Biomedical subjects

Shunsuke Sato

Publications and source records attributed to Shunsuke Sato.

6 recordsLinked to original sources

PubMed2003

Classifying lower limb dynamics in Parkinson's disease.

To classify lower limb dynamics in patients with Parkinson's disease (PD), we conducted a clinical study by using pedaling exercise.Twenty-seven patients with idiopathic PD were included in this study. We measured rotational velocities of pedals during pedaling movements with a newly developed ergometer. The velocity waveforms exhibited different characteristics among patients, which could be categorized into four different clusters. In cluster 1, the amplitude on each side was constant and the relative phase was locked at 180 degrees. The pattern was the same as seen in normal subjects. In cluster 2, the amplitude on each side was constant, but the relative phase was locked at 90 degrees. In cluster 3, the amplitude on each side was modulated, and the relative phase drifted monotonously from 0 to 360 degrees during pedaling cycles. In cluster 4, the amplitude on each side was synchronously and irregularly modulated, and the relative phase fluctuated with intermittent spike-like decrement. In order to evaluate, the correlation between pattern and severity of PD, we divided 13 patients, who underwent measurement of pedaling patterns more than three times, into three groups, and found that the abnormal coordination pattern correlated with the presence of freezing phenomenon in patients with PD. Our clinical analysis may contribute in analyzing and classifying the dynamics of PD.

Adult↗

PubMed2003

The role of the human supplementary motor area in reactive motor operation.

The role of the supplementary motor area (SMA) in reactive motor operation was investigated with functional magnetic resonance imaging in 13 normal subjects. A visual cue was presented at a regular (1 Hz) or irregular (mean, 1 Hz) rate, and the subject pressed a button with the right index finger in a predictive or reactive manner. Brain regions associated with reactive movement were detected by comparing reactive with predictive movement tasks, and those with irregular movement by comparing irregular and regular cueing tasks. During regular cueing, the SMA showed greater activation for reactive than predictive movement. However, the SMA was equally activated between regular and irregular cueing once the subject reacted to the cue. The SMA is likely involved in reactive adjustment of movement to the external cue.

Adult↗

PubMed2003

A coupled oscillator model of disordered interlimb coordination in patients with Parkinson's disease.

Coordination between the left and right limbs during cyclic movements, which can be characterized by the amplitude of each limb's oscillatory movement and relative phase, is impaired in patients with Parkinson's disease (PD). A pedaling exercise on an ergometer in a recent clinical study revealed several types of coordination disorder in PD patients. These include an irregular and burst-like amplitude modulation with intermittent changes in its relative phase, a typical sign of chaotic behavior in nonlinear dynamical systems. This clinical observation leads us to hypothesize that emergence of the rhythmic motor behaviors might be concerned with nonlinearity of an underlying dynamical system. In order to gain insight into this hypothesis, we consider a simple hard-wired central pattern generator model consisting of two identical oscillators connected by reciprocal inhibition. In the model, each oscillator acts as a neural half-center controlling movement of a single limb, either left or right, and receives a control input modeling a flow of descending signals from higher motor centers. When these two control inputs are tonic-constant and identical, the model has left-right symmetry and basically exhibits ordered coordination with an alternating periodic oscillation. We show that, depending on the intensities of these two control inputs and on the difference between them that introduces asymmetry into the model, the model can reproduce several behaviors observed in the clinical study. Bifurcation analysis of the model clarifies two possible mechanisms for the generation of disordered coordination in the model: one is the spontaneous symmetry-breaking bifurcation in the model with the left-right symmetry. The other is related to the degree of asymmetry reflecting the difference between the two control inputs. Finally, clinical implications by the model's dynamics are briefly discussed.

Ataxia↗

PubMed2003

Possible functional roles of phase resetting during walking.

