Discovering dynamic task-modulated functional networks with specific spectral modes using MEG
Zhu, 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. NeuroImage, 218, Article 116924. https://doi.org/10.1016/j.neuroimage.2020.116924
Liu, Jia |
DisciplinePsykologiaTietotekniikkaMonitieteinen aivotutkimuskeskusHyvinvoinnin tutkimuksen yhteisöPsychologyMathematical Information TechnologyCentre for Interdisciplinary Brain ResearchSchool of Wellbeing
© 2020 the Authors
Efficient 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. ...
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Additional information about fundingThis 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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