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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: Feb 28, 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

Interpretable Network-Level Biomarker Discovery for Alzheimer's Stage Assessment Using Resting-State fNIRS Complexity

Min-Kyoung Kang¹, Agatha Elisabet¹, So-Hyeon Yoo²

¹School of Mechanical Engineering, Pusan National University, Busan 46241, Republic of Korea.

|February 27, 2026

Summary

This summary is machine-generated.

This study presents a new graph-based method for resting-state fNIRS analysis, identifying brain network biomarkers for mild cognitive impairment and Alzheimer's disease. The framework offers reproducible and interpretable insights into neurodegenerative changes.

Keywords:

Alzheimer’s disease functional near-infrared spectroscopy (fNIRS)graph neural networks resting-state brain networks signal complexity

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Area of Science:

Neuroscience
Biomedical Engineering
Data Science

Background:

Resting-state functional near-infrared spectroscopy (fNIRS) is valuable for Alzheimer's disease (AD) assessment.
Existing fNIRS methods often lack network-level insights and reproducibility.
Coordinated network dynamics are crucial for understanding neurodegenerative processes.

Purpose of the Study:

To develop a reproducible and interpretable graph-based framework for resting-state fNIRS.
To enable network-level biomarker discovery for Alzheimer's disease (AD) and mild cognitive impairment (MCI).
To move beyond static, channel-wise analyses towards dynamic network assessments.

Main Methods:

Represented resting-state prefrontal fNIRS signals as subject-level graphs.
Utilized sliding-window analysis to capture nonlinear signal complexity fluctuations for edge computation.
Employed graph neural networks (GNNs) for network pattern identification and interpretability analysis.

Main Results:

The complexity-fluctuation graph approach surpassed conventional amplitude-based connectivity.
Identified statistically significant prefrontal network biomarkers distinguishing MCI from healthy aging (p=0.001).
Observed more heterogeneous network patterns in AD, with MCI showing more consistent alterations.

Conclusions:

Established a reproducible and interpretable framework for fNIRS analysis focusing on complexity dynamics.
Network alterations are most consistently detected at the MCI stage, indicating its significance.
The framework shows potential for longitudinal monitoring and clinical assessment of neurodegenerative diseases.