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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
Detecting Mild Cognitive Impairment to Alzheimer's Disease Progression by fMRI Using Convolutional Neural Network and
Sima Ghafoori1, Ahmad Shalbaf1
1Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Basic and Clinical Neuroscience
|June 4, 2026
Summary
This study predicts Alzheimer's disease progression in mild cognitive impairment patients using deep learning and fMRI data. The CNN-LSTM model achieved 92.47% accuracy, aiding early diagnosis and treatment strategies.
Area of Science:
- Neuroimaging
- Artificial Intelligence
- Gerontology
Background:
- Mild cognitive impairment (MCI) precedes Alzheimer's disease (AD), with a significant risk of progression.
- Predicting MCI to AD progression is crucial for timely intervention and resource management.
Purpose of the Study:
- To develop a diagnostic tool for predicting AD progression in MCI patients.
- To leverage resting-state fMRI, clinical, and demographic data for enhanced prediction accuracy.
Main Methods:
- Utilized deep learning models, specifically 3D convolutional neural networks (CNN) and long short-term memory (LSTM) networks.
- Trained models on 266 samples from 81 MCI subjects with longitudinal data (average 5-year follow-up).
- Integrated clinical attributes with fMRI data for model enhancement.
Main Results:
- The combined CNN-LSTM model integrating clinical attributes achieved the highest validation accuracy of 92.47%.
- Demonstrated high performance in predicting MCI to AD progression.
Conclusions:
- Deep learning approaches show significant potential for analyzing fMRI data in predicting neurodegenerative disease progression.
- Integrating diverse data types (fMRI, clinical, demographic) enhances predictive model efficiency and accuracy for MCI to AD conversion.
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