Identifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition
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
Xu, Jing |
© 2021 the Authors
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. ...
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN Search the Publication Forum1534-4320
Publication in research information system
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Additional information about fundingNational Foundation in China (Grant Number: 2020-JCJQ-JJ-252 and JCKY2019110B009) Fundamental Research Funds for the Central Universities (Grant Number: DUT2019 and DUT20LAB303) 10.13039/501100004543-China Scholarship Council (Grant Number: 201706060262 and 201706060263) 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 91748105)
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