Neurology

Brain morphological changes in hypokinetic dysarthria of Parkinson's disease and use of machine learning to predict severity.

BACKGROUND: Up to 90% of patients with Parkinson's disease (PD) eventually develop the speech and voice disorder referred to as hypokinetic dysarthria (HD). However, the brain morphological changes associated with HD have not been investigated. Moreover, no reliable model for predicting the severity of HD based on neuroimaging has yet been developed.

Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke.

. Accurate prediction of clinical impairment in upper-extremity motor function following therapy in chronic stroke patients is a difficult task for clinicians but is key in prescribing appropriate therapeutic strategies. Machine learning is a highly promising avenue with which to improve prediction accuracy in clinical practice. . The objective was to evaluate the performance of 5 machine learning...

Optic Disc and Cup Image Segmentation Utilizing Contour-Based Transformation and Sequence Labeling Networks.

Optic disc (OD) and optic cup (OC) segmentation are important steps for automatic screening and diagnosing of optic nerve head abnormalities such as g...

Automatic optic nerve head localization and cup-to-disc ratio detection using state-of-the-art deep-learning architectures.

Computer vision has greatly advanced recently. Since AlexNet was first introduced, many modified deep learning architectures have been developed and t...

Brain-Controlled Robotic Arm System Based on Multi-Directional CNN-BiLSTM Network Using EEG Signals.

Brain-machine interfaces (BMIs) can be used to decode brain activity into commands to control external devices. This paper presents the decoding of in...

Toward a unified framework for interpreting machine-learning models in neuroimaging.

Machine learning is a powerful tool for creating computational models relating brain function to behavior, and its use is becoming widespread in neuro...

Automatic Seizure Detection using Fully Convolutional Nested LSTM.

The automatic seizure detection system can effectively help doctors to monitor and diagnose epilepsy thus reducing their workload. Many outstanding st...

Deep-learning- and pharmacophore-based prediction of RAGE inhibitors.

The receptor for advanced glycation end products (RAGE) has been identified as a therapeutic target in a host of pathological diseases, including Alzh...

Performance Evaluation of Convolutional Neural Network for Hand Gesture Recognition Using EMG.

Electromyography (EMG) is a measure of electrical activity generated by the contraction of muscles. Non-invasive surface EMG (sEMG)-based pattern reco...

Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback.

Optimizing neurofeedback (NF) and brain-computer interface (BCI) implementations constitutes a challenge across many fields and has so far been addres...

Could automated machine-learned MRI grading aid epidemiological studies of lumbar spinal stenosis? Validation within the Wakayama spine study.

BACKGROUND: MRI scanning has revolutionized the clinical diagnosis of lumbar spinal stenosis (LSS). However, there is currently no consensus as to how...

Stroke Prediction with Machine Learning Methods among Older Chinese.

Timely stroke diagnosis and intervention are necessary considering its high prevalence. Previous studies have mainly focused on stroke prediction with...

Connectome-Based Propagation Model in Amyotrophic Lateral Sclerosis.

OBJECTIVE: Clinical trials in amyotrophic lateral sclerosis (ALS) continue to rely on survival or functional scales as endpoints, despite the emergenc...

Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography.

Traumatic brain injury (TBI) is one of the injuries that can bring serious consequences if medical attention has been delayed. Commonly, analysis of c...

A Myoelectric Control Interface for Upper-Limb Robotic Rehabilitation Following Spinal Cord Injury.

Spinal cord injury (SCI) is a widespread, life-altering injury leading to impairment of sensorimotor function that, while once thought to be permanent...

Double-Criteria Active Learning for Multiclass Brain-Computer Interfaces.

Recent technological advances have enabled researchers to collect large amounts of electroencephalography (EEG) signals in labeled and unlabeled datas...

Molecular and DNA Artificial Neural Networks via Fractional Coding.

This article considers implementation of artificial neural networks (ANNs) using molecular computing and DNA based on fractional coding. Prior work ha...

An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding.

Previous studies of Brain Computer Interfaces (BCI) based on scalp electroencephalography (EEG) have demonstrated the feasibility of decoding kinemati...

Applying deep learning to single-trial EEG data provides evidence for complementary theories on action control.

Efficient action control is indispensable for goal-directed behaviour. Different theories have stressed the importance of either attention or response...

New criteria for global stability of neutral-type Cohen-Grossberg neural networks with multiple delays.

The significant contribution of this paper is the addressing the stability issue of neutral-type Cohen-Grossberg neural networks possessing multiple t...