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PubMed1984

Social-network considerations in the alcohol field.

This chapter reviews the literature on social networks in the alcohol field. The review focuses on the dynamics of the social-network system and network analysis. Stressors to recently urbanized persons are also examined in a discussion of social adaptation, social identity, and networks. The findings of several studies that are summarized indicate that there is a crisis in changing cultural styles and social values that develops after migration and resettlement. An aspect of this review suggests that social networks may be utilized as stress-buffering strategies in both a constructive and a destructive fashion during these periods of crisis. The author describes these networks as pathways to care and illustrates how they might play a significant role in alcoholism-treatment and recovery programs. Finally, the author concludes with some possible directions for future research on the systematic study of social bonds.

Alcohol Drinking↗
PubMed2005

Formation and structure of ramified charge transportation networks in an electromechanical system.

We present findings in an experiment where we obtain stationary ramified transportation networks in a macroscopic nonbiological system. Our purpose here is to introduce the phenomenology of the experiment. We describe the dynamical formation of the network which consists of three growth stages: (I) strand formation, (II) boundary formation, and (III) geometric expansion. We find that the system forms statistically robust network features, like the number of termini and the number of branch points. We also find that the networks are usually trees, meaning that they lack closed loops; indeed, we find that loops are unstable in the network. Finally, we find that the final topology of the network is sensitive to the initial conditions of the particles, in particular to its geometry.

Journal Article↗
PubMed1981

Analysis of a schizophrenic psychosocial network.

The amorphous concept of social support systems merits construction of a conceptually coherent theoretical model linked to social theory and amenable to empirical investigation. The social network paradigm is presented as such a model. The model is further defined in terms of the intimate psychosocial network, which has been empirically studied with the Pattison Psychosocial Kinship Inventory. The characteristics of the normal network are shown to differ substantially in the schizophrenic network. The structure and functions of the schizophrenic network are illustrated in a case study analysis. The schizophrenic network is shown to exhibit dynamics that generate and perpetuate psychotic behavior. A strategy for network intervention is described, based on the model of structural change in the network social system.

Adult↗
PubMed2006

Parameter estimation for stiff equations of biosystems using radial basis function networks.

BACKGROUND: The modeling of dynamic systems requires estimating kinetic parameters from experimentally measured time-courses. Conventional global optimization methods used for parameter estimation, e.g. genetic algorithms (GA), consume enormous computational time because they require iterative numerical integrations for differential equations. When the target model is stiff, the computational time for reaching a solution increases further. RESULTS: In an attempt to solve this problem, we explored a learning technique that uses radial basis function networks (RBFN) to achieve a parameter estimation for biochemical models. RBFN reduce the number of numerical integrations by replacing derivatives with slopes derived from the distribution of searching points. To introduce a slight search bias, we implemented additional data selection using a GA that searches data-sparse areas at low computational cost. In addition, we adopted logarithmic transformation that smoothes the fitness surface to obtain a solution simply. We conducted numerical experiments to validate our methods and compared the results with those obtained by GA. We found that the calculation time decreased by more than 50% and the convergence rate increased from 60% to 90%. CONCLUSION: In this work, our RBFN technique was effective for parameter optimization of stiff biochemical models.

Algorithms↗
PubMed2000

Generalized and partial synchronization of coupled neural networks.

Synchronization of neural signals has been proposed as a temporal coding scheme representing cooperated computation in distributed cortical networks. Previous theoretical studies in that direction mainly focused on the synchronization of coupled oscillatory subsystems and neglected more complex dynamical modes, that already exist on the single-unit level. In this paper we study the parametrized time-discrete dynamics of two coupled recurrent networks of graded neurons. Conditions for the existence of partially synchronized dynamics of these systems are derived, referring to a situation where only subsets of neurons in each sub-network are synchronous. The coupled networks can have different architectures and even a different number of neurons. Periodic as well as quasiperiodic and chaotic attractors constrained to a manifold M of synchronized components are observed. Examples are discussed for coupled 3-neuron networks having different architectures, and for coupled 2-neuron and 3-neuron networks. Partial synchronization of different degrees is demonstrated by numerical results for selected sets of parameters. In conclusion, the results show that synchronization phenomena far beyond completely synchronized oscillations can occur even in simple coupled networks. The type of the synchronization depends in an intricate way on stimuli, history and connectivity as well as other parameters of the network. Specific inputs can further switch between different operational modes in a complex way, suggesting a similarly rich spatio-temporal behaviour in real neural systems.

