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Total 123 results found since Jan 2013.

Brain activation is related to smoothness of upper limb movements after stroke.
This study suggests that recruitment of secondary motor areas at 6 weeks after stroke is highly associated with increased jerk during reaching and grasping. As jerk represents the change in acceleration, the recruitment of additional sensorimotor areas seems to reflect a type of control in which deviations from an optimal movement pattern are continuously corrected. This relationship suggests that additional recruitment of sensorimotor areas after stroke may not correspond to restitution of motor function, but more likely to adaptive motor learning strategies to compensate for motor impairments. PMID: 26979435 [PubMe...
Source: Experimental Brain Research - March 15, 2016 Category: Neuroscience Authors: Buma FE, van Kordelaar J, Raemaekers M, van Wegen EE, Ramsey NF, Kwakkel G Tags: Exp Brain Res Source Type: research

Assessment of biofeedback rehabilitation in post-stroke patients combining fMRI and gait analysis: a case study
Conclusions: Our findings showed that this methodology allows evaluation of the relationship between alterations in gait and brain activation of a post-stroke patient. Such methodology, if applied on a larger sample subjects, could provide information about the specific motor area involved in a rehabilitation treatment.
Source: Journal of NeuroEngineering and Rehabilitation - April 9, 2014 Category: Rehabilitation Authors: Silvia Del DinAlessandra BertoldoZimi SawachaJohanna JonsdottirMarco RabuffettiClaudio CobelliMaurizio Ferrarin Source Type: research

Cognitive state following mild stroke: A matter of hippocampal mean diffusivity
This article is protected by copyright. All rights reserved.
Source: Hippocampus - July 27, 2015 Category: Neurology Authors: Efrat Kliper, Einor Ben Assayag, Amos D. Korczyn, Eitan Auriel, Ludmila Shopin, Hen Hallevi, Shani Shenhar‐Tsarfaty, Anat Mike, Moran Artzi, Ilana Klovatch, Natan M. Bornstein, Dafna Ben Bashat Tags: Research Article Source Type: research

Using convolutional neural network to analyze brain MRI images for predicting functional outcomes of stroke
AbstractNowadays, the physicians usually predict functional outcomes of stroke based on clinical experiences and big data, so we wish to develop a model to accurately identify imaging features for predicting functional outcomes of stroke patients. Using magnetic resonance imaging of ischemic and hemorrhagic stroke patients, we developed and trained a VGG-16 convolutional neural network (CNN) to predict functional outcomes after 28-day hospitalization. A total of 44 individuals (24 men and 20 women) were recruited from Taoyuan General Hospital and China Medical University Hsinchu Hospital to enroll in the study. Based on ...
Source: Medical and Biological Engineering and Computing - August 2, 2022 Category: Biomedical Engineering Source Type: research

Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review
Biomed Res Int. 2022 Nov 14;2022:2456550. doi: 10.1155/2022/2456550. eCollection 2022.ABSTRACTIschemic stroke is a cerebrovascular disease with a high morbidity and mortality rate, which poses a serious challenge to human health and life. Meanwhile, the management of ischemic stroke remains highly dependent on manual visual analysis of noncontrast computed tomography (CT) or magnetic resonance imaging (MRI). However, artifacts and noise of the equipment as well as the radiologist experience play a significant role on diagnostic accuracy. To overcome these defects, the number of computer-aided diagnostic (CAD) methods for i...
Source: Biomed Res - November 24, 2022 Category: Research Authors: Liyuan Cui Zhiyuan Fan Yingjian Yang Rui Liu Dajiang Wang Yingying Feng Jiahui Lu Yifeng Fan Source Type: research

Prognosis of ischemic stroke predicted by machine learning based on multi-modal MRI radiomics
ConclusionThe ML models based on muti-modal MRI radiomics are of high value for predicting clinical outcomes in acute stroke patients.
Source: Frontiers in Psychiatry - January 9, 2023 Category: Psychiatry Source Type: research

IS 12. Plasticity in stroke patients: Why brain stimulation may (not) work
Advances in brain imaging techniques allow us to study not just what the brain looks like but how it works. When applied to people who have suffered a stroke this technology has demonstrated reorganization of the way surviving brain regions function. These findings give hope to the idea that new treatments can be designed and more effectively targeted towards individual patients.So how can we measure these changes in organization in the human brain? Brain imaging techniques such as functional magnetic resonance imaging (fMRI) have developed to the point where a detailed appreciation of the damage to brain structures and th...
Source: Clinical Neurophysiology - September 1, 2013 Category: Neuroscience Authors: N. Ward Tags: Society Proceedings Source Type: research

