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dc.contributor.authorCarter, Matthew Edwarden_US
dc.date.accessioned2014-08-26T08:00:15Z
dc.date.available2014-08-26T08:00:15Z
dc.date.issued2014-08-25en_US
dc.identifier.othervt_gsexam:3564en_US
dc.identifier.urihttp://hdl.handle.net/10919/50418
dc.description.abstractModelling neuronal interactions using a directed network can be used to provide insight into the activity of the brain during experimental tasks. Magnetoencephalography (MEG) allows for the observation of the fast neuronal dynamics necessary to characterize the activity of sources and their interactions. A network representation of these sources and their con- nections can be formed by mapping them to nodes and their connection strengths to edge weights. Dynamic Causal Modelling (DCM) presents a Bayesian framework to estimate the parameters of these networks, as well as the ability to test hypotheses on the structure of the network itself using Bayesian model comparison. DCM uses a neurologically-informed representation of the active neural sources, which leads to an underdetermined system and increased complexity in estimating the network parameters. This work shows that inform- ing the MEG DCM source location with prior distributions defined using a MEG source localization algorithm improves model selection accuracy. DCM inversion of a group of can- didate models shows an enhanced ability to identify a ground-truth network structure when source-localized prior means are used.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis Item is protected by copyright and/or related rights. Some uses of this Item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectDynamic Causal Modellingen_US
dc.subjectMEGen_US
dc.subjectBayesian Statisticsen_US
dc.subjectPrior Distributionsen_US
dc.subjectSystem Identificationen_US
dc.titleSetting location priors using beamforming improves model comparison in MEG-DCMen_US
dc.typeThesisen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineElectrical Engineeringen_US
dc.contributor.committeechairWyatt, Christopher L.en_US
dc.contributor.committeememberBaumann, William T.en_US
dc.contributor.committeememberBeex, Aloysius A.en_US


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