Tensor-Based Multi-index Representation Learning for Major Depression Disorder Detection with Resting-State fMRI

Major depressive disorder (MDD) is a common and costly mental illness whose pathophysiology is difficult to clarify. Resting-state functional MRI (rs-fMRI) provides a non-invasive solution for the study of functional brain network abnormalities in MDD patients. Existing studies have shown that multiple indexes derived from rs-fMRI, such as fractional amplitude of low-frequency fluctuations (fALFF), voxel-mirrored homotopic connectivity (VMHC), and degree centrality (DC) help depict functional mechanisms of brain disorders from different perspectives. However, previous methods generally treat these indexes independently, without considering their potentially complementary relationship. Moreover, it is usually very challenging to effectively fuse multi-index representations for disease analysis, due to the significant heterogeneity among indexes in the feature distribution. In this paper, we propose a tensor-based multi-index representation learning (TMRL) framework for fMRI-based MDD detection. In TMRL, we first generate multi-index representations (i.e., fALFF, VMHC and DC) for each subject, followed by patch selection via group comparison for each index. We further develop a tensor-based multi-task learning model (with a tensor-based regularizer) to align multi-index representations into a common latent space, followed by MDD prediction. Experimental results on 533 subjects with rs-fMRI data demonstrate that the TMRL outperforms several state-of-the-art methods in MDD identification.