A1 Journal article (refereed)
Identifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition (2021)

Liu, W., Wang, X., Xu, J., Chang, Yi., Hämäläinen, T., & Cong, F. (2021). Identifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, 1895-1904. https://doi.org/10.1109/tnsre.2021.3111564

JYU authors or editors

Publication details

All authors or editors: Liu, Wenya; Wang, Xiulin; Xu, Jing; Chang, Yi.; Hämäläinen, Timo; Cong, Fengyu

Journal or series: IEEE Transactions on Neural Systems and Rehabilitation Engineering

ISSN: 1534-4320

eISSN: 1558-0210

Publication year: 2021

Volume: 29

Pages range: 1895-1904

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/tnsre.2021.3111564

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/77853

Web address of parallel published publication (pre-print): https://www.biorxiv.org/content/10.1101/2021.04.23.441123v1

Abstract

Previous researches demonstrate that major depression disorder (MDD) is associated with widespread network dysconnectivity, and the dynamics of functional connectivity networks are important to delineate the neural mechanisms of MDD. Neural oscillations exert a key role in coordinating the activity of remote brain regions, and various assemblies of oscillations can modulate different networks to support different cognitive tasks. Studies have demonstrated that the dysconnectivity of electroencephalography (EEG) oscillatory networks is related with MDD. In this study, we investigated the oscillatory hyperconnectivity and hypoconnectivity networks in MDD under a naturalistic and continuous stimuli condition of music listening. With the assumption that the healthy group and the MDD group share similar brain topology from the same stimuli and also retain individual brain topology for group differences, we applied the coupled nonnegative tensor decomposition algorithm on two adjacency tensors with the dimension of time × frequency × connectivity × subject, and imposed double-coupled constraints on spatial and spectral modes. The music-induced oscillatory networks were identified by a correlation analysis approach based on the permutation test between extracted temporal factors and musical features. We obtained three hyperconnectivity networks from the individual features of MDD and three hypoconnectivity networks from common features. The results demonstrated that the dysfunction of oscillatory networks could affect the involvement in music perception for MDD patients. Those oscillatory dysconnectivity networks may provide promising references to reveal the pathoconnectomics of MDD and potential biomarkers for the diagnosis of MDD.

Keywords: depression (mental disorders); neural networks (biology); oscillations; stimuli (role related to effect); music; EEG; signal processing; signal analysis; cognitive neuroscience

Free keywords: dynamic functional connectivity; coupled tensor decomposition; major depression disorder, naturalistic music stimuli, oscillatory networks

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2021

JUFO rating: 2