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At least 1,225 records · Page 68Linked to original sources
PubMed1997

Dynamical aspects of the EEG in different psychopathological states in an interview situation: a pilot study.

Dynamical brain states can be characterized by non-linear measures of EEG. The present study shows that critical transitions, i.e., abrupt changes from one dynamic pattern of neural mass activity to another one, may be detected by abrupt variations in local chaoticity. Using an ambulatory device, EEG was recorded from 10 patients with a schizophrenic and two patients with an affective disorder during a series of 25-min interviews. Dynamical aspects, in particular, phase transitions in the EEG-dynamics of the EEG were characterized by means of a measure that continuously estimates the chaoticity of the EEG signal and is thus related to its predictability. Results indicate simpler dynamics of the EEG time series in paranoid-hallucinatory patients, while at the same time these patients tended to exhibit more abrupt transitions/unit of time between different dynamical EEG states. Such sudden phase transitions in brain activity were significantly enhanced prior to expressions of thought disorders that were detected by the interviewer and an observer in the conversation, compared with time periods during the interview without such symptoms.

Adult↗
PubMed2001

Nonlinear dynamics and chaos theory: concepts and applications relevant to pharmacodynamics.

The theory of nonlinear dynamical systems (chaos theory), which deals with deterministic systems that exhibit a complicated, apparently random-looking behavior, has formed an interdisciplinary area of research and has affected almost every field of science in the last 20 years. Life sciences are one of the most applicable areas for the ideas of chaos because of the complexity of biological systems. It is widely appreciated that chaotic behavior dominates physiological systems. This is suggested by experimental studies and has also been encouraged by very successful modeling. Pharmacodynamics are very tightly associated with complex physiological processes, and the implications of this relation demand that the new approach of nonlinear dynamics should be adopted in greater extent in pharmacodynamic studies. This is necessary not only for the sake of more detailed study, but mainly because nonlinear dynamics suggest a whole new rationale, fundamentally different from the classic approach. In this work the basic principles of dynamical systems are presented and applications of nonlinear dynamics in topics relevant to drug research and especially to pharmacodynamics are reviewed. Special attention is focused on three major fields of physiological systems with great importance in pharmacotherapy, namely cardiovascular, central nervous, and endocrine systems, where tools and concepts from nonlinear dynamics have been applied.

Biopharmaceutics↗
PubMed2001

Prey preference, intraguild predation and population dynamics of an arthropod food web on plants.

The theory of intraguild predation (IGP) largely studies effects on equilibrium densities of predators and prey, while experiments mostly concern transient dynamics. We studied the effects of an intraguild (IG) predator, the bug Orius laevigatus, on the population dynamics of IG-prey, the predatory mite Phytoseiulus persimilis, and a shared prey, the phytophagous two-spotted spider mite Tetranychus urticae, as well as on the performance of cucumber plants in a greenhouse. The interaction of the predatory mite and the spider mite is highly unstable, and ends either by herbivores overexploiting the plant or predators exterminating the herbivores. We studied the effect of IGP on the transient dynamics of this system, and compared the dynamics with that predicted by a simple population-dynamical model with IGP added. Behavioural studies showed that the predatory bug and the predatory mite were both attracted to plants infested by spider mites and that the two predators did not avoid plants occupied by the other predator. Observations on foraging behaviour of the predatory bug showed that it attacks and kills large numbers of predatory mites and spider mites. The model predicts strong effects of predation and prey preference by the predatory bugs on the dynamics of predatory mites and spider mites. However, experiments in which the predatory bug was added to populations of predatory mites and spider mites had little or no effect on numbers of both mite species, and cucumber plant and fruit weight.

Animals↗
PubMed2005

Fractal and noisy CBV dynamics in humans: influence of age and gender.

