Solute diffusion is hindered in the mitochondrial matrix.

Intracellular chemical reactions generally constitute reaction-diffusion systems located inside nanostructured compartments like the cytosol, nucleus, endoplasmic reticulum, Golgi, and mitochondrion. Understanding the properties of such systems requires quantitative information about solute diffusion. Here we present a novel approach that allows determination of the solvent-dependent solute diffusion constant (D(solvent)) inside cell compartments with an experimentally quantifiable nanostructure. In essence, our method consists of the matching of synthetic fluorescence recovery after photobleaching (FRAP) curves, generated by a mathematical model with a realistic nanostructure, and experimental FRAP data. As a proof of principle, we assessed D(solvent) of a monomeric fluorescent protein (AcGFP1) and its tandem fusion (AcGFP1(2)) in the mitochondrial matrix of HEK293 cells. Our results demonstrate that diffusion of both proteins is substantially slowed by barriers in the mitochondrial matrix (cristae), suggesting that cells can control the dynamics of biochemical reactions in this compartment by modifying its nanostructure.

Amplitude modulation of nuclear Ca2+ signals in human skeletal myotubes: a possible role for nuclear Ca2+ buffering.

Video-rate confocal microscopy of Indo-1-loaded human skeletal myotubes was used to assess the relationship between the changes in sarcoplasmic ([Ca(2+)](S)) and nuclear ([Ca(2+)](N)) Ca(2+) concentration during low- and high-frequency electrostimulation. A single stimulus of 10 ms duration transiently increased [Ca(2+)] in both compartments with the same time of onset. Rate and amplitude of the [Ca(2+)] rise were significantly lower in the nucleus (4.0- and 2.5-fold, respectively). Similarly, [Ca(2+)](N) decayed more slowly than [Ca(2+)](S) (mono-exponential time constants of 6.1 and 2.5 s, respectively). After return of [Ca(2+)] to the prestimulatory level, a train of 10 stimuli was applied at a frequency of 1 Hz. The amplitude of the first [Ca(2+)](S) transient was 25% lower than that of the preceding single transient. Thereafter, [Ca(2+)](S) increased stepwise to a maximum that equalled that of the single transient. Similarly, the amplitude of the first [Ca(2+)](N) transient was 20% lower than that of the preceding single transient. In contrast to [Ca(2+)](S), [Ca(2+)](N) then increased to a maximum that was 2.3-fold higher than that of the single transient and equalled that of [Ca(2+)](S). In the nucleus, and to a lesser extent in the sarcoplasm, [Ca(2+)] decreased faster at the end of the stimulus train than after the preceding single stimulus (time constants of 3.3 and 2.1 s, respectively). To gain insight into the molecular principles underlying the shaping of the nuclear Ca(2+) signal, a 3-D mathematical model was constructed. Intriguingly, quantitative modelling required the inclusion of a satiable nuclear Ca(2+) buffer. Alterations in the concentration of this putative buffer had dramatic effects on the kinetics of the nuclear Ca(2+) signal. This finding unveils a possible mechanism by which the skeletal muscle can adapt to changes in physiological demand.

Movement parameters that distinguish between voluntary movements and levodopa-induced dyskinesia in Parkinson's disease.

It is well known that long-term use of levodopa by patients with Parkinson's disease causes dyskinesia. Several methods have been proposed for the automatic, unsupervised detection and classification of levodopa induced dyskinesia. Recently, we have demonstrated that neural networks are highly successful to detect dyskinesia and to distinguish dyskinesia from voluntary movements. The aim of this study was to use the trained neural networks to extract parameters, which are important to distinguish between dyskinesia and voluntary movements. Thirteen patients were continuously monitored in a home-like situation performing in about 35 daily life tasks for a period of approximately 2.5 h. Behavior of the patients was measured using triaxial accelerometers, which were placed at six different positions of the body. A neural network was trained to assess the severity of dyskinesia. The neural network was able to assess the severity of dyskinesia and could distinguish dyskinesia from voluntary movements in daily life. For the trunk and the leg, the important parameters appeared to be the percentage of time that the trunk or leg was moving and the standard deviation of the segment velocity of the less dyskinetic leg. For the arm, the combination of the percentage of time, that the wrist was moving, and the percentage of time, that a patient was sitting, explained the largest part of the variance of the output. Dyskinesia differs from voluntary movements in the fact that dyskinetic movements tend to have lower frequencies than voluntary movements and in the fact that movements of different body segments are not well coordinated in dyskinesia.

Input-driven oscillations in networks with excitatory and inhibitory neurons with dynamic synapses.

Previous work has shown that networks of neurons with two coupled layers of excitatory and inhibitory neurons can reveal oscillatory activity. For example, Börgers and Kopell (2003) have shown that oscillations occur when the excitatory neurons receive a sufficiently large input. A constant drive to the excitatory neurons is sufficient for oscillatory activity. Other studies (Doiron, Chacron, Maler, Longtin, & Bastian, 2003; Doiron, Lindner, Longtin, Maler, & Bastian, 2004) have shown that networks of neurons with two coupled layers of excitatory and inhibitory neurons reveal oscillatory activity only if the excitatory neurons receive correlated input, regardless of the amount of excitatory input. In this study, we show that these apparently contradictory results can be explained by the behavior of a single model operating in different regimes of parameter space. Moreover, we show that adding dynamic synapses in the inhibitory feedback loop provides a robust network behavior over a broad range of stimulus intensities, contrary to that of previous models. A remarkable property of the introduction of dynamic synapses is that the activity of the network reveals synchronized oscillatory components in the case of correlated input, but also reflects the temporal behavior of the input signal to the excitatory neurons. This allows the network to encode both the temporal characteristics of the input and the presence of spatial correlations in the input simultaneously.

