Strength and similarity guided group-level brain functional network construction for MCI diagnosis. (April 2019)

• Record Type: Journal Article
• Authors: Zhang, Yu; Zhang, Han; Chen, Xiaobo; Liu, Mingxia; Zhu, Xiaofeng; Lee, Seong-Whan; Shen, Dinggang
• Is Part Of: Pattern recognition. Volume 88(2019:Apr.)
• Page Start: 421
• Page End: 430
• Publication Date: 2019-04
• Subjects: Alzheimers disease -- Mild cognitive impairment -- Resting-state functional magnetic resonance imaging (rs-fMRI) -- Functional connectivity -- Brain functional network -- Group sparse representation -- Diagnosis; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.12.001
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: Sparse representation-based brain functional network modeling often results in large inter-subject variability in the network structure. This could reduce the statistical power in group comparison, or even deteriorate the generalization capability of the individualized diagnosis of brain diseases. Although group sparse representation (GSR) can alleviate such a limitation by increasing network similarity across subjects, it could, in turn, fail in providing satisfactory separability between the subjects from different groups (e.g., patients vs. controls). In this study, we propose to integrate individual functional connectivity (FC) information into the GSR-based network construction framework to achieve higher between-group separability while maintaining the merit of within-group consistency. Our method was based on an observation that the subjects from the same group have generally more similar FC patterns than those from different groups. To this end, we propose our new method, namely "strength and similarity guided GSR (SSGSR)", which exploits both BOLD signal temporal correlation-based "low-order" FC (LOFC) and inter-subject LOFC-profile similarity-based "high-order" FC (HOFC) as two priors to jointly guide the GSR-based network modeling. Extensive experimental comparisons are carried out, with the rs-fMRI data from mild cognitive impairment (MCI) subjects and healthy controls, between the proposed algorithm and other state-of-the-art brain network modeling approaches. Individualized MCI identification results show that our method could achieve a balance between the individually consistent brain functional network construction and the adequately maintained inter-group brain functional network distinctions, thus leading to a more accurate classification result. Our method also provides a promising and generalized solution for the future connectome-based individualized diagnosis of brain disease.