Biomedical subjects

John J Jeka

Publications and source records attributed to John J Jeka.

10 recordsLinked to original sources

PubMed2006

Multisensory reweighting of vision and touch is intact in healthy and fall-prone older adults.

Unexplained falls in older adults are thought to arise from subtle deficits in multiple components of the postural control system, including peripheral sensory loss and central sensory processing. One commonly proposed central sensory processing deficit is a decline in the adaptive use of changing or conflicting sensory inputs for estimating body dynamics, i.e., multisensory reweighting. We examined the assumption of impaired multisensory reweighting in healthy and fall-prone older adults using quantitative methods that have previously demonstrated reweighting in young adults. Standing subjects were exposed to simultaneous medio-lateral oscillatory visual and fingertip touch inputs at varying relative amplitudes. No group differences in overall levels of vision and touch gain were found. Both healthy and fall-prone older adults demonstrated the same pattern of adaptive gain change as healthy young adults. Like the young adults, both elderly groups displayed clear evidence of intra- and inter-sensory reweighting to both vision and touch motion stimuli. These data suggest that, for small amplitude vision and touch stimuli, the central sensory reweighting adaptation process remains intact in healthy and fall-prone older adults with sufficiently intact peripheral sensation.

Accidental Falls↗

PubMed2006

Modeling the dynamics of sensory reweighting.

Reweighting sensory information adaptively is considered critical for flexible postural control, but little is known of the time scale of the reweighting process. We analyzed the transient dynamics of sensory reweighting in a previously published nonlinear adaptive model of sensory integration in the human postural control system. The model's dynamics of adaptation were tested in response to abrupt changes in the amplitude of the motion of the visual surround. In addition to qualitatively reproducing the correct asymptotic response to such changes in visual amplitude, as previously found, the model qualitatively reproduced the asymmetric transient response elucidated in recent experiments (Oie et al. in Gait Posture 2005). In particular, the model adapts at an initially rapid rate to a switch from low to high amplitude visual motion, but at an initially slower rate upon the return to low amplitude motion. The observed temporal asymmetry has potential functional value. Rapid downweighting of a visual stimulus that suddenly increases is necessary to prevent loss of upright equilibrium. A visual stimulus that decreases in amplitude does not pose a threat to upright balance, allowing for slower upweighting without functional consequence.

Adaptation, Physiological↗

PubMed2005

Slow dynamics of postural sway are in the feedback loop.

Postural sway is considered to have two fundamental stochastic components, a slow nonoscillatory component and a faster damped-oscillatory component. The slow component has been shown to account for the majority of sway variance during quiet stance. Postural control is generally viewed as a feedback loop in which sway is detected by sensory systems and appropriate motor commands are generated to stabilize the body's orientation. Whereas the mechanistic source for the damped-oscillatory sway component is most likely feedback control of an inverted pendulum, the underlying basis for the slow component is less clear. We investigated whether the slow process was inside or outside the feedback loop by providing standing subjects with sum-of-sines visual motion. Linear stochastic models were fit to the experimental sway trajectories to determine the stochastic structure of sway as well as the transfer function from visual motion to sway. The results supported a fifth-order stochastic model, consisting of a slow process and two damped-oscillatory components. Importantly, the slow process was determined to be inside the feedback loop. This supports the hypothesis that the slow component is due to errors in state estimation because state estimation is inside the feedback loop rather than a moving reference point or an exploratory process outside the feedback loop.

Adult↗

PubMed2005

Comparing internal models of the dynamics of the visual environment.

It is well known that the human postural control system responds to motion of the visual scene, but the implicit assumptions it makes about the visual environment and what quantities, if any, it estimates about the visual environment are unknown. This study compares the behavior of four models of the human postural control system to experimental data. Three include internal models that estimate the state of the visual environment, implicitly assuming its dynamics to be that of a linear stochastic process (respectively, a random walk, a general first-order process, and a general second-order process). In each case, all of the coefficients that describe the process are estimated by an adaptive scheme based on maximum likelihood. The fourth model does not estimate the state of the visual environment. It adjusts sensory weights to minimize the mean square of the control signal without making any specific assumptions about the dynamic properties of the environmental motion. We find that both having an internal model of the visual environment and its type make a significant difference in how the postural system responds to motion of the visual scene. Notably, the second-order process model outperforms the human postural system in its response to sinusoidal stimulation. Specifically, the second-order process model can correctly identify the frequency of the stimulus and completely compensate so that the motion of the visual scene has no effect on sway. In this case the postural control system extracts the same information from the visual modality as it does when the visual scene is stationary. The fourth model that does not simulate the motion of the visual environment is the only one that reproduces the experimentally observed result that, across different frequencies of sinusoidal stimulation, the gain with respect to the stimulus drops as the amplitude of the stimulus increases but the phase remains roughly constant. Our results suggest that the human postural control system does not estimate the state of the visual environment to respond to sinusoidal stimuli.

