Brain network embedding is the process of converting brain network data to discriminative representations of subjects, so that patients with brain disorders and normal controls can be easily separated. Computer-aided diagnosis based on such representations is potentially transformative for investigating disease mechanisms and for informing therapeutic interventions. However, existing methods either limit themselves to extracting graph-theoretical measures and subgraph patterns, or fail to incorporate brain network properties and domain knowledge in medical science. In this paper, we propose t-BNE, a novel Brain Network Embedding model based on constrained tensor factorization. t-BNE incorporates 1) symmetric property of brain networks, 2) side information guidance to obtain representations consistent with auxiliary measures, 3) orthogonal constraint to make the latent factors distinct with each other, and 4) classifier learning procedure to introduce supervision from labeled data. The Alternating Direction Method of Multipliers (ADMM) framework is utilized to solve the optimization objective. We evaluate t-BNE on three EEG brain network datasets. Experimental results illustrate the superior performance of the proposed model on graph classification tasks with significant improvement 20.51%, 6.38% and 12.85%, respectively. Furthermore, the derived factors are visualized which could be informative for investigating disease mechanisms under different emotion regulation tasks.
at. REF propose a constrained tensor factorization algorithm; t-BNE, to learn brain network representations.
t-BNE: Tensor-based Brain Network Embedding
SDM
Computer Science