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PubMed2005

Analyzing functional connectivity using a network likelihood model of ensemble neural spiking activity.

Analyzing the dependencies between spike trains is an important step in understanding how neurons work in concert to represent biological signals. Usually this is done for pairs of neurons at a time using correlation-based techniques. Chornoboy, Schramm, and Karr (1988) proposed maximum likelihood methods for the simultaneous analysis of multiple pair-wise interactions among an ensemble of neurons. One of these methods is an iterative, continuous-time estimation algorithm for a network likelihood model formulated in terms of multiplicative conditional intensity functions. We devised a discrete-time version of this algorithm that includes a new, efficient computational strategy, a principled method to compute starting values, and a principled stopping criterion. In an analysis of simulated neural spike trains from ensembles of interacting neurons, the algorithm recovered the correct connectivity matrices and interaction parameters. In the analysis of spike trains from an ensemble of rat hippocampal place cells, the algorithm identified a connectivity matrix and interaction parameters consistent with the pattern of conjoined firing predicted by the overlap of the neurons' spatial receptive fields. These results suggest that the network likelihood model can be an efficient tool for the analysis of ensemble spiking activity.

Action Potentials↗
PubMed1996

Dynamics of functional connectivity in visual cortical networks: an overview.

The aim of the Royaumont Symposium was to review various dynamic aspects of adaptive changes in functional connectivity, expressed in cortical networks during development, learning, and possibly during recognition and cognitive processing. The link between the various experimental and theoretical models was the comparison of cellular and molecular mechanisms that could be involved in the up- and down-regulation of functional connectivity, over different time scales. These processes have been investigated using several approaches in parallel: 1) at the molecular/subcellular level, to identify postsynaptic receptors (NMDA, mGluR) and second messengers (calcium protein kinases and phosphatases) involved in the induction of synaptic potentiation and depression, and to characterize diffusible factors (NO), released pre- or postsynaptically, involved in the spatial generalization of local changes to neighboring synapses; 2) at the level of integrating networks, to develop electrophysiological (single and multiple recording), pharmacological and optical imaging techniques in order to compare the dynamics of adaptive processes put into play during the natural development of cortical specificity and connectivity, versus those triggered during forced regimes of temporal correlations between pre- and post synaptic activities. Both in vitro and in vivo approaches have been combined in the primary visual cortex of the developing and adult vertebrate (rat, guinea-pig, ferret, cat and monkey). The various forms of 'slow' synaptic plasticity, demonstrated during epigenesis and selective phases of learning in the adult, can be compared with 'fast' forms of functional coupling (or synchronous firing) shown to develop during the time span required for perception and cognitive processing Phenomenology of the dynamics in functional connectivity and their relative dependence on temporal correlation in neuronal activity have been analyzed in each of these situations. Experimental results have been compared at different levels of neuronal integration (synapse, column map and cell assembly) in order to gain a better understanding of functional grouping within cortical networks.

Adaptation, Physiological↗
PubMed2006

Small-world network organization of functional connectivity of EEG slow-wave activity during sleep.

OBJECTIVE: To analyze the functional connectivity patterns of the EEG slow-wave activity during the different sleep stages and Cyclic Alternating Pattern (CAP) conditions, using concepts derived from Graph Theory. METHODS: We evaluated spatial patterns of EEG slow-wave synchronization between all possible pairs of electrodes (19) placed over the scalp of 10 sleeping healthy young normal subjects using two graph theoretical measures: the clustering coefficient (Cp) and the characteristic path length (Lp). The measures were obtained during the different sleep stages and CAP conditions from the real EEG connectivity networks and randomized control (surrogate) networks (Cp-s and Lp-s). RESULTS: Cp and Cp/Cp-s increased significantly from wakefulness to sleep while Lp and Lp/Lp-s did not show changes. Cp/Cp-s was higher for A1 phases, compared to B phases of CAP. CONCLUSIONS: The network organization of the EEG slow-wave synchronization during sleep shows features characteristic of small-world networks (high Cp combined with low Lp); this type of organization is slightly but significantly more evident during the CAP A1 subtypes. SIGNIFICANCE: Our results show feasibility of using graph theoretical measures to characterize the complexity of brain networks during sleep and might indicate sleep, and the A1 phases of CAP in particular, as a period during which slow-wave synchronization shows optimal network organization for information processing.

Adult↗
PubMed2005

Undirected graphs of frequency-dependent functional connectivity in whole brain networks.

