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dc.contributor.authorZhu, Yongjie
dc.contributor.authorLiu, Jia
dc.contributor.authorYe, Chaoxiong
dc.contributor.authorMathiak, Klaus
dc.contributor.authorAstikainen, Piia
dc.contributor.authorRistaniemi, Tapani
dc.contributor.authorCong, Fengyu
dc.date.accessioned2020-06-02T08:29:41Z
dc.date.available2020-06-02T08:29:41Z
dc.date.issued2020
dc.identifier.citationZhu, Y., Liu, J., Ye, C., Mathiak, K., Astikainen, P., Ristaniemi, T., & Cong, F. (2020). Discovering dynamic task-modulated functional networks with specific spectral modes using MEG. <i>NeuroImage</i>, <i>218</i>, Article 116924. <a href="https://doi.org/10.1016/j.neuroimage.2020.116924" target="_blank">https://doi.org/10.1016/j.neuroimage.2020.116924</a>
dc.identifier.otherCONVID_35761215
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/69643
dc.description.abstractEfficient neuronal communication between brain regions through oscillatory synchronization at certain frequencies is necessary for cognition. Such synchronized networks are transient and dynamic, established on the timescale of milliseconds in order to support ongoing cognitive operations. However, few studies characterizing dynamic electrophysiological brain networks have simultaneously accounted for temporal non-stationarity, spectral structure, and spatial properties. Here, we propose an analysis framework for characterizing the large-scale phase-coupling network dynamics during task performance using magnetoencephalography (MEG). We exploit the high spatiotemporal resolution of MEG to measure time-frequency dynamics of connectivity between parcellated brain regions, yielding data in tensor format. We then use a tensor component analysis (TCA)-based procedure to identify the spatio-temporal-spectral modes of covariation among separate regions in the human brain. We validate our pipeline using MEG data recorded during a hand movement task, extracting a transient motor network with beta-dominant spectral mode, which is significantly modulated by the movement task. Next, we apply the proposed pipeline to explore brain networks that support cognitive operations during a working memory task. The derived results demonstrate the temporal formation and dissolution of multiple phase-coupled networks with specific spectral modes, which are associated with face recognition, vision, and movement. The proposed pipeline can characterize the spectro-temporal dynamics of functional connectivity in the brain on the subsecond timescale, commensurate with that of cognitive performance.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofseriesNeuroImage
dc.rightsCC BY-NC-ND 4.0
dc.subject.othertensor decomposition
dc.subject.otherMEG
dc.subject.otherfunctional connectivity
dc.subject.otherfrequency-specific oscillations
dc.subject.otherdynamic brain networks
dc.subject.othercanonical polyadic decomposition
dc.titleDiscovering dynamic task-modulated functional networks with specific spectral modes using MEG
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202006023901
dc.contributor.laitosPsykologian laitosfi
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosDepartment of Psychologyen
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiainePsykologiafi
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMonitieteinen aivotutkimuskeskusfi
dc.contributor.oppiainePsychologyen
dc.contributor.oppiaineMathematical Information Technologyen
dc.contributor.oppiaineCentre for Interdisciplinary Brain Researchen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.relation.issn1053-8119
dc.relation.volume218
dc.type.versionpublishedVersion
dc.rights.copyright© 2020 the Authors
dc.rights.accesslevelopenAccessfi
dc.subject.ysoaivotutkimus
dc.subject.ysohermoverkot (biologia)
dc.subject.ysoMEG
dc.subject.ysosignaalinkäsittely
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p23705
jyx.subject.urihttp://www.yso.fi/onto/yso/p38811
jyx.subject.urihttp://www.yso.fi/onto/yso/p3329
jyx.subject.urihttp://www.yso.fi/onto/yso/p12266
dc.rights.urlhttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.relation.doi10.1016/j.neuroimage.2020.116924
jyx.fundinginformationThis work was supported by the National Natural Science Foundation of China (Grant No. 91748105&81471742), the Fundamental Research Funds for the Central Universities [DUT2019] in Dalian University of Technology in China, and the scholarship from China Scholarship Council (No. 201600090042; No. 201600090044). Y. Zhu was also supported by the Mobility Grant from the University of Jyvaskyla.


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