Prefrontal tDCS Decreases Pain in Patients with Multiple Sclerosis.

BACKGROUND: In the last few years, transcranial direct current stimulation (tDCS) has emerged as an appealing therapeutic option to improve brain functions. Promising data support the role of prefrontal tDCS in augmenting cognitive performance and ameliorating several neuropsychiatric symptoms, namely pain, fatigue, mood disturbances, and attentional impairment. Such symptoms are commonly encountered in patients with multiple sclerosis (MS). OBJECTIVE: The main objective of the current work was to evaluate the tDCS effects over the left dorsolateral prefrontal cortex (DLPFC) on pain in MS patients.Our secondary outcomes were to study its influence on attention, fatigue, and mood. MATERIALS AND METHODS: Sixteen MS patients with chronic neuropathic pain were enrolled in a randomized, sham-controlled, and cross-over study.Patients randomly received two anodal tDCS blocks (active or sham), each consisting of three consecutive daily tDCS sessions, and held apart by 3 weeks. Evaluations took place before and after each block. To evaluate pain, we used the Brief Pain Inventory (BPI) and the Visual Analog Scale (VAS). Attention was assessed using neurophysiological parameters and the Attention Network Test (ANT). Changes in mood and fatigue were measured using various scales. RESULTS: Compared to sham, active tDCS yielded significant analgesic effects according to VAS and BPI global scales.There were no effects of any block on mood, fatigue, or attention. CONCLUSION: Based on our results, anodal tDCS over the left DLPFC appears to act in a selective manner and would ameliorate specific symptoms, particularly neuropathic pain. Analgesia might have occurred through the modulation of the emotional pain network. Attention, mood, and fatigue were not improved in this work. This could be partly attributed to the short protocol duration, the small sample size, and the heterogeneity of our MS cohort. Future large-scale studies can benefit from comparing the tDCS effects over different cortical sites, changing the stimulation montage, prolonging the duration of protocol, and coupling tDCS with neuroimaging techniques for a better understanding of its possible mechanism of action.

Effects of transcranial random noise stimulation (tRNS) on affect, pain and attention in multiple sclerosis.

PURPOSE: Pain and cognitive impairment are frequent symptoms in patients with multiple sclerosis (MS). Neglecting experimental pain and paying attention to demanding tasks is reported to decrease the pain intensity. Little is known about the interaction between chronic neuropathic pain and attention disorders in MS. Recently, transcranial direct current stimulation (tDCS) was used to modulate various cognitive and motor symptoms in MS. We aimed to study the effects of transcranial random noise stimulation (tRNS), a form of transcranial electric stimulation, over the left dorsolateral prefrontal cortex (DLPFC) on attention and neuropathic pain in MS patients. METHODS: 16 MS patients were included in a randomized, sham-controlled, cross-over study. Each patient randomly received two tRNS blocks, separated by three weeks of washout interval. Each block consisted of three consecutive daily sessions of either active or sham tRNS. The patients were evaluated for pain, attention and mood and further underwent an electrophysiological evaluation. RESULTS: Compared to sham, tRNS showed a trend to decrease the N2-P2 amplitudes of pain related evoked potentials and improve pain ratings. Attention performance and mood scales did not change after stimulations. CONCLUSIONS: This study suggests the role of tRNS in pain modulation, which could have been more evident with longer stimulation protocols.

Tremor in multiple sclerosis: The intriguing role of the cerebellum.

Tremor is frequently encountered in multiple sclerosis (MS) patients. However, its underlying pathophysiological mechanisms remain poorly understood. Our aim was to assess the potential role of the cerebellum and brain stem structures in the generation of MS tremor.We performed accelerometric (ACC) and electromyographic(EMG) assessment of tremor in 32MS patients with manual clumsiness. In addition to clinical examination, patients underwent a neurophysiological exploration of the brainstem and cerebellar functions,which consisted of blink and masseter inhibitory reflexes, cerebello-thalamo-cortical inhibition (CTCi), and somatosensory evoked potentials. Tremor was clinically visible in 18 patients and absent in 14. Patients with visible tremor had more severe score of ataxia and clinical signs of cerebellar dysfunction, as well as a more reduced CTCi on neurophysiological investigation. However, ACC and EMG recordings confirmed the presence of a real rhythmic activity in only one patient. In most MS patients, the clinically visible tremor corresponded to a pseudorhythmic activity without coupling between ACC and EMG recordings. Cerebellar dysfunction may contribute to the occurrence of this pseudorhythmic activity mimicking tremor during posture and movement execution.

Bilateral thalamic stimulation induces insomnia in patients treated for intractable tremor.

