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Updated: May 11, 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

Prognostic model for predicting Alzheimer's disease conversion using functional connectome manifolds.

Sunghun Kim^1,2, Mansu Kim³, Jong-Eun Lee^1,2

¹Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea.

Alzheimer'S Research & Therapy

|October 10, 2024

Summary

This study developed a new prognostic model to predict Alzheimer's disease (AD) conversion risk using brain functional connectivity (FC) and Cox regression. The model shows promise for early intervention in individuals at risk of AD progression.

Keywords:

Alzheimer’s disease Cox regression Disease conversion Functional connectivity Gradient

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Last Updated: May 11, 2026

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

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Published on: August 7, 2017

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Published on: October 13, 2023

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

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Published on: December 15, 2023

Area of Science:

Neuroimaging
Neurodegenerative disease research
Biostatistics

Background:

Early detection of Alzheimer's disease (AD) is critical for management.
Predicting conversion from mild cognitive impairment (MCI) to AD is essential.
Existing neuroimaging studies often overlook temporal conversion information.

Purpose of the Study:

Develop a prognostic model for AD conversion.
Utilize functional connectivity (FC) and Cox regression for event modeling.
Incorporate temporal information for improved prediction accuracy.

Main Methods:

Utilized Alzheimer's Disease Neuroimaging Initiative and Open Access Series of Imaging Studies datasets.
Generated cortex-wide functional connectivity gradients and subcortical-weighted manifold degrees from MRI data.
Employed penalized Cox regression with elastic net for risk score calculation, including clinical factors.

Main Results:

The prognostic model accurately predicted AD conversion risk.
Key brain regions for prediction included association/visual cortices, caudate, and hippocampus.
The risk score correlated with disease progression markers and clinical severity, validated on independent data.

Conclusions:

A novel prognostic model for predicting AD conversion risk was developed.
The model utilizes imaging-derived manifolds and clinical factors.
The risk score offers potential for early intervention in at-risk individuals.