Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

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

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

Published on: August 7, 2017

Time Distributed Classification of Alzheimer's Disease on MRI Scans.

Mehmet Sait Dundar^1,2, Bulent Yilmaz³

¹Department of Electrical and Computer Engineering, Graduate School of Engineering and Sciences, Abdullah Gul University, Kayseri, Turkey.

NMR in Biomedicine

|June 4, 2026

Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Neural and emotional responses to mood induction and emotional design in multimedia learning.

BMC neuroscience·2026

Same author

Serum and Cervical Flushing Fluid Eicosapentaenoic Acid Levels in Patients With Unexplained Infertility Versus Healthy Controls.

Lipids·2026

Same author

Predicting multiple sclerosis prognosis using AI and machine learning: integrating clinical, immunological, and radiological variables.

Frontiers in neurology·2026

Same author

Comparison of Serum and Cervical Mucus Docosahexaenoic Acid (DHA) Levels in Patients with Polycystic Ovary Syndrome and Healthy Controls.

Journal of clinical medicine·2025

Same author

Artificial intelligence predicts pregnancy complications based on cytokine profiles.

The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians·2025

Same author

Modeling and simulation of an effectual triangular slotted UWB flexible antenna for breast cancer detection and healthcare monitoring.

PloS one·2025

This study developed advanced neuroimaging techniques to accurately diagnose Alzheimer's disease (AD) and mild cognitive impairment (MCI) using MRI scans. Machine learning models achieved high accuracy in differentiating disease stages, aiding early detection and management.

Area of Science:

Neuroimaging
Computational Neuroscience
Machine Learning

Background:

Alzheimer's disease (AD) diagnosis relies on neuroimaging and cognitive assessments.
Sophisticated methods are needed for accurate early detection of AD and mild cognitive impairment (MCI).

Purpose of the Study:

To develop and evaluate computational models for categorizing individuals as cognitively normal (CN), MCI, or AD using MRI data.
To integrate volumetric analysis and deep learning for enhanced diagnostic accuracy.

Main Methods:

Volumetric feature analysis (cortical thickness, grey/white matter, CSF, intracranial volume) from MRI data.
Linear regression to derive volumetric change rates and machine learning classifiers (Random Forest).
Hybrid deep learning model (3D ResNet-101 CNN and LSTM) for spatial and temporal MRI analysis.

Keywords:

3D CNN Alzheimers disease LSTM MRI machine learning temporal analysis volumetric analysis

More Related Videos

Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451

Hybrid PET/MRI Imaging of Alzheimer's Disease Based on ¹⁸F-AV-1451

Published on: April 18, 2025

Related Experiment Videos

Last Updated: Jun 5, 2026

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

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

Published on: August 7, 2017

Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451

Hybrid PET/MRI Imaging of Alzheimer's Disease Based on ¹⁸F-AV-1451

Published on: April 18, 2025

Main Results:

Random Forest model using cortical thickness slopes achieved 82.5% accuracy differentiating AD from CN.
Hybrid CNN-LSTM model attained 96.7% accuracy for AD vs. CN classification.
Deep learning approach improved MCI case distinction, though with some discrepancies.

Conclusions:

Integrating volumetric statistical analysis with deep learning shows significant potential for automated AD categorization.
Combined spatial and temporal MRI data analysis enhances neuroimaging diagnostic capabilities for AD.
This approach supports earlier diagnosis and better monitoring of Alzheimer's disease progression.