Discovering and characterizing dynamic functional brain networks in task FMRI

Many existing studies for the mapping of function brain networks impose an implicit assumption that the networks’ spatial distributions are constant over time. However, the latest research reports reveal that functional brain networks are dynamical and have time-varying spatial patterns. Furthermore...

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Bibliographic Details
Published in:Brain imaging and behavior Vol. 14; no. 5; pp. 1660 - 1673
Main Authors: Ge, Bao, Wang, Huan, Wang, Panpan, Tian, Yin, Zhang, Xin, Liu, Tianming
Format: Journal Article
Language:English
Published: New York Springer US 01.10.2020
Springer Nature B.V
Subjects:
Biomedical and Life Sciences
Biomedicine
Brain
Brain - diagnostic imaging
Brain Mapping
Coding
Data management
Dictionaries
Functional magnetic resonance imaging
Game theory
Humans
Learning
Magnetic Resonance Imaging
Mapping
Networks
Neural coding
Neuropsychology
Neuroradiology
Neurosciences
Original Research
Psychiatry
Spatial distribution
Dynamic function brain network
Dictionary learning and sparse coding
Task-fMRI
ISSN:1931-7557, 1931-7565, 1931-7565
Online Access:Get full text
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Summary:Many existing studies for the mapping of function brain networks impose an implicit assumption that the networks’ spatial distributions are constant over time. However, the latest research reports reveal that functional brain networks are dynamical and have time-varying spatial patterns. Furthermore, how these functional networks evolve over time has not been elaborated and explained in sufficient details yet. In this paper, we aim to discover and characterize the dynamics of functional brain networks via a windowed group-wise dictionary learning and sparse coding approach. First, we aggregated the sampled subjects’ fMRI signals into one big data matrix, and learned a common dictionary for all individuals via a group-wise dictionary learning step. Second, we obtained the dynamic time-varying functional networks by using the windowed time-varying sparse coding approach. Experimental results demonstrated that our windowed group-wise dictionary learning and sparse coding method can effectively detect the task-evoked networks and also characterize how these networks evolve over time. This work sheds novel insights on the dynamics mechanism of functional brain networks.
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ISSN:1931-7557
1931-7565
1931-7565
DOI:10.1007/s11682-019-00096-6