Discovering and characterizing dynamic functional brain networks in task FMRI
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ORIGINAL RESEARCH
Discovering and characterizing dynamic functional brain networks in task FMRI Bao Ge 1,2 & Huan Wang 2 & Panpan Wang 2 & Yin Tian 3 & Xin Zhang 4 & Tianming Liu 5
# Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract 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. Keywords Dynamic function brain network . Task-fMRI . Dictionary learning and sparse coding
Introduction Construction and mapping of functional brain networks has been an interesting and important issue in brain science, since it has very significant implications in revealing the brain’s
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11682-019-00096-6) contains supplementary material, which is available to authorized users. * Tianming Liu [email protected] 1
Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi’an, China
2
School of Physics & Information Technology, Shaanxi Normal University, Xi’an, China
3
Department of Communication and Command, National University of Defense Technology, Xi’an, China
4
Institute of Medical Research, Northwestern Polytechnical University, Xi’an, China
5
Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
functional dynamic behaviors (Sporns 2010; Shine et al. 2016). In most of current studies, the networks were identified from the whole fMRI time series, which imposes an implicit assumption of spatial and temporal stationarity throughout the measurement period, resulting in that the functional connectivity (including strength and directionality) is constant and network’s spatial distribution is also constant through time (Hutchison et al. 2013; Calhoun et al. 2014). This assumption has guided many previous studies in functional brain networks, and substantial p
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