STUDY OBJECTIVES: To explore the influence of acute bilateral ventral intermediate thalamic nucleus (VIM) stimulation on sleep. DESIGN: Three consecutive full-night polysomnography recordings were made in the laboratory. After the habituation night, a random order for night ON-stim and OFF-stim was applied for the second and third nights. SETTING: Sleep disorders unit of a university hospital. PATIENTS: Eleven patients with bilateral stimulation of the ventral intermediate nucleus of the thalamus (VIM) for drug-resistant tremor. MEASUREMENTS: Sleep measures on polysomnography. RESULTS: Total sleep time was reduced during night ON-stim compared to OFF- stim, as well as rapid eye movement sleep percentage while the percentage of N2 increased. Wakefulness after sleep onset time was increased. CONCLUSION: Our results show that bilateral stimulation of the VIM nuclei reduces sleep and could be associated with insomnia.

An automatic electroencephalography blinking artefact detection and removal method based on template matching and ensemble empirical mode decomposition.

Electrooculographic (EOG) artefacts are one of the most common causes of Electroencephalogram (EEG) distortion. In this paper, we propose a method for EOG Blinking Artefacts (BAs) detection and removal from EEG. Normalized Correlation Coefficient (NCC), based on a predetermined BA template library was used for detecting the BA. Ensemble Empirical Mode Decomposition (EEMD) was applied to the contaminated region and a statistical algorithm determined which Intrinsic Mode Functions (IMFs) correspond to the BA. The proposed method was applied in simulated EEG signals, which were contaminated with artificially created EOG BAs, increasing the Signal-to-Error Ratio (SER) of the EEG Contaminated Region (CR) by 35 dB on average.

Phase space and power spectral approaches for EEG-based automatic sleep-wake classification in humans: a comparative study using short and standard epoch lengths.

Sleep disorders in humans have become a public health issue in recent years. Sleep can be analysed by studying the electroencephalogram (EEG) recorded during a night's sleep. Alternating between sleep-wake stages gives information related to the sleep quality and quantity since this alternating pattern is highly affected during sleep disorders. Spectral composition of EEG signals varies according to sleep stages, alternating phases of high energy associated to low frequency (deep sleep) with periods of low energy associated to high frequency (wake and light sleep). The analysis of sleep in humans is usually made on periods (epochs) of 30-s length according to the original Rechtschaffen and Kales sleep scoring manual. In this work, we propose a new phase space-based (mainly based on Poincaré plot) algorithm for automatic classification of sleep-wake states in humans using EEG data gathered over relatively short-time periods. The effectiveness of our approach is demonstrated through a series of experiments involving EEG data from seven healthy adult female subjects and was tested on epoch lengths ranging from 3-s to 30-s. The performance of our phase space approach was compared to a 2-dimensional state space approach using the power spectral (PS) in two selected human-specific frequency bands. These powers were calculated by dividing integrated spectral amplitudes at selected human-specific frequency bands. The comparison demonstrated that the phase space approach gives better performance in the case of short as well as standard 30-s epoch lengths.

Automatic removal of high-amplitude stimulus artefact from neuronal signal recorded in the subthalamic nucleus.

Over the last few years, deep brain stimulation (DBS) with targets such as the subthalamic nucleus or the pallidum were found to be beneficial in the treatment of Parkinson's disease and dystonia. The investigation of the mechanisms of action of DBS by recording concomitant neural activities in basal ganglia is hampered by the large stimulus artefacts (SA). Approaches to remove the SA with conventional filters, or other conventional digital methods, are not always effective due to the significant overlap between the spectral contents of the neuronal signal and the SA. Thus, such approaches may produce a significant residual SA or alter the neuronal signal dynamics by removing its frequency contents. In this work, we propose a method based on an on-line SA template extraction and on the Ensemble empirical mode decomposition (EEMD) to automatically detect and remove the dynamics of the SA without altering the embedded dynamics of the neuronal signal during stimulation. The results, based on real signals recorded in the subthalamic nucleus during Motor cortex stimulation (MCS) experiments, show that this technique, which may be applied on-line, effectively identifies, separates and removes the SA, and uncovers neuronal potentials superimposed on the artefact.

Nonlinear and nonstationary framework for feature extraction and classification of motor imagery.

In this work we investigate a nonlinear approach for feature extraction of Electroencephalogram (EEG) signals in order to classify motor imagery for Brain Computer Interface (BCI). This approach is based on the Empirical Mode Decomposition (EMD) and band power (BP). The EMD method is a data-driven technique to analyze non-stationary and nonlinear signals. It generates a set of stationary time series called Intrinsic Mode Functions (IMF) to represent the original data. These IMFs are analyzed with the power spectral density (PSD) to study the active frequency range correspond to the motor imagery for each subject. Then, the band power is computed within a certain frequency range in the channels. Finally, the data is reconstructed with only the specific IMFs and then the band power is employed on the new database. The classification of motor imagery was performed by using two classifiers, Linear Discriminant Analysis (LDA) and Hidden Markov Models (HMMs). The results obtained show that the EMD method allows the most reliable features to be extracted from EEG and that the classification rate obtained is higher and better than using only the direct BP approach.