Noisy galvanic vestibular stimulation promotes GABA release in the substantia nigra and improves locomotion in hemiparkinsonian rats

Dopamine related disorders usually respond to dopaminergic drugs, but not all symptoms are equally responsive. In Parkinson’s disease (PD) in particular, axial symptoms resulting in impaired gait and postural control are difficult to treat. Stochastic vestibular stimulation (SVS) has been put forward as a method to improve CNS function in dopamine related disorders, but the mechanisms of action are not well understood. This thesis aimed to investigate the effects of SVS on neuronal brain activity and to evaluate the possible enhancing effect of SVS on motor control in PD and on cognitive functions and motor learning in Attention deficit hyperactivity disorder (ADHD). Behavioural tests were conducted in the 6-OHDA rat model of PD using the accelerating Rotarod and the Montoya skilled reach test to evaluate the effect of SVS on motor control. The effect of SVS on brain activity was assessed using in vivo microdialysis and immunohistochemistry. We evaluated the effect of SVS on postural control and Parkinsonism in patients with PD and the effect of SVS on cognitive function in people with ADHD. The behavioural animal studies indicate that SVS may have an enhancing effect on locomotion, but not skilled forepaw function. SVS increased GABA transmission in the ipsilesional substantia nigra (SN) and may have a rebalancing effect on dysfunctional brain activity. SVS increased c-Fos activity more than levodopa and saline in the vestibular nucleus of all animals. c-Fos expression was also higher in this region in the 6-OHDA lesioned than in shamlesioned animals, supporting the theory that SVS may have larger effects in the dopamine depleted brain. SVS increased c-Fos expression in the habenula nucleus substantially more than levodopa did. Furthermore, SVS and levodopa had similar effects on many brain regions, including the striatum, where saline had no effect. The clinical studies revealed improvement of postural control in PD during SVS. There was a trend towards reduced Parkinsonism during SVS when off levodopa. No substantial effects were found on cognitive performance in ADHD. In PD, SVS may improve motor control by inhibiting the overactive SN, possibly through a non-dopaminergic modulatory pathway involving increased neurotransmission in the habenula nucleus. SVS could be trialled in larger studies to evaluate long-term effects on treatment resistant axial symptoms associated with PD

Sensitivity of Local Dynamic Stability of Over-Ground Walking to Balance Impairment Due to Galvanic Vestibular Stimulation

Impaired balance control during gait can be detected by local dynamic stability measures. For clinical applications, the use of a treadmill may be limiting. Therefore, the aim of this study was to test sensitivity of these stability measures collected during short episodes of over-ground walking by comparing normal to impaired balance control. Galvanic vestibular stimulation (GVS) was used to impair balance control in 12 healthy adults, while walking up and down a 10 m hallway. Trunk kinematics, collected by an inertial sensor, were divided into episodes of one stroll along the hallway. Local dynamic stability was quantified using short-term Lyapunov exponents (λs), and subjected to a bootstrap analysis to determine the effects of number of episodes analysed on precision and sensitivity of the measure. λs increased from 0.50 ± 0.06 to 0.56 ± 0.08 (p = 0.0045) when walking with GVS. With increasing number of episodes, coefficients of variation decreased from 10 ± 1.3% to 5 ± 0.7% and the number of p values >0.05 from 42 to 3.5%, indicating that both precision of estimates of λs and sensitivity to the effect of GVS increased. λs calculated over multiple episodes of over-ground walking appears to be a suitable measure to calculate local dynamic stability on group level

Fractal analyses reveal independent complexity and predictability of gait

Locomotion is a natural task that has been assessed for decades and used as a proxy to highlight impairments of various origins. So far, most studies adopted classical linear analyses of spatio-temporal gait parameters. Here, we use more advanced, yet not less practical, non-linear techniques to analyse gait time series of healthy subjects. We aimed at finding more sensitive indexes related to spatio-temporal gait parameters than those previously used, with the hope to better identify abnormal locomotion. We analysed large-scale stride interval time series and mean step width in 34 participants while altering walking direction (forward vs. backward walking) and with or without galvanic vestibular stimulation. The Hurst exponent α and the Minkowski fractal dimension D were computed and interpreted as indexes expressing predictability and complexity of stride interval time series, respectively. These holistic indexes can easily be interpreted in the framework of optimal movement complexity. We show that α and D accurately capture stride interval changes in function of the experimental condition. Walking forward exhibited maximal complexity (D) and hence, adaptability. In contrast, walking backward and/or stimulation of the vestibular system decreased D. Furthermore, walking backward increased predictability (α) through a more stereotyped pattern of the stride interval and galvanic vestibular stimulation reduced predictability. The present study demonstrates the complementary power of the Hurst exponent and the fractal dimension to improve walking classification. Our developments may have immediate applications in rehabilitation, diagnosis, and classification procedures

Virtual signals of head rotation induce gravity‐dependent inferences of linear acceleration

This dataset contains data from participants' perceptions in different orientations relative to gravity under electrical vestibular stimulation (EVS), a computational model of central vestibular processing of EVS, and code used for analysis and visualization of experimental and model simulation outcomes. Instructions: 1- Download all data files and Matlab functions and ensure they are all in the same directory 2- Open EVS_Mechanistic_Model.m, Figure_5_Code.m or Figure_6_Code.m with Matlab 3- Make sure Matlab is currently in the folder where you put the files or add that folder to the path 4- Run the cod