Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization
Wang, X., Liu, W., Wang, X., Mu, Z., Xu, J., Chang, Y., Zhang, Q., Wu, J., & Cong, F. (2021). Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization. Frontiers in Human Neuroscience, 15, Article 799288. https://doi.org/10.3389/fnhum.2021.799288
Published inFrontiers in Human Neuroscience
Mu, Zhen |
Xu, Jing |
DisciplineTietotekniikkaSecure Communications Engineering and Signal ProcessingOhjelmisto- ja tietoliikennetekniikkaMathematical Information TechnologySecure Communications Engineering and Signal ProcessingSoftware and Communications Engineering
© 2021 Wang, Liu, Wang, Mu, Xu, Chang, Zhang, Wu and Cong
Ongoing electroencephalography (EEG) signals are recorded as a mixture of stimulus-elicited EEG, spontaneous EEG and noises, which poses a huge challenge to current data analyzing techniques, especially when different groups of participants are expected to have common or highly correlated brain activities and some individual dynamics. In this study, we proposed a data-driven shared and unshared feature extraction framework based on nonnegative and coupled tensor factorization, which aims to conduct group-level analysis for the EEG signals from major depression disorder (MDD) patients and healthy controls (HC) when freely listening to music. Constrained tensor factorization not only preserves the multilinear structure of the data, but also considers the common and individual components between the data. The proposed framework, combined with music information retrieval, correlation analysis, and hierarchical clustering, facilitated the simultaneous extraction of shared and unshared spatio-temporal-spectral feature patterns between/in MDD and HC groups. Finally, we obtained two shared feature patterns between MDD and HC groups, and obtained totally three individual feature patterns from HC and MDD groups. The results showed that the MDD and HC groups triggered similar brain dynamics when listening to music, but at the same time, MDD patients also brought some changes in brain oscillatory network characteristics along with music perception. These changes may provide some basis for the clinical diagnosis and the treatment of MDD patients. ...
PublisherFrontiers Media SA
Publication in research information system
MetadataShow full item record
Additional information about fundingThis work is supported by National Natural Science Foundation of China (grant no. 91748105), National Foundation in China (no. JCKY2019110B009 and 2020-JCJQ-JJ-252), the Fundamental Research Funds for the Central Universities (DUT2019 and DUT20LAB303) in Dalian University of Technology in China, Dalian Science and Technology Innovation Fund Project (2021JJ12SN38), and the scholarship from China scholarship Council (no. 201706060263). ...
Showing items with similar title or keywords.
Identifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition Liu, Wenya; Wang, Xiulin; Xu, Jing; Chang, Yi.; Hämäläinen, Timo; Cong, Fengyu (Institute of Electrical and Electronics Engineers (IEEE), 2021)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 ...
Zhu, Yongjie; Wang, Xiaoyu; Mathiak, Klaus; Toiviainen, Petri; Ristaniemi, Tapani; Xu, Jing; Chang, Yi; Cong, Fengyu (World Scientific, 2021)To examine the electrophysiological underpinnings of the functional networks involved in music listening, previous approaches based on spatial independent component analysis (ICA) have recently been used to ongoing ...
Wang, Deqing; Zhu, Yongjie; Ristaniemi, Tapani; Cong, Fengyu (Elsevier BV, 2018)Background Preprocessed Event-related potential (ERP) data are usually organized in multi-way tensor, in which tensor decomposition serves as a powerful tool for data processing. Due to the limitation of computation burden ...
Discovering hidden brain network responses to naturalistic stimuli via tensor component analysis of multi-subject fMRI data Hu, Guoqiang; Li, Huanjie; Zhao, Wei; Hao, Yuxing; Bai, Zonglei; Nickerson, Lisa D.; Cong, Fengyu (Elsevier, 2022)The study of brain network interactions during naturalistic stimuli facilitates a deeper understanding of human brain function. To estimate large-scale brain networks evoked with naturalistic stimuli, a tensor component ...
Liu, Wenya; Zhang, Chi; Wang, Xiaoyu; Xu, Jing; Chang, Yi; Ristaniemi, Tapani; Cong, Fengyu (Elsevier BV, 2020)Objective The functional connectivity (FC) of major depression disorder (MDD) has not been well studied under naturalistic and continuous stimuli conditions. In this study, we investigated the frequency-specific FC of MDD ...