Algorithms↗

PubMed2004

Nonlinear postural control in response to visual translation.

Recent models of human postural control have focused on the nonlinear properties inherent to fusing sensory information from multiple modalities. In general, these models are underconstrained, requiring additional experimental data to clarify the properties of such nonlinearities. Here we report an experiment suggesting that new or multiple mechanisms may be needed to capture the integration of vision into the postural control scheme. Subjects were presented with visual displays whose motion consisted of two components: a constant-amplitude, 0.2 Hz oscillation, and constant-velocity translation from left to right at velocities between 0 cm/s and 4 cm/s. Postural sway variability increased systematically with translation velocity, but remained below that observed in the eyes-closed condition, indicating that the postural control system is able to use visual information to stabilize sway even at translation velocities as high as 4 cm/s. Gain initially increased as translation velocity increased from 0 cm/s to 1 cm/s and then decreased. The changes in gain and variability provided a clear indication of nonlinearity in the postural response across conditions, which were interpreted in terms of sensory reweighting. The fact that gain did not decrease at low translation velocities suggests that the postural control system is able to decompose relative visual motion into environmental motion and self-motion. The eventual decrease in gain suggests that nonlinearities in sensory noise levels (state-dependent noise) may also contribute to the sensory reweighting involved in postural control. These results provide important constraints and suggest that multiple mechanisms may be required to model the nonlinearities involved in sensory fusion for upright stance control.

Adult↗

PubMed2003

Postural control in children. Coupling to dynamic somatosensory information.

The purpose of this investigation was to determine whether the coupling between dynamic somatosensory information and body sway is similar in children and adults. Thirty children (4-, 6-, and 8-year-olds) and 10 adults stood upright, with feet parallel, and lightly contacting the fingertip to a rigid metal plate that moved rhythmically at 0.2, 0.5, and 0.8 Hz. Light touch to the moving contact surface induced postural sway in all participants. The somatosensory stimulus produced a broadband frequency response in children, while the adult response was primarily at the driving frequency. Gain, as a function of frequency, was qualitatively the same in children and adults. Phase decreased less in 4-year-olds than other age groups, suggesting a weaker coupling to position information in the sensory stimulus. Postural sway variability was larger in children than adults. These findings suggest that, even as young as age 6, children show well-developed coupling to the sensory stimulus. However, unlike adults, this coupling is not well focused at the frequency specified by the somatosensory signal. Children may be unable to uncouple from sensory information that is less relevant to the task, resulting in a broadband response in their frequency spectrum. Moreover, higher sway variability may not result from the sensory feedback process, but rather from the children's underdeveloped ability to estimate an internal model of body orientation.

Adult↗

PubMed2002

Limited control strategies with the loss of vestibular function.

When subjects stand on an unstable or compliant support surface, rather than a stable one, vestibular information becomes more important for the control of posture. We investigated how subjects with bilateral vestibular loss (BVL) controlled their upright posture, with and without light-touch contact at the fingertip, while standing on a support surface, sinusoidally rotating at different frequencies. Subjects stood with eyes closed on a platform that rotated +/-1.2 degrees around an axis directly beneath the midline of the ankle for frequencies ranging from 0.01 to 0.4 Hz for two sensory conditions: (1) with light, nonsupportive touch (less than 1 N vertical force) on a stationary surface; or (2) with the fingertip held in a position directly above the contact surface (no contact). Gain, phase, and variability of the center of mass (CoM) and the finger were analyzed to compare BVL subjects with healthy controls in the no-touch and light-touch conditions. Three important results were observed: First, CoM gain and variability of BVL subjects was distinctly higher than control subjects with no-touch contact, particularly at the higher platform frequencies. Second, with light-touch contact, BVL and control subjects showed equivalent gain, variability, and phase. Third, multiple relationships between the finger and the CoM were observed in control subjects, whereas BVL subjects implemented a single finger/CoM control scheme. The results are explained in terms of three interacting factors: the transfer function of the vestibular system, a sensory reweighting mechanism, and the inertial properties of the body. Moreover, multiple control strategies observed in control subjects suggest a more flexible control system than that of individuals with severely diminished vestibular function.

