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Results for “functional network connectivity”

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At least 235 records · Page 13Linked to original sources

PubMed1997

Coarse-grain parallel computing for very large scale neural simulations in the NEXUS simulation environment.

We describe a neural simulator designed for simulating very large scale models of cortical architectures. This simulator, NEXUS, uses coarse-grain parallel computing by distributing computation and data onto multiple conventional workstations connected via a local area network. Coarse-grain parallel computing offers natural advantages in simulating functionally segregated neural processes. We partition a complete model into modules with locally dense connections--a module may represent a cortical area, column, layer, or functional entity. Asynchronous data communications among workstations are established through the Network File System, which, together with the implicit modularity, decreases communications overhead, and increases overall performance. Coarse-grain parallelism also benefits from the standardization of conventional workstations and LAN, including portability between generations and vendors.

Computer Communication Networks↗

PubMed1979

The collagen network of the heart.

An extensive collagen skeleton in the myocardium is clearly demonstrable by scanning electron microscopy. This system mechanically connects myocytes to contiguous myocytes, connects myocytes to contiguous capillaries, and is responsible for orienting myocytes into anatomical units. The extent and location of the system involves it in a variety of physical parameters of the heart under both normal and abnormal conditions. The myocyte to myocyte collagen struts may provide for sarcomere alignment from cell to cell, ensure equal stretch of contiguous cells, and prevent slippage of adjacent cells. The numerous myocyte to web network connections would ensure that the entire group of cells function in a consistent fashion. The origin and course of the myocyte to capillary collagen struts could account for capillary patency during systole in the presence of high ventricular wall pressures. The complex web network surrounds groups of myocytes and is mechanically coupled to the contained myocytes. The loose collagen connections between groups easily permits slippage and rearrangement of groups of cells as expected in acute dilation. The web network itself could be the site of some of the viscoelastic properties of the heart.

Animals↗

PubMed1996

Imaging motor-to-sensory discharges in the human brain: an experimental tool for the assessment of functional connectivity.

We present a new approach to studying functional connectivity in the human brain. This approach is based on the observation that when we engage in motor activity, a discharge corollary to the motor command is sent from motor to sensory structures. Thus, as long as movement-related sensory input is either prevented or masked, modulation of neuronal activity in sensory structures would indicate the presence of functional connectivity between the motor and the sensory regions. Using positron emission tomography, such a central interaction between motor and sensory regions can be assessed by measuring regional changes in cerebral blood flow (CBF) in sensory regions. In this paper, we describe the experimental design and the results of two studies of corollary discharges, namely those generated during eye movements and speech. In these studies, a graded approach was used to establish the relationship between the number of eye movements or utterances and CBF in visual or auditory regions, respectively. Significant covariations between the number of movements and CBF in sensory regions were found, thus indicating the presence of functional connectivity between motor and sensory regions. In addition, interregional CBF covariations were computed and the effect of removing the intersubject variance on these covariations was evaluated. The corollary-discharge-based approach to studying functional connectivity is discussed in the context of more traditional computational approaches to network analysis in functional brain imaging.

Brain↗

PubMedDate not supplied

Information processing with frequency-dependent synaptic connections.

The efficacy of synaptic transmission between two neurons changes as a function of the history of previous activations of the synaptic connection. This history dependence can be characterized by examining the dependence of transmission on the frequency of stimulation. In this framework synaptic plasticity can also be examined in terms of changes in the frequency dependence of transmission and not merely in terms of synaptic strength which constitutes only a linear scaling mechanism. Recent work shows that the frequency dependence of transmission determines the content of information transmitted between neurons and that synaptic modifications can change the content of information transmitted. Multipatch-clamp recordings revealed that the frequency dependence of transmission is potentially unique for each synaptic connection made by a single axon and that the class of pre-postsynaptic neuron determines the class of frequency dependence (activity independent), while the unique activity relationship between any two neurons could determine the precise values of the parameters within a specific class (activity dependent). The content of information transmitted between neurons is also formalized to provide synaptic transfer functions which can be used to determine the role of the synaptic connection within a network of neurons. It is proposed that deriving synaptic transfer functions is crucial in order to understand the link between synaptic transmission and information processing within networks of neurons and to understand the link between synaptic plasticity and learning and memory.

Algorithms↗

PubMed2001

Ocular motor system: anatomy and functional magnetic resonance imaging.

