Neurology

Inferring protein expression changes from mRNA in Alzheimer's dementia using deep neural networks.

Identifying the molecular systems and proteins that modify the progression of Alzheimer's disease and related dementias (ADRD) is central to drug target selection. However, discordance between mRNA and protein abundance, and the scarcity of proteomic data, has limited our ability to advance candidate targets that are mainly based on gene expression. Therefore, by using a deep neural network that p...

Quantitative Evaluation System of Upper Limb Motor Function of Stroke Patients Based on Desktop Rehabilitation Robot.

Rehabilitation training and movement evaluation after stroke have become a research hotspot as stroke has become a very common and harmful disease. However, traditional rehabilitation training and evaluation are mainly conducted under the guidance of rehabilitation doctors. The evaluation process is time-consuming and the evaluation results are greatly influenced by doctors. In this study, a deskt...

GuiltyTargets: Prioritization of Novel Therapeutic Targets With Network Representation Learning.

The majority of clinical trials fail due to low efficacy of investigated drugs, often resulting from a poor choice of target protein. Existing computa...

Genetic Algorithms for Optimized Diagnosis of Alzheimer's Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging.

Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized...

Automated characterisation of microglia in ageing mice using image processing and supervised machine learning algorithms.

The resident macrophages of the central nervous system, microglia, are becoming increasingly implicated as active participants in neuropathology and a...

A neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG.

Interictal High Frequency Oscillations (HFO) are measurable in scalp EEG. This development has aroused interest in investigating their potential as bi...

Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference.

Although deep networks have been shown to perform very well on a variety of medical imaging tasks, inference in the presence of pathology presents sev...

Deep Learning-Based Neuromelanin MRI Changes of Isolated REM Sleep Behavior Disorder.

BACKGROUND: Isolated REM sleep behavior disorder (iRBD) is considered a prodromal stage of parkinsonism. Neurodegenerative changes in the substantia n...

Assessment of Smartphone-Based Spiral Tracing in Multiple Sclerosis Reveals Intra-Individual Reproducibility as a Major Determinant of the Clinical Utility of the Digital Test.

Technological advances, lack of medical professionals, high cost of face-to-face encounters, and disasters such as the COVID-19 pandemic fuel the tele...

A Deep Learning Approach to Predicting Disease Progression in Multiple Sclerosis Using Magnetic Resonance Imaging.

OBJECTIVES: Magnetic resonance imaging (MRI) is an important tool for diagnosis and monitoring of disease course in multiple sclerosis (MS). However, ...

Orthogonal representations for robust context-dependent task performance in brains and neural networks.

How do neural populations code for multiple, potentially conflicting tasks? Here we used computational simulations involving neural networks to define...

The Effect of Robot-Mediated Virtual Reality Gaming on Upper Limb Spasticity Poststroke: A Randomized-Controlled Trial.

Stroke is a common reason for motor disability and is often associated with spasticity and poor motor function of the upper limbs involved. Spasticit...

Investigating EEG-based functional connectivity patterns for multimodal emotion recognition.

Previous studies on emotion recognition from electroencephalography (EEG) mainly rely on single-channel-based feature extraction methods, which ignore...

Continuous Scoring of Depression From EEG Signals via a Hybrid of Convolutional Neural Networks.

Depression score is traditionally determined by taking the Beck depression inventory (BDI) test, which is a qualitative questionnaire. Quantitative sc...

Sequential endoscopic and robot-assisted surgical solutions for a rare fungal spondylodiscitis, secondary lumbar spinal stenosis, and subsequent discal pseudocyst causing acute cauda equina syndrome: a case report.

BACKGROUND: Fungal spondylodiscitis is a rare infectious disease. The secondary lumbar spinal stenosis and postoperative discal pseudocyst were even r...

Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia.

INTRODUCTION: Ultrasound-guided regional anesthesia (UGRA) involves the acquisition and interpretation of ultrasound images to delineate sonoanatomy. ...

Robot-assisted distal training improves upper limb dexterity and function after stroke: a systematic review and meta-regression.

INTRODUCTION: Stroke is one of the top 10 causes of death worldwide, and more than half of stroke patients face distal upper extremity dysfunction. Co...

Natural language processing for automated surveillance of intraoperative neuromonitoring in spine surgery.

We sought to develop natural language processing (NLP) methods for automated detection and characterization of neuromonitoring documentation from free...

Deep-learning online EEG decoding brain-computer interface using error-related potentials recorded with a consumer-grade headset.

Brain-computer interfaces (BCIs) allow subjects with sensorimotor disability to interact with the environment. Non-invasive BCIs relying on EEG signal...

Effects of Balance Exercise Assist Robot training for patients with hemiparetic stroke: a randomized controlled trial.

BACKGROUND: Robot-assisted rehabilitation for patients with stroke is promising. However, it is unclear whether additional balance training using a ba...