Aged↗

PubMed2002

Behavioral and electrocortical evidence of an interaction between probability and task metrics in movement preparation.

Recent neurophysiological evidence suggests that cognitive factors shape neural activity in cortical areas such as parietal (area 5), premotor, and primary motor cortex. The implication of these findings is that behavioral signatures of cognitive factors and movement-specific factors should likewise be interdependent. The present study provides evidence of this interdependence in both behavioral (reaction time) and electrophysiological (P300) measures. Subjects performed a two-choice pointing task, in which the angular distance between the two required movement directions (task metrics) and the probability of the two responses was varied. In a control condition, a single reaction was required in response to both stimuli to test for the influence of stimulus metrics. Results from the pointing task showed a clear interaction between the metrics and effects of probability. When the potential targets were widely separated (120 degrees), stimulus probability influences reaction time and P300 amplitude in the classic fashion (longer reaction times and larger P300 amplitudes to less probable responses). When pointing to targets that were narrowly separated (20 degrees), probability had no effect: both rare and frequent targets were "functionally frequent." The same interaction was not observed in the control condition, indicating that metrics were primarily influencing movement preparation rather than stimulus processing. The results are consistent with the theoretical framework of dynamic field theory and demonstrate that metrics are an important factor that must be taken into account when assessing the processes associated with movement preparation.

Adolescent↗

PubMed2002

Multisensory fusion and the stochastic structure of postural sway.

We analyze the stochastic structure of postural sway and demonstrate that this structure imposes important constraints on models of postural control. Linear stochastic models of various orders were fit to the center-of-mass trajectories of subjects during quiet stance in four sensory conditions: (i) light touch and vision, (ii) light touch, (iii) vision, and (iv) neither touch nor vision. For each subject and condition, the model of appropriate order was determined, and this model was characterized by the eigenvalues and coefficients of its autocovariance function. In most cases, postural-sway trajectories were similar to those produced by a third-order model with eigenvalues corresponding to a slow first-order decay plus a faster-decaying damped oscillation. The slow-decay fraction, which we define as the slow-decay autocovariance coefficient divided by the total variance, was usually near 1. We compare the stochastic structure of our data to two linear control-theory models: (i) a proportional-integral-derivative control model in which the postural system's state is assumed to be known, and (ii) an optimal-control model in which the system's state is estimated based on noisy multisensory information using a Kalman filter. Under certain assumptions, both models have eigenvalues consistent with our results. However, the slow-decay fraction predicted by both models is less than we observe. We show that our results are more consistent with a modification of the optimal-control model in which noise is added to the computations performed by the state estimator. This modified model has a slow-decay fraction near 1 in a parameter regime in which sensory information related to the body's velocity is more accurate than sensory information related to position and acceleration. These findings suggest that: (i) computation noise is responsible for much of the variance observed in postural sway, and (ii) the postural control system under the conditions tested resides in the regime of accurate velocity information.

Adult↗

PubMed2002

Multisensory fusion: simultaneous re-weighting of vision and touch for the control of human posture.

We examined the generally held belief that the postural control system is able to re-weight its available sensory inputs in order to optimize stance control in altered sensory environments. Our view is that previous accounts of sensory re-weighting provide only indirect evidence, which is subject to alternative explanations. The present results provide strong evidence for sensory re-weighting as the primary mechanism for changes observed in postural sway between conditions. Subjects were presented with small-amplitude, oscillatory visual and somatosensory stimuli at 0.20 and 0.28 Hz, respectively, in five conditions that manipulated the amplitudes of stimulus motion. Gain calculated in each trial with respect to each of the two stimuli was found to change systematically as stimulus motion amplitudes changed across condition. The observed pattern of gain rules out a constant-weight, linear account of posture and is consistent with the re-weighting hypothesis. Parameter fits of a third-order, linear stochastic model to postural sway trajectories in each condition showed that changes in gain across condition were primarily due to changes in coupling coefficients rather than changes in parameters that characterize the stability of the postural system. Visual gain was found to depend upon visual motion amplitude and touch gain was found to depend upon touch motion amplitude, indicating intra-modality dependencies. Visual gain also depended upon touch motion amplitude, indicating an inter-modality dependence. To our knowledge, simultaneous re-weighting of more than one sensory input has never been rigorously demonstrated. These techniques may be able to resolve the source of balance control deficits across populations with far more certainty than currently possible.

Adult↗