The walking rhythm is known to show phase shift or "reset" in response to external impulsive perturbations. We tried to elucidate functional roles of the phase reset possibly used for the neural control of locomotion. To this end, a system with a double pendulum as a simplified model of the locomotor control and a model of bipedal locomotion were employed and analyzed in detail. In these models, a movement corresponding to the normal steady-state walking was realized as a stable limit cycle solution of the system. Unexpected external perturbations applied to the system can push the state point of the system away from its limit cycle, either outside or inside the basin of attraction of the limit cycle. Our mathematical analyses of the models suggested functional roles of the phase reset during walking as follows. Function 1: an appropriate amount of the phase reset for a given perturbation can contribute to relocating the system's state point outside the basin of attraction of the limit cycle back to the inside. Function 2: it can also be useful to reduce the convergence time (the time necessary for the state point to return to the limit cycle). In experimental studies during walking of animals and humans, the reset of walking rhythm induced by perturbations was investigated using the phase transition curve (PTC) or the phase resetting curve (PRC) representing phase-dependent responses of the walking. We showed, for the simple double-pendulum model, the existence of the optimal phase control and the corresponding PTC that could optimally realize the aforementioned functions in response to impulsive force perturbations. Moreover, possible forms of PRC that can avoid falling against the force perturbations were predicted by the biped model, and they were compared with the experimentally observed PRC during human walking. Finally, physiological implications of the results were discussed.

Models, Neurological↗

PubMed2002

MEG responses during rhythmic finger tapping in humans to phasic stimulation and their interpretation based on neural mechanisms.

The phase-resetting experiment was applied to human periodic finger tapping to understand how its rhythm is controlled by the internal neural clock that is assumed to exist. In the experiment, the right periodic tapping movement was disturbed transiently by a series of left finger taps in response to impulsive auditory cues presented randomly at various phases within the tapping cycle. After each left finger tap, the original periodic tapping was reestablished within several tapping cycles. Influences of the disturbance on the periodic right finger tapping varied depending on the phase of the periodic right finger tapping at which each left finger tap was made. It was confirmed that the periodic tapping was disturbed not by the auditory cues but by the left finger taps. Based on this fact, in this paper each single left tap was considered as the stimulus, and the phase of the periodic tapping of the right index finger when the left tap was executed as the phase of the stimulus. Responses of the neural activities (magnetoencephalography, MEG), the tapping movement, and the corresponding muscle activities (electromyography) were simultaneously measured. Phase-resetting curves (PRCs) representing the degree of phase reset as a function of the phase of the stimulus were obtained both for the left sensorimotor cortex MEG response and for the right index finger tapping response. The shapes of both PRCs were similar, suggesting that the phase reset of the left sensorimotor cortex activities and that of the finger tapping rhythm were the same. Four out of eight subjects showed type-0 reset in Winfree's definition, and the others showed type-1 reset. For general limit-cycle oscillators, type-0 reset is obtained for relatively strong perturbations and type 1 for weak perturbations. It was shown that the transient response of MEG to the single left tap stimuli in type-0 subjects, where the phase was progressively reset, were different from those in type-1 subjects. Based on detailed analysis of the differences, a neural network model for the phase reset of the tapping rhythm is proposed.

Acoustic Stimulation↗

PubMed2002

Algorithm for vector autoregressive model parameter estimation using an orthogonalization procedure.

We review the derivation of the fast orthogonal search algorithm, first proposed by Korenberg, with emphasis on its application to the problem of estimating coefficient matrices of vector autoregressive models. New aspects of the algorithm not previously considered are examined. One of these is the application of the algorithm to estimate coefficient matrices of a vector autoregressive process with time-varying coefficients when multiple realizations of the said process are available. Computer simulations were also performed to characterize the statistical properties of the estimates. The results show that even for shorter time series the algorithm works well and obtains good estimates of the time-varying parameters. Statistical characterization indicates that the standard deviation of the estimates decreases as 1 square root N (N being the length of the time series), a typical behavior of least-squares estimators. Another key aspect of the approach, which has previously been considered, is its direct extension to the parameter estimation of vector nonlinear autoregressive models. Nonlinear terms can be added to the model and the same algorithm can be applied to effectively estimate their associated parameters. Using chaotic time series generated from the Lorenz equations, the algorithm produces a model that captures the nonlinear structure of the data and exhibits the same chaotic attractor as that of the original system.

Algorithms↗