Neural Networks, Computer↗
PubMed2006

Oscillatory neuronal dynamics during language comprehension.

Language comprehension involves two basic operations: the retrieval of lexical information (such as phonologic, syntactic, and semantic information) from long-term memory, and the unification of this information into a coherent representation of the overall utterance. Neuroimaging studies using hemodynamic measures such as PET and fMRI have provided detailed information on which areas of the brain are involved in these language-related memory and unification operations. However, much less is known about the dynamics of the brain's language network. This chapter presents a literature review of the oscillatory neuronal dynamics of EEG and MEG data that can be observed during language comprehension tasks. From a detailed review of this (rapidly growing) literature the following picture emerges: memory retrieval operations are mostly accompanied by increased neuronal synchronization in the theta frequency range (4-7 Hz). Unification operations, in contrast, induce high-frequency neuronal synchronization in the beta (12-30 Hz) and gamma (above 30 Hz) frequency bands. A desynchronization in the (upper) alpha frequency band is found for those studies that use secondary tasks, and seems to correspond with attentional processes, and with the behavioral consequences of the language comprehension process. We conclude that it is possible to capture the dynamics of the brain's language network by a careful analysis of the event-related changes in power and coherence of EEG and MEG data in a wide range of frequencies, in combination with subtle experimental manipulations in a range of language comprehension tasks. It appears then that neuronal synchrony is a mechanism by which the brain integrates the different types of information about language (such as phonological, orthographic, semantic, and syntactic information) represented in different brain areas.

Brain↗
PubMed2005

Charting epilepsy by searching for intelligence in network space with the help of evolving autonomous agents.

The problem of demarcating neural network space is formidable. A simple fully connected recurrent network of five units (binary activations, synaptic weight resolution of 10) has 3.2 *10(26) possible initial states. The problem increases drastically with scaling. Here we consider three complementary approaches to help direct the exploration to distinguish epileptic from healthy networks. [1] First, we perform a gross mapping of the space of five-unit continuous recurrent networks using randomized weights and initial activations. The majority of weight patterns (>70%) were found to result in neural assemblies exhibiting periodic limit-cycle oscillatory behavior. [2] Next we examine the activation space of non-periodic networks demonstrating that the emergence of paroxysmal activity does not require changes in connectivity. [3] The next challenge is to focus the search of network space to identify networks with more complex dynamics. Here we rely on a major available indicator critical to clinical assessment but largely ignored by epilepsy modelers, namely: behavioral states. To this end, we connected the above network layout to an external robot in which interactive states were evolved. The first random generation showed a distribution in line with approach [1]. That is, the predominate phenotypes were fixed-point or oscillatory with seizure-like motor output. As evolution progressed the profile changed markedly. Within 20 generations the entire population was able to navigate a simple environment with all individuals exhibiting multiply-stable behaviors with no cases of default locked limit-cycle oscillatory motor behavior. The resultant population may thus afford us a view of the architectural principles demarcating healthy biological networks from the pathological. The approach has an advantage over other epilepsy modeling techniques in providing a way to clarify whether observed dynamics or suggested therapies are pointing to computational viability or dead space.

Action Potentials↗
PubMed2005

Dopamine modulation of spike dynamics in bursting neurons.

The pyloric network of the lobster stomatogastric ganglion is a prime example of an oscillatory neural circuit. In our previous study on the firing patterns of pyloric neurons we observed characteristic temporal structures termed 'interspike interval (ISI) signatures' which were found to depend on the synaptic connectivity of the network. Dopamine, a well-known modulator of the pyloric network, is known to affect inhibitory synapses so it might also tune the fine temporal structure of intraburst spikes, a phenomenon not previously investigated. In the recent work we study the DA modulation of ISI patterns of two identified pyloric neurons in normal conditions and after blocking their glutamatergic synaptic connections. Dopamine (10-50 microM) strongly regularizes the firing of the lateral pyloric (LP) and pyloric dilator (PD) neurons by increasing the reliability of recurrent spike patterns. The most dramatic effect is observed in the LP, where precisely replicated spike multiplets appear in a normally 'noisy' neuron. The DA-induced regularization of intraburst spike patterns requires functional glutamatergic inputs to the LP neuron and this effect cannot be mimicked by simple intracellular depolarization. Inhibitory synaptic inputs arriving before the bursts are important factors in shaping the intraburst spike dynamics of both the PD and the LP neurons. Our data reveal a novel aspect of chemical neuromodulation in oscillatory neural networks. This effect sets in at concentrations lower than those affecting the overall burst pattern of the network. The sensitivity of intraburst spike dynamics to preceding synaptic inputs also suggests a novel method of temporal coding in neural bursters.