We explored properties of whole brain networks based on multivariate spectral analysis of human functional magnetic resonance imaging (fMRI) time-series measured in 90 cortical and subcortical subregions in each of five healthy volunteers studied in the (no-task) resting state. We note that undirected graphs representing conditional independence between multivariate time-series can be more readily approached in the frequency domain than the time domain. Estimators of partial coherency and normalized partial mutual information phi, an integrated measure of partial coherence over an arbitrary frequency band, are applied. Using these tools, we replicate the prior observations that bilaterally homologous brain regions tend to be strongly connected and functional connectivity is generally greater at low frequencies [0.0004, 0.1518 Hz]. We also show that long-distance intrahemispheric connections between regions of prefrontal and parietal cortex were more salient at low frequencies than at frequencies greater than 0.3 Hz, whereas many local or short-distance connections, such as those comprising segregated dorsal and ventral paths in posterior cortex, were also represented in the graph of high-frequency connectivity. We conclude that the partial coherency spectrum between a pair of human brain regional fMRI time-series depends on the anatomical distance between regions: long-distance (greater than 7 cm) edges represent conditional dependence between bilaterally symmetric neocortical regions, and between regions of prefrontal and parietal association cortex in the same hemisphere, are predominantly subtended by low-frequency components.

Brain↗
PubMed2002

Functional connectivity in the resting brain: a network analysis of the default mode hypothesis.

Functional imaging studies have shown that certain brain regions, including posterior cingulate cortex (PCC) and ventral anterior cingulate cortex (vACC), consistently show greater activity during resting states than during cognitive tasks. This finding led to the hypothesis that these regions constitute a network supporting a default mode of brain function. In this study, we investigate three questions pertaining to this hypothesis: Does such a resting-state network exist in the human brain? Is it modulated during simple sensory processing? How is it modulated during cognitive processing? To address these questions, we defined PCC and vACC regions that showed decreased activity during a cognitive (working memory) task, then examined their functional connectivity during rest. PCC was strongly coupled with vACC and several other brain regions implicated in the default mode network. Next, we examined the functional connectivity of PCC and vACC during a visual processing task and show that the resultant connectivity maps are virtually identical to those obtained during rest. Last, we defined three lateral prefrontal regions showing increased activity during the cognitive task and examined their resting-state connectivity. We report significant inverse correlations among all three lateral prefrontal regions and PCC, suggesting a mechanism for attenuation of default mode network activity during cognitive processing. This study constitutes, to our knowledge, the first resting-state connectivity analysis of the default mode and provides the most compelling evidence to date for the existence of a cohesive default mode network. Our findings also provide insight into how this network is modulated by task demands and what functions it might subserve.

Adult↗
PubMed2002

Constrained synaptic connectivity in functional mammalian neuronal networks grown on patterned surfaces.

The use of ordered neuronal networks in vitro is a promising approach to study the development and the activity of small neuronal assemblies. However, in previous attempts, sufficient growth control and physiological maturation of neurons could not be achieved. Here we describe an original protocol in which polylysine patterns confine the adhesion of cellular bodies to prescribed spots and the neuritic growth to thin lines. Hippocampal neurons in these networks are maintained healthy in serum free medium up to 5 weeks in vitro. Electrophysiology and immunochemistry show that neurons exhibit mature excitatory and inhibitory synapses and calcium imaging reveals spontaneous activity of neurons in isolated networks. We demonstrate that neurons in these geometrical networks form functional synapses preferentially to their first neighbors. We have, therefore, established a simple and robust protocol to constrain both the location of neuronal cell bodies and their pattern of connectivity. Moreover, the long term maintenance of the geometry and the physiology of the networks raises the possibility of new applications for systematic screening of pharmacological agents and for electronic to neuron devices.

Action Potentials↗
PubMed2006

Analysis of synchrony demonstrates 'pain networks' defined by rapidly switching, task-specific, functional connectivity between pain-related cortical structures.

