Effects of reflex delays on postural control during unstable seated balance

Patients with low-back pain (LBP) exhibit longer trunk muscle reflex latencies and poorer postural control than healthy individuals. We hypothesized that balance during a simulated postural control task would become impaired when the delays exhibited by LBP patients were incorporated into neuromuscular control. The task chosen for this investigation was seated balancing, which emphasizes trunk muscles’ contribution in postural control. This task was modeled in Simulink? as a fourth order linearized dynamic system with feedback delays. Optimization (minimizing error between experimental and model data) of state variables was used to determine neuromuscular control parameters. Experimental data were obtained from 7 subjects during 5 perturbation trials while balancing on the seat with eyes closed. Model accuracy, reflecting the ability of the model to capture the dynamics of seated balance, was correlated with seated balance performance (r=0.91, p<0.001). To minimize the risk of erroneous findings from inaccurate modeling, only the best five balancers’ data were used for hypothesis testing. In these five subjects, feedback delays in modeled neuromuscular control were increased to determine their effect on task stability, trunk displacement and trunk moment. Simulations showed that longer delays found in LBP, in general, did not produce unstable balancing, but did result in increased trunk displacement (p<0.001) and trunk moment (p=0.001). This impairment in neuromuscular control in chronic LBP patients could possibly exacerbate their condition by increasing tissue strain (more spinal displacement) and stress (more spinal loading).     

Learning the dynamics of reaching movements results in the modification of arm impedance and long-latency perturbation responses

 Some characteristics of arm movements that humans exhibit during learning the dynamics of reaching are consistent with a theoretical framework where training results in motor commands that are gradually modified to predict and compensate for novel forces that may act on the hand. As a first approximation, the motor control system behaves as an adapting controller that learns an internal model of the dynamics of the task. It approximates inverse dynamics and predicts motor commands that are appropriate for a desired limb trajectory. However, we had previously noted that subtle motion characteristics observed during changes in task dynamics challenged this simple model and raised the possibility that adaptation also involved sensory–motor feedback pathways. These pathways reacted to sensory feedback during the course of the movement. Here we hypothesize that adaptation to dynamics might also involve a modification of how the CNS responds to sensory feedback. We tested this through experiments that quantified how the motor system's response to errors during voluntary movements changed as it adapted to dynamics of a force field. We describe a nonlinear approach that approximates the impedance of the arm, i.e., force response as a function of arm displacement trajectory. We observe that after adaptation, the impedance function changes in a way that closely matches and counters the effect of the force field. This is particularly prominent in the long-latency (>100 ms) component of response to perturbations. Therefore, it appears that practice not only modifies the internal model with which the brain generates motor commands that initiate a movement, but also the internal model with which sensory feedback is integrated with the ongoing descending commands in order to respond to error during the movement. Received: 10 January 2001 / Accepted in revised form: 30 May 2001     

Delayed feedback control requires an internal forward model

Biological motor control provides highly effective solutions to difficult control problems in spite of the complexity of the plant and the significant delays in sensory feedback. Such delays are expected to lead to non trivial stability issues and lack of robustness of control solutions. However, such difficulties are not observed in biological systems under normal operating conditions. Based on early suggestions in the control literature, a possible solution to this conundrum has been the suggestion that the motor system contains within itself a forward model of the plant (e.g., the arm), which allows the system to ‘simulate’ and predict the effect of applying a control signal. In this work, we formally define the notion of a forward model for deterministic control problems, and provide simple conditions that imply its existence for tasks involving delayed feedback control. As opposed to previous work which dealt mostly with linear plants and quadratic cost functions, our results apply to rather generic control systems, showing that any controller (biological or otherwise) which solves a set of tasks, must contain within itself a forward plant model. We suggest that our results provide strong theoretical support for the necessity of forward models in many delayed control problems, implying that they are not only useful, but rather, mandatory, under general conditions.     

