Severe Neuromuscular Disabilities Research Articles

Short-chain acyl-CoA dehydrogenase deficiency: from gene to cell pathology and possible disease mechanisms.

Short-chain acyl-CoA dehydrogenase deficiency (SCADD) is an inherited disorder of mitochondrial fatty acid oxidation that is characterized by the presence of increased butyrylcarnitine and ethylmalonic acid (EMA) concentrations in plasma and urine. Individuals with symptomatic SCADD may show relatively severe phenotype, while the majority of those who are diagnosed through newborn screening by tandem mass spectrometry may remain asymptomatic. As such, the associated clinical symptoms are very diverse, ranging from severe metabolic or neuromuscular disabilities to asymptomatic. Molecular analysis of affected individuals has identified rare gene variants along with two common gene variants, c.511C>T and c.625G>A. In vitro studies have demonstrated that the common variants as well as the great majority of rare variants, which are missense variants, impair folding, that may lead to toxic accumulation of the encoded protein, and/or metabolites, and initiate excessive production of ROS and chronic oxidative stress. It has been suggested that this cell toxicity in combination with yet unknown factors can trigger disease development. This association and the full implications of SCADD are not commonly appreciated. Accordingly, there is a worldwide discussion of the relationship of clinical manifestation to SCADD, and whether SCAD gene variants are disease associated at all. Therefore, SCADD is not part of the newborn screening programs in most countries, and consequently many patients with SCAD gene variants do not get a diagnosis and the possibilities to be followed up during development.

P300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls

ObjectiveBrain-computer interfaces (BCIs) aimed at restoring communication to people with severe neuromuscular disabilities often use event-related potentials (ERPs) in scalp-recorded EEG activity. Up to the present, most research and development in this area has been done in the laboratory with young healthy control subjects. In order to facilitate the development of BCI most useful to people with disabilities, the present study set out to: (1) determine whether people with amyotrophic lateral sclerosis (ALS) and healthy, age-matched volunteers (HVs) differ in the speed and accuracy of their ERP-based BCI use; (2) compare the ERP characteristics of these two groups; and (3) identify ERP-related factors that might enable improvement in BCI performance for people with disabilities. MethodsSixteen EEG channels were recorded while people with ALS or healthy age-matched volunteers (HVs) used a P300-based BCI. The subjects with ALS had little or no remaining useful motor control (mean ALS Functional Rating Scale-Revised 9.4 (±9.5SD) (range 0–25)). Each subject attended to a target item as the items in a 6×6 visual matrix flashed. The BCI used a stepwise linear discriminant function (SWLDA) to determine the item the user wished to select (i.e., the target item). Offline analyses assessed the latencies, amplitudes, and locations of ERPs to the target and non-target items for people with ALS and age-matched control subjects. ResultsBCI accuracy and communication rate did not differ significantly between ALS users and HVs. Although ERP morphology was similar for the two groups, their target ERPs differed significantly in the location and amplitude of the late positivity (P300), the amplitude of the early negativity (N200), and the latency of the late negativity (LN). ConclusionsThe differences in target ERP components between people with ALS and age-matched HVs are consistent with the growing recognition that ALS may affect cortical function. The development of BCIs for use by this population may begin with studies in HVs but also needs to include studies in people with ALS. Their differences in ERP components may affect the selection of electrode montages, and might also affect the selection of presentation parameters (e.g., matrix design, stimulation rate). SignificanceP300-based BCI performance in people severely disabled by ALS is similar to that of age-matched control subjects. At the same time, their ERP components differ to some degree from those of controls. Attention to these differences could contribute to the development of BCIs useful to those with ALS and possibly to others with severe neuromuscular disabilities.

A Brain–Computer Interface-Based Vehicle Destination Selection System Using P300 and SSVEP Signals

In this paper, we propose a novel driver–vehicle interface for individuals with severe neuromuscular disabilities to use intelligent vehicles by using P300 and steady-state visual evoked potential (SSVEP) brain–computer interfaces (BCIs) to select a destination and test its performance in the laboratory and real driving conditions. The proposed interface consists of two components: the selection component based on a P300 BCI and the confirmation component based on an SSVEP BCI. Furthermore, the accuracy and selection time models of the interface are built to help analyze the performance of the entire system. Experimental results from 16 participants collected in the laboratory and real driving scenarios show that the average accuracy of the system in the real driving conditions is about 99% with an average selection time of about 26 s. More importantly, the proposed system improves the accuracy of destination selection compared with a single P300 BCI-based selection system, particularly for those participants with relatively low level of accuracy in using the P300 BCI. This study not only provides individuals with severe motor disabilities with an interface to use intelligent vehicles and thus improve their mobility, but also facilitates the research on driver–vehicle interface, multimodal interaction, and intelligent vehicles. Furthermore, it opens an avenue on how cognitive neuroscience may be applied to intelligent vehicles.

Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study

Objective. The P300 speller is a brain–computer interface (BCI) that can possibly restore communication abilities to individuals with severe neuromuscular disabilities, such as amyotrophic lateral sclerosis (ALS), by exploiting elicited brain signals in electroencephalography (EEG) data. However, accurate spelling with BCIs is slow due to the need to average data over multiple trials to increase the signal-to-noise ratio (SNR) of the elicited brain signals. Probabilistic approaches to dynamically control data collection have shown improved performance in non-disabled populations; however, validation of these approaches in a target BCI user population has not occurred. Approach. We have developed a data-driven algorithm for the P300 speller based on Bayesian inference that improves spelling time by adaptively selecting the number of trials based on the acute SNR of a user’s EEG data. We further enhanced the algorithm by incorporating information about the user’s language. In this current study, we test and validate the algorithms online in a target BCI user population, by comparing the performance of the dynamic stopping (DS) (or early stopping) algorithms against the current state-of-the-art method, static data collection, where the amount of data collected is fixed prior to online operation. Main results. Results from online testing of the DS algorithms in participants with ALS demonstrate a significant increase in communication rate as measured in bits/min (100–300%), and theoretical bit rate (100–550%), while maintaining selection accuracy. Participants also overwhelmingly preferred the DS algorithms. Significance. We have developed a viable BCI algorithm that has been tested in a target BCI population which has the potential for translation to improve BCI speller performance towards more practical use for communication.

The Extended Role of Total Laryngectomy in Patients with Severe Neuromuscular Disabilities

Amongst the myriad of problems that patients with severe neurodisability encounter is that of recurrent airway obstructions and intractable aspiration pneumonias due to dysfunction of the protective mechanism of the larynx. Here, we describe the use of total laryngectomy as a surgical option for these patients. Case was 24 year old male with severe cerebral palsy suffering from recurrent episodes of airway obstruction and aspiration pneumonia. In the severely disabled patient with recurrent aspiration pneumonias, total laryngectomy may present as a viable option, especially in those patients who have been previously incapable of effective verbal communication.

Preprocessing by a Bayesian single-trial event-related potential estimation technique allows feasibility of an assistive single-channel P300-based brain-computer interface.

A major clinical goal of brain-computer interfaces (BCIs) is to allow severely paralyzed patients to communicate their needs and thoughts during their everyday lives. Among others, P300-based BCIs, which resort to EEG measurements, have been successfully operated by people with severe neuromuscular disabilities. Besides reducing the number of stimuli repetitions needed to detect the P300, a current challenge in P300-based BCI research is the simplification of system's setup and maintenance by lowering the number N of recording channels. By using offline data collected in 30 subjects (21 amyotrophic lateral sclerosis patients and 9 controls) through a clinical BCI with N = 5 channels, in the present paper we show that a preprocessing approach based on a Bayesian single-trial ERP estimation technique allows reducing N to 1 without affecting the system's accuracy. The potentially great benefit for the practical usability of BCI devices (including patient acceptance) that would be given by the reduction of the number N of channels encourages further development of the present study, for example, in an online setting.

Bayesian Approach to Dynamically Controlling Data Collection in P300 Spellers

P300 spellers provide a noninvasive method of communication for people who may not be able to use other communication aids due to severe neuromuscular disabilities. However, P300 spellers rely on event-related potentials (ERPs) which often have low signal-to-noise ratios (SNRs). In order to improve detection of the ERPs, P300 spellers typically collect multiple measurements of the electroencephalography (EEG) response for each character. The amount of collected data can affect both the accuracy and the communication rate of the speller system. The goal of the present study was to develop an algorithm that would automatically determine the necessary amount of data to collect during operation. Dynamic data collection was controlled by a threshold on the probabilities that each possible character was the target character, and these probabilities were continually updated with each additional measurement. This Bayesian technique differs from other dynamic data collection techniques by relying on a participant-independent, probability-based metric as the stopping criterion. The accuracy and communication rate for dynamic and static data collection in P300 spellers were compared for 26 users. Dynamic data collection resulted in a significant increase in accuracy and communication rate.

