Deterministic and stochastic features of rhythmic human movement

The dynamics of rhythmic movement has both deterministic and stochastic features. We advocate a recently established analysis method that allows for an unbiased identification of both types of system components. The deterministic components are revealed in terms of drift coefficients and vector fields, while the stochastic components are assessed in terms of diffusion coefficients and ellipse fields. The general principles of the procedure and its application are explained and illustrated using simulated data from known dynamical systems. Subsequently, we exemplify the method’s merits in extracting deterministic and stochastic aspects of various instances of rhythmic movement, including tapping, wrist cycling and forearm oscillations. In particular, it is shown how the extracted numerical forms can be analysed to gain insight into the dependence of dynamical properties on experimental conditions.     

Intercellular and systemic movement of RNA silencing signals

In most eukaryotes, double-stranded RNA is processed into small RNAs that are potent regulators of gene expression. This gene silencing process is known as RNA silencing or RNA interference (RNAi) and, in plants and nematodes, it is associated with the production of a mobile signal that can travel from cell-to-cell and over long distances. The sequence-specific nature of systemic RNA silencing indicates that a nucleic acid is a component of the signalling complex. Recent work has shed light on the mobile RNA species, the genes involved in the production and transport of the signal. This review discusses the advances in systemic RNAi and presents the current challenges and questions in this rapidly evolving field.     

Chloroplasts continuously monitor photoreceptor signals during accumulation movement

Under low light conditions, chloroplasts gather at a cell surface to maximize light absorption for efficient photosynthesis, which is called the accumulation response. Phototropin1 (phot1) and phototropin2 (phot2) were identified as blue light photoreceptors in the accumulation response that occurs in Arabidopsis thaliana and Adiantum capillus-veneris with neochrome1 (neo1) as a red light photoreceptor in A. capillus-veneris. However, the signal molecule that is emitted from the photoreceptors and transmitted to the chloroplasts is not known. To investigate this topic, the accumulation response was induced by partial cell irradiation with a microbeam of red, blue and far-red light in A. capillus-veneris gametophyte cells. Chloroplasts moved towards the irradiated region and were able to sense the signal as long as its signal flowed. The signal from neo1 had a longer life than the signal that came from phototropins. When two microbeams with the same wavelength and the same fluence rate were placed 20 μm apart from each other and were applied to a dark-adapted cell, chloroplasts at an equidistant position always moved towards the center (midpoint) of the two microbeams, but not towards either one. This result indicates that chloroplasts are detecting the concentration of the signal but not the direction of signal flow. Chloroplasts repeatedly move and stop at roughly 10 s intervals during the accumulation response, suggesting that they monitor the intermittent signal waves from photoreceptors.     

A stochastic theory of phase transitions in human hand movement

The order parameter equation for the relative phase of correlated hand movements, derived in a previous paper by Haken et al. (1985), is extended to a time-dependent stochastic differential equation. Its solutions are determined close to stationary points and for the transition region. Remarkably good agreement between this theory and recent experiments done by Kelso and Scholz (1985) is found, and new predictions are offered.     

Transformation of kinematic characteristics of a precise movement after change in a spatial task

Brain mechanisms of motor programming were studied with the use of the model of learning precise horizontal elbow flexion. To exclude visual control and make learning to be based, predominantly, on proprioception, experiments were carried out in darkness. The target position was not demonstrated beforehand. Subject (S) had to find an adequate angle of flexion during training with a short light-diode flash which marked the moment of target reaching. Ss were asked to perform a precise horizontal elbow flexion as fast as possible. Movement amplitude, velocity and acceleration were on-line recorded. Ten Ss were divided in two groups. The first group was initially trained to make the precise movement with the preset amplitude of 70 degrees and the second group had to perform similar movement with the amplitude of 55 degrees. Each S was trained to the moment of acquisition of a stable skill (within the 5% error of preset flexion amplitude). After that the target position was unex pectedly changed (from 70 for 55 degree or visa verse). This work was a continuation of our earlier search for a mathematical hypothesis most correctly explaining the central mechanism of motor learning. The dynamics of kinematic characteristics of learning in our experiments fitted well to A. Barto and J. Houk's "Cerebellar Model of Timing and Prediction". A comparison of a computer simulation of this model to the learning characteristics allowed us to make some refinements of the model very important for data analysis possible under conditions of noninvasive investigations. The analysis of acceleration dynamics not considered by the authors of the model made it possible to identify this index with the "pulse phase" similar to the period of LTD of Purkinje cells (the key mechanism of the model). We took such an interpretation as principal in our analysis of experimental data. We analyzed integrals of positive and negative acceleration which made it possible to gain a deeper insight into the physiological mechanism of a replacement of one central command by the other as a consequence of change in spatial task conditions.     

Decoding hand movement velocity from electroencephalogram signals during a drawing task

Background  

Decoding neural activities associated with limb movements is the key of motor prosthesis control. So far, most of these studies have been based on invasive approaches. Nevertheless, a few researchers have decoded kinematic parameters of single hand in non-invasive ways such as magnetoencephalogram (MEG) and electroencephalogram (EEG). Regarding these EEG studies, center-out reaching tasks have been employed. Yet whether hand velocity can be decoded using EEG recorded during a self-routed drawing task is unclear.     

