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Results for “functional network connectivity”

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At least 325 records · Page 18Linked to original sources

PubMed2006

Arithmetic processing in the brain shaped by cultures.

The universal use of Arabic numbers in mathematics raises a question whether these digits are processed the same way in people speaking various languages, such as Chinese and English, which reflect differences in Eastern and Western cultures. Using functional MRI, we demonstrated a differential cortical representation of numbers between native Chinese and English speakers. Contrasting to native English speakers, who largely employ a language process that relies on the left perisylvian cortices for mental calculation such as a simple addition task, native Chinese speakers, instead, engage a visuo-premotor association network for the same task. Whereas in both groups the inferior parietal cortex was activated by a task for numerical quantity comparison, functional MRI connectivity analyses revealed a functional distinction between Chinese and English groups among the brain networks involved in the task. Our results further indicate that the different biological encoding of numbers may be shaped by visual reading experience during language acquisition and other cultural factors such as mathematics learning strategies and education systems, which cannot be explained completely by the differences in languages per se.

Adult↗

PubMed2004

Functional evolution of the yeast protein interaction network.

Protein interactions are central to most biological processes. We investigated the dynamics of emergence of the protein interaction network of Saccharomyces cerevisiae by mapping origins of proteins on an evolutionary tree. We demonstrate that evolutionary periods are characterized by distinct connectivity levels of the emerging proteins. We found that the most-connected group of proteins dates to the eukaryotic radiation, and the more ancient group of pre-eukaryotic proteins is less connected. We show that functional classes have different average connectivity levels and that the time of emergence of these functional classes parallels the observed connectivity variation in evolution. We take these findings as evidence that the evolution of function might be the reason for the differences in connectivity throughout evolutionary time. We propose that the understanding of the mechanisms that generate the scale-free protein interaction network, and possibly other biological networks, requires consideration of protein function.

Computational Biology↗

PubMed2000

Polyhedral units and network connectivity in calcium aluminosilicate glasses from high-energy X-Ray diffraction

Structure factors for Ca (x/2)Al xSi 1-xO (2) glasses (x = 0,0.25,0. 5,0.67) extended to a wave vector of magnitude Q = 40 A (-1) have been obtained by high-energy x-ray diffraction. For the first time, it is possible to resolve the contributions of Si-O, Al-O, and Ca-O coordination polyhedra to the experimental atomic pair distribution functions. It has been found that the connectivity of Si/Al-O tetrahedral network decreases with increasing x due to the emerging of nonbridging oxygens located on Si-O tetrahedra. Calcium maintains a rather uniform coordination sphere for all values of x and so it plays a certain role in determining the glass structure.

Journal Article↗

PubMed2005

The yeast kinome displays scale free topology with functional hub clusters.

BACKGROUND: The availability of interaction databases provides an opportunity for researchers to utilize immense amounts of data exclusively in silico. Recently there has been an emphasis on studying the global properties of biological interactions using network analysis. While this type of analysis offers a wide variety of global insights it has surprisingly not been used to examine more localized interactions based on mechanism. In as such we have particular interest in the role of key topological components in signal transduction cascades as they are vital regulators of healthy and diseased cell states. RESULTS: We have used publicly available databases and a novel software tool termed Hubview to model the interactions of a subset of the yeast interactome, specifically protein kinases and their interaction partners. Analysis of the connectivity distribution has inferred a fat-tailed degree distribution with parameters consistent with those found in other biological networks. In addition, Hubview identified a functional clustering of a large group of kinases, distributed between three separate groupings. The complexity and average degree for each of these clusters is indicative of a specialized function (cell cycle propagation, DNA repair and pheromone response) and relative age for each cluster. CONCLUSION: Using connectivity analysis on a functional subset of proteins we have evidence that reinforces the scale free topology as a model for protein network evolution. We have identified the hub components of the kinase network and observed a tendency for these kinases to cluster together on a functional basis. As such, these results suggest an inherent trend to preserve scale free characteristics at a domain based modular level within large evolvable networks.

