Dynamic Causal Modeling Across Network Topologies

dc.contributor.authorZaghlool, Shaza B.en
dc.contributor.committeechairWyatt, Christopher L.en
dc.contributor.committeememberLaConte, Stephen M.en
dc.contributor.committeememberLaurienti, Paul J.en
dc.contributor.committeememberBeex, Aloysius A.en
dc.contributor.committeememberBaumann, William T.en
dc.contributor.departmentElectrical and Computer Engineeringen
dc.description.abstractDynamic Causal Modeling (DCM) uses dynamical systems to represent the high-level neural processing strategy for a given cognitive task. The logical network topology of the model is specified by a combination of prior knowledge and statistical analysis of the neuro-imaging signals. Parameters of this a-priori model are then estimated and competing models are compared to determine the most likely model given experimental data. Inter-subject analysis using DCM is complicated by differences in model topology, which can vary across subjects due to errors in the first-level statistical analysis of fMRI data or variations in cognitive processing. This requires considerable judgment on the part of the experimenter to decide on the validity of assumptions used in the modeling and statistical analysis; in particular, the dropping of subjects with insufficient activity in a region of the model and ignoring activation not included in the model. This manual data filtering is required so that the fMRI model's network size is consistent across subjects. This thesis 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 data set 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.degreePh. D.en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.subjectDynamic Causal Modelingen
dc.subjectMissing Dataen
dc.titleDynamic Causal Modeling Across Network Topologiesen
thesis.degree.disciplineComputer Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.namePh. D.en


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