Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Interannual Dynamics of Macrobenthic Communities near a Coastal Nuclear Power Plant: Environmental Drivers and Risks of Cooling Source Blockage.

Biology·2026
Same author

Pharmacological effects, classification, genetic and molecular studies of different chemotypes essential oil of <i>Perilla frutescens</i> (L.) Britt.: A review.

Journal of pharmaceutical analysis·2026
Same author

Effects of ACT on the Executive Function and Emotional Distress of Older Adults With Subjective Cognitive Decline.

American journal of psychotherapy·2026
Same author

A Chitosan-Dextran Hydrogel Based on a Site-Isolated Grafting Strategy for Long-Term Wet Tissue Adhesion and Wound Healing.

Advanced healthcare materials·2026
Same author

Interfacial Polarization Engineering in MXene-Polymer Nanofibers for High-Output Triboelectric Nanogenerators.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

m⁶A-associated GAS6 expression is associated with pathogenic activation of fibroblast-like synoviocytes in rheumatoid arthritis.

Scientific reports·2026

Related Experiment Video

Updated: Jun 3, 2025

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.1K

Unveiling neural activity changes in mild cognitive impairment using microstate analysis and machine learning.

Xiaotian Wu1, Yanli Liu1, Jiajun Che2,3

  • 1Department of Biomedical Engineering, Chengde Medical University, Chengde City, Hebei Province, China.

Journal of Alzheimer'S Disease : JAD
|January 8, 2025
PubMed
Summary
This summary is machine-generated.

Electroencephalogram (EEG) microstate analysis reveals distinct neurophysiological patterns in mild cognitive impairment (MCI). These findings offer new insights into brain activity changes and potential diagnostic markers for cognitive decline.

Keywords:
Alzheimer's diseasecognitive declinemachine learningmicrostatesmild cognitive impairmentneural dynamicsresting-state EEG

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

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

Published on: December 15, 2023

947

Related Experiment Videos

Last Updated: Jun 3, 2025

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.1K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

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

Published on: December 15, 2023

947

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Biomedical Engineering

Background:

  • Mild cognitive impairment (MCI) is a precursor to Alzheimer's disease (AD), necessitating research into its underlying neural mechanisms.
  • Electroencephalogram (EEG) microstates offer a window into brain activity dynamics, but comprehensive characterization in MCI is lacking.
  • Understanding neurophysiological changes in MCI is critical for early detection and intervention strategies.

Purpose of the Study:

  • To investigate neurophysiological alterations in MCI using a detailed analysis of EEG microstate features.
  • To explore both traditional temporal features and advanced entropy measures of EEG microstates.
  • To identify specific microstate patterns associated with cognitive decline in MCI.

Main Methods:

  • Resting-state EEG data were acquired from 69 individuals with MCI and healthy controls (HC).
  • Microstate analysis extracted conventional (duration, coverage) and entropy-based features.
  • Statistical analysis, Principal Component Analysis (PCA), and Machine Learning (ML) were used to identify MCI-specific patterns.

Main Results:

  • MCI patients exhibited altered microstate dynamics, including longer coverage/duration in Microstate C and shorter in A, B, and D compared to HCs.
  • PCA identified two key components, dominated by microstate dynamics and entropy, explaining over 75% of variance.
  • ML models demonstrated high accuracy in differentiating MCI from healthy control patterns.

Conclusions:

  • Comprehensive EEG microstate analysis provides novel insights into neurophysiological changes in MCI.
  • Specific alterations in microstate temporal and entropy features are associated with MCI.
  • EEG microstates show significant potential for studying complex neural changes in cognitive decline and AD research.