G5 Doctoral dissertation (article)
Identifying task-related dynamic electrophysiological brain connectivity (2020)

Zhu, Yongjie (2020). Identifying task-related dynamic electrophysiological brain connectivity. JYU dissertations, 305. Jyväskylä: Jyväskylän yliopisto. http://urn.fi/URN:ISBN:978-951-39-8348-2

JYU authors or editors

Publication details

All authors or editors: Zhu, Yongjie

eISBN: 978-951-39-8348-2

Journal or series: JYU dissertations

eISSN: 2489-9003

Publication year: 2020

Number in series: 305

Number of pages in the book: 1 verkkoaineisto (68 sivua, 37 sivua useina numerointijaksoina, 24 numeroimatonta sivua)

Publisher: Jyväskylän yliopisto

Place of Publication: Jyväskylä

Publication country: Finland

Publication language: English

Persistent website address: http://urn.fi/URN:ISBN:978-951-39-8348-2

Open Access: Publication published in an open access channel


How does human cognition emerge from neural dynamics? A proposed hypothesis states that efficient neuronal communication between brain regions through oscillatory synchronization gives the basis for cognitive processing. These synchronized oscillatory networks are transiently forming and dissolving at the timescale of milliseconds to support specific cognitive functions. However, unlike resting-state networks, there is still no appropriate technique for characterizing the complicated organization of such cognitive networks during task performance, especially naturalistic tasks (e.g., music listening). In this thesis, we exploit the high spatiotemporal resolution of electro- or magnetoencephalography (EEG/MEG) to match the rapid timescales of synchronized neural populations and develop EEG/MEG analysis tools to probe the reconfiguration of electrophysiology brain networks during cognitive task performance. In the first study, we applied CANDECOMP/PARAFAC (CP) tensor decomposition to single-trial wavelet-transformed representations of sourcelevel EEG data recorded in a simplified and controlled task, to extract the stimuliinduced oscillatory brain activity. In the second study, by combining spatial Fourier independent component analysis with acoustic feature extraction, we probed the spatial-spectral signatures of brain patterns during continuously listening to natural music. In the third study, we examined the connectivity dynamics during natural speech comprehension via performing principal component analysis on envelope-based connectivity measurements concatenated across time or subjects. In the fourth study, we introduced a novel approach based on CP decomposition to investigate the task-related functional networks with a distinct spectrum during self-peace movement and working memory tasks. Then, we extended this tensor-based method of analyzing network dynamics during natural music listening in the fifth study. In conclusion, these studies introduce novel approaches based on matrix or tensor decomposition to allow for multi-way connectivity analysis considering its non-stationarity, frequency-specificity, and spatial topography.

Keywords: brain research; cognitive neuroscience; stimuli (role related to effect); listening; neural networks (biology); EEG; MEG; signal analysis; signal processing

Free keywords: naturalistic stimuli; brain networks; functional connectivity; dynamics; frequency-specificity; tensor decomposition

Contributing organizations

Ministry reporting: Yes

Last updated on 2020-19-11 at 16:29