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Alzheimer's Disease: Overview

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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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Updated: Mar 7, 2026

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

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Predicting brain network changes in Alzheimer's disease with link prediction algorithms.

Sadegh Sulaimany¹, Mohammad Khansari², Peyman Zarrineh¹

¹Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. amasoudin@ut.ac.ir.

Molecular Biosystems

|February 16, 2017

Summary

This summary is machine-generated.

This study introduces Mixed Link Prediction (MLP) to model brain network changes in Alzheimer's disease (AD) and mild cognitive impairment (MCI). The Adamic and Adar algorithm best predicted disease progression by simulating both added and removed brain connections.

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Area of Science:

Neuroimaging
Network Science
Computational Neuroscience

Background:

Alzheimer's disease (AD) and mild cognitive impairment (MCI) involve changes in brain connectivity.
Link prediction is a network analysis technique that can model these changes.

Purpose of the Study:

To introduce a novel Mixed Link Prediction (MLP) approach to model brain network alterations across dementia stages.
To evaluate the predictability of cognitive impairment progression using MLP.

Main Methods:

Analysis of 3-tesla whole-brain diffusion-weighted images from 202 participants (controls, early MCI, late MCI, AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
Application of established link prediction algorithms within the MLP framework to simulate simultaneous addition and removal of links in brain networks.
Identification of the optimal algorithm for predicting disease progression.

Main Results:

The Adamic and Adar algorithm demonstrated the best fit and highest accuracy in predicting subsequent stages of dementia from preceding ones.
MLP, by incorporating both link addition and removal, offers a more comprehensive simulation of disease-related brain changes compared to methods predicting only added links.
Results align with existing computational neuroimaging and clinical findings.

Conclusions:

Mixed Link Prediction (MLP) provides a robust framework for understanding dynamic brain network changes in cognitive decline.
The Adamic and Adar algorithm shows significant potential for predicting Alzheimer's disease and mild cognitive impairment progression.
This approach can be refined for enhanced accuracy in modeling neurodegenerative diseases.