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Resting state networks in empirical and simulated dynamic functional connectivity

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dc.contributor.author Glomb, Katharina
dc.contributor.author Ponce-Alvarez, Adrián
dc.contributor.author Gilson, Matthieu
dc.contributor.author Ritter, Petra
dc.contributor.author Deco, Gustavo
dc.date.accessioned 2017-12-22T12:10:34Z
dc.date.available 2017-12-22T12:10:34Z
dc.date.issued 2017
dc.identifier.citation Glomb K, Ponce-Alvarez A, Gilson M, Ritter P, Deco G. Resting state networks in empirical and simulated dynamic functional connectivity. NeuroImage. 2017;159:388-402. DOI: 10.1016/j.neuroimage.2017.07.065
dc.identifier.issn 1053-8119
dc.identifier.uri http://hdl.handle.net/10230/33567
dc.description.abstract It is well-established that patterns of functional connectivity (FC) - measures of correlated activity between pairs of voxels or regions observed in the human brain using neuroimaging - are robustly expressed in spontaneous activity during rest. These patterns are not static, but exhibit complex spatio-temporal dynamics. Over the last years, a multitude of methods have been proposed to reveal these dynamics on the level of the whole brain. One finding is that the brain transitions through different FC configurations over time, and substantial effort has been put into characterizing these configurations. However, the dynamics governing these transitions are more elusive, specifically, the contribution of stationary vs. non-stationary dynamics is an active field of inquiry. In this study, we use a whole-brain approach, considering FC dynamics between 66 ROIs covering the entire cortex. We combine an innovative dimensionality reduction technique, tensor decomposition, with a mean field model which possesses stationary dynamics. It has been shown to explain resting state FC averaged over time and multiple subjects, however, this average FC summarizes the spatial distribution of correlations while hiding their temporal dynamics. First, we apply tensor decomposition to resting state scans from 24 healthy controls in order to characterize spatio-temporal dynamics present in the data. We simultaneously utilize temporal and spatial information by creating tensors that are subsequently decomposed into sets of brain regions (“communities”) that share similar temporal dynamics, and their associated time courses. The tensors contain pairwise FC computed inside of overlapping sliding windows. Communities are discovered by clustering features pooled from all subjects, thereby ensuring that they generalize. We find that, on the group level, the data give rise to four distinct communities that resemble known resting state networks (RSNs): default mode network, visual network, control networks, and somatomotor network. Second, we simulate data with our stationary mean field model whose nodes are connected according to results from DTI and fiber tracking. In this model, all spatio-temporal structure is due to noisy fluctuations around the average FC. We analyze the simulated data in the same way as the empirical data in order to determine whether stationary dynamics can explain the emergence of distinct FC patterns (RSNs) which have their own time courses. We find that this is the case for all four networks using the spatio-temporal information revealed by tensor decomposition if nodes in the simulation are connected according to model-based effective connectivity. Furthermore, we find that these results require only a small part of the FC values, namely the highest values that occur across time and ROI pair. Our findings show that stationary dynamics can account for the emergence of RSNs. We provide an innovative method that does not make strong assumptions about the underlying data and is generally applicable to resting state or task data from different subject populations.
dc.description.sponsorship This work was supported by the European Union, FP7 Marie Curie ITN “INDIREA” (Grant N. 606901; KG), FP7 FET ICT Flagship Human Brain Project (Grant N. 604102; MG), ERC Advanced Human Brain Project (Grant N. 604102; GD), European Union Horizon2020 (ERC Consolidator grant BrainModes 683049; PR); the Spanish Ministry for Economy, Industry and Competitiveness (MINECO) project “PIRE-PICCS” (Grant N. PCIN-2015-079; KG), SEMAINE ERA-Net NEURON Project (Grant N. PCIN2013-026; APA), and ICoBAM (Grant N. PSI2013-42091-P; GD); the James S. McDonnell Foundation (Brain Network Recovery Group, Grant N. JSMF22002082; PR); the German Ministry of Education and Research (Grant N. 01GQ1504A and 01GQ0971-5; PR); the Max-Planck Society (Minerva Program; PR).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Elsevier
dc.relation.ispartof NeuroImage. 2017;159:388-402.
dc.rights © Elsevier http://dx.doi.org/10.1016/j.neuroimage.2017.07.065
dc.title Resting state networks in empirical and simulated dynamic functional connectivity
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1016/j.neuroimage.2017.07.065
dc.subject.keyword fMRI
dc.subject.keyword Human
dc.subject.keyword Functional connectivity
dc.subject.keyword Dynamic functional connectivity
dc.subject.keyword Tensor decomposition
dc.subject.keyword Feature extraction
dc.subject.keyword Mean field models
dc.subject.keyword Whole-brain models
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/606901
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/604102
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/683049
dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/PCIN-2015-079
dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/PCIN2013-026
dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/PSI2013-42091-P
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/submittedVersion


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