Self-administered non-invasive vagus nerve stimulation therapy for severe pharmacoresistant restless legs syndrome: outcomes at 6 months.

Severe pharmacoresistant restless legs syndrome (RLS) is difficult to manage and a source of suffering to patients. We studied the effectiveness at 6 months of an innovative treatment: transauricular vagus nerve stimulation (taVNS) in the left cymba concha in a case series of 15 patients, 53% male, mean (SD) age 62.7 (12.3) years with severe pharmacoresistant RLS (mean [SD] International Restless Legs Rating Scale [IRLS] score of 31.9 [2.9]) at baseline. Following an 8-week non-randomised hospital-based study with eight 1-h sessions of taVNS, patients were trained to administer taVNS at home and were followed up for 6 months. The primary outcome measure was the IRLS score, secondary outcome measures were quality of life, mood disorders using the Hospital Anxiety and Depression scale (HAD) subscales for depression (HADD) and anxiety (HADA). At the 6-month follow-up 13/15 patients continued to use weekly taVNS. Symptom severity decreased (mean [SD] IRLS score 22.2 [9.32] at 6 months, p = 0.0005). Four of the 15 patients had an IRLS score of <20 at 6 months and two an IRLS score of 5. Quality of life significantly improved compared to baseline (mean [SD] score at baseline 49.3 [18.1] versus 65.66 [22.58] at 6 months, p = 0.0005) as did anxiety and depression symptoms (mean [SD] HADA score at baseline 8.9 [5.4] versus 7.53 [4.42] at 6 months, p = 0.029; and HADD score at baseline 5.2 [4.5] versus 4.73 [4.44] at 6 months, p = 0.03). Treatment was well tolerated, and no adverse events were reported. Our case series shows a potential role for self-administered taVNS in patients with severe pharmacoresistant RLS. Randomised controlled trials are needed to confirm the utility of taVNS.

Noninvasive Vagus Nerve Stimulation: A New Therapeutic Approach for Pharmacoresistant Restless Legs Syndrome.

AIMS: This work aimed to study the effect of noninvasive vagus nerve stimulation on severe restless legs syndrome (RLS) resistant to pharmacotherapy. MATERIALS AND METHODS: Patients with severe pharmacoresistant RLS were recruited from a tertiary care sleep center. Intervention was one-hour weekly sessions of transauricular vagus nerve stimulation (tVNS) in the left cymba concha, for eight weeks. The primary outcome measure was the score on the International Restless Legs Rating Scale (IRLS); secondary outcome measures were quality of life (Restless Legs Syndrome Quality of Life scale [RLSQOL]), mood disorders using the Hospital Anxiety and Depression scale subscale for depression (HADD) and Hospital Anxiety and Depression scale subscale for anxiety (HADA), and objective sleep latency, sleep duration, efficiency, and leg movement time measured by actigraphy. RESULTS: Fifteen patients, 53% male, aged mean 62.7 ± 12.3 years with severe RLS, reduced quality of life, and symptoms of anxiety and depression, were included. The IRLS improved from baseline to session eight: IRLS 31.9 ± 2.9 vs 24.6 ± 5.9 p = 0.0003. Of these participants, 27% (4/15) had a total response with a decrease below an IRLS score of 20; 40% (6/15) a partial response with an improvement in the IRLS > 5 but an IRLS above 20; and 33% (5/15) were nonresponders. After tVNS, quality of life improved (RLSQOL 49.3 ± 18.1 vs 80.0 ± 19.6 p = 0.0005), as did anxiety (HADA 8.9 ± 5.4 vs 6.2 ± 5.0 p = 0.001) and depression (HADD 5.2 ± 4.5 vs 4.0 ± 4.0 p = 0.01). No significant change was found in actigraphic outcome measures. CONCLUSIONS: In this pilot study, tVNS improved the symptoms of RLS in 66% of participants (10/15) with severe pharmacoresistant RLS, with concomitant improvements in quality of life and mood. Randomized controlled trials evaluating therapeutic efficacy of tVNS in RLS are needed to confirm these promising findings.

SenseJoy, a pluggable solution for assessing user behavior during powered wheelchair driving tasks.

