dc.contributor.author | Keilholz, Shella | |
dc.contributor.author | Caballero Gaudes, César | |
dc.contributor.author | Bandettini, Peter | |
dc.contributor.author | Deco, Gustavo | |
dc.contributor.author | Calhoun, Vince | |
dc.date | 2018-09-06 | |
dc.date.accessioned | 2017-12-05T10:45:27Z | |
dc.date.available | 2017-12-05T10:45:27Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Keilholz Shella, Caballero-Gaudes Cesar, Bandettini Peter, Deco Gustavo, and Calhoun Vince. Brain Connectivity. October 2017, 7(8): 465-481. https://doi.org/10.1089/brain.2017.0543 | es_ES |
dc.identifier.issn | 2158-0014 | |
dc.identifier.uri | http://hdl.handle.net/10810/23965 | |
dc.description | Online Ahead of Editing: September 6, 2017 | es_ES |
dc.description.abstract | Time-resolved analysis of resting-state functional magnetic resonance imaging (rs-fMRI) data allows researchers to extract more information about brain function than traditional functional connectivity analysis, yet a number of challenges in data analysis and interpretation remain. This article briefly summarizes common methods for time-resolved analysis and presents some of the pressing issues and opportunities in the field. From there, the discussion moves to interpretation of the network dynamics observed with rs-fMRI and the role that rs-fMRI can play in elucidating the large-scale organization of brain activity. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Brain Connectivity | es_ES |
dc.rights | info:eu-repo/semantics/restrictedAccess | es_ES |
dc.subject | dynamic analysis | es_ES |
dc.subject | dynamic connectivity | es_ES |
dc.subject | network dynamics | es_ES |
dc.subject | time-resolved resting-state fMRI | es_ES |
dc.title | Time-Resolved Resting-State Functional Magnetic Resonance Imaging Analysis: Current Status, Challenges, and New Directions | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © Mary Ann Liebert, Inc. | es_ES |
dc.relation.publisherversion | http://www.liebertpub.com/nv/resources-tools/self-archiving/51/ | es_ES |
dc.identifier.doi | 10.1089/brain.2017.0543 | |