Noisy Stimulation Research Articles

Oscillation suppression effects of intermittent noisy deep brain stimulation induced by coordinated reset pattern based on a computational model

Noisy stimulation (NS) has been proposed to alleviate the pathological state in a computational model of Parkinson’s disease. Based on a resonance effect, NS modulates the neural firing pattern so as to suppress the excessive beta (12–35 Hz) synchronization and improve the relay reliability of the thalamus. However, in common with clinically-used pulsed high-frequency stimulation, the use of a continuous stimulation waveform for NS still may require an energy expenditure that is greater than necessary. A coordinated reset (CR) method to optimize NS is explored in this work that considers that the neural nature of the Parkinsonian condition is the enhancement of synchronization. CR methods has an advantage in disturbing the abnormal synchronous rhythm. Thesimulation resultsshow that coordinated reset noisy stimulation (CRNS) can further inhibit the enhanced pathological synchronization in a basal ganglia-thalamus network model with lower energy expenditure, as compared to basic NS. Moreover, two kinds of intermittent CRNS strategies are further derived by adding and adjusting the stimulation-off phases in the CRNS strategy. It is found that m:n ON-OFF CRNS strategies and delayed CRNS may further reduce the stimulation energy cost by 21.2% and 15.3% without compromising control performance, respectively. This work provides a new insight into the optimization of deep brain stimulation and may guide a new approach to treating Parkinson’s disease by optimizing the noise-induced improvement of the basal ganglia dysfunction.

Estimation of biophysical parameters in a neuron model under random fluctuations

In this paper, an attempt has been made to estimate the biophysical parameters in an improved version of Morris–Lecar (M–L) neuron model in a noisy environment. To observe the influence of noisy stimulation in estimation procedure, a Gaussian white noise has been added to the membrane voltage of the model system. Estimation of the parameters has been investigated by a proposed algorithm. The denoising technique (local projection method) has been applied to reduce the influence of noisy stimuli and the effectiveness of the method is reported. The proposed scheme performs well for an excitable neuron model and provides good estimates between the estimated parameters and the actual values in a reasonable way. This approach can be used for parameter estimation for other nonlinear dynamical systems.

Extracellular stimulation with human "noisy" electromyographic patterns facilitates myotube activity.

Electrical stimulation (ES) of skeletal muscle partially mimics the benefits of physical activity. However, the stimulation protocols applied clinically to date, often cause unpleasant symptoms and muscle fatigue. Here, we compared the efficiency of a "noisy" stimulus waveform derived from human electromyographic (EMG) muscle patterns, with stereotyped 45 and 1 Hz electrical stimulations applied to mouse myotubes in vitro. Human gastrocnemius medialis electromyograms recorded from volunteers during real locomotor activity were used as a template for a noisy stimulation, called EMGstim. The stimulus-induced electrical activity, intracellular Ca(2+) dynamics and mechanical twitches in the myotubes were assessed using whole-cell perforated patch-clamp, Ca(2+) imaging and optical visualization techniques. EMGstim was more efficient in inducing myotube cell firing, [Ca(2+)]i changes and contractions compared with more conventional electrical stimulation. Its stimulation strength was also much lower than the minimum required to induce contractions via stereotyped stimulation protocols. We conclude that muscle cells in vitro can be more efficiently depolarized using the "noisy" stochastic stimulation pattern, EMGstim, a finding that suggests a way to favor a higher level of electrical activity in a larger number of cells.

