Filtered By:
Specialty: Neurology
Education: Learning
This page shows you your search results in order of date. This is page number 4.
Order by Relevance | Date
Total 1309 results found since Jan 2013.
Moderate-intensity cardiovascular exercise performed before motor practice attenuates offline implicit motor learning in stroke survivors but not age-matched neurotypical adults
Exp Brain Res. 2023 Jul 3. doi: 10.1007/s00221-023-06659-w. Online ahead of print.ABSTRACTThe acute impact of cardiovascular exercise on implicit motor learning of stroke survivors is still unknown. We investigated the effects of cardiovascular exercise on implicit motor learning of mild-moderately impaired chronic stroke survivors and neurotypical adults. We addressed whether exercise priming effects are time-dependent (e.g., exercise before or after practice) in the encoding (acquisition) and recall (retention) phases. Forty-five stroke survivors and 45 age-matched neurotypical adults were randomized into three sub-group...
Source: Brain Research - July 3, 2023 Category: Neurology Authors: Giordano Marcio Gatinho Bonuzzi Flavio Henrique Bastos Nicolas Schweighofer Eric Wade Carolee Joyce Winstein Camila Torriani-Pasin Source Type: research
The natural history of epilepsy and nonepileptic seizures in Sturge-Weber syndrome: A retrospective case-note review
This study explored the natural history of epileptic and nonepileptic seizures into adulthood in patients with SWS, and their treatment, and investigated whether any clinical factors predict which symptoms a patient will experience during adulthood.METHODS: A retrospective case-note review of a cohort of 26 adults with SWS at the National Hospital for Neurology and Neurosurgery (NHNN). Childhood data were also recorded, where available, to enable review of change/development of symptoms over time.RESULTS: The course of epilepsy showed some improvement in adulthood - seventeen adults continued to have seizures, while six pa...
Source: Epilepsy and Behaviour - June 22, 2023 Category: Neurology Authors: Rhian Male Sofia H Eriksson Source Type: research
OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features
ConclusionThe OEDL approach proposed herein could effectively achieve improved stroke prognosis prediction performance, the effect of using combined data modeling was significantly better than that of single clinical or radiomics feature models, and the proposed method had a better intervention guidance value. Our approach is beneficial for optimizing the early clinical intervention process and providing the necessary clinical decision support for personalized treatment.
Source: Frontiers in Neurology - June 21, 2023 Category: Neurology Source Type: research
Computed tomography angiography-based radiomics model for predicting carotid atherosclerotic plaque vulnerability
This study aimed to identify radiomic features associated with the neovascularization of CAP and construct a prediction model for CAP vulnerability based on radiomic features. CTA data and clinical data of patients with CAPs who underwent CTA and CEUS between January 2018 and December 2021 in Beijing Hospital were retrospectively collected. The data were divided into a training cohort and a testing cohort using a 7:3 split. According to the examination of CEUS, CAPs were dichotomized into vulnerable and stable groups. 3D Slicer software was used to delineate the region of interest in CTA images, and the Pyradiomics package...
Source: Frontiers in Neurology - June 16, 2023 Category: Neurology Source Type: research
A blended neurostimulation protocol to delineate cortico-muscular and spino-muscular dynamics following neuroplastic adaptation
In this paper we propose a novel neurostimulation protocol that provides an intervention-based assessment to distinguish the contributions of different motor control networks in the cortico-spinal system. Specifically, we use a combination of non-invasive brain stimulation and neuromuscular stimulation to probe neuromuscular system behavior with targeted impulse-response system identification. In this protocol, we use an in-house developed human-machine interface (HMI) for an isotonic wrist movement task, where the user controls a cursor on-screen. During the task, we generate unique motor evoked potentials based on trigge...
Source: Frontiers in Neurology - June 15, 2023 Category: Neurology Source Type: research
Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis
ConclusionML can be used as an assessment tool for predicting the motor function in patients with 3–6 months of post-stroke. Additionally, the study found that ML models with radiomics as a predictive variable were also demonstrated to have good predictive capabilities. This systematic review provides valuable guidance for the future optimization of ML prediction systems that predict poor motor outcomes in stroke patients.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022335260, identifier: CRD42022335260.
