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000943298 1001_ $$0P:(DE-Juel1)179582$$aDomhof, Justin$$b0$$eCorresponding author
000943298 1112_ $$aNIC Symposium 2022$$cJülich$$d2022-09-29 - 2022-09-30$$wGermany
000943298 245__ $$aReliability and subject specificity of personalized dynamical whole-brain models
000943298 260__ $$c2022
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000943298 520__ $$aDynamical whole-brain models originally provided a biophysically-inspiredapproach to investigate the relationship between the structural (SC) andfunctional (FC) brain connectivity and are also used nowadays to studythe dynamical regimes of the brain and how these relate to various subjecttraits. Nevertheless, it is unclear how the modeling results perform in termsof test-retest reliability and subject specificity. We systematically assessthese aspects of the modeling results and examine how they relate tothe reliability and subject specificity of empirical data.We used the empirical SC and FC matrices of 200 healthy unrelated subjectsfrom the Human Connectome Project to build individual models based on networksof neural mass models and systems of coupled phase oscillators. The lattermodel used region-specific natural frequencies extracted from empirical datathat were either subject specific or the same for all subjects to varythe extent of model personalization. The models were simulated for a broadrange of parameter settings to yield the simulated FC matrices exhibitingthe highest correlation with the empirical FCs.We show that the reliability of the simulated FC can exceed that of theempirical one, especially, for the structural atlases and for thepersonalized models.  Also, the subject specificity of the simulated FC mayoutperform that of the empirical one, where the personalized phase oscillator modelwith subject-specific frequencies generated FCs with a much higher subject specificitythan the other, less personalized modeling paradigms. In addition, the atlashas a larger influence on the reliability and specificity of the simulated FCthan on that of the empirical FC, where a distinction between structurally- andfunctionally-derived atlases can be made.Taken together, our results indicate that whole-brain dynamical modelscan generate simulated connectomes with high reliability and (subject)specificity and may outperform the empirical data in this respect.In turn, this suggests that these models potentially reduce the variance inthe empirical FC across different realizations for a single subject by providinga reliable model fit for further analyses. We underline the critical roles thatthe parcellation and model implementation have on the modeling results. Ourfindings also suggest that the application of the dynamical whole-brain modelingshould be tightly connected with an estimate of the reliability of the results.
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000943298 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x2
000943298 536__ $$0G:(EU-Grant)826421$$aVirtualBrainCloud - Personalized Recommendations for Neurodegenerative Disease (826421)$$c826421$$fH2020-SC1-DTH-2018-1$$x3
000943298 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b1
000943298 7001_ $$0P:(DE-Juel1)131880$$aPopovych, Oleksandr$$b2$$eCorresponding author
000943298 8564_ $$uhttps://www.john-von-neumann-institut.de/en/news/nic-symposium/nic-symposium-2022
000943298 8564_ $$uhttps://juser.fz-juelich.de/record/943298/files/Poster_CJINM71_Domhof_et_al.pdf$$yOpenAccess
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