Multigene Family↗

PubMed2026

Genome-Wide Characterization of β-Glucosidase (TaBGLU) Genes in Bread Wheat and Their Expression Under Drought, Cold, and Combined Stress.

Glycoside hydrolase 1 (GH1) β-glucosidases were known to activate hormone conjugates and defense metabolites, yet their genomic organization and stress-response dynamics in wheat remained incompletely defined. We therefore performed an integrated characterization of TaBGLUs spanning phylogeny, gene structure and conserved motifs, subcellular localization, promoter cis-elements, Gene Ontology enrichment, protein-protein interaction networks, and targeted expression profiling. Wheat TaBGLUs partitioned into well-supported clades that shared canonical GH1 catalytic residues and a largely conserved motif scaffold. Subcellular localization predictions indicated predominant nuclear and chloroplast targeting, with a smaller cohort directed to secretory or endomembrane compartments. Promoters were enriched for light-responsive, hormone-related (ABA, JA/SA, auxin, GA) and stress-associated (MYB/WRKY, heat, low temperature) cis-elements, and functional annotations were consistent with roles in carbohydrate and cell-wall metabolism, hormone homeostasis, and defense. Network analysis revealed a densely connected TaBGLU submodule embedded within broader carbohydrate and defense interaction networks, suggesting coordinated or cooperative functions. Expression profiling under cold, drought, and combined drought and cold demonstrated broad stress inducibility, with early activation detected by 6 h, cold-responsive maxima typically at 12 h, drought-responsive peaks predominating at 24 h, and combined stress eliciting both earlier and more sustained expression maxima between 12-24 h. Representative strongly responsive genes included TaBGLU20, TaBGLU44, TaBGLU6, and TaBGLU23, which showed pronounced late induction under combined stress, TaBGLU30, which exhibited an earlier combined-stress peak, and TaBGLU12, which displayed a marked late drought-specific response. Taken together, this integrated genomic, regulatory, and expression atlas refined the wheat BGLU repertoire relative to previous gene model inventories, highlighted candidate TaBGLUs with central network positions and strong stress inducibility, and provided concrete entry points for functional validation and breeding for improved stress resilience.

Triticum↗

PubMed2004

Rheology of binary colloidal structures assembled via specific biological cross-linking.

The selectivity and range of energies offered by specific biological interactions serve as valuable tools for engineering the assembly of colloidal particles into novel materials. In this investigation, high affinity biological interactions between biotin-coated "A" particles (RA = 0.475 microm) and streptavidin-coated "B" particles (RB = 2.75 microm) drive the self-assembly of a series of binary colloidal structures, from colloidal micelles (a large B particle coated by smaller A particles) to elongated chain microstructures (alternating A and B particles), as the relative number of small (A) to large (B) particles (2 < or = NA/NB < or = 200) is decreased at a low total volume fraction (10(-4) < or = phiT < or = 10(-3)). At a significantly higher total volume fraction (phiT > or = 10(-1)) and a low number ratio (NA/NB = 2), the rheological behavior of volume-filling particle networks connected by streptavidin-biotin bonds is characterized. The apparent viscosity (eta) as a function of the shear rate gamma, measured for networks at phiT = 0.1 and 0.2, exhibits shear-rate-dependent flow behavior, and both the apparent viscosity and the extent of shear thinning increase upon an increase of a factor of 2 in the total volume fraction. Micrographs taken before and after shearing show a structural breakdown of the flocculated binary particle network into smaller flocs, and ultimately a fluidlike suspension, with increasing shear rate. Rheological measurements provide further proof that suspension microstructure is governed by specific biomolecular interactions, as control experiments in which the streptavidin molecules on particles were blocked displayed Newtonian flow behavior. This investigation represents the first attempt at measuring the rheology of colloidal suspensions where assembly is driven by biomolecular cross-linking.