Action Potentials↗
PubMed1996

Dynamic patterns of allergic patch test reactions to 10 European standard allergens. An analysis of data recorded by the Information Network of Departments of Dermatology (IVDK).

Insight into the dynamic time courses of patch test reactions may be useful for patch test evaluation. To put this to the proof, we have analysed the time courses of allergic patch test reactions that were filed between 1990 and 1994 by the Information Network of Departments of Dermatology in Germany (IVDK). Based on reaction strength at days 1 to 3, each allergic patch test was assigned to 1 of 5 distinct dynamic reaction patterns: crescendo, plateau, decrescendo, crescendo/decrescendo, or decrescendo/ crescendo. The frequencies of these patterns were determined for those 10 standard allergens yielding the highest numbers of allergic reactions (n between 180 and 1240), and dynamic patterns were classified as type 1 (crescendo or plateau patterns) or type 2 (other patterns). Influences of population characteristics upon the classification of patterns were evaluated by logistic regression analyses. Different frequencies of reaction patterns were found for distinct allergens. For all 10 allergens, type-1 patterns were significantly more often observed for allergic reactions assessed as clinically relevant. For 5 allergens, female sex of patients was associated with a lower frequency of type-2 patterns. A history of atopic dermatitis and the age of patients were only weakly associated with a certain classification of reaction patterns in some of the allergens. In a comparison of reaction patterns obtained with different allergens in the same population, we found that nickel sulfate elicited type 1 reaction patterns more often than fragrance mix. We conclude that the dynamic reaction patterns should be taken into account in the evaluation of patch tests. The relation of type-1 patterns to clinical relevance of the reaction needs further attention.

Adult↗
PubMed2006

Scale-free topologies and activatory-inhibitory interactions.

A simple model of activatory-inhibitory interactions controlling the activity of agents (substrates) through a "saturated response" dynamical rule in a scale-free network is thoroughly studied. After discussing the most remarkable dynamical features of the model, namely fragmentation and multistability, we present a characterization of the temporal (periodic and chaotic) fluctuations of the quasi-stasis asymptotic states of network activity. The double (both structural and dynamical) source of entangled complexity of the system temporal fluctuations, as an important partial aspect of the correlation structure-function problem, is further discussed in light of the numerical results, with a view on potential applications of these general results.

Action Potentials↗
PubMed2004

A green fluorescent protein fusion to actin-binding domain 2 of Arabidopsis fimbrin highlights new features of a dynamic actin cytoskeleton in live plant cells.

The actin cytoskeleton coordinates numerous cellular processes required for plant development. The functions of this network are intricately linked to its dynamic arrangement, and thus progress in understanding how actin orchestrates cellular processes relies on critical evaluation of actin organization and turnover. To investigate the dynamic nature of the actin cytoskeleton, we used a fusion protein between green fluorescent protein (GFP) and the second actin-binding domain (fABD2) of Arabidopsis (Arabidopsis thaliana) fimbrin, AtFIM1. The GFP-fABD2 fusion protein labeled highly dynamic and dense actin networks in diverse species and cell types, revealing structural detail not seen with alternative labeling methods, such as the commonly used mouse talin GFP fusion (GFP-mTalin). Further, we show that expression of the GFP-fABD2 fusion protein in Arabidopsis, unlike GFP-mTalin, has no detectable adverse effects on plant morphology or development. Time-lapse confocal microscopy and fluorescence recovery after photobleaching analyses of the actin cytoskeleton labeled with GFP-fABD2 revealed that lateral-filament migration and sliding of individual actin filaments or bundles are processes that contribute to the dynamic and continually reorganizing nature of the actin scaffold. These new observations of the dynamic actin cytoskeleton in plant cells using GFP-fABD2 reveal the value of this probe for future investigations of how actin filaments coordinate cellular processes required for plant development.

