title: Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer's disease.
abstract: The study of brain networks by resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method for identifying patients with dementia from healthy controls (HC). Using graph theory, different aspects of the brain network can be efficiently characterized by calculating measures of integration and segregation. In this study, we combined a graph theoretical approach with advanced machine learning methods to study the brain network in 89 patients with mild cognitive impairment (MCI), 34 patients with Alzheimer's disease (AD), and 45 age-matched HC. The rs-fMRI connectivity matrix was constructed using a brain parcellation based on a 264 putative functional areas. Using the optimal features extracted from the graph measures, we were able to accurately classify three groups (i.e., HC, MCI, and AD) with accuracy of 88.4 %. We also investigated performance of our proposed method for a binary classification of a group (e.g., MCI) from two other groups (e.g., HC and AD). The classification accuracies for identifying HC from AD and MCI, AD from HC and MCI, and MCI from HC and AD, were 87.3, 97.5, and 72.0 %, respectively. In addition, results based on the parcellation of 264 regions were compared to that of the automated anatomical labeling atlas (AAL), consisted of 90 regions. The accuracy of classification of three groups using AAL was degraded to 83.2 %. Our results show that combining the graph measures with the machine learning approach, on the basis of the rs-fMRI connectivity analysis, may assist in diagnosis of AD and MCI.
authors.affiliation: Department of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran. khazaee.a@gmail.com.
authors.forename: Ali
authors.initials: A
authors.lastname: Khazaee
authors.affiliation: Department of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran.
authors.forename: Ata
authors.initials: A
authors.lastname: Ebrahimzadeh
authors.affiliation: Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA.|Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, TN, USA.|Neuroscience Institute, Le Bonheur Children's Hospital, Memphis, TN, USA.
authors.forename: Abbas
authors.initials: A
authors.lastname: Babajani-Feremi
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000145:classification / Q000000981:diagnostic imaging* / Q000503:physiopathology; D001921:Brain / Q000000981:diagnostic imaging* / Q000502:physiology / Q000503:physiopathology; D001931:Brain Mapping / Q000379:methods*; D060825:Cognitive Dysfunction / Q000145:classification / Q000000981:diagnostic imaging* / Q000503:physiopathology; D019992:Databases as Topic; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D009434:Neural Pathways / Q000000981:diagnostic imaging / Q000502:physiology / Q000503:physiopathology; D012146:Rest; D012680:Sensitivity and Specificity
keywords: Alzheimer’s disease (AD); Graph theory; Machine learning; Mild cognitive impairment (MCI); Resting-state functional magnetic resonance imaging (rs-fMRI); Support vector machine (SVM)