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Results for “functional network connectivity”

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PubMed2000

Generalized radial basis function networks for classification and novelty detection: self-organization of optimal Bayesian decision.

By adding reverse connections from the output layer to the central layer it is shown how a generalized radial basis functions (GRBF) network can self-organize to form a Bayesian classifier, which is also capable of novelty detection. For this purpose, three stochastic sequential learning rules are introduced from biological considerations which pertain to the centers, the shapes, and the widths of the receptive fields of the neurons and allow ajoint optimization of all network parameters. The rules are shown to generate maximum-likelihood estimates of the class-conditional probability density functions of labeled data in terms of multivariate normal mixtures. Upon combination with a hierarchy of deterministic annealing procedures, which implement a multiple-scale approach, the learning process can avoid the convergence problems hampering conventional expectation-maximization algorithms. Using an example from the field of speech recognition, the stages of the learning process and the capabilities of the self-organizing GRBF classifier are illustrated.

Artificial Intelligence↗

PubMed2001

Networks with trainable amplitude of activation functions.

Network training algorithms have heavily concentrated on the learning of connection weights. Little effort has been made to learn the amplitude of activation functions, which defines the range of values that the function can take. This paper introduces novel algorithms to learn the amplitudes of nonlinear activations in layered networks, without any assumption on their analytical form. Three instances of the algorithms are developed: (i) a common amplitude is shared among all nonlinear units; (ii) each layer has its own amplitude; and (iii) neuron-specific amplitudes are allowed. The algorithms can also be seen as a particular double-step gradient-descent procedure, as gradient-driven adaptive learning rate schemes, or as weight-grouping techniques that are consistent with known scaling laws for regularization with weight decay. As a side effect, a self-pruning mechanism of redundant neurons may emerge. Experimental results on function approximation, classification, and regression tasks, with synthetic and real-world data, validate the approach and show that the algorithms speed up convergence and modify the search path in the weight space, possibly reaching deeper minima that may also improve generalization.

Algorithms↗

PubMed1997

[High-field (3.OT) MR system: advantages in functional analysis].

In order to elucidate the precise mechanisms underlying higher brain function, investigations need to focus not only on functionality of neuronal networks but also the underlying connectivity. Newly developed magnetic resonance (MR) techniques offer revolutional, non-invasive tools for both functionality and connectivity analysis in humans, namely, BOLD (blood oxygen level dependent) functional magnetic resonance imaging (MRI) and three dimensional anisotropy contrast (3DAC) magnetic resonance axonograpy (MRX). The former technique makes use of changes in regional deoxyhemoglobin concentration associated with brain function, while the latter utilizes directional information of apparent diffusivity observed in axonal fibers. In these critically important but highly demanding imaging techniques, high-field (> or = 3.OT) MR systems possess significant advantages over conventional systems.

Axons↗

PubMed2005

Investigating the neural basis for functional and effective connectivity. Application to fMRI.

Viewing cognitive functions as mediated by networks has begun to play a central role in interpreting neuroscientific data, and studies evaluating interregional functional and effective connectivity have become staples of the neuroimaging literature. The neurobiological substrates of functional and effective connectivity are, however, uncertain. We have constructed neurobiologically realistic models for visual and auditory object processing with multiple interconnected brain regions that perform delayed match-to-sample (DMS) tasks. We used these models to investigate how neurobiological parameters affect the interregional functional connectivity between functional magnetic resonance imaging (fMRI) time-series. Variability is included in the models as subject-to-subject differences in the strengths of anatomical connections, scan-to-scan changes in the level of attention, and trial-to-trial interactions with non-specific neurons processing noise stimuli. We find that time-series correlations between integrated synaptic activities between the anterior temporal and the prefrontal cortex were larger during the DMS task than during a control task. These results were less clear when the integrated synaptic activity was haemodynamically convolved to generate simulated fMRI activity. As the strength of the model anatomical connectivity between temporal and frontal cortex was weakened, so too was the strength of the corresponding functional connectivity. These results provide a partial validation for using fMRI functional connectivity to assess brain interregional relations.

Auditory Perception↗

PubMed2025

Depth-dependent multi-kingdom microbial interactions and biogeochemical cycling genes in eutrophic shallow lake sediments.