Feedback-mediated dynamics in two coupled nephrons

Previously, we developed a dynamic model for the tubuloglomerular feedback (TGF) system in a single, short-looped nephron of the mammalian kidney. In that model, a semi-linear hyperbolic partial differential equation was used to represent two fundamental processes of solute transport in the nephron’s thick ascending limb (TAL): chloride advection by fluid flow along the TAL lumen and transepithelial chloride transport from the lumen to the interstitium. An empirical function and a time delay were used to relate glomerular filtration rate to the chloride concentration at the macula densa of the TAL. Analysis of the model equations indicated that stable limit-cycle oscillations (LCO) in nephron fluid flow and chloride concentration can emerge for sufficiently large feedback gain magnitude and time delay. In this study, the single-nephron model was extended to two nephrons, which were coupled through their filtration rates. Explicit analytical conditions were obtained for bifurcation loci corresponding to two special cases: (1) identical time delays but differing feedback gains, and (2) identical gains but differing delays. Similar to the case of a single nephron, our analysis indicates that stable LCO can emerge in coupled nephrons for sufficiently large gains and delays. However, these LCO may emerge at lower values of the feedback gain, relative to a single (i.e., uncoupled) nephron, or at shorter delays, provided the delays are sufficiently close. These results suggest that, in vivo, if two nephrons are sufficiently similar, then coupling will tend to increase the likelihood of LCO.     

Effect of an additional manual motor task on the maintenance of equilibrium in the frontal and sagittal planes in standing subjects

The postural oscillations of a standing subject during an additional manual motor task consisting in holding a movable ball in the center of a flat box were studied. The movements of the center of pressure (CP) in the frontal and sagittal planes were studied when subjects were standing on a stable rigid support and on a movable unstable support. The effect of the additional motor task on the movement of the CP depended on the stability of the support. When the additional task was performed, the sagittal movements of the CP increased in the case a movable support and did not increase when the support was stable. The additional task decreased the frontal movements of CP in the case of a stable support, and it did not change the frontal movements of CP when the support was unstable. Thus, the performance of an additional motor task led to a reduction of the efficiency of the postural control system in maintaining equilibrium on an unstable support. This decrease may be due to a greater cortical influence on the posture control system in subjects standing on a movable support in comparison with this influence in the case of a stable support.     

Delays in Human-Computer Interaction and Their Effects on Brain Activity

The temporal contingency of feedback is an essential requirement of successful human-computer interactions. The timing of feedback not only affects the behavior of a user but is also accompanied by changes in psychophysiology and neural activity. In three fMRI experiments we systematically studied the impact of delayed feedback on brain activity while subjects performed an auditory categorization task. In the first fMRI experiment, we analyzed the effects of rare and thus unexpected delays of different delay duration on brain activity. In the second experiment, we investigated if users can adapt to frequent delays. Therefore, delays were presented as often as immediate feedback. In a third experiment, the influence of interaction outage was analyzed by measuring the effect of infrequent omissions of feedback on brain activity. The results show that unexpected delays in feedback presentation compared to immediate feedback stronger activate inter alia bilateral the anterior insular cortex, the posterior medial frontal cortex, the left inferior parietal lobule and the right inferior frontal junction. The strength of this activation increases with the duration of the delay. Thus, delays interrupt the course of an interaction and trigger an orienting response that in turn activates brain regions of action control. If delays occur frequently, users can adapt, delays become expectable, and the brain activity in the observed network diminishes over the course of the interaction. However, introducing rare omissions of expected feedback reduces the system’s trustworthiness which leads to an increase in brain activity not only in response to such omissions but also following frequently occurring and thus expected delays.     

