S.F. Campfens

Sensory reweighting of proprioceptive information of the left and right leg during human balance control

To keep balance, information from different sensory systems is integrated to generate corrective torques. Current literature suggests that this information is combined according to the sensory reweighting hypothesis, i.e., more reliable information is weighted more strongly than less reliable information. In this approach, no distinction has been made between the contributions of both legs. In this study, we investigated how proprioceptive information from both legs is combined to maintain upright stance. Healthy subjects maintained balance with eyes closed while proprioceptive information of each leg was perturbed independently by continuous rotations of the support surfaces (SS) and the human body by platform translation. Two conditions were tested: perturbation amplitude of one SS was increased over trials while the other SS 1) did not move or 2) was perturbed with constant amplitude. With the use of system identification techniques, the response of the ankle torques to the perturbation amplitudes (i.e., the torque sensitivity functions) was determined and how much each leg contributed to stabilize stance (i.e., stabilizing mechanisms) was estimated. Increased amplitude of one SS resulted in a decreased torque sensitivity. The torque sensitivity to the constant perturbed SS showed no significant differences. The properties of the stabilizing mechanisms remained constant during perturbations of each SS. This study demonstrates that proprioceptive information from each leg is weighted independently and that the weight decreases with perturbation amplitude. Weighting of proprioceptive information of one leg has no influence on the weight of the proprioceptive information of the other leg. According to the sensory reweighting hypothesis, vestibular information must be up-weighted, because closing the eyes eliminates visual information.

A Bilateral Ankle Manipulator to Investigate Human Balance Control

The ankles play an important role in human balance. In most studies investigating balance control the contribution of the left and right leg is not separated. However, in certain pathologies such as stroke and Parkinsons disease, balance control can be asymmetric. Here, a bilateral ankle perturbator (BAP) is presented, which applies support surface rotations to both ankles independently. The device consists of two small foot-size support surfaces, which are independently actuated. The BAP device can operate in either angle or torque control mode. The device is able to apply support surface rotations up to 8.6° with a bandwidth of 42 Hz. Additionally the platforms can be replaced by 6-DoF force plates to measure the center of pressure underneath each foot. With the optional force plates the bandwidth decreases to 16 Hz as a result of the additional weight. Two possible applications of the device to investigate human balance control are demonstrated: ankle stiffness by applying minimum jerk profiles and sensory reweighting of the proprioceptive information. In conclusion, we developed a bilateral ankle perturbator which is able to apply support surface rotations to both ankles independently. The major application of the device will be to investigate the contribution of both ankles to human balance control, and the interactions in balance control between both legs.

Stretch Evoked Potentials in Healthy Subjects and After Stroke: A Potential Measure for Proprioceptive Sensorimotor Function

Sensory feedback is of vital importance in motor control, yet rarely assessed in diseases with impaired motor function like stroke. Muscle stretch evoked potentials (StrEPs) may serve as a measure of cortical sensorimotor activation in response to proprioceptive input. The aim of this study is: 1) to determine early and late features of the StrEP and 2) to explore whether StrEP waveform and features can be measured after stroke. Consistency of StrEP waveforms and features was evaluated in 22 normal subjects. StrEP features and similarity between hemispheres were evaluated in eight subacute stroke subjects. StrEPs of normal subjects had a consistent shape across conditions and sessions (mean cross correlation waveforms > 0.75). Stroke subjects showed heterogeneous StrEP waveforms. Stroke subjects presented a normal early peak (40 ms after movement onset) but later peaks had abnormal amplitudes and latencies. No significant differences between stroke subjects with good and poor motor function were found (P > 0.14). With the consistent responses of normal subjects the StrEP meets a prerequisite for potential clinical value. Recording of StrEPs is feasible even in subacute stroke survivors with poor motor function. How StrEP features relate to clinical phenotypes and recovery needs further investigation.

