Neurology

Identification of therapeutic targets for Alzheimer's Disease Treatment using bioinformatics and machine learning.

Alzheimer's disease (AD) is a complex neurodegenerative disorder that currently lacks effective treatment options. This study aimed to identify potential therapeutic targets for the treatment of AD using comprehensive bioinformatics methods and machine learning algorithms. By integrating differential gene expression analysis, weighted gene co-expression network analysis, Mfuzz clustering, single-c...

Graph convolution network-based eeg signal analysis: a review.

With the advancement of artificial intelligence technology, more and more effective methods are being used to identify and classify Electroencephalography (EEG) signals to address challenges in healthcare and brain-computer interface fields. The applications and major achievements of Graph Convolution Network (GCN) techniques in EEG signal analysis are reviewed in this paper. Through an exhaustive...

Adjacent point aided vertebral landmark detection and Cobb angle measurement for automated AIS diagnosis.

Adolescent Idiopathic Scoliosis (AIS) is a prevalent structural deformity disease of human spine, and accurate assessment of spinal anatomical paramet...

A temporal-spatial feature fusion network for emotion recognition with individual differences reduction.

PURPOSE: In the context of EEG-based emotion recognition tasks, a conventional strategy involves the extraction of spatial and temporal features, subs...

Artificial intelligence for segmentation and classification in lumbar spinal stenosis: an overview of current methods.

PURPOSE: Lumbar spinal stenosis (LSS) is a frequently occurring condition defined by narrowing of the spinal or nerve root canal due to degenerative c...

UK Biobank MRI data can power the development of generalizable brain clocks: A study of standard ML/DL methodologies and performance analysis on external databases.

In this study, we present a comprehensive pipeline to train and compare a broad spectrum of machine learning and deep learning brain clocks, integrati...

Transcutaneous spinal cord stimulation combined with robotic-assisted body weight-supported treadmill training enhances motor score and gait recovery in incomplete spinal cord injury: a double-blind randomized controlled clinical trial.

BACKGROUND: Although transcutaneous spinal cord stimulation (tSCS) has been suggested as a safe and feasible intervention for gait rehabilitation, no ...

Paradigms of intraoperative neuromonitoring in paediatric thyroid surgery.

The larynx of children and adolescents is still in the developmental phase and the anatomical structure is still very small and sensitive. The higher ...

Enhancing neural stem cell integration in the injured spinal cord through targeted PTEN modulation.

JOURNAL/nrgr/04.03/01300535-202604000-00039/figure1/v/2025-06-30T060627Z/r/image-tiff Spinal cord injury results in permanent loss of neurological fun...

Spatially resolved transcriptomics and graph-based deep learning improve accuracy of routine CNS tumor diagnostics.

The diagnostic landscape of brain tumors integrates comprehensive molecular markers alongside traditional histopathological evaluation. DNA methylatio...

Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface.

Creating an intracortical brain computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experi...

Machine learning to predict stroke risk from routine hospital data: A systematic review.

PURPOSE: Stroke remains a leading cause of morbidity and mortality. Despite this, current risk stratification tools such as CHADS-VASc and QRISK3 are ...

Molecular Insights into α-Synuclein Fibrillation: A Raman Spectroscopy and Machine Learning Approach.

The aggregation of α-synuclein is crucial to the development of Lewy body diseases, including Parkinson's disease and dementia with Lewy bodies. The a...

Alzheimer's disease: an integrative bioinformatics and machine learning analysis reveals glutamine metabolism-associated gene biomarkers.

BACKGROUND: Alzheimer's disease (AD), a hallmark of age-related cognitive decline, is defined by its unique neuropathology. Metabolic dysregulation, p...

Preclinical Cognitive Markers of Alzheimer Disease and Early Diagnosis Using Virtual Reality and Artificial Intelligence: Literature Review.

BACKGROUND: This review explores the potential of virtual reality (VR) and artificial intelligence (AI) to identify preclinical cognitive markers of A...

Comparative efficacy of robot-assisted therapy associated with other different interventions on upper limb rehabilitation after stroke: A protocol for a network meta-analysis.

INTRODUCTION: Post-stroke movement disorders are common, especially upper limb dysfunction, which seriously affects the physical and mental health of ...

Identifying Primary Sites of Spinal Metastases: Expert-Derived Features vs. ResNet50 Model Using Nonenhanced MRI.

BACKGROUND: The spinal column is a frequent site for metastases, affecting over 30% of solid tumor patients. Identifying the primary tumor is essentia...

Prediction of Pharmacoresistance in Drug-Naïve Temporal Lobe Epilepsy Using Ictal EEGs Based on Convolutional Neural Network.

Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications (ASMs), a condition known as pharmacoresistant epi...

Development and applications of a machine learning model for an in-depth analysis of pentylenetetrazol-induced seizure-like behaviors in adult zebrafish.

Epilepsy, a neurological disorder causing recurring seizures, is often studied in zebrafish by exposing animals to pentylenetetrazol (PTZ), which indu...

Beyond averaging: A transformer approach to decoding event related brain potentials.

The objective of this study is to assess the potential of a transformer-based deep learning approach applied to event-related brain potentials (ERPs) ...