Sistema de Teleoperación Multi-Robot Basado en Interfaz Cerebro-Computador

This paper reports multi-robot teleoperation system based on a brain-computer interface, which allows the user to control simultaneously a non-predefined number of robots via internet, only by brain activity. These devices are oriented to people with severe neuromuscular disabilities, providing them telepresence with a interaction mode based on their thoughts. This work is an extension of a teleoperation system which has been improved to support the teleoperation of N robots, and that uses small and portable robots (of two orders of dimension below the original). The brain-computer interface is based on EEG with a P300-based protocol of control, and the robots are able to navigate, interact by sending alarms, and visually perceive the environments. The system has been validated with two healthy users and the main result is that all of them were able to successfully solve the proposed tasks with no failures, which demonstrates the validity and high robustness of the prototype.

Sistema de Teleoperación Multi-Robot basado en Interfaz Cerebro-Computador

This paper reports multi-robot teleoperation system based on a brain-computer interface, which allows the user to control simultaneously a non-predefined number of robots via internet, only by brain activity. These devices are oriented to people with severe neuromuscular disabilities, providing them telepresence with a interaction mode based on their thoughts. This work is an extension of a teleoperation system which has been improved to support the teleoperation of N robots, and that uses small and portable robots (of two orders of dimension below the original). The brain-computer interface is based on EEG with a P300-based protocol of control, and the robots are able to navigate, interact by sending alarms, and visually perceive the environments. The system has been validated with two healthy users and the main result is that all of them were able to successfully solve the proposed tasks with no failures, which demonstrates the validity and high robustness of the prototype.

Does the ‘P300’ speller depend on eye gaze?

Many people affected by debilitating neuromuscular disorders such as amyotrophic lateral sclerosis, brainstem stroke or spinal cord injury are impaired in their ability to, or are even unable to, communicate. A brain–computer interface (BCI) uses brain signals, rather than muscles, to re-establish communication with the outside world. One particular BCI approach is the so-called ‘P300 matrix speller’ that was first described by Farwell and Donchin (1988 Electroencephalogr. Clin. Neurophysiol. 70 510–23). It has been widely assumed that this method does not depend on the ability to focus on the desired character, because it was thought that it relies primarily on the P300-evoked potential and minimally, if at all, on other EEG features such as the visual-evoked potential (VEP). This issue is highly relevant for the clinical application of this BCI method, because eye movements may be impaired or lost in the relevant user population. This study investigated the extent to which the performance in a ‘P300’ speller BCI depends on eye gaze. We evaluated the performance of 17 healthy subjects using a ‘P300’ matrix speller under two conditions. Under one condition (‘letter’), the subjects focused their eye gaze on the intended letter, while under the second condition (‘center’), the subjects focused their eye gaze on a fixation cross that was located in the center of the matrix. The results show that the performance of the ‘P300’ matrix speller in normal subjects depends in considerable measure on gaze direction. They thereby disprove a widespread assumption in BCI research, and suggest that this BCI might function more effectively for people who retain some eye-movement control. The applicability of these findings to people with severe neuromuscular disabilities (particularly in eye-movements) remains to be determined.

Evaluation of P300-Based Brain-Computer Interface in Real-World Contexts

Despite recent advances in brain-computer interface (BCI) development, system usability still remains a large oversight. The goal of this study was to investigate the usability of a P300-based BCI system, P300 Speller, by assessing how background noise and interface color contrast affect user performance and BCI usage preference. Fifteen able-bodied participants underwent a 2 (low and high interface color contrast) × 3 (low, medium, and high background noise level) within-subjects design experiment, in which participants were asked to type six 10-character phrases in the P300 Speller paradigm. The overall accuracy in the study was 80.2%. Participants showed higher accuracy, higher information transfer rate, bigger amplitude, and smaller latency in the high interface color contrast condition than in the low contrast condition. Participants had better performance in the noisy condition than in the quiet condition, but the background noise effects were not statistically significant in the present study. These results should give some insight to the real-world applicability of the current P300 Speller as a nonmuscular communication system, especially for individuals with severe neuromuscular disabilities.

