@Article{cmes.2020.08999,
AUTHOR = {Zhuqing Jiao, Yixin Ji, Tingxuan Jiao, Shuihua Wang},
TITLE = {Extracting Sub-Networks from Brain Functional Network Using Graph Regularized Nonnegative Matrix Factorization},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {123},
YEAR = {2020},
NUMBER = {2},
PAGES = {845--871},
URL = {http://www.techscience.com/CMES/v123n2/38702},
ISSN = {1526-1506},
ABSTRACT = {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 subnetwork 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 lowdimensional space, which provides a new idea for studying brain functional connectomes.},
DOI = {10.32604/cmes.2020.08999}
}