Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
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The relation was investigated between hemiparetic arm function improvement and brain cortical perfusion (BCP) change during voluntary muscle contraction (VOL), EMG-controlled FES (EMG-FES) and simple electrical muscle stimulation (ES) before and after EMG-FES therapy in chronic stroke patients. ⋯ The sensory motor integration during EMG-FES therapy might facilitate BCP of the ipsilesional SMC and result in functional improvement of hemiparetic upper extremity.
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The aim of this study was to determine the optimum interpulse interval (OIPI) for transcranial electrical train stimulation to elicit muscle motor evoked potentials (TES-MEP) with maximal amplitude in upper and lower extremities during intra-operative spinal cord monitoring. ⋯ Based on the results of this study, it is advisable to perform a set-up procedure for each individual patient undergoing TES-MEP to determine the optimal parameter settings when using supramaximal intensity of TES.
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Comparative Study
Comparison of spherical and realistically shaped boundary element head models for transcranial magnetic stimulation navigation.
MRI-guided real-time transcranial magnetic stimulation (TMS) navigators that apply electromagnetic modeling have improved the utility of TMS. However, their accuracy and speed depends on the assumed volume conductor geometry. Spherical models found in present navigators are computationally fast but may be inaccurate in some areas. Realistically shaped boundary-element models (BEMs) could increase accuracy at a moderate computational cost, but it is unknown which model features have the largest influence on accuracy. Thus, we compared different types of spherical models and BEMs. ⋯ Realistically shaped BEMs may increase TMS navigation accuracy in several brain areas, such as in prefrontal regions often targeted in clinical applications.
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In some anti-ganglioside antibody-mediated neuropathies, human and experimental data suggest a common pathogenic mechanism of dysfunction/disruption at the node of Ranvier resulting in a pathophysiologic continuum from transitory nerve conduction failure to axonal degeneration. The traditional classification of polyneuropathies into demyelinating or axonal may generate some confusion in the electrophysiological diagnosis of Guillain-Barré syndrome subtypes associated with anti-ganglioside antibodies. ⋯ Moreover the term axonal may be misleading as it is commonly associated to axonal degeneration and not to a transitory, promptly reversible, dysfunction of the excitable axolemma. To focus on the site of nerve injury and overcome the classification difficulties, we propose the new category of nodo-paranodopathy which seems appropriate to various acute and chronic neuropathies associated with anti-ganglioside antibodies and we think better systematizes the neuropathies characterized by an autoimmune attack targeting the nodal region.
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Randomized Controlled Trial
A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder.
The problem of identifying, in advance, the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we investigate the performance of the proposed machine learning (ML) methodology (based on the pre-treatment electroencephalogram (EEG)) for prediction of response to treatment with a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD). ⋯ The proposed approach offers the potential to improve the treatment of major depression and to reduce health care costs.