The complexity of spontaneous cerebral blood volume (CBV) fluctuations can emerge from random, fractal, or chaotic processes. Our aims were to define the contribution of these patterns to the observed complexity and to evaluate the effect of age and gender on it. The total hemoglobin content as the measure of CBV was monitored by near-infrared spectroscopy on volunteers (men n = 19, age = 20 to 78 years; women n = 23, age = 21 to 79 years). Random and fractal patterns were distinguished by the spectral index (beta). Chaos was identified by surrogate analysis of the correlation dimension (a static chaotic parameter, the dimension of the correlation integral) and the largest Lyapunov exponent (a dynamic chaotic parameter, the rate of exponential divergence of the system states from a perturbed initial condition over the chaotic attractor). In spontaneous CBV fluctuations, both fast random and slow fractal dynamics are present separately in their spectra by a cutoff frequency, f'. Below f' the pattern is fractal, in that power rises inversely with frequency as 1/f(beta). f' decreases with age in men and women alike (F1: up to 0.12+/-0.06 Hz versus F2: up to 0.05+/-0.04 Hz at P = 0.015, and M1: up to 0.16+/-0.05 Hz versus M2: up to 0.11+/-0.04 Hz at P = 0.044). Neither pre- nor postmenopausal age groups (1 and 2, respectively) showed a (low)beta gender difference. Surrogate analysis showed that CBV dynamics cannot be characterized on the grounds of deterministic chaos. Cerebral blood volume fluctuates in a complex, bimodal manner in humans, in that the fast dynamics has no structure, while the slow dynamics exhibits a self-similar, that is, fractal temporal structure. The range of fluctuation amplitudes produced by fractal dynamics is always larger than that of random fluctuations, and it shrinks with an altered structuring in aging women only.

Adult↗
PubMed1999

Non-linear cardiac dynamics and morning dip: an unsound circadian rhythm.

The frequency of sudden cardiac death increases in the morning. The relationship between decreased complexity of heart rate dynamics and sudden cardiac death has been documented. An understanding of circadian variation in the complexity of cardiac dynamics may be important to predict and prevent sudden cardiac death. Dynamic 24-h electrocardiographic recordings were obtained from 30 healthy ambulant subjects aged 41-50 years, and the digitized data were partitioned into sections of 30 min duration. For each section, four indexes obtained from separate algorithms of non-linear dynamics of the RR interval--modified correlation dimension, Lyapunov exponent, approximate entropy, and fractal dimension--were calculated. Normalized low-(0.04-0.15 hertz) and high-frequency (> 0.15 hertz) components were also calculated. All four indexes of non-linear dynamics showed a remarkably similar circadian rhythm: a prominent morning dip preceded by a steep decline during the late night, a recovery during the evening and a peak around midnight. In the morning, the low-frequency component rose rapidly with concomitant reduction in the high-frequency component. The complexity of cardiac dynamics decreases significantly in the morning, and this may contribute to the ominously increased rate of cardiac death in the morning hours.

Adult↗
PubMed2006

Changes over time in the spatiotemporal dynamics of cyclic populations of field voles (Microtus agrestis L.).

We demonstrate changes over time in the spatial and temporal dynamics of an herbivorous small rodent by analyzing time series of population densities obtained at 21 locations on clear cuts within a coniferous forest in Britain from 1984 to 2004. Changes had taken place in the amplitude, periodicity, and synchrony of cycles and density-dependent feedback on population growth rates. Evidence for the presence of a unidirectional traveling wave in rodent abundance was strong near the beginning of the study but had disappeared near the end. This study provides empirical support for the hypothesis that the temporal (such as delayed density dependence structure) and spatial (such as traveling waves) dynamics of cyclic populations are closely linked. The changes in dynamics were markedly season specific, and changes in overwintering dynamics were most pronounced. Climatic changes, resulting in a less seasonal environment with shorter winters near the end of the study, are likely to have caused the changes in vole dynamics. Similar changes in rodent dynamics and the climate as reported from Fennoscandia indicate the involvement of large-scale climatic variables.

Animals↗
PubMed2002

Dynamics of stability: the physiologic basis of functional health and frailty.

Under basal resting conditions most healthy physiologic systems demonstrate highly irregular, complex dynamics that represent interacting regulatory processes operating over multiple time scales. These processes prime the organism for an adaptive response, making it ready and able to react to sudden physiologic stresses. When the organism is perturbed or deviates from a given set of boundary conditions, most physiologic systems evoke closed-loop responses that operate over relatively short periods of time to restore the organism to equilibrium. This transiently alters the dynamics to a less complex, dominant response mode, which is denoted "reactive tuning." Aging and disease are associated with a loss of complexity in resting dynamics and maladaptive responses to perturbations. These alterations in the dynamics of physiologic systems lead to functional decline and frailty. Nonlinear mathematical techniques that quantify physiologic dynamics may predict the onset of frailty, and interventions aimed toward restoring healthy dynamics may prevent functional decline.