Domain-general subregions of the medial prefrontal cortex contribute to recovery of language after stroke
AbstractWe hypothesized that the recovery of speech production after left hemisphere stroke not only depends on the integrity of language-specialized brain systems, but also on ‘domain-general’ brain systems that have much broader functional roles. The presupplementary motor area/dorsal anterior cingulate forms part of the cingular-opercular network, which has a broad role in cognition and learning. Consequently, we have previously suggested that variability in the rec overy of speech production after aphasic stroke may relate in part to differences in patients’ abilities to engage this domain-general brain region. T...
Source: Brain - June 27, 2017 Category: Neurology Source Type: research

Improving Ischemic Stroke Care With MRI and Deep Learning Artificial Intelligence
Advanced magnetic resonance imaging has been used as selection criteria for both acute ischemic stroke treatment and secondary prevention. The use of artificial intelligence, and in particular, deep learning, to synthesize large amounts of data and to understand better how clinical and imaging data can be leveraged to improve stroke care promises a new era of stroke care. In this article, we review common deep learning model structures for stroke imaging, evaluation metrics for model performance, and studies that investigated deep learning application in acute ischemic stroke care and secondary prevention.
Source: Topics in Magnetic Resonance Imaging - August 1, 2021 Category: Radiology Tags: Review Articles Source Type: research

Neural substrates underlying stimulation-enhanced motor skill learning after stroke
Motor skill learning is one of the key components of motor function recovery after stroke, especially recovery driven by neurorehabilitation. Transcranial direct current stimulation can enhance neurorehabilitation and motor skill learning in stroke patients. However, the neural mechanisms underlying the retention of stimulation-enhanced motor skill learning involving a paretic upper limb have not been resolved. These neural substrates were explored by means of functional magnetic resonance imaging. Nineteen chronic hemiparetic stroke patients participated in a double-blind, cross-over randomized, sham-controlled experiment...
Source: Brain - January 6, 2015 Category: Neurology Authors: Lefebvre, S., Dricot, L., Laloux, P., Gradkowski, W., Desfontaines, P., Evrard, F., Peeters, A., Jamart, J., Vandermeeren, Y. Tags: Original Articles Source Type: research

Reports Ipsilesional anodal tDCS enhances the functional benefits of rehabilitation in patients after stroke
Anodal transcranial direct current stimulation (tDCS) can boost the effects of motor training and facilitate plasticity in the healthy human brain. Motor rehabilitation depends on learning and plasticity, and motor learning can occur after stroke. We tested whether brain stimulation using anodal tDCS added to motor training could improve rehabilitation outcomes in patients after stroke. We performed a randomized, controlled trial in 24 patients at least 6 months after a first unilateral stroke not directly involving the primary motor cortex. Patients received either anodal tDCS (n = 11) or sham treatment (n = 13) paired wi...
Source: Science Translational Medicine - March 16, 2016 Category: Biomedical Science Authors: Allman, C., Amadi, U., Winkler, A. M., Wilkins, L., Filippini, N., Kischka, U., Stagg, C. J., Johansen-Berg, H. Tags: Reports Source Type: research

Increased functional connectivity one week after motor learning and tDCS in stroke patients
Publication date: 6 January 2017 Source:Neuroscience, Volume 340 Author(s): Stéphanie Lefebvre, Laurence Dricot, Patrice Laloux, Philippe Desfontaines, Frédéric Evrard, André Peeters, Jacques Jamart, Yves Vandermeeren Recent studies using resting-state functional magnetic resonance imaging (rs-fMRI) demonstrated that changes in functional connectivity (FC) after stroke correlate with recovery. The aim of this study was to explore whether combining motor learning to dual transcranial direct current stimulation (dual-tDCS, applied over both primary motor cortices (M1)) modulated FC in stroke patients. Twenty-two chronic...
Source: Neuroscience - November 25, 2016 Category: Neuroscience Source Type: research

A Machine Learning Approach for Classifying Ischemic Stroke Onset Time From Imaging
Current clinical practice relies on clinical history to determine the time since stroke (TSS) onset. Imaging-based determination of acute stroke onset time could provide critical information to clinicians in deciding stroke treatment options, such as thrombolysis. The patients with unknown or unwitnessed TSS are usually excluded from thrombolysis, even if their symptoms began within the therapeutic window. In this paper, we demonstrate a machine learning approach for TSS classification using routinely acquired imaging sequences. We develop imaging features from the magnetic resonance (MR) images and train machine learning ...
Source: IEE Transactions on Medical Imaging - June 30, 2019 Category: Biomedical Engineering Source Type: research