Microorganisms are pivotal to lake ecosystem biogeochemical cycles, yet existing research often focuses on single microbial kingdoms or surface sediments, neglecting multi-kingdom interactions and depth-resolved dynamics. To address these gaps, we used metagenomic sequencing to characterize microbial communities and their functional associations across overlying water and 0-45 cm sediments in four shallow lakes of the middle Yangtze River basin, China. Despite increasing bacterial and fungal diversity with depth, the 0-9 cm surface sediments exhibited the strongest multi-kingdom network connectivity and the greatest microbial stability. Functional genes exhibited clear depth-dependent patterns: nitrogen cycling genes, including those involved in dissimilatory nitrate reduction to ammonium, were most enriched in the upper 0-9 cm of sediment; methane cycling genes were positively correlated with depth; phosphorus cycling genes and some sulfur cycling genes, such as assimilatory sulphate reduction, declined with depth. Sediment microbial assembly was dominated by deterministic processes, in which the vertical distribution of functional genes was primarily dictated by heavy metals and conventional environmental indicators. These findings highlight depth-specific multi-kingdom microbial interactions and their associations with biogeochemical cycling, advancing lacustrine microbial ecology understanding and providing references for lake conservation under environmental change.

Lakes↗

PubMed1994

Effects of fetal calf serum and disruption of cadherin function on the formation of bile canaliculi between hepatocytes.

The polarization of hepatocytes to form a connected network of bile canaliculi (BC) is necessary for the function of the liver. Hepatocyte polarization may be controlled by soluble factors and/or physical interactions between cells. Monolayer cultures of embryonic chicken hepatocytes in DMEM supplemented with ornithine, dexamethasone, and insulin express BC-specific antigens for at least 7 days. However, BC-specific antigen expression is lost within 3 days of culture initiation in DMEM containing 10% fetal calf serum. The dedifferentiating effects of fetal calf serum (FCS) can be reversed. Furthermore, cultures in medium containing ornithine, dexamethasone, insulin, and 10% FCS appear identical to cultures grown in 10% FCS alone. Thus FCS contains a soluble inhibitor of hepatocyte polarization. Aggregate cultures grown in suspension maintain hepatocyte polarization for 10-12 days. This may be due to the increased cell-cell contact between hepatocytes in aggregate culture or to more normal contact with the extracellular matrix. We have evaluated the role of cadherin-mediated interactions on hepatocyte polarization. Anti-E-cadherin Fab' fragments disrupted the formation of long networks of BC in monolayer cultures but did not stop polarized expression of BC-specific antigens. The BC antigens in anti-E-cadherin-treated cells were concentrated in small areas between cells and were present at lower levels uniformly on the cell surface. These results indicate that E-cadherin is required for the formation of extended BC networks, but that other factors are responsible for maintaining the synthesis and localization of BC-specific antigens.

Animals↗

PubMed2003

Measuring information integration.

BACKGROUND: To understand the functioning of distributed networks such as the brain, it is important to characterize their ability to integrate information. The paper considers a measure based on effective information, a quantity capturing all causal interactions that can occur between two parts of a system. RESULTS: The capacity to integrate information, or Phi, is given by the minimum amount of effective information that can be exchanged between two complementary parts of a subset. It is shown that this measure can be used to identify the subsets of a system that can integrate information, or complexes. The analysis is applied to idealized neural systems that differ in the organization of their connections. The results indicate that Phi is maximized by having each element develop a different connection pattern with the rest of the complex (functional specialization) while ensuring that a large amount of information can be exchanged across any bipartition of the network (functional integration). CONCLUSION: Based on this analysis, the connectional organization of certain neural architectures, such as the thalamocortical system, are well suited to information integration, while that of others, such as the cerebellum, are not, with significant functional consequences. The proposed analysis of information integration should be applicable to other systems and networks.

Brain↗

PubMed2006

Interactive atrial neural network: Determining the connections between ganglionated plexi.

BACKGROUND: The electrophysiologic functions of the intrinsic cardiac autonomic nervous system (ANS) are not well understood. OBJECTIVES: The purpose of this study was to investigate the functional interactions between ganglionated plexi within the intrinsic cardiac ANS. METHODS: The hearts of 21 dogs were exposed via right and/or left thoracotomy to expose the (1) anterior right ganglionated plexi near the caudal end of the sinoatrial node, (2) inferior right ganglionated plexi at the junction of inferior vena cava and atria, and (3) superior left ganglionated plexi near the junction of left superior pulmonary vein and left pulmonary artery. Ganglionated plexi were stimulated at 0.6 to 8.0 V (square waves, 20 Hz, 0.1-ms duration). Sinus rate, AH interval during atrial pacing, and ventricular rate during atrial fibrillation were compared before and after ganglionated plexi stimulation and after their ablation. RESULTS: Anterior right ganglionated plexi stimulation induced significant AH prolongation and slowing of ventricular rate and sinus rate. When inferior right ganglionated plexi was ablated, slowing of sinus rate by anterior right ganglionated plexi stimulation was unaltered, but inhibition of AV conduction was eliminated. Superior left ganglionated plexi stimulation induced similar effects on sinus and AV nodal function, and sinus rate slowing was markedly attenuated by anterior right ganglionated plexi ablation. Ablation of both anterior right ganglionated plexi and inferior right ganglionated plexi eliminated AV conduction inhibition but not sinus rate slowing by superior left ganglionated plexi stimulation. CONCLUSION: This study provides functional evidence for the interconnections between ganglionated plexi to modulate sinus and AV nodal function, supporting clinical evidence that interconnections within the intrinsic cardiac ANS are critical elements in identifying the targets for atrial fibrillation ablation.