Directional coherence disentangles causality within the sensorimotor loop, but cannot open the loop

More and more studies indicate that corticomuscular coherence in the beta band (15–30 Hz), which expresses the functional coupling between the cortex and the muscles, originates from the interaction within the sensorimotor loop (e.g. Witham et al. 2011). The phase of the corticomuscular coherence expresses the relative time–frequency relationship and is often explained as to result from the efferent delay between the cortex and the muscles. In a recent issue of The Journal of Physiology, Witham and co-workers (2011) demonstrated that the slope of the phase of corticomuscular coherence is less negative than would be expected of pure efferent pathways and even becomes positive in some subjects (negative slopes indicate that the muscle lags the brain). This is a clear indication of a bidirectional coupling between EEG and EMG; in other words, the signals are part of a closed-loop system. However, the authors also use the phase of the directional coherence to assess the delays in the efferent and afferent pathways, which will give erroneous results in a closed-loop system, like the sensorimotor loop. The causality of signals within a closed loop is difficult to assess. For example, in the sensorimotor loop it is not obvious whether cortical activity leads muscle activity – suggesting an efferent pathway; or cortical activity lags muscle activity – suggesting an afferent pathway. In the sensorimotor loop EEG and EMG signals will contain a combination of afferent and efferent influences. As the authors demonstrate, directional coherence provides a good measure to disentangle the causal relationships of the signals within the sensorimotor loop. With directional coherence multivariate autoregressive (MVAR) modelling is used to uncover causality. MVAR modelling is a common technique which disentangles the recorded signals at a certain time instant as a weighted sum of the signals’ previous values and (unknown) external noise sources, which enter the model just before the signals (see Fig. 1). The directed coherence is calculated using the directional transfer function Hij(f) (e.g. Witham et al. eqn (3)). The directional transfer function Hij(f) is calculated, which represents: ‘the causal influence of signal j on signal i’. Figure 1 Schematic representation of the sensorimotor loop as a closed-loop system Although this is a widely accepted expression, it is a simplified expression. The precise expression would be that the direction transfer function represents the causal influence of external noise source j on signal i (Kaminski & Blinowska, 1991). In other words the directional transfer function expresses how much signal i depends on the unknown external noise source which enters the model just before signal j. The simplification on what directional transfer function represents has a tremendous effect on the understanding of the phase of the directional coherence. In Witham et al. (2011), the authors assume that the phase of the directional coherence represents the relative delay between signal i and signal j. With this assumption the slope of the phase would represent the delays in the open-loop transfer functions, i.e. the relative delay of the efferent (EEG to EMG: Heff) and afferent (EMG to EEG: Haff) pathways. However the directional transfer function is a closed-loop transfer function between noise source j and signal i. In that sense directional transfer functions allow the disentangling of the causality within a closed loop, but the phase of the directional coherence presents the relative delay between the signal within the sensorimotor loop (EEG and EMG) and the unknown external noise sources ɛ (i.e. the cortical and afferent drive). This effect contributes to the observation of Witham and co-workers that the delays measured by using the directed coherence were often larger than would be expected the known conduction delays from the cortex to muscle assesses with stimulation and peripheral nerve stimulation. In conclusion, the technique based on multivariate AR modelling – like directional coherence – decomposes signals within a closed loop as a weighted combination of external sources and the signals’ past, and allows disentangling of the causality. The phase of the directional coherence, however, describes the relative delay between the signals and the unknown external sources, and is not a direct measure of the phase of the inferred open-loop transfer function, like the efferent and afferent pathways. New techniques which are able to assess the open-loop transfer functions are highly desirable. The application of controlled external perturbations could be a promising way (Campfens et al. 2011).

P7.11 Corticomuscular system tunes to external perturbations during a motor task as revealed by corticomuscular coherence

P7.10 Cortical sources of resting state electroencephalographic rhythms in Parkinson’s disease related dementia and Alzheimer’s disease P. Buffo1, F. Vecchio2, M.F. De Pandis3, C. Babiloni4, P.M. Rossini5 1Dip. Fisiologia e Farmacologia, University “Sapienza”, Rome, Italy, 2A.Fa.R., Dip. Neurosci. Osp. FBF; Isola Tiberina, Rome, Italy, 3Casa di Cura San Raffaele Cassino, Cassino, Italy, 4Department of Biomedical Sciences, University of Foggia, Foggia, Italy, 5Neurol. University “Campus Biomedico”, Rome, Italy