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001006719 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-01798
001006719 037__ $$aFZJ-2023-01798
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001006719 1001_ $$0P:(DE-Juel1)178611$$aJung, Kyesam$$b0$$ufzj
001006719 245__ $$aImpact of data processing parameters on whole-brain dynamical models$$f - 2023-06-21
001006719 260__ $$c2023
001006719 300__ $$a121
001006719 3367_ $$2DataCite$$aOutput Types/Dissertation
001006719 3367_ $$2ORCID$$aDISSERTATION
001006719 3367_ $$2BibTeX$$aPHDTHESIS
001006719 3367_ $$02$$2EndNote$$aThesis
001006719 3367_ $$0PUB:(DE-HGF)11$$2PUB:(DE-HGF)$$aDissertation / PhD Thesis$$bphd$$mphd$$s1694092955_15201
001006719 3367_ $$2DRIVER$$adoctoralThesis
001006719 502__ $$aDissertation, Heinrich-Heine-Universität Düsseldorf, 2023$$bDissertation$$cHeinrich-Heine-Universität Düsseldorf$$d2023$$o2023-09-01
001006719 520__ $$aMagnetic resonance imaging (MRI) in neuroscience is one of the most powerful non-invasivemethods to measure the human brain. Neuroimaging studies have been using MRI to extractstructural and functional properties from the brain. In computational neuroscience, whole-brainmodeling employs MRI data as a backbone and allows researchers to scrutinize simulatedwhole-brain dynamics in silico by exploring free parameters of whole-brain models. However,MRI data processing has no standardized method because of the lack of ground truth of thehuman brain. Thus, using different softwares and data processing parameters can induceinconsistent results and lead to different conclusions across studies. Besides, the impact of dataprocessing on whole-brain models has not been clearly understood. Therefore, I performedthree studies considering conditions of MRI data processing for whole-brain modeling andinvestigated the impact of data processing parameters on whole-brain models. In study 1, varieddata processing was used to calculate the structural connectome, which can directly influencewhole-brain models. Subsequently, these different whole-brain models strongly influencedsimulated results and the subjects were stratified based on empirical and simulated data. Instudy 2, different brain parcellation schemes were used for data processing. Empirical andsimulated results from different parcellation schemes showed inter-individual variability viadata variables. In these respects, in study 3, varied functional data processing was used forwhole-brain dynamical modeling. Afterwards, the empirical and simulated results withdifferent conditions were used for the classification of patients with Parkinson’s disease againsthealthy subjects. The classification performance was affected by the functional data processingconditions. Furthermore, whole-brain modeling improved the performance when the empiricaldata are complemented by the simulation results. From these studies in the thesis, varying MRIdata processing parameters does not only impact empirical data but also leads to differentsimulation results in whole-brain dynamical modeling and its application.
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001006719 9141_ $$y2023
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