Exploring Oscillatory Dysconnectivity Networks in Major Depression During Resting State Using Coupled Tensor Decomposition

• Published in: IEEE transactions on biomedical engineering 2022-08, Vol.69 (8), p.2691-2700
• Main Authors: Liu, Wenya, Wang, Xiulin, Hamalainen, Timo, Cong, Fengyu
• Format: Article
• Language: English
• Subjects: Algorithms; Brain; Brain modeling; Cluster analysis; Clustering; Computational neuroscience; Coupled tensor decomposition; Decomposition; dynamic functional connectivity; Education; EEG; Electroencephalography; Indexes; major depression disorder; Mathematical analysis; Mental depression; Mental disorders; Networks; Neural networks; Optimization; Oscillations; oscillatory networks; Phase lag; Tensors; Time-frequency analysis; Windows; Windows (intervals)
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2022.3152413

Dysconnectivity of large-scale brain networks has been linked to major depression disorder (MDD) during resting state. Recent researches show that the temporal evolution of brain networks regulated by oscillations reveals novel mechanisms and neural characteristics of MDD. Our study applied a novel coupled tensor decomposition model to investigate the dysconnectivity networks characterized by spatio-temporal-spectral modes of covariation in MDD using resting electroencephalography. The phase lag index is used to calculate the functional connectivity within each time window at each frequency bin. Then, two adjacency tensors with the dimension of time × frequency × connectivity × subject are constructed for the healthy group and the major depression group. We assume that the two groups share the same features for group similarity and retain individual characteristics for group differences. Considering that the constructed tensors are nonnegative and the components in spectral and adjacency modes are partially consistent among the two groups, we formulate a double-coupled nonnegative tensor decomposition model. To reduce computational complexity, we introduce the low-rank approximation. Then, the fast hierarchical alternative least squares algorithm is applied for model optimization. After clustering analysis, we summarize four oscillatory networks characterizing the healthy group and four oscillatory networks characterizing the major depression group, respectively. The proposed model may reveal novel mechanisms of pathoconnectomics in MDD during rest, and it can be easily extended to other psychiatric disorders.