Estimation by humans of signals simulating different sound movement directions and specificity of the perception of these signals by patients with temporal epilepsy

The perception of moving sound stimuli that imitate directional sound source movement was studied in healthy subjects and in patients with temporal lobe lesions, as well as in a group of patients with simultaneous lesions of the temporal cortex and hippocampus. Under the conditions of dichotic stimulation of patients with the rightor left-side foci of convulsive activity, the nature and length of the trajectories of the emerging subjective sound images (SSI) were estimated depending on the direction of movement and interaural time difference (700, 400, 200 μs). The audiograms of all patients did not differ from those of healthy subjects, suggesting that the auditory sensitivity of patients remained unimpaired. However, in the patients, the trajectories were shorter than the trajectories in healthy subjects at all the values of the initial time delay and at all the directions of SSI movements. In patients with the cortical temporal epilepsy, changes of the subjective sound field were the most significant in the case of the right-side localization of foci of the convulsive activity. In patients with simultaneous lesions of the temporal cortex and hippocampus, the averaged trajectories of SSI movement differed significantly from those in the group of healthy subjects (p < 0.01) and in patients with a relatively isolated lesion of the temporal cortex (p < 0.05); these trajectories were independent of the initial delay. The mediobasal structures of the temporal lobe that are involved in the epileptic process proved to play a significant role in the perception and estimation of the moving sound stimuli, although they do not belong to the auditory system proper. The possible mechanisms underlying disorders in patients with temporal epilepsy are discussed.     

A stochastic model for chemosensory cell movement: application to neutrophil and macrophage persistence and orientation

Two central features of leukocyte chemosensory movement behavior demand fundamental theoretical understanding. In uniform concentrations of chemoattractant, these cells exhibit a persistent random walk, with a characteristic “persistence time” between significant changes in direction. In chemoattractant concentration gradients, they demonstrate a biased random walk, with an “orientation bias” characterizing the fraction of cells moving up the gradient. A coherent picture of cell-movement responses to chemoattractant requires that both the persistence time and the orientation bias be explained within a unifying framework. In this paper we offer the possibility that “noise” in the cellular signal perception/response mechanism can simultaneously account for these two key phenomena. In particular, we report on a stochastic mathematical model for cell locomotion based on kinetic fluctuations in chemoattractant receptor binding. This model proves to be capable of stimulating cell paths similar to those observed experimentally for two cell types examined to date: neutrophils and alveolar macrophages, under conditions of uniform chemoattractant concentrations as well as chemoattractant concentration gradients. Further, this model can quantitatively predict both cell persistence time and dependence of orientation bias on gradient size. The model also successfully predicts that an increase in persistence time is associated with a decrease in orientation for typical system parameter values, as is observed for alveolar macrophages in comparison to neutrophils. Thus, the concept of signal “noise” can quantitatively unify the major characteristics of leukocyte random motility and chemotaxis. The same level of noise large enough to account for the observed frequency of turning in uniform environments is simultaneously small enough to allow for the observed degree of directional bias in gradients. This suggests that chemosensory cell movement behavior may be based on a “usefully” imperfect integrated signal response system, which allows both random and directed searches under appropriate conditions.     

Eradication-resolution dynamics with stochastic flare-ups

In infectious disease as well as in cancer, the ultimate outcome of the curative response, mediated by the body itself or through drug treatment, is either successful eradication or a resurgence of the disease (“flare-up” or “relapse”), depending on random fluctuations that dominate the dynamics of the system when the number of diseased cells has become very low. The presence of a low-numbers bottle-neck in the dynamics, which is unavoidable if eradication is to take place at all, renders at least one phase of the dynamics essentially stochastic. However, the eradicating agents (e.g. immune cells, drug molecules) generally remain at high numbers during the critical bottle-neck phase, sufficiently so to warrant a deterministic treatment. This leads us to consider a hybrid stochastic-deterministic approach where the infected cells are treated stochastically whereas the eradicating agents are treated deterministically. Exploiting the fact that the number of eradicating agents typically decreases monotonically during the resolution phase of the response, we derive a set of coupled first-order differential equations that describe the probability of ultimate eradication as a function of the system's state, and we consider a number of biomedical applications.     

Trivial influences: A doubly stochastic poisson process model permits the detection of arbitrarily small electromagnetic signals

If a weak, exogenous, extremely low-frequency (ELF) electric or magnetic field is to produce biological sequelae, then there must exist averaging sufficient to lift some primary effect of that field above the endogenous stochastic variations of the biological system. One way in which a field could accomplish this is by changing the intensity of some stochastic operation that controls an important and not trivially reversible biological transformation. In this paper, this operation is modeled as a doubly stochastic Poisson process. It is then shown, first, that (in theory) even a minuscule exogenous influence might appreciably shift the incidence of a sufficiently rare transformation and, second, that this shift might be observable if a trial were allowed to run long enough over a sufficiently large population of exposed entities. © 1995 Wiley-Liss, Inc.     

Enhancement of information transmission of sub-threshold signals applied to distal positions of dendritic trees in hippocampal CA1 neuron models with stochastic resonance

Stochastic resonance (SR) has been shown to enhance the signal-to-noise ratio and detection of low level signals in neurons. It is not yet clear how this effect of SR plays an important role in the information processing of neural networks. The objective of this article is to test the hypothesis that information transmission can be enhanced with SR when sub-threshold signals are applied to distal positions of the dendrites of hippocampal CA1 neuron models. In the computer simulation, random sub-threshold signals were presented repeatedly to a distal position of the main apical branch, while the homogeneous Poisson shot noise was applied as a background noise to the mid-point of a basal dendrite in the CA1 neuron model consisting of the soma with one sodium, one calcium, and five potassium channels. From spike firing times recorded at the soma, the mutual information and information rate of the spike trains were estimated. The simulation results obtained showed a typical resonance curve of SR, and that as the activity (intensity) of sub-threshold signals increased, the maximum value of the information rate tended to increased and eventually SR disappeared. It is concluded that SR can play a key role in enhancing the information transmission of sub-threshold stimuli applied to distal positions on the dendritic trees.