional hurdle model is applied to modeling hospitalizations within the first 2 years from initiation of dialysis, with a high percentage of zeros, in the Comprehensive Dialysis Study participants. Hospitalization counts are modeled as a function of sparse longitudinal measurements of serum albumin concentrations, patient demographics, and comorbidities. Simulation studies are used to study finite sample properties of the proposed method and include comparisons with an adaptation of standard principal components regression. Copyright © 2014 John Wiley & Sons, Ltd.

N-mixture models for estimating population size from spatially replicated counts

USGS Publications Warehouse

Royle, J. Andrew

2004-01-01

Spatial replication is a common theme in count surveys of animals. Such surveys often generate sparse count data from which it is difficult to estimate population size while formally accounting for detection probability. In this article, i describe a class of models (n-mixture models) which allow for estimation of population size from such data. The key idea is to view site-specific population sizes, n, as independent random variables distributed according to some mixing distribution (e.g., Poisson). Prior parameters are estimated from the marginal likelihood of the data, having integrated over the prior distribution for n. Carroll and lombard (1985, journal of american statistical association 80, 423-426) proposed a class of estimators based on mixing over a prior distribution for detection probability. Their estimator can be applied in limited settings, but is sensitive to prior parameter values that are fixed a priori. Spatial replication provides additional information regarding the parameters of the prior distribution on n that is exploited by the n-mixture models and which leads to reasonable estimates of abundance from sparse data. A simulation study demonstrates superior operating characteristics (bias, confidence interval coverage) of the n-mixture estimator compared to the caroll and lombard estimator. Both estimators are applied to point count data on six species of birds illustrating the sensitivity to choice of prior on p and substantially different estimates of abundance as a consequence.

A general parallel sparse-blocked matrix multiply for linear scaling SCF theory

NASA Astrophysics Data System (ADS)

Challacombe, Matt

2000-06-01

A general approach to the parallel sparse-blocked matrix-matrix multiply is developed in the context of linear scaling self-consistent-field (SCF) theory. The data-parallel message passing method uses non-blocking communication to overlap computation and communication. The space filling curve heuristic is used to achieve data locality for sparse matrix elements that decay with “separation”. Load balance is achieved by solving the bin packing problem for blocks with variable size.With this new method as the kernel, parallel performance of the simplified density matrix minimization (SDMM) for solution of the SCF equations is investigated for RHF/6-31G ∗∗ water clusters and RHF/3-21G estane globules. Sustained rates above 5.7 GFLOPS for the SDMM have been achieved for (H 2 O) 200 with 95 Origin 2000 processors. Scalability is found to be limited by load imbalance, which increases with decreasing granularity, due primarily to the inhomogeneous distribution of variable block sizes.

Large Scale Density Estimation of Blue and Fin Whales (LSD)

DTIC Science & Technology

2015-09-30

1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Large Scale Density Estimation of Blue and Fin Whales ...sensors, or both. The goal of this research is to develop and implement a new method for estimating blue and fin whale density that is effective over...develop and implement a density estimation methodology for quantifying blue and fin whale abundance from passive acoustic data recorded on sparse

Advancing Underwater Acoustic Communication for Autonomous Distributed Networks via Sparse Channel Sensing, Coding, and Navigation Support

DTIC Science & Technology

2013-09-30

underwater acoustic communication technologies for autonomous distributed underwater networks, through innovative signal processing, coding, and navigation...in real enviroments , an offshore testbed has been developed to conduct field experimetns. The testbed consists of four nodes and has been deployed...Leadership by the Connecticut Technology Council. Dr. Zhaohui Wang joined the faculty of the Department of Electrical and Computer Engineering at

Off-Grid Direction of Arrival Estimation Based on Joint Spatial Sparsity for Distributed Sparse Linear Arrays

PubMed Central

Liang, Yujie; Ying, Rendong; Lu, Zhenqi; Liu, Peilin

2014-01-01

In the design phase of sensor arrays during array signal processing, the estimation performance and system cost are largely determined by array aperture size. In this article, we address the problem of joint direction-of-arrival (DOA) estimation with distributed sparse linear arrays (SLAs) and propose an off-grid synchronous approach based on distributed compressed sensing to obtain larger array aperture. We focus on the complex source distribution in the practical applications and classify the sources into common and innovation parts according to whether a signal of source can impinge on all the SLAs or a specific one. For each SLA, we construct a corresponding virtual uniform linear array (ULA) to create the relationship of random linear map between the signals respectively observed by these two arrays. The signal ensembles including the common/innovation sources for different SLAs are abstracted as a joint spatial sparsity model. And we use the minimization of concatenated atomic norm via semidefinite programming to solve the problem of joint DOA estimation. Joint calculation of the signals observed by all the SLAs exploits their redundancy caused by the common sources and decreases the requirement of array size. The numerical results illustrate the advantages of the proposed approach. PMID:25420150

Parafunctional loading and occlusal device on stress distribution around implants: A 3D finite element analysis.

PubMed

Borges Radaelli, Manuel Tomás; Idogava, Henrique Takashi; Spazzin, Aloisio Oro; Noritomi, Pedro Yoshito; Boscato, Noéli

2018-04-30

An occlusal device is frequently recommended for patients with bruxism to protect implant-supported restorations and prevent marginal bone loss. Scientific evidence to support this treatment is lacking. The purpose of this 3-dimensional (3D) finite element study was to evaluate the influence of an acrylic resin occlusal device, implant length, and insertion depth on stress distribution with functional and parafunctional loadings. Computer-aided design software was used to construct 8 models. The models were composed of a mandibular bone section including the second premolar and first and second molars. Insertion depths (bone level and 2 mm subcrestal) were simulated at the first molar. Three natural antagonist maxillary teeth and the placement or not of an occlusal device were simulated. Functional (200-N axial and 10-N oblique) and parafunctional (1000-N axial and 25-N oblique) forces were applied. Finite element analysis (FEA) was used to determine the maximum principal stress for the cortical and trabecular bone and von Mises for implant and prosthetic abutment. Stress concentration was observed at the abutment-implant and the implant-bone interfaces. Occlusal device placement changed the pattern of stress distribution and reduced stress levels from parafunctional loading in all structures, except in the trabecular bone. Implants with subcrestal insertion depths had reduced stress at the implant-abutment interface and cortical bone around the implant abutment, while the stress increased in the bone in contact with the implant. Parafunctional loading increased the stress levels in all structures when compared with functional loading. An occlusal device resulted in the lowest stress levels at the abutment and implant and the most favorable stress distribution between the cortical and trabecular bone. Under parafunctional loading, an occlusal device was more effective in reducing stress distribution for longer implants inserted at bone level. Subcrestally, implant insertion yielded the most favorable biomechanical conditions at the abutment-implant interface and at the coronal surface of the cortical bone, mainly when there was no occlusal device. Copyright © 2018 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

Improved Estimation and Interpretation of Correlations in Neural Circuits

PubMed Central

Yatsenko, Dimitri; Josić, Krešimir; Ecker, Alexander S.; Froudarakis, Emmanouil; Cotton, R. James; Tolias, Andreas S.

2015-01-01

Ambitious projects aim to record the activity of ever larger and denser neuronal populations in vivo. Correlations in neural activity measured in such recordings can reveal important aspects of neural circuit organization. However, estimating and interpreting large correlation matrices is statistically challenging. Estimation can be improved by regularization, i.e. by imposing a structure on the estimate. The amount of improvement depends on how closely the assumed structure represents dependencies in the data. Therefore, the selection of the most efficient correlation matrix estimator for a given neural circuit must be determined empirically. Importantly, the identity and structure of the most efficient estimator informs about the types of dominant dependencies governing the system. We sought statistically efficient estimators of neural correlation matrices in recordings from large, dense groups of cortical neurons. Using fast 3D random-access laser scanning microscopy of calcium signals, we recorded the activity of nearly every neuron in volumes 200 μm wide and 100 μm deep (150–350 cells) in mouse visual cortex. We hypothesized that in these densely sampled recordings, the correlation matrix should be best modeled as the combination of a sparse graph of pairwise partial correlations representing local interactions and a low-rank component representing common fluctuations and external inputs. Indeed, in cross-validation tests, the covariance matrix estimator with this structure consistently outperformed other regularized estimators. The sparse component of the estimate defined a graph of interactions. These interactions reflected the physical distances and orientation tuning properties of cells: The density of positive ‘excitatory’ interactions decreased rapidly with geometric distances and with differences in orientation preference whereas negative ‘inhibitory’ interactions were less selective. Because of its superior performance, this ‘sparse+latent’ estimator likely provides a more physiologically relevant representation of the functional connectivity in densely sampled recordings than the sample correlation matrix. PMID:25826696

Comparative Distribution of Relaxin-3 Inputs and Calcium-Binding Protein-Positive Neurons in Rat Amygdala

PubMed Central

Santos, Fabio N.; Pereira, Celia W.; Sánchez-Pérez, Ana M.; Otero-García, Marcos; Ma, Sherie; Gundlach, Andrew L.; Olucha-Bordonau, Francisco E.

2016-01-01

The neural circuits involved in mediating complex behaviors are being rapidly elucidated using various newly developed and powerful anatomical and molecular techniques, providing insights into the neural basis for anxiety disorders, depression, addiction, and dysfunctional social behaviors. Many of these behaviors and associated physiological processes involve the activation of the amygdala in conjunction with cortical and hippocampal circuits. Ascending subcortical projections provide modulatory inputs to the extended amygdala and its related nodes (or “hubs”) within these key circuits. One such input arises from the nucleus incertus (NI) in the tegmentum, which sends amino acid- and peptide-containing projections throughout the forebrain. Notably, a distinct population of GABAergic NI neurons expresses the highly-conserved neuropeptide, relaxin-3, and relaxin-3 signaling has been implicated in the modulation of reward/motivation and anxiety- and depressive-like behaviors in rodents via actions within the extended amygdala. Thus, a detailed description of the relaxin-3 innervation of the extended amygdala would provide an anatomical framework for an improved understanding of NI and relaxin-3 modulation of these and other specific amygdala-related functions. Therefore, in this study, we examined the distribution of NI projections and relaxin-3-positive elements (axons/fibers/terminals) within the amygdala, relative to the distribution of neurons expressing the calcium-binding proteins, parvalbumin (PV), calretinin (CR) and/or calbindin. Anterograde tracer injections into the NI revealed a topographic distribution of NI efferents within the amygdala that was near identical to the distribution of relaxin-3-immunoreactive fibers. Highest densities of anterogradely-labeled elements and relaxin-3-immunoreactive fibers were observed in the medial nucleus of the amygdala, medial divisions of the bed nucleus of the stria terminalis (BST) and in the endopiriform nucleus. In contrast, sparse anterogradely-labeled and relaxin-3-immunoreactive fibers were observed in other amygdala nuclei, including the lateral, central and basal nuclei, while the nucleus accumbens lacked any innervation. Using synaptophysin as a synaptic marker, we identified relaxin-3 positive synaptic terminals in the medial amygdala, BST and endopiriform nucleus of amygdala. Our findings demonstrate the existence of topographic NI and relaxin-3-containing projections to specific nuclei of the extended amygdala, consistent with a likely role for this putative integrative arousal system in the regulation of amygdala-dependent social and emotional behaviors. PMID:27092060

Comparative Distribution of Relaxin-3 Inputs and Calcium-Binding Protein-Positive Neurons in Rat Amygdala.

PubMed

Santos, Fabio N; Pereira, Celia W; Sánchez-Pérez, Ana M; Otero-García, Marcos; Ma, Sherie; Gundlach, Andrew L; Olucha-Bordonau, Francisco E

2016-01-01

The neural circuits involved in mediating complex behaviors are being rapidly elucidated using various newly developed and powerful anatomical and molecular techniques, providing insights into the neural basis for anxiety disorders, depression, addiction, and dysfunctional social behaviors. Many of these behaviors and associated physiological processes involve the activation of the amygdala in conjunction with cortical and hippocampal circuits. Ascending subcortical projections provide modulatory inputs to the extended amygdala and its related nodes (or "hubs") within these key circuits. One such input arises from the nucleus incertus (NI) in the tegmentum, which sends amino acid- and peptide-containing projections throughout the forebrain. Notably, a distinct population of GABAergic NI neurons expresses the highly-conserved neuropeptide, relaxin-3, and relaxin-3 signaling has been implicated in the modulation of reward/motivation and anxiety- and depressive-like behaviors in rodents via actions within the extended amygdala. Thus, a detailed description of the relaxin-3 innervation of the extended amygdala would provide an anatomical framework for an improved understanding of NI and relaxin-3 modulation of these and other specific amygdala-related functions. Therefore, in this study, we examined the distribution of NI projections and relaxin-3-positive elements (axons/fibers/terminals) within the amygdala, relative to the distribution of neurons expressing the calcium-binding proteins, parvalbumin (PV), calretinin (CR) and/or calbindin. Anterograde tracer injections into the NI revealed a topographic distribution of NI efferents within the amygdala that was near identical to the distribution of relaxin-3-immunoreactive fibers. Highest densities of anterogradely-labeled elements and relaxin-3-immunoreactive fibers were observed in the medial nucleus of the amygdala, medial divisions of the bed nucleus of the stria terminalis (BST) and in the endopiriform nucleus. In contrast, sparse anterogradely-labeled and relaxin-3-immunoreactive fibers were observed in other amygdala nuclei, including the lateral, central and basal nuclei, while the nucleus accumbens lacked any innervation. Using synaptophysin as a synaptic marker, we identified relaxin-3 positive synaptic terminals in the medial amygdala, BST and endopiriform nucleus of amygdala. Our findings demonstrate the existence of topographic NI and relaxin-3-containing projections to specific nuclei of the extended amygdala, consistent with a likely role for this putative integrative arousal system in the regulation of amygdala-dependent social and emotional behaviors.

Neurofilament protein defines regional patterns of cortical organization in the macaque monkey visual system: a quantitative immunohistochemical analysis

NASA Technical Reports Server (NTRS)

Hof, P. R.; Morrison, J. H.; Bloom, F. E. (Principal Investigator)

1995-01-01

Visual function in monkeys is subserved at the cortical level by a large number of areas defined by their specific physiological properties and connectivity patterns. For most of these cortical fields, a precise index of their degree of anatomical specialization has not yet been defined, although many regional patterns have been described using Nissl or myelin stains. In the present study, an attempt has been made to elucidate the regional characteristics, and to varying degrees boundaries, of several visual cortical areas in the macaque monkey using an antibody to neurofilament protein (SMI32). This antibody labels a subset of pyramidal neurons with highly specific regional and laminar distribution patterns in the cerebral cortex. Based on the staining patterns and regional quantitative analysis, as many as 28 cortical fields were reliably identified. Each field had a homogeneous distribution of labeled neurons, except area V1, where increases in layer IVB cell and in Meynert cell counts paralleled the increase in the degree of eccentricity in the visual field representation. Within the occipitotemporal pathway, areas V3 and V4 and fields in the inferior temporal cortex were characterized by a distinct population of neurofilament-rich neurons in layers II-IIIa, whereas areas located in the parietal cortex and part of the occipitoparietal pathway had a consistent population of large labeled neurons in layer Va. The mediotemporal areas MT and MST displayed a distinct population of densely labeled neurons in layer VI. Quantitative analysis of the laminar distribution of the labeled neurons demonstrated that the visual cortical areas could be grouped in four hierarchical levels based on the ratio of neuron counts between infragranular and supragranular layers, with the first (areas V1, V2, V3, and V3A) and third (temporal and parietal regions) levels characterized by low ratios and the second (areas MT, MST, and V4) and fourth (frontal regions) levels characterized by high to very high ratios. Such density trends may correspond to differential representation of corticocortically (and corticosubcortically) projecting neurons at several functional steps in the integration of the visual stimuli. In this context, it is possible that neurofilament protein is crucial for the unique capacity of certain subsets of neurons to perform the highly precise mapping functions of the monkey visual system.

Testing Mechanisms and Scales of Equilibrium Using Textural and Compositional Analysis of Porphyroblasts in Rocks with Heterogeneous Garnet Distributions

NASA Astrophysics Data System (ADS)

Ruthven, R. C.; Ketcham, R. A.; Kelly, E. D.

2015-12-01

Three-dimensional textural analysis of garnet porphyroblasts and electron microprobe analyses can, in concert, be used to pose novel tests that challenge and ultimately increase our understanding of metamorphic crystallization mechanisms. Statistical analysis of high-resolution X-ray computed tomography (CT) data of garnet porphyroblasts tells us the degree of ordering or randomness of garnets, which can be used to distinguish the rate-limiting factors behind their nucleation and growth. Electron microprobe data for cores, rims, and core-to-rim traverses are used as proxies to ascertain porphyroblast nucleation and growth rates, and the evolution of sample composition during crystallization. MnO concentrations in garnet cores serve as a proxy for the relative timing of nucleation, and rim concentrations test the hypothesis that MnO is in equilibrium sample-wide during the final stages of crystallization, and that concentrations have not been greatly altered by intracrystalline diffusion. Crystal size distributions combined with compositional data can be used to quantify the evolution of nucleation rates and sample composition during crystallization. This study focuses on quartzite schists from the Picuris Mountains with heterogeneous garnet distributions consisting of dense and sparse layers. 3D data shows that the sparse layers have smaller, less euhedral garnets, and petrographic observations show that sparse layers have more quartz and less mica than dense layers. Previous studies on rocks with homogeneously distributed garnet have shown that crystallization rates are diffusion-controlled, meaning that they are limited by diffusion of nutrients to growth and nucleation sites. This research extends this analysis to heterogeneous rocks to determine nucleation and growth rates, and test the assumption of rock-wide equilibrium for some major elements, among a set of compositionally distinct domains evolving in mm- to cm-scale proximity under identical P-T conditions.

The Episodic Memory System: Neurocircuitry and Disorders

PubMed Central

Dickerson, Bradford C; Eichenbaum, Howard

2010-01-01

The ability to encode and retrieve our daily personal experiences, called episodic memory, is supported by the circuitry of the medial temporal lobe (MTL), including the hippocampus, which interacts extensively with a number of specific distributed cortical and subcortical structures. In both animals and humans, evidence from anatomical, neuropsychological, and physiological studies indicates that cortical components of this system have key functions in several aspects of perception and cognition, whereas the MTL structures mediate the organization and persistence of the network of memories whose details are stored in those cortical areas. Structures within the MTL, and particularly the hippocampus, have distinct functions in combining information from multiple cortical streams, supporting our ability to encode and retrieve details of events that compose episodic memories. Conversely, selective damage in the hippocampus, MTL, and other structures of the large-scale memory system, or deterioration of these areas in several diseases and disorders, compromises episodic memory. A growing body of evidence is converging on a functional organization of the cortical, subcortical, and MTL structures that support the fundamental features of episodic memory in humans and animals. PMID:19776728

Cobtorin target analysis reveals that pectin functions in the deposition of cellulose microfibrils in parallel with cortical microtubules.

PubMed

Yoneda, Arata; Ito, Takuya; Higaki, Takumi; Kutsuna, Natsumaro; Saito, Tamio; Ishimizu, Takeshi; Osada, Hiroyuki; Hasezawa, Seiichiro; Matsui, Minami; Demura, Taku

2010-11-01

Cellulose and pectin are major components of primary cell walls in plants, and it is believed that their mechanical properties are important for cell morphogenesis. It has been hypothesized that cortical microtubules guide the movement of cellulose microfibril synthase in a direction parallel with the microtubules, but the mechanism by which this alignment occurs remains unclear. We have previously identified cobtorin as an inhibitor that perturbs the parallel relationship between cortical microtubules and nascent cellulose microfibrils. In this study, we searched for the protein target of cobtorin, and we found that overexpression of pectin methylesterase and polygalacturonase suppressed the cobtorin-induced cell-swelling phenotype. Furthermore, treatment with polygalacturonase restored the deposition of cellulose microfibrils in the direction parallel with cortical microtubules, and cobtorin perturbed the distribution of methylated pectin. These results suggest that control over the properties of pectin is important for the deposition of cellulose microfibrils and/or the maintenance of their orientation parallel with the cortical microtubules. © 2010 The Authors. The Plant Journal © 2010 Blackwell Publishing Ltd.