Aged↗
PubMed2026

Dynamic metabolic modelling of ATP allocation during viral infection.

Viral pathogens, like SARS-CoV-2, hijack the host's macromolecular production machinery, imposing an energetic burden that is distributed across cellular metabolism. To explore the dynamic metabolic tension between the host's survival and viral replication, we developed a computational framework that uses genome-scale models to perform dynamic flux balance analysis of human cell metabolism during virus infections. Relative to previous models, our framework addresses the physiology of viral infections of non-proliferating host cells through two new features. First, by incorporating the lipid content of SARS-CoV-2 biomass, we discovered activation of previously overlooked pathways giving rise to new predictions of possible drug targets. Furthermore, we introduce a dynamic model that simulates the partitioning of resources between the virus and the host cell, capturing the extent to which the competition depletes the human cells from essential ATP. By incorporating viral dynamics into our COMETS framework for spatio-temporal modelling of metabolism, we provide a mechanistic, dynamic and generalizable starting point for bridging systems biology modelling with viral pathogenesis. This framework could be extended to broadly incorporate phage dynamics in microbial systems and ecosystems.

Humans↗
PubMed1998

Empirical determinants of measles metapopulation dynamics in England and Wales.

A key issue in metapopulation dynamics is the relative impact of internal patch dynamics and coupling between patches. This problem can be addressed by analysing large spatiotemporal data sets, recording the local and global dynamics of metapopulations. In this paper, we analyse the dynamics of measles meta-populations in a large spatiotemporal case notification data set, collected during the pre-vaccination era in England and Wales. Specifically, we use generalized linear statistical models to quantify the relative importance of local influences (birth rate and population size) and regional coupling on local epidemic dynamics. Apart from the proportional effect of local population size on case totals, the models indicate patterns of local and regional dynamic influences which depend on the current state of epidemics. Birth rate and geographic coupling are not associated with the size of major epidemics. By contrast, minor epidemics--and especially the incidence of local extinction of infection--are influenced both by birth rate and geographical coupling. Birth rate at a lag of four years provides the best fit, reflecting the delayed recruitment of susceptibles to school cohorts. A hierarchical index of spatial coupling to large centres provides the best spatial model. The model also indicates that minor epidemics and extinction patterns are more strongly influenced by this regional effect than the local impact of birth rate.

Disease Outbreaks↗
PubMed2000

Environmental colour affects aspects of single-species population dynamics.

Single-species populations of ciliates (Colpidium and Paramecium) experienced constant temperature or white or reddened temperature fluctuations in aquatic microcosms in order to test three hypotheses about how environmental colour influences population dynamics. (i) Models predict that the colour of population dynamics is tinged by the colour of the environmental variability. However, environmental colour had no effect on the colour of population dynamics. All population dynamics in this experiment were reddened, regardless of environmental colour. (ii) Models predict that populations will track reddened environmental variability more closely than white environmental variability and that populations with a higher intrinsic growth rate (r) will track environmental variability more closely than populations with a low r. The experimental populations behaved as predicted. (iii) Models predict that population variability is determined by interaction between r and the environmental variability. The experimental populations behaved as predicted. These results show that (i) reddened population dynamics may need no special explanation, such as reddened environments, spatial subdivision or interspecific interactions, and (ii) and (iii) that population dynamics are sensitive to environmental colour, in agreement with population models. Correct specification of the colour of the environmental variability in models is required for accurate predictions. Further work is needed to study the effects of environmental colour on communities and ecosystems.

Animals↗
PubMed2001

Multiple dynamics in a single predator-prey system: experimental effects of food quality.

Recent work with the freshwater zooplankton Daphnia has suggested that the quality of its algal prey can have a significant effect on its demographic rates and life-history patterns. Predator-prey theory linking food quantity and food quality predicts that a single system should be able to display two distinct patterns of population dynamics. One pattern is predicted to have high herbivore and low algal biomass dynamics (high HBD), whereas the other is predicted to have low herbivore and high algal biomass dynamics (low HBD). Despite these predictions and the stoichiometric evidence that many phytoplankton communities may have poor access to food of quality, there have been few tests of whether a dynamic predator-prey system can display both of these distinct patterns. Here we report, to the authors' knowledge, the first evidence for two dynamical patterns, as predicted by theory, in a single predator-prey system. We show that the high HBD is a result of food quantity effects and that the low HBD is a result of food quality effects, which are maintained by phosphorus limitation in the predator. These results provide an important link between the known effects of nutrient limitation in herbivores and the significance of prey quality in predator-prey population dynamics in natural zooplankton communities.

