Resting state networks in empirical and simulated dynamic functional connectivity

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 sp...

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Detalles Bibliográficos
Autores: Glomb, Katharina, Ponce-Alvarez, Adrián, Gilson, Matthieu, Ritter, Petra, Deco, Gustavo
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2017
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/33567
Acceso en línea:http://hdl.handle.net/10230/33567
http://dx.doi.org/10.1016/j.neuroimage.2017.07.065
Access Level:acceso abierto
Palabra clave:fMRI
Human
Functional connectivity
Dynamic functional connectivity
Tensor decomposition
Feature extraction
Mean field models
Whole-brain models
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dc.title.none.fl_str_mv Resting state networks in empirical and simulated dynamic functional connectivity
title Resting state networks in empirical and simulated dynamic functional connectivity
spellingShingle Resting state networks in empirical and simulated dynamic functional connectivity
Glomb, Katharina
fMRI
Human
Functional connectivity
Dynamic functional connectivity
Tensor decomposition
Feature extraction
Mean field models
Whole-brain models
title_short Resting state networks in empirical and simulated dynamic functional connectivity
title_full Resting state networks in empirical and simulated dynamic functional connectivity
title_fullStr Resting state networks in empirical and simulated dynamic functional connectivity
title_full_unstemmed Resting state networks in empirical and simulated dynamic functional connectivity
title_sort Resting state networks in empirical and simulated dynamic functional connectivity
dc.creator.none.fl_str_mv Glomb, Katharina
Ponce-Alvarez, Adrián
Gilson, Matthieu
Ritter, Petra
Deco, Gustavo
author Glomb, Katharina
author_facet Glomb, Katharina
Ponce-Alvarez, Adrián
Gilson, Matthieu
Ritter, Petra
Deco, Gustavo
author_role author
author2 Ponce-Alvarez, Adrián
Gilson, Matthieu
Ritter, Petra
Deco, Gustavo
author2_role author
author
author
author
dc.subject.none.fl_str_mv fMRI
Human
Functional connectivity
Dynamic functional connectivity
Tensor decomposition
Feature extraction
Mean field models
Whole-brain models
topic fMRI
Human
Functional connectivity
Dynamic functional connectivity
Tensor decomposition
Feature extraction
Mean field models
Whole-brain models
description 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.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017
2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/33567
http://dx.doi.org/10.1016/j.neuroimage.2017.07.065
url http://hdl.handle.net/10230/33567
http://dx.doi.org/10.1016/j.neuroimage.2017.07.065
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv NeuroImage. 2017;159:388-402.
info:eu-repo/grantAgreement/EC/FP7/606901
info:eu-repo/grantAgreement/EC/FP7/604102
info:eu-repo/grantAgreement/EC/H2020/683049
info:eu-repo/grantAgreement/ES/1PE/PCIN-2015-079
info:eu-repo/grantAgreement/ES/1PE/PCIN2013-026
info:eu-repo/grantAgreement/ES/1PE/PSI2013-42091-P
dc.rights.none.fl_str_mv © Elsevier http://dx.doi.org/10.1016/j.neuroimage.2017.07.065
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © Elsevier http://dx.doi.org/10.1016/j.neuroimage.2017.07.065
eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
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spelling Resting state networks in empirical and simulated dynamic functional connectivityGlomb, KatharinaPonce-Alvarez, AdriánGilson, MatthieuRitter, PetraDeco, GustavofMRIHumanFunctional connectivityDynamic functional connectivityTensor decompositionFeature extractionMean field modelsWhole-brain modelsIt 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.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).Elsevier201720172017info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/33567http://dx.doi.org/10.1016/j.neuroimage.2017.07.065reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésNeuroImage. 2017;159:388-402.info:eu-repo/grantAgreement/EC/FP7/606901info:eu-repo/grantAgreement/EC/FP7/604102info:eu-repo/grantAgreement/EC/H2020/683049info:eu-repo/grantAgreement/ES/1PE/PCIN-2015-079info:eu-repo/grantAgreement/ES/1PE/PCIN2013-026info:eu-repo/grantAgreement/ES/1PE/PSI2013-42091-P© Elsevier http://dx.doi.org/10.1016/j.neuroimage.2017.07.065info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/335672026-06-12T07:21:37Z
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