Neurology

Latest AI and machine learning research in neurology for healthcare professionals.

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Analyses of the mechanism and therapeutic targets of senescence related genes in ischemic stroke with multi-omics approach.

Ischemic stroke (IS) affects 11 million people annually, posing substantial clinical and economic bu...

A comprehensive explainable AI approach for enhancing transparency and interpretability in stroke prediction.

Stroke is among the leading causes of death, especially among old adults. Thus, the mortality rate a...

CMOS-compatible flash-gated thyristor-based neuromorphic module with small area and low energy consumption for in-memory computing.

In-memory computing (IMC) is a technology that enables efficient analog vector-matrix multiplication...

Self-Supervised Neuron Morphology Representation with Graph Transformer.

Effective representation of neuronal morphology is essential for cell typing and understanding brain...

Evaluating changes in hypoglossal nerve stimulator use over time and long-term adherence.

PURPOSE: Evaluate patient phenotypes and longitudinal patterns of hypoglossal nerve stimulator (HGNS...

Clinical Translation of Integrated PET-MRI for Neurodegenerative Disease.

The prevalence of Alzheimer's disease and other dementias is increasing as populations live longer l...

Graph Convolutional Neural Network-Enabled Frontier Molecular Orbital Prediction: A Case Study with Neurotransmitters and Antidepressants.

With the advancement of artificial intelligence-embedded methodologies, their application to predict...

Developing an accessible dementia assessment tool: Leveraging a residual network, the trail making test, and demographic data.

BackgroundThe global burden of Alzheimer's disease and related dementias is rapidly increasing, part...

Characterizing structure-function coupling in subjective memory complaints of preclinical Alzheimer's disease.

BackgroundSubjective cognitive decline (SCD) is recognized as an early phase in the progression of A...

Reflections on dynamic prediction of Alzheimer's disease: advancements in modeling longitudinal outcomes and time-to-event data.

BACKGROUND: Individualized prediction of health outcomes supports clinical medicine and decision mak...

Transcriptional modulation unique to vulnerable motor neurons predicts ALS across species and SOD1 mutations.

Amyotrophic lateral sclerosis (ALS) is characterized by the progressive loss of motor neurons (MNs) ...

BDEC: Brain Deep Embedded Clustering Model for Resting State fMRI Group-Level Parcellation of the Human Cerebral Cortex.

OBJECTIVE: To develop a robust group-level brain parcellation method using deep learning based on re...

M4CEA: A Knowledge-guided Foundation Model for Childhood Epilepsy Analysis.

Existing electroencephalogram (EEG)-based deep learning models are mainly designed for single or sev...

Multi-tissue Methylation Analysis of Alzheimer's Disease: Insights into Pathways, Modules, and Key Genes.

DNA methylation plays a crucial role in the onset and progression of Alzheimer's disease (AD). Genom...

Prediction of Epilepsy Seizure Based on Cepstrum Analysis and Deep Learning.

There are specific changes in the frequency components of the electroencephalogram (EEG) signals bef...

Developing an explainable machine learning and fog computing-based visual rating scale for the prediction of dementia progression.

Recently, dementia research has primarily concentrated on using Magnetic Resonance Imaging (MRI) to ...

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