Learning combined feedback and feedforward control of a musculoskeletal system

The goal of this paper is the learning of neuromuscular control, given the following necessary conditions: (1) time delays in the control loop, (2) non-linear muscle characteristics, (3) learning of feedforward and feedback control, (4) possibility of feedback gain modulation during a task. A control system and learning methodology that satisfy those conditions is given. The control system contains a neural network, comprising both feedforward and feedback control. The learning method is backpropagation through time with an explicit sensitivity model. Results will be given for a one degree of freedom arm with two muscles. Good control results are achieved which compare well with experimental data. Analysis of the controller shows that significant differences in controller characteristics are found if the loop delays are neglected. During a control task the system shows feedback gain modulation, similar to experimentally found reflex gain modulation during rapid voluntary contraction. If only limited feedback information is available to the controller the system learns to co-contract the antagonistic muscle pair. In this way joint stiffness increases and stable control is more easily maintained. Received: 7 November 1995 / Accepted in revised form: 13 February 1996     

Motor skill learning, retention, and control deficits in Parkinson's disease

Parkinson's disease, which affects the basal ganglia, is known to lead to various impairments of motor control. Since the basal ganglia have also been shown to be involved in learning processes, motor learning has frequently been investigated in this group of patients. However, results are still inconsistent, mainly due to skill levels and time scales of testing. To bridge across the time scale problem, the present study examined de novo skill learning over a long series of practice sessions that comprised early and late learning stages as well as retention. 19 non-demented, medicated, mild to moderate patients with Parkinson's disease and 19 healthy age and gender matched participants practiced a novel throwing task over five days in a virtual environment where timing of release was a critical element. Six patients and seven control participants came to an additional long-term retention testing after seven to nine months. Changes in task performance were analyzed by a method that differentiates between three components of motor learning prominent in different stages of learning: Tolerance, Noise and Covariation. In addition, kinematic analysis related the influence of skill levels as affected by the specific motor control deficits in Parkinson patients to the process of learning. As a result, patients showed similar learning in early and late stages compared to the control subjects. Differences occurred in short-term retention tests; patients' performance constantly decreased after breaks arising from poorer release timing. However, patients were able to overcome the initial timing problems within the course of each practice session and could further improve their throwing performance. Thus, results demonstrate the intact ability to learn a novel motor skill in non-demented, medicated patients with Parkinson's disease and indicate confounding effects of motor control deficits on retention performance.     

Proprioceptive Feedback and Brain Computer Interface (BCI) Based Neuroprostheses

Brain computer interface (BCI) technology has been proposed for motor neurorehabilitation, motor replacement and assistive technologies. It is an open question whether proprioceptive feedback affects the regulation of brain oscillations and therefore BCI control. We developed a BCI coupled on-line with a robotic hand exoskeleton for flexing and extending the fingers. 24 healthy participants performed five different tasks of closing and opening the hand: (1) motor imagery of the hand movement without any overt movement and without feedback, (2) motor imagery with movement as online feedback (participants see and feel their hand, with the exoskeleton moving according to their brain signals, (3) passive (the orthosis passively opens and closes the hand without imagery) and (4) active (overt) movement of the hand and rest. Performance was defined as the difference in power of the sensorimotor rhythm during motor task and rest and calculated offline for different tasks. Participants were divided in three groups depending on the feedback receiving during task 2 (the other tasks were the same for all participants). Group 1 (n = 9) received contingent positive feedback (participants'' sensorimotor rhythm (SMR) desynchronization was directly linked to hand orthosis movements), group 2 (n = 8) contingent “negative” feedback (participants'' sensorimotor rhythm synchronization was directly linked to hand orthosis movements) and group 3 (n = 7) sham feedback (no link between brain oscillations and orthosis movements). We observed that proprioceptive feedback (feeling and seeing hand movements) improved BCI performance significantly. Furthermore, in the contingent positive group only a significant motor learning effect was observed enhancing SMR desynchronization during motor imagery without feedback in time. Furthermore, we observed a significantly stronger SMR desynchronization in the contingent positive group compared to the other groups during active and passive movements. To summarize, we demonstrated that the use of contingent positive proprioceptive feedback BCI enhanced SMR desynchronization during motor tasks.     