Animals↗
PubMed2004

Spatially structured population dynamics in feral oilseed rape.

We studied the population dynamics of feral oilseed rape (Brassica napus) for 10 years (1993-2002) in 3658 adjacent permanent 100 m quadrats in the verges of the M25 motorway around London, UK. The aim was to determine the relative importance of different factors affecting the observed temporal patterns of population dynamics and their spatial correlations. A wide range of population dynamics was observed (downward or upward trends, cycles, local extinctions and recolonizations), but overall the populations were not self-replacing (lambda < 1). Many quadrats remained unoccupied throughout the study period, but a few were occupied at high densities for all 10 years. Most quadrats showed transient oilseed rape populations, lasting 1-4 years. There were strong spatial patterns in mean population density, associated with soil conditions and the successional age of the plant community dominating the verge, and these large-scale spatial patterns were highly consistent from year to year. The importance of seed spilled from trucks in transit to the processing plant at Erith in Kent was confirmed: rape populations were significantly higher on the 'to Erith' verge than the 'from Erith' verge (overall mean 2.83-fold greater stem density). Quadrats in which lambda > 1 were much more frequent in the 'to Erith' verge, indicating that seed immigration can give the spurious impression of self-replacing population dynamics in time-series analysis. There was little evidence of a pervasive Moran effect, and climatic forcing did not produce widespread large-scale synchrony in population dynamics for the motorway as a whole; just 23% of quadrats had significant rank correlations with the mean time-series. There was, however, significant local spatial synchrony of population dynamics, apparently associated with soil disturbance and seed input. This study draws attention to the possibility that different processes may impose population synchrony at different scales. We hypothesize that synchrony in this system is driven by at least three processes: small-scale, local forcing caused by soil disturbance, intermediate-scale forcing as a result of seed input, and large-scale climatic forcing (e.g. winter rainfall) that affects the motorway as a whole.

Brassica napus↗
PubMed2006

Complex population dynamics and complex causation: devils, details and demography.

Population dynamics result from the interplay of density-independent and density-dependent processes. Understanding this interplay is important, especially for being able to predict near-term population trajectories for management. In recent years, the study of model systems-experimental, observational and theoretical-has shed considerable light on the way that the both density-dependent and -independent aspects of the environment affect population dynamics via impacting on the organism's life history and therefore demography. These model-based approaches suggest that (i) individuals in different states differ in their demographic performance, (ii) these differences generate structure that can fluctuate independently of current total population size and so can influence the dynamics in important ways, (iii) individuals are strongly affected by both current and past environments, even when the past environments may be in previous generations and (iv) dynamics are typically complex and transient due to environmental noise perturbing complex population structures. For understanding population dynamics of any given system, we suggest that 'the devil is in the detail'. Experimental dissection of empirical systems is providing important insights into the details of the drivers of demographic responses and therefore dynamics and should also stimulate theory that incorporates relevant biological mechanism.

Adaptation, Physiological↗
PubMed2005

Dynamics of a neural system with a multiscale architecture.

The architecture of the brain is characterized by a modular organization repeated across a hierarchy of spatial scales-neurons, minicolumns, cortical columns, functional brain regions, and so on. It is important to consider that the processes governing neural dynamics at any given scale are not only determined by the behaviour of other neural structures at that scale, but also by the emergent behaviour of smaller scales, and the constraining influence of activity at larger scales. In this paper, we introduce a theoretical framework for neural systems in which the dynamics are nested within a multiscale architecture. In essence, the dynamics at each scale are determined by a coupled ensemble of nonlinear oscillators, which embody the principle scale-specific neurobiological processes. The dynamics at larger scales are 'slaved' to the emergent behaviour of smaller scales through a coupling function that depends on a multiscale wavelet decomposition. The approach is first explicated mathematically. Numerical examples are then given to illustrate phenomena such as between-scale bifurcations, and how synchronization in small-scale structures influences the dynamics in larger structures in an intuitive manner that cannot be captured by existing modelling approaches. A framework for relating the dynamical behaviour of the system to measured observables is presented and further extensions to capture wave phenomena and mode coupling are suggested.

