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 18,428 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Individuals post-stroke sustain motor deficits years after the stroke. Despite recent advancements in the applications of non-invasive brain stimulation techniques and Deep Brain Stimulation in humans, there is a lack of evidence supporting their use for rehabilitation after brain lesions. Non-invasive brain stimulation is already in use for treating motor deficits in individuals with Parkinson's disease and post-stroke. Deep Brain Stimulation has become an established treatment for individuals with movement disorders, such as Parkinson's disease, essential tremor, epilepsy, cerebral palsy and dystonia. It has also been utilized for the treatment of Tourette's syndrome, Alzheimer's disease and neuropsychiatric conditions such as obsessive-compulsive disorder, major depression and anorexia nervosa. There exists growing scientific knowledge from animal studies supporting the use of Deep Brain Stimulation to enhance motor recovery after brain damage. Nevertheless, these results are currently not applicable to humans. This review details the current literature supporting the use of these techniques to enhance motor recovery, both from human and animal studies, aiming to encourage development in this domain.
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PMID:Can neuromodulation techniques optimally exploit cerebello-thalamo-cortical circuit properties to enhance motor learning post-stroke? 3119 94

We present a model for digital neural impairment screening and self-assessment, which can evaluate cognitive and motor deficits for patients with symptoms of central nervous system (CNS) disorders, such as mild cognitive impairment (MCI), Parkinson's disease (PD), Huntington's disease (HD), or dementia. The data was collected with an Android mobile application that can track cognitive, hand tremor, energy expenditure, and speech features of subjects. We extracted 238 features as the model inputs using 16 tasks, 12 of them were based on a self-administered cognitive testing (SAGE) methodology and others used finger tapping and voice features acquired from the sensors of a smart mobile device (smartphone or tablet). Fifteen subjects were involved in the investigation: 7 patients with neurological disorders (1 with Parkinson's disease, 3 with Huntington's disease, 1 with early dementia, 1 with cerebral palsy, 1 post-stroke) and 8 healthy subjects. The finger tapping, SAGE, energy expenditure, and speech analysis features were used for neural impairment evaluations. The best results were achieved using a fusion of 13 classifiers for combined finger tapping and SAGE features (96.12% accuracy), and using bidirectional long short-term memory (BiLSTM) (94.29% accuracy) for speech analysis features.
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PMID:A Mobile Application for Smart Computer-Aided Self-Administered Testing of Cognition, Speech, and Motor Impairment. 3251 23


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