Contribution of different classes of glutamate receptors in the corticostriatal polysynaptic responses from striatal direct and indirect projection neurons

PubMed Central

2013-01-01

Background Previous work showed differences in the polysynaptic activation of GABAergic synapses during corticostriatal suprathreshold responses in direct and indirect striatal projection neurons (dSPNs and iSPNs). Here, we now show differences and similarities in the polysynaptic activation of cortical glutamatergic synapses on the same responses. Corticostriatal contacts have been extensively studied. However, several questions remain unanswered, e.g.: what are the differences and similarities in the responses to glutamate in dSPNs and iSPNs? Does glutamatergic synaptic activation exhibits a distribution of latencies over time in vitro? That would be a strong suggestion of polysynaptic cortical convergence. What is the role of kainate receptors in corticostriatal transmission? Current-clamp recordings were used to answer these questions. One hypothesis was: if prolonged synaptic activation distributed along time was present, then it would be mainly generated from the cortex, and not from the striatum. Results By isolating responses from AMPA-receptors out of the complex suprathreshold response of SPNs, it is shown that a single cortical stimulus induces early and late synaptic activation lasting hundreds of milliseconds. Prolonged responses depended on cortical stimulation because they could not be elicited using intrastriatal stimulation, even if GABAergic transmission was blocked. Thus, the results are not explained by differences in evoked inhibition. Moreover, inhibitory participation was larger after cortical than after intrastriatal stimulation. A strong activation of interneurons was obtained from the cortex, demonstrating that polysynaptic activation includes the striatum. Prolonged kainate (KA) receptor responses were also elicited from the cortex. Responses of dSPNs and iSPNs did not depend on the cortical area stimulated. In contrast to AMPA-receptors, responses from NMDA- and KA-receptors do not exhibit early and late responses, but generate slow responses that contribute to plateau depolarizations. Conclusions As it has been established in previous physiological studies in vivo, synaptic invasion over different latencies, spanning hundreds of milliseconds after a single stimulus strongly indicates convergent polysynaptic activation. Interconnected cortical neurons converging on the same SPNs may explain prolonged corticostriatal responses. Glutamate receptors participation in these responses is described as well as differences and similarities between dSPNs and iSPNs. PMID:23782743

Trait-Related Cortical-Subcortical Dissociation in Bipolar Disorder: Analysis of Network Degree Centrality.

PubMed

Zhou, Qian; Womer, Fay Y; Kong, Lingtao; Wu, Feng; Jiang, Xiaowei; Zhou, Yifang; Wang, Dahai; Bai, Chuan; Chang, Miao; Fan, Guoguang; Xu, Ke; He, Yong; Tang, Yanqing; Wang, Fei

2017-05-01

Bipolar disorder is a systemic brain disorder. Accumulated evidence suggested that cortical-subcortical imbalance could be a trait-related pathogenic factor of bipolar disorder. Degree centrality, a robust index of focal connectivity in which the number of direct connections from one node to all nodes is counted, has not previously been studied in bipolar disorder as a whole. Resting state functional magnetic resonance imaging was performed on 52 patients with DSM-IV bipolar I disorder and 70 healthy controls recruited between September 2009 and July 2014. Degree centrality was calculated within cerebral gray matter for each subject and compared between patients with bipolar disorder and healthy controls. Hub distributions of both groups were explored. Effects of medication exposure and mood state on degree centrality, as well as cortical-subcortical degree centrality correlations, were explored. Compared to healthy controls, patients with bipolar disorder exhibited significant decrease in degree centrality in cortical regions, including the middle temporal pole, inferior temporal gyrus, and ventral prefrontal cortex, but showed significant increase in degree centrality mainly in subcortical regions, including caudate, thalamus, parahippocampal gyrus, hippocampi, anterior cingulate, insula, and amygdala, and a small portion of cortical regions, such as superior and middle frontal gyrus (P < .05, corrected). Spatial distributions of the 2 groups were very similar. No significant effects of medication exposure or mood state on degree centrality were found. Patients with bipolar disorder also showed significant decrease in cortical-subcortical degree centrality correlation (P = .003). These findings further contribute to the mounting evidence of cortical-subcortical dissociation in bipolar disorder pathophysiology. In addition, this study supports the continued development and implementation of graph-based techniques to enhance our understanding of the underlying neural mechanisms in mental disorders such as bipolar disorder, which are increasingly viewed as systemic brain disorders rather than disorders arising from disruption within a single structure or a limited number of structures. Due to the heterogeneity of our sample, as well as the small sample size of each medication and mood state subgroups, further investigation is needed to support our findings. © Copyright 2016 Physicians Postgraduate Press, Inc.

Asymmetric TDP pathology in primary progressive aphasia with right hemisphere language dominance.

PubMed

Kim, Garam; Vahedi, Shahrooz; Gefen, Tamar; Weintraub, Sandra; Bigio, Eileen H; Mesulam, Marek-Marsel; Geula, Changiz

2018-01-30

To quantitatively examine the regional densities and hemispheric distribution of the 43-kDa transactive response DNA-binding protein (TDP-43) inclusions, neurons, and activated microglia in a left-handed patient with right hemisphere language dominance and logopenic-variant primary progressive aphasia (PPA). Phosphorylated TDP-43 inclusions, neurons, and activated microglia were visualized with immunohistochemical and histologic methods. Markers were quantified bilaterally with unbiased stereology in language- and memory-related cortical regions. Clinical MRI indicated cortical atrophy in the right hemisphere, mostly in the temporal lobe. Significantly higher densities of TDP-43 inclusions were present in right language-related temporal regions compared to the left or to other right hemisphere regions. The memory-related entorhinal cortex (ERC) and language regions without significant atrophy showed no asymmetry. Activated microglia displayed extensive asymmetry (R > L). A substantial density of neurons remained in all areas and showed no hemispheric asymmetry. However, perikaryal size was significantly smaller in the right hemisphere across all regions except the ERC. To demonstrate the specificity of this finding, sizes of residual neurons were measured in a right-handed case with PPA and were found to be smaller in the language-dominant left hemisphere. The distribution of TDP-43 inclusions and microglial activation in right temporal language regions showed concordance with anatomic distribution of cortical atrophy and clinical presentation. The results revealed no direct relationship between density of TDP-43 inclusions and activated microglia. Reduced size of the remaining neurons is likely to contribute to cortical atrophy detected by MRI. These findings support the conclusion that there is no obligatory relationship between logopenic PPA and Alzheimer pathology. © 2018 American Academy of Neurology.

Distribution of Response Time, Cortical, and Cardiac Correlates during Emotional Interference in Persons with Subclinical Psychotic Symptoms

PubMed Central

Holper, Lisa K. B.; Aleksandrowicz, Alekandra; Müller, Mario; Ajdacic-Gross, Vladeta; Haker, Helene; Fallgatter, Andreas J.; Hagenmuller, Florence; Kawohl, Wolfram; Rössler, Wulf

2016-01-01

A psychosis phenotype can be observed below the threshold of clinical detection. The study aimed to investigate whether subclinical psychotic symptoms are associated with deficits in controlling emotional interference, and whether cortical brain and cardiac correlates of these deficits can be detected using functional near-infrared spectroscopy (fNIRS). A data set derived from a community sample was obtained from the Zurich Program for Sustainable Development of Mental Health Services. 174 subjects (mean age 29.67 ± 6.41, 91 females) were assigned to four groups ranging from low to high levels of subclinical psychotic symptoms (derived from the Symptom Checklist-90-R). Emotional interference was assessed using the emotional Stroop task comprising neutral, positive, and negative conditions. Statistical distributional methods based on delta plots [behavioral response time (RT) data] and quantile analysis (fNIRS data) were applied to evaluate the emotional interference effects. Results showed that both interference effects and disorder-specific (i.e., group-specific) effects could be detected, based on behavioral RTs, cortical hemodynamic signals (brain correlates), and heart rate variability (cardiac correlates). Subjects with high compared to low subclinical psychotic symptoms revealed significantly reduced amplitudes in dorsolateral prefrontal cortices (interference effect, p < 0.001) and middle temporal gyrus (disorder-specific group effect, p < 0.001), supported by behavioral and heart rate results. The present findings indicate that distributional analyses methods can support the detection of emotional interference effects in the emotional Stroop. The results suggested that subjects with high subclinical psychosis exhibit enhanced emotional interference effects. Based on these observations, subclinical psychosis may therefore prove to represent a valid extension of the clinical psychosis phenotype. PMID:27660608

Distribution of Response Time, Cortical, and Cardiac Correlates during Emotional Interference in Persons with Subclinical Psychotic Symptoms.

PubMed

Holper, Lisa K B; Aleksandrowicz, Alekandra; Müller, Mario; Ajdacic-Gross, Vladeta; Haker, Helene; Fallgatter, Andreas J; Hagenmuller, Florence; Kawohl, Wolfram; Rössler, Wulf

2016-01-01

A psychosis phenotype can be observed below the threshold of clinical detection. The study aimed to investigate whether subclinical psychotic symptoms are associated with deficits in controlling emotional interference, and whether cortical brain and cardiac correlates of these deficits can be detected using functional near-infrared spectroscopy (fNIRS). A data set derived from a community sample was obtained from the Zurich Program for Sustainable Development of Mental Health Services. 174 subjects (mean age 29.67 ± 6.41, 91 females) were assigned to four groups ranging from low to high levels of subclinical psychotic symptoms (derived from the Symptom Checklist-90-R). Emotional interference was assessed using the emotional Stroop task comprising neutral, positive, and negative conditions. Statistical distributional methods based on delta plots [behavioral response time (RT) data] and quantile analysis (fNIRS data) were applied to evaluate the emotional interference effects. Results showed that both interference effects and disorder-specific (i.e., group-specific) effects could be detected, based on behavioral RTs, cortical hemodynamic signals (brain correlates), and heart rate variability (cardiac correlates). Subjects with high compared to low subclinical psychotic symptoms revealed significantly reduced amplitudes in dorsolateral prefrontal cortices (interference effect, p < 0.001) and middle temporal gyrus (disorder-specific group effect, p < 0.001), supported by behavioral and heart rate results. The present findings indicate that distributional analyses methods can support the detection of emotional interference effects in the emotional Stroop. The results suggested that subjects with high subclinical psychosis exhibit enhanced emotional interference effects. Based on these observations, subclinical psychosis may therefore prove to represent a valid extension of the clinical psychosis phenotype.

Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex.

PubMed

Romero-Garcia, Rafael; Whitaker, Kirstie J; Váša, František; Seidlitz, Jakob; Shinn, Maxwell; Fonagy, Peter; Dolan, Raymond J; Jones, Peter B; Goodyer, Ian M; Bullmore, Edward T; Vértes, Petra E

2018-05-01

Complex network topology is characteristic of many biological systems, including anatomical and functional brain networks (connectomes). Here, we first constructed a structural covariance network from MRI measures of cortical thickness on 296 healthy volunteers, aged 14-24 years. Next, we designed a new algorithm for matching sample locations from the Allen Brain Atlas to the nodes of the SCN. Subsequently we used this to define, transcriptomic brain networks by estimating gene co-expression between pairs of cortical regions. Finally, we explored the hypothesis that transcriptional networks and structural MRI connectomes are coupled. A transcriptional brain network (TBN) and a structural covariance network (SCN) were correlated across connection weights and showed qualitatively similar complex topological properties: assortativity, small-worldness, modularity, and a rich-club. In both networks, the weight of an edge was inversely related to the anatomical (Euclidean) distance between regions. There were differences between networks in degree and distance distributions: the transcriptional network had a less fat-tailed degree distribution and a less positively skewed distance distribution than the SCN. However, cortical areas connected to each other within modules of the SCN had significantly higher levels of whole genome co-expression than expected by chance. Nodes connected in the SCN had especially high levels of expression and co-expression of a human supragranular enriched (HSE) gene set that has been specifically located to supragranular layers of human cerebral cortex and is known to be important for large-scale, long-distance cortico-cortical connectivity. This coupling of brain transcriptome and connectome topologies was largely but not entirely accounted for by the common constraint of physical distance on both networks. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Sparse reconstruction localization of multiple acoustic emissions in large diameter pipelines

NASA Astrophysics Data System (ADS)

Dubuc, Brennan; Ebrahimkhanlou, Arvin; Salamone, Salvatore

2017-04-01

A sparse reconstruction localization method is proposed, which is capable of localizing multiple acoustic emission events occurring closely in time. The events may be due to a number of sources, such as the growth of corrosion patches or cracks. Such acoustic emissions may yield localization failure if a triangulation method is used. The proposed method is implemented both theoretically and experimentally on large diameter thin-walled pipes. Experimental examples are presented, which demonstrate the failure of a triangulation method when multiple sources are present in this structure, while highlighting the capabilities of the proposed method. The examples are generated from experimental data of simulated acoustic emission events. The data corresponds to helical guided ultrasonic waves generated in a 3 m long large diameter pipe by pencil lead breaks on its outer surface. Acoustic emission waveforms are recorded by six sparsely distributed low-profile piezoelectric transducers instrumented on the outer surface of the pipe. The same array of transducers is used for both the proposed and the triangulation method. It is demonstrated that the proposed method is able to localize multiple events occurring closely in time. Furthermore, the matching pursuit algorithm and the basis pursuit densoising approach are each evaluated as potential numerical tools in the proposed sparse reconstruction method.

Intercomparison of two BRDF models in the estimation of the directional emissivity in MIR channel from MSG1-SEVIRI data.

PubMed

Jiang, Geng-Ming; Li, Zhao-Liang

2008-11-10

This work intercompared two Bi-directional Reflectance Distribution Function (BRDF) models, the modified Minnaert's model and the RossThick-LiSparse-R model, in the estimation of the directional emissivity in Middle Infra-Red (MIR) channel from the data acquired by the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) onboard the first Meteosat Second Generation (MSG1). The bi-directional reflectances in SEVIRI channel 4 (3.9 microm) were estimated from the combined MIR and Thermal Infra-Red (TIR) data and then were used to estimate the directional emissivity in this channel with aid of the BRDF models. The results show that: (1) Both models can relatively well describe the non-Lambertian reflective behavior of land surfaces in SEVIRI channel 4; (2) The RossThick-LiSparse-R model is better than the modified Minnaert's model in modeling the bi-directional reflectances, and the directional emissivities modeled by the modified Minnaert's model are always lower than the ones obtained by the RossThick-LiSparse-R model with averaged emissivity differences of approximately 0.01 and approximately 0.04 over the vegetated and bare areas, respectively. The use of the RossThick-LiSparse-R model in the estimation of the directional emissivity in MIR channel is recommended.

Polarity of the ascidian egg cortex and relocalization of cER and mRNAs in the early embryo.

PubMed

Prodon, François; Dru, Philippe; Roegiers, Fabrice; Sardet, Christian

2005-06-01

The mature ascidian oocyte is a large cell containing cytoplasmic and cortical domains polarized along a primary animal-vegetal (a-v) axis. The oocyte cortex is characterized by a gradient distribution of a submembrane monolayer of cortical rough endoplasmic reticulum (cER) and associated maternal postplasmic/PEM mRNAs (cER-mRNA domain). Between fertilization and first cleavage, this cER-mRNA domain is first concentrated vegetally and then relocated towards the posterior pole via microfilament-driven cortical contractions and spermaster-microtubule-driven translocations. The cER-mRNA domain further concentrates in a macroscopic cortical structure called the centrosome attracting body (CAB), which mediates a series of asymmetric divisions starting at the eight-cell stage. This results in the segregation of determinant mRNAs and their products in posterior cells of the embryo precursors of the muscle and germ line. Using two species of ascidians (Ciona intestinalis and Phallusia mammillata), we have pursued and amplified the work initiated in Halocynthia roretzi. We have analysed the cortical reorganizations in whole cells and in cortical fragments isolated from oocytes and from synchronously developing zygotes and embryos. After fertilization, we observe that a cortical patch rich in microfilaments encircles the cER-mRNA domain, concentrated into a cortical cap at the vegetal/contraction pole (indicating the future dorsal pole). Isolated cortices also retain microtubule asters rich in cER (indicating the future posterior pole). Before mitosis, parts of the cER-mRNA domain are detected, together with short microtubules, in isolated posterior (but not anterior) cortices. At the eight-cell stage, the posteriorly located cER-mRNA domain undergoes a cell-cycle-dependant compaction into the CAB. The CAB with embedded centrosomal microtubules can be isolated with cortical fragments from eight-cell-stage embryos. These and previous observations indicate that cytoskeleton-driven repositioning and compaction of a polarized cortical domain made of rough ER is a conserved mechanism used for polarization and segregation of cortical maternal mRNAs in embryos of evolutionarily distant species of ascidians.

No relative expansion of the number of prefrontal neurons in primate and human evolution.

PubMed

Gabi, Mariana; Neves, Kleber; Masseron, Carolinne; Ribeiro, Pedro F M; Ventura-Antunes, Lissa; Torres, Laila; Mota, Bruno; Kaas, Jon H; Herculano-Houzel, Suzana

2016-08-23

Human evolution is widely thought to have involved a particular expansion of prefrontal cortex. This popular notion has recently been challenged, although controversies remain. Here we show that the prefrontal region of both human and nonhuman primates holds about 8% of cortical neurons, with no clear difference across humans and other primates in the distribution of cortical neurons or white matter cells along the anteroposterior axis. Further, we find that the volumes of human prefrontal gray and white matter match the expected volumes for the number of neurons in the gray matter and for the number of other cells in the white matter compared with other primate species. These results indicate that prefrontal cortical expansion in human evolution happened along the same allometric trajectory as for other primate species, without modification of the distribution of neurons across its surface or of the volume of the underlying white matter. We thus propose that the most distinctive feature of the human prefrontal cortex is its absolute number of neurons, not its relative volume.

Healthy and pathological cerebellar Spiking Neural Networks in Vestibulo-Ocular Reflex.

PubMed

Antonietti, Alberto; Casellato, Claudia; Geminiani, Alice; D'Angelo, Egidio; Pedrocchi, Alessandra

2015-01-01

Since the Marr-Albus model, computational neuroscientists have been developing a variety of models of the cerebellum, with different approaches and features. In this work, we developed and tested realistic artificial Spiking Neural Networks inspired to this brain region. We tested in computational simulations of the Vestibulo-Ocular Reflex protocol three different models: a network equipped with a single plasticity site, at the cortical level; a network equipped with a distributed plasticity, at both cortical and nuclear levels; a network with a pathological plasticity mechanism at the cortical level. We analyzed the learning performance of the three different models, highlighting the behavioral differences among them. We proved that the model with a distributed plasticity produces a faster and more accurate cerebellar response, especially during a second session of acquisition, compared with the single plasticity model. Furthermore, the pathological model shows an impaired learning capability in Vestibulo-Ocular Reflex acquisition, as found in neurophysiological studies. The effect of the different plasticity conditions, which change fast and slow dynamics, memory consolidation and, in general, learning capabilities of the cerebellar network, explains differences in the behavioral outcome.

Treatment of Memory Disorders in Gulf War Illness with High-Definition Transcranial Direct Cortical Stimulation

DTIC Science & Technology

2017-10-01

AWARD NUMBER: W81XWH-16-1-0521 TITLE: Treatment of Memory Disorders in Gulf War Illness with High - Definition Transcranial Direct Cortical...Approved for Public Release; Distribution Unlimited The views, opinions and/or findings contained in this report are those of the author(s) and should...Sep 2017 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Treatment of Memory Disorders in Gulf War Illness with High -Definition Transcranial Direct

Femoral neck BMD is a strong predictor of hip fracture susceptibility in elderly men and women because it detects cortical bone instability: the Rotterdam Study.

