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Updated: Jun 5, 2026
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
Summary
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.
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