Biotin↗

PubMedDate not supplied

Recurrent neural networks of integrate-and-fire cells simulating short-term memory and wrist movement tasks derived from continuous dynamic networks.

Dynamic recurrent neural networks composed of units with continuous activation functions provide a powerful tool for simulating a wide range of behaviors, since the requisite interconnections can be readily derived by gradient descent methods. However, it is not clear whether more realistic integrate-and-fire cells with comparable connection weights would perform the same functions. We therefore investigated methods to convert dynamic recurrent neural networks of continuous units into networks with integrate-and-fire cells. The transforms were tested on two recurrent networks derived by backpropagation. The first simulates a short-term memory task with units that mimic neural activity observed in cortex of monkeys performing instructed delay tasks. The network utilizes recurrent connections to generate sustained activity that codes the remembered value of a transient cue. The second network simulates patterns of neural activity observed in monkeys performing a step-tracking task with flexion/extension wrist movements. This more complicated network provides a working model of the interactions between multiple spinal and supraspinal centers controlling motoneurons. Our conversion algorithm replaced each continuous unit with multiple integrate-and-fire cells that interact through delayed "synaptic potentials". Successful transformation depends on obtaining an appropriate fit between the activation function of the continuous units and the input-output relation of the spiking cells. This fit can be achieved by adapting the parameters of the synaptic potentials to replicate the input-output behavior of a standard sigmoidal activation function (shown for the short-term memory network). Alternatively, a customized activation function can be derived from the input-output relation of the spiking cells for a chosen set of parameters (demonstrated for the wrist flexion/extension network). In both cases the resulting networks of spiking cells exhibited activity that replicated the activity of corresponding continuous units. This confirms that the network solutions obtained through backpropagation apply to spiking networks and provides a useful method for deriving recurrent spiking networks performing a wide range of functions.

Animals↗

PubMed2005

It's HIP to be a hub: new trends for old-fashioned proteins.

Many endocytic proteins shuttle between the nucleus and the cytoplasm; however, their putative function in the nucleus is unclear. Now, new data demonstrate that huntingtin interacting protein 1 (HIP1), an endocytic protein, modulates the transcriptional activity of nuclear hormone receptors. In network theory, therefore, HIP1 can be regarded as a hub connecting heterogeneous functional "territories:" a possibility with important physiological and pathological implications.

Cell Nucleus↗

PubMed2005

An analysis of the class of gene regulatory functions implied by a biochemical model.

Understanding the integrated behavior of genetic regulatory networks, in which genes regulate one another's activities via RNA and protein products, is emerging as a dominant problem in systems biology. One widely studied class of models of such networks includes genes whose expression values assume Boolean values (i.e., on or off). Design decisions in the development of Boolean network models of gene regulatory systems include the topology of the network (including the distribution of input- and output-connectivity) and the class of Boolean functions used by each gene (e.g., canalizing functions, post functions, etc.). For example, evidence from simulations suggests that biologically realistic dynamics can be produced by scale-free network topologies with canalizing Boolean functions. This work seeks further insights into the design of Boolean network models through the construction and analysis of a class of models that include more concrete biochemical mechanisms than the usual abstract model, including genes and gene products, dimerization, cis-binding sites, promoters and repressors. In this model, it is assumed that the system consists of N genes, with each gene producing one protein product. Proteins may form complexes such as dimers, trimers, etc. The model also includes cis-binding sites to which proteins may bind to form activators or repressors. Binding affinities are based on structural complementarity between proteins and binding sites, with molecular binding sites modeled by bit-strings. Biochemically plausible gene expression rules are used to derive a Boolean regulatory function for each gene in the system. The result is a network model in which both topological features and Boolean functions arise as emergent properties of the interactions of components at the biochemical level. A highly biased set of Boolean functions is observed in simulations of networks of various sizes, suggesting a new characterization of the subset of Boolean functions that are likely to appear in gene regulatory networks.