PubMed

Rivadeneira, Fernando; Zillikens, M Carola; De Laet, Chris Edh; Hofman, Albert; Uitterlinden, André G; Beck, Thomas J; Pols, Huibert Ap

2007-11-01

We studied HSA measurements in relation to hip fracture risk in 4,806 individuals (2,740 women). Hip fractures (n = 147) occurred at the same absolute levels of bone instability in both sexes. Cortical instability (propensity of thinner cortices in wide diameters to buckle) explains why hip fracture risk at different BMD levels is the same across sexes. Despite the sexual dimorphism of bone, hip fracture risk is very similar in men and women at the same absolute BMD. We aimed to elucidate the main structural properties of bone that underlie the measured BMD and that ultimately determines the risk of hip fracture in elderly men and women. This study is part of the Rotterdam Study (a large prospective population-based cohort) and included 147 incident hip fracture cases in 4,806 participants with DXA-derived hip structural analysis (mean follow-up, 8.6 yr). Indices compared in relation to fracture included neck width, cortical thickness, section modulus (an index of bending strength), and buckling ratio (an index of cortical bone instability). We used a mathematical model to calculate the hip fracture distribution by femoral neck BMD, BMC, bone area, and hip structure analysis (HSA) parameters (cortical thickness, section modulus narrow neck width, and buckling ratio) and compared it with prospective data from the Rotterdam Study. In the prospective data, hip fracture cases in both sexes had lower BMD, thinner cortices, greater bone width, lower strength, and higher instability at baseline. In fractured individuals, men had an average BMD that was 0.09 g/cm(2) higher than women (p < 0.00001), whereas no significant difference in buckling ratios was seen. Modeled fracture distribution by BMD and buckling ratio levels were in concordance to the prospective data and showed that hip fractures seem to occur at the same absolute levels of bone instability (buckling ratio) in both men and women. No significant differences were observed between the areas under the ROC curves of BMD (0.8146 in women and 0.8048 in men) and the buckling ratio (0.8161 in women and 0.7759 in men). The buckling ratio (an index of bone instability) portrays in both sexes the critical balance between cortical thickness and bone width. Our findings suggest that extreme thinning of cortices in expanded bones plays a key role on local susceptibility to fracture. Even though the buckling ratio does not offer additional predictive value, these findings improve our understanding of why low BMD is a good predictor of fragility fractures.

Temporal lobe stimulation reveals anatomic distinction between auditory naming processes.

PubMed

Hamberger, M J; Seidel, W T; Goodman, R R; Perrine, K; McKhann, G M

2003-05-13

Language errors induced by cortical stimulation can provide insight into function(s) supported by the area stimulated. The authors observed that some stimulation-induced errors during auditory description naming were characterized by tip-of-the-tongue responses or paraphasic errors, suggesting expressive difficulty, whereas others were qualitatively different, suggesting receptive difficulty. They hypothesized that these two response types reflected disruption at different stages of auditory verbal processing and that these "subprocesses" might be supported by anatomically distinct cortical areas. To explore the topographic distribution of error types in auditory verbal processing. Twenty-one patients requiring left temporal lobe surgery underwent preresection language mapping using direct cortical stimulation. Auditory naming was tested at temporal sites extending from 1 cm from the anterior tip to the parietal operculum. Errors were dichotomized as either "expressive" or "receptive." The topographic distribution of error types was explored. Sites associated with the two error types were topographically distinct from one another. Most receptive sites were located in the middle portion of the superior temporal gyrus (STG), whereas most expressive sites fell outside this region, scattered along lateral temporal and temporoparietal cortex. Results raise clinical questions regarding the inclusion of the STG in temporal lobe epilepsy surgery and suggest that more detailed cortical mapping might enable better prediction of postoperative language decline. From a theoretical perspective, results carry implications regarding the understanding of structure-function relations underlying temporal lobe mediation of auditory language processing.

Theta-Modulated Gamma-Band Synchronization Among Activated Regions During a Verb Generation Task

PubMed Central

Doesburg, Sam M.; Vinette, Sarah A.; Cheung, Michael J.; Pang, Elizabeth W.

2012-01-01

Expressive language is complex and involves processing within a distributed network of cortical regions. Functional MRI and magnetoencephalography (MEG) have identified brain areas critical for expressive language, but how these regions communicate across the network remains poorly understood. It is thought that synchronization of oscillations between neural populations, particularly at a gamma rate (>30 Hz), underlies functional integration within cortical networks. Modulation of gamma rhythms by theta-band oscillations (4–8 Hz) has been proposed as a mechanism for the integration of local cell coalitions into large-scale networks underlying cognition and perception. The present study tested the hypothesis that these oscillatory mechanisms of functional integration were present within the expressive language network. We recorded MEG while subjects performed a covert verb generation task. We localized activated cortical regions using beamformer analysis, calculated inter-regional phase locking between activated areas, and measured modulation of inter-regional gamma synchronization by theta phase. The results show task-dependent gamma-band synchronization among regions activated during the performance of the verb generation task, and we provide evidence that these transient and periodic instances of high-frequency connectivity were modulated by the phase of cortical theta oscillations. These findings suggest that oscillatory synchronization and cross-frequency interactions are mechanisms for functional integration among distributed brain areas supporting expressive language processing. PMID:22707946

The Roots of Alzheimer's Disease: Are High-Expanding Cortical Areas Preferentially Targeted?†.

PubMed

Fjell, Anders M; Amlien, Inge K; Sneve, Markus H; Grydeland, Håkon; Tamnes, Christian K; Chaplin, Tristan A; Rosa, Marcello G P; Walhovd, Kristine B

2015-09-01

Alzheimer's disease (AD) is regarded a human-specific condition, and it has been suggested that brain regions highly expanded in humans compared with other primates are selectively targeted. We calculated shared and unique variance in the distribution of AD atrophy accounted for by cortical expansion between macaque and human, affiliation to the default mode network (DMN), ontogenetic development and normal aging. Cortical expansion was moderately related to atrophy, but a critical discrepancy was seen in the medial temporo-parietal episodic memory network. Identification of "hotspots" and "coldspots" of expansion across several primate species did not yield compelling evidence for the hypothesis that highly expanded regions are specifically targeted. Controlling for distribution of atrophy in aging substantially attenuated the expansion-AD relationship. A path model showed that all variables explained unique variance in AD atrophy but were generally mediated through aging. This supports a systems-vulnerability model, where critical networks are subject to various negative impacts, aging in particular, rather than being selectively targeted in AD. An alternative approach is suggested, focused on the interplay of the phylogenetically old and preserved medial temporal lobe areas with more highly expanded association cortices governed by different principles of plasticity and stability. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Neural Correlates of Visual–Spatial Attention in Electrocorticographic Signals in Humans

PubMed Central

Gunduz, Aysegul; Brunner, Peter; Daitch, Amy; Leuthardt, Eric C.; Ritaccio, Anthony L.; Pesaran, Bijan; Schalk, Gerwin

2011-01-01

Attention is a cognitive selection mechanism that allocates the limited processing resources of the brain to the sensory streams most relevant to our immediate goals, thereby enhancing responsiveness and behavioral performance. The underlying neural mechanisms of orienting attention are distributed across a widespread cortical network. While aspects of this network have been extensively studied, details about the electrophysiological dynamics of this network are scarce. In this study, we investigated attentional networks using electrocorticographic (ECoG) recordings from the surface of the brain, which combine broad spatial coverage with high temporal resolution, in five human subjects. ECoG was recorded when subjects covertly attended to a spatial location and responded to contrast changes in the presence of distractors in a modified Posner cueing task. ECoG amplitudes in the alpha, beta, and gamma bands identified neural changes associated with covert attention and motor preparation/execution in the different stages of the task. The results show that attentional engagement was primarily associated with ECoG activity in the visual, prefrontal, premotor, and parietal cortices. Motor preparation/execution was associated with ECoG activity in premotor/sensorimotor cortices. In summary, our results illustrate rich and distributed cortical dynamics that are associated with orienting attention and the subsequent motor preparation and execution. These findings are largely consistent with and expand on primate studies using intracortical recordings and human functional neuroimaging studies. PMID:22046153

Category-Selectivity in Human Visual Cortex Follows Cortical Topology: A Grouped icEEG Study

PubMed Central

Conner, Christopher Richard; Whaley, Meagan Lee; Baboyan, Vatche George; Tandon, Nitin

2016-01-01

Neuroimaging studies suggest that category-selective regions in higher-order visual cortex are topologically organized around specific anatomical landmarks: the mid-fusiform sulcus (MFS) in the ventral temporal cortex (VTC) and lateral occipital sulcus (LOS) in the lateral occipital cortex (LOC). To derive precise structure-function maps from direct neural signals, we collected intracranial EEG (icEEG) recordings in a large human cohort (n = 26) undergoing implantation of subdural electrodes. A surface-based approach to grouped icEEG analysis was used to overcome challenges from sparse electrode coverage within subjects and variable cortical anatomy across subjects. The topology of category-selectivity in bilateral VTC and LOC was assessed for five classes of visual stimuli—faces, animate non-face (animals/body-parts), places, tools, and words—using correlational and linear mixed effects analyses. In the LOC, selectivity for living (faces and animate non-face) and non-living (places and tools) classes was arranged in a ventral-to-dorsal axis along the LOS. In the VTC, selectivity for living and non-living stimuli was arranged in a latero-medial axis along the MFS. Written word-selectivity was reliably localized to the intersection of the left MFS and the occipito-temporal sulcus. These findings provide direct electrophysiological evidence for topological information structuring of functional representations within higher-order visual cortex. PMID:27272936

Deconstructing white matter connectivity of human amygdala nuclei with thalamus and cortex subdivisions in vivo.

PubMed

Abivardi, Aslan; Bach, Dominik R

2017-08-01

Structural alterations in long-range amygdala connections are proposed to crucially underlie several neuropsychiatric disorders. While progress has been made in elucidating the function of these connections, our understanding of their structure in humans remains sparse and non-systematic. Harnessing diffusion-weighted imaging and probabilistic tractography in humans, we investigate connections between two main amygdala nucleus groups, thalamic nuclei, and cortex. We first parcellated amygdala into deep (basolateral) and superficial (centrocortical) nucleus groups, and thalamus into six subregions, using previously established protocols based on connectivity. Cortex was parcellated based on T1-weighted images. We found substantial amygdala connections to thalamus, with different patterns for the two amygdala nuclei. Crucially, we describe direct subcortical connections between amygdala and paraventricular thalamus. Different from rodents but similar to non-human primates, these are more pronounced for basolateral than centrocortical amygdala. Substantial white-matter connectivity between amygdala and visual pulvinar is also more pronounced for basolateral amygdala. Furthermore, we establish detailed connectivity profiles for basolateral and centrocortical amygdala to cortical regions. These exhibit cascadic connections with sensory cortices as suggested previously based on tracer methods in non-human animals. We propose that the quantitative connectivity profiles provided here may guide future work on normal and pathological function of human amygdala. Hum Brain Mapp 38:3927-3940, 2017. © 2017 Wiley Periodicals, Inc. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Deconstructing white matter connectivity of human amygdala nuclei with thalamus and cortex subdivisions in vivo

PubMed Central

2017-01-01

Abstract Structural alterations in long‐range amygdala connections are proposed to crucially underlie several neuropsychiatric disorders. While progress has been made in elucidating the function of these connections, our understanding of their structure in humans remains sparse and non‐systematic. Harnessing diffusion‐weighted imaging and probabilistic tractography in humans, we investigate connections between two main amygdala nucleus groups, thalamic nuclei, and cortex. We first parcellated amygdala into deep (basolateral) and superficial (centrocortical) nucleus groups, and thalamus into six subregions, using previously established protocols based on connectivity. Cortex was parcellated based on T1‐weighted images. We found substantial amygdala connections to thalamus, with different patterns for the two amygdala nuclei. Crucially, we describe direct subcortical connections between amygdala and paraventricular thalamus. Different from rodents but similar to non‐human primates, these are more pronounced for basolateral than centrocortical amygdala. Substantial white‐matter connectivity between amygdala and visual pulvinar is also more pronounced for basolateral amygdala. Furthermore, we establish detailed connectivity profiles for basolateral and centrocortical amygdala to cortical regions. These exhibit cascadic connections with sensory cortices as suggested previously based on tracer methods in non‐human animals. We propose that the quantitative connectivity profiles provided here may guide future work on normal and pathological function of human amygdala. Hum Brain Mapp 38:3927–3940, 2017. © 2017 Wiley Periodicals, Inc. PMID:28512761

Glutamatergic and neurometabolic alterations in chronic cocaine users measured with (1) H-magnetic resonance spectroscopy.

PubMed

Hulka, Lea M; Scheidegger, Milan; Vonmoos, Matthias; Preller, Katrin H; Baumgartner, Markus R; Herdener, Marcus; Seifritz, Erich; Henning, Anke; Quednow, Boris B

2016-01-01

Cocaine addiction is a chronically relapsing disorder that is associated with harmful consequences. Relapses occur frequently and effective pharmacotherapies are currently sparse. Preclinical studies suggest that altered glutamatergic signaling is crucial for the maintenance of cocaine self-administration. However, the translational validity of these models is currently unknown. Therefore, we investigated potential differences of glutamate, glutamine and further metabolite levels in the pregenual anterior cingulate cortex (pgACC) and the right dorsolateral prefrontal cortex (rDLPFC) of chronic cocaine users and controls using the PRior knOwledge FITting 2.0 tool in combination with two-dimensional J-resolved single-voxel (1) H-magnetic resonance spectroscopy at 3T and voxel tissue composition and relaxation correction. Glutamate and glutamine levels did not differ between cocaine users and controls, but higher weekly cocaine use and higher cocaine hair concentrations were associated with lower glutamine/creatine ratios in the pgACC. Interestingly, cocaine users exhibited higher glucose/total creatine ratios than controls in the pgACC and higher choline/creatine ratios in the pgACC and rDLPFC. These results imply that cocaine use is associated with altered cortical glucose metabolism and membrane turnover. Finally, cocaine use over the past 6 months appears to decrease cortical glutamine levels indicating changes in glutamate cycling. © 2014 Society for the Study of Addiction.

Modulation of postsynaptic potentials in rat cortical neurons by valerian extracts macerated with different alcohols: involvement of adenosine A(1)- and GABA(A)-receptors.

PubMed

Sichardt, K; Vissiennon, Z; Koetter, U; Brattström, A; Nieber, K

2007-10-01

Valeriana officinalis (valerian) is used traditionally as a mild sedative. Research into valerian is sparse, and studies differ greatly with respect to design, measures and preparations used. This study compares the action of a methanol (M-E), ethanol (E-E) and an extract macerated with ethylacetate (EA-E) from roots of valerian (Valeriana officinalis L., Valerianaceae) on postsynaptic potentials (PSPs) in cortical neurons. Intracellular recordings were performed in rat brain slice preparations containing pyramidal cells of the cingulate cortex. PSPs were induced by electrical field stimulation. The M-E induced strong inhibition in the concentration range 0.1-15 mg/mL, whereas the E-E (1-10 mg/mL) did not influence significantly the PSPs. The maximum inhibition induced by the M-E was completely antagonized by 1,3-dipropyl-8-cyclopentylxanthine (DPCPX, 0.1 microm), an antagonist on the adenosine A(1) receptor. Contrary to the M-E, the EA-E (10 mg/mL) induced an increase of the PSPs, which was completely blocked by the GABA(A) receptor antagonist picrotoxin (100 microm). The data suggest that activation of adenosine A(1) and GABA(A) receptors is mediated by different components within the valerian extract. The two mechanisms may contribute independently to the sleep-inducing effect of valerian.

Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex

NASA Astrophysics Data System (ADS)

Ohki, Kenichi; Chung, Sooyoung; Ch'ng, Yeang H.; Kara, Prakash; Reid, R. Clay

2005-02-01

Neurons in the cerebral cortex are organized into anatomical columns, with ensembles of cells arranged from the surface to the white matter. Within a column, neurons often share functional properties, such as selectivity for stimulus orientation; columns with distinct properties, such as different preferred orientations, tile the cortical surface in orderly patterns. This functional architecture was discovered with the relatively sparse sampling of microelectrode recordings. Optical imaging of membrane voltage or metabolic activity elucidated the overall geometry of functional maps, but is averaged over many cells (resolution >100µm). Consequently, the purity of functional domains and the precision of the borders between them could not be resolved. Here, we labelled thousands of neurons of the visual cortex with a calcium-sensitive indicator in vivo. We then imaged the activity of neuronal populations at single-cell resolution with two-photon microscopy up to a depth of 400µm. In rat primary visual cortex, neurons had robust orientation selectivity but there was no discernible local structure; neighbouring neurons often responded to different orientations. In area 18 of cat visual cortex, functional maps were organized at a fine scale. Neurons with opposite preferences for stimulus direction were segregated with extraordinary spatial precision in three dimensions, with columnar borders one to two cells wide. These results indicate that cortical maps can be built with single-cell precision.

The effect of preterm birth on brainstem, middle latency and cortical auditory evoked responses (BMC AERs).

PubMed

Pasman, J W; Rotteveel, J J; de Graaf, R; Stegeman, D F; Visco, Y M

1992-12-01

Recent studies on the maturation of auditory brainstem evoked responses (ABRs) present conflicting results, whereas only sparse reports exist with respect to the maturation of middle latency auditory evoked responses (MLRs) and auditory cortical evoked responses (ACRs). The present study reports the effect of preterm birth on the maturation of auditory evoked responses in low risk preterm infants (27-34 weeks conceptional age). The ABRs indicate a consistent trend towards longer latencies for all individual ABR components and towards longer interpeak latencies in preterm infants. The MLR shows longer latencies for early component P0 in preterm infants. The ACRs show a remarkable difference between preterm and term infants. At 40 weeks CA the latencies of ACR components Na and P2 are significantly longer in term infants, whereas at 52 weeks CA the latencies of the same ACR components are shorter in term infants. The results support the hypothesis that retarded myelination of the central auditory pathway is partially responsible for differences found between preterm infants and term infants with respect to late ABR components and early MLR component P0. Furthermore, mild conductive hearing loss in preterm infants may also play its role. A more complex mechanism is implicated to account for the findings noted with respect to MLR component Na and ACR components Na and P2.

Brain connections of words, perceptions and actions: A neurobiological model of spatio-temporal semantic activation in the human cortex.

PubMed

Tomasello, Rosario; Garagnani, Max; Wennekers, Thomas; Pulvermüller, Friedemann

2017-04-01

Neuroimaging and patient studies show that different areas of cortex respectively specialize for general and selective, or category-specific, semantic processing. Why are there both semantic hubs and category-specificity, and how come that they emerge in different cortical regions? Can the activation time-course of these areas be predicted and explained by brain-like network models? In this present work, we extend a neurocomputational model of human cortical function to simulate the time-course of cortical processes of understanding meaningful concrete words. The model implements frontal and temporal cortical areas for language, perception, and action along with their connectivity. It uses Hebbian learning to semantically ground words in aspects of their referential object- and action-related meaning. Compared with earlier proposals, the present model incorporates additional neuroanatomical links supported by connectivity studies and downscaled synaptic weights in order to control for functional between-area differences purely due to the number of in- or output links of an area. We show that learning of semantic relationships between words and the objects and actions these symbols are used to speak about, leads to the formation of distributed circuits, which all include neuronal material in connector hub areas bridging between sensory and motor cortical systems. Therefore, these connector hub areas acquire a role as semantic hubs. By differentially reaching into motor or visual areas, the cortical distributions of the emergent 'semantic circuits' reflect aspects of the represented symbols' meaning, thus explaining category-specificity. The improved connectivity structure of our model entails a degree of category-specificity even in the 'semantic hubs' of the model. The relative time-course of activation of these areas is typically fast and near-simultaneous, with semantic hubs central to the network structure activating before modality-preferential areas carrying semantic information. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

A gradient in cortical pathology in multiple sclerosis by in vivo quantitative 7 T imaging

PubMed Central

Louapre, Céline; Govindarajan, Sindhuja T.; Giannì, Costanza; Nielsen, A. Scott; Cohen-Adad, Julien; Sloane, Jacob; Kinkel, Revere P.