A novel P300-based brain–computer interface stimulus presentation paradigm: Moving beyond rows and columns

An electroencephalographic brain-computer interface (BCI) can provide a non-muscular means of communication for people with amyotrophic lateral sclerosis (ALS) or other neuromuscular disorders. We present a novel P300-based BCI stimulus presentation - the checkerboard paradigm (CBP). CBP performance is compared to that of the standard row/column paradigm (RCP) introduced by Farwell and Donchin (1988). Using an 8x9 matrix of alphanumeric characters and keyboard commands, 18 participants used the CBP and RCP in counter-balanced fashion. With approximately 9-12 min of calibration data, we used a stepwise linear discriminant analysis for online classification of subsequent data. Mean online accuracy was significantly higher for the CBP, 92%, than for the RCP, 77%. Correcting for extra selections due to errors, mean bit rate was also significantly higher for the CBP, 23 bits/min, than for the RCP, 17 bits/min. Moreover, the two paradigms produced significantly different waveforms. Initial tests with three advanced ALS participants produced similar results. Furthermore, these individuals preferred the CBP to the RCP. These results suggest that the CBP is markedly superior to the RCP in performance and user acceptability. The CBP has the potential to provide a substantially more effective BCI than the RCP. This is especially important for people with severe neuromuscular disabilities.

A $\mu $-Rhythm Matched Filter for Continuous Control of a Brain-Computer Interface

A brain-computer interface (BCI) is a system that provides an alternate nonmuscular communication/control channel for individuals with severe neuromuscular disabilities. With proper training, individuals can learn to modulate the amplitude of specific electroencephalographic (EEG) components (e.g., the 8-12 Hz mu rhythm and 18-26 Hz beta rhythm) over the sensorimotor cortex and use them to control a cursor on a computer screen. Conventional spectral techniques for monitoring the continuous amplitude fluctuations fail to capture essential amplitude/phase relationships of the mu and beta rhythms in a compact fashion and, therefore, are suboptimal. By extracting the characteristic mu rhythm for a user, the exact morphology can be characterized and exploited as a matched filter. A simple, parameterized model for the characteristic mu rhythm is proposed and its effectiveness as a matched filter is examined online for a one-dimensional cursor control task. The results suggest that amplitude/phase coupling exists between the mu and beta bands during event-related desynchronization, and that an appropriate matched filter can provide improved performance.

Clinical Indices and Body Composition Changes After Feeding Children By Gastrostomy

LEARNING OUTCOME: To define the outcome of adequate weight gain reflecting fat and lean mass for children with neurological disabilities fed by gastrostomy.This study addressed a commonly held impression that an outcome of gastrostomy feeding is to make children fat, particularly those with neurological impairments. A repeated measure study identified body composition and clinical correlates longitudinally in 12 underweight children who had laporoscopic surgery for gastrostomy placement and fundoplication. The baseline was the surgery date, with assessments every three months. The subjects were four to eight years, diagnosed with spastic quadriplegia, and were non-ambulatory. Eleven were diagnosed with failure-to-thrive. Four had interfering conditions effecting recumbent length, such as hip dislocation surgical correction or scoliosis. The usual post-surgical services were provided with nutritional assessments that included weight, triceps skinfolds fat mass, arm circumference, head circumference, recumbent length, and Dual Electron X-ray Absorptiometry (DEXA). The subjects gained an average of 3.8 kg over 9 months. Increase in both lean (r=0.914) and fat (r=0.834) compartments were significantly correlated to weight (p<001), and paralleled correction of failure to thrive as defined by increase in z-score for weight for age. The study did not suggest a period for detecting a significant changes; 3,6 or 9 months were all improved over baseline on a continuum of growth. Triceps skinfolds fat mass significantly correlated with fat mass (r=0.782) but not with fat-free mass (r=0.574). None of the children had triceps fat mass greater than mid-range, even with rapid weight gain. Percent body fat was not a good clinical indicator; weight and triceps skinfolds fat mass were significant outcome measures compared to DEXA. The use of DEXA measurements gave confirmatory but not additional information to the clinical correlates. For these subjects added weight after gastrostomy feeding was initiated was not only fat, but significant lean body mass, in spite of severe neuromuscular disabilities.