P146. Comparison of alpha-entrainment with oscillatory and noisy low-voltage stimulation

Introduction Non-invasive brain stimulation is an emerging field in clinical neurology. Frequency- and noise specific stimulation can induce specific oscillatory activity patterns that are associated with a recovery-related brain state. However, our understanding of the underlying neurophysiological mechanisms remain incomplete, which is a major limitation for transitioning into clinical practice. One notion is the existence of a common framework with oscillatory and noisy components. Here, we studied the influence of a random noise stimulation with added alternating current stimulation in the retinofugal pathway on the entrainment of alpha-power over the visual cortex. Methods We applied retinofugal alternating current stimulation (rtACS) in each subject’s individual alpha-frequency (IAF) and retinofugal random noise stimulation (rtRNS) on 10 healthy subjects. Varying current intensities were applied over a stimulation period of 30 s followed by a 40 s free interval respectively. EEG measurements were recorded prior, during and after stimulation over a total duration of 12 min. Results rtACS in IAF entrains occipital alpha power with increasing stimulation intensity. rtRNS has strong facilitatory effects on alpha activity ( p p = 0.069). Discussion As random noise stimulation improved alpha power in all intensity ranges while showing a trend towards inhibiting alpha activity in combination with oscillatory stimulation, we argue that our findings are suggestive of noise acting as a controller of oscillations in neural networks. Hence, a common framework consisting of oscillatory and noisy stimulation might modify brain-states more effectively.

Investigating the effects of noisy stimulation on the retinofugal pathway

Investigating the effects of noisy stimulation on the retinofugal pathway

Noisy Galvanic Vestibular Stimulation Modulates the Amplitude of EEG Synchrony Patterns

Noisy galvanic vestibular stimulation has been associated with numerous cognitive and behavioural effects, such as enhancement of visual memory in healthy individuals, improvement of visual deficits in stroke patients, as well as possibly improvement of motor function in Parkinson’s disease; yet, the mechanism of action is unclear. Since Parkinson’s and other neuropsychiatric diseases are characterized by maladaptive dynamics of brain rhythms, we investigated whether noisy galvanic vestibular stimulation was associated with measurable changes in EEG oscillatory rhythms within theta (4–7.5 Hz), low alpha (8–10 Hz), high alpha (10.5–12 Hz), beta (13–30 Hz) and gamma (31–50 Hz) bands. We recorded the EEG while simultaneously delivering noisy bilateral, bipolar stimulation at varying intensities of imperceptible currents – at 10, 26, 42, 58, 74 and 90% of sensory threshold – to ten neurologically healthy subjects. Using standard spectral analysis, we investigated the transient aftereffects of noisy stimulation on rhythms. Subsequently, using robust artifact rejection techniques and the Least Absolute Shrinkage Selection Operator regression and cross-validation, we assessed the combinations of channels and power spectral features within each EEG frequency band that were linearly related with stimulus intensity. We show that noisy galvanic vestibular stimulation predominantly leads to a mild suppression of gamma power in lateral regions immediately after stimulation, followed by delayed increase in beta and gamma power in frontal regions approximately 20–25 s after stimulation ceased. Ongoing changes in the power of each oscillatory band throughout frontal, central/parietal, occipital and bilateral electrodes predicted the intensity of galvanic vestibular stimulation in a stimulus-dependent manner, demonstrating linear effects of stimulation on brain rhythms. We propose that modulation of neural oscillations is a potential mechanism for the previously-described cognitive and motor effects of vestibular stimulation, and noisy galvanic vestibular stimulation may provide an additional non-invasive means for neuromodulation of functional brain networks.

External stimulation induces switches between neural oscillations: an illustrative feedback model