2015-01-01

We used a surface-based analysis of T2* relaxation rates at 7 T magnetic resonance imaging, which allows sampling quantitative T2* throughout the cortical width, to map in vivo the spatial distribution of intracortical pathology in multiple sclerosis. Ultra-high resolution quantitative T2* maps were obtained in 10 subjects with clinically isolated syndrome/early multiple sclerosis (≤3 years disease duration), 18 subjects with relapsing-remitting multiple sclerosis (≥4 years disease duration), 13 subjects with secondary progressive multiple sclerosis, and in 17 age-matched healthy controls. Quantitative T2* maps were registered to anatomical cortical surfaces for sampling T2* at 25%, 50% and 75% depth from the pial surface. Differences in laminar quantitative T2* between each patient group and controls were assessed using general linear model (P < 0.05 corrected for multiple comparisons). In all 41 multiple sclerosis cases, we tested for associations between laminar quantitative T2*, neurological disability, Multiple Sclerosis Severity Score, cortical thickness, and white matter lesions. In patients, we measured, T2* in intracortical lesions and in the intracortical portion of leukocortical lesions visually detected on 7 T scans. Cortical lesional T2* was compared with patients’ normal-appearing cortical grey matter T2* (paired t-test) and with mean cortical T2* in controls (linear regression using age as nuisance factor). Subjects with multiple sclerosis exhibited relative to controls, independent from cortical thickness, significantly increased T2*, consistent with cortical myelin and iron loss. In early disease, T2* changes were focal and mainly confined at 25% depth, and in cortical sulci. In later disease stages T2* changes involved deeper cortical laminae, multiple cortical areas and gyri. In patients, T2* in intracortical and leukocortical lesions was increased compared with normal-appearing cortical grey matter (P < 10−10 and P < 10−7), and mean cortical T2* in controls (P < 10−5 and P < 10−6). In secondary progressive multiple sclerosis, T2* in normal-appearing cortical grey matter was significantly increased relative to controls (P < 0.001). Laminar T2* changes may, thus, result from cortical pathology within and outside focal cortical lesions. Neurological disability and Multiple Sclerosis Severity Score correlated each with the degree of laminar quantitative T2* changes, independently from white matter lesions, the greatest association being at 25% depth, while they did not correlate with cortical thickness and volume. These findings demonstrate a gradient in the expression of cortical pathology throughout stages of multiple sclerosis, which was associated with worse disability and provides in vivo evidence for the existence of a cortical pathological process driven from the pial surface. PMID:25681411

No estuarine intertidal bathymetry? No worries! Estimating intertidal depth contours from readily available GIS data

EPA Science Inventory

The importance of littoral elevation to the distribution of intertidal species has long been a cornerstone of estuarine ecology and its historical importance to navigation cannot be understated. However, historically, intertidal elevation measurements have been sparse likely due ...

Got Game

ERIC Educational Resources Information Center

Lum, Lydia

2007-01-01

Around the country, disabled sports are often treated like second-class siblings to their able-bodied counterparts, largely because the latter bring in prestigious tournaments and bowl games, lucrative TV contracts and national exposure for top athletes and coaches. Because disabled people are so sparsely distributed in the general population, it…

Does the visual system of the flying fox resemble that of primates? The distribution of calcium-binding proteins in the primary visual pathway of Pteropus poliocephalus.

PubMed

Ichida, J M; Rosa, M G; Casagrande, V A

2000-01-31

It has been proposed that flying foxes and echolocating bats evolved independently from early mammalian ancestors in such a way that flying foxes form one of the suborders most closely related to primates. A major piece of evidence offered in support of a flying fox-primate link is the highly developed visual system of flying foxes, which is theorized to be primate-like in several different ways. Because the calcium-binding proteins parvalbumin (PV) and calbindin (CB) show distinct and consistent distributions in the primate visual system, the distribution of these same proteins was examined in the flying fox (Pteropus poliocephalus) visual system. Standard immunocytochemical techniques reveal that PV labeling within the lateral geniculate nucleus (LGN) of the flying fox is sparse, with clearly labeled cells located only within layer 1, adjacent to the optic tract. CB labeling in the LGN is profuse, with cells labeled in all layers throughout the nucleus. Double labeling reveals that all PV+ cells also contain CB, and that these cells are among the largest in the LGN. In primary visual cortex (V1) PV and CB label different classes of non-pyramidal neurons. PV+ cells are found in all cortical layers, although labeled cells are found only rarely in layer I. CB+ cells are found primarily in layers II and III. The density of PV+ neuropil correlates with the density of cytochrome oxidase staining; however, no CO+ or PV+ or CB+ patches or blobs are found in V1. These results show that the distribution of calcium-binding proteins in the flying fox LGN is unlike that found in primates, in which antibodies for PV and CB label specific separate populations of relay cells that exist in different layers. Indeed, the pattern of calcium-binding protein distribution in the flying fox LGN is different from that reported in any other terrestrial mammal. Within V1 no PV+ patches, CO blobs, or patchy distribution of CB+ neuropil that might reveal interblobs characteristic of primate V1 are found; however, PV and CB are found in separate populations of non-pyramidal neurons. The types of V1 cells labeled with antibodies to PV and CB in all mammals examined including the flying fox suggest that the similarities in the cellular distribution of these proteins in cortex reflect the fact that this feature is common to all mammals.

Cortical Neural Computation by Discrete Results Hypothesis

PubMed Central

Castejon, Carlos; Nuñez, Angel

2016-01-01

One of the most challenging problems we face in neuroscience is to understand how the cortex performs computations. There is increasing evidence that the power of the cortical processing is produced by populations of neurons forming dynamic neuronal ensembles. Theoretical proposals and multineuronal experimental studies have revealed that ensembles of neurons can form emergent functional units. However, how these ensembles are implicated in cortical computations is still a mystery. Although cell ensembles have been associated with brain rhythms, the functional interaction remains largely unclear. It is still unknown how spatially distributed neuronal activity can be temporally integrated to contribute to cortical computations. A theoretical explanation integrating spatial and temporal aspects of cortical processing is still lacking. In this Hypothesis and Theory article, we propose a new functional theoretical framework to explain the computational roles of these ensembles in cortical processing. We suggest that complex neural computations underlying cortical processing could be temporally discrete and that sensory information would need to be quantized to be computed by the cerebral cortex. Accordingly, we propose that cortical processing is produced by the computation of discrete spatio-temporal functional units that we have called “Discrete Results” (Discrete Results Hypothesis). This hypothesis represents a novel functional mechanism by which information processing is computed in the cortex. Furthermore, we propose that precise dynamic sequences of “Discrete Results” is the mechanism used by the cortex to extract, code, memorize and transmit neural information. The novel “Discrete Results” concept has the ability to match the spatial and temporal aspects of cortical processing. We discuss the possible neural underpinnings of these functional computational units and describe the empirical evidence supporting our hypothesis. We propose that fast-spiking (FS) interneuron may be a key element in our hypothesis providing the basis for this computation. PMID:27807408

Cortical Neural Computation by Discrete Results Hypothesis.

PubMed

Castejon, Carlos; Nuñez, Angel

2016-01-01

One of the most challenging problems we face in neuroscience is to understand how the cortex performs computations. There is increasing evidence that the power of the cortical processing is produced by populations of neurons forming dynamic neuronal ensembles. Theoretical proposals and multineuronal experimental studies have revealed that ensembles of neurons can form emergent functional units. However, how these ensembles are implicated in cortical computations is still a mystery. Although cell ensembles have been associated with brain rhythms, the functional interaction remains largely unclear. It is still unknown how spatially distributed neuronal activity can be temporally integrated to contribute to cortical computations. A theoretical explanation integrating spatial and temporal aspects of cortical processing is still lacking. In this Hypothesis and Theory article, we propose a new functional theoretical framework to explain the computational roles of these ensembles in cortical processing. We suggest that complex neural computations underlying cortical processing could be temporally discrete and that sensory information would need to be quantized to be computed by the cerebral cortex. Accordingly, we propose that cortical processing is produced by the computation of discrete spatio-temporal functional units that we have called "Discrete Results" (Discrete Results Hypothesis). This hypothesis represents a novel functional mechanism by which information processing is computed in the cortex. Furthermore, we propose that precise dynamic sequences of "Discrete Results" is the mechanism used by the cortex to extract, code, memorize and transmit neural information. The novel "Discrete Results" concept has the ability to match the spatial and temporal aspects of cortical processing. We discuss the possible neural underpinnings of these functional computational units and describe the empirical evidence supporting our hypothesis. We propose that fast-spiking (FS) interneuron may be a key element in our hypothesis providing the basis for this computation.

Nano-structural, compositional and micro-architectural signs of cortical bone fragility at the superolateral femoral neck in elderly hip fracture patients vs. healthy aged controls.

PubMed

Milovanovic, Petar; Rakocevic, Zlatko; Djonic, Danijela; Zivkovic, Vladimir; Hahn, Michael; Nikolic, Slobodan; Amling, Michael; Busse, Bjoern; Djuric, Marija

2014-07-01

To unravel the origins of decreased bone strength in the superolateral femoral neck, we assessed bone structural features across multiple length scales at this cortical fracture initiating region in postmenopausal women with hip fracture and in aged-matched controls. Our combined methodological approach encompassed atomic force microscopy (AFM) characterization of cortical bone nano-structure, assessment of mineral content/distribution via quantitative backscattered electron imaging (qBEI), measurement of bone material properties by reference point indentation, as well as evaluation of cortical micro-architecture and osteocyte lacunar density. Our findings revealed a wide range of differences between the fracture group and the controls, suggesting a number of detrimental changes at various levels of cortical bone hierarchical organization that may render bone fragile. Namely, mineral crystals at external cortical bone surfaces of the fracture group were larger (65.22nm±41.21nm vs. 36.75nm±18.49nm, p<0.001), and a shift to a higher mineral content and more homogenous mineralization profile as revealed via qBEI were found in the bone matrix of the fracture group. Fracture cases showed nearly 35% higher cortical porosity and showed significantly reduced osteocyte lacunar density compared to controls (226±27 vs. 247±32#/mm(2), p=0.05). Along with increased crystal size, a shift towards higher mineralization and a tendency to increased cortical porosity and reduced osteocyte lacunar number delineate that cortical bone of the superolateral femoral neck bears distinct signs of fragility at various levels of its structural organization. These results contribute to the understanding of hierarchical bone structure changes in age-related fragility. Copyright © 2014 Elsevier Inc. All rights reserved.

Detection and characterization of nonspecific, sparsely-populated binding modes in the early stages of complexation

PubMed Central

Cardone, A.; Bornstein, A.; Pant, H. C.; Brady, M.; Sriram, R.; Hassan, S. A.

2015-01-01

A method is proposed to study protein-ligand binding in a system governed by specific and non-specific interactions. Strong associations lead to narrow distributions in the proteins configuration space; weak and ultra-weak associations lead instead to broader distributions, a manifestation of non-specific, sparsely-populated binding modes with multiple interfaces. The method is based on the notion that a discrete set of preferential first-encounter modes are metastable states from which stable (pre-relaxation) complexes at equilibrium evolve. The method can be used to explore alternative pathways of complexation with statistical significance and can be integrated into a general algorithm to study protein interaction networks. The method is applied to a peptide-protein complex. The peptide adopts several low-population conformers and binds in a variety of modes with a broad range of affinities. The system is thus well suited to analyze general features of binding, including conformational selection, multiplicity of binding modes, and nonspecific interactions, and to illustrate how the method can be applied to study these problems systematically. The equilibrium distributions can be used to generate biasing functions for simulations of multiprotein systems from which bulk thermodynamic quantities can be calculated. PMID:25782918

Sparse Reconstruction for Temperature Distribution Using DTS Fiber Optic Sensors with Applications in Electrical Generator Stator Monitoring

PubMed Central

Bazzo, João Paulo; Pipa, Daniel Rodrigues; da Silva, Erlon Vagner; Martelli, Cicero; Cardozo da Silva, Jean Carlos

2016-01-01

This paper presents an image reconstruction method to monitor the temperature distribution of electric generator stators. The main objective is to identify insulation failures that may arise as hotspots in the structure. The method is based on temperature readings of fiber optic distributed sensors (DTS) and a sparse reconstruction algorithm. Thermal images of the structure are formed by appropriately combining atoms of a dictionary of hotspots, which was constructed by finite element simulation with a multi-physical model. Due to difficulties for reproducing insulation faults in real stator structure, experimental tests were performed using a prototype similar to the real structure. The results demonstrate the ability of the proposed method to reconstruct images of hotspots with dimensions down to 15 cm, representing a resolution gain of up to six times when compared to the DTS spatial resolution. In addition, satisfactory results were also obtained to detect hotspots with only 5 cm. The application of the proposed algorithm for thermal imaging of generator stators can contribute to the identification of insulation faults in early stages, thereby avoiding catastrophic damage to the structure. PMID:27618040

ESTIMATION OF FUNCTIONALS OF SPARSE COVARIANCE MATRICES.

PubMed

Fan, Jianqing; Rigollet, Philippe; Wang, Weichen

High-dimensional statistical tests often ignore correlations to gain simplicity and stability leading to null distributions that depend on functionals of correlation matrices such as their Frobenius norm and other ℓ r norms. Motivated by the computation of critical values of such tests, we investigate the difficulty of estimation the functionals of sparse correlation matrices. Specifically, we show that simple plug-in procedures based on thresholded estimators of correlation matrices are sparsity-adaptive and minimax optimal over a large class of correlation matrices. Akin to previous results on functional estimation, the minimax rates exhibit an elbow phenomenon. Our results are further illustrated in simulated data as well as an empirical study of data arising in financial econometrics.

ESTIMATION OF FUNCTIONALS OF SPARSE COVARIANCE MATRICES

PubMed Central

Fan, Jianqing; Rigollet, Philippe; Wang, Weichen

2016-01-01

High-dimensional statistical tests often ignore correlations to gain simplicity and stability leading to null distributions that depend on functionals of correlation matrices such as their Frobenius norm and other ℓr norms. Motivated by the computation of critical values of such tests, we investigate the difficulty of estimation the functionals of sparse correlation matrices. Specifically, we show that simple plug-in procedures based on thresholded estimators of correlation matrices are sparsity-adaptive and minimax optimal over a large class of correlation matrices. Akin to previous results on functional estimation, the minimax rates exhibit an elbow phenomenon. Our results are further illustrated in simulated data as well as an empirical study of data arising in financial econometrics. PMID:26806986

Bayesian X-ray computed tomography using a three-level hierarchical prior model

NASA Astrophysics Data System (ADS)

Wang, Li; Mohammad-Djafari, Ali; Gac, Nicolas

2017-06-01

In recent decades X-ray Computed Tomography (CT) image reconstruction has been largely developed in both medical and industrial domain. In this paper, we propose using the Bayesian inference approach with a new hierarchical prior model. In the proposed model, a generalised Student-t distribution is used to enforce the Haar transformation of images to be sparse. Comparisons with some state of the art methods are presented. It is shown that by using the proposed model, the sparsity of sparse representation of images is enforced, so that edges of images are preserved. Simulation results are also provided to demonstrate the effectiveness of the new hierarchical model for reconstruction with fewer projections.

Compressed sampling and dictionary learning framework for wavelength-division-multiplexing-based distributed fiber sensing.

PubMed

Weiss, Christian; Zoubir, Abdelhak M

2017-05-01

We propose a compressed sampling and dictionary learning framework for fiber-optic sensing using wavelength-tunable lasers. A redundant dictionary is generated from a model for the reflected sensor signal. Imperfect prior knowledge is considered in terms of uncertain local and global parameters. To estimate a sparse representation and the dictionary parameters, we present an alternating minimization algorithm that is equipped with a preprocessing routine to handle dictionary coherence. The support of the obtained sparse signal indicates the reflection delays, which can be used to measure impairments along the sensing fiber. The performance is evaluated by simulations and experimental data for a fiber sensor system with common core architecture.

The maturation of cortical sleep rhythms and networks over early development.

PubMed

Chu, C J; Leahy, J; Pathmanathan, J; Kramer, M A; Cash, S S

2014-07-01

Although neuronal activity drives all aspects of cortical development, how human brain rhythms spontaneously mature remains an active area of research. We sought to systematically evaluate the emergence of human brain rhythms and functional cortical networks over early development. We examined cortical rhythms and coupling patterns from birth through adolescence in a large cohort of healthy children (n=384) using scalp electroencephalogram (EEG) in the sleep state. We found that the emergence of brain rhythms follows a stereotyped sequence over early development. In general, higher frequencies increase in prominence with striking regional specificity throughout development. The coordination of these rhythmic activities across brain regions follows a general pattern of maturation in which broadly distributed networks of low-frequency oscillations increase in density while networks of high frequency oscillations become sparser and more highly clustered. Our results indicate that a predictable program directs the development of key rhythmic components and physiological brain networks over early development. This work expands our knowledge of normal cortical development. The stereotyped neurophysiological processes observed at the level of rhythms and networks may provide a scaffolding to support critical periods of cognitive growth. Furthermore, these conserved patterns could provide a sensitive biomarker for cortical health across development. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

The maturation of cortical sleep rhythms and networks over early development

PubMed Central

Chu, CJ; Leahy, J; Pathmanathan, J; Kramer, MA; Cash, SS

2014-01-01

Objective Although neuronal activity drives all aspects of cortical development, how human brain rhythms spontaneously mature remains an active area of research. We sought to systematically evaluate the emergence of human brain rhythms and functional cortical networks over early development. Methods We examined cortical rhythms and coupling patterns from birth through adolescence in a large cohort of healthy children (n=384) using scalp electroencephalogram (EEG) in the sleep state. Results We found that the emergence of brain rhythms follows a stereotyped sequence over early development. In general, higher frequencies increase in prominence with striking regional specificity throughout development. The coordination of these rhythmic activities across brain regions follows a general pattern of maturation in which broadly distributed networks of low-frequency oscillations increase in density while networks of high frequency oscillations become sparser and more highly clustered. Conclusion Our results indicate that a predictable program directs the development of key rhythmic components and physiological brain networks over early development. Significance This work expands our knowledge of normal cortical development. The stereotyped neurophysiological processes observed at the level of rhythms and networks may provide a scaffolding to support critical periods of cognitive growth. Furthermore, these conserved patterns could provide a sensitive biomarker for cortical health across development. PMID:24418219

The evolution of cortical development: the synapsid-diapsid divergence.

PubMed

Goffinet, Andre M

2017-11-15

The cerebral cortex covers the rostral part of the brain and, in higher mammals and particularly humans, plays a key role in cognition and consciousness. It is populated with neuronal cell bodies distributed in radially organized layers. Understanding the common and lineage-specific molecular mechanisms that orchestrate cortical development and evolution are key issues in neurobiology. During evolution, the cortex appeared in stem amniotes and evolved divergently in two main branches of the phylogenetic tree: the synapsids (which led to present day mammals) and the diapsids (reptiles and birds). Comparative studies in organisms that belong to those two branches have identified some common principles of cortical development and organization that are possibly inherited from stem amniotes and regulated by similar molecular mechanisms. These comparisons have also highlighted certain essential features of mammalian cortices that are absent or different in diapsids and that probably evolved after the synapsid-diapsid divergence. Chief among these is the size and multi-laminar organization of the mammalian cortex, and the propensity to increase its area by folding. Here, I review recent data on cortical neurogenesis, neuronal migration and cortical layer formation and folding in this evolutionary perspective, and highlight important unanswered questions for future investigation. © 2017. Published by The Company of Biologists Ltd.

The cortical structure of consolidated memory: a hypothesis on the role of the cingulate-entorhinal cortical connection.

