A1 Journal article (refereed)
Exploring Oscillatory Dysconnectivity Networks in Major Depression during Resting State Using Coupled Tensor Decomposition (2022)

Liu, W., Wang, X., Hämäläinen, T., & Cong, F. (2022). Exploring Oscillatory Dysconnectivity Networks in Major Depression during Resting State Using Coupled Tensor Decomposition. IEEE Transactions on Biomedical Engineering, 69(8), 2691-2700. https://doi.org/10.1109/TBME.2022.3152413

JYU authors or editors

Publication details

All authors or editors: Liu, Wenya; Wang, Xiulin; Hämäläinen, Timo; Cong, Fengyu

Journal or series: IEEE Transactions on Biomedical Engineering

ISSN: 0018-9294

eISSN: 1558-2531

Publication year: 2022

Volume: 69

Issue number: 8

Pages range: 2691-2700

Publisher: IEEE

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/TBME.2022.3152413

Publication open access: Not open

Publication channel open access: 

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/85291

Abstract

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 lowrank 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.

Keywords: brain; brain research; mental disorders; depression (mental disorders); EEG; neural networks (information technology); neural computation; modelling (representation)

Free keywords: dynamic functional connectivity; coupled tensor decomposition; major depression disorder; oscillatory networks; tensors; brain modeling; electroencephalography; time-frequency analysis

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2022

JUFO rating: 2