Neurology

Single-cell omics: moving towards a new era in ischemic stroke research.

Ischemic stroke (IS) is a highly complex and heterogeneous disease involving multiple pathophysiological events. A better understanding of the pathophysiology of IS will enhance preventive, diagnostic and therapeutic strategies. Despite significant advances in modern medicine, the molecular mechanisms of IS are still largely unknown. The high-throughput omics approach opens new avenues for identif...

The interaction of UBR4, LRP1, and OPHN1 in refractory epilepsy: Drosophila model to investigate the oligogenic effect on epilepsy.

Refractory epilepsy is an intractable neurological disorder that can be associated with oligogenic/polygenic etiologies. Through trio-based whole-exome sequencing analysis, we identified a clinical case of refractory epilepsy with three candidate gene variants: UBR4, LRP1, and OPHN1. Utilizing the Gal4-UAS system and double-balancer tool, we generated single, double, and triple knockdown Drosophil...

A subject transfer neural network fuses Generator and Euclidean alignment for EEG-based motor imagery classification.

BACKGROUND: Brain-computer interface (BCI) facilitates the connection between human brain and computer, enabling individuals to control external devic...

Distinct neurodynamics of functional brain networks in Alzheimer's disease and frontotemporal dementia as revealed by EEG

Objective While Alzheimer's disease (AD) and frontotemporal dementia (FTD) show some common memory deficits, these two disorders show partially over...

Exploring Gender Differences in Chronic Pain Discussions on Reddit

Pain is an inherent part of human existence, manifesting as both physical and emotional experiences, and can be categorized as either acute or chron...

EEG-based prediction of reaction time during sleep deprivation.

Prolonged wakefulness is known to adversely affect basic cognitive abilities such as object recognition and decision-making. It affects the dynamics o...

Multigranular Evaluation for Brain Visual Decoding

Existing evaluation protocols for brain visual decoding predominantly rely on coarse metrics that obscure inter-model differences, lack neuroscienti...

ArteryX: Advancing Brain Artery Feature Extraction with Vessel-Fused Networks and a Robust Validation Framework

Cerebrovascular pathology significantly contributes to cognitive decline and neurological disorders, underscoring the need for advanced tools to ass...

Interpretable EEG-to-Image Generation with Semantic Prompts

Decoding visual experience from brain signals offers exciting possibilities for neuroscience and interpretable AI. While EEG is accessible and tempo...

eegFloss: A Python package for refining sleep EEG recordings using machine learning models

Electroencephalography (EEG) allows monitoring of brain activity, providing insights into the functional dynamics of various brain regions and their...

Sample-Efficient Reinforcement Learning Controller for Deep Brain Stimulation in Parkinson's Disease

Deep brain stimulation (DBS) is an established intervention for Parkinson's disease (PD), but conventional open-loop systems lack adaptability, are ...

Uncovering Neuroimaging Biomarkers of Brain Tumor Surgery with AI-Driven Methods

Brain tumor resection is a complex procedure with significant implications for patient survival and quality of life. Predictions of patient outcomes...

Artificial intelligence models using F-wave responses predict amyotrophic lateral sclerosis.

Nerve conduction F-wave studies contain crucial information about subclinical motor dysfunction that can be used to diagnose patients with amyotrophic...

Prediction of tissue and clinical thrombectomy outcome in acute ischaemic stroke using deep learning.

The advent of endovascular thrombectomy has significantly improved outcomes for stroke patients with intracranial large vessel occlusion, yet individu...

Deep learning analyses of splicing variants identify the link of PCP4 with amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a severe motor neuron disease, with most sporadic cases lacking clear genetic causes. Abnormal pre-mRNA splicin...

An Explainable Transformer Model for Alzheimer's Disease Detection Using Retinal Imaging

Alzheimer's disease (AD) is a neurodegenerative disorder that affects millions worldwide. In the absence of effective treatment options, early diagn...

Transformer Model for Alzheimer's Disease Progression Prediction Using Longitudinal Visit Sequences

Alzheimer's disease (AD) is a neurodegenerative disorder with no known cure that affects tens of millions of people worldwide. Early detection of AD...

MLASDO: a software tool to detect and explain clinical and omics inconsistencies applied to the Parkinson's Progression Markers Initiative cohort

Inconsistencies between clinical and omics data may arise within medical cohorts. The identification, annotation and explanation of anomalous omics-...

From Video to EEG: Adapting Joint Embedding Predictive Architecture to Uncover Visual Concepts in Brain Signal Analysis

EEG signals capture brain activity with high temporal and low spatial resolution, supporting applications such as neurological diagnosis, cognitive ...

RECA-PD: A Robust Explainable Cross-Attention Method for Speech-based Parkinson's Disease Classification

Parkinson's Disease (PD) affects over 10 million people globally, with speech impairments often preceding motor symptoms by years, making speech a v...