PubMed

Insel, Nathan; Takehara-Nishiuchi, Kaori

2013-11-01

Daily experiences are represented by networks of neurons distributed across the neocortex, bound together for rapid storage and later retrieval by the hippocampus. While the hippocampus is necessary for retrieving recent episode-based memory associations, over time, consolidation processes take place that enable many of these associations to be expressed independent of the hippocampus. It is generally thought that mechanisms of consolidation involve synaptic weight changes between cortical regions; or, in other words, the formation of "horizontal" cortico-cortical connections. Here, we review anatomical, behavioral, and physiological data which suggest that the connections in and between the entorhinal and cingulate cortices may be uniquely important for the long-term storage of memories that initially depend on the hippocampus. We propose that current theories of consolidation that divide memory into dual systems of hippocampus and neocortex might be improved by introducing a third, middle layer of entorhinal and cingulate allocortex, the synaptic weights within which are necessary and potentially sufficient for maintaining initially hippocampus-dependent associations over long time periods. This hypothesis makes a number of still untested predictions, and future experiments designed to address these will help to fill gaps in the current understanding of the cortical structure of consolidated memory. Copyright © 2013 Elsevier Inc. All rights reserved.

Imaging the cortical effect of lamotrigine in patients with idiopathic generalized epilepsy: a low-resolution electromagnetic tomography (LORETA) study.

PubMed

Clemens, Béla; Piros, Pálma; Bessenyei, Mónika; Tóth, Márton; Hollódy, Katalin; Kondákor, István

2008-10-01

Anatomical localization of the cortical effect of lamotrigine (LTG) in patients with idiopathic generalized epilepsy (IGE). 19 patients with untreated IGE were investigated. EEG was recorded in the untreated condition and 3 months later when LTG treatment abolished the seizures. 19-channel EEG was recorded, and a total of 2min artifact-free, waking EEG was processed to low-resolution electromagnetic tomography (LORETA) analysis. Activity (that is, current source density, A/m(2)) was computed in four frequency bands (delta, theta, alpha, and beta), for 2394 voxels that represented the cortical gray matter and the hippocampi. Group differences between the untreated and treated conditions were computed for the four bands and all voxels by multiple t-tests for interdependent datasets. The results were presented in terms of anatomical distribution and statistical significance. p<0.01 (uncorrected) changes (decrease of activity) emerged in the theta and the alpha bands. Theta activity decreased in a large cluster of voxels including parts of the temporal, parietal, occipital cortex bilaterally, and in the transverse temporal gyri, insula, hippocampus, and uncus on the right side. Alpha activity decreased in a relatively smaller cortical area involving the right temporo-parietal junction and surrounding parts of the cortex, and part of the insula on the right side. LTG decreased theta activity in several cortical areas where abnormally increased theta activity had been found in a prior study in another cohort of untreated IGE patients [Clemens, B., Bessenyei, M., Piros, P., Tóth, M., Seress, L., Kondákor, I., 2007b. Characteristic distribution of interictal brain electrical activity in idiopathic generalized epilepsy. Epilepsia 48, 941-949]. These LTG-related changes might be related to the decrease of seizure propensity in IGE.

Linking physics with physiology in TMS: a sphere field model to determine the cortical stimulation site in TMS.

PubMed

Thielscher, Axel; Kammer, Thomas

2002-11-01

A fundamental problem of transcranial magnetic stimulation (TMS) is determining the site and size of the stimulated cortical area. In the motor system, the most common procedure for this is motor mapping. The obtained two-dimensional distribution of coil positions with associated muscle responses is used to calculate a center of gravity on the skull. However, even in motor mapping the exact stimulation site on the cortex is not known and only rough estimates of its size are possible. We report a new method which combines physiological measurements with a physical model used to predict the electric field induced by the TMS coil. In four subjects motor responses in a small hand muscle were mapped with 9-13 stimulation sites at the head perpendicular to the central sulcus in order to keep the induced current direction constant in a given cortical region of interest. Input-output functions from these head locations were used to determine stimulator intensities that elicit half-maximal muscle responses. Based on these stimulator intensities the field distribution on the individual cortical surface was calculated as rendered from anatomical MR data. The region on the cortical surface in which the different stimulation sites produced the same electric field strength (minimal variance, 4.2 +/- 0.8%.) was determined as the most likely stimulation site on the cortex. In all subjects, it was located at the lateral part of the hand knob in the motor cortex. Comparisons of model calculations with the solutions obtained in this manner reveal that the stimulated cortex area innervating the target muscle is substantially smaller than the size of the electric field induced by the coil. Our results help to resolve fundamental questions raised by motor mapping studies as well as motor threshold measurements.

Centrality of prefrontal and motor preparation cortices to Tourette Syndrome revealed by meta-analysis of task-based neuroimaging studies.

PubMed

Polyanska, Liliana; Critchley, Hugo D; Rae, Charlotte L

2017-01-01

Tourette Syndrome (TS) is a neurodevelopmental condition characterized by chronic multiple tics, which are experienced as compulsive and 'unwilled'. Patients with TS can differ markedly in the frequency, severity, and bodily distribution of tics. Moreover, there are high comorbidity rates with attention deficit hyperactivity disorder (ADHD), obsessive compulsive disorder (OCD), anxiety disorders, and depression. This complex clinical profile may account for apparent variability of findings across neuroimaging studies that connect neural function to cognitive and motor behavior in TS. Here we crystalized information from neuroimaging regarding the functional circuitry of TS, and furthermore, tested specifically for neural determinants of tic severity, by applying activation likelihood estimation (ALE) meta-analyses to neuroimaging (activation) studies of TS. Fourteen task-based studies (13 fMRI and one H2O-PET) met rigorous inclusion criteria. These studies, encompassing 25 experiments and 651 participants, tested for differences between TS participants and healthy controls across cognitive, motor, perceptual and somatosensory domains. Relative to controls, TS participants showed distributed differences in the activation of prefrontal (inferior, middle, and superior frontal gyri), anterior cingulate, and motor preparation cortices (lateral premotor cortex and supplementary motor area; SMA). Differences also extended into sensory (somatosensory cortex and the lingual gyrus; V4); and temporo-parietal association cortices (posterior superior temporal sulcus, supramarginal gyrus, and retrosplenial cortex). Within TS participants, tic severity (reported using the Yale Global Tic Severity Scale; YGTSS) selectively correlated with engagement of SMA, precentral gyrus, and middle frontal gyrus across tasks. The dispersed involvement of multiple cortical regions with differences in functional reactivity may account for heterogeneity in the symptomatic expression of TS and its comorbidities. More specifically for tics and tic severity, the findings reinforce previously proposed contributions of premotor and lateral prefrontal cortices to tic expression.

Probabilistic map of critical functional regions of the human cerebral cortex: Broca's area revisited.

PubMed

Tate, Matthew C; Herbet, Guillaume; Moritz-Gasser, Sylvie; Tate, Joseph E; Duffau, Hugues

2014-10-01

The organization of basic functions of the human brain, particularly in the right hemisphere, remains poorly understood. Recent advances in functional neuroimaging have improved our understanding of cortical organization but do not allow for direct interrogation or determination of essential (versus participatory) cortical regions. Direct cortical stimulation represents a unique opportunity to provide novel insights into the functional distribution of critical epicentres. Direct cortical stimulation (bipolar, 60 Hz, 1-ms pulse) was performed in 165 consecutive patients undergoing awake mapping for resection of low-grade gliomas. Tasks included motor, sensory, counting, and picture naming. Stimulation sites eliciting positive (sensory/motor) or negative (speech arrest, dysarthria, anomia, phonological and semantic paraphasias) findings were recorded and mapped onto a standard Montreal Neurological Institute brain atlas. Montreal Neurological Institute-space functional data were subjected to cluster analysis algorithms (K-means, partition around medioids, hierarchical Ward) to elucidate crucial network epicentres. Sensorimotor function was observed in the pre/post-central gyri as expected. Articulation epicentres were also found within the pre/post-central gyri. However, speech arrest localized to ventral premotor cortex, not the classical Broca's area. Anomia/paraphasia data demonstrated foci not only within classical Wernicke's area but also within the middle and inferior frontal gyri. We report the first bilateral probabilistic map for crucial cortical epicentres of human brain functions in the right and left hemispheres, including sensory, motor, and language (speech, articulation, phonology and semantics). These data challenge classical theories of brain organization (e.g. Broca's area as speech output region) and provide a distributed framework for future studies of neural networks. © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Time-Frequency Signal Representations Using Interpolations in Joint-Variable Domains

DTIC Science & Technology

2016-06-14

distribution kernels,” IEEE Trans. Signal Process., vol. 42, no. 5, pp. 1156–1165, May 1994. [25] G. S. Cunningham and W. J. Williams , “Kernel...interpolated data. For comparison, we include sparse reconstruction and WVD and Choi– Williams distribution (CWD) [23], which are directly applied to...Prentice-Hall, 1995. [23] H. I. Choi and W. J. Williams , “Improved time-frequency representa- tion of multicomponent signals using exponential kernels

Prominent microglial activation in cortical white matter is selectively associated with cortical atrophy in primary progressive aphasia.

PubMed

Ohm, D T; Kim, G; Gefen, T; Rademaker, A; Weintraub, S; Bigio, E H; Mesulam, M-M; Rogalski, E; Geula, C

2018-04-21

Primary progressive aphasia (PPA) is a clinical syndrome characterized by selective language impairments associated with focal cortical atrophy favouring the language dominant hemisphere. PPA is associated with Alzheimer's disease (AD), frontotemporal lobar degeneration (FTLD) and significant accumulation of activated microglia. Activated microglia can initiate an inflammatory cascade that may contribute to neurodegeneration, but their quantitative distribution in cortical white matter and their relationship with cortical atrophy remain unknown. We investigated white matter activated microglia and their association with grey matter atrophy in 10 PPA cases with either AD or FTLD-TDP pathology. Activated microglia were quantified with optical density measures of HLA-DR immunoreactivity in two regions with peak cortical atrophy, and one nonatrophied region within the language dominant hemisphere of each PPA case. Nonatrophied contralateral homologues of the language dominant regions were examined for hemispheric asymmetry. Qualitatively, greater densities of activated microglia were observed in cortical white matter when compared to grey matter. Quantitative analyses revealed significantly greater densities of activated microglia in the white matter of atrophied regions compared to nonatrophied regions in the language dominant hemisphere (P < 0.05). Atrophied regions of the language dominant hemisphere also showed significantly more activated microglia compared to contralateral homologues (P < 0.05). White matter activated microglia accumulate more in atrophied regions in the language dominant hemisphere of PPA. While microglial activation may constitute a response to neurodegenerative processes in white matter, the resultant inflammatory processes may also exacerbate disease progression and contribute to cortical atrophy. © 2018 British Neuropathological Society.

Cortical layers: Cyto-, myelo-, receptor- and synaptic architecture in human cortical areas.

PubMed

Palomero-Gallagher, Nicola; Zilles, Karl

2017-08-12

Cortical layers have classically been identified by their distinctive and prevailing cell types and sizes, as well as the packing densities of cell bodies or myelinated fibers. The densities of multiple receptors for classical neurotransmitters also vary across the depth of the cortical ribbon, and thus determine the neurochemical properties of cyto- and myeloarchitectonic layers. However, a systematic comparison of the correlations between these histologically definable layers and the laminar distribution of transmitter receptors is currently lacking. We here analyze the densities of 17 different receptors of various transmitter systems in the layers of eight cytoarchitectonically identified, functionally (motor, sensory, multimodal) and hierarchically (primary and secondary sensory, association) distinct areas of the human cerebral cortex. Maxima of receptor densities are found in different layers when comparing different cortical regions, i.e. laminar receptor densities demonstrate differences in receptorarchitecture between isocortical areas, notably between motor and primary sensory cortices, specifically the primary visual and somatosensory cortices, as well as between allocortical and isocortical areas. Moreover, considerable differences are found between cytoarchitectonical and receptor architectonical laminar patterns. Whereas the borders of cyto- and myeloarchitectonic layers are well comparable, the laminar profiles of receptor densities rarely coincide with the histologically defined borders of layers. Instead, highest densities of most receptors are found where the synaptic density is maximal, i.e. in the supragranular layers, particularly in layers II-III. The entorhinal cortex as an example of the allocortex shows a peculiar laminar organization, which largely deviates from that of all the other cortical areas analyzed here. Copyright © 2017. Published by Elsevier Inc.

Attractor neural networks with resource-efficient synaptic connectivity

NASA Astrophysics Data System (ADS)

Pehlevan, Cengiz; Sengupta, Anirvan

Memories are thought to be stored in the attractor states of recurrent neural networks. Here we explore how resource constraints interplay with memory storage function to shape synaptic connectivity of attractor networks. We propose that given a set of memories, in the form of population activity patterns, the neural circuit choses a synaptic connectivity configuration that minimizes a resource usage cost. We argue that the total synaptic weight (l1-norm) in the network measures the resource cost because synaptic weight is correlated with synaptic volume, which is a limited resource, and is proportional to neurotransmitter release and post-synaptic current, both of which cost energy. Using numerical simulations and replica theory, we characterize optimal connectivity profiles in resource-efficient attractor networks. Our theory explains several experimental observations on cortical connectivity profiles, 1) connectivity is sparse, because synapses are costly, 2) bidirectional connections are overrepresented and 3) are stronger, because attractor states need strong recurrence.

Low excitatory innervation balances high intrinsic excitability of immature dentate neurons

PubMed Central

Dieni, Cristina V.; Panichi, Roberto; Aimone, James B.; Kuo, Chay T.; Wadiche, Jacques I.; Overstreet-Wadiche, Linda

2016-01-01

Persistent neurogenesis in the dentate gyrus produces immature neurons with high intrinsic excitability and low levels of inhibition that are predicted to be more broadly responsive to afferent activity than mature neurons. Mounting evidence suggests that these immature neurons are necessary for generating distinct neural representations of similar contexts, but it is unclear how broadly responsive neurons help distinguish between similar patterns of afferent activity. Here we show that stimulation of the entorhinal cortex in mouse brain slices paradoxically generates spiking of mature neurons in the absence of immature neuron spiking. Immature neurons with high intrinsic excitability fail to spike due to insufficient excitatory drive that results from low innervation rather than silent synapses or low release probability. Our results suggest that low synaptic connectivity prevents immature neurons from responding broadly to cortical activity, potentially enabling excitable immature neurons to contribute to sparse and orthogonal dentate representations. PMID:27095423

Low excitatory innervation balances high intrinsic excitability of immature dentate neurons

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

Dieni, Cristina V.; Panichi, Roberto; Aimone, James B.

Persistent neurogenesis in the dentate gyrus produces immature neurons with high intrinsic excitability and low levels of inhibition that are predicted to be more broadly responsive to afferent activity than mature neurons. Mounting evidence suggests that these immature neurons are necessary for generating distinct neural representations of similar contexts, but it is unclear how broadly responsive neurons help distinguish between similar patterns of afferent activity. Here we show that stimulation of the entorhinal cortex in mouse brain slices paradoxically generates spiking of mature neurons in the absence of immature neuron spiking. Immature neurons with high intrinsic excitability fail to spikemore » due to insufficient excitatory drive that results from low innervation rather than silent synapses or low release probability. Here, our results suggest that low synaptic connectivity prevents immature neurons from responding broadly to cortical activity, potentially enabling excitable immature neurons to contribute to sparse and orthogonal dentate representations.« less

Low excitatory innervation balances high intrinsic excitability of immature dentate neurons

DOE PAGES

Dieni, Cristina V.; Panichi, Roberto; Aimone, James B.; ...

2016-04-20

Persistent neurogenesis in the dentate gyrus produces immature neurons with high intrinsic excitability and low levels of inhibition that are predicted to be more broadly responsive to afferent activity than mature neurons. Mounting evidence suggests that these immature neurons are necessary for generating distinct neural representations of similar contexts, but it is unclear how broadly responsive neurons help distinguish between similar patterns of afferent activity. Here we show that stimulation of the entorhinal cortex in mouse brain slices paradoxically generates spiking of mature neurons in the absence of immature neuron spiking. Immature neurons with high intrinsic excitability fail to spikemore » due to insufficient excitatory drive that results from low innervation rather than silent synapses or low release probability. Here, our results suggest that low synaptic connectivity prevents immature neurons from responding broadly to cortical activity, potentially enabling excitable immature neurons to contribute to sparse and orthogonal dentate representations.« less

Digital Correlation In Laser-Speckle Velocimetry

NASA Technical Reports Server (NTRS)

Gilbert, John A.; Mathys, Donald R.

1992-01-01

Periodic recording helps to eliminate spurious results. Improved digital-correlation process extracts velocity field of two-dimensional flow from laser-speckle images of seed particles distributed sparsely in flow. Method which involves digital correlation of images recorded at unequal intervals, completely automated and has potential to be fastest yet.

Bayesian Semiparametric Structural Equation Models with Latent Variables

ERIC Educational Resources Information Center

Yang, Mingan; Dunson, David B.

2010-01-01

Structural equation models (SEMs) with latent variables are widely useful for sparse covariance structure modeling and for inferring relationships among latent variables. Bayesian SEMs are appealing in allowing for the incorporation of prior information and in providing exact posterior distributions of unknowns, including the latent variables. In…

Selective Vulnerability of Cortical Border Zone to Microembolic Infarct.

PubMed

Bergui, Mauro; Castagno, Davide; D'Agata, Federico; Cicerale, Alessandro; Anselmino, Matteo; Maria Ferrio, Federica; Giustetto, Carla; Halimi, Franck; Scaglione, Marco; Gaita, Fiorenzo

2015-07-01

Endovascular procedures, including atrial fibrillation transcatheter ablation, may cause microembolization of brain arteries. Microemboli often cause small sized and clinically silent cerebral ischemias (SCI). These lesions are clearly visible on early postoperative magnetic resonance diffusion-weighted images. We analyzed SCI distribution in a population of patients submitted to atrial fibrillation transcatheter ablation. Seventy-eight of 927 consecutive patients submitted to atrial fibrillation transcatheter ablation were found positive for acute SCI on a postoperative magnetic resonance. SCI were identified and marked, and their coordinates were transformed from native space into the International Consortium for Brain Mapping/Montreal Neurological Institute space. We then computed the voxel-wise probability distribution map of the SCI using the activation likelihood estimation approach. SCI were more commonly found in the cortex. In supratentorial regions, SCI selectively involved cortical border zone between anterior, middle, and posterior cerebral arteries; in infratentorial regions, distal territory of posteroinferior cerebellar artery. Possible explanations include selective embolization, linked to the vascular anatomy of pial arteries supplying those territories, reduced clearance of emboli in a relatively hypoperfused zone, or a combination of both. This particular distribution of lesions has been reported in both animal models and in patients with microemboli of different sources. A selective vulnerability of cortical border zone to microemboli occurring during atrial fibrillation transcatheter ablation was observed. We hypothesize that such selectivity may apply to microemboli of different sources. © 2015 American Heart Association, Inc.

Association of In Vivo [18F]AV-1451 Tau PET Imaging Results With Cortical Atrophy and Symptoms in Typical and Atypical Alzheimer Disease

PubMed Central

Xia, Chenjie; Makaretz, Sara J.; Caso, Christina; McGinnis, Scott; Gomperts, Stephen N.; Sepulcre, Jorge; Gomez-Isla, Teresa; Hyman, Bradley T.; Schultz, Aaron; Vasdev, Neil; Johnson, Keith A.