Imaging studies indicate that experimental pain is processed in multiple cortical areas which are often characterized as a network. However, the functional connectivity within the network and the other properties of the network is poorly understood. Substantial evidence demonstrates that synchronous oscillations between two cortical areas may indicate functional connectivity between those areas. We test the hypothesis that cortical areas with pain-related activity are functionally connected during attention to a painful stimulus. We stimulated with a painful, cutaneous, laser stimulus and recorded the response directly from the cortical surface (electrocorticography--ECoG) over primary somatosensory (SI), parasylvian (PS), and medial frontal (MF) cortex through subdural electrodes implanted for treatment of epilepsy. The results demonstrate synchrony of ECoGs between cortical structures receiving input from nociceptors, as indicated by the occurrence of laser-evoked potentials (LEPs) and/or event-related desynchronization (ERD). Prior to the stimulus, directed attention to the painful stimulus consistently increased the degree of synchrony between SI and PS regions, as the subject anticipated the stimulus. After the laser stimulus, directed attention to the painful stimulus consistently increased the degree of synchrony between SI and MF cortex, as the subject responded by counting the stimulus. Therefore, attention to painful stimuli always enhanced synchrony between cortical pain-related structures. The pattern of this synchrony changed as the patient switched tasks from anticipation of the stimulus to counting the stimulus. These results are the first compelling evidence of pain networks characterized by rapidly switching, task-specific functional connectivity.

Adult↗
PubMed1997

Intermediate filaments and cytoplasmic networking: new connections and more functions.

Recent research highlights the roles of cytoskeletal intermediate filaments (IFs) and their interactions with both the cell surface and other cytoskeletal systems in maintaining cellular integrity and the mechanical properties of cytoplasm. This has been demonstrated by analyses of mutations in IF-associated proteins (IFAPs) that are involved in connecting IFs to cell surface junctions. New data also point to the role of IFAPs as molecular 'nuts and bolts' in the construction of an integrated cytoplasmic architecture. This is highlighted by the initial descriptions of a family of multifunctional molecules that are capable of bridging IFs to other cytoskeletal elements. These findings, together with the development of specific peptide inhibitors capable of disassembling IF networks in vivo, are paving the way to the identification of new cellular functions for IFs and IFAPs.

Cytoplasm↗
PubMed2006

Functional connectivity in a baseline resting-state network in autism.

Brain activity in people with high-functioning autism has been shown to be atypical in a number of ways, including reduced synchronization across areas of activation measured by functional magnetic resonance imaging. This activation atypicality has been observed mostly during the performance of cognitive tasks. This study compares the resting-state network of 57 participants with autism and 57 control participants matched for age and intelligence quotient. The results indicate that both groups have a resting-state network that is very similar both in volume and in organization, but in autism this network is much more loosely connected. This functional underconnectivity was observed in the anterior-posterior connections. The results expand the theory of cortical underconnectivity in autism to the resting state of the brain.

Adult↗
PubMed2006

Independent component model of the default-mode brain function: Assessing the impact of active thinking.

The "default-mode" network is an ensemble of cortical regions, which are typically deactivated during demanding cognitive tasks in functional magnetic resonance imaging (fMRI) studies. Using functional connectivity, this network can be conceptualized and studied as a "stand-alone" function or system. Regardless of the task, independent component analysis (ICA) produces a picture of the "default-mode" function even when the subject is performing a simple sensori-motor task or just resting in the scanner. This has boosted the use of default-mode fMRI for non-invasive research in brain disorders. Here, we studied the effect of cognitive load modulation of fMRI responses on the ICA-based pictures of the default-mode function. In a standard graded working memory study based on the n-back task, we used group-level ICA to explore the variability of the default-mode network related to the engagement in the task, in 10 healthy volunteers. The analysis of the default-mode components highlighted similarities and differences in the layout under three different cognitive loads. We found a load-related general increase of deactivation in the cortical network. Nonetheless, a variable recruitment of the cingulate regions was evident, with greater extension of the anterior and lesser extension of the posterior clusters when switching from lower to higher working memory loads. A co-activation of the hippocampus was only found under no working memory load. As a generalization of our results, the variability of the default-mode pattern may link the default-mode system as a whole to cognition and may more directly support use of the ICA model for evaluating cognitive decline in brain disorders.

Adult↗
PubMed2004

Decomposition of metabolic network into functional modules based on the global connectivity structure of reaction graph.