Oscillatory activity in neural systems plays an important role in information processing. For instance, specific cognitive tasks evoke oscillations in certain frequency bands, which are characteristic for the corresponding experimental paradigm. Examples are the theta-rhythm (4-8Hz) in memory tasks or the gamma-rhythm (3060Hz) in visual binding tasks. Moreover, changing the external activation of a neural system may lead to the activation of additional oscillations in specific frequency bands or may even yield a switch between specific frequency bands. In the last years, such a switch has been found experimentally in-vivo in some neural systems, such as in the mammalian olfactory bulb [1] and in the primary sensory area of weakly electric fish [2]. Both systems exhibit a feedback topology between two or more interacting populations and an external stimulation that originates from another brain area. More detailed, the mammalian olfactory bulb is subject to the activation by the receptor cells, which are rhythmically activated by odours due to the breathing cycle. Applying an odour, the Local Field Potentials (LFP) exhibit high gamma-activity in the inhalation phase and switches to high beta-activity in the exhalationn phase. In weakly electric fish, the primary sensory area ELL receives input from electro-receptors on the skin of the fish. In the case of spatially uncorrelated noisy stimulation of the skin receptors, single neurons fire mainly in the beta-band, while a strong spatial input correlation switches on an additional spiking rythm in the gamma-band. To explain such switches in frequency bands in feedback systems, the current work presents a neural population rate model of a rather general feedback system [3], that allows to derive mathematical conditions for the switch between frequency bands subject to the spatial correlation of an external input. The proposed model explains the switch between frequency bands as a selective activation of linear spatial oscillation modes. The centre frequency and the width of the bands and the properties of the spatial oscillation modes to be activated result from the intrinsic properties of the feedback system. Such properties are the synaptic time constants and delays between system components. Moreover, the spatial interactions in the feedback system determines the spatial frequency of the oscillation modes. The essential selection of the frequency band happens via the coupling of the spatially extended external input with the spatial modes. For illustration reasons, one may think of a guitar string, whose oscillation frequency depends on the manner how it is stricken.

Stochastic Resonance‐Like Phenomena in the Context of the Alimentary Tract

External noisy stimulation induces Ca(2+) binding protein dynamics synchronization. Cell coupling chain fluctuation may lead to internal order facilitating information (generation and propagation). Body rhythms require cellular synchronization by interaction between nonlinear biological systems in a fluctuating environment. Could stochastic resonance add or subtract efficacy in digestive tolerogenic selection or disease?

Behavioural and physiological reactivity to noise in the newborn

To assess the electromyographic (EMG) and behavioural reactivity of a group of newborn infants exposed to noisy stimulation of various intensity recorded in the Paediatric intensive care Unit (PICU). The study was performed at the nursery of the Paediatrics Department (University of Padova) on a group of 21 healthy newborns (mean 39 weeks of gestation), assessed between 24 and 72 h after birth. The study involved taking EMG recordings of the corrugator supercilii muscle and assessing the infant's behaviour at the baseline (15 seconds before stimulation), during noisy stimulation (for 1-2 seconds) and during recovery (15 seconds in three subphases). The noises, previously recorded in PICU, had four different intensities and were administered in random order to all infants. Descriptive analysis and repeated-measures analysis of variance (anova) were performed on the EMG and behavioural data. The infants demonstrated a significant reaction to the noises both in the EMG recordings and in behavioural changes, especially during intense noisy stimulation. The reaction lasted longer than the stimulation period, preventing the infants from returning to the baseline condition. Exposure to high-intensity noise produced in PICU causes evident behavioural and physiological effects (EMG). This is a field of study that could have important repercussions, given the medium- and long-term effects of repeated noise stimulation.

Noise-induced transition in excitable neuron models.

We studied the influence of noisy stimulation on the Hodgkin-Huxley neuron model. Rather than examining the noise-related variability of the discharge times of the model--as has been done previously--our study focused on the effect of noise on the stationary distributions of the membrane potential and gating variables of the model. We observed that a gradual increase in the noise intensity did not result in a gradual change of the distributions. Instead, we could identify a critical intermediate noise range in which the shapes of the distributions underwent a drastic qualitative change. Namely, they moved from narrow unimodal Gaussian-like shapes associated with low noise intensities to ones that spread widely at large noise intensities. In particular, for the membrane potential and the sodium activation variable, the distributions changed from unimodal to bimodal. Thus, our investigation revealed a noise-induced transition in the Hodgkin-Huxley model. In order to further characterize this phenomenon, we considered a reduced one-dimensional model of an excitable system, namely the active rotator. For this model, our analysis indicated that the noise-induced transition is associated with a deterministic bifurcation of approximate equations governing the dynamics of the mean and variance of the state variable. Finally, we shed light on the possible functional importance of this noise-induced transition in neuronal coding by determining its effect on the spike timing precision in models of neuronal ensembles.