DNA-Binding Proteins↗

PubMed2003

The primate basal ganglia: parallel and integrative networks.

The basal ganglia and frontal cortex operate together to execute goal directed behaviors. This requires not only the execution of motor plans, but also the behaviors that lead to this execution, including emotions and motivation that drive behaviors, cognition that organizes and plans the general strategy, motor planning, and finally, the execution of that plan. The components of the frontal cortex that mediate these behaviors, are reflected in the organization, physiology, and connections between areas of frontal cortex and in their projections through basal ganglia circuits. This comprises a series of parallel pathways. However, this model does not address how information flows between circuits thereby developing new learned behaviors (or actions) from a combination of inputs from emotional, cognitive, and motor cortical areas. Recent anatomical evidence from primates demonstrates that the neuro-networks within basal ganglia pathways are in a position to move information across functional circuits. Two networks are: the striato-nigral-striatal network and the thalamo-cortical-thalamic network. Within each of these sets of connected structures, there are both reciprocal connections linking up regions associated with similar functions and non-reciprocal connections linking up regions that are associated with different cortical basal ganglia circuits. Each component of information (from limbic to motor outcome) sends both feedback connection, and also a feedforward connection, allowing the transfer of information. Information is channeled from limbic, to cognitive, to motor circuits. Action decision-making processes are thus influenced by motivation and cognitive inputs, allowing the animal to respond appropriate to environmental cues.

Animals↗

PubMed2006

From molecular to modular cardiology. How to interpret the millions of data that came out from large scale analysis of gene expression?

Cell biology is in transition from reductionism, to a more integrated science which is now preoccupied by molecular interactions acting in modules. Large-scale quantitative analysis of gene expression, including cDNA microarrays and proteomic analysis, is now applied to heart failure and atherosclerosis. The technology is still at the beginning and is limited by variations in the array platforms and gene products as well as sensitivity or specificity of the selected probes. These limitations are progressively going to be reduced, but still they do exist. Biological systems are scale free networks made from genes, proteins or traits that interact one another and form networks and functional modules. Networks emerge through the addition of new nodes which are preferentially attached to more connected nodes to form hubs, according to the "rich-gets-richer" mechanism, and there are large networks which include central genes (nexus). Both hubs and nexus are attractive candidate for targeting new therapy. An important study from King JY et al. (Physiol Genomics 2005; 23: 103-18) exemplifies this concept by showing the first realistic pathways to understand atherosclerosis. The 4 steps of the design are based on histological grading and microarrays analysis and include an association network constructed from PubMed and the construction of sub-networks in which genes whose expression was differentially regulated were indicated. Connectivity analysis networks revealed new important modular pathways. In heart failure, no attempts have been made to organize the data into functional modulus. Since the causes of heart failure are well documented, the problem is to identify functional modules responsible for myocardial dysfunction. Several potential functional modules can be identified so far. Indeed, cardiac remodeling results from two types of changes in gene expression, namelly the reexpression of the foetal programme which has a mechanical origin and several well documented interfering determinants that modified the basic remodelling, including senescence, obesity, diabetes, ischemia, and the neurohormonal reaction.

Coronary Artery Disease↗

PubMed1990

Vasopressinergic neurons in periependymal and periventricular areas of the rostral third ventricle of the rat.

On the lateral wall of the rostral third ventricle, an area separates the ependyma from the neurosecretory PVN neurons. Since VP from the latter discharges into the vasculature, the above area may be regarded as constituting an interface between the ventricular and vascular compartments of the CNS. As VP release into the two compartments is integrated, the interface region has been explored for possible existence of a neural infrastructure that would allow such an integration. Immunohistochemical staining for VP following colchicine treatment reveals the presence of an elaborate vasopressinergic network in the interface region that is divisible into a medial periependymal and a lateral periventricular area. A closer examination indicates that the ependymal, periependymal, periventricular and PVN areas (in that order medio-laterally) are all interconnected through this network. The medial area appears to be receptive in nature, while the connectivity of the lateral area points to an effector function. All in all, such a neural network would provide a sound morphological basis for integration of neuroendocrine mechanisms modulating VP release into the ventricular and vascular compartments of the CNS.

