A1 Journal article (refereed)
Discovering dynamic task-modulated functional networks with specific spectral modes using MEG (2020)


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


JYU authors or editors


Publication details

All authors or editors: Zhu, Yongjie; Liu, Jia; Ye, Chaoxiong; Mathiak, Klaus; Astikainen, Piia; Ristaniemi, Tapani; Cong, Fengyu

Journal or series: NeuroImage

ISSN: 1053-8119

eISSN: 1095-9572

Publication year: 2020

Volume: 218

Article number: 116924

Publisher: Elsevier

Publication country: Netherlands

Publication language: English

DOI: https://doi.org/10.1016/j.neuroimage.2020.116924

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/69643


Abstract

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.


Keywords: brain research; neural networks (biology); MEG; signal processing

Free keywords: tensor decomposition; MEG; functional connectivity; frequency-specific oscillations; dynamic brain networks; canonical polyadic decomposition


Contributing organizations


Ministry reporting: Yes

Reporting Year: 2020

JUFO rating: 2


Last updated on 2022-20-09 at 13:48