This article reviews current positron emission tomography and functional MR imaging (fMRI) studies of brain activation and indicates some of the insights they provide into the neuronal network mediating motion-perception and ocular motor control. The complexity of the network and the activation of the entire system seemingly independent of the specific ocular motor task performed makes functional interpretation extremely difficult. At the same time, however, the complexity illustrates the dilemma of associating a particular locus with one specific function. Caution is required to differentiate specific from nonspecific effects, to identify activations and deactivations, and to distinguish which of the systems has been activated inadvertently through poor stimulus control. The next step required for meaningful correlations of structure with function is for fMRI studies to test neurologic patients with distinct and separate lesions of the network. Such a comparison must evaluate the effect of the ocular motor deficit on the lesion site and the connected network in the acute stage and then following functional recovery caused by plasticity and neuronal repair.

Brain↗

PubMed1997

A penalty-function approach for pruning feedforward neural networks.

This article proposes the use of a penalty function for pruning feedforward neural network by weight elimination. The penalty function proposed consists of two terms. The first term is to discourage the use of unnecessary connections, and the second term is to prevent the weights of the connections from taking excessively large values. Simple criteria for eliminating weights from the network are also given. The effectiveness of this penalty function is tested on three well-known problems: the contiguity problem, the parity problems, and the monks problems. The resulting pruned networks obtained for many of these problems have fewer connections than previously reported in the literature.

Algorithms↗

PubMed2004

Cerebral modeling and dynamic Bayesian networks.

The understanding and the prediction of the clinical outcomes of focal or degenerative cerebral lesions, as well as the assessment of rehabilitation procedures, necessitate knowing the cerebral substratum of cognitive or sensorimotor functions. This is achieved by activation studies, where subjects are asked to perform a specific task while data of their brain functioning are obtained through functional neuroimaging techniques. Such studies, as well as animal experiments, have shown that sensorimotor or cognitive functions are the offspring of the activity of large-scale networks of anatomically connected cerebral regions. However, no one-to-one correspondence between activated networks adn functions can be found. Our research aims at understanding how the activation of large-scale networks derives from cerebral information processing mechanisms, which can only explain apparently conflicting activation data. Our work falls at the crossroads of neuroimaging interpretation techniques and computational neuroscience. Since knowledge in cognitive neuroscience is permanently evolving, our research aims more precisely at defining a new modeling formalism and at building a flexible simulator, allowing a quick implementation of the models, for a better interpretation of cerebral functional images. It also aims at providing plausible models, at eht level of large-scale networks, of cerebral information processing mechanisms in humans. In this paper, we propose a formalism, based on dynamic Bayesian networks (DBNs), that respects the following constraints: an oriented, networks architecture, whose nodes (the cerebral structures) can all be different, the implementation of causality--the activation of the structure is caused by upstream nodes' activation--the explicit representation of different time scales (from 1 ms for the cerebral activity to many seconds for a PET scan image acquisition), the representation of cerebral information at the integrated level of neuronal populations, the imprecision of functional neuroimaging data, the nonlinearity and the uncertainty in cerebral mechanisms, and brain's plasticity (learning, reorganization, modulation). One of the main problems, nonlinearity, has been tackled thanks to new extensions of the Kalman filter. The capabilities of the formalism's current version are illustrated by the modeling of a phoneme categorization process, explaining the different cerebral activations in normal and dyslexic subjects.

Bayes Theorem↗

PubMed2003

[Modeling dynamics of gene net, regulating the cell cycle in mammalian cells].

The study of the molecular mechanisms determining cellular programs of proliferation, differentiation, and apoptosis is currently attracting much attention. Recent studies have demonstrated that the system of cell-cycle control based on the transcriptional regulation of the expression of specific genes is responsible for the transition between programs. These groups of functionally connected genes from so-called gene networks characterized by numerous feedbacks and a complex behavioral dynamics. Computer simulation methods have been applied to studying the dynamics of gene networks regulating the cell cycle of vertebrates. The data on the regulation of the key genes obtained from the CYCLE-TRRD database have been used as a basis to construct gene networks of different degrees of complexity controlling the G1/S transition, one of the most important stages of the cell cycle. The behavior dynamics of the model constructed has been analyzed. Two qualitatively different functional modes of the system has been obtained. It has also been shown that the transition between these modes depends on the duration of the proliferation signal. It has also been demonstrated that the additional feedback from factor E2F to genes c-fos and c-jun, which was predicted earlier based on the computer analysis of promoters, plays an important role in the transition of the cell to the S phase.

Animals↗

PubMed2003

Self-organizing neural network model of motion processing in the visual cortex during smooth pursuit.

A physiologically based neural network model was constructed to study cortical motion processing during pursuit eye movements. The model consists of three layers of computational units, simulating information processing by direction selective neurons in the primary visual cortex (V1), motion selective neurons in the middle-temporal area, and pursuit selective neurons in the middle-superior-temporal (MST) area. MST units integrate visual and eye-movement related information, and their connections develop during an unsupervised training process. The resulting MST units represent a transition from retinal to real-world reference frame. By analyzing the model connectivity, mechanisms underlying the functions performed by the network are studied.

