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Model-based whole-brain effective connectivity to study distributed cognition in health and disease

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dc.contributor.author Gilson, Matthieu
dc.contributor.author Zamora-López, Gorka
dc.contributor.author Pallarés, Vicente
dc.contributor.author Adhikari, Mohit H.
dc.date.accessioned 2020-04-27T08:27:13Z
dc.date.available 2020-04-27T08:27:13Z
dc.date.issued 2020
dc.identifier.citation Gilson M, Zamora Lopez G, Pallarés V, Adhikari MH. Model-based whole-brain effective connectivity to study distributed cognition in health and disease. Netw Neurosci. 2020 Apr 1;4(2):338-73. DOI: 10.1162/netn_a_00117
dc.identifier.issn 2472-1751
dc.identifier.uri http://hdl.handle.net/10230/44334
dc.description.abstract Neuroimaging techniques are now widely used to study human cognition. The functional associations between brain areas have become a standard proxy to describe how cognitive processes are distributed across the brain network. Among the many analysis tools available, dynamic models of brain activity have been developed to overcome the limitations of original connectivity measures such as functional connectivity. This goes in line with the many efforts devoted to the assessment of directional interactions between brain areas from the observed neuroimaging activity. This opinion article provides an overview of our model-based whole-brain effective connectivity to analyze fMRI data, while discussing the pros and cons of our approach with respect to other established approaches. Our framework relies on the multivariate Ornstein-Uhlenbeck (MOU) process and is thus referred to as MOU-EC. Once tuned, the model provides a directed connectivity estimate that reflects the dynamical state of BOLD activity, which can be used to explore cognition. We illustrate this approach using two applications on task-evoked fMRI data. First, as a connectivity measure, MOU-EC can be used to extract biomarkers for task-specific brain coordination, understood as the patterns of areas exchanging information. The multivariate nature of connectivity measures raises several challenges for whole-brain analysis, for which machine-learning tools present some advantages over statistical testing. Second, we show how to interpret changes in MOU-EC connections in a collective and model-based manner, bridging with network analysis. Our framework provides a comprehensive set of tools that open exciting perspectives to study distributed cognition, as well as neuropathologies.
dc.description.sponsorship Mario Senden, Horizon 2020 Framework Programme (http://dx.doi.org/10.13039/100010661), Award ID: Human Brain Project SGA2 No. 785907. Gorka Zamora-López, Horizon 2020 Framework Programme (http://dx.doi.org/10.13039/100010661), Award ID: Human Brain Project SGA2 No. 785907. Matthieu Gilson, Horizon 2020 Framework Programme, Award ID: Human Brain Project SGA2 No. 785907. Gustavo Deco, Horizon 2020 Framework Programme (http://dx.doi.org/10.13039/100010661), Award ID: Human Brain Project SGA2 No. 785907. Andrea Insabato, H2020 Marie Skłodowska-Curie Actions (http://dx.doi.org/10.13039/100010665), Award ID: MSCA grant agreement No. 841684. Gustavo Deco, Agencia Estatal de Investigación (http://dx.doi.org/10.13039/501100011033), Award ID: PSI2016-75688-P. Gustavo Deco, Consell Català de Recercai Innovació (http://dx.doi.org/10.13039/501100002810), Award ID: AGAUR Programme 2017 899 SGR 1545. Maurizio Corbetta, Italian Ministry of Research (MIUR), Award ID: Progetto Dipartimenti di Eccellenza Neuro-DiP. Maurizio Corbetta, Horizon 2020 Framework Programme (http://dx.doi.org/10.13039/100010661), Award ID: FLAG-ERA JTC.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher MIT Press
dc.relation.ispartof Network Neuroscience. 2020 Apr 1;4(2):338-73
dc.rights This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/legalcode), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.title Model-based whole-brain effective connectivity to study distributed cognition in health and disease
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1162/netn_a_00117
dc.subject.keyword fMRI
dc.subject.keyword Cognition
dc.subject.keyword Whole-brain dynamic model
dc.subject.keyword Effective connectivity
dc.subject.keyword Connectivity estimation
dc.subject.keyword Machine learning
dc.subject.keyword Classification
dc.subject.keyword Biomarker
dc.subject.keyword Network theory
dc.subject.keyword Recurrent network
dc.subject.keyword Dynamic communicability and flow
dc.subject.keyword Community analysis
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/785907
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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