2017-01-01

Importance Previous postmortem studies have long demonstrated that neurofibrillary tangles made of hyperphosphorylated tau proteins are closely associated with Alzheimer disease clinical phenotype and neurodegeneration pattern. Validating these associations in vivo will lead to new diagnostic tools for Alzheimer disease and better understanding of its neurobiology. Objective To examine whether topographical distribution and severity of hyperphosphorylated tau pathologic findings measured by fluorine 18–labeled AV-1451 ([18F]AV-1451) positron emission tomographic (PET) imaging are linked with clinical phenotype and cortical atrophy in patients with Alzheimer disease. Design, Setting, and Participants This observational case series, conducted from July 1, 2012, to July 30, 2015, in an outpatient referral center for patients with neurodegenerative diseases, included 6 patients: 3 with typical amnesic Alzheimer disease and 3 with atypical variants (posterior cortical atrophy, logopenic variant primary progressive aphasia, and corticobasal syndrome). Patients underwent [18F]AV-1451 PET imaging to measure tau burden, carbon 11–labeled Pittsburgh Compound B ([11C]PiB) PET imaging to measure amyloid burden, and structural magnetic resonance imaging to measure cortical thickness. Seventy-seven age-matched controls with normal cognitive function also underwent structural magnetic resonance imaging but not tau or amyloid PET imaging. Main Outcomes and Measures Tau burden, amyloid burden, and cortical thickness. Results In all 6 patients (3 women and 3 men; mean age 61.8 years), the underlying clinical phenotype was associated with the regional distribution of the [18F]AV-1451 signal. Furthermore, within 68 cortical regions of interest measured from each patient, the magnitude of cortical atrophy was strongly correlated with the magnitude of [18F]AV-1451 binding (3 patients with amnesic Alzheimer disease, r = –0.82; P < .001; r = –0.70; P < .001; r = –0.58; P < .001; and 3 patients with nonamnesic Alzheimer disease, r = –0.51; P < .001; r = –0.63; P < .001; r = –0.70; P < .001), but not of [11C]PiB binding. Conclusions and Relevance These findings provide further in vivo evidence that distribution of the [18F]AV-1451 signal as seen on results of PET imaging is a valid marker of clinical symptoms and neurodegeneration. By localizing and quantifying hyperphosphorylated tau in vivo, results of tau PET imaging will likely serve as a key biomarker that links a specific type of molecular Alzheimer disease neuropathologic condition with clinically significant neurodegeneration, which will likely catalyze additional efforts to develop disease-modifying therapeutics. PMID:28241163

Sparse Representation with Spatio-Temporal Online Dictionary Learning for Efficient Video Coding.

PubMed

Dai, Wenrui; Shen, Yangmei; Tang, Xin; Zou, Junni; Xiong, Hongkai; Chen, Chang Wen

2016-07-27

Classical dictionary learning methods for video coding suer from high computational complexity and interfered coding eciency by disregarding its underlying distribution. This paper proposes a spatio-temporal online dictionary learning (STOL) algorithm to speed up the convergence rate of dictionary learning with a guarantee of approximation error. The proposed algorithm incorporates stochastic gradient descents to form a dictionary of pairs of 3-D low-frequency and highfrequency spatio-temporal volumes. In each iteration of the learning process, it randomly selects one sample volume and updates the atoms of dictionary by minimizing the expected cost, rather than optimizes empirical cost over the complete training data like batch learning methods, e.g. K-SVD. Since the selected volumes are supposed to be i.i.d. samples from the underlying distribution, decomposition coecients attained from the trained dictionary are desirable for sparse representation. Theoretically, it is proved that the proposed STOL could achieve better approximation for sparse representation than K-SVD and maintain both structured sparsity and hierarchical sparsity. It is shown to outperform batch gradient descent methods (K-SVD) in the sense of convergence speed and computational complexity, and its upper bound for prediction error is asymptotically equal to the training error. With lower computational complexity, extensive experiments validate that the STOL based coding scheme achieves performance improvements than H.264/AVC or HEVC as well as existing super-resolution based methods in ratedistortion performance and visual quality.

A comparative study on the stress distribution around dental implants in three arch form models for replacing six implants using finite element analysis.

PubMed

Zarei, Maryam; Jahangirnezhad, Mahmoud; Yousefimanesh, Hojatollah; Robati, Maryam; Robati, Hossein

2018-01-01

Dental implant is a method to replacement of missing teeth. It is important for replacing the missed anterior teeth. In vitro method is a safe method for evaluation of stress distribution. Finite element analysis as an in vitro method evaluated stress distribution around replacement of six maxillary anterior teeth implants in three models of maxillary arch. In this in vitro study, using ABAQUS software (Simulia Corporation, Vélizy-Villacoublay, France), implant simulation was performed for reconstruction of six maxillary anterior teeth in three models. Two implants were placed on both sides of the canine tooth region (A model); two implants on both sides of the canine tooth region and another on one side of the central incisor region (B model); and two implants on both sides of the canine tooth region and two implants in the central incisor area (C model). All implants evaluated in three arch forms (tapered, ovoid, and square). Data were analyzed by finite analysis software. Von Mises stress by increasing of implant number was reduced. In a comparison of A model in each maxillary arch, the stress created in the cortical and cancellous bones in the square arch was less than ovoid and tapered arches. The stress created in implants and cortical and cancellous bones in C model was less than A and B models. The C model (four-implant) reduced the stress distribution in cortical and cancellous bones, but this pattern must be evaluated according to arch form and cost benefit of patients.

Laminar microvascular transit time distribution in the mouse somatosensory cortex revealed by Dynamic Contrast Optical Coherence Tomography

PubMed Central

Merkle, Conrad W.; Srinivasan, Vivek J.

2015-01-01

The transit time distribution of blood through the cerebral microvasculature both constrains oxygen delivery and governs the kinetics of neuroimaging signals such as blood-oxygen-level-dependent functional Magnetic Resonance Imaging (BOLD fMRI). However, in spite of its importance, capillary transit time distribution has been challenging to quantify comprehensively and efficiently at the microscopic level. Here, we introduce a method, called Dynamic Contrast Optical Coherence Tomography (DyC-OCT), based on dynamic cross-sectional OCT imaging of an intravascular tracer as it passes through the field-of-view. Quantitative transit time metrics are derived from temporal analysis of the dynamic scattering signal, closely related to tracer concentration. Since DyC-OCT does not require calibration of the optical focus, quantitative accuracy is achieved even deep in highly scattering brain tissue where the focal spot degrades. After direct validation of DyC-OCT against dilution curves measured using a fluorescent plasma label in surface pial vessels, we used DyC-OCT to investigate the transit time distribution in microvasculature across the entire depth of the mouse somatosensory cortex. Laminar trends were identified, with earlier transit times and less heterogeneity in the middle cortical layers. The early transit times in the middle cortical layers may explain, at least in part, the early BOLD fMRI onset times observed in these layers. The layer-dependencies in heterogeneity may help explain how a single vascular supply manages to deliver oxygen to individual cortical layers with diverse metabolic needs. PMID:26477654

Laminar microvascular transit time distribution in the mouse somatosensory cortex revealed by Dynamic Contrast Optical Coherence Tomography.

PubMed

Merkle, Conrad W; Srinivasan, Vivek J

2016-01-15

The transit time distribution of blood through the cerebral microvasculature both constrains oxygen delivery and governs the kinetics of neuroimaging signals such as blood-oxygen-level-dependent functional Magnetic Resonance Imaging (BOLD fMRI). However, in spite of its importance, capillary transit time distribution has been challenging to quantify comprehensively and efficiently at the microscopic level. Here, we introduce a method, called Dynamic Contrast Optical Coherence Tomography (DyC-OCT), based on dynamic cross-sectional OCT imaging of an intravascular tracer as it passes through the field-of-view. Quantitative transit time metrics are derived from temporal analysis of the dynamic scattering signal, closely related to tracer concentration. Since DyC-OCT does not require calibration of the optical focus, quantitative accuracy is achieved even deep in highly scattering brain tissue where the focal spot degrades. After direct validation of DyC-OCT against dilution curves measured using a fluorescent plasma label in surface pial vessels, we used DyC-OCT to investigate the transit time distribution in microvasculature across the entire depth of the mouse somatosensory cortex. Laminar trends were identified, with earlier transit times and less heterogeneity in the middle cortical layers. The early transit times in the middle cortical layers may explain, at least in part, the early BOLD fMRI onset times observed in these layers. The layer-dependencies in heterogeneity may help explain how a single vascular supply manages to deliver oxygen to individual cortical layers with diverse metabolic needs. Copyright © 2015 Elsevier Inc. All rights reserved.

Claustrum projections to prefrontal cortex in the capuchin monkey (Cebus apella)

PubMed Central

Reser, David H.; Richardson, Karyn E.; Montibeller, Marina O.; Zhao, Sherry; Chan, Jonathan M. H.; Soares, Juliana G. M.; Chaplin, Tristan A.; Gattass, Ricardo; Rosa, Marcello G. P.

2014-01-01

We examined the pattern of retrograde tracer distribution in the claustrum following intracortical injections into the frontal pole (area 10), and in dorsal (area 9), and ventral lateral (area 12) regions of the rostral prefrontal cortex in the tufted capuchin monkey (Cebus apella). The resulting pattern of labeled cells was assessed in relation to the three-dimensional geometry of the claustrum, as well as recent reports of claustrum-prefrontal connections in other primates. Claustrum-prefrontal projections were extensive, and largely concentrated in the ventral half of the claustrum, especially in the rostral 2/3 of the nucleus. Our data are consistent with a topographic arrangement of claustrum-cortical connections in which prefrontal and association cortices receive connections largely from the rostral and medial claustrum. Comparative aspects of claustrum-prefrontal topography across primate species and the implications of claustrum connectivity for understanding of cortical functional networks are explored, and we hypothesize that the claustrum may play a role in controlling or switching between resting state and task-associated cortical networks. PMID:25071475

Pycortex: an interactive surface visualizer for fMRI

PubMed Central

Gao, James S.; Huth, Alexander G.; Lescroart, Mark D.; Gallant, Jack L.

2015-01-01

Surface visualizations of fMRI provide a comprehensive view of cortical activity. However, surface visualizations are difficult to generate and most common visualization techniques rely on unnecessary interpolation which limits the fidelity of the resulting maps. Furthermore, it is difficult to understand the relationship between flattened cortical surfaces and the underlying 3D anatomy using tools available currently. To address these problems we have developed pycortex, a Python toolbox for interactive surface mapping and visualization. Pycortex exploits the power of modern graphics cards to sample volumetric data on a per-pixel basis, allowing dense and accurate mapping of the voxel grid across the surface. Anatomical and functional information can be projected onto the cortical surface. The surface can be inflated and flattened interactively, aiding interpretation of the correspondence between the anatomical surface and the flattened cortical sheet. The output of pycortex can be viewed using WebGL, a technology compatible with modern web browsers. This allows complex fMRI surface maps to be distributed broadly online without requiring installation of complex software. PMID:26483666

Populations of Bactrocera oleae (Diptera: Tephritidae) and Its Parasitoids in Himalayan Asia

USDA-ARS?s Scientific Manuscript database

For a biological control program against olive fruit fly, Bactrocera oleae Rossi, olives were collected in the Himalayan foothills (China, Nepal, India, and Pakistan) to discover new natural enemies. Wild olives, Olea europaea ssp. cuspidata (Wall ex. G. Don), were sparsely distributed and fly-infes...

Populations of Bactrocera oleae (Diptera: Tephritidae) and Its Parasitoids in Himalayan Asia

USDA-ARS?s Scientific Manuscript database

For a biological control program against olive fruit fly, Bactrocera oleae Rossi, olives were collected in the Himalayan foothills (China, Nepal, India, and Pakistan) to discover new natural enemies. Wild olives, Olea europaea ssp. cuspidata (Wall ex. G. Don), were sparsely distributed and fly-infe...

Feminism, Neoliberalism, and Social Studies

ERIC Educational Resources Information Center

Schmeichel, Mardi

2011-01-01

The purpose of this article is to analyze the sparse presence of women in social studies education and to consider the possibility of a confluence of feminism and neoliberalism within the most widely distributed National Council for the Social Studies (NCSS) publication, "Social Education." Using poststructural conceptions of discourse, the author…

Sparse Distributed Representation and Hierarchy: Keys to Scalable Machine Intelligence

DTIC Science & Technology

2016-04-01

Lesher, Jasmin Leveille, and Oliver Layton Neurithmic Systems, LLC APRIL 2016 Final Report Approved for public release...61101E 6. AUTHOR(S) Gerard (Rod) Rinkus, Greg Lesher, Jasmin Leveille, and Oliver Layton 5d. PROJECT NUMBER 1000 5e. TASK NUMBER N/A 5f. WORK

Geographic Mobility of Manpower in the USSR.

ERIC Educational Resources Information Center

Kossov, V. V.; Tatevosoc, R. V.

1984-01-01

The Soviet Union is experiencing substantial reduction in the growth of the working-age population, accompanied by a shift in the distribution of population growth. The government is using various means to encourage workers to move to the sparsely populated developing regions and away from the large cities. (SK)

45 CFR 303.20 - Minimum organizational and staffing requirements.

Code of Federal Regulations, 2011 CFR

2011-10-01

... payments or social services functions under title IV-A or XX of the Act. In the case of a sparsely... social worker. (2) The assistance payments function means activities related to determination of... financial and medical assistance and commodities distribution or food stamps. (3) The social services...

Monitoring NEON terrestrial sites phenology with daily MODIS BRDF/albedo product and landsat data

USDA-ARS?s Scientific Manuscript database

The MODerate resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) and albedo products (MCD43) have already been in production for more than a decade. The standard product makes use of a linear “kernel-driven” RossThick-LiSparse Reciprocal (RTLSR) BRDF m...

[Oxygen diffusion through the venule walls in the rat cerebral cortex during breathing with pure oxygen].

PubMed

Vovenko, E P; Sokolova, I B; Loshchagin, O V

2002-03-01

Using oxygen microelectrodes, distribution of oxygen tension (pO2) has been studied in venules of the rat brain cortex at normobaric hyperoxia (spontaneous breathing with pure oxygen). It has been shown that inhalation of oxygen results in sharp increase of pO2 in majority of the venules under study. The pO2 distribution along the length of venous microvessels of 7-280 microns in diameter is best approximated by equation: pO2 = 76.44 e-0.0008D, n = 407. The pO2 distribution was characterised by extremely high pO2 values (180-240 mm Hg) in some minute venules. Heterogeneity of pO2 distribution in venous microvessels at hyperoxia was shown to be significantly increased. Profiles of pO2 between neighbouring arterioles and venules were for the first time measured. The data clearly evidenced that O2 diffusional shunting took place between cortical arterioles and venules, provided they were distanced from each other for not over 80-100 microns. Distribution of pO2 in venules has been shown to be dependent on the blood flow in the brain cortical microvessels.

A comparison of adaptive sampling designs and binary spatial models: A simulation study using a census of Bromus inermis

USGS Publications Warehouse

Irvine, Kathryn M.; Thornton, Jamie; Backus, Vickie M.; Hohmann, Matthew G.; Lehnhoff, Erik A.; Maxwell, Bruce D.; Michels, Kurt; Rew, Lisa

2013-01-01

Commonly in environmental and ecological studies, species distribution data are recorded as presence or absence throughout a spatial domain of interest. Field based studies typically collect observations by sampling a subset of the spatial domain. We consider the effects of six different adaptive and two non-adaptive sampling designs and choice of three binary models on both predictions to unsampled locations and parameter estimation of the regression coefficients (species–environment relationships). Our simulation study is unique compared to others to date in that we virtually sample a true known spatial distribution of a nonindigenous plant species, Bromus inermis. The census of B. inermis provides a good example of a species distribution that is both sparsely (1.9 % prevalence) and patchily distributed. We find that modeling the spatial correlation using a random effect with an intrinsic Gaussian conditionally autoregressive prior distribution was equivalent or superior to Bayesian autologistic regression in terms of predicting to un-sampled areas when strip adaptive cluster sampling was used to survey B. inermis. However, inferences about the relationships between B. inermis presence and environmental predictors differed between the two spatial binary models. The strip adaptive cluster designs we investigate provided a significant advantage in terms of Markov chain Monte Carlo chain convergence when trying to model a sparsely distributed species across a large area. In general, there was little difference in the choice of neighborhood, although the adaptive king was preferred when transects were randomly placed throughout the spatial domain.

Amplitude envelope correlations measure synchronous cortical oscillations in performing musicians.

PubMed

Zamm, Anna; Debener, Stefan; Bauer, Anna-Katharina R; Bleichner, Martin G; Demos, Alexander P; Palmer, Caroline

2018-05-14

A major question facing cognitive neuroscience is measurement of interbrain synchrony between individuals performing joint actions. We describe the application of a novel method for measuring musicians' interbrain synchrony: amplitude envelope correlations (AECs). Amplitude envelopes (AEs) reflect energy fluctuations in cortical oscillations over time; AE correlations measure the degree to which two envelope fluctuations are temporally correlated, such as cortical oscillations arising from two individuals performing a joint action. Wireless electroencephalography was recorded from two pianists performing a musical duet; an analysis pipeline is described for computing AEs of cortical oscillations at the duet performance frequency (number of tones produced per second) to test whether these oscillations reflect the temporal dynamics of partners' performances. The pianists' AE correlations were compared with correlations based on a distribution of AEs simulated from white noise signals using the same methods. The AE method was also applied to the temporal characteristics of the pianists' performances, to show that the observed pair's AEs reflect the temporal dynamics of their performance. AE correlations offer a promising approach for assessing interbrain correspondences in cortical activity associated with performing joint tasks. © 2018 New York Academy of Sciences.

Evolution and development of the mammalian cerebral cortex.

PubMed

Molnár, Zoltán; Kaas, Jon H; de Carlos, Juan A; Hevner, Robert F; Lein, Ed; Němec, Pavel

2014-01-01

Comparative developmental studies of the mammalian brain can identify key changes that can generate the diverse structures and functions of the brain. We have studied how the neocortex of early mammals became organized into functionally distinct areas, and how the current level of cortical cellular and laminar specialization arose from the simpler premammalian cortex. We demonstrate the neocortical organization in early mammals, which helps to elucidate how the large, complex human brain evolved from a long line of ancestors. The radial and tangential enlargement of the cortex was driven by changes in the patterns of cortical neurogenesis, including alterations in the proportions of distinct progenitor types. Some cortical cell populations travel to the cortex through tangential migration whereas others migrate radially. A number of recent studies have begun to characterize the chick, mouse and human and nonhuman primate cortical transcriptome to help us understand how gene expression relates to the development and anatomical and functional organization of the adult neocortex. Although all mammalian forms share the basic layout of cortical areas, the areal proportions and distributions are driven by distinct evolutionary pressures acting on sensory and motor experiences during the individual ontogenies. © 2014 S. Karger AG, Basel.

Markov Chain Monte Carlo Inference of Parametric Dictionaries for Sparse Bayesian Approximations

PubMed Central

Chaspari, Theodora; Tsiartas, Andreas; Tsilifis, Panagiotis; Narayanan, Shrikanth

2016-01-01

Parametric dictionaries can increase the ability of sparse representations to meaningfully capture and interpret the underlying signal information, such as encountered in biomedical problems. Given a mapping function from the atom parameter space to the actual atoms, we propose a sparse Bayesian framework for learning the atom parameters, because of its ability to provide full posterior estimates, take uncertainty into account and generalize on unseen data. Inference is performed with Markov Chain Monte Carlo, that uses block sampling to generate the variables of the Bayesian problem. Since the parameterization of dictionary atoms results in posteriors that cannot be analytically computed, we use a Metropolis-Hastings-within-Gibbs framework, according to which variables with closed-form posteriors are generated with the Gibbs sampler, while the remaining ones with the Metropolis Hastings from appropriate candidate-generating densities. We further show that the corresponding Markov Chain is uniformly ergodic ensuring its convergence to a stationary distribution independently of the initial state. Results on synthetic data and real biomedical signals indicate that our approach offers advantages in terms of signal reconstruction compared to previously proposed Steepest Descent and Equiangular Tight Frame methods. This paper demonstrates the ability of Bayesian learning to generate parametric dictionaries that can reliably represent the exemplar data and provides the foundation towards inferring the entire variable set of the sparse approximation problem for signal denoising, adaptation and other applications. PMID:28649173

Neuroinflammatory component of gray matter pathology in multiple sclerosis.

