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| Hauptseite > Publikationsdatenbank > Impact of data processing parameters on whole-brain dynamical models > print |
| Hauptseite > Publikationsdatenbank > Impact of data processing parameters on whole-brain dynamical models > print |
| 001 | 1006719 | ||
| 005 | 20230907204635.0 | ||
| 024 | 7 | _ | |a 10.34734/FZJ-2023-01798 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2023-01798 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Jung, Kyesam |0 P:(DE-Juel1)178611 |b 0 |u fzj |
| 245 | _ | _ | |a Impact of data processing parameters on whole-brain dynamical models |f - 2023-06-21 |
| 260 | _ | _ | |c 2023 |
| 300 | _ | _ | |a 121 |
| 336 | 7 | _ | |a Output Types/Dissertation |2 DataCite |
| 336 | 7 | _ | |a DISSERTATION |2 ORCID |
| 336 | 7 | _ | |a PHDTHESIS |2 BibTeX |
| 336 | 7 | _ | |a Thesis |0 2 |2 EndNote |
| 336 | 7 | _ | |a Dissertation / PhD Thesis |b phd |m phd |0 PUB:(DE-HGF)11 |s 1694092955_15201 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a doctoralThesis |2 DRIVER |
| 502 | _ | _ | |a Dissertation, Heinrich-Heine-Universität Düsseldorf, 2023 |c Heinrich-Heine-Universität Düsseldorf |b Dissertation |d 2023 |o 2023-09-01 |
| 520 | _ | _ | |a Magnetic 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. |
| 536 | _ | _ | |a 5231 - Neuroscientific Foundations (POF4-523) |0 G:(DE-HGF)POF4-5231 |c POF4-523 |f POF IV |x 0 |
| 536 | _ | _ | |a 5232 - Computational Principles (POF4-523) |0 G:(DE-HGF)POF4-5232 |c POF4-523 |f POF IV |x 1 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1006719/files/Jung%2C%20Kyesam%20-%20Dissertation%20Urfassung%20mit%20den%20Publikationen.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:1006719 |p openaire |p open_access |p VDB |p driver |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)178611 |
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| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-523 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Neuromorphic Computing and Network Dynamics |9 G:(DE-HGF)POF4-5232 |x 1 |
| 914 | 1 | _ | |y 2023 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 920 | _ | _ | |l yes |
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| 980 | _ | _ | |a phd |
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