graph theory

Background and Aim : Alzheimer’s disease (AD) is a progressive disease that causes problems of cognitive and memory functions decline. Patients with AD usually lose their ability to manage their daily life. Exploring the progression of the brain from normal controls (NC) to AD is an essential part of human research. Although connection changes have been found in the progression, the connection mechanism that drives these changes remains incompletely understood. The purpose of this study is to explore the connection changes in brain networks in the process from NC to AD, and uncovers the underlying connection mechanism that shapes the topologies of AD brain networks. In particular, we propose a mutual information brain network model (MINM) from the perspective of graph theory to achieve our aim. MINM concerns the question of estimating the connection probability between two cortical regions with the consideration of both the mutual information of their observed network topologies and their Euclidean distance in anatomical space. In addition, MINM considers establishing and deleting connections, simultaneously, during the networks modeling from the stage ofNCtoAD.ExperimentsshowthatMINMissufficienttocaptureanimpressiverangeoftopological propertiesofrealbrainnetworkssuchascharacteristicpathlength,networkefficiency,andtransitivity, and it also provides an excellent fit to the real brain networks in degree distribution compared to experiential models. Thus, we anticipate that MINM may explain the connection mechanism for the formation of the brain network organization in AD patientsMethods : Data used in this study were recruited from the public resting-statefunctionalmagneticresonance imaging (rs-fMRI) datasets named Alzheimer’s Disease Neuroimaging Initiative (ADNI) (http://adni. loni.ucla.edu) consisting of a total of 147 participants. They are divided into three groups: normal controls (NC) group, mild cognitive impairment (MCI) group, and Alzheimer’s disease (AD) group. The NC participants were non-depressed, non-demented, and had an average MMSE score of 28.72. The MCI group had an averageMMSEscoreof27.68. PatientswithADhadanaverageMMSEscoreof22.36. Eachparticipant underwent a scan session using a 3.0T Philips MRI scanner. All the resting fMRI scans were collected axially by adopting an echo-planar imaging (EPI) sequence with the following parameters: repetition time (TR) = 3000 ms; echo time (TE) = 30 ms; axial slices = 48; slice thickness = 3.313 mm; slice acquisition order = sequential ascending; and flip angle (FA) = 80.0◦. Participants were informed to relax their minds and keep their eyes closed during the scanning to obtain resting state MRIsResults : 1 Conclusion : We have investigated how topological-based mutual information and Euclidean distance are adopted in the simulation of brain network topologies with AD. We also concentrate on how connections are established or deleted among different brain regions. Our ambition is to uncover the fundamentalconnectionmechanismthatfacilitatesthealterationsofbrainnetworksintheprogression from NC to AD. We demonstrate that adding the mutual information into our model can promote the modeling performance in this progression. Successful models of AD brain networks have been instrumental in understanding how structural brain organizations affect the ability of cognition. Our work has opened new avenues toward the diagnosis and treatment of AD

Keywords:

Alzheimer’s disease , graph theory , mutual information , network model , connection mechanism , functional magnetic resonance imaging , topological structures , anatomical distance