Neurology

EEG may serve as a biomarker in Huntington's disease using machine learning automatic classification.

Reliable markers measuring disease progression in Huntington's disease (HD), before and after disease manifestation, may guide a therapy aimed at slowing or halting disease progression. Quantitative electroencephalography (qEEG) may provide a quantification method for possible (sub)cortical dysfunction occurring prior to or concomitant with motor or cognitive disturbances observed in HD. In this p...

Self-selected speed gait training in Parkinson's disease: robot-assisted gait training with virtual reality versus gait training on the ground.

BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disorder causing progressive gait disability. Although robot-assisted gait training (RAGT) using the Lokomat device has been demonstrated to improve gait in PD, it is not clear what the best training settings are, in particular if a self-selected treadmill speed can give better results.

Identification of Hidden Sources by Estimating Instantaneous Causality in High-Dimensional Biomedical Time Series.

The study of connectivity patterns of a system's variables, such as multi-channel electroencephalograms (EEG), is of utmost importance towards a bette...

Sparse Representation-Based Extreme Learning Machine for Motor Imagery EEG Classification.

Classification of motor imagery (MI) electroencephalogram (EEG) plays a vital role in brain-computer interface (BCI) systems. Recent research has show...

The revised Cerebral Recovery Index improves predictions of neurological outcome after cardiac arrest.

OBJECTIVE: Analysis of the electroencephalogram (EEG) background pattern helps predicting neurological outcome of comatose patients after cardiac arre...

Machine Learning for Predicting Cognitive Diseases: Methods, Data Sources and Risk Factors.

Machine learning and data mining approaches are being successfully applied to different fields of life sciences for the past 20 years. Medicine is one...

Intravenous Fluid for the Treatment of Emergency Department Patients With Migraine Headache: A Randomized Controlled Trial.

STUDY OBJECTIVE: The objective of this pilot study is to assess the feasibility and necessity of performing a large-scale trial to measure the effect ...

3D regression neural network for the quantification of enlarged perivascular spaces in brain MRI.

Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging marker for cerebral small vessel disease, and have been shown to be related t...

Representations of regular and irregular shapes by deep Convolutional Neural Networks, monkey inferotemporal neurons and human judgments.

Recent studies suggest that deep Convolutional Neural Network (CNN) models show higher representational similarity, compared to any other existing obj...

Increased risk of group B Streptococcus causing meningitis in infants with mannose-binding lectin deficiency.

OBJECTIVES: To evaluate the association of mannose-binding lectin (MBL) deficiency with susceptibility and clinical features of group B Streptococcus ...

JOURNAL CLUB: Use of Gradient Boosting Machine Learning to Predict Patient Outcome in Acute Ischemic Stroke on the Basis of Imaging, Demographic, and Clinical Information.

OBJECTIVE: When treatment decisions are being made for patients with acute ischemic stroke, timely and accurate outcome prediction plays an important ...

Deep Learning for MR Angiography: Automated Detection of Cerebral Aneurysms.

Purpose To develop and evaluate a supportive algorithm using deep learning for detecting cerebral aneurysms at time-of-flight MR angiography to provid...

Evaluating performance of neural codes in model neural communication networks.

Information needs to be appropriately encoded to be reliably transmitted over physical media. Similarly, neurons have their own codes to convey inform...

Electroencephalography-based machine learning for cognitive profiling in Parkinson's disease: Preliminary results.

BACKGROUND: Cognitive symptoms are common in patients with Parkinson's disease. Characterization of a patient's cognitive profile is an essential step...

Roles for globus pallidus externa revealed in a computational model of action selection in the basal ganglia.

The basal ganglia are considered vital to action selection - a hypothesis supported by several biologically plausible computational models. Of the sev...

Bispectrum Features and Multilayer Perceptron Classifier to Enhance Seizure Prediction.

The ability to accurately forecast seizures could significantly improve the quality of life of patients with drug-refractory epilepsy. Prediction capa...

Afferent Input Induced by Rhythmic Limb Movement Modulates Spinal Neuronal Circuits in an Innovative Robotic In Vitro Preparation.

Locomotor patterns are mainly modulated by afferent feedback, but its actual contribution to spinal network activity during continuous passive limb tr...

Control of a Robot Arm Using Decoded Joint Angles from Electrocorticograms in Primate.

Electrocorticogram (ECoG) is a well-known recording method for the less invasive brain machine interface (BMI). Our previous studies have succeeded in...

Seizure forecasting using single robust linear feature as correlation vector of seizure-like events in brain slices preparation in vitro.

Epilepsy is a neurological disorder affecting 50 million individuals globally. Modern research has inspected the likelihood of forecasting epileptic s...

Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification.

Mental tasks classification is increasingly recognized as a major challenge in the field of EEG signal processing and analysis. State-of-the-art appro...