Simultaneous differential network analysis and classification for matrix-variate data with application to brain connectivity.

ABSTRACT: Growing evidence has shown that the brain connectivity network experiences alterations for complex diseases such as Alzheimer's disease (AD). Network comparison, also known as differential network analysis, is thus particularly powerful to reveal the disease pathologies and identify clinical biomarkers for medical diagnoses (classification). Data from neurophysiological measurements are multidimensional and in matrix-form. Naive vectorization method is not sufficient as it ignores the structural information within the matrix. In the article, we adopt the Kronecker product covariance matrices framework to capture both spatial and temporal correlations of the matrix-variate data while the temporal covariance matrix is treated as a nuisance parameter. By recognizing that the strengths of netw

SUBMITTER: Chen H 

PROVIDER: S-EPMC9295187 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature