scholarly journals FINGER TREMOR IN TABETIC PATIENTS AND ITS BEARING ON THE MECHANISM PRODUCING THE RHYTHM OF PHYSIOLOGICAL TREMOR

1958 ◽  
Vol 21 (2) ◽  
pp. 101-108 ◽  
Author(s):  
A. M. Halliday ◽  
J. W. T. Redfearn
2015 ◽  
Vol 113 (2) ◽  
pp. 647-656 ◽  
Author(s):  
Carlijn Andrea Vernooij ◽  
Martin Lakie ◽  
Raymond Francis Reynolds

Two frequency peaks of variable preponderance have been reported for human physiological finger tremor. The high-frequency peak (20–25 Hz, seen only in postural tremor) is generally attributed to mechanical resonance, whereas the lower frequency peak (8–12 Hz, seen in both postural and kinetic tremor) is usually attributed to synchronous central or reflexive neural drive. In this study, we determine whether mechanical resonance could generate both peaks. In relaxed subjects, an artificial finger tremor was evoked by random mechanical perturbations of the middle finger or random electrical muscular stimulation of the finger extensor muscle. The high and the low frequencies observed in physiological tremor could both be created by either type of artificial input at appropriate input intensity. Resonance, inferred from cross-spectral gain and phase, occurred at both frequencies. To determine any neural contribution, we compared truly passive subjects with those who exhibited some electromyographic (EMG) activity in the finger extensor; artificially created tremor spectra were almost identical between groups. We also applied electrical stimuli to two clinically deafferented subjects lacking stretch reflexes. They exhibited the same artificial tremor spectrum as control subjects. These results suggest that both typical physiological finger tremor frequencies can be reproduced by random artificial input; neither requires synchronized neural input. We therefore suggest that mechanical resonance could generate both dominant frequency peaks characteristic of physiological finger tremor. The inverse relationship between the input intensity and the resulting tremor frequency can be explained by a movement-dependent reduction in muscle stiffness, a conjecture we support using a simple computational model.


1995 ◽  
Vol 14 (1) ◽  
pp. 37-47
Author(s):  
Tiejun Miao ◽  
Kazuyoshi Sakamoto
Keyword(s):  

1998 ◽  
Vol 08 (07) ◽  
pp. 1505-1516 ◽  
Author(s):  
J. Timmer

Empirical time series often contain observational noise. We investigate the effect of this noise on the estimated parameters of models fitted to the data. For data of physiological tremor, i.e. a small amplitude oscillation of the outstretched hand of healthy subjects, we compare the results for a linear model that explicitly includes additional observational noise to one that ignores this noise. We discuss problems and possible solutions for nonlinear deterministic as well as nonlinear stochastic processes. Especially we discuss the state space model applicable for modeling noisy stochastic systems and Bock's algorithm capable for modeling noisy deterministic systems.


1978 ◽  
Vol 41 (3) ◽  
pp. 557-571 ◽  
Author(s):  
J. H. Allum ◽  
V. Dietz ◽  
H. J. Freund

1. Tremor force was recorded during stationary isometric contractions of intrinsic hand muscles of normal subjects. Subjects maintained a steady force level between their thumb and forefinger for 30 s. The force level varied from weak (0.2 kg) to strong contractions (7 kg). These experimental conditions were the same as those in two preceding studies, where single motor-unit activity (14) and the correlation between the discharges of two simultaneously recorded motor units and physiological tremor (11) have been investigated. 2. Two alterations of the power spectra were observed at successively stronger contractions: increase of tremor amplitude and changes in the shape of the power spectrum. At all force levels, the power spectra of tremor force show the well-known decay of tremor amplitude from the lower to the higher frequencies with a local peak at 6--10 Hz. This peak does not show a significant change with respect to frequency when the force level is varied. It is shifted toward lower frequencies in a pathological condition (Parkinsonism) where the recruitment firing rates of the motor units are significantly lower than in the normal. 3. Higher frequencies (greater than 20 Hz) are barely present in the power spectrum during the very weak contractions. They become significant as the contractions become stronger. 4. The steep decay of the power spectrum toward higher frequencies has a similar slope (--43 dB/decade) as the reduction in amplitude of the unfused part of the muscle contractions with increasing stimulus rates (--38 dB/decade). The cutoff of the power spectrum above 25 Hz parallels the achievement of total fusion of muscle twitches above this rate. 5. The results are consistent with the hypothesis that the power spectrum over the range of 6--25 Hz is mainly caused by the unfused parts of the twitch contractions of motor units firing between recruitment (6--8/s) and total fusion of the twitches (25--30/s). The decline of the power spectrum toward higher frequencies can be explained by mechanical damping, which results from increasing fusion of the twitch contractions. The low-frequency part of the power spectrum is assumed to be the result of the slow force deviations produced by changes in the net output of the motoneuron pool. 6. These assumptions were supported by additional animal experiments where the number and rate of force-producing elements could be controlled. Bundles of ventral root filaments innervating cat soleus and gastrocnemius muscles were stimulated synchronously and asynchronously at a number of different rates. The force output of the strain gauge was recorded, filtered, and analyzed in the same way as the human force records. 7. Stimualtion of one nerve bundle at one fixed frequency led to a sharp peak in the power spectrum at that frequency plus peaks of decreasing height representing the harmonics of the stimulation frequency. The height of the peaks decreased at --37 dB/decade. 8...


1992 ◽  
Vol 28 (Supplement) ◽  
pp. 182-183
Author(s):  
Kazuyoshi Sakamoto ◽  
Li Zhou ◽  
Kazuo Matsuura ◽  
Naoaki Itakura ◽  
Satoshi Hanba
Keyword(s):  

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