October 18, 2018
Sadegh Sulaimany

Sadegh Sulaimany

Academic rank: Assistant professor
Address: Room 102 Computer Engineering Group Engineering Dept.
Education: PhD. in Bioinformatics
Phone: 08733627722 (داخلی 3336)
Faculty: Faculty of Engineering

Research

Title
Predicting brain network changes in Alzheimer’s disease with link prediction algorithms
Type Article
Keywords
Mixed Link Prediction, Brain Network, Alzheimer’s Disease
Year
2017
Journal Molecular BioSystems
DOI 10.1039/C6MB00815A
Researchers Sadegh Sulaimany، Mohammad Khansari، Peyman Zarrineh، Madelaine Daianu، Neda Jahanshad، Paul Thompson، Ali Masoudi-Nejad

Abstract

Link prediction is a promising research area for modeling various types of networks and has mainly focused on predicting missing links. Link prediction methods may be valuable for describing brain connectivity, as it changes in Alzheimer's disease (AD) and its precursor, mild cognitive impairment (MCI). Here, we analyzed 3-tesla whole-brain diffusion-weighted images from 202 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) - 50 healthy controls, 72 with earlyMCI (eMCI) and 38 with lateMCI (lMCI) and 42 AD patients. We introduce a novel approach for Mixed Link Prediction (MLP) to test and define the percent of predictability of each heightened stage of dementia from its previous, less impaired stage, in the simplest case. Using well-known link prediction algorithms as the core of MLP, we propose a new approach that predicts stages of cognitive impairment by simultaneously adding and removing links in the brain networks of elderly individuals. We found that the optimal algorithm, called "Adamic and Adar", had the best fit and most accurately predicted the stages of AD from their previous stage. When compared to the other link prediction algorithms, that mainly only predict the added links, our proposed approach can more inclusively simulate the brain changes during disease by both adding and removing links of the network. Our results are also in line with computational neuroimaging and clinical findings and can be improved for better results.