PubMed

Herranz, Elena; Giannì, Costanza; Louapre, Céline; Treaba, Constantina A; Govindarajan, Sindhuja T; Ouellette, Russell; Loggia, Marco L; Sloane, Jacob A; Madigan, Nancy; Izquierdo-Garcia, David; Ward, Noreen; Mangeat, Gabriel; Granberg, Tobias; Klawiter, Eric C; Catana, Ciprian; Hooker, Jacob M; Taylor, Norman; Ionete, Carolina; Kinkel, Revere P; Mainero, Caterina

2016-11-01

In multiple sclerosis (MS), using simultaneous magnetic resonance-positron emission tomography (MR-PET) imaging with 11 C-PBR28, we quantified expression of the 18kDa translocator protein (TSPO), a marker of activated microglia/macrophages, in cortex, cortical lesions, deep gray matter (GM), white matter (WM) lesions, and normal-appearing WM (NAWM) to investigate the in vivo pathological and clinical relevance of neuroinflammation. Fifteen secondary-progressive MS (SPMS) patients, 12 relapsing-remitting MS (RRMS) patients, and 14 matched healthy controls underwent 11 C-PBR28 MR-PET. MS subjects underwent 7T T2*-weighted imaging for cortical lesion segmentation, and neurological and cognitive evaluation. 11 C-PBR28 binding was measured using normalized 60- to 90-minute standardized uptake values and volume of distribution ratios. Relative to controls, MS subjects exhibited abnormally high 11 C-PBR28 binding across the brain, the greatest increases being in cortex and cortical lesions, thalamus, hippocampus, and NAWM. MS WM lesions showed relatively modest TSPO increases. With the exception of cortical lesions, where TSPO expression was similar, 11 C-PBR28 uptake across the brain was greater in SPMS than in RRMS. In MS, increased 11 C-PBR28 binding in cortex, deep GM, and NAWM correlated with neurological disability and impaired cognitive performance; cortical thinning correlated with increased thalamic TSPO levels. In MS, neuroinflammation is present in the cortex, cortical lesions, deep GM, and NAWM, is closely linked to poor clinical outcome, and is at least partly linked to neurodegeneration. Distinct inflammatory-mediated factors may underlie accumulation of cortical and WM lesions. Quantification of TSPO levels in MS could prove to be a sensitive tool for evaluating in vivo the inflammatory component of GM pathology, particularly in cortical lesions. Ann Neurol 2016;80:776-790. © 2016 American Neurological Association.

Large-scale modeling of the primary visual cortex: influence of cortical architecture upon neuronal response.

PubMed

McLaughlin, David; Shapley, Robert; Shelley, Michael

2003-01-01

A large-scale computational model of a local patch of input layer 4 [Formula: see text] of the primary visual cortex (V1) of the macaque monkey, together with a coarse-grained reduction of the model, are used to understand potential effects of cortical architecture upon neuronal performance. Both the large-scale point neuron model and its asymptotic reduction are described. The work focuses upon orientation preference and selectivity, and upon the spatial distribution of neuronal responses across the cortical layer. Emphasis is given to the role of cortical architecture (the geometry of synaptic connectivity, of the ordered and disordered structure of input feature maps, and of their interplay) as mechanisms underlying cortical responses within the model. Specifically: (i) Distinct characteristics of model neuronal responses (firing rates and orientation selectivity) as they depend upon the neuron's location within the cortical layer relative to the pinwheel centers of the map of orientation preference; (ii) A time independent (DC) elevation in cortico-cortical conductances within the model, in contrast to a "push-pull" antagonism between excitation and inhibition; (iii) The use of asymptotic analysis to unveil mechanisms which underly these performances of the model; (iv) A discussion of emerging experimental data. The work illustrates that large-scale scientific computation--coupled together with analytical reduction, mathematical analysis, and experimental data, can provide significant understanding and intuition about the possible mechanisms of cortical response. It also illustrates that the idealization which is a necessary part of theoretical modeling can outline in sharp relief the consequences of differing alternative interpretations and mechanisms--with final arbiter being a body of experimental evidence whose measurements address the consequences of these analyses.

Influence of mesh density, cortical thickness and material properties on human rib fracture prediction.

PubMed

Li, Zuoping; Kindig, Matthew W; Subit, Damien; Kent, Richard W

2010-11-01

The purpose of this paper was to investigate the sensitivity of the structural responses and bone fractures of the ribs to mesh density, cortical thickness, and material properties so as to provide guidelines for the development of finite element (FE) thorax models used in impact biomechanics. Subject-specific FE models of the second, fourth, sixth and tenth ribs were developed to reproduce dynamic failure experiments. Sensitivity studies were then conducted to quantify the effects of variations in mesh density, cortical thickness, and material parameters on the model-predicted reaction force-displacement relationship, cortical strains, and bone fracture locations for all four ribs. Overall, it was demonstrated that rib FE models consisting of 2000-3000 trabecular hexahedral elements (weighted element length 2-3mm) and associated quadrilateral cortical shell elements with variable thickness more closely predicted the rib structural responses and bone fracture force-failure displacement relationships observed in the experiments (except the fracture locations), compared to models with constant cortical thickness. Further increases in mesh density increased computational cost but did not markedly improve model predictions. A ±30% change in the major material parameters of cortical bone lead to a -16.7 to 33.3% change in fracture displacement and -22.5 to +19.1% change in the fracture force. The results in this study suggest that human rib structural responses can be modeled in an accurate and computationally efficient way using (a) a coarse mesh of 2000-3000 solid elements, (b) cortical shells elements with variable thickness distribution and (c) a rate-dependent elastic-plastic material model. Copyright © 2010 IPEM. Published by Elsevier Ltd. All rights reserved.

Zic deficiency in the cortical marginal zone and meninges results in cortical lamination defects resembling those in type II lissencephaly.

PubMed

Inoue, Takashi; Ogawa, Masaharu; Mikoshiba, Katsuhiko; Aruga, Jun

2008-04-30

The formation of the highly organized cortical structure depends on the production and correct placement of the appropriate number and types of neurons. The Zic family of zinc-finger transcription factors plays essential roles in regulating the proliferation and differentiation of neuronal progenitors in the medial forebrain and the cerebellum. Examination of the expression of Zic genes demonstrated that Zic1, Zic2, and Zic3 were expressed by the progenitor cells in the septum and cortical hem, the sites of generation of the Cajal-Retzius (CR) cells. Immunohistochemical studies have revealed that Zic proteins were abundantly expressed in the meningeal cells and that the majority of the CR cells distributed in the medial and dorsal cortex also expressed Zic proteins in the mid-late embryonic and postnatal cortical marginal zones. During embryonic cortical development, Zic1/Zic3 double-mutant and hypomorphic Zic2 mutant mice showed a reduction in the number of CR cells in the rostral cortex, whereas the cell number remained unaffected in the caudal cortex. These mutants also showed mislocalization of the CR cells and cortical lamination defects, resembling the changes noted in type II (cobblestone) lissencephaly, throughout the brain. In the Zic1/3 mutant, reduced proliferation of the meningeal cells was observed before the thinner and disrupted organization of the pial basement membrane (BM) with reduced expression of the BM components and the meningeal cell-derived secretory factor. These defects correlated with the changes in the end feet morphology of the radial glial cells. These findings indicate that the Zic genes play critical roles in cortical development through regulating the proliferation of meningeal cells and the pial BM assembly.

Robustness-Based Design Optimization Under Data Uncertainty

NASA Technical Reports Server (NTRS)

Zaman, Kais; McDonald, Mark; Mahadevan, Sankaran; Green, Lawrence

2010-01-01

This paper proposes formulations and algorithms for design optimization under both aleatory (i.e., natural or physical variability) and epistemic uncertainty (i.e., imprecise probabilistic information), from the perspective of system robustness. The proposed formulations deal with epistemic uncertainty arising from both sparse and interval data without any assumption about the probability distributions of the random variables. A decoupled approach is proposed in this paper to un-nest the robustness-based design from the analysis of non-design epistemic variables to achieve computational efficiency. The proposed methods are illustrated for the upper stage design problem of a two-stage-to-orbit (TSTO) vehicle, where the information on the random design inputs are only available as sparse point and/or interval data. As collecting more data reduces uncertainty but increases cost, the effect of sample size on the optimality and robustness of the solution is also studied. A method is developed to determine the optimal sample size for sparse point data that leads to the solutions of the design problem that are least sensitive to variations in the input random variables.

Microbial ecology of extreme environments: Antarctic yeasts and growth in substrate-limited habitats

NASA Technical Reports Server (NTRS)

Vishniac, H. S.

1984-01-01

An extreme environment is by definition one with a depauperate biota. While the Ross Desert is by no means homogeneous, the most exposed and arid habitats, soils in the unglaciated high valleys, do indeed contain a very sparse biota of low diversity. So sparse that the natives could easily be outnumbered by airborne exogenous microbes. Native biota must be capable of overwintering as well as growing in the high valley summer. Tourists may undergo a few divisions before contributing their enzymes and, ultimately, elements to the soil - or may die before landing. The simplest way to demonstrate the indigenicity of a particular microbe is therefore to establish unique distribution; occurrence only in the habitat in question precludes foreign origin.

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

Chen, Chao; Pouransari, Hadi; Rajamanickam, Sivasankaran

We present a parallel hierarchical solver for general sparse linear systems on distributed-memory machines. For large-scale problems, this fully algebraic algorithm is faster and more memory-efficient than sparse direct solvers because it exploits the low-rank structure of fill-in blocks. Depending on the accuracy of low-rank approximations, the hierarchical solver can be used either as a direct solver or as a preconditioner. The parallel algorithm is based on data decomposition and requires only local communication for updating boundary data on every processor. Moreover, the computation-to-communication ratio of the parallel algorithm is approximately the volume-to-surface-area ratio of the subdomain owned by everymore » processor. We also provide various numerical results to demonstrate the versatility and scalability of the parallel algorithm.« less

The Human Thalamus Is an Integrative Hub for Functional Brain Networks

PubMed Central

Bertolero, Maxwell A.

2017-01-01

The thalamus is globally connected with distributed cortical regions, yet the functional significance of this extensive thalamocortical connectivity remains largely unknown. By performing graph-theoretic analyses on thalamocortical functional connectivity data collected from human participants, we found that most thalamic subdivisions display network properties that are capable of integrating multimodal information across diverse cortical functional networks. From a meta-analysis of a large dataset of functional brain-imaging experiments, we further found that the thalamus is involved in multiple cognitive functions. Finally, we found that focal thalamic lesions in humans have widespread distal effects, disrupting the modular organization of cortical functional networks. This converging evidence suggests that the human thalamus is a critical hub region that could integrate diverse information being processed throughout the cerebral cortex as well as maintain the modular structure of cortical functional networks. SIGNIFICANCE STATEMENT The thalamus is traditionally viewed as a passive relay station of information from sensory organs or subcortical structures to the cortex. However, the thalamus has extensive connections with the entire cerebral cortex, which can also serve to integrate information processing between cortical regions. In this study, we demonstrate that multiple thalamic subdivisions display network properties that are capable of integrating information across multiple functional brain networks. Moreover, the thalamus is engaged by tasks requiring multiple cognitive functions. These findings support the idea that the thalamus is involved in integrating information across cortical networks. PMID:28450543

Joint cross-correlation analysis reveals complex, time-dependent functional relationship between cortical neurons and arm electromyograms

PubMed Central

Zhuang, Katie Z.; Lebedev, Mikhail A.

2014-01-01

Correlation between cortical activity and electromyographic (EMG) activity of limb muscles has long been a subject of neurophysiological studies, especially in terms of corticospinal connectivity. Interest in this issue has recently increased due to the development of brain-machine interfaces with output signals that mimic muscle force. For this study, three monkeys were implanted with multielectrode arrays in multiple cortical areas. One monkey performed self-timed touch pad presses, whereas the other two executed arm reaching movements. We analyzed the dynamic relationship between cortical neuronal activity and arm EMGs using a joint cross-correlation (JCC) analysis that evaluated trial-by-trial correlation as a function of time intervals within a trial. JCCs revealed transient correlations between the EMGs of multiple muscles and neural activity in motor, premotor and somatosensory cortical areas. Matching results were obtained using spike-triggered averages corrected by subtracting trial-shuffled data. Compared with spike-triggered averages, JCCs more readily revealed dynamic changes in cortico-EMG correlations. JCCs showed that correlation peaks often sharpened around movement times and broadened during delay intervals. Furthermore, JCC patterns were directionally selective for the arm-reaching task. We propose that such highly dynamic, task-dependent and distributed relationships between cortical activity and EMGs should be taken into consideration for future brain-machine interfaces that generate EMG-like signals. PMID:25210153

Reduced Current Spread by Concentric Electrodes in Transcranial Electrical Stimulation (tES).

PubMed

Bortoletto, M; Rodella, C; Salvador, R; Miranda, P C; Miniussi, C

2016-01-01

We propose the use of a new montage for transcranial direct current stimulation (tDCS), called concentric electrodes tDCS (CE-tDCS), involving two concentric round electrodes that may improve stimulation focality. To test efficacy and focality of CE-tDCS, we modelled the current distribution and tested physiological effects on cortical excitability. Motor evoked potentials (MEPs) from first dorsal interosseous (FDI) and abductor digiti minimi (ADM) were recorded before and after the delivery of anodal, cathodal and sham stimulation on the FDI hotspot for 10 minutes. MEP amplitude of FDI increased after anodal-tDCS and decreased after cathodal-tDCS, supporting the efficacy of CE-tDCS in modulating cortical excitability. Moreover, modelled current distribution and no significant effects of stimulation on MEP amplitude of ADM suggest high focality of CE-tDCS. CE-tDCS may allow a better control of current distribution and may represent a novel tool for applying tDCS and other transcranial current stimulation approaches. Copyright © 2016 Elsevier Inc. All rights reserved.

Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity

PubMed Central

Nessler, Bernhard; Pfeiffer, Michael; Buesing, Lars; Maass, Wolfgang

2013-01-01

The principles by which networks of neurons compute, and how spike-timing dependent plasticity (STDP) of synaptic weights generates and maintains their computational function, are unknown. Preceding work has shown that soft winner-take-all (WTA) circuits, where pyramidal neurons inhibit each other via interneurons, are a common motif of cortical microcircuits. We show through theoretical analysis and computer simulations that Bayesian computation is induced in these network motifs through STDP in combination with activity-dependent changes in the excitability of neurons. The fundamental components of this emergent Bayesian computation are priors that result from adaptation of neuronal excitability and implicit generative models for hidden causes that are created in the synaptic weights through STDP. In fact, a surprising result is that STDP is able to approximate a powerful principle for fitting such implicit generative models to high-dimensional spike inputs: Expectation Maximization. Our results suggest that the experimentally observed spontaneous activity and trial-to-trial variability of cortical neurons are essential features of their information processing capability, since their functional role is to represent probability distributions rather than static neural codes. Furthermore it suggests networks of Bayesian computation modules as a new model for distributed information processing in the cortex. PMID:23633941

Enhancing the Temporal Complexity of Distributed Brain Networks with Patterned Cerebellar Stimulation

PubMed Central

Farzan, Faranak; Pascual-Leone, Alvaro; Schmahmann, Jeremy D.; Halko, Mark

2016-01-01

Growing evidence suggests that sensory, motor, cognitive and affective processes map onto specific, distributed neural networks. Cerebellar subregions are part of these networks, but how the cerebellum is involved in this wide range of brain functions remains poorly understood. It is postulated that the cerebellum contributes a basic role in brain functions, helping to shape the complexity of brain temporal dynamics. We therefore hypothesized that stimulating cerebellar nodes integrated in different networks should have the same impact on the temporal complexity of cortical signals. In healthy humans, we applied intermittent theta burst stimulation (iTBS) to the vermis lobule VII or right lateral cerebellar Crus I/II, subregions that prominently couple to the dorsal-attention/fronto-parietal and default-mode networks, respectively. Cerebellar iTBS increased the complexity of brain signals across multiple time scales in a network-specific manner identified through electroencephalography (EEG). We also demonstrated a region-specific shift in power of cortical oscillations towards higher frequencies consistent with the natural frequencies of targeted cortical areas. Our findings provide a novel mechanism and evidence by which the cerebellum contributes to multiple brain functions: specific cerebellar subregions control the temporal dynamics of the networks they are engaged in. PMID:27009405

Effects of oxotremorine on local glucose utilization in the rat cerebral cortex

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

Dam, M.; Wamsley, J.K.; Rapoport, S.I.

The (/sup 14/C)2-deoxy-D-glucose technique was used to examine the effects of central muscarinic stimulation on local cerebral glucose utilization (LCGU) in the cerebral cortex of the unanesthetized rat. Systemic administration of the muscarinic agonist oxotremorine (OXO, 0.1 to 1.0 mg/kg, i.p.) increased LCGU in the neocortex, mesocortex, and paleocortex. In the neocortex, OXO was more potent in elevating LCGU of the auditory, frontal, and sensorimotor regions compared with the visual cortex. Within these neocortical regions, OXO effects were greatest in cortical layers IV and V. OXO effects were more dramatic in the neocortex than in the meso- or paleocortex, andmore » no significant effect occurred in the perirhinal and pyriform cortices. OXO-induced LCGU increases were not influenced by methylatropine (1 mg/kg, s.c.) but were antagonized completely by scopolamine (2.5 mg/kg, i.p.). Scopolamine reduced LCGU in layer IV of the auditory cortex and in the retrosplenial cortex. The distribution and magnitude of the cortical LCGU response to OXO apparently were related to the distributions of cholinergic neurochemical markers, especially high affinity muscarinic binding sites.« less

A Comparison between Model Base Hardconstrain, Bandlimited, and Sparse-Spike Seismic Inversion: New Insights for CBM Reservoir Modelling on Muara Enim Formation, South Sumatra

NASA Astrophysics Data System (ADS)

Mohamad Noor, Faris; Adipta, Agra

2018-03-01

Coal Bed Methane (CBM) as a newly developed resource in Indonesia is one of the alternatives to relieve Indonesia’s dependencies on conventional energies. Coal resource of Muara Enim Formation is known as one of the prolific reservoirs in South Sumatra Basin. Seismic inversion and well analysis are done to determine the coal seam characteristics of Muara Enim Formation. This research uses three inversion methods, which are: model base hard- constrain, bandlimited, and sparse-spike inversion. Each type of seismic inversion has its own advantages to display the coal seam and its characteristic. Interpretation result from the analysis data shows that the Muara Enim coal seam has 20 (API) gamma ray value, 1 (gr/cc) – 1.4 (gr/cc) from density log, and low AI cutoff value range between 5000-6400 (m/s)*(g/cc). The distribution of coal seam is laterally thinning northwest to southeast. Coal seam is seen biasedly on model base hard constraint inversion and discontinued on band-limited inversion which isn’t similar to the geological model. The appropriate AI inversion is sparse spike inversion which has 0.884757 value from cross plot inversion as the best correlation value among the chosen inversion methods. Sparse Spike inversion its self-has high amplitude as a proper tool to identify coal seam continuity which commonly appears as a thin layer. Cross-sectional sparse spike inversion shows that there are possible new boreholes in CDP 3662-3722, CDP 3586-3622, and CDP 4004-4148 which is seen in seismic data as a thick coal seam.

Multiple Kernel Sparse Representation based Orthogonal Discriminative Projection and Its Cost-Sensitive Extension.

PubMed

Zhang, Guoqing; Sun, Huaijiang; Xia, Guiyu; Sun, Quansen

2016-07-07

Sparse representation based classification (SRC) has been developed and shown great potential for real-world application. Based on SRC, Yang et al. [10] devised a SRC steered discriminative projection (SRC-DP) method. However, as a linear algorithm, SRC-DP cannot handle the data with highly nonlinear distribution. Kernel sparse representation-based classifier (KSRC) is a non-linear extension of SRC and can remedy the drawback of SRC. KSRC requires the use of a predetermined kernel function and selection of the kernel function and its parameters is difficult. Recently, multiple kernel learning for SRC (MKL-SRC) [22] has been proposed to learn a kernel from a set of base kernels. However, MKL-SRC only considers the within-class reconstruction residual while ignoring the between-class relationship, when learning the kernel weights. In this paper, we propose a novel multiple kernel sparse representation-based classifier (MKSRC), and then we use it as a criterion to design a multiple kernel sparse representation based orthogonal discriminative projection method (MK-SR-ODP). The proposed algorithm aims at learning a projection matrix and a corresponding kernel from the given base kernels such that in the low dimension subspace the between-class reconstruction residual is maximized and the within-class reconstruction residual is minimized. Furthermore, to achieve a minimum overall loss by performing recognition in the learned low-dimensional subspace, we introduce cost information into the dimensionality reduction method. The solutions for the proposed method can be efficiently found based on trace ratio optimization method [33]. Extensive experimental results demonstrate the superiority of the proposed algorithm when compared with the state-of-the-art methods.