Animals↗

PubMed2004

Prediction and identification using wavelet-based recurrent fuzzy neural networks.

This paper presents a wavelet-based recurrent fuzzy neural network (WRFNN) for prediction and identification of nonlinear dynamic systems. The proposed WRFNN model combines the traditional Takagi-Sugeno-Kang (TSK) fuzzy model and the wavelet neural networks (WNN). This paper adopts the nonorthogonal and compactly supported functions as wavelet neural network bases. Temporal relations embedded in the network are caused by adding some feedback connections representing the memory units into the second layer of the feedforward wavelet-based fuzzy neural networks (WFNN). An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the degree measure to obtain the number of fuzzy rules and wavelet functions. Meanwhile, the parameter learning is based on the gradient descent method for adjusting the shape of the membership function and the connection weights of WNN. Finally, computer simulations have demonstrated that the proposed WRFNN model requires fewer adjustable parameters and obtains a smaller rms error than other methods.

Algorithms↗

PubMed2004

Scaling self-organizing maps to model large cortical networks.

Self-organizing computational models with specific intracortical connections can explain many functional features of visual cortex, such as topographic orientation and ocular dominance maps. However, due to their computational requirements, it is difficult to use such detailed models to study large-scale phenomenal like object segmentation and binding, object recognition, tilt illusions, optic flow, and fovea-periphery differences. This article introduces two techniques that make large simulations practical. First, we show how parameter scaling equations can be derived for laterally connected self-organizing models. These equations result in quantitatively equivalent maps over a wide range of simulation sizes, making it possible to debug small simulations and then scale them up only when needed. Parameter scaling also allows detailed comparison of biological maps and parameters between individuals and species with different brain region sizes. Second, we use parameter scaling to implement a new growing map method called GLISSOM, which dramatically reduces the memory and computational requirements of large self-organizing networks. With GLISSOM, it should be possible to simulate all of human V1 at the single-column level using current desktop workstations. We are using these techniques to develop a new simulator Topographica, which will help make it practical to perform detailed studies of large-scale phenomena in topographic maps.

Animals↗

PubMed2001

Changes in cat medullary neurone firing rates and synchrony following induction of respiratory long-term facilitation.

1. Long-term facilitation is a respiratory memory expressed as an increase in motor output lasting more than an hour. This change is induced by repeated hypoxia, stimulation of carotid chemoreceptors, or electrical stimulation of the carotid sinus nerve or brainstem mid-line. The present work addressed the hypothesis that persistent changes in medullary respiratory neural networks contribute to long-term facilitation. 2. Carotid chemoreceptors were stimulated by close arterial injection of CO(2)-saturated saline solution. Phrenic nerve efferent activity and up to 30 single medullary neurones were recorded simultaneously in nucleus tractus solitarii (NTS) including the dorsal respiratory group (DRG), Botzinger-ventral respiratory group (Böt-VRG), and nucleus raphe obscurus of nine adult cats, anaesthetized, injected with a neuromuscular blocking agent, vagotomized and artificially ventilated. 3. The firing rates of 87 of 105 neurones (83 %) changed following induction of long-term facilitation. Nine of eleven DRG and Böt-VRG putative premotor inspiratory neurones had increased firing rates with long-term facilitation. Fourteen of twenty-one raphe obscurus neurones with control firing rates less than 4 Hz had significant long-term increases in activity. 4. Cross-correlogram analysis suggested that there were changes in effective connectivity of neuron pairs with long-term facilitation. Joint peristimulus time histograms and pattern detection methods used with 'gravity' analysis also detected changes in short time scale correlations associated with long-term facilitation. 5. The results suggest that changes in firing rates and synchrony of VRG and DRG premotor neurones and altered effective connectivity among other functionally antecedent elements of the medullary respiratory network contribute to the expression of long-term facilitation.

