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000915899 005__ 20230106131531.0
000915899 037__ $$aFZJ-2022-05768
000915899 041__ $$aEnglish
000915899 1001_ $$0P:(DE-Juel1)178611$$aJung, Kyesam$$b0$$ufzj
000915899 1112_ $$aBernstein Conference$$cBerlin$$d2022-09-13 - 2022-09-16$$wGermany
000915899 245__ $$aWhole-brain dynamical modeling for classification of Parkinson’s disease
000915899 260__ $$c2022
000915899 3367_ $$033$$2EndNote$$aConference Paper
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000915899 520__ $$aSimulated whole-brain connectomes demonstrate an enhanced inter-individual variability depending on data processing and modeling approach. By considering the human brain connectome as an individualized attribute, we investigate how empirical and simulated whole-brain connectome-derived features can be utilized to classify patients with Parkinson's disease against healthy controls in light of varying data processing and model validation. To this end, we applied simulated blood oxygenation level-dependent signals derived by a whole-brain dynamical model simulating electrical signals of neuronal populations to reveal differences between patients and controls. In addition to the widely used model validation via fitting the dynamical model to empirical neuroimaging data, we invented a model validation against behavioral data, such as subject classes, which we refer to as behavioral model fitting and show that it can be beneficial for Parkinsonian patient classification. Furthermore, the results of machine-learning reported in this study also demonstrated that performance of the patient classification can be improved when the empirical data are complemented by the simulation results. We also showed that temporal filtering of blood oxygenation level-dependent signals influences the prediction results, where the filtering in the low-frequency band is advisable for Parkinsonian patient classification. In addition, composing the feature space of empirical and simulated data from multiple brain parcellation schemes provided complementary features that improve prediction performance. Based on our findings, we suggest including the simulation results with empirical data is effective for inter-individual research and its clinical application.
000915899 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x0
000915899 7001_ $$0P:(DE-HGF)0$$aFlorin, Esther$$b1
000915899 7001_ $$0P:(DE-Juel1)172843$$aPatil, Kaustubh$$b2$$ufzj
000915899 7001_ $$0P:(DE-HGF)0$$aCaspers, Julian$$b3
000915899 7001_ $$0P:(DE-HGF)0$$aRubbert, Christian$$b4
000915899 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b5$$ufzj
000915899 7001_ $$0P:(DE-Juel1)131880$$aPopovych, Oleksandr$$b6$$eCorresponding author$$ufzj
000915899 8564_ $$uhttps://juser.fz-juelich.de/record/915899/files/115_KyesamJung.pdf$$yRestricted
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000915899 9141_ $$y2022
000915899 920__ $$lyes
000915899 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
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