Neurology

Improving Stroke Risk Prediction in the General Population: A Comparative Assessment of Common Clinical Rules, a New Multimorbid Index, and Machine-Learning-Based Algorithms.

BACKGROUND: There are few large studies examining and predicting the diversified cardiovascular/noncardiovascular comorbidity relationships with stroke. We investigated stroke risks in a very large prospective cohort of patients with multimorbidity, using two common clinical rules, a clinical multimorbid index and a machine-learning (ML) approach, accounting for the complex relationships among var...

Effects of spectral features of EEG signals recorded with different channels and recording statuses on ADHD classification with deep learning.

Early diagnosis of attention deficit and hyperactivity disorder (ADHD) by experts is difficult. Some solutions using electroencephalography (EEG) signals have been presented in the literature to solve this problem. However, few studies have aimed to determine which recording statuses and which channels are effective for the diagnosis of ADHD. In this study, the effects of photic stimuli at differe...

Epileptic Seizures Detection Using Deep Learning Techniques: A Review.

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging...

Dexmedetomidine and Magnesium Sulfate as Adjuvant to 0.5% Ropivacaine in Supraclavicular Brachial Plexus Block: A Comparative Evaluation.

BACKGROUND: Dexmedetomidine and magnesium sulfate (MgSO) as an adjuvant to local anesthetics and ultrasound guidance improves the quality of periphera...

The adjunct of transcranial direct current stimulation to Robot-assisted therapy in upper limb post-stroke treatment.

Transcranial direct current stimulation (TDCS) and robot-assisted therapy (RAT) proved to be promising interventions in post-stroke rehabilitation. Ho...

Epileptic EEG Classification by Using Time-Frequency Images for Deep Learning.

Epilepsy is one of the most common brain disorders worldwide. The most frequently used clinical tool to detect epileptic events and monitor epilepsy p...

Incidence of Recurrent Laryngeal Nerve Palsy in Robot-Assisted Versus Conventional Minimally Invasive McKeown Esophagectomy in Prone Position: A Propensity Score-Matched Study.

BACKGROUND: Esophagectomy with lymphadenectomy is the principal treatment for localized esophageal cancer. Conventional minimally invasive esophagecto...

Ischemic stroke-induced polyaxonal innervation at the neuromuscular junction is attenuated by robot-assisted mechanical therapy.

Ischemic stroke is a leading cause of disability world-wide. Mounting evidence supports neuromuscular pathology following stroke, yet mechanisms of dy...

Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis.

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signal...

Cortical Thickness from MRI to Predict Conversion from Mild Cognitive Impairment to Dementia in Parkinson Disease: A Machine Learning-based Model.

Background Group comparison results associating cortical thinning and Parkinson disease (PD) dementia (PDD) are limited in their application to clinic...

Expression-EEG Bimodal Fusion Emotion Recognition Method Based on Deep Learning.

As one of the key issues in the field of emotional computing, emotion recognition has rich application scenarios and important research value. However...

Robot-Assisted Stereotactic Shunting as a Novel Treatment for Pontine Glioependymal Cysts.

In this case report, the authors describe the first case of a glioependymal cyst of the brainstem managed by robot-assisted, stereotactic, cysto-ventr...

Using deep learning convolutional neural networks to automatically perform cerebral aqueduct CSF flow analysis.

Since the development of phase-contrast magnetic resonance imaging (PC-MRI), quantification of cerebrospinal fluid (CSF) flow across the cerebral aque...

Predicting phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins.

BACKGROUND: Post-translational modification (PTM) is a biological process that alters proteins and is therefore involved in the regulation of various ...

Predicting optimal deep brain stimulation parameters for Parkinson's disease using functional MRI and machine learning.

Commonly used for Parkinson's disease (PD), deep brain stimulation (DBS) produces marked clinical benefits when optimized. However, assessing the larg...

Discrimination between healthy and patients with Parkinson's disease from hand resting activity using inertial measurement unit.

BACKGROUND: Parkinson's disease (PD) is a neurological disease that affects the motor system. The associated motor symptoms are muscle rigidity or sti...

Machine Learning Algorithm Identifies the Importance of Environmental Factors for Hospital Discharge to Home of Stroke Patients using Wheelchair after Discharge.

BACKGROUND AND PURPOSE: Physical environmental factors are generally likely to become barriers for discharge to home of wheelchair users, compared wit...

Spectroscopic and deep learning-based approaches to identify and quantify cerebral microhemorrhages.

Cerebral microhemorrhages (CMHs) are associated with cerebrovascular disease, cognitive impairment, and normal aging. One method to study CMHs is to a...

Delayed brain development of Rolandic epilepsy profiled by deep learning-based neuroanatomic imaging.

OBJECTIVES: Although Rolandic epilepsy (RE) has been regarded as a brain developmental disorder, neuroimaging studies have not yet ascertained whether...

Application of deep learning to understand resilience to Alzheimer's disease pathology.

People who have Alzheimer's disease neuropathologic change (ADNC) typically associated with dementia but not the associated cognitive decline can be c...