Action Potentials↗

PubMed2007

Chaperones as parts of organelle networks.

The efficiency, divergence, and specificity of virtually all intracellular metabolic and signalling pathways largely depend on their compartmentalized organization. A corollary of the requirement of compartmentalization is the dynamic structural partition of the intracellular space by endomembrane systems. A branch of these membranes communicate with the extracellular space through the endo- and exocytotic processes. Others, like the mitochondrial and endoplasmic reticulum networks accomplish a further role, being fundamental for the maintenance of cellular energy balance and for determination of cell fate under stress conditions. Recent structural and functional studies revealed that the interaction of these networks and the connectivity state of mitochondria controls metabolic flow, protein transport, intracellular Ca2+ signalling, and cell death. Moreover, reflecting the fact that the above processes are accomplished in a microdomain between collaborating organelle membranes, the existence of macromolecular complexes at their contact sites have also been revealed. Being not only assistants of nascent protein folding, chaperones are proposed to participate in assembling and maintaining the function of the above complexes. In this chapter we discuss recently found examples of such an assembly of protein interactions driven by chaperone proteins, and their role in regulating physiological and pathological processes.

Animals↗

PubMed1999

Network analysis in episodic encoding and retrieval of word-pair associates: a PET study.

The involvement of distributed brain regions in declarative memory has been hypothesized based on studies with verbal memory tasks. To characterize episodic declarative memory function further, 14 right-handed volunteers performed a visual verbal learning task using paired word associates. The volunteers underwent positron emission tomography. 15O-butanol was used as a tracer of regional cerebral blood flow (rCBF). Inter-regional functional interactions were assessed based on within-task, across-subject inter-regional rCBF correlations. Anatomical connections between brain areas were based on known anatomy. Structural equation modelling was used to calculate the path coefficients representing the magnitudes of the functional influences of each area on the ones to which it is connected by anatomical pathways. The encoding and the retrieval network elicit similarities in a general manner but also differences. Strong functional linkages involving visual integration areas, parahippocampal regions, left precuneus and cingulate gyrus were found in both encoding and retrieval; the functional linkages between posterior regions and prefrontal regions were more closely linked during encoding, whereas functional linkages between the left parahippocampal region and posterior cingulate as well as extrastriate areas and posterior cingulate gyrus were stronger during retrieval. In conclusion, these findings support the idea of a global bihemispheric, asymmetric encoding/retrieval network subserving episodic declarative memory. Our results further underline the role of the precuneus in episodic memory, not only during retrieval but also during encoding.

Adult↗

PubMed2006

Consistent resting-state networks across healthy subjects.

Functional MRI (fMRI) can be applied to study the functional connectivity of the human brain. It has been suggested that fluctuations in the blood oxygenation level-dependent (BOLD) signal during rest reflect the neuronal baseline activity of the brain, representing the state of the human brain in the absence of goal-directed neuronal action and external input, and that these slow fluctuations correspond to functionally relevant resting-state networks. Several studies on resting fMRI have been conducted, reporting an apparent similarity between the identified patterns. The spatial consistency of these resting patterns, however, has not yet been evaluated and quantified. In this study, we apply a data analysis approach called tensor probabilistic independent component analysis to resting-state fMRI data to find coherencies that are consistent across subjects and sessions. We characterize and quantify the consistency of these effects by using a bootstrapping approach, and we estimate the BOLD amplitude modulation as well as the voxel-wise cross-subject variation. The analysis found 10 patterns with potential functional relevance, consisting of regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the so-called default-mode network, each with BOLD signal changes up to 3%. In general, areas with a high mean percentage BOLD signal are consistent and show the least variation around the mean. These findings show that the baseline activity of the brain is consistent across subjects exhibiting significant temporal dynamics, with percentage BOLD signal change comparable with the signal changes found in task-related experiments.

Brain↗