Animals↗

PubMed1993

The visual system and levels of perception: properties of neuromental organization.

To see whether the mental and the neural have common attributes that could resolve some of the traditional dichotomies, we review neuroscientific data on the visual system. The results show that neuronal and perceptual function share a parallel and hierarchical architecture which is manifest not only in the anatomy and physiology of the visual system, but also in normal perception and in the deficits caused by lesions in different parts of the system. Based on the description of parallel hierarchical levels of active information processing in the visual brain, we suggest a concept of dissociable levels of perception, advocating that the phenomenal perception and recognition is realized in the functional integrity of a network of reciprocal cortico-cortical connections. The properties shared by neuronal and perceptional functions provide a basis for a neuromental monism in which both functions are attributed a causal role.

Cerebral Cortex↗

PubMed2006

OR/AND neurons and the development of interpretable logic models.

In this paper, we are concerned with the concept of fuzzy logic networks and logic-based data analysis realized within this framework. The networks under discussion are homogeneous architectures comprising of OR/AND neurons originally introduced by Hirota and Pedrycz. Being treated here as generic processing units, OR/AND neurons are neurofuzzy constructs that exhibit well-defined logic characteristics and are endowed with a high level of parametric flexibility and come with significant interpretation abilities. The composite logic nature of the logic neurons becomes instrumental in covering a broad spectrum of logic dependencies whose character spread in-between between those being captured by plain and and or logic descriptors (connectives). From the functional standpoint, the developed network realizes a logic approximation of multidimensional mappings between unit hypercubes, that is transformations from [0, 1]n to [0, 1]m. The way in which the structure of the network has been formed is highly modular and becomes reflective of a general concept of decomposition of logic expressions and Boolean functions (as being commonly encountered in two-valued logic). In essence, given a collection of input variables, selected is their subset and transformed into new composite variable, which in turn is used in the consecutive module of the network. These intermediate synthetic variables are the result of the successive problem (mapping) decomposition. The development of the network is realized through genetic optimization. This helps address important issues of structural optimization (where we are concerned with a selection of a subset of variables and their allocation within the network) and reaching a global minimum when carrying out an extensive parametric optimization (adjustments of the connections of the neurons). The paper offers a comprehensive and user-interactive design procedure including a simple pruning mechanism whose intention is to enhance the interpretability of the network while reducing its size. The experimental studies comprise of three parts. First, we demonstrate the performance of the network on Boolean data (that leads to some useful comparative observations considering a wealth of optimization tools available in two-valued logic and digital systems). Second, we discuss synthetic multivalued data that helps focus on the approximation abilities of the network. Finally, show the generation of logic expressions describing selected data sets coming from the machine learning repository.

Algorithms↗

PubMed2005

Constraining the connectivity of neuronal networks cultured on microelectrode arrays with microfluidic techniques: a step towards neuron-based functional chips.

In vitro culture of small neuronal networks with pre-defined topological features is particularly desirable when the electrical activity of such assemblies can be monitored for long periods of time. Indeed, it is hoped that such networks, with pre-determined connectivity, will provide unique insights into the structure/function relationship of biological neural networks and their properties of self-organization. However, the experimental techniques that have been developed so far for that purpose have either failed to provide very long-term pattern definition and retention, or they have not shown potential for integration into more complex microfluidic devices. To address this problem, three-dimensional microfluidic systems in poly(dimethylsiloxane) (PDMS) were fabricated and used in conjunction with both custom-made and commercially available planar microelectrode arrays (pMEAs). Various types of primary neuronal cell cultures were established inside these systems. Extracellular electrical signals were successfully recorded from all types of cells placed inside the patterns, and this bioelectrical activity was present for several weeks. The advantage of this approach is that it can be further integrated with microfluidic devices and pMEAs to yield, for example, complex neuron-based biosensors or chips for pharmacological screening.

