Related Experiment Video

Updated: Jun 5, 2026

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

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
PubMed
Abstract

Insights

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.
Keywords:
Alzheimer’s disease (AD)Convolutional neural network (CNN)Deep learning (DL)Long-short term memory (LSTM)Magnetic resonance imaging (MRI)Mild cognitive impairment (MCI)

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