Brain↗
PubMed2004

Trajectory priming with dynamic fuzzy networks in nonlinear optimal control.

Fuzzy logic systems have been recognized as a robust and attractive alternative to some classical control methods. The application of classical fuzzy logic (FL) technology to dynamic system control has been constrained by the nondynamic nature of popular FL architectures. Many difficulties include large rule bases (i.e., curse of dimensionality), long training times, etc. These problems can be overcome with a dynamic fuzzy network (DFN), a network with unconstrained connectivity and dynamic fuzzy processing units called "feurons." In this study, DFN as an optimal control trajectory priming system is considered as a nonlinear optimization with dynamic equality constraints. The overall algorithm operates as an autotrainer for DFN (a self-learning structure) and generates optimal feed-forward control trajectories in a significantly smaller number of iterations. For this, DFN encapsulates and generalizes the optimal control trajectories. By the algorithm, the time-varying optimal feedback gains are also generated along the trajectory as byproducts. This structure assists the speeding up of trajectory calculations for intelligent nonlinear optimal control. For this purpose, the direct-descent-curvature algorithm is used with some modifications [called modified-descend-controller (MDC) algorithm] for the nonlinear optimal control computations. The algorithm has numerically generated robust solutions with respect to conjugate points. The minimization of an integral quadratic cost functional subject to dynamic equality constraints (which is DFN) is considered for trajectory obtained by MDC tracking applications. The adjoint theory (whose computational complexity is significantly less than direct method) has been used in the training of DFN, which is as a quasilinear dynamic system. The updating of weights (identification of DFN parameters) are based on Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. Simulation results are given for controlling a difficult nonlinear second-order system using fully connected three-feuron DFN.

Fuzzy Logic↗
PubMed2004

Nonlinear dynamical factor analysis for state change detection.

Changes in a dynamical process are often detected by monitoring selected indicators directly obtained from the process observations, such as the mean values or variances. Standard change detection algorithms such as the Shewhart control charts or the cumulative sum (CUSUM) algorithm are often based on such first- and second-order statistics. Much better results can be obtained if the dynamical process is properly modeled, for example by a nonlinear state-space model, and then the accuracy of the model is monitored over time. The success of the latter approach depends largely on the quality of the model. In practical applications like industrial processes, the state variables, dynamics, and observation mapping are rarely known accurately. Learning from data must be used; however, methods for the simultaneous estimation of the state and the unknown nonlinear mappings are very limited. We use a novel method of learning a nonlinear state-space model, the nonlinear dynamical factor analysis (NDFA) algorithm. It takes a set of multivariate observations over time and fits blindly a generative dynamical latent variable model, resembling nonlinear independent component analysis. We compare the performance of the model in process change detection to various traditional methods. It is shown that NDFA outperforms the classical methods by a wide margin in a variety of cases where the underlying process dynamics changes.

Factor Analysis, Statistical↗
PubMed1996

A new type of self-organization associated with chaotic dynamics in neural networks.

A new type of self-organized dynamics is presented, in relation with chaos in neural networks. One is chaotic itinerancy and the other is chaos-driven contraction dynamics. The former is addressed as a universal behavior in high-dimensional dynamical systems. In particular, it can be viewed as one possible form of memory dynamics in brain. The latter gives rise to singular-continuous nowhere-differentiable attractors. These dynamics can be related to each other in the context of dimensionality and of chaotic information processings. Possible roles of these complex dynamics in brain are also discussed.

Animals↗
PubMed2001

Spatio-temporal dynamics and transition from asymptotic equilibrium to bounded oscillations in Chrysomya albiceps (Diptera, Calliphoridae).

The sensitivity of parameters that govern the stability of population size in Chrysomya albiceps and describe its spatial dynamics was evaluated in this study. The dynamics was modeled using a density-dependent model of population growth. Our simulations show that variation in fecundity and mainly in survival has marked effect on the dynamics and indicates the possibility of transitions from one-point equilibrium to bounded oscillations. C. albiceps exhibits a two-point limit cycle, but the introduction of diffusive dispersal induces an evident qualitative shift from two-point limit cycle to a one fixed-point dynamics. Population dynamics of C. albiceps is here compared to dynamics of Cochliomyia macellaria, C. megacephala and C. putoria.

Animals↗