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Alzheimer's Disease: Overview

Alzheimer's Disease: Overview

Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...

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Related Experiment Video

Updated: Apr 16, 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

Structural-Functional Connectome Generation via Diffusion-Guided Graph Transformer for Alzheimer's Disease Analysis.

Yifei Tang, Heng Kong, Hongjie Jiang

IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society

|April 14, 2026

Summary

This summary is machine-generated.

This study introduces DiffusionBrain, a new method for analyzing brain networks in Alzheimer's Disease (AD). DiffusionBrain integrates structural and functional connections for improved diagnosis and understanding of AD's impact on the brain.

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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Area of Science:

Neuroscience
Medical Imaging
Computational Biology

Background:

Alzheimer's Disease (AD) diagnosis relies on understanding brain network abnormalities.
Current methods analyze structural or functional networks separately, missing crucial complementary information.
Existing tools are time-consuming and require subjective parameter tuning.

Purpose of the Study:

To develop a novel paradigm, DiffusionBrain, for generating integrated structural-functional brain networks.
To apply DiffusionBrain for enhanced diagnosis and analysis of Alzheimer's Disease.
To overcome limitations of unimodal brain network analysis.

Main Methods:

Designed a Graph Prompt Fusion Module (GPFM) for multimodal feature extraction and fusion.
Developed a Dual Diffusion-guided Graph Transformer (DDGT) for efficient network generation.
Implemented a Graph Alignment Module (GAM) for deep fusion of structural and functional networks.
Utilized the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset for validation.

Main Results:

DiffusionBrain effectively captures complex interactions between structural and functional brain networks.
The method successfully reveals abnormal connection patterns characteristic of Alzheimer's Disease.
Demonstrated superior performance in modeling multimodal brain networks compared to existing approaches.

Conclusions:

DiffusionBrain offers a powerful new paradigm for multimodal brain network modeling.
This approach enhances the identification of Alzheimer's Disease biomarkers and pathological mechanisms.
Provides critical insights for early diagnosis and intervention strategies in Alzheimer's Disease.