Zhuqing Jiao1, *, Yixin Ji1, Tingxuan Jiao1, Shuihua Wang2, *
CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.2, pp. 845-871, 2020, DOI:10.32604/cmes.2020.08999
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… More >
Login
Register
