Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Alzheimer's Disease: Overview

Alzheimer's Disease: Overview

Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Mitigating structural recalcitrance: Microwave pretreatment governs anatomy-dependent moisture migration and dimensional stability of moso bamboo.

Bioresource technology·2026

Same author

Hyperspectral geographical origin identification of Bupleurum via spatial-spectral features based on adaptive weighted quaternion zernike moments.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026

Same author

Spatial atlas of human diabetic kidney uncovered podocyte-driven metabolic-inflammatory crosstalk via glycerolipid reprogramming and DUSP4/MMP3 axis.

Fundamental research·2026

Same author

Proteomics in environmental pollution research: Advances, challenges, and future directions.

Journal of proteomics·2026

Same author

Efficacy and safety of BCMA- or GPRC5D-directed CD3 bispecific antibodies in relapsed/refractory multiple myeloma: a systematic review and meta-analysis of prospective clinical trials and real-world studies.

Frontiers in immunology·2026

Same author

KSDiffusion: conditional diffusion for kinase-specific phosphorylation site prediction under data-limited and imbalanced regimes.

Briefings in bioinformatics·2026

Same journal

The predictive power of neuropsychological measures in MCI: early detection of dementia conversion.

Frontiers in aging neuroscience·2026

Same journal

Age-associated neuronal micronuclei formation and transfer to microglia.

Frontiers in aging neuroscience·2026

Same journal

Ershiwuwei Shanhu Pill ameliorates cognitive impairment in Alzheimer's disease mice by remodeling gut microbiota and host serum metabolites.

Frontiers in aging neuroscience·2026

Same journal

Behind the scenes of a 7T MRI clinical study in Alzheimer's disease: challenges and recommendations for future research.

Frontiers in aging neuroscience·2026

Same journal

Striatal functional connectivity alterations in mild cognitive impairment subtypes defined by CSF A/T biomarkers.

Frontiers in aging neuroscience·2026

Same journal

State sensitivity and five-year longitudinal stability of resting-state EEG biomarker candidates in healthy adults.

Frontiers in aging neuroscience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 21, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Alzheimer's Disease Analysis Algorithm Based on No-threshold Recurrence Plot Convolution Network.

Xuemei Li¹, Tao Zhou², Shi Qiu³

¹School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu, China.

Frontiers in Aging Neuroscience

|May 27, 2022

Summary

This summary is machine-generated.

This study introduces a novel method for early Alzheimer's disease detection using multi-channel electroencephalography (EEG) signals. The approach combines advanced signal processing and feature fusion for improved diagnostic accuracy.

Keywords:

Alzheimer's disease EEG PLV no-threshold recursive graph

More Related Videos

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Related Experiment Videos

Last Updated: Sep 21, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Area of Science:

Neurology
Biomedical Engineering
Signal Processing

Background:

Alzheimer's disease (AD) is a progressive neurological disorder impacting cognition and behavior.
Early diagnosis and treatment are crucial for managing AD.
Electroencephalography (EEG) offers a non-invasive method for AD detection.

Purpose of the Study:

To enhance the analysis of single-channel EEG signals for more accurate AD detection.
To develop a robust framework for analyzing multi-channel EEG data.
To improve the characterization of EEG signals for AD diagnosis.

Main Methods:

A Phase Locking Value (PLV) framework was developed to analyze relationships between multiple EEG channels.
A threshold-free recursive plot convolution network was employed for 1D EEG signal to 2D representation.
A fusion algorithm integrating clinical and imaging features was proposed.

Main Results:

The proposed algorithm demonstrated good performance in detecting Alzheimer's disease.
The method showed robustness in its diagnostic capabilities.
The combination of multi-channel analysis, advanced representation, and feature fusion improved detection accuracy.

Conclusions:

The developed approach offers a promising, non-invasive tool for early Alzheimer's disease detection.
Multi-channel EEG analysis combined with feature fusion enhances diagnostic accuracy.
This method provides a robust framework for neurological disorder detection using EEG.