Show simple item record

dc.contributor.authorDiez, I.
dc.contributor.authorErramuzpe, A.
dc.contributor.authorEscudero, I.
dc.contributor.authorMateos, B.
dc.contributor.authorCabrera, A.
dc.contributor.authorMarinazzo, D.
dc.contributor.authorSanz-Arigita, E.J.
dc.contributor.authorStramaglia, S. 
dc.contributor.authorCortes Diaz, J.M.
dc.date.accessioned2016-06-13T13:44:26Z
dc.date.available2016-06-13T13:44:26Z
dc.date.issued2015-12-31
dc.identifier.issn2158-0014
dc.identifier.urihttp://hdl.handle.net/20.500.11824/273
dc.description.abstractThe resting brain dynamics self-organize into a finite number of correlated patterns known as resting-state networks (RSNs). It is well known that techniques such as independent component analysis can separate the brain activity at rest to provide such RSNs, but the specific pattern of interaction between RSNs is not yet fully understood. To this aim, we propose here a novel method to compute the information flow (IF) between different RSNs from resting-state magnetic resonance imaging. After hemodynamic response function blind deconvolution of all voxel signals, and under the hypothesis that RSNs define regions of interest, our method first uses principal component analysis to reduce dimensionality in each RSN to next compute IF (estimated here in terms of transfer entropy) between the different RSNs by systematically increasing k (the number of principal components used in the calculation). When k=1, this method is equivalent to computing IF using the average of all voxel activities in each RSN. For k≥1, our method calculates the k multivariate IF between the different RSNs. We find that the average IF among RSNs is dimension dependent increasing from k=1 (i.e., the average voxel activity) up to a maximum occurring at k=5 and to finally decay to zero for k≥10. This suggests that a small number of components (close to five) is sufficient to describe the IF pattern between RSNs. Our method-addressing differences in IF between RSNs for any generic data-can be used for group comparison in health or disease. To illustrate this, we have calculated the inter-RSN IF in a data set of Alzheimer's disease (AD) to find that the most significant differences between AD and controls occurred for k=2, in addition to AD showing increased IF w.r.t. controls. The spatial localization of the k=2 component, within RSNs allows the characterization of IF differences between AD and controls.
dc.formatapplication/pdf
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectAlzheimer's disease
dc.subjectfunctional magnetic resonance imaging
dc.subjectindependent component analysis
dc.subjectmultivariate Granger causality
dc.subjectresting state networks
dc.titleInformation Flow between Resting-State Networks
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doi10.1089/brain.2014.0337
dc.relation.publisherversionhttp://online.liebertpub.com/doi/10.1089/brain.2014.0337
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersionen_US
dc.journal.titleBrain Connectivityen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España