Actins↗
PubMed2005

Actin dynamics: growth from dendritic branches.

The dendritic nucleation model was devised to explain the cycle of actin dynamics resulting in actin filament network assembly and disassembly in two contexts--at the leading edge of motile cells and in the actin comet tails of intracellular pathogenic bacteria and viruses. Due to the detailed nature of its biochemical predictions, the model has provided an excellent focus for subsequent experimentation. This review summarizes recent work on actin dynamics in the context of the dendritic nucleation model. One outcome of this research is the possibility that additional proteins, as well as the six proteins included in the original model, might increase the efficiency of dendritic nucleation or modify the resulting actin network. In addition, actin dynamics at the leading edge might be influenced by a second actin filament network, independent of dendritic nucleation.

Actins↗
PubMed1998

Dynamic cross-linking by alpha-actinin determines the mechanical properties of actin filament networks.

We used smooth muscle alpha-actinin to evaluate the contribution of cross-linker dynamics to the mechanical properties of actin filament networks. Recombinant actin-binding domain (residues 2-269) binds actin filaments with a Kd of 1 microM at 25 degrees C, 20 times stronger than actin-binding domain produced by thermolysin digestion of native alpha-actinin (residues 25-257). Between 8 and 25 degrees C the rate constants for recombinant actin-binding domain to bind to (0.8-2.7 microM-1 s-1) and dissociate from (0.2-2.4 s-1) actin filaments depend on temperature. At 8 degrees C actin filaments cross-linked with alpha-actinin are stiff and nearly solid, whereas at 25 degrees C the mechanical properties approach those of actin filaments alone. In these experiments, high actin concentrations kept most of the alpha-actinin bound to actin and temperature varied a single parameter, cross-linker dynamics, because the mechanical properties of pure actin filaments (a viscoelastic gel) or biotinylated actin filaments cross-linked irreversibly by avidin (a stiff viscoelastic solid) depend little on temperature. These results show that the rate of exchange of dynamic cross-links between actin filaments is an important determinant of the mechanical properties of the networks.

Actinin↗
PubMed2002

Three-dimensional reconstruction of cytoplasmic membrane networks in parietal cells.

There is general agreement that stimulation and consequent secretion of gastric parietal cells result in a great expansion of the apical canalicular membrane at the expense of an extensive intracellular network of membranes rich in the gastric proton pump (H,K-ATPase). However, there is ongoing controversy as to the precise nature of the intracellular membrane network, conventionally called tubulovesicles. At the heart of this controversy lies the question of whether tubulovesicles are a distinct membrane compartment or whether they are continuous with the apical plasma membrane. To address this controversy we used high-pressure, rapid freezing techniques to fix non-stimulated (resting) rabbit gastric glands for electron microscopy. Ultra-thin (60-70 nm) serial sections were used for conventional TEM; 400-500 nm sections were used for tomography. Images were digitized and models constructed using Midas and Imod software (http://bio3d.colorado.edu ). Images were aligned and contours drawn on specific cellular structures. The contours from a stack of serial sections were arranged into objects and meshed into 3D structures. For resting parietal cells our findings are as follows: (1) The apical canaliculus is a microvilli-decorated, branching membrane network that extends into and throughout the parietal cell. This agrees well with a host of previous studies. (2) The plentiful mitochondria form an extensive reticular network throughout the cytoplasm. This has not previously been reported for the parietal cell, and the significance of this observation and the dynamics of the mitochondrial network remain unknown. (3) H,K-ATPase-rich membranes do include membrane tubules and vesicles; however, the tubulovesicular compartment is chiefly comprised of small stacks of cisternae. Thus a designation of tubulocisternae seems appropriate; however, in the resting cell there are no continuities between the apical canaliculus and the tubulocisternae or between tubulocisternae. These data support the recruitment-recycling model of parietal cell stimulation.

Animals↗
PubMed2005

[Self-organizing neural networks for automatic detection and classification of contrast (media) enhancement of lesions in dynamic MR-mammography].

