Neurology

Identifying natural inhibitors against FUS protein in dementia through machine learning, molecular docking, and dynamics simulation.

Dementia, a complex and debilitating spectrum of neurodegenerative diseases, presents a profound challenge in the quest for effective treatments. The FUS protein is well at the center of this problem, as it is frequently dysregulated in the various disorders. We chose a route of computational work that involves targeting natural inhibitors of the FUS protein, offering a novel treatment strategy. W...

Multi-source sparse broad transfer learning for parkinson's disease diagnosis via speech.

Diagnosing Parkinson's disease (PD) via speech is crucial for its non-invasive and convenient data collection. However, the small sample size of PD speech data impedes accurate recognition of PD speech. Therefore, we propose a novel multi-source sparse broad transfer learning (SBTL) method, inspired by incremental broad learning, which balances model learning capability and the overfitting associa...

Machine Learning-based World Health Organization Disability Assessment Schedule for persons with Parkinson's disease.

INTRODUCTION: The World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) is a well-known measure to assess disability in persons wi...

Machine learning enables high-throughput, low-replicate screening for novel anti-seizure targets and compounds using combined movement and calcium fluorescence in larval zebrafish.

Identifying new anti-seizure medications (ASMs) is difficult due to limitations in animal-based assays. Zebrafish (Danio rerio) serve as a model for c...

FLANet: A multiscale temporal convolution and spatial-spectral attention network for EEG artifact removal with adversarial training.

Denoising artifacts, such as noise from muscle or cardiac activity, is a crucial and ubiquitous concern in neurophysiological signal processing, parti...

EEG-based fatigue state evaluation by combining complex network and frequency-spatial features.

BACKGROUND: The proportion of traffic accidents caused by fatigue driving is increasing year by year, which has aroused wide concerns for researchers....

Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network.

Previous deep learning-based brain network research has made significant progress in understanding the pathophysiology of schizophrenia. However, it i...

Specific endophenotypes in EEG microstates for methamphetamine use disorder.

BACKGROUND: Electroencephalogram (EEG) microstates, which reflect large-scale resting-state networks of the brain, have been proposed as potential end...

Enhanced electroencephalogram signal classification: A hybrid convolutional neural network with attention-based feature selection.

Accurate recognition and classification of motor imagery electroencephalogram (MI-EEG) signals are crucial for the successful implementation of brain-...

Hybrid deep learning based stroke detection using CT images with routing in an IoT environment.

Stroke remains a leading global health concern and early diagnosis and accurate identification of stroke lesions are essential for improving treatment...

Multi-knowledge informed deep learning model for multi-point prediction of Alzheimer's disease progression.

The diagnosis of Alzheimer's disease (AD) based on visual features-informed by clinical knowledge has achieved excellent results. Our study endeavors ...

Machine learning techniques for independent gait recovery prediction in acute anterior circulation ischemic stroke.

OBJECTIVE: This study aimed to develop and validate a machine learning-based predictive model for gait recovery in patients with acute anterior circul...

Multi-branch convolutional neural network with cross-attention mechanism for emotion recognition.

Research on emotion recognition is an interesting area because of its wide-ranging applications in education, marketing, and medical fields. This stud...

Biomechanical Risk Classification in Repetitive Lifting Using Multi-Sensor Electromyography Data, Revised National Institute for Occupational Safety and Health Lifting Equation, and Deep Learning.

Repetitive lifting tasks in occupational settings often result in shoulder injuries, impacting both health and productivity. Accurately assessing the ...

External validation of 12 existing survival prediction models for patients with spinal metastases.

BACKGROUND CONTEXT: Survival prediction models for patients with spinal metastases may inform patients and clinicians in shared decision-making.

Development of a machine learning model and a web application for predicting neurological outcome at hospital discharge in spinal cord injury patients.

BACKGROUND: Spinal cord injury (SCI) is a devastating condition with profound physical, psychological, and socioeconomic consequences. Despite advance...

Machine Learning and Experiments Revealed Key Genes Related to PANoptosis Linked to Drug Prediction and Immune Landscape in Spinal Cord Injury.

Spinal cord injury (SCI) is a severe central nervous system injury without effective therapies. PANoptosis is involved in the development of many dise...

Integrating neuroscience and artificial intelligence: EEG analysis using ensemble learning for diagnosis Alzheimer's disease and frontotemporal dementia.

BACKGROUND: Alzheimer's disease (AD) and frontotemporal dementia (FTD) are both progressive neurological disorders that affect the elderly. Distinguis...

Differential diagnosis of multiple system atrophy with predominant parkinsonism and Parkinson's disease using neural networks (part II).

Neural networks (NNs) possess the capability to learn complex data relationships, recognize inherent patterns by emulating human brain functions, and ...

Unveiling encephalopathy signatures: A deep learning approach with locality-preserving features and hybrid neural network for EEG analysis.

EEG signals exhibit spatio-temporal characteristics due to the neural activity dispersion in space over the brain and the dynamic temporal patterns of...