BACKGROUND: The complex task of Electric Powered Wheelchairs (EPW) prescription relies mainly on personal experience and subjective observations despite standardized processes and protocols. The most informative measurements come from joystick monitoring, but recording direct joystick outputs require to disassemble the joystick. We propose a new solution called "SenseJoy" that is easy to plug on a joystick and is suitable to characterize the driver behavior by estimating the joystick command. METHODS: SenseJoy is a pluggable system embedded on EPW built with a 3D accelerometer and a 2D gyrometer placed within the joystick and another 3D accelerometer located at the basis of the joystick. Data is sampled at 39 Hz and processed offline. First, SenseJoy sensitivity is assessed on wheelchair driving tasks performed by a group of 8 drivers (31 ± 8 years old, including one driver with left hemiplegia, one with cerebral palsy) in a lab environment. Direct joystick measurements are compared with SenseJoy estimations in different driving exercises. A second group of 5 drivers is recorded in the ecological context of a rehabilitation center (41 ± 10 years old, with two tetraplegic drivers, one tetraplegic driver with cognitive disorder, one driver post-stroke, one driver with right hemiplegia). The measurements from all groups of drivers are evaluated with an unsupervised statistical analysis, to estimate driving profile clusters. RESULTS: The SenseJoy is able to measure the EPW joystick inclination angles with a resolution of 1.31% and 1.23% in backward/forward and left/right directions respectively. A statistical validation ensures that the classical joystick-based indicators are equivalent when acquired with the SenseJoy or with a direct joystick output connection. Using an unsupervised methodology, based on a similarity matrix between subjects, it is possible to characterize the driver profile from real data. CONCLUSION: SenseJoy is a pluggable system for assessing the joystick controls during EPW driving tasks. This system can be plugged on any EPW equipped with a joystick control interface. We demonstrate that it correctly estimates the performance indicators and it is able to characterize driving profile. The system is suitable and efficient to assist therapists in their recommendation, by providing objective measures with a fast installation process.

A systematic evaluation of Euclidean alignment with deep learning for EEG decoding.

Objective:Electroencephalography signals are frequently used for various Brain-Computer interface (BCI) tasks. While deep learning (DL) techniques have shown promising results, they are hindered by the substantial data requirements. By leveraging data from multiple subjects, transfer learning enables more effective training of DL models. A technique that is gaining popularity is Euclidean alignment (EA) due to its ease of use, low computational complexity, and compatibility with DL models. However, few studies evaluate its impact on the training performance of shared and individual DL models. In this work, we systematically evaluate the effect of EA combined with DL for decoding BCI signals.Approach:We used EA as a pre-processing step to train shared DL models with data from multiple subjects and evaluated their transferability to new subjects.Main results:Our experimental results show that it improves decoding in the target subject by 4.33% and decreases convergence time by more than 70%. We also trained individual models for each subject to use as a majority-voting ensemble classifier. In this scenario, using EA improved the 3-model ensemble accuracy by 3.71%. However, when compared to the shared model with EA, the ensemble accuracy was 3.62% lower.Significance:EA succeeds in the task of improving transfer learning performance with DL models and, could be used as a standard pre-processing technique.

Measuring neuronal avalanches to inform brain-computer interfaces.

Large-scale interactions among multiple brain regions manifest as bursts of activations called neuronal avalanches, which reconfigure according to the task at hand and, hence, might constitute natural candidates to design brain-computer interfaces (BCIs). To test this hypothesis, we used source-reconstructed magneto/electroencephalography during resting state and a motor imagery task performed within a BCI protocol. To track the probability that an avalanche would spread across any two regions, we built an avalanche transition matrix (ATM) and demonstrated that the edges whose transition probabilities significantly differed between conditions hinged selectively on premotor regions in all subjects. Furthermore, we showed that the topology of the ATMs allows task-decoding above the current gold standard. Hence, our results suggest that neuronal avalanches might capture interpretable differences between tasks that can be used to inform brain-computer interfaces.

Accounting for endogenous effects in decision-making with a non-linear diffusion decision model.