Brain Mapping↗

PubMed2003

Executive functions and their disorders.

The term executive function defines complex cognitive processing requiring the co-ordination of several subprocesses to achieve a particular goal. Neuropsychological evidence suggests that executive processing is intimately connected with the intact function of the frontal cortices. Executive dysfunction has been associated with a range of disorders, and is generally attributed to structural or functional frontal pathology. Neuroimaging, with PET and fMRI, has confirmed the relationship; however, attempts to link specific aspects of executive functioning to discrete prefrontal foci have been inconclusive. Instead, the emerging view suggests that executive function is mediated by dynamic and flexible networks, that can be characterised using functional integration and effective connectivity analyses. This view is compatible with the clinical presentation of executive dysfunction associated with a range of pathologies, and also with evidence that recovery of executive function can occur after traumatic brain injury, perhaps due to functional reorganisation within executive networks.

Brain Injuries↗

PubMed2005

White matter tractography by anisotropic wavefront evolution and diffusion tensor imaging.

Determination of axonal pathways provides an invaluable means to study the connectivity of the human brain and its functional network. Diffusion tensor imaging (DTI) is unique in its ability to capture the restricted diffusion of water molecules which can be used to infer the directionality of tissue components. In this paper, we introduce a white matter tractography method based on anisotropic wavefront propagation in diffusion tensor images. A front propagates in the white matter with a speed profile governed by the isocontour of the diffusion tensor ellipsoid. By using the ellipsoid, we avoid possible misclassification of the principal eigenvector in oblate regions. The wavefront evolution is described by an anisotropic version of the static Hamilton-Jacobi equation, which is solved by a sweeping method in order to obtain correct arrival times. Pathways of connection are determined by tracing minimum-cost trajectories using the characteristic vector field of the resulting partial differential equation. A validity index is described to rate the goodness of the resulting pathways with respect to the directionality of the tensor field. Connectivity results using normal human DTI brain images are illustrated and discussed. We also compared our method with a similar level set-based tractography technique, and found that the anisotropic evolution increased the validity index of the obtained pathways by 18%.

Algorithms↗

PubMed2006

Gene loss rate: a probabilistic measure for the conservation of eukaryotic genes.

The rate of conservation of a gene in evolution is believed to be correlated with its biological importance. Recent studies have devised various conservation measures for genes and have shown that they are correlated with several biological characteristics of functional importance. Specifically, the state-of-the-art propensity for gene loss (PGL) measure was shown to be strongly correlated with gene essentiality and its number of protein-protein interactions (PPIs). The observed correlation between conservation and functional importance varies however between conservation measures, underscoring the need for accurate and general measures for the rate of gene conservation. Here we develop a novel maximum-likelihood approach to computing the rate in which a gene is lost in evolution, motivated by the same principles as those underlying PGL. However, in difference to PGL which considers only the most parsimonious ancestral states of the internal nodes of the phylogenetic tree relating the species, our approach weighs in a probabilistic manner all possible ancestral states, and includes the branch length information as part of the probabilistic model. In application to data of 16 eukaryotic genomes, our approach shows higher correlations with experimental data than PGL, including data on gene lethality, level of connectivity in a PPI network and coherence within functionally related genes.

Algorithms↗

PubMed2004

Regularising neural networks using flexible multivariate activation function.

This paper presents a new general neural structure based on nonlinear flexible multivariate function that can be viewed in the framework of the generalised regularisation networks theory. The proposed architecture is based on multi-dimensional adaptive cubic spline basis activation function that collects information from the previous network layer in aggregate form. In other words, each activation function represents a spline function of a subset of previous layer outputs so the number of network connections (structural complexity) can be very low with respect to the problem complexity. A specific learning algorithm, based on the adaptation of local parameters of the activation function, is derived. This fact improve the network generalisation capabilities and speed up the convergence of the learning process. At last, some experimental results demonstrating the effectiveness of the proposed architecture, are presented.

Learning↗

PubMed2003

Cell-cell signaling during synapse formation in the CNS.

Synapses join individual nerve cells into a functional network. Specific cell-cell signaling events regulate synapse formation during development and thereby generate a highly reproducible connectivity pattern. The accuracy of this process is fundamental for normal brain function, and aberrant connectivity leads to nervous system disorders. However, despite the overall precision with which neuronal circuits are formed, individual synapses and synaptic networks are also plastic and can readily adapt to external stimuli or perturbations. In recent studies, several trans-synaptic signaling systems have been identified that can mediate various aspects of synaptic differentiation in the central nervous system. It appears that these individual pathways functionally cooperate, thereby generating robustness and flexibility, which ensure normal nervous system function.

