Quantifying Postural Control during Exergaming Using Multivariate Whole-Body Movement Data: A Self-Organizing Maps Approach.

BACKGROUND: Exergames are becoming an increasingly popular tool for training balance ability, thereby preventing falls in older adults. Automatic, real time, assessment of the user's balance control offers opportunities in terms of providing targeted feedback and dynamically adjusting the gameplay to the individual user, yet algorithms for quantification of balance control remain to be developed. The aim of the present study was to identify movement patterns, and variability therein, of young and older adults playing a custom-made weight-shifting (ice-skating) exergame. METHODS: Twenty older adults and twenty young adults played a weight-shifting exergame under five conditions of varying complexity, while multi-segmental whole-body movement data were captured using Kinect. Movement coordination patterns expressed during gameplay were identified using Self Organizing Maps (SOM), an artificial neural network, and variability in these patterns was quantified by computing Total Trajectory Variability (TTvar). Additionally a k Nearest Neighbor (kNN) classifier was trained to discriminate between young and older adults based on the SOM features. RESULTS: Results showed that TTvar was significantly higher in older adults than in young adults, when playing the exergame under complex task conditions. The kNN classifier showed a classification accuracy of 65.8%. CONCLUSIONS: Older adults display more variable sway behavior than young adults, when playing the exergame under complex task conditions. The SOM features characterizing movement patterns expressed during exergaming allow for discriminating between young and older adults with limited accuracy. Our findings contribute to the development of algorithms for quantification of balance ability during home-based exergaming for balance training.

High-frequency electrical stimulation suppresses cholinergic accumbens interneurons in acute rat brain slices through GABA(B) receptors.

The nucleus accumbens is selected as a surgical target in deep brain stimulation for treating refractory obsessive-compulsive disorder (OCD). One of the therapeutic benefits of this procedure is that the abnormal hyper-functioning prefrontal cortex of patients with OCD is restored during stimulation. One hypothesis regarding the mechanism of deep brain stimulation is that the neuronal electrophysiological properties are directly altered by electrical stimulation; another hypothesis assumes that the stimulation induces selective neuron transmitter release, such as γ-aminobutyric acid (GABA). In this study, we used multi-electrode arrays with electrode size of 40 × 40 µm to record electrophysiological signals from the large nucleus accumbens neurons in acute rat brain slices while applying electrical stimulation simultaneously. We revealed that high-frequency stimulation (HFS, 140 Hz) suppressed the spontaneous neuronal firing rate significantly, whereas low-frequency stimulation (LFS, 10 Hz) did not. Both HFS and LFS have no effect on neuronal firing pattern or on neuronal oscillation synchrony. GABAB receptor antagonism reversed the HFS-provoked neuronal inhibition, whereas GABAA receptor blockade failed to affect it. The recorded neurons were pharmacologically identified to be cholinergic interneurons. We propose that HFS has a direct suppressive effect on the identified accumbal acetylcholine (ACh) interneurons by enhancing GABA release in the stimulated region. Potentially, suppressed ACh interneurons decrease the disinhibiting function of medium-sized spiny neurons in the striato-thalamo-cortical circuit. This finding might give an indication of the mechanism of the therapeutic effect of HFS in nucleus accumbens on restoring the abnormal hyperactive prefrontal cortex status in OCD.

Suitability of Kinect for measuring whole body movement patterns during exergaming.

Exergames provide a challenging opportunity for home-based training and evaluation of postural control in the elderly population, but affordable sensor technology and algorithms for assessment of whole body movement patterns in the home environment are yet to be developed. The aim of the present study was to evaluate the use of Kinect, a commonly available video game sensor, for capturing and analyzing whole body movement patterns. Healthy adults (n=20) played a weight shifting exergame under five different conditions with varying amplitudes and speed of sway movement, while 3D positions of ten body segments were recorded in the frontal plane using Kinect and a Vicon 3D camera system. Principal Component Analysis (PCA) was used to extract and compare movement patterns and the variance in individual body segment positions explained by these patterns. Using the identified patterns, balance outcome measures based on spatiotemporal sway characteristics were computed. The results showed that both Vicon and Kinect capture >90% variance of all body segment movements within three PCs. Kinect-derived movement patterns were found to explain variance in trunk movements accurately, yet explained variance in hand and foot segments was underestimated and overestimated respectively by as much as 30%. Differences between both systems with respect to balance outcome measures range 0.3-64.3%. The results imply that Kinect provides the unique possibility of quantifying balance ability while performing complex tasks in an exergame environment.