The Drift-Diffusion Model (DDM) is widely accepted for two-alternative forced-choice decision paradigms thanks to its simple formalism and close fit to behavioral and neurophysiological data. However, this formalism presents strong limitations in capturing inter-trial dynamics at the single-trial level and endogenous influences. We propose a novel model, the non-linear Drift-Diffusion Model (nl-DDM), that addresses these issues by allowing the existence of several trajectories to the decision boundary. We show that the non-linear model performs better than the drift-diffusion model for an equivalent complexity. To give better intuition on the meaning of nl-DDM parameters, we compare the DDM and the nl-DDM through correlation analysis. This paper provides evidence of the functioning of our model as an extension of the DDM. Moreover, we show that the nl-DDM captures time effects better than the DDM. Our model paves the way toward more accurately analyzing across-trial variability for perceptual decisions and accounts for peri-stimulus influences.

Class-distinctiveness-based frequency band selection on the Riemannian manifold for oscillatory activity-based BCIs: preliminary results.

Considering user-specific settings is known to enhance Brain-Computer Interface (BCI) performances. In particular, the optimal frequency band for oscillatory activity classification is highly user-dependent and many frequency band selection methods have been developed in the past two decades. However, it is not well studied whether those conventional methods can be efficiently applied to the Riemannian BCIs, a recent family of BCI systems that utilize the non-Euclidean nature of the data unlike conventional BCI pipelines. In this paper, we proposed a novel frequency band selection method working on the Riemannian manifold. The frequency band is selected considering the class distinctiveness as quantified based on the inter-class distance and the intra-class variance on the manifold. An advantage of this method is that the frequency bandwidth can be adjusted for each individual without intensive optimization steps. In a comparative experiment using a public dataset of motor imagery-based BCI, our method showed a substantial improvement in average accuracy over both a fixed broad frequency band and a popular conventional frequency band selection method. In particular, our method substantially improved performances for subjects with initially low accuracies. This preliminary result suggests the importance of developing new user-specific setting algorithms considering the manifold properties, rather than directly applying methods developed prior to the rise of the Riemannian BCIs.

Functional Connectivity Ensemble Method to Enhance BCI Performance (FUCONE).

OBJECTIVE: Relying on the idea that functional connectivity provides important insights on the underlying dynamic of neuronal interactions, we propose a novel framework that combines functional connectivity estimators and covariance-based pipelines to improve the classification of mental states, such as motor imagery. METHODS: A Riemannian classifier is trained for each estimator and an ensemble classifier combines the decisions in each feature space. A thorough assessment of the functional connectivity estimators is provided and the best performing pipeline among those tested, called FUCONE, is evaluated on different conditions and datasets. RESULTS: Using a meta-analysis to aggregate results across datasets, FUCONE performed significantly better than all state-of-the-art methods. CONCLUSION: The performance gain is mostly imputable to the improved diversity of the feature spaces, increasing the robustness of the ensemble classifier with respect to the inter- and intra-subject variability. SIGNIFICANCE: Our results offer new insights into the need to consider functional connectivity-based methods to improve the BCI performance.

Randomized Cross Over Study Assessing the Efficacy of Non-invasive Stimulation of the Vagus Nerve in Patients With Axial Spondyloarthritis Resistant to Biotherapies: The ESNV-SPA Study Protocol.

Axial spondyloarthritis (SpA), is a major cause of chronic pain and disability that profoundly alters the quality of life of patients. Nearly half of patients with SpA usually develop drug resistance. Non-pharmacological treatments targeting inflammation are an attractive alternative to drug administration. Vagus nerve stimulation (VNS), by promoting a cholinergic anti-inflammatory reflex holds promise for treating inflammatory disease. Inflammatory reflex signaling, which is enhanced by electrically stimulating the vagus nerve, significantly reduces cytokine production and attenuates disease severity in animal models of endotoxemia, sepsis, colitis, and other preclinical models of inflammatory diseases. It has been proposed that vagal efferent fibers release acetylcholine (Ach), which can interact with α7-subunit-containing nicotinic receptors expressed by tissue macrophages and other immune cells to rapidly inhibit the synthesis/release of pro-inflammatory cytokines such as TNFα, IL-1ß, IL-6, and IL-18. External vagal nerve stimulation devices are now available that do not require surgery nor implantation to non-invasively stimulate the vagal nerve. This double-blind randomized cross-over clinical trial aims to study the change in SpA disease activity, according to Assessment in Ankylosing Spondylitis 20 (ASAS20) definition, after 12 weeks of non-invasive VNS treatment vs. non-specific dummy stimulation (control group). One hundred and twenty adult patients with drug resistant SpA, meeting the ASAS classification criteria, will be included in the study. Patients will be randomized into two parallel groups according to a cross over design: either active VNS for 12 weeks, then dummy stimulation for 12 weeks, or dummy stimulation for 12 weeks, then active VNS for 12 weeks. The two stimulation periods will be separated by a 4 weeks wash-out period. A transcutaneous auricular vagus nerve stimulator Tens Eco Plus SCHWA MEDICOTM France will be used in this study. The active VNS stimulation will be applied in the cymba conchae of the left ear upon the auricular branch of the vagus nerve, using low intensity (2-5 mA), once à week, during 1 h. Dummy stimulation will be performed under the same conditions and parameters as active VNS stimulation, but at an irrelevant anatomical site: the left ear lobule. This multicenter study was registered on ClinicalTrials.gov: NCT04286373.