Ambulatory motor assessment in Parkinson's disease.

We developed an algorithm that distinguishes between on and off states in patients with Parkinson's disease during daily life activities. Twenty-three patients were monitored continuously in a home-like situation for approximately 3 hours while they carried out normal daily-life activities. Behavior and comments of patients during the experiment were used to determine the on and off periods by a trained observer. Behavior of the patients was measured using triaxial accelerometers, which were placed at six different positions on the body. Parameters related to hypokinesia (percentage movement), bradykinesia (mean velocity), and tremor (percentage peak frequencies above 4 Hz) were used to distinguish between on and off states. The on-off detection was evaluated using sensitivity and specificity. The performance for each patient was defined as the average of the sensitivity and specificity. The best performance to classify on and off states was obtained by analysis of movements in the frequency domain with a sensitivity of 0.97 and a specificity of 0.97. We conclude that our algorithm can distinguish between on and off states with a sensitivity and specificity near 0.97. This method, together with our previously published method to detect levodopa-induced dyskinesia, can automatically assess the motor state of Parkinson's disease patients and can operate successfully in unsupervised ambulatory conditions.

The structure of frontoparallel haptic space is task dependent.

In three experiments, we investigated the structure of frontoparallel haptic space. In the first experiment, we asked blindfolded participants to rotate a matching bar so that it felt parallel to the reference bar, the bars could be at various positions in the frontoparallel plane. Large systematic errors were observed, in which orientations that were perceived to be parallel were not physically parallel. In two subsequent experiments, we investigated the origin of these errors. In Experiment 2, we asked participants to verbally report the orientation of haptically presented bars. In this task, participants made errors that were considerably smaller than those made in Experiment 1. In Experiment 3, we asked participants to set bars in a verbally instructed orientation, and they also made errors significantly smaller than those observed in Experiment 1. The data suggest that the errors in the matching task originate from the transfer of the reference orientation to the matching-bar position.

An evaluation of the minimum-jerk and minimum torque-change principles at the path, trajectory, and movement-cost levels.

In the present study we evaluated the minimum jerk and the minimum torque-change model at the path, trajectory, and movement-cost levels. To date, most evaluations of these models have mainly been restricted to path comparisons. Assessments of the time courses of realized jerk and torque changes are surprisingly lacking. Moreover, the extent to which the presumed optimized parameters change as a function of the duration and other temporal features of aiming movements has never been investigated, most probably because the models presuppose movement time. In order to fill this gap, we analyzed a subset of the data of an earlier experiment in which 12 participants performed leftward and rightward planar pointing movements. Hand displacements and joint excursions were recorded with a 3D motion-tracking system and subsequently evaluated by means of model-based analyses. The results show that despite a good agreement between observed paths and predicted paths, especially by the minimum torque-change model, the time courses of jerk and torque changes of observed and modeled movements differed considerably. These differences could mainly be attributed to asymmetrical properties of the time functions of slow movements. Variations of movement costs as a function of movement time and skewness of tangential velocity profiles show that, especially at high movement speed, costs increase exponentially with departures of symmetry. It is concluded that trajectory-formation models have limited explanatory power in situations that require demanding information processing during the homing-in phase of goal-directed movements. However, for slow movements, deviations from the optimal timing profiles require little extra costs in terms of jerk or torque change.

Modeling kinematics and dynamics of human arm movements.

A central problem in motor control relates to the coordination of the arm's many degrees of freedom. This problem concerns the many arm postures (kinematics) that correspond to the same hand position in space and the movement trajectories between begin and end position (dynamics) that result in the same arm postures. The aim of this study was to compare the predictions for arm kinematics by various models on human motor control with experimental data and to study the relation between kinematics and dynamics. Goal-directed arm movements were measured in 3-D space toward far and near targets. The results demonstrate that arm postures for a particular target depend on previous arm postures, contradicting Donders's law. The minimum-work and minimum-torque-change models, on the other hand, predict a much larger effect of initial posture than observed. These data suggest that both kinematics and dynamics affect postures and that their relative contribution might depend on instruction and task complexity.

Automatic assessment of levodopa-induced dyskinesias in daily life by neural networks.

We developed an objective and automatic procedure to assess the severity of levodopa-induced dyskinesia (LID) in patients with Parkinson's disease during daily life activities. Thirteen patients were continuously monitored in a home-like situation for a period of approximately 2.5 hours. During this time period, the patients performed approximately 35 functional daily life activities. Behavior of the patients was measured using triaxial accelerometers, which were placed at six different positions on the body. A neural network was trained to assess the severity of LID using various variables of the accelerometer signals. Neural network scores were compared with the assessment by physicians, who evaluated the continuously videotaped behavior of the patients off-line. The neural network correctly classified dyskinesia or the absence of dyskinesia in 15-minute intervals in 93.7, 99.7, and 97.0% for the arm, trunk, and leg, respectively. In the few cases of misclassification, the rating by the neural network was in the class next to that indicated by the physicians using the AIMS score (scale 0-4). Analysis of the neural networks revealed several new variables, which are relevant for assessing the severity of LID. The results indicate that the neural network can accurately assess the severity of LID and could distinguish LID from voluntary movements in daily life situations.

Associative memory with dynamic synapses.

We have examined a role of dynamic synapses in the stochastic Hopfield-like network behavior. Our results demonstrate an appearance of a novel phase characterized by quick transitions from one memory state to another. The network is able to retrieve memorized patterns corresponding to classical ferromagnetic states but switches between memorized patterns with an intermittent type of behavior. This phenomenon might reflect the flexibility of real neural systems and their readiness to receive and respond to novel and changing external stimuli.