Animals↗

PubMed2004

Drosophila homeodomain protein Nkx6 coordinates motoneuron subtype identity and axonogenesis.

The regulatory networks acting in individual neurons to control their stereotyped differentiation, connectivity, and function are not well understood. Here, we demonstrate that homeodomain protein Nkx6 is a key member of the genetic network of transcription factors that specifies neuronal fates in Drosophila. Nkx6 collaborates with the homeodomain protein Hb9 to specify ventrally projecting motoneuron fate and to repress dorsally projecting motoneuron fate. While Nkx6 acts in parallel with hb9 to regulate motoneuron fate, we find that Nkx6 plays a distinct role to promote axonogenesis, as axon growth of Nkx6-positive motoneurons is severely compromised in Nkx6 mutant embryos. Furthermore, Nkx6 is necessary for the expression of the neural adhesion molecule Fasciclin III in Nkx6-positive motoneurons. Thus, this work demonstrates that Nkx6 acts in a specific neuronal population to link neuronal subtype identity to neuronal morphology and connectivity.

Animals↗

PubMed1996

Functional GABAergic synaptic connection in neonatal mouse barrel cortex.

Intracortical inhibition is crucial to proper functioning of the mature neocortex, yet, paradoxically, is reported to be rare or absent in the neonatal animal. We reexamined this issue by recording whole-cell postsynaptic currents (PSCs) of barrel cortex neurons in thalamocortical brain slices from neonatal mice. Monosynaptic, excitatory thalamocortical responses were elicited in layers V/VI neurons as early as postnatal day 0 (P0, the first 24 hr after birth) and in presumptive layer IV as early as P2. At very low stimulation frequencies, the monosynaptic response was invariably followed by a prolonged (up to 1 sec) synaptic barrage, which fatigued at stimulus repetition rates of 2/min or higher. This barrage consisted of postsynaptic responses to spiking activity in neighboring cortical cells, because (1) it could also be evoked by intracortical stimulation in coronal slices and (2) it was abolished by antagonists to NMDA receptors (NMDARs), even when NMDARs on the recorded cell were under a voltage-dependent block. Some of the larger polysynaptic events changed polarity at a negative reversal potential and were blocked by GABAA receptor (GABAAR) antagonists, with a concurrent enhancement of the extracellular field potential, indicating that they were GABAAR- mediated, CI-dependent inhibitory PSCs (IPSCs). We conclude that a network of functional intracortical GABAAR-mediated synaptic connections exists from the earliest postnatal ages, although it gives rise to responses that differ from mature IPSCs in reversal potential and latency.

6-Cyano-7-nitroquinoxaline-2,3-dione↗

PubMed2006

Analysis of synchronization between two modules of pulse neural networks with excitatory and inhibitory connections.

To study the synchronized oscillations among distant neurons in the visual cortex, we analyzed the synchronization between two modules of pulse neural networks using the phase response function. It was found that the intermodule connections from excitatory to excitatory ensembles tend to stabilize the antiphase synchronization and that the intermodule connections from excitatory to inhibitory ensembles tend to stabilize the in-phase synchronization. It was also found that the intermodule synchronization was more noticeable when the inner-module synchronization was weak.

Action Potentials↗

PubMed2003

Impaired thalamocortical connectivity in humans during general-anesthetic-induced unconsciousness.

Whereas converging lines of evidence suggest that anesthetic-induced unconsciousness may result from disruption of functional interactions within neural networks involving the thalamus and cerebral cortex, the effects anesthetics have on human thalamocortical connectivity remain unexamined with current neuroimaging techniques. To address this issue we retrospectively analyzed positron emission tomography data from 11 volunteers scanned for regional cerebral glucose utilization (rCMRglu) when awake and again during isoflurane- (n = 6) or halothane- (n = 5) induced unconsciousness using statistical parametric mapping (SPM99) and structural equation modeling. A main effect analysis, contrasting awake and unconscious metabolic activity, localized a discrete region of the left va/vl thalamus whose relative rCMRglu activity was significantly suppressed (P < 0.05, corrected) during the unconscious state. To identify brain regions whose functional connectivity with this region of the thalamus was impaired during the unconscious state, a psychophysiological interaction analysis was performed. This analysis revealed effects predominantly in topographically related areas of the primary motor and supplementary motor association cortices. Structural equation modeling of a neuroanatomical network encompassing these empirically identified regions revealed significant state-related changes in effective connectivity (chi(2)diff (6)-15.88; P < 0.05) which primarily involved impairment of thalamocortical and corticocortical projections during the unconscious state. These findings support the hypothesis that a mechanistic component underlying general-anesthetic-induced unconsciousness involves disruption of functional interactions within thalamocortical neural networks.

Anesthesia, General↗