title: Predicting brain network changes in Alzheimer's disease with link prediction algorithms.
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.
authors.affiliation: Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. amasoudin@ut.ac.ir.
authors.forename: Sadegh
authors.initials: S
authors.lastname: Sulaimany
authors.affiliation: Department of New Sciences and Technologies (FNST), University of Tehran, Tehran, Iran.
authors.forename: Mohammad
authors.initials: M
authors.lastname: Khansari
authors.affiliation: Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. amasoudin@ut.ac.ir.
authors.forename: Peyman
authors.initials: P
authors.lastname: Zarrineh
authors.affiliation: Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Marina del Rey, CA, USA.
authors.forename: Madelaine
authors.initials: M
authors.lastname: Daianu
authors.affiliation: Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Marina del Rey, CA, USA.
authors.forename: Neda
authors.initials: N
authors.lastname: Jahanshad
authors.affiliation: Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Marina del Rey, CA, USA.
authors.forename: Paul M
authors.initials: PM
authors.lastname: Thompson
authors.affiliation: Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. amasoudin@ut.ac.ir.
authors.forename: Ali
authors.identifier: http://orcid.org/0000-0003-0659-5183
authors.initials: A
authors.lastname: Masoudi-Nejad
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000503:physiopathology*; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping; D016022:Case-Control Studies; D016208:Databases, Factual; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008959:Models, Neurological; D009434:Neural Pathways; D057188:Workflow