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@PHDTHESIS{Jung:1006719,
author = {Jung, Kyesam},
title = {{I}mpact of data processing parameters on whole-brain
dynamical models},
school = {Heinrich-Heine-Universität Düsseldorf},
type = {Dissertation},
reportid = {FZJ-2023-01798},
pages = {121},
year = {2023},
note = {Dissertation, Heinrich-Heine-Universität Düsseldorf,
2023},
abstract = {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.},
cin = {INM-7},
cid = {I:(DE-Juel1)INM-7-20090406},
pnm = {5231 - Neuroscientific Foundations (POF4-523) / 5232 -
Computational Principles (POF4-523)},
pid = {G:(DE-HGF)POF4-5231 / G:(DE-HGF)POF4-5232},
typ = {PUB:(DE-HGF)11},
doi = {10.34734/FZJ-2023-01798},
url = {https://juser.fz-juelich.de/record/1006719},
}