title: Hyper-connectivity of functional networks for brain disease diagnosis.
abstract: Exploring structural and functional interactions among various brain regions enables better understanding of pathological underpinnings of neurological disorders. Brain connectivity network, as a simplified representation of those structural and functional interactions, has been widely used for diagnosis and classification of neurodegenerative diseases, especially for Alzheimer's disease (AD) and its early stage - mild cognitive impairment (MCI). However, the conventional functional connectivity network is usually constructed based on the pairwise correlation among different brain regions and thus ignores their higher-order relationships. Such loss of high-order information could be important for disease diagnosis, since neurologically a brain region predominantly interacts with more than one other brain regions. Accordingly, in this paper, we propose a novel framework for estimating the hyper-connectivity network of brain functions and then use this hyper-network for brain disease diagnosis. Here, the functional connectivity hyper-network denotes a network where each of its edges representing the interactions among multiple brain regions (i.e., an edge can connect with more than two brain regions), which can be naturally represented by a hyper-graph. Specifically, we first construct connectivity hyper-networks from the resting-state fMRI (R-fMRI) time series by using sparse representation. Then, we extract three sets of brain-region specific features from the connectivity hyper-networks, and further exploit a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Finally, we use multi-kernel support vector machine (SVM) for classification. The experimental results on both MCI dataset and attention deficit hyperactivity disorder (ADHD) dataset demonstrate that, compared with the conventional connectivity network-based methods, the proposed method can not only improve the classification performance, but also help discover disease-related biomarkers important for disease diagnosis.
authors.affiliation: Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; Department of Computer Science and Technology, Anhui Normal University, Wuhu, 241000, China. Electronic address: jbiao@nuaa.edu.cn.
authors.forename: Biao
authors.initials: B
authors.lastname: Jie
authors.affiliation: Department of Biomedical Engineering, National University of Singapore, 119077, Singapore.
authors.forename: Chong-Yaw
authors.initials: CY
authors.lastname: Wee
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea. Electronic address: dgshen@med.unc.edu.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
authors.affiliation: Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China. Electronic address: dqzhang@nuaa.edu.cn.
authors.forename: Daoqiang
authors.initials: D
authors.lastname: Zhang
mesh_terms: D000368:Aged; D019540:Area Under Curve; D001289:Attention Deficit Disorder with Hyperactivity / Q000000981:diagnostic imaging*; D001921:Brain / Q000000981:diagnostic imaging*; D001931:Brain Mapping / Q000379:methods; D060825:Cognitive Dysfunction / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: Alzheimer's disease; Classification; Functional MR imaging; Hyper-network