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Biomedical subjects

S A Werness

Publications and source records attributed to S A Werness.

8 recordsLinked to original sources
PubMed1994

Short-term effects of smoking marijuana on balance in patients with multiple sclerosis and normal volunteers.

A double-blind randomized placebo-controlled study of inhaled marijuana smoke on postural responses was performed in 10 adult patients with spastic multiple sclerosis (MS) and 10 normal volunteers matched as closely as possible for age, sex, and weight. A computer-controlled dynamic posturographic platform with a video line scan camera measured shoulder displacement in response to pseudorandom platform movements. Premarijuana smoking patient tracking was inferior to that of the normal volunteers as indicated by the higher noise variance of the former. Smoking one marijuana cigarette containing 1.54% delta 9-tetrahydrocannabinol increased postural tracking error in both the patients and normal control subjects with both eyes open and closed; this untoward effect was greatest for the patients. The tracking error was also accompanied by a decrease in response speed for the patients with their eyes closed. Marijuana smoking further impairs posture and balance in patients with spastic MS.

Adult↗
PubMed1993

Associative learning in a network model of Hermissenda crassicornis. II. Experiments.

A companion paper in a previous issue of this journal presented a resistance-capacitance circuit computer model of the four-neuron visual-vestibular network of the invertebrate marine mollusk Hermissenda crassicornis. In the present paper, we demonstrate that changes in the model's output in response to simulated associative training is quantitatively similar to behavioral and electrophysiological changes in response to associative training of Hermissenda crassicornis. Specifically, the model demonstrates many characteristics of conditioning: sensitivity to stimulus contingency, stimulus specificity, extinction, and savings. The model's learning features also are shown to be devoid of non-associative components. Thus, this computational model is an excellent tool for examining the information flow and dynamics of biological associative learning and for uncovering insights concerning associative learning, memory, and recall that can be applied to the development of artificial neural networks.

Animals↗
PubMed1992

Associative learning in a network model of Hermissenda crassicornis. I. Theory.

A time-varying Resistance-Capacitance (RC) circuit computer model was constructed based on known membrane and synaptic properties of the visual-vestibular network of the marine snail Hermissenda crassicornis. Specific biophysical properties and synaptic connections of identified neurons are represented as lumped parameters (circuit elements) in the model; in the computer simulation, differential equations are approximated by difference equations. The model's output, membrane potential, an indirect measure of firing frequency, closely parallels the behavioral and electrophysiologic outputs of Hermissenda in response to the same input stimuli presented during and after associative learning. The parallelism of the computer modeled and the biologic outputs suggests that the model captures the features necessary and sufficient for associative learning.

Animals↗
PubMed1986

Dynamic posture analysis of Spacelab-1 crew members.

Dynamic posture testing was conducted on the science crew of the Spacelab-1 mission on a single axis linear motion platform. Tests took place in pre- and post-flight sessions lasting approximately 20 min each. The pre-flight tests were widely spaced over the several months prior to the mission while the post-flight tests were conducted over the first, second, fourth, and sixth days after landing. Two of the crew members were also tested on the day of landing. Consistent with previous postural testing conducted on flight crews, these crew members were able to complete simple postural tasks to an acceptable level even in the first few hours after landing. Our tests were designed to induce dynamic postural responses using a variety of stimuli and from these responses, evaluate subtle changes in the postural control system which had occurred over the duration of the flight. Periodic sampling post-flight allowed us to observe the time course of readaptation to terrestrial life. Our observations of hip and shoulder position, when subjected to careful analysis, indicated modification of the postural response from pre- to post-flight and that demonstrable adjustments in the dynamic control of their postural systems were taking place in the first few days after flight. For transient stimuli where the platform on which they were asked to stand quickly moved a few centimeters fore or aft then stopped, ballistic or open loop 'programs' would closely characterize the response. During these responses the desired target position was not always achieved and of equal importance not always properly corrected some 15 seconds after the platform ceased to move. The persistent observation was that the subjects had a much stronger dependence on visual stabilization post-flight than pre-flight. This was best illustrated by a slow or only partial recovery to an upward posture after a transient base-of-support movement with eyes open. Postural responses to persistent wideband pseudorandom base-of-support translation were modeled as time invarient linear systems arrived at by Kalman adaptive filter techniques. Derived model parameters such as damping factor and fundamental frequency of the closed loop system showed significant modification between pre- and post-flight. This phenomenon is best characterized by movement of the poles toward increasing stability. While pre-flight data tended to show shoulders and hips moving in phase with each other, post-flight data showed a more disjoint behavior.(ABSTRACT TRUNCATED AT 400 WORDS)

Adaptation, Physiological↗
PubMed1984

Parametric analysis of dynamic postural responses.

A detailed theoretical understanding of postural control mechanisms must be preceded by careful quantification of both the deterministic and stochastic aspects of postural behavior of normal and abnormal subjects under various dynamic conditions. Toward this end, concise parametric transfer function plus noise models were derived for both shoulder and waist position data obtained by applying a linear anterior-posterior bandlimited pseudorandom disturbance to the base of support of human subjects. Model orders as well as model parameters were determined empirically. One advantage of this modeling procedure is the conciseness of the postural models, permitting easy statistical analysis of the data obtained under different dynamic conditions from many subjects. Model features, including pole and zero locations, from 6 normal subjects each tested on 5 consecutive days under 3 input amplitudes and eyes open and closed conditions are presented. The resulting transfer function models consist of only 1 or 2 poles near the integration position on the Z plane unit circle and 0 to 2 zeros. Locations of the poles indicate that the eyes closed responses are more oscillatory, less damped, and with higher gains than the eyes open responses. These transfer functions are similar to nonparametric ones of other authors. The noise model orders are also small. Their spectra are those of low pass systems. Also, the quantity and frequency range of the postural noise is positively related to the amplitude of platform motion as well as related to the presence or absence of vision.

Adult↗
PubMedDate not supplied

A computer program for linear nonparametric and parametric identification of biological data.

A computer program package for parametric ad nonparametric linear system identification of both static and dynamic biological data, written for an LSI-11 minicomputer with 28 K of memory, is described. The program has 11 possible commands including an instructional help command. A user can perform nonparametric spectral analysis and estimation of autocorrelation and partial autocorrelation functions of univariate data and estimate nonparametrically the transfer function and possibly an associated noise series of bivariate data. In addition, the commands provide the user the means to derive a parametric autoregressive moving average model for univariate data, to derive a parametric transfer function and noise model for bivariate data, and to perform several model evaluation tests such as pole-zero cancellation, examination of residual whiteness and uncorrelatedness with the input. The program, consisting of a main program and driver subroutine as well as six overlay segments, may be run interactively or automatically.

Computers↗