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Related Experiment Video

Updated: Mar 6, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

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Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM.

Seyed Hani Hojjati1, Ata Ebrahimzadeh1, Ali Khazaee2

  • 1Department of Electrical Engineering, Babol University of Technology, Babol, Iran.

Journal of Neuroscience Methods
|March 14, 2017
PubMed
Summary

This study uses resting-state fMRI and machine learning to accurately identify mild cognitive impairment (MCI) patients who will develop Alzheimer's disease (AD). This approach aids in early AD diagnosis by detecting brain changes linked to MCI conversion.

Keywords:
Alzheimer’s disease (AD)Graph theoryMCI converterMCI non-converterMachine learning approachMild cognitive impairment (MCI)Resting-state fMRI

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Related Experiment Videos

Last Updated: Mar 6, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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Area of Science:

  • Neuroimaging
  • Machine Learning
  • Neurology

Background:

  • Investigated the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD).
  • Differentiated between MCI converters (MCI-C) and MCI non-converters (MCI-NC) using resting-state functional MRI (rs-fMRI).

Purpose of the Study:

  • To develop a method for predicting AD progression in MCI patients.
  • To utilize rs-fMRI data for early diagnosis of Alzheimer's disease.

Main Methods:

  • Employed graph theory and machine learning, specifically a support vector machine (SVM).
  • Utilized a novel feature selection algorithm to identify optimal features from local and global graph measures derived from rs-fMRI.
  • Included 18 MCI-C and 62 MCI-NC participants.

Main Results:

  • Achieved high classification accuracy (91.4%), sensitivity (83.24%), specificity (90.1%), and AUC (0.95) in distinguishing MCI-C from MCI-NC.
  • Identified specific brain regions affected in Alzheimer's disease progression.

Conclusions:

  • The proposed approach shows potential for early AD diagnosis.
  • rs-fMRI data can predict MCI to AD conversion by revealing underlying affected brain regions.