001010417 001__ 1010417
001010417 005__ 20240201205521.0
001010417 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-03046
001010417 037__ $$aFZJ-2023-03046
001010417 041__ $$aEnglish
001010417 1001_ $$0P:(DE-Juel1)178611$$aJung, Kyesam$$b0$$eCorresponding author
001010417 1112_ $$aOrganization for Human Brain Mapping (OHBM)$$cMontreal$$d2023-07-22 - 2023-07-26$$wCanada
001010417 245__ $$aWhole-brain dynamical modeling for classification of Parkinson's disease
001010417 260__ $$c2023
001010417 3367_ $$033$$2EndNote$$aConference Paper
001010417 3367_ $$2BibTeX$$aINPROCEEDINGS
001010417 3367_ $$2DRIVER$$aconferenceObject
001010417 3367_ $$2ORCID$$aCONFERENCE_POSTER
001010417 3367_ $$2DataCite$$aOutput Types/Conference Poster
001010417 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1706800648_2171$$xAfter Call
001010417 500__ $$aThis work was supported by the Portfolio Theme Supercomputing and Modeling for the Human Brain by the Helmholtz association (https://www.helmholtz.de/en), the Human Brain Project, and the European Union’s Horizon 2020 Research and Innovation Programme (https://cordis.europa.eu) under Grant Agreements 785907 (HBP SGA2), 945539 (HBP SGA3), and 826421 (VirtualBrainCloud). Open access publication was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 491111487. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
001010417 520__ $$aIntroduction:Simulated whole-brain connectomes demonstrate disparate inter-individual variability depending on dataprocessing and modeling approach (Domhof et al., 2021; Jung et al., 2021; Popovych et al., 2021). Byconsidering the human brain connectome as an individualized attribute, we investigate how empirical andsimulated whole-brain connectome-derived features can be utilized to classify patients with Parkinson'sdisease against healthy controls in light of varying data processing and model validation.Methods:This study included 51 (30 males) healthy controls and 65 (45 males) patients with Parkinson's disease.Diffusion-weighted MRI (dMRI), T1-weighted MRI and resting-state functional MRI (rsfMRI) were acquired inthe subjects. Empirical functional connectivity (eFC) was calculated by Pearson correlation between regionsof a whole-brain parcellation using blood oxygenation level-dependent (BOLD) signals extracted from thersfMRI. Empirical structural connectivity (eSC) was reconstructed using extracted streamlines connectingregions of the parcellation from whole-brain tractography that was calculated using dMRI. We used twodifferent parcellation schemes based on functional and structural brain properties for calculation of the eFCand eSC. We also simulated BOLD signals by a whole-brain dynamical model of Jansen-Rit type (Jansen etal., 1995) derived from the eSC serving as a network backbone. The simulated data was used to calculatethe simulated FC that was employed together with eFC and eSC to reveal differences between patients andcontrols. In this study, we applied four temporal filtering conditions in four frequency bands to empirical andsimulated BOLD signals. In addition to the widely used model validation via fitting the dynamical model toempirical neuroimaging data (Deco et al., 2015; Honey et al., 2009; Naskar et al., 2021), we invented amodel validation against behavioral data, such as subject classes, which we refer to as behavioral modelfitting and applied it to a machine-learning (ML) classification of Parkinsonian patients.Results:The results of ML investigation demonstrated that performance of the patient classification can besignificantly improved when the empirical data are complemented by the simulation results. This issupported by both integrative performance measures (Fig. 1) and predicted probabilities for individualsubjects (Fig. 2). We also showed that temporal filtering of empirical and simulated BOLD signals influencesthe prediction results, where the filtering in the low-frequency band is advisable for Parkinsonian patientclassification. In addition, composing the feature space of empirical and simulated data from multiple brainparcellation schemes provided complementary features that further improved the prediction performance.The best performance (median of balanced accuracies) was 0.65 for unseen subjects (cf. 0.61 using theempirical data).
001010417 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001010417 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x1
001010417 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x2
001010417 536__ $$0G:(EU-Grant)826421$$aVirtualBrainCloud - Personalized Recommendations for Neurodegenerative Disease (826421)$$c826421$$fH2020-SC1-DTH-2018-1$$x3
001010417 7001_ $$0P:(DE-HGF)0$$aFlorin, Esther$$b1
001010417 7001_ $$0P:(DE-Juel1)172843$$aPatil, Kaustubh$$b2
001010417 7001_ $$0P:(DE-Juel1)144344$$aCaspers, Julian$$b3
001010417 7001_ $$0P:(DE-HGF)0$$aRubbert, Christian$$b4
001010417 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b5
001010417 7001_ $$0P:(DE-Juel1)131880$$aPopovych, Oleksandr$$b6
001010417 8564_ $$uhttps://juser.fz-juelich.de/record/1010417/files/OHBM%20%202023%20Jung%20poster.pdf$$yOpenAccess
001010417 909CO $$ooai:juser.fz-juelich.de:1010417$$popen_access$$popenaire$$pVDB$$pdriver$$pec_fundedresources
001010417 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178611$$aForschungszentrum Jülich$$b0$$kFZJ
001010417 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)178611$$a HHU Düsseldorf$$b0
001010417 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a HHU Düsseldorf$$b1
001010417 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172843$$aForschungszentrum Jülich$$b2$$kFZJ
001010417 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)172843$$a HHU Düsseldorf$$b2
001010417 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)144344$$a HHU Düsseldorf$$b3
001010417 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a HHU Düsseldorf$$b4
001010417 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131678$$aForschungszentrum Jülich$$b5$$kFZJ
001010417 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)131678$$a HHU Düsseldorf$$b5
001010417 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131880$$aForschungszentrum Jülich$$b6$$kFZJ
001010417 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)131880$$a HHU Düsseldorf$$b6
001010417 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5232$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
001010417 9141_ $$y2023
001010417 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001010417 920__ $$lyes
001010417 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
001010417 980__ $$aposter
001010417 980__ $$aVDB
001010417 980__ $$aI:(DE-Juel1)INM-7-20090406
001010417 980__ $$aUNRESTRICTED
001010417 9801_ $$aFullTexts