A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Zhu, Yongjie; Liu, Jia; Ye, Chaoxiong; Mathiak, Klaus; Astikainen, Piia; Ristaniemi, Tapani; Cong, Fengyu
Lehti tai sarja: NeuroImage
ISSN: 1053-8119
eISSN: 1095-9572
Julkaisuvuosi: 2020
Volyymi: 218
Artikkelinumero: 116924
Kustantaja: Elsevier
Julkaisumaa: Alankomaat
Julkaisun kieli: englanti
DOI: https://doi.org/10.1016/j.neuroimage.2020.116924
Julkaisun avoin saatavuus: Avoimesti saatavilla
Julkaisukanavan avoin saatavuus: Osittain avoin julkaisukanava
Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/69643
Tiivistelmä
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.
YSO-asiasanat: aivotutkimus; hermoverkot (biologia); MEG; signaalinkäsittely
Vapaat asiasanat: tensor decomposition; MEG; functional connectivity; frequency-specific oscillations; dynamic brain networks; canonical polyadic decomposition
Liittyvät organisaatiot
OKM-raportointi: Kyllä
Raportointivuosi: 2020
JUFO-taso: 2