Exergaming for balance training of elderly: state of the art and future developments.

Fall injuries are responsible for physical dysfunction, significant disability, and loss of independence among elderly. Poor postural control is one of the major risk factors for falling but can be trained in fall prevention programs. These however suffer from low therapy adherence, particularly if prevention is the goal. To provide a fun and motivating training environment for elderly, exercise games, or exergames, have been studied as balance training tools in the past years. The present paper reviews the effects of exergame training programs on postural control of elderly reported so far. Additionally we aim to provide an in-depth discussion of technologies and outcome measures utilized in exergame studies. Thirteen papers were included in the analysis. Most of the reviewed studies reported positive results with respect to improvements in balance ability after a training period, yet few reached significant levels. Outcome measures for quantification of postural control are under continuous dispute and no gold standard is present. Clinical measures used in the studies reviewed are well validated yet only give a global indication of balance ability. Instrumented measures were unable to detect small changes in balance ability as they are mainly based on calculating summary statistics, thereby ignoring the time-varying structure of the signals. Both methods only allow for measuring balance after the exergame intervention program. Current developments in sensor technology allow for accurate registration of movements and rapid analysis of signals. We propose to quantify the time-varying structure of postural control during gameplay using low-cost sensor systems. Continuous monitoring of balance ability leaves the user unaware of the measurements and allows for generating user-specific exergame training programs and feedback, both during one game and in timeframes of weeks or months. This approach is unique and unlocks the as of yet untapped potential of exergames as balance training tools for community dwelling elderly.

Phase-dependent effects of stimuli locked to oscillatory activity in cultured cortical networks.

The archetypal activity pattern in cultures of dissociated neurons is spontaneous network-wide bursting. Bursts may interfere with controlled activation of synaptic plasticity, but can be suppressed by the application of stimuli at a sufficient rate. We sinusoidally modulated (4 Hz) the pulse rate of random background stimulation (RBS) and found that cultures were more active, burst less frequently, and expressed oscillatory activity. Next, we studied the effect of phase-locked tetani (four pulses, 200 s(-1)) on network activity. Tetani were applied to one electrode at the peak or trough of mRBS stimulation. We found that when tetani were applied at the peak of modulated RBS (mRBS), a significant potentiation of poststimulus histograms (PSTHs) occurred. Conversely, tetani applied at the trough resulted in a small but insignificant depression of PSTHs. In addition to PSTHs, electrode-specific firing rate profiles within spontaneous bursts before and after mRBS were analyzed. Here, significant changes in firing rate profiles were found only for stimulation at the peak of mRBS. Our study shows that rhythmic activity in culture is possible, and that the network responds differentially to strong stimuli depending on the phase at which they are delivered. This suggests that plasticity mechanisms may be differentially accessible in an oscillatory state.

The effect of slow electrical stimuli to achieve learning in cultured networks of rat cortical neurons.

Learning, or more generally, plasticity may be studied using cultured networks of rat cortical neurons on multi electrode arrays. Several protocols have been proposed to affect connectivity in such networks. One of these protocols, proposed by Shahaf and Marom, aimed to train the input-output relationship of a selected connection in a network using slow electrical stimuli. Although the results were quite promising, the experiments appeared difficult to repeat and the training protocol did not serve as a basis for wider investigation yet. Here, we repeated their protocol, and compared our 'learning curves' to the original results. Although in some experiments the protocol did not seem to work, we found that on average, the protocol showed a significantly improved stimulus response indeed. Furthermore, the protocol always induced functional connectivity changes that were much larger than changes that occurred after a comparable period of random or no stimulation. Finally, our data shows that stimulation at a fixed electrode induces functional connectivity changes of similar magnitude as stimulation through randomly varied sites; both larger than spontaneous connectivity fluctuations. We concluded that slow electrical stimulation always induced functional connectivity changes, although uncontrolled. The magnitude of change increased when we applied the adaptive (closed-loop) training protocol. We hypothesize that networks develop an equilibrium between connectivity and activity. Induced connectivity changes depend on the combination of applied stimulus and initial connectivity. Plain stimuli may drive networks to the nearest equilibrium that accommodates this input, whereas adaptive stimulation may direct the space for exploration and force networks to a new balance, at a larger distance from the initial state.

