Strength, physical activity, and fasciculations in patients with ALS
ABSTRACT Fasciculations are a nearly universal feature in people with amyotrophic lateral sclerosis (ALS). The prognostic value of fasciculations remains uncertain. Twenty-four patients with ALS were evaluated for the effects of atrophy, limb weakness, disease duration, and physical activity on fasciculation frequency (as measured by surface electromyography and clinical counting). Variables were compared by multiple linear regression. As strength of the limb deteriorated, the number of fasciculations in the same limb increased, as long as physical activity was maintained or increased. Fasciculation frequency was not associated with the duration of ALS (r = 0.22; p = 0.30) and was independent of the degree of limb weakness (p>0.05) and limb atrophy (p>0.05). No prediction of disease duration could be made based on fasciculation frequency alone. Fasciculations therefore appear to have diagnostic, but not prognostic, utility in the care of people with ALS.
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ABSTRACT: Our objective was to determine the prevalence and distribution of fasciculations in healthy adults and to assess the effect of age, caffeine and exercise. Fasciculations were studied with ultrasonography in 58 healthy adults in various age categories. Questionnaires were used to determine effect of caffeine and regular exercise on the presence of fasciculations. Finally, we tested the effect of strenuous exercise on fasciculations in 10 healthy adults. Twenty-five subjects (43%) showed fasciculations on ultrasonography, mostly in the abductor hallucis longus muscle. Fasciculations were only sporadically encountered in muscle groups above the knee. Subjects with fasciculations were significantly older than those without. Caffeine and regular physical exercise did not influence the prevalence of fasciculations. However, strenuous physical exercise caused a temporary increase in fasciculations, but only in lower leg muscles. Fasciculations above the knee should raise suspicion and may warrant further investigation.Amyotrophic Lateral Sclerosis 07/2009; 11(1-2):181-6. DOI:10.3109/17482960903062137 · 2.37 Impact Factor
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ABSTRACT: Respiratory muscle involvement is one of the main prognostic factors in amyotrophic lateral sclerosis (ALS). Acute respiratory failure is sometimes the first manifestation of the disease, although onset can be more insidious. In the present retrospective study, it was proposed to review the clinical features and to assess the effects of non-invasive ventilation (NIV) on the prognosis of patients with respiratory onset, which was taken to be present when the first symptoms of muscular weakness were dyspnoea at exertion, dyspnoea at rest, or orthopnoea. Respiratory onset ALS is uncommon, since it accounts for less than 3% of ALS cases. ALS with respiratory onset has some common clinical features: male predominance, frequent camptocormia or dropped head, frequent widespread fasciculations, limb mobility fairly well preserved and significant weight loss in the early stages. ALS patients with respiratory onset still have a poor prognosis compared with those with bulbar or spinal forms. NIV should be proposed promptly because it improves the symptoms, general state of health and survival time. Efforts should be made to inform general practitioners and chest physicians and remind them that neuromuscular respiratory insufficiency may be attributable to this particular form of ALS.Amyotrophic Lateral Sclerosis 12/2009; 11(4):379-82. DOI:10.3109/17482960903426543 · 2.37 Impact Factor
Chapter: Brain-Computer Interfaces[Show abstract] [Hide abstract]
ABSTRACT: Brain–computer interface (BCI) systems detect changes in brain signals that reflect human intention, then translate these signals to control monitors or external devices (for a comprehensive review, see ). BCIs typically measure electrical signals resulting from neural firing (i.e. neuronal action potentials, Electroencephalogram (ECoG), or Electroencephalogram (EEG)). Sophisticated pattern recognition and classification algorithms convert neural activity into the required control signals. BCI research has focused heavily on developing powerful signal processing and machine learning techniques to accurately classify neural activity [2–4].Edited by Graimann, Bernhard and Pfurtscheller, Gert and Allison, Brendan, 01/2010: pages 65-78-78; Springer., ISBN: 978-3-642-02090-2