In-vivo proton MR-spectroscopy of the human brain: assessment of N-acetylaspartate (NAA) reduction as a marker for neurodegeneration

Summary.  Proton magnetic resonance spectroscopy (¹H-MRS) is a non-invasive method to investigate changes in brain metabolite composition in different cerebral diseases. We performed proton spectroscopy in patients with dementia of the Alzheimer's type (AD) and in patients with motor neuron disease (MND) with the aim to detect the specific metabolic pattern for these neurodegenerative disorders. In the MND group we found a significant reduction of NAA/tCr metabolite ratios in the motor cortex, which correlates with the disease severity and the clinical lateralization of neurological symptoms and further decreases in the time course of the disease. In AD patients a reduction of NAA/tCr was observed in the medial temporal lobe. Since NAA is exclusively expressed in neurons as shown by immunohistochemical studies, reduced NAA levels suggest neuronal loss or dysfunction in the observed regions. The observed regional metabolic alterations reflect the neuronal basis of the characteristic neurological symptoms in AD (dementia) and MND (muscular palsy) and mirrors the disease progress over time. Received June 29, 2001 Accepted August 6, 2001 Published online August 9, 2002     

Seidel–Herzel model of human baroreflex in cardiorespiratory system with stochastic delays

The stochastic versus deterministic solution of the Seidel–Herzel model describing the baroreceptor control loop (which regulates the short-time heart rate) are compared with the aim of exploring the heart rate variability. The deterministic model solutions are known to bifurcate from the stable to sustained oscillatory solutions if time delays in transfer of signals by sympathetic nervous system to the heart and vasculature are changed. Oscillations in the heart rate and blood pressure are physiologically crucial since they are recognized as Mayer waves. We test the role of delays of the sympathetic stimulation in reconstruction of the known features of the heart rate. It appears that realistic histograms and return plots are attainable if sympathetic time delays are stochastically perturbed, namely, we consider a perturbation by a white noise. Moreover, in the case of stochastic model the bifurcation points vanish and Mayer oscillations in heart period and blood pressure are observed for whole considered space of sympathetic time delays.      

Parkinsonian tremor and simplification in network dynamics

We explore the behavior of richly connected inhibitory neural networks under parameter changes that correspond to weakening of synaptic efficacies between network units, and show that transitions from irregular to periodic dynamics are common in such systems. The weakening of these connections leads to a reduction in the number of units that effectively drive the dynamics and thus to simpler behavior. We hypothesize that the multiple interconnecting loops of the brain’s motor circuitry, which involve many inhibitory connections, exhibit such transitions. Normal physiological tremor is irregular while other forms of tremor show more regular oscillations. Tremor in Parkinson’s disease, for example, stems from weakened synaptic efficacies of dopaminergic neurons in the nigro-striatal pathway, as in our general model. The multiplicity of structures involved in the production of symptoms in Parkinson’s disease and the reversibility of symptoms by pharmacological and surgical manipulation of connection parameters suggest that such a neural network model is appropriate. Furthermore, fixed points that can occur in the network models are suggestive of akinesia in Parkinson’s disease. This model is consistent with the view that normal physiological systems can be regulated by robust and richly connected feedback networks with complex dynamics, and that loss of complexity in the feedback structure due to disease leads to more orderly behavior.     

Attention modulation regulates both motor and non-motor performance: a high-density EEG study in Parkinson's disease

We have previously shown that, in early stages of Parkinson's disease (PD), patients with higher reaction times are also more impaired in visual sequence learning, suggesting that movement preparation shares resources with the learning of visuospatial sequences. Here, we ascertained whether, in patients with PD, the pattern of the neural correlates of attentional processes of movement planning predict sequence learning and working memory abilities. High density Electroencephalography (EEG, 256 electrodes) was recorded in 19 patients with PD performing reaching movements in a choice reaction time paradigm. Patients were also tested with Digit Span and performed a visuomotor sequence learning task that has an important declarative learning component. We found that attenuation of alpha/beta oscillatory activity before the stimulus presentation in frontoparietal regions significantly correlated with reaction time in the choice reaction time task, similarly to what we had previously found in normal subjects. In addition, such activity significantly predicted the declarative indices of sequence learning and the scores in the Digit Span task. These findings suggest that some motor and non motor PD signs might have common neural bases, and thus, might have a similar response to the same behavioral therapy. In addition, these results might help in designing and testing the efficacy of novel rehabilitative approaches to improve specific aspects of motor performance in PD and other neurological disorders.     

