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dc.contributor.authorSafari, Ali
dc.contributor.authorMoretti, Paolo
dc.contributor.authorDiez, Ibai
dc.contributor.authorCortés, Jesús M.
dc.contributor.authorMuñoz Martínez, Miguel Ángel 
dc.date.accessioned2021-06-18T10:00:49Z
dc.date.available2021-06-18T10:00:49Z
dc.date.issued2021-02-10
dc.identifier.citationPublisher version: A. Safari, P. Moretti, I. Diez et al., Persistence of hierarchical network organization and emergent topologies in models of func-tional connectivity, Neurocomputing [https://doi.org/10.1016/j.neucom.2021.02.096]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/69273
dc.descriptionData available to us are from 30 healthy subjects (14 males, 16 females) with age between 22 and 35. Data were provided through the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. To build the connectivity matrices (for further details see [51] ), we processed the same-subject structure–function triple acquisitions of magnetic resonance imaging (MRI) – see Appendix for details on acquisition parameters – consisting of: 1. High-resolution anatomical MRI (used for the mask segmentation of gray matter, white matter and cerebrospinal fluid, and for the transformation to common-space of the functional and the diffusion data), 2. Functional MRI at rest (used for extracting region time series of the blood-oxygen-dependent signal, after removal of movement artifacts and physiological noise, but not the global signal regression), and 3. Diffusion MRI (used for building SC matrices after fitting a diffusion tensor to each voxel, running a deterministic tractography algorithm using the UCL Camino Diffusion MRI Toolkit [52] , and counting the number of streamlines connecting all pairs within the regions, each one containing on average 66 voxels.es_ES
dc.descriptionWe acknowledge the Spanish Ministry and Agencia Estatal de investigación (AEI) through grant FIS2017-84256-P (European Regional Development Fund), as well as the Consejería de Conocimiento, Investigación Universidad, Junta de Andalucía and European Regional Development Fund, Ref. A-FQM-175-UGR18 and SOMM17/6105/UGR for financial support. AS and PM acknowledge financial support from the Deutsche Forschungsgemeinschaft, under grants MO 3049/1-1 and MO 3049/3-1.es_ES
dc.description.abstractFunctional networks provide a topological description of activity patterns in the brain, as they stem from the propagation of neural activity on the underlying anatomical or structural network of synaptic connections. This latter is well known to be organized in hierarchical and modular way. While it is assumed that structural networks shape their functional counterparts, it is also hypothesized that alterations of brain dynamics come with transformations of functional connectivity. In this computational study, we introduce a novel methodology to monitor the persistence and breakdown of hierarchical order in functional networks, generated from computational models of activity spreading on both synthetic and real structural connectomes. We show that hierarchical connectivity appears in functional networks in a persistent way if the dynamics is set to be in the quasi-critical regime associated with optimal processing capabilities and normal brain function, while it breaks down in other (supercritical) dynamical regimes, often associated with pathological conditions. Our results offer important clues for the study of optimal neurocomputing architectures and processes, which are capable of controlling patterns of activity and information flow. We conclude that functional connectivity patterns achieve optimal balance between local specialized processing (i.e. segregation) and global integration by inheriting the hierarchical organization of the underlying structural architecture.es_ES
dc.description.sponsorshipConsejería de Conocimientoes_ES
dc.description.sponsorshipInvestigación Universidad, Junta de Andalucía A-FQM-175-UGR18es_ES
dc.description.sponsorshipSpanish Ministryes_ES
dc.description.sponsorshipNational Institutes of Health NIHes_ES
dc.description.sponsorshipNIH Blueprint for Neuroscience Researches_ES
dc.description.sponsorshipMcDonnell Center for Systems Neurosciencees_ES
dc.description.sponsorshipDeutsche Forschungsgemeinschaft MO 3049/1-1,MO 3049/3-1 DFGes_ES
dc.description.sponsorshipEuropean Regional Development Fund ERDFes_ES
dc.description.sponsorshipAgencia Estatal de Investigación FIS2017-84256-P AEIes_ES
dc.language.isoenges_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectBrain networkes_ES
dc.subjectFunctional connectivityes_ES
dc.subjectHierarchical modular networkses_ES
dc.subjectSegregation integrationes_ES
dc.titlePersistence of hierarchical network organization and emergent topologies in models of functional connectivityes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1016/j.neucom.2021.02.096
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersiones_ES


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