The effect of learning on bursting.

We have studied the effect that learning a new stimulus-response (SR) relationship had within a neuronal network cultured on a multielectrode array. For training, we applied repetitive focal electrical stimulation delivered at a low rate (<1/s). Stimulation was withdrawn when a desired SR success ratio was achieved. It has been shown elsewhere, and we verified that this training algorithm, named conditional repetitive stimulation (CRS), can be used to strengthen an initially weak SR. So far, it remained unclear what the role of the rest of the network during learning was. We therefore studied the effect of CRS on spontaneously occurring network bursts. To this end, we made profiles of the firing rates within network bursts. We have earlier shown that these profiles change shape on a time base of several hours during spontaneous development. We show here that profiles of summed activity, called burst profiles, changed shape at an increased rate during CRS. This suggests that the whole network was involved in making the changes necessary to incorporate the desired SR relationship. However, a local (path-specific) component to learning was also found by analyzing profiles of single-electrode-activity phase profiles. Phase profiles that were not part of the SR relationship changed far less during CRS than the phase profiles of the electrodes that were part of the SR relationship. Finally, the manner in which phase profiles changed shape varied and could not be linked to the SR relationship.

Analysis of cultured neuronal networks using intraburst firing characteristics.

It is an open question whether neuronal networks, cultured on multielectrode arrays, retain any capability to usefully process information (learning and memory). A necessary prerequisite for learning is that stimulation can induce lasting changes in the network. To observe these changes, one needs a method to describe the network in sufficient detail, while stable in normal circumstances. We analyzed the spontaneous bursting activity that is encountered in dissociated cultures of rat neocortical cells. Burst profiles (BPs) were made by estimating the instantaneous array-wide firing frequency. The shape of the BPs was found to be stable on a time scale of hours. Spatiotemporal detail is provided by analyzing the instantaneous firing frequency per electrode. The resulting phase profiles (PPs) were estimated by aligning BPs to their peak spiking rate over a period of 15 min. The PPs reveal a stable spatiotemporal pattern of activity during bursts over a period of several hours, making them useful for plasticity and learning studies. We also show that PPs can be used to estimate conditional firing probabilities. Doing so, yields an approach in which network bursting behavior and functional connectivity can be studied.

Changes within bursts during learning in dissociated neural networks.

We have studied the effect of imprinting a new stimulus-response (SR) relationship into a neuronal network cultured on a multi electrode array (MEA). We have used the Conditional Repetitive Stimulation (CRS) algorithm introduced by Shahaf et al in 2004. In this algorithm focal electrical stimulation is delivered at a low rate (<<1 Hz) and is withdrawn when a desired response is observed. We confirmed that CRS could train the network to strengthen an initially weak SR relationship. With the acquisition of a new SR relationship, we studied its effect on network activity. Specifically, spontaneously occurring network bursts measured before, during and after training were analyzed. The total firing rate within bursts was estimated with a temporal resolution of milliseconds (burst profiles). We have shown earlier that these profiles change shape on a time base of several hours during spontaneous development. We show that the rate of change of the profiles during training (i.e. CRS) was higher than when no stimulation was applied.

Do external stimuli, applied to train cultured cortical networks, disturb the balance between activity and connectivity?

Learning, or more generally, plasticity may be studied using cultured neuronal networks on multi electrode arrays. Many protocols have been proposed to change connectivity in such networks. So far, only one of these protocols, proposed by Shahaf and Marom, aimed to change the input-output relationship of a selected connection in the network. Although the results were quite promising, the experiments appeared difficult to repeat and the protocol did not serve as a basis for wider investigation yet. Here, we repeated their protocol, and compared our 'learning curves' to the original results. Although in some experiments the protocol did not seem to work, we found that on average, the protocol showed a significant learning effect indeed. We frequently found learning curves that initially declined as in the original results, but then increased again before finally settling at a low level.