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dc.contributor.authorLiu, Wenya
dc.contributor.authorWang, Xiulin
dc.contributor.authorXu, Jing
dc.contributor.authorChang, Yi.
dc.contributor.authorHämäläinen, Timo
dc.contributor.authorCong, Fengyu
dc.date.accessioned2021-09-20T10:28:42Z
dc.date.available2021-09-20T10:28:42Z
dc.date.issued2021
dc.identifier.citationLiu, 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. <i>IEEE Transactions on Neural Systems and Rehabilitation Engineering</i>, <i>29</i>, 1895-1904. <a href="https://doi.org/10.1109/tnsre.2021.3111564" target="_blank">https://doi.org/10.1109/tnsre.2021.3111564</a>
dc.identifier.otherCONVID_100915391
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/77853
dc.description.abstractPrevious 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.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofseriesIEEE Transactions on Neural Systems and Rehabilitation Engineering
dc.rightsCC BY 4.0
dc.subject.otherdynamic functional connectivity
dc.subject.othercoupled tensor decomposition
dc.subject.othermajor depression disorder, naturalistic music stimuli, oscillatory networks
dc.titleIdentifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202109204926
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange1895-1904
dc.relation.issn1534-4320
dc.relation.volume29
dc.type.versionpublishedVersion
dc.rights.copyright© 2021 the Authors
dc.rights.accesslevelopenAccessfi
dc.subject.ysosignaalianalyysi
dc.subject.ysomasennus
dc.subject.ysohermoverkot (biologia)
dc.subject.ysoEEG
dc.subject.ysokognitiivinen neurotiede
dc.subject.ysosignaalinkäsittely
dc.subject.ysoärsykkeet
dc.subject.ysomusiikki
dc.subject.ysovärähtelyt
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p26805
jyx.subject.urihttp://www.yso.fi/onto/yso/p7995
jyx.subject.urihttp://www.yso.fi/onto/yso/p38811
jyx.subject.urihttp://www.yso.fi/onto/yso/p3328
jyx.subject.urihttp://www.yso.fi/onto/yso/p23133
jyx.subject.urihttp://www.yso.fi/onto/yso/p12266
jyx.subject.urihttp://www.yso.fi/onto/yso/p2943
jyx.subject.urihttp://www.yso.fi/onto/yso/p1808
jyx.subject.urihttp://www.yso.fi/onto/yso/p708
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1109/tnsre.2021.3111564
jyx.fundinginformationNational 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)
dc.type.okmA1


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