PURPOSE: Investigation and statistical evaluation of "Self-Organizing Maps," a special type of neural networks in the field of artificial intelligence, classifying contrast enhancing lesions in dynamic MR-mammography. MATERIAL AND METHODS: 176 investigations with proven histology after core biopsy or operation were randomly divided into two groups. Several Self-Organizing Maps were trained by investigations of the first group to detect and classify contrast enhancing lesions in dynamic MR-mammography. Each single pixel's signal/time curve of all patients within the second group was analyzed by the Self-Organizing Maps. The likelihood of malignancy was visualized by color overlays on the MR-images. At last assessment of contrast-enhancing lesions by each different network was rated visually and evaluated statistically. RESULTS: A well balanced neural network achieved a sensitivity of 90.5 % and a specificity of 72.2 % in predicting malignancy of 88 enhancing lesions. Detailed analysis of false-positive results revealed that every second fibroadenoma showed a "typical malignant" signal/time curve without any chance to differentiate between fibroadenomas and malignant tissue regarding contrast enhancement alone; but this special group of lesions was represented by a well-defined area of the Self-Organizing Map. DISCUSSION: Self-Organizing Maps are capable of classifying a dynamic signal/time curve as "typical benign" or "typical malignant." Therefore, they can be used as second opinion. In view of the now known localization of fibroadenomas enhancing like malignant tumors at the Self-Organizing Map, these lesions could be passed to further analysis by additional post-processing elements (e.g., based on T2-weighted series or morphology analysis) in the future.

Algorithms↗
PubMed1999

Non-linear time series analysis of intracranial EEG recordings in patients with epilepsy--an overview.

Deterministic chaos offers a striking explanation for apparently irregular behavior, a characteristic feature of brain electrical activity. The framework of the theory of non-linear dynamics provides new concepts and powerful algorithms to analyze such time series. However, different influencing factors render the use of non-linear measures in a strict sense problematic. Nevertheless, if interpreted with care, particularly the correlation dimension or the Lyapunov-exponents provide a means to reliably characterize different states of normal and pathological brain function. This overview summarizes recent findings applying this concept in the field of epileptology that promise to be important for clinical practice. Non-linear measures extracted from the intra-cranially recorded EEG allow (a) localization of epileptogenic areas in different cerebral regions even during seizure-free intervals, (b) investigation of the influence of anticonvulsive drugs and (c) detection of features predictive of imminent seizure activity. Moreover, particularly the dimensional complexity proves a valuable parameter reflecting spatially distributed neuronal activity during verbal learning and memory processes. Specific changes in time of this non-linear measure allow the prediction of memory performance and, in addition, represent an estimate of the recruitment potency in the anterior mesial temporal lobes. Thus, the application of non-linear time series analysis to brain electrical activity offers new information about the dynamics of the underlying neuronal networks.

Electroencephalography↗
PubMed2000

Learning chaotic attractors by neural networks.

An algorithm is introduced that trains a neural network to identify chaotic dynamics from a single measured time series. During training, the algorithm learns to short-term predict the time series. At the same time a criterion, developed by Diks, van Zwet, Takens, and de Goede (1996) is monitored that tests the hypothesis that the reconstructed attractors of model-generated and measured data are the same. Training is stopped when the prediction error is low and the model passes this test. Two other features of the algorithm are (1) the way the state of the system, consisting of delays from the time series, has its dimension reduced by weighted principal component analysis data reduction, and (2) the user-adjustable prediction horizon obtained by "error propagation"-partially propagating prediction errors to the next time step. The algorithm is first applied to data from an experimental-driven chaotic pendulum, of which two of the three state variables are known. This is a comprehensive example that shows how well the Diks test can distinguish between slightly different attractors. Second, the algorithm is applied to the same problem, but now one of the two known state variables is ignored. Finally, we present a model for the laser data from the Santa Fe time-series competition (set A). It is the first model for these data that is not only useful for short-term predictions but also generates time series with similar chaotic characteristics as the measured data.

Algorithms↗
PubMed2005

Arm retraction potential of branched polymers in the absence of dynamic dilution.

We study the stress relaxation of model polymer networks containing low contents of star shaped and linear dangling polymers. As compared with their melts, the behavior of star and dangling polymers leads to a dynamic response with unprecedented large relaxation times. By comparing data of star melts with those corresponding to stars and dangling chains residing in polymer networks, we were able to identify the effects of dynamic dilution clearly. Since in polymer networks the dynamic dilution effect is suppressed, we were able by the first time to experimentally test the validity of the potential for arm retraction proposed by Pearson and Helfand.

Journal Article↗