Source: Frontiers in Neurology - June 13, 2023 Category: Neurology Source Type: research
CT Angiography Radiomics Combining Traditional Risk Factors to Predict Brain Arteriovenous Malformation Rupture: a Machine Learning, Multicenter Study
This study aimed to develop a machine learning model for predicting brain arteriovenous malformation (bAVM) rupture using a combination of traditional risk factors and radiomics features. This multicenter retrospective study enrolled 586 patients with unruptured bAVMs from 2010 to 2020. All patients were grouped into the hemorrhage (n = 368) and non-hemorrhage (n = 218) groups. The bAVM nidus were segmented on CT angiography images using Slicer software, and radiomic features were extracted using Pyradiomics. The dataset included a training set and an independent testing set. The machine learning model was developed on the...
Source: Translational Stroke Research - June 13, 2023 Category: Neurology Source Type: research
Predicting DWI-FLAIR mismatch on NCCT: the role of artificial intelligence in hyperacute decision making
ConclusionThe DWI-FLAIR mismatch may be reckoned using NCCT images through advanced artificial intelligence techniques.
Source: Frontiers in Neurology - June 12, 2023 Category: Neurology Source Type: research
Delirium detection using wearable sensors and machine learning in patients with intracerebral hemorrhage
ConclusionsWe found that actigraphy in conjunction with machine learning models improves clinical detection of delirium in patients with stroke, thus paving the way to make actigraph-assisted predictions clinically actionable.
Source: Frontiers in Neurology - June 9, 2023 Category: Neurology Source Type: research
Prediction of subjective cognitive decline after corpus callosum infarction by an interpretable machine learning-derived early warning strategy
ConclusionOur study firstly demonstrated that the LR-model with 9 common variables has the best-performance to predict the risk of post-stroke SCD due to CC infarcton. Particularly, the combination of LR-model and SHAP-explainer could aid in achieving personalized risk prediction and be served as a decision-making tool for early intervention since its poor long-term outcome.
Source: Frontiers in Neurology - June 9, 2023 Category: Neurology Source Type: research
Machine learning-based identification of symptomatic carotid atherosclerotic plaques with dual-energy computed tomography angiography
This study aimed to develop and validate a machine learning model incorporating both dual-energy computed tomography (DECT) angiography quantitative parameters and clinically relevant risk factors for the identification of symptomatic carotid plaques to prevent acute cerebrovascular events.
Source: Journal of Stroke and Cerebrovascular Diseases - June 7, 2023 Category: Neurology Authors: Ling-Jie Wang, Pei-Qing Zhai, Li-Li Xue, Cai-Yun Shi, Qian Zhang, Hua Zhang Source Type: research
A brain CT-based approach for predicting and analyzing stroke-associated pneumonia from intracerebral hemorrhage
DiscussionOur findings suggest that our method is effective in classifying the development of pneumonia based on brain CT scans. Furthermore, we identified distinct characteristics, such as volume and distribution, of ICH in four different types of SAP.
Source: Frontiers in Neurology - June 2, 2023 Category: Neurology Source Type: research
Predictive model, miRNA-TF network, related subgroup identification and drug prediction of ischemic stroke complicated with mental disorders based on genes related to gut microbiome
ConclusionThrough comprehensive analysis, a diagnostic prediction model with good effect was obtained. Both the training group (AUC 0.82, CI 0.93–0.71) and the verification group (AUC 0.81, CI 0.90–0.72) had a good phenotype in the qRT-PCR test. And in verification group 2 we validated between the two groups with and without carotid-related ischemic cerebrovascular events (AUC 0.87, CI 1–0.64). MiRNA (hsa-mir-129-2-3p, has-mir-335-5p, and has-mir-16-5p) and TFs (CREB1, FOXL1), which may be related to IS, were obtained.
Source: Frontiers in Neurology - May 26, 2023 Category: Neurology Source Type: research
CT-based thrombus radiomics nomogram for predicting secondary embolization during mechanical thrombectomy for large vessel occlusion
ConclusionThis nomogram could be used to optimize the surgical MT procedure for LVO based on the risk of developing SE.
Source: Frontiers in Neurology - May 12, 2023 Category: Neurology Source Type: research
