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dc.contributor.advisorMoorhead, Bill
dc.contributor.advisorRoberts, Neil
dc.contributor.advisorLawrie, Stephen M.
dc.contributor.authorDauvermann, Maria Regina
dc.date.accessioned2015-04-13T13:18:16Z
dc.date.available2015-04-13T13:18:16Z
dc.date.issued2014-11-28
dc.identifier.urihttp://hdl.handle.net/1842/10022
dc.description.abstractSchizophrenia is a complex and severe psychiatric disorder with positive symptoms, negative symptoms and cognitive deficits. Preclinical neurobiological studies showed that alterations of dopaminergic and glutamatergic neurotransmitter circuits involving the prefrontal cortex resulted in cognitive impairment such as working memory. Functional activation and functional connectivity findings of functional Magnetic Resonance Imaging (fMRI) data provided support for prefrontal dysfunction during fMRI working memory tasks in individuals with schizophrenia. However, these findings do not offer a neurobiological interpretation of the fMRI data. Biophysical modelling of functional large-scale networks has been designed for the analysis of fMRI data, which can be interpreted in a mechanistic way. This approach may enable the interpretation of fMRI data in terms of altered synaptic plasticity processes found in schizophrenia. One such process is gating mechanism, which has been shown to be altered for the thalamo-cortical and meso-cortical connection in schizophrenia. The primary aim of the thesis was to investigate altered synaptic plasticity and gating mechanisms with Dynamic Causal Modelling (DCM) within functional large-scale networks during two fMRI tasks in individuals with schizophrenia. Applying nonlinear DCM to the verbal fluency fMRI task of the Edinburgh High Risk Study, we showed that the connection strengths with nonlinear modulation for the thalamo-cortical connection was reduced in subjects at high familial risk of schizophrenia when compared to healthy controls. These results suggest that nonlinear DCM enables the investigation of altered synaptic plasticity and gating mechanism from fMRI data. For the Scottish Family Mental Health Study, we reported two different optimal linear models for individuals with established schizophrenia (EST) and healthy controls during working memory function. We suggested that this result may indicate that EST and healthy controls used different functional large-scale networks. The results of nonlinear DCM analyses may suggest that gating mechanism was intact in EST and healthy controls. In conclusion, the results presented in this thesis give evidence for the role of synaptic plasticity processes as assessed in functional large-scale networks during cognitive tasks in individuals with schizophrenia.en
dc.contributor.sponsorotheren
dc.language.isoenen
dc.publisherThe University of Edinburghen
dc.relation.hasversionDauvermann MR, Whalley HC, Schmidt A, Lee GL, Romaniuk L, Roberts N, Johnstone EC, Lawrie SM, Moorhead TWJ. Computational neuropsychiatry – schizophrenia as a cognitive brain network disorder. Frontiers in Schizophrenia (2014). 5:30. eCollection 2014. doi: 10.3389/fpsyt.2014.00030.en
dc.relation.hasversionDauvermann MR, Whalley HC, Romaniuk L, Valton V, Owens DGC, Johnstone EC, Lawrie SM, Moorhead TWJ. The Application of Nonlinear Dynamic Causal Modelling for fMRI in Subjects at High Genetic Risk of Schizophrenia. NeuroImage 2013; 73:16-29. doi: 10.1016/j.neuroimage.2013.01.063.en
dc.relation.hasversionDauvermann MR, Mukherjee P, Moorhead TWJ, Stanfield AC, Fusar-Poli P, Lawrie SM, Whalley HC. Relationship between gyrification and functional connectivity of the prefrontal cortex in subjects at high risk of schizophrenia. Curr Pharm Design 2012;18(4),434-442.en
dc.subjectschizophreniaen
dc.subjectDynamic Causal Modellingen
dc.subjectDCMen
dc.subjectverbal fluencyen
dc.subjectworking memoryen
dc.titleInvestigation into functional large-scale networks in individuals with schizophrenia using fMRI data and Dynamic Causal Modellingen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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