Action Potentials↗

PubMed2000

Functional synaptic connections among respiratory neurons.

This presentation focuses on the application of methods to determine functional connections between neurons in the respiratory network of adult decerebrate rats. We employ a general network investigation paradigm that first examines the intracellular recordings of a respiratory neuron and then determines which neurons synapse with it to produce the observed membrane potential changes. It is used to pursue the source of respiratory excitation and inhibition from its arrival at phrenic motoneurons to respiratory neurons in the medulla, and then examine some of the interactions among these neurons that shape their patterns of activity. Findings include a demonstration that phrenic motoneuron activity is determined by excitation from medullary inspiratory premotor neurons and inhibition by Bötzinger complex expiratory neurons, and that the latter neurons inhibit both medullary inspiratory premotor neurons and themselves. We conclude that these functional interconnections explain the activity patterns of some respiratory neurons, but the connections between neurons thought to be involved in rhythm generation remain to be demonstrated in adult rats.

Animals↗

PubMed2004

The nonconserved wrapping of conserved protein folds reveals a trend toward increasing connectivity in proteomic networks.

Although protein folding domains are generally conserved for function across distant homologous sequences, one crucial structural feature is not conserved: the wrapping of backbone hydrogen bonds, that is, the extent to which they are intramolecularly desolvated and thereby protected from water attack. Extensive data on protein complex interfaces led us to postulate that insufficiently wrapped backbone hydrogen bonds in monomeric domains must be adhesive, and therefore determinants of interactivity, a result that has been experimentally confirmed. Here, we show that the wrapping of certain conserved folds becomes progressively poorer as species diverge in some lineages. This trend is thus concurrent with a progressive enhancement of the interactivity of individual domains sharing the conserved fold. Such increase in interactivity is predicted to impose an "evolutionary brake" on the overall speed of sequence divergence. This phenomenon follows when more and more residues become engaged in protein associations and thus become functionally indispensable. For complete proteomes for which statistically significant structural data are available, scale-free network statistics based solely on the distribution of folding domains, catalogued by their number of wrapping defects, best describe the proteomic connectivity. Thus, the intermolecular connectivity may be effectively used as a measure of species complexity. Our results might contribute to explaining how interactome complexity may be achieved without a dramatic increase in genome size.

Animals↗

PubMed2006

Topological determinants of epileptogenesis in large-scale structural and functional models of the dentate gyrus derived from experimental data.

In temporal lobe epilepsy, changes in synaptic and intrinsic properties occur on a background of altered network architecture resulting from cell loss and axonal sprouting. Although modeling studies using idealized networks indicated the general importance of network topology in epilepsy, it is unknown whether structural changes that actually take place during epileptogenesis result in hyperexcitability. To answer this question, we built a 1:1 scale structural model of the rat dentate gyrus from published in vivo and in vitro cell type-specific connectivity data. This virtual dentate gyrus in control condition displayed globally and locally well connected ("small world") architecture. The average number of synapses between any two neurons in this network of over one million cells was less than three, similar to that measured for the orders of magnitude smaller C. elegans nervous system. To study how network architecture changes during epileptogenesis, long-distance projecting hilar cells were gradually removed in the structural model, causing massive reductions in the number of total connections. However, as long as even a few hilar cells survived, global connectivity in the network was effectively maintained and, as a result of the spatially restricted sprouting of granule cell axons, local connectivity increased. Simulations of activity in a functional dentate network model, consisting of over 50,000 multicompartmental single-cell models of major glutamatergic and GABAergic cell types, revealed that the survival of even a small fraction of hilar cells was enough to sustain networkwide hyperexcitability. These data indicate new roles for fractionally surviving long-distance projecting hilar cells observed in specimens from epilepsy patients.

Animals↗

PubMed2003

Two mitofusin proteins, mammalian homologues of FZO, with distinct functions are both required for mitochondrial fusion.