Riemann-Based Algorithms Assessment for Single- and Multiple-Trial P300 Classification in Non-Optimal Environments.

The P300 wave is commonly used in Brain-Computer Interface technology due to its higher bit rates when compared to other BCI paradigms. P300 classification pipelines based on Riemannian Geometry provide accuracies on par with state-of-the-art pipelines, without having the need for spatial filters, and also possess the ability to be calibrated with little data. In this study, five different P300 detection pipelines are compared, with three of them using Riemannian Geometry as either feature extraction or classification algorithms. The goal of this study is to assess the viability of Riemannian Geometry-based methods in non-optimal environments with sudden background noise changes, rather than maximizing classification accuracy values. For fifteen subjects, the average single-trial accuracy obtained for each pipeline was: 56.06% for Linear Discriminant Analysis (LDA), 72.13% for Bayesian Linear Discriminant Analysis (BLDA), 63.56% for Riemannian Minimum Distance to Mean (MDM), 69.22% for Riemannian Tangent Space with Logistic Regression (TS-LogR), and 63.30% for Riemannian Tangent Space with Support Vector Machine (TS-SVM). The results are higher for the pipelines based on BLDA and TS-LogR, suggesting that they could be viable methods for the detection of the P300 component when maximizing the bit rate is needed. For multiple-trial classification, the BLDA pipeline converged faster towards higher average values, closely followed by the TS-LogR pipeline. The two remaining Riemannian methods' accuracy also increases with the number of trials, but towards a lower value compared to the aforementioned ones. Single-stimulus detection metrics revealed that the TS-LogR pipeline can be a viable classification method, as its results are only slightly lower than those obtained with BLDA. P300 waveforms were also analyzed to check for evidence of the component being elicited. Finally, a questionnaire was used to retrieve the most intuitive focusing methods employed by the subjects.

SMR/Theta Neurofeedback Training Improves Cognitive Performance and EEG Activity in Elderly With Mild Cognitive Impairment: A Pilot Study.

Background: Neurofeedback (NF) training, as a method of self-regulation of brain activity, may be beneficial in elderly patients with mild cognitive impairment (MCI). In this pilot study, we investigated whether a sensorimotor (SMR)/theta NF training could improve cognitive performance and brain electrical activity in elderly patients with MCI. Methods: Twenty elderly patients with MCI were assigned to 20 consecutive sessions of sensorimotor (SMR)/theta NF training, during 10 weeks, on a basis of two sessions each week. Neuropsychological assessments and questionnaires, as well as electroencephalogram (EEG), were performed and compared between baseline (T0), after the last NF training session at 10 weeks (T1), and 1-month follow-up (T2). Results: Repeated measures ANOVA revealed that from baseline to post-intervention, participants showed significant improvement in the Montreal cognitive assessment (MoCa, F = 4.78; p = 0.012), the delayed recall of the Rey auditory verbal learning test (RAVLT, F = 3.675; p = 0.032), the Forward digit span (F = 13.82; p < 0.0001), the Anxiety Goldberg Scale (F = 4.54; p = 0.015), the Wechsler Adult Intelligence Score-Fourth Edition (WAIS-IV; F = 24.75; p < 0.0001), and the Mac Nair score (F = 4.47; p = 0.016). EEG theta power (F = 4.44; p = 0.016) and alpha power (F = 3.84; p = 0.027) during eyes-closed resting-state significantly increased after the NF training and showed sustained improvement at a 1-month follow-up. Conclusion: Our results suggest that NF training could be effective to reduce cognitive deficits in elderly patients with MCI and improve their EEG activity. If these findings are confirmed by randomized controlled studies with larger samples of patients, NF could be seen as a useful non-invasive, non-pharmacological tool for preventing further decline, rehabilitation of cognitive function in the elderly. Clinical Trial Registration: This pilot study was a preliminary step before the trial registered in www.ClinicalTrials.gov, under the number of NCT03526692.

