Ordinal Pattern: A New Descriptor for Brain Connectivity Networks

• Published in: IEEE transactions on medical imaging Vol. 37; no. 7; pp. 1711 - 1722
• Main Authors: Zhang, Daoqiang, Huang, Jiashuang, Jie, Biao, Du, Junqiang, Tu, Liyang, Liu, Mingxia
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Attention Deficit Disorder with Hyperactivity - diagnostic imaging; Attention deficit hyperactivity disorder; Brain; Brain - diagnostic imaging; Brain - physiology; brain disease diagnosis; Brain diseases; Brain mapping; Brain Mapping - methods; Brain modeling; classification; Connectivity network; Diagnosis; Diseases; Feature extraction; Functional magnetic resonance imaging; Humans; Image Interpretation, Computer-Assisted - methods; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical diagnosis; Medical imaging; Nerve Net - diagnostic imaging; Nerve Net - physiology; Network analysis; network descriptor; Networks; Neural networks; Neurodegenerative diseases; Neuroimaging; Neurology; NMR; Nuclear magnetic resonance; regression; Regression analysis; State of the art; Structure-function relationships; Weight
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2018.2798500

Brain connectivity networks based on magnetic resonance imaging (MRI) or functional MRI (fMRI) data provide a straightforward way to quantify the structural or functional systems of the brain. Currently, there are several network descriptors developed for representing and analyzing brain connectivity networks. However, most of them are designed for unweighted networks, regardless of the valuable weight information of edges, or do not take advantage of the ordinal relationship of weighted edges (even though they are designed for weighted networks). In this paper, we propose a new network descriptor ( i.e. , ordinal pattern that contains a sequence of weighted edges) for brain connectivity network analysis. Compared with previous network properties, the proposed ordinal patterns cannot only take advantage of the weight information of edges but also explicitly model the ordinal relationship of weighted edges in brain connectivity networks. We further develop an ordinal pattern-based learning framework for brain disease diagnosis using resting-state fMRI data. Specifically, we first construct a set of brain functional connectivity networks, where each network is corresponding to a particular subject. We then develop an algorithm to identify ordinal patterns that frequently appear in brain connectivity networks of patients and normal controls. We further perform discriminative ordinal pattern selection and extract feature representations for subjects based on the selected ordinal patterns, followed by a learning model for automated brain disease diagnosis. Experimental results on both Alzheimer's Disease Neuroimaging Initiative and attention deficit hyperactivity disorder-200 data sets demonstrate that our method outperforms the several state-of-the-art approaches in the tasks of disease classification and clinical score regression.