MOTIVATION: Metabolic networks are organized in a modular, hierarchical manner. Methods for a rational decomposition of the metabolic network into relatively independent functional subsets are essential to better understand the modularity and organization principle of a large-scale, genome-wide network. Network decomposition is also necessary for functional analysis of metabolism by pathway analysis methods that are often hampered by the problem of combinatorial explosion due to the complexity of metabolic network. Decomposition methods proposed in literature are mainly based on the connection degree of metabolites. To obtain a more reasonable decomposition, the global connectivity structure of metabolic networks should be taken into account. RESULTS: In this work, we use a reaction graph representation of a metabolic network for the identification of its global connectivity structure and for decomposition. A bow-tie connectivity structure similar to that previously discovered for metabolite graph is found also to exist in the reaction graph. Based on this bow-tie structure, a new decomposition method is proposed, which uses a distance definition derived from the path length between two reactions. An hierarchical classification tree is first constructed from the distance matrix among the reactions in the giant strong component of the bow-tie structure. These reactions are then grouped into different subsets based on the hierarchical tree. Reactions in the IN and OUT subsets of the bow-tie structure are subsequently placed in the corresponding subsets according to a 'majority rule'. Compared with the decomposition methods proposed in literature, ours is based on combined properties of the global network structure and local reaction connectivity rather than, primarily, on the connection degree of metabolites. The method is applied to decompose the metabolic network of Escherichia coli. Eleven subsets are obtained. More detailed investigations of the subsets show that reactions in the same subset are really functionally related. The rational decomposition of metabolic networks, and subsequent studies of the subsets, make it more amenable to understand the inherent organization and functionality of metabolic networks at the modular level. SUPPLEMENTARY INFORMATION: http://genome.gbf.de/bioinformatics/

Algorithms↗
PubMed2000

Universal learning network and its application to chaos control.

Universal Learning Networks (ULNs) are proposed and their application to chaos control is discussed. ULNs provide a generalized framework to model and control complex systems. They consist of a number of inter-connected nodes where the nodes may have any continuously differentiable nonlinear functions in them and each pair of nodes can be connected by multiple branches with arbitrary time delays. Therefore, physical systems, which can be described by differential or difference equations and also their controllers, can be modeled in a unified way, and so ULNs may form a super set of neural networks and fuzzy neural networks. In order to optimize the ULNs, a generalized learning algorithm is derived, in which both the first order derivatives (gradients) and the higher order derivatives are incorporated. The derivatives are calculated by using forward or backward propagation schemes. These algorithms for calculating the derivatives are extended versions of Back Propagation Through Time (BPTT) and Real Time Recurrent Learning (RTRL) of Williams in the sense that generalized node functions, generalized network connections with multi-branch of arbitrary time delays, generalized criterion functions and higher order derivatives can be deal with. As an application of ULNs, a chaos control method using maximum Lyapunov exponent of ULNs is proposed. Maximum Lyapunov exponent of ULNs can be formulated by using higher order derivatives of ULNs, and the parameters of ULNs can be adjusted so that the maximum Lyapunov exponent approaches the target value. From the simulation results, it has been shown that a fully connected ULN with three nodes is able to display chaotic behaviors.

Artificial Intelligence↗
PubMed2001

Solving the binding problem of the brain with bi-directional functional connectivity.

We propose a neural network model which gives one solution to the binding problem on the basis of 'functional connectivity' and bidirectional connections. Here, 'functional connectivity' is dynamic neuronal connectivity peculiar to temporal spike coding neural networks with coincidence detector neurons. The model consists of a single primary map and two higher modules which extract two different features shown on the primary map. There exist three layers in each higher module and the layers are connected bi-directionally. An object in the outer world is represented by a 'global dynamical cell assembly' which is organized across the primary map and the two higher modules. Detailed, but spatially localized, information is coded in the primary map, whereas coarse, but spatially extracted information or globally integrated information is coded in the higher modules. Computer simulations of the proposed model show that multiple cell assemblies sharing the same neurons partially can co-exist. Furthermore, we introduce a three-dimensional J-PSTH (Joint-Peri Stimulus Time Histogram) which is capable of tracking such cell assemblies, altering its constituent neurons as in our proposed model.

Action Potentials↗
PubMedDate not supplied

A neural network model of chemotaxis predicts functions of synaptic connections in the nematode Caenorhabditis elegans.

The anatomical connectivity of the nervous system of the nematode Caenorhabditis elegans has been almost completely described, but determination of the neurophysiological basis of behavior in this system is just beginning. Here we used an optimization algorithm to search for patterns of connectivity sufficient to compute the sensorimotor transformation underlying C. elegans chemotaxis, a simple form of spatial orientation behavior in which turning probability is modulated by the rate of change of chemical concentration. Optimization produced differentiator networks capable of simulating chemotaxis. A surprising feature of these networks was inhibitory feedback connections on all neurons. Further analysis showed that feedback regulates the latency between sensory input and behavior. Common patterns of connectivity between the model and biological networks suggest new functions for previously identified connections in the C. elegans nervous system.