Mitochondria are dynamic organelles that undergo frequent fission and fusion or branching. Although these morphologic changes are considered crucial for cellular functions, the underlying mechanisms remain elusive, especially in mammalian cells. We characterized two rat mitochondrial outer membrane proteins, Mfn1 and Mfn2, with distinct tissue expressions, that are homologous to Drosophila Fzo, a GTPase involved in mitochondrial fusion. Expression of the GTPase-domain mutant of Mfn2 (Mfn2(K109T)) in HeLa cells induced mitochondrial fragmentation in which Mfn2(K109T) localized at the restricted domains. Immuno-electronmicroscopy revealed that Mfn2(K109T) was concentrated at the contact domains between adjacent mitochondria, suggesting that fusion of the outer membrane was arrested at some intermediate step. Mfn1 expression induced highly connected tubular network structures depending on the functional GTPase domain. The Mfn1-induced tubular networks were suppressed by co-expression with Mfn2. In vivo depletion of either isoform by RNA interference revealed that both are required to maintain normal mitochondrial morphology. The fusion of differentially-labeled mitochondria in HeLa cells subjected to depletion of either Mfn isoform and subsequent cell fusion by hemagglutinating virus of Japan revealed that both proteins have distinct functions in mitochondrial fusion. We conclude that the two Mfn isoforms cooperate in mitochondrial fusion in mammalian cells.

Animals↗

PubMed2003

Necessary and sufficient condition for absolute stability of normal neural networks.

Globally convergent dynamics of a class of neural networks with normal connection matrices is studied by using the Lyapunov function method and spectral analysis of the connection matrices. It is shown that the networks are absolutely stable if and only if all the real parts of the eigenvalues of the connection matrices are nonpositive. This extends an existing result on symmetric neural networks to a larger class including certain asymmetric networks. Further extension of the present result to certain non-normal case leads naturally to a quasi-normal matrix condition, which may be interpreted as a generalization of the so-called principle of detailed balance for the connection weights or the quasi-symmetry condition that was previously proposed in the literature in association with symmetric neural networks. These results are of particular interest in neural optimization and classification problems.

Models, Theoretical↗

PubMed2006

Influence of metabolic network structure and function on enzyme evolution.

BACKGROUND: Most studies of molecular evolution are focused on individual genes and proteins. However, understanding the design principles and evolutionary properties of molecular networks requires a system-wide perspective. In the present work we connect molecular evolution on the gene level with system properties of a cellular metabolic network. In contrast to protein interaction networks, where several previous studies investigated the molecular evolution of proteins, metabolic networks have a relatively well-defined global function. The ability to consider fluxes in a metabolic network allows us to relate the functional role of each enzyme in a network to its rate of evolution. RESULTS: Our results, based on the yeast metabolic network, demonstrate that important evolutionary processes, such as the fixation of single nucleotide mutations, gene duplications, and gene deletions, are influenced by the structure and function of the network. Specifically, central and highly connected enzymes evolve more slowly than less connected enzymes. Also, enzymes carrying high metabolic fluxes under natural biological conditions experience higher evolutionary constraints. Genes encoding enzymes with high connectivity and high metabolic flux have higher chances to retain duplicates in evolution. In contrast to protein interaction networks, highly connected enzymes are no more likely to be essential compared to less connected enzymes. CONCLUSION: The presented analysis of evolutionary constraints, gene duplication, and essentiality demonstrates that the structure and function of a metabolic network shapes the evolution of its enzymes. Our results underscore the need for systems-based approaches in studies of molecular evolution.

Amino Acid Substitution↗

PubMed2001

Multimodal characterisation of cortical areas by multivariate analyses of receptor binding and connectivity data.

Cortical areas are regarded as fundamental structural and functional units within the information processing networks of the brain. Their properties have been described extensively by cyto-, myelo- and chemoarchitectonics, cortical and extracortical connectivity patterns, receptive field mapping, activation properties, lesion effects, and other structural and functional characteristics. Systematic integrative approaches aiming at multimodal characterisations of cortical areas or at the delineation of global features of the cortical network, however, are still scarce and usually limited to a single data modality, such as cytoarchitectonical or tract tracing data. Here we describe a methodological framework for the systematic evaluation, comparison and integration of different data modalities from the brain and demonstrate its practical application and significance in the analysis of receptor binding and connectivity data within the motor and visual cortices of macaque monkeys. The framework builds on algorithmic methods to convert data between different cortical parcellation schemes, as well as on statistical techniques for the exploration of multivariate data sets comprising data of different types and scales. Thereby, we establish a relationship between intrinsic area properties as expressed by quantitative receptor binding, and extrinsic inter-area communication, which relies on anatomical connectivity. Our analyses provide preliminary evidence for a good correspondence of these two data types in the motor cortex, and their partial discrepancy in the visual cortex, raising hypotheses about the different organisational aspects highlighted by receptors and connectivity. The methodological framework presented here is flexible enough to accommodate a wide range of further data modalities, and is specific enough to permit novel insights and predictions concerning brain organisation. Thus, this approach promises to be very useful in the endeavour to characterise multimodal structure-function relationships in the brain.

Animals↗