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Related Concept Videos

Alzheimer's Disease: Overview01:26

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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.
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Alzheimer disease stages identification based on correlation transfer function system using resting-state functional

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  • 1Computers and Systems Department, Electronics Research Institute, Giza, Egypt.

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Correlated transfer function (CorrTF) effectively identifies Alzheimer's disease (AD) stages using resting-state fMRI data. This novel biomarker achieves high accuracy in distinguishing between normal cognition and various stages of cognitive impairment.

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Area of Science:

  • Neuroimaging
  • Biomarker Discovery
  • Machine Learning

Background:

  • Alzheimer's disease (AD) significantly impacts quality of life, causing memory loss and cognitive decline.
  • Resting-state functional magnetic resonance imaging (rs-fMRI) is a key tool for analyzing brain regions in AD.
  • Identifying reliable biomarkers for early AD detection remains a critical challenge.

Purpose of the Study:

  • To evaluate the efficacy of correlated transfer function (CorrTF) as a novel biomarker for AD detection using rs-fMRI.
  • To distinguish between different stages of Alzheimer's disease and mild cognitive impairment (MCI).
  • To explore brain regions exhibiting significant changes in CorrTF features across AD stages.

Main Methods:

  • rs-fMRI data preprocessing to reduce noise and retain essential information.
  • Brain parcellation into 116 regions using the Automated Anatomical Labeling (AAL) atlas.
  • Extraction of CorrTF features from regional time series and classification using hierarchical and flat Support Vector Machine (SVM) schemes.

Main Results:

  • The proposed framework achieved high classification accuracies: 98.2% for hierarchical SVM and 95.5% for flat SVM.
  • Ten-fold cross-validation was employed to ensure robust performance evaluation.
  • Significant changes in CorrTF connection strengths were observed across different AD stages.

Conclusions:

  • CorrTF is a promising and effective biomarker for the early identification of Alzheimer's disease.
  • Analysis of CorrTF feature strengths aids in identifying affected brain regions and their associations during AD progression.
  • The study highlights the potential of rs-fMRI combined with CorrTF for improved AD diagnosis and understanding.