Extracting Sub-Networks from Brain Functional Network Using Graph Regularized Nonnegative Matrix Factorization

• Published in: Computer modeling in engineering & sciences Vol. 123; no. 2; pp. 845 - 871
• Main Authors: Jiao, Zhuqing, Ji, Yixin, Jiao, Tingxuan, Wang, Shuihua
• Format: Journal Article
• Language: English
• Published: Henderson Tech Science Press 01.01.2020
• Subjects: Abnormalities; Agglomeration; Aggregation Matrix; Brain; Brain Functional Network; Factorization; Functional Connectivity; Graph Regularized Nonnegative Matrix Factorization (gnmf); Mathematical analysis; Matrix algebra; Matrix methods; Medical imaging; Networks; Regularization; Statistical analysis; Sub-Network
• ISSN: 1526-1492; 1526-1506; 1526-1506
• DOI: 10.32604/cmes.2020.08999

Currently, functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders. If one brain disease just manifests as some cognitive dysfunction, it means that the disease may affect some local connectivity in the brain functional network. That is, there are functional abnormalities in the sub-network. Therefore, it is crucial to accurately identify them in pathological diagnosis. To solve these problems, we proposed a sub-network extraction method based on graph regularization nonnegative matrix factorization (GNMF). The dynamic functional networks of normal subjects and early mild cognitive impairment (eMCI) subjects were vectorized and the functional connection vectors (FCV) were assembled to aggregation matrices. Then GNMF was applied to factorize the aggregation matrix to get the base matrix, in which the column vectors were restored to a common sub-network and a distinctive sub-network, and visualization and statistical analysis were conducted on the two sub-networks, respectively. Experimental results demonstrated that, compared with other matrix factorization methods, the proposed method can more obviously reflect the similarity between the common sub-network of eMCI subjects and normal subjects, as well as the difference between the distinctive sub-network of eMCI subjects and normal subjects, Therefore, the high-dimensional features in brain functional networks can be best represented locally in the low-dimensional space, which provides a new idea for studying brain functional connectomes.