Risk-Sensitive Optimal Feedback Control Accounts for Sensorimotor Behavior under Uncertainty

Many aspects of human motor behavior can be understood using optimality principles such as optimal feedback control. However, these proposed optimal control models are risk-neutral; that is, they are indifferent to the variability of the movement cost. Here, we propose the use of a risk-sensitive optimal controller that incorporates movement cost variance either as an added cost (risk-averse controller) or as an added value (risk-seeking controller) to model human motor behavior in the face of uncertainty. We use a sensorimotor task to test the hypothesis that subjects are risk-sensitive. Subjects controlled a virtual ball undergoing Brownian motion towards a target. Subjects were required to minimize an explicit cost, in points, that was a combination of the final positional error of the ball and the integrated control cost. By testing subjects on different levels of Brownian motion noise and relative weighting of the position and control cost, we could distinguish between risk-sensitive and risk-neutral control. We show that subjects change their movement strategy pessimistically in the face of increased uncertainty in accord with the predictions of a risk-averse optimal controller. Our results suggest that risk-sensitivity is a fundamental attribute that needs to be incorporated into optimal feedback control models.     

ACE I/D polymorphism in Alzheimer’s disease

Angiotensin-converting enzyme (ACE) has been reported to show altered activity in patients with neurological diseases. The recent studies found that a 287 bp insertion/deletion (I/D) polymorphism of the ACE gene may be associated with susceptibility to Alzheimer’s disease (AD) but the results have been heterogenous between studies in Europe. In the present study we examined for the first time the association of ACE I/D polymorphism along with APOE genotype in 70 sporadic AD and 126 control subjects in Slovak Caucasians (Central Europe). An increased risk for AD was observed in subjects with at least one APOE*E4 allele (OR=3.99, 95% CI=1.97–8.08). No significant differences for the genotype distribution or the allele frequency were revealed comparing controls and patients for ACE gene. Gene-gene interaction analysis showed increase of the risk to develop AD in subjects carrying both the ACE DD genotype and the APOE*E4 allele (OR=10.32, 95% C.I. 2.67–39.81).     

Winner-take-all selection in a neural system with delayed feedback

We consider the effects of temporal delay in a neural feedback system with excitation and inhibition. The topology of our model system reflects the anatomy of the avian isthmic circuitry, a feedback structure found in all classes of vertebrates. We show that the system is capable of performing a 'winner-take-all' selection rule for certain combinations of excitatory and inhibitory feedback. In particular, we show that when the time delays are sufficiently large a system with local inhibition and global excitation can function as a 'winner-take-all' network and exhibit oscillatory dynamics. We demonstrate how the origin of the oscillations can be attributed to the finite delays through a linear stability analysis.     

A multisensory integration model of human stance control

A model is presented to study and quantify the contribution of all available sensory information to human standing based on optimal estimation theory. In the model, delayed sensory information is integrated in such a way that a best estimate of body orientation is obtained. The model approach agrees with the present theory of the goal of human balance control. The model is not based on purely inverted pendulum body dynamics, but rather on a three-link segment model of a standing human on a movable support base. In addition, the model is non-linear and explicitly addresses the problem of multisensory integration and neural time delays. A predictive element is included in the controller to compensate for time delays, necessary to maintain erect body orientation. Model results of sensory perturbations on total body sway closely resemble experimental results. Despite internal and external perturbations, the controller is able to stabilise the model of an inherently unstable standing human with neural time delays of 100 ms. It is concluded, that the model is capable of studying and quantifying multisensory integration in human stance control. We aim to apply the model in (1) the design and development of prostheses and orthoses and (2) the diagnosis of neurological balance disorders. Received: 25 August 1997 / Accepted in revised form: 8 December 1998