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  3. Predicting The Conversion From Mild Cognitive Impairment To Alzheimer's Disease Using Graph Frequency Bands And Functional Connectivity-based Features.
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  3. Predicting The Conversion From Mild Cognitive Impairment To Alzheimer's Disease Using Graph Frequency Bands And Functional Connectivity-based Features.

Related Experiment Video

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

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

Published on: August 7, 2017

Predicting the Conversion From Mild Cognitive Impairment to Alzheimer's Disease Using Graph Frequency Bands and

Jafar Zamani1, Alireza Talesh Jafadideh2

  • 1Department of Psychiatry and Behavioral Sciences, Stanford University, California, United States.

Basic and Clinical Neuroscience
|June 4, 2026

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
Alzheimer’s disease (AD)ClassificationConnectivity-based featuresGraph signal processingMild cognitive impairment (MCI)

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Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
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Related Experiment Videos

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

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

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Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
09:38

Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Published on: November 14, 2017

Predicting Alzheimer's disease progression from mild cognitive impairment is vital. Machine learning models using resting-state fMRI data can identify at-risk individuals with high accuracy, aiding early diagnosis.

Area of Science:

  • Neuroimaging
  • Machine Learning
  • Computational Neuroscience

Background:

  • Accurate prediction of mild cognitive impairment (MCI) progression to Alzheimer's disease (AD) is critical for timely intervention.
  • Resting-state functional magnetic resonance imaging (rs-fMRI) and machine learning show promise for classifying AD and MCI.
  • Identifying predictive biomarkers for MCI-to-AD conversion is an ongoing research challenge.

Purpose of the Study:

  • To develop and validate a machine learning framework for predicting MCI progression to AD using rs-fMRI data.
  • To identify a parsimonious set of features for accurate classification of stable (sMCI) versus progressive (pMCI) individuals.
  • To enhance the precision of early AD diagnosis and inform intervention strategies.

Main Methods:

  • Utilized rs-fMRI data from 142 sMCI and 136 pMCI patients in the ADNI cohort.
  • Applied graph signal processing to filter rs-fMRI data into low, middle, and high-frequency bands.
  • Extracted connectivity-based features and employed particle swarm optimization (PSO) for feature selection, followed by SVM classification.

    • Main Results:

    • A reduced set of five features, selected via PSO, achieved 77% accuracy, 70% specificity, and 83% sensitivity using an SVM classifier.
    • Key predictive features included graph metrics (clustering coefficient, modularity) and network properties (radius, eccentricity) across different frequency bands.
    • The proposed method demonstrated high classification performance with a significantly reduced feature set.

    Conclusions:

    • The developed framework effectively predicts MCI progression to AD using a concise set of rs-fMRI derived graph features.
    • This approach offers a promising tool for improving the accuracy of early AD risk assessment.
    • The findings support the potential for advanced neuroimaging analysis in guiding early diagnosis and personalized treatment strategies.