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Related Concept Videos

Dementia01:30

Dementia

83
Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
The progression of dementia is generally gradual....
83

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Related Experiment Video

Updated: May 23, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome

Thawirasm Jungrungrueang1, Sawrawit Chairat1, Kasidach Rasitanon2

  • 1Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand.

Scientific Reports
|May 19, 2025
PubMed
Summary

This study reveals dynamic electroencephalography (EEG) connectivity patterns can differentiate dementia subtypes like Alzheimer's disease (AD) and frontotemporal dementia (FD) with high accuracy, offering a promising biomarker for early diagnosis.

Keywords:
Aging disordersAlzheimer’s diseaseBrain connectivityConvolutional neural networkElectroencephalogramFrontotemporal dementia

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Area of Science:

  • Neuroscience
  • Biomarkers
  • Medical Imaging

Background:

  • Dementia spectrum disorders present a growing global health challenge, necessitating early detection for effective management.
  • Resting-state electroencephalography (EEG) offers a non-invasive method to assess brain function.

Purpose of the Study:

  • To identify dynamic EEG functional connectivity patterns characteristic of Alzheimer's disease (AD) and frontotemporal dementia (FD).
  • To evaluate the potential of these dynamic connectivity features as biomarkers for dementia subtypes.
  • To develop and assess a deep learning model for classifying dementia subtypes using EEG data.

Main Methods:

  • Extraction of statistical features (mean, variance, skewness, Shannon entropy) from resting-state EEG functional connectivity.
  • Analysis of alterations in Alpha, Delta, Theta, Beta, and Gamma frequency bands.
  • Development of a convolutional neural network (CNN) model incorporating dynamic EEG features for classification.

Main Results:

  • Generalized disruption of Alpha-band connectivity observed across dementia subtypes.
  • Distinctive patterns identified: Delta-band hyperconnectivity in AD and disrupted phase-based connectivity in FD (Theta, Beta, Gamma bands).
  • High classification accuracies achieved: 93.6% for multiclass (AD, FD, controls), 97.8% for AD vs. controls, 96.7% for FD vs. controls, and 97.4% for AD vs. FD.

Conclusions:

  • Dynamic EEG functional connectivity patterns serve as effective biomarkers for differentiating dementia subtypes.
  • A high-performance deep learning framework utilizing these patterns shows promise for early dementia screening and diagnosis.
  • Further research into network dynamics and task-based EEG could enhance diagnostic capabilities and personalized treatment strategies.