Missing data estimation in fMRI dynamic causal modeling

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dc.contributor.authorZaghlool, Shaza B.en
dc.contributor.authorWyatt, Christopher Leeen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2019-11-04T18:34:09Zen
dc.date.available2019-11-04T18:34:09Zen
dc.date.issued2014-07-04en
dc.description.abstractDynamic Causal Modeling (DCM) can be used to quantify cognitive function in individuals as effective connectivity. However, ambiguity among subjects in the number and location of discernible active regions prevents all candidate models from being compared in all subjects, precluding the use of DCM as an individual cognitive phenotyping tool. This paper proposes a solution to this problem by treating missing regions in the first-level analysis as missing data, and performing estimation of the time course associated with any missing region using one of four candidate methods: zero-filling, average-filling, noise-filling using a fixed stochastic process, or one estimated using expectation-maximization. The effect of this estimation scheme was analyzed by treating it as a preprocessing step to DCM and observing the resulting effects on model evidence. Simulation studies show that estimation using expectation-maximization yields the highest classification accuracy using a simple loss function and highest model evidence, relative to other methods. This result held for various dataset sizes and varying numbers of model choice. In real data, application to Go/No-Go and Simon tasks allowed computation of signals from the missing nodes and the consequent computation of model evidence in all subjects compared to 62 and 48 percent respectively if no preprocessing was performed. These results demonstrate the face validity of the preprocessing scheme and open the possibility of using single-subject DCM as an individual cognitive phenotyping tool.en
dc.description.notesResearch reported in this publication was supported by National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award number R01NS070917. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.en
dc.description.sponsorshipNational Institute of Neurological Disorders and Stroke of the National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Neurological Disorders & Stroke (NINDS) [R01NS070917]en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.3389/fnins.2014.00191en
dc.identifier.eissn1662-453Xen
dc.identifier.other191en
dc.identifier.pmid25071435en
dc.identifier.urihttp://hdl.handle.net/10919/95247en
dc.identifier.volume8en
dc.language.isoenen
dc.publisherFrontiersen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectdynamic causal modelingen
dc.subjectexpectation-maximizationen
dc.subjectmissing dataen
dc.titleMissing data estimation in fMRI dynamic causal modelingen
dc.title.serialFrontiers in Neuroscienceen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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