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Quantifying information flow in fMRI using the Kullbakc-Leibler divergence

Seghouane, Abd-Krim

Description

Extracting the directional interaction between activated brain areas from functional magnetic resonance imaging (fMRI) time series measurements of their activity is a significant step in understanding the process of brain functions. In this paper, the directional interaction between fMRI time series characterizing the activity of two neuronal sites is quantified using a measure derived from the Kullback-Leibler divergence. A parametric approach based on the autoregressive (AR) and...[Show more]

dc.contributor.authorSeghouane, Abd-Krim
dc.coverage.spatialChicago USA
dc.date.accessioned2015-12-10T23:09:13Z
dc.date.createdMarch 30-April 2 2011
dc.identifier.isbn9781424441280
dc.identifier.urihttp://hdl.handle.net/1885/63390
dc.description.abstractExtracting the directional interaction between activated brain areas from functional magnetic resonance imaging (fMRI) time series measurements of their activity is a significant step in understanding the process of brain functions. In this paper, the directional interaction between fMRI time series characterizing the activity of two neuronal sites is quantified using a measure derived from the Kullback-Leibler divergence. A parametric approach based on the autoregressive (AR) and autoregressive exogenous (ARX) modelling is proposed for estimating this measure. The links between the proposed measure and other existing information measures for quantifying the directional interaction between neuronal sites is discussed. The significance and effectiveness of the proposed measure is illustrated on both simulated and real fMRI data sets.
dc.publisherIEEE Signal Processing Society
dc.relation.ispartofseriesIEEE International Symposium on Biomedical Imaging 2011
dc.sourceFrom Nano to Macro
dc.subjectKeywords: Auto-regressive; Brain areas; Brain functions; Directional interactions; effective connectivity; fMRI data; Functional magnetic resonance imaging; Functional MRI; Information flows; Information measures; Kullback Leibler divergence; Parametric approach; B effective connectivity; Functional MRI; information flow; Kullback-Leibler divergence
dc.titleQuantifying information flow in fMRI using the Kullbakc-Leibler divergence
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2011
local.identifier.absfor080106 - Image Processing
local.identifier.ariespublicationu4334215xPUB800
local.type.statusPublished Version
local.contributor.affiliationSeghouane, Abd-Krim, College of Engineering and Computer Science, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.startpage1569
local.bibliographicCitation.lastpage1572
local.identifier.doi10.1109/ISBI.2011.5872701
local.identifier.absseo970109 - Expanding Knowledge in Engineering
local.identifier.absseo970106 - Expanding Knowledge in the Biological Sciences
dc.date.updated2016-02-24T11:03:10Z
local.identifier.scopusID2-s2.0-80055040234
CollectionsANU Research Publications

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