Action Potentials↗
PubMed2004

Stimulus-induced change in long-range temporal correlations and scaling behaviour of sensorimotor oscillations.

The human brain spontaneously generates large-scale network oscillations at around 10 and 20 Hz. The amplitude envelope of these oscillations fluctuates intermittently and was recently reported to exhibit power-law decay of the autocorrelation for hundreds of seconds. This indicates that the underlying networks are in a dynamic state resembling the self-organized critical state known to exist in many complex systems. Based on the mechanism of how correlations emerge in these systems, we hypothesized that the physiological basis of long-range power-law correlations is the buildup of a memory of past activity by a continuous modification of the network's functional connectivity by the ongoing oscillations. In this framework, exogenous perturbations of ongoing oscillations would degrade or abolish this dynamic network memory. We investigated the sensitivity of the temporal correlations in sensorimotor 10- and 20-Hz oscillations to median nerve stimulation that is known to have immediate effects on ongoing oscillations. Our results show that the amplitude fluctuations of these oscillations were effectively modulated by the somatosensory stimuli but still exhibited long-range temporal correlations and power-law scaling behaviour. The magnitude of the temporal correlations was, however, attenuated and the power-law exponents were decreased. This implies that the stimuli indeed degraded the network's memory of its past.

Adult↗
PubMed2006

Functional and evolutionary inference in gene networks: does topology matter?

The relationship between the topology of a biological network and its functional or evolutionary properties has attracted much recent interest. It has been suggested that most, if not all, biological networks are 'scale free.' That is, their connections follow power-law distributions, such that there are very few nodes with very many connections and vice versa. The number of target genes of known transcriptional regulators in the yeast, Saccharomyces cerevisiae, appears to follow such a distribution, as do other networks, such as the yeast network of protein-protein interactions. These findings have inspired attempts to draw biological inferences from general properties associated with scale-free network topology. One often cited general property is that, when compromised, highly connected nodes will tend to have a larger effect on network function than sparsely connected nodes. For example, more highly connected proteins are more likely to be lethal when knocked out. However, the correlation between lethality and connectivity is relatively weak, and some highly connected proteins can be removed without noticeable phenotypic effect. Similarly, network topology only weakly predicts the response of gene expression to environmental perturbations. Evolutionary simulations of gene-regulatory networks, presented here, suggest that such weak or non-existent correlations are to be expected, and are likely not due to inadequacy of experimental data. We argue that 'top-down' inferences of biological properties based on simple measures of network topology are of limited utility, and we present simulation results suggesting that much more detailed information about a gene's location in a regulatory network, as well as dynamic gene-expression data, are needed to make more meaningful functional and evolutionary predictions. Specifically, we find in our simulations that: (1) the relationship between a gene's connectivity and its fitness effect upon knockout depends on its equilibrium expression level; (2) correlation between connectivity and genetic variation is virtually non-existent, yet upon independent evolution of networks with identical topologies, some nodes exhibit consistently low or high polymorphism; and (3) certain genes show low polymorphism yet high divergence among independent evolutionary runs. This latter pattern is generally taken as a signature of positive selection, but in our simulations its cause is often neutral coevolution of regulatory inputs to the same gene.

Algorithms↗
PubMed1995

Artificial neural network and classical least-squares methods for neurotransmitter mixture analysis.

Identification of individual components in biological mixtures can be a difficult problem regardless of the analytical method employed. In this work, Raman spectroscopy was chosen as a prototype analytical method due to its inherent versatility and applicability to aqueous media, making it useful for the study of biological samples. Artificial neural networks (ANNs) and the classical least-squares (CLS) method were used to identify and quantify the Raman spectra of the small-molecule neurotransmitters and mixtures of such molecules. The transfer functions used by a network, as well as the architecture of a network, played an important role in the ability of the network to identify the Raman spectra of individual neurotransmitters and the Raman spectra of neurotransmitter mixtures. Specifically, networks using sigmoid and hyperbolic tangent transfer functions generalized better from the mixtures in the training data set to those in the testing data sets than networks using sine functions. Networks with connections that permit the local processing of inputs generally performed better than other networks on all the testing data sets. and better than the CLS method of curve fitting, on novel spectra of some neurotransmitters. The CLS method was found to perform well on noisy, shifted, and difference spectra.

Acetylcholine↗