Weighted graph regularized sparse brain network construction for MCI identification. (June 2019)

• Record Type: Journal Article
• Authors: Yu, Renping; Qiao, Lishan; Chen, Mingming; Lee, Seong-Whan; Fei, Xuan; Shen, Dinggang
• Is Part Of: Pattern recognition. Volume 90(2019:Jun.)
• Page Start: 220
• Page End: 231
• Publication Date: 2019-06
• Subjects: Graph Laplacian regularization -- Sparse representation -- Brain functional network -- Mild cognitive impairment (MCI); Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.01.015
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Integrate the data similarity and locality to sparse modeling of brain functional network. A unified framework integrates intrinsic correlation, local manifold structure, and sparsity . Solve the controversial point of graph Laplacian in the self-representation model. MCI classification based on fMRI shows our method is more effective (accuracy = 88.89%). Abstract: Brain functional networks (BFNs) constructed from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely applied to the analysis and diagnosis of brain diseases, such as Alzheimer's disease and its prodrome, namely mild cognitive impairment (MCI). Constructing a meaningful brain network based on, for example, sparse representation (SR) is the most essential step prior to the subsequent analysis or disease identification. However, the independent coding process of SR fails to capture the intrinsic locality and similarity characteristics in the data. To address this problem, we propose a novel weighted graph (Laplacian) regularized SR framework, based on which BFN can be optimized by considering both intrinsic correlation similarity and local manifold structure in the data, as well as sparsity prior of the brain connectivity. Additionally, the non-convergence of the graph Laplacian in the self-representation model has been solved properly. Combined with a pipeline of sparse feature selection and classification, the effectiveness of our proposed method is demonstrated by identifying MCI based on the constructed BFNs.