Solving large tomographic linear systems: size reduction and error estimation

NASA Astrophysics Data System (ADS)

Voronin, Sergey; Mikesell, Dylan; Slezak, Inna; Nolet, Guust

2014-10-01

We present a new approach to reduce a sparse, linear system of equations associated with tomographic inverse problems. We begin by making a modification to the commonly used compressed sparse-row format, whereby our format is tailored to the sparse structure of finite-frequency (volume) sensitivity kernels in seismic tomography. Next, we cluster the sparse matrix rows to divide a large matrix into smaller subsets representing ray paths that are geographically close. Singular value decomposition of each subset allows us to project the data onto a subspace associated with the largest eigenvalues of the subset. After projection we reject those data that have a signal-to-noise ratio (SNR) below a chosen threshold. Clustering in this way assures that the sparse nature of the system is minimally affected by the projection. Moreover, our approach allows for a precise estimation of the noise affecting the data while also giving us the ability to identify outliers. We illustrate the method by reducing large matrices computed for global tomographic systems with cross-correlation body wave delays, as well as with surface wave phase velocity anomalies. For a massive matrix computed for 3.7 million Rayleigh wave phase velocity measurements, imposing a threshold of 1 for the SNR, we condensed the matrix size from 1103 to 63 Gbyte. For a global data set of multiple-frequency P wave delays from 60 well-distributed deep earthquakes we obtain a reduction to 5.9 per cent. This type of reduction allows one to avoid loss of information due to underparametrizing models. Alternatively, if data have to be rejected to fit the system into computer memory, it assures that the most important data are preserved.

First record of Tettigettalna mariae Quartau & Boulard, 1995 (Insecta: Hemiptera: Cicadoidea) in Spain

PubMed Central

2013-01-01

Abstract Tettigettalna mariae Quartau & Boulard 1995 is recorded for the first time in Spain. Thought to be endemic to Portugal (occurring in the southern province of Algarve), the present paper adds its distribution to southern Spain, being an Iberian endemism. The acoustic signals of the new specimens collected were recorded in different localities of Huelva province, in Andalusia during August 2012. According to their present known distribution, specimens of Tettigettalna mariae tend to be sparsely distributed in small range populations in southern Iberian Peninsula, favouring wooded areas with Pinus pinea. PMID:24723772

Sparse and redundant representations for inverse problems and recognition

NASA Astrophysics Data System (ADS)

Patel, Vishal M.

Sparse and redundant representation of data enables the description of signals as linear combinations of a few atoms from a dictionary. In this dissertation, we study applications of sparse and redundant representations in inverse problems and object recognition. Furthermore, we propose two novel imaging modalities based on the recently introduced theory of Compressed Sensing (CS). This dissertation consists of four major parts. In the first part of the dissertation, we study a new type of deconvolution algorithm that is based on estimating the image from a shearlet decomposition. Shearlets provide a multi-directional and multi-scale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets. We develop a deconvolution algorithm that allows for the approximation inversion operator to be controlled on a multi-scale and multi-directional basis. Furthermore, we develop a method for the automatic determination of the threshold values for the noise shrinkage for each scale and direction without explicit knowledge of the noise variance using a generalized cross validation method. In the second part of the dissertation, we study a reconstruction method that recovers highly undersampled images assumed to have a sparse representation in a gradient domain by using partial measurement samples that are collected in the Fourier domain. Our method makes use of a robust generalized Poisson solver that greatly aids in achieving a significantly improved performance over similar proposed methods. We will demonstrate by experiments that this new technique is more flexible to work with either random or restricted sampling scenarios better than its competitors. In the third part of the dissertation, we introduce a novel Synthetic Aperture Radar (SAR) imaging modality which can provide a high resolution map of the spatial distribution of targets and terrain using a significantly reduced number of needed transmitted and/or received electromagnetic waveforms. We demonstrate that this new imaging scheme, requires no new hardware components and allows the aperture to be compressed. Also, it presents many new applications and advantages which include strong resistance to countermesasures and interception, imaging much wider swaths and reduced on-board storage requirements. The last part of the dissertation deals with object recognition based on learning dictionaries for simultaneous sparse signal approximations and feature extraction. A dictionary is learned for each object class based on given training examples which minimize the representation error with a sparseness constraint. A novel test image is then projected onto the span of the atoms in each learned dictionary. The residual vectors along with the coefficients are then used for recognition. Applications to illumination robust face recognition and automatic target recognition are presented.

Spatiotemporal characteristics of sleep spindles depend on cortical location.

PubMed

Piantoni, Giovanni; Halgren, Eric; Cash, Sydney S

2017-02-01

Since their discovery almost one century ago, sleep spindles, 0.5-2s long bursts of oscillatory activity at 9-16Hz during NREM sleep, have been thought to be global and relatively uniform throughout the cortex. Recent work, however, has brought this concept into question but it remains unclear to what degree spindles are global or local and if their properties are uniform or location-dependent. We addressed this question by recording sleep in eight patients undergoing evaluation for epilepsy with intracranial electrocorticography, which combines high spatial resolution with extensive cortical coverage. We find that spindle characteristics are not uniform but are strongly influenced by the underlying cortical regions, particularly for spindle density and fundamental frequency. We observe both highly isolated and spatially distributed spindles, but in highly skewed proportions: while most spindles are restricted to one or very few recording channels at any given time, there are spindles that occur over widespread areas, often involving lateral prefrontal cortices and superior temporal gyri. Their co-occurrence is affected by a subtle but significant propagation of spindles from the superior prefrontal regions and the temporal cortices towards the orbitofrontal cortex. This work provides a brain-wide characterization of sleep spindles as mostly local graphoelements with heterogeneous characteristics that depend on the underlying cortical area. We propose that the combination of local characteristics and global organization reflects the dual properties of the thalamo-cortical generators and provides a flexible framework to support the many functions ascribed to sleep in general and spindles specifically. Copyright © 2016 Elsevier Inc. All rights reserved.

Sequence of information processing for emotions based on the anatomic dialogue between prefrontal cortex and amygdala.

PubMed

Ghashghaei, H T; Hilgetag, C C; Barbas, H

2007-02-01

The prefrontal cortex and the amygdala have synergistic roles in regulating purposive behavior, effected through bidirectional pathways. Here we investigated the largely unknown extent and laminar relationship of prefrontal input-output zones linked with the amygdala using neural tracers injected in the amygdala in rhesus monkeys. Prefrontal areas varied vastly in their connections with the amygdala, with the densest connections found in posterior orbitofrontal and posterior medial cortices, and the sparsest in anterior lateral prefrontal areas, especially area 10. Prefrontal projection neurons directed to the amygdala originated in layer 5, but significant numbers were also found in layers 2 and 3 in posterior medial and orbitofrontal cortices. Amygdalar axonal terminations in prefrontal cortex were most frequently distributed in bilaminar bands in the superficial and deep layers, by columns spanning the entire cortical depth, and less frequently as small patches centered in the superficial or deep layers. Heavy terminations in layers 1-2 overlapped with calbindin-positive inhibitory neurons. A comparison of the relationship of input to output projections revealed that among the most heavily connected cortices, cingulate areas 25 and 24 issued comparatively more projections to the amygdala than they received, whereas caudal orbitofrontal areas were more receivers than senders. Further, there was a significant relationship between the proportion of 'feedforward' cortical projections from layers 2-3 to 'feedback' terminations innervating the superficial layers of prefrontal cortices. These findings indicate that the connections between prefrontal cortices and the amygdala follow similar patterns as corticocortical connections, and by analogy suggest pathways underlying the sequence of information processing for emotions.

Layer-specific gene expression in epileptogenic type II focal cortical dysplasia: normal-looking neurons reveal the presence of a hidden laminar organization

PubMed Central

2014-01-01

Background Type II focal cortical dysplasias (FCDs) are malformations of cortical development characterised by the disorganisation of the normal neocortical structure and the presence of dysmorphic neurons (DNs) and balloon cells (BCs). The pathogenesis of FCDs has not yet been clearly established, although a number of histopathological patterns and molecular findings suggest that they may be due to abnormal neuronal and glial proliferation and migration processes. In order to gain further insights into cortical layering disruption and investigate the origin of DNs and BCs, we used in situ RNA hybridisation of human surgical specimens with a neuropathologically definite diagnosis of Type IIa/b FCD and a panel of layer-specific genes (LSGs) whose expression covers all cortical layers. We also used anti-phospho-S6 ribosomal protein antibody to investigate mTOR pathway hyperactivation. Results LSGs were expressed in both normal and abnormal cells (BCs and DNs) but their distribution was different. Normal-looking neurons, which were visibly reduced in the core of the lesion, were apparently located in the appropriate cortical laminae thus indicating a partial laminar organisation. On the contrary, DNs and BCs, labelled with anti-phospho-S6 ribosomal protein antibody, were spread throughout the cortex without any apparent rule and showed a highly variable LSG expression pattern. Moreover, LSGs did not reveal any differences between Type IIa and IIb FCD. Conclusion These findings suggest the existence of hidden cortical lamination involving normal-looking neurons, which retain their ability to migrate correctly in the cortex, unlike DNs which, in addition to their morphological abnormalities and mTOR hyperactivation, show an altered migratory pattern. Taken together these data suggest that an external or environmental hit affecting selected precursor cells during the very early stages of cortical development may disrupt normal cortical development. PMID:24735483

Random-access scanning microscopy for 3D imaging in awake behaving animals

PubMed Central

Nadella, K. M. Naga Srinivas; Roš, Hana; Baragli, Chiara; Griffiths, Victoria A.; Konstantinou, George; Koimtzis, Theo; Evans, Geoffrey J.; Kirkby, Paul A.; Silver, R. Angus

2018-01-01

Understanding how neural circuits process information requires rapid measurements from identified neurons distributed in 3D space. Here we describe an acousto-optic lens two-photon microscope that performs high-speed focussing and line-scanning within a volume spanning hundreds of micrometres. We demonstrate its random access functionality by selectively imaging cerebellar interneurons sparsely distributed in 3D and by simultaneously recording from the soma, proximal and distal dendrites of neocortical pyramidal cells in behaving mice. PMID:27749836

Time-Frequency Based Instantaneous Frequency Estimation of Sparse Signals from an Incomplete Set of Samples

DTIC Science & Technology

2014-06-17

100 0 2 4 Wigner distribution 0 50 100 0 0.5 1 Auto-correlation function 0 50 100 0 2 4 L- Wigner distribution 0 50 100 0 0.5 1 Auto-correlation function ...bilinear or higher order autocorrelation functions will increase the number of missing samples, the analysis shows that accurate instantaneous...frequency estimation can be achieved even if we deal with only few samples, as long as the auto-correlation function is properly chosen to coincide with

A Distributed-Memory Package for Dense Hierarchically Semi-Separable Matrix Computations Using Randomization

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

Rouet, François-Henry; Li, Xiaoye S.; Ghysels, Pieter

In this paper, we present a distributed-memory library for computations with dense structured matrices. A matrix is considered structured if its off-diagonal blocks can be approximated by a rank-deficient matrix with low numerical rank. Here, we use Hierarchically Semi-Separable (HSS) representations. Such matrices appear in many applications, for example, finite-element methods, boundary element methods, and so on. Exploiting this structure allows for fast solution of linear systems and/or fast computation of matrix-vector products, which are the two main building blocks of matrix computations. The compression algorithm that we use, that computes the HSS form of an input dense matrix, reliesmore » on randomized sampling with a novel adaptive sampling mechanism. We discuss the parallelization of this algorithm and also present the parallelization of structured matrix-vector product, structured factorization, and solution routines. The efficiency of the approach is demonstrated on large problems from different academic and industrial applications, on up to 8,000 cores. Finally, this work is part of a more global effort, the STRUctured Matrices PACKage (STRUMPACK) software package for computations with sparse and dense structured matrices. Hence, although useful on their own right, the routines also represent a step in the direction of a distributed-memory sparse solver.« less

A Distributed-Memory Package for Dense Hierarchically Semi-Separable Matrix Computations Using Randomization

DOE PAGES

Rouet, François-Henry; Li, Xiaoye S.; Ghysels, Pieter; ...

2016-06-30

In this paper, we present a distributed-memory library for computations with dense structured matrices. A matrix is considered structured if its off-diagonal blocks can be approximated by a rank-deficient matrix with low numerical rank. Here, we use Hierarchically Semi-Separable (HSS) representations. Such matrices appear in many applications, for example, finite-element methods, boundary element methods, and so on. Exploiting this structure allows for fast solution of linear systems and/or fast computation of matrix-vector products, which are the two main building blocks of matrix computations. The compression algorithm that we use, that computes the HSS form of an input dense matrix, reliesmore » on randomized sampling with a novel adaptive sampling mechanism. We discuss the parallelization of this algorithm and also present the parallelization of structured matrix-vector product, structured factorization, and solution routines. The efficiency of the approach is demonstrated on large problems from different academic and industrial applications, on up to 8,000 cores. Finally, this work is part of a more global effort, the STRUctured Matrices PACKage (STRUMPACK) software package for computations with sparse and dense structured matrices. Hence, although useful on their own right, the routines also represent a step in the direction of a distributed-memory sparse solver.« less

Individual snag detection using neighborhood attribute filtered airborne lidar data

Treesearch

Brian M. Wing; Martin W. Ritchie; Kevin Boston; Warren B. Cohen; Michael J. Olsen

2015-01-01

The ability to estimate and monitor standing dead trees (snags) has been difficult due to their irregular and sparse distribution, often requiring intensive sampling methods to obtain statistically significant estimates. This study presents a new method for estimating and monitoring snags using neighborhood attribute filtered airborne discrete-return lidar data. The...

Ex-situ conservation of Quercus oglethorpensis in living collections of arboreta and botanical gardens.

Treesearch

Matthew S. Lobdell; Patrick G. Thompson

2017-01-01

Quercus oglethorpensis (Oglethorpe oak) is an endangered species native to the southeastern United States. It is threatened by land use changes, competition, and chestnut blight disease caused by Cryphonectria parasitica. The species is distributed sparsely over a linear distance of ca. 950 km. Its range includes several...

A practical modification of horizontal line sampling for snag and cavity tree inventory

Treesearch

M. J. Ducey; G. J. Jordan; J. H. Gove; H. T. Valentine

2002-01-01

Snags and cavity trees are important structural features in forests, but they are often sparsely distributed, making efficient inventories problematic. We present a straightforward modification of horizontal line sampling designed to facilitate inventory of these features while remaining compatible with commonly employed sampling methods for the living overstory. The...

Sparse distributed memory prototype: Principles of operation

NASA Technical Reports Server (NTRS)

Flynn, Michael J.; Kanerva, Pentti; Ahanin, Bahram; Bhadkamkar, Neal; Flaherty, Paul; Hickey, Philip

1988-01-01

Sparse distributed memory is a generalized random access memory (RAM) for long binary words. Such words can be written into and read from the memory, and they can be used to address the memory. The main attribute of the memory is sensitivity to similarity, meaning that a word can be read back not only by giving the original right address but also by giving one close to it as measured by the Hamming distance between addresses. Large memories of this kind are expected to have wide use in speech and scene analysis, in signal detection and verification, and in adaptive control of automated equipment. The memory can be realized as a simple, massively parallel computer. Digital technology has reached a point where building large memories is becoming practical. The research is aimed at resolving major design issues that have to be faced in building the memories. The design of a prototype memory with 256-bit addresses and from 8K to 128K locations for 256-bit words is described. A key aspect of the design is extensive use of dynamic RAM and other standard components.

Sparse Feature Selection Identifies H2A.Z as a Novel, Pattern-Specific Biomarker for Asymmetrically Self-Renewing Distributed Stem Cells

PubMed Central

Huh, Yang Hoon; Noh, Minsoo; Burden, Frank R.; Chen, Jennifer C.; Winkler, David A.; Sherley, James L.

2015-01-01

There is a long-standing unmet clinical need for biomarkers with high specificity for distributed stem cells (DSCs) in tissues, or for use in diagnostic and therapeutic cell preparations (e.g., bone marrow). Although DSCs are essential for tissue maintenance and repair, accurate determination of their numbers for medical applications has been problematic. Previous searches for biomarkers expressed specifically in DSCs were hampered by difficulty obtaining pure DSCs and by the challenges in mining complex molecular expression data. To identify DSC such useful and specific biomarkers, we combined a novel sparse feature selection method with combinatorial molecular expression data focused on asymmetric self-renewal, a conspicuous property of DSCs. The analysis identified reduced expression of the histone H2A variant H2A.Z as a superior molecular discriminator for DSC asymmetric self-renewal. Subsequent molecular expression studies showed H2A.Z to be a novel “pattern-specific biomarker” for asymmetrically self-renewing cells with sufficient specificity to count asymmetrically self-renewing DSCs in vitro and potentially in situ. PMID:25636161

Coverage maximization under resource constraints using a nonuniform proliferating random walk.

PubMed

Saha, Sudipta; Ganguly, Niloy

2013-02-01

Information management services on networks, such as search and dissemination, play a key role in any large-scale distributed system. One of the most desirable features of these services is the maximization of the coverage, i.e., the number of distinctly visited nodes under constraints of network resources as well as time. However, redundant visits of nodes by different message packets (modeled, e.g., as walkers) initiated by the underlying algorithms for these services cause wastage of network resources. In this work, using results from analytical studies done in the past on a K-random-walk-based algorithm, we identify that redundancy quickly increases with an increase in the density of the walkers. Based on this postulate, we design a very simple distributed algorithm which dynamically estimates the density of the walkers and thereby carefully proliferates walkers in sparse regions. We use extensive computer simulations to test our algorithm in various kinds of network topologies whereby we find it to be performing particularly well in networks that are highly clustered as well as sparse.

Two-dimensional shape recognition using sparse distributed memory

NASA Technical Reports Server (NTRS)

Kanerva, Pentti; Olshausen, Bruno

1990-01-01

Researchers propose a method for recognizing two-dimensional shapes (hand-drawn characters, for example) with an associative memory. The method consists of two stages: first, the image is preprocessed to extract tangents to the contour of the shape; second, the set of tangents is converted to a long bit string for recognition with sparse distributed memory (SDM). SDM provides a simple, massively parallel architecture for an associative memory. Long bit vectors (256 to 1000 bits, for example) serve as both data and addresses to the memory, and patterns are grouped or classified according to similarity in Hamming distance. At the moment, tangents are extracted in a simple manner by progressively blurring the image and then using a Canny-type edge detector (Canny, 1986) to find edges at each stage of blurring. This results in a grid of tangents. While the technique used for obtaining the tangents is at present rather ad hoc, researchers plan to adopt an existing framework for extracting edge orientation information over a variety of resolutions, such as suggested by Watson (1987, 1983), Marr and Hildreth (1980), or Canny (1986).

Some methods of encoding simple visual images for use with a sparse distributed memory, with applications to character recognition

NASA Technical Reports Server (NTRS)

Jaeckel, Louis A.

1989-01-01

To study the problems of encoding visual images for use with a Sparse Distributed Memory (SDM), I consider a specific class of images- those that consist of several pieces, each of which is a line segment or an arc of a circle. This class includes line drawings of characters such as letters of the alphabet. I give a method of representing a segment of an arc by five numbers in a continuous way; that is, similar arcs have similar representations. I also give methods for encoding these numbers as bit strings in an approximately continuous way. The set of possible segments and arcs may be viewed as a five-dimensional manifold M, whose structure is like a Mobious strip. An image, considered to be an unordered set of segments and arcs, is therefore represented by a set of points in M - one for each piece. I then discuss the problem of constructing a preprocessor to find the segments and arcs in these images, although a preprocessor has not been developed. I also describe a possible extension of the representation.