Randomized Controlled Study Evaluating Efficiency of Low Intensity Transcranial Direct Current Stimulation (tDCS) for Dyspnea Relief in Mechanically Ventilated COVID-19 Patients in ICU: The tDCS-DYSP-COVID Protocol.

The severe respiratory distress syndrome linked to the new coronavirus disease (COVID-19) includes unbearable dyspneic suffering which contributes to the deterioration of the prognosis of patients in intensive care unit (ICU). Patients are put on mechanical ventilation to reduce respiratory suffering and preserve life. Despite this mechanical ventilation, most patients continue to suffer from dyspnea. Dyspnea is a major source of suffering in intensive care and one of the main factors that affect the prognosis of patients. The development of innovative methods for its management, especially non-drug management is more than necessary. In recent years, numerous studies have shown that transcranial direct current stimulation (tDCS) could modulate the perception of acute or chronic pain. In the other hand, it has been shown that the brain zones activated during pain and dyspnea are close and/or superimposed, suggesting that brain structures involved in the integration of aversive emotional component are shared by these two complex sensory experiences. Therefore, it can be hypothesized that stimulation by tDCS with regard to the areas which, in the case of pain have activated one or more of these brain structures, may also have an effect on dyspnea. In addition, our team recently demonstrated that the application of tDCS on the primary cortical motor area can modulate the excitability of the respiratory neurological pathways. Indeed, tDCS in anodal or cathodal modality reduced the excitability of the diaphragmatic cortico-spinal pathways in healthy subjects. We therefore hypothesized that tDCS could relieve dyspnea in COVID-19 patients under mechanical ventilation in ICU. This study was designed to evaluate effects of two modalities of tDCS (anodal and cathodal) vs. placebo, on the relief of dyspnea in COVID-19 patients requiring mechanical ventilation in ICU. Trial Registration: This protocol is derived from the tDCS-DYSP-REA project registered on ClinicalTrials.gov NCT03640455. It will however be registered under its own NCT number.

Influence of extracellular oscillations on neural communication: a computational perspective.

Neural communication generates oscillations of electric potential in the extracellular medium. In feedback, these oscillations affect the electrochemical processes within the neurons, influencing the timing and the number of action potentials. It is unclear whether this influence should be considered only as noise or it has some functional role in neural communication. Through computer simulations we investigated the effect of various sinusoidal extracellular oscillations on the timing and number of action potentials. Each simulation is based on a multicompartment model of a single neuron, which is stimulated through spatially distributed synaptic activations. A thorough analysis is conducted on a large number of simulations with different models of CA3 and CA1 pyramidal neurons which are modeled using realistic morphologies and active ion conductances. We demonstrated that the influence of the weak extracellular oscillations, which are commonly present in the brain, is rather stochastic and modest. We found that the stronger fields, which are spontaneously present in the brain only in some particular cases (e.g., during seizures) or that can be induced externally, could significantly modulate spike timings.

Time-frequency selection in two bipolar channels for improving the classification of motor imagery EEG.

Time and frequency information is essential to feature extraction in a motor imagery BCI, in particular for systems based on a few channels. In this paper, we propose a novel time-frequency selection method based on a criterion called Time-frequency Discrimination Factor (TFDF) to extract discriminative event-related desynchronization (ERD) features for BCI data classification. Compared to existing methods, the proposed approach generates better classification performances (mean kappa coefficient= 0.62) on experimental data from the BCI competition IV dataset IIb, with only two bipolar channels.