000865117 001__ 865117
000865117 005__ 20210130002918.0
000865117 0247_ $$2Handle$$a2128/23264
000865117 037__ $$aFZJ-2019-04669
000865117 1001_ $$0P:(DE-Juel1)164577$$aManos, Thanos$$b0$$ufzj
000865117 1112_ $$aCNS*2019$$cBarcelona$$d2019-07-13 - 2019-07-17$$wSpain
000865117 245__ $$aImpact of brain parcellation on parameter optimization of the whole-brain dynamical models
000865117 260__ $$c2019
000865117 3367_ $$033$$2EndNote$$aConference Paper
000865117 3367_ $$2BibTeX$$aINPROCEEDINGS
000865117 3367_ $$2DRIVER$$aconferenceObject
000865117 3367_ $$2ORCID$$aCONFERENCE_POSTER
000865117 3367_ $$2DataCite$$aOutput Types/Conference Poster
000865117 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1586171614_14019$$xOther
000865117 520__ $$aRecent progress in neuroimaging techniques has advanced our understanding of structural and functional properties of the brain. Resting-state functional connectivity (FC) analysis has brought new insights to the inter-individual variability [1]. Using diffusion-weighted magnetic resonance imaging, one can retrieve the basic features of the anatomical architecture of brain networks, i.e. structural connectivity (SC) [2]. Empirical SC (eSC) and FC (eFC) can be used to build and validate large-scale mathematical models of the brain dynamics being in the focus of research nowadays [3, 4]. In this work, we set out to investigate the impact of different brain atlases on the dynamics of the whole-brain computational models and their optimal parameters fitted to the neuroimaging data, resulting in the optimal agreement between empirical and simulated data. We considered a sample of 23 healthy subjects from the Human Connectome Project database [5] and 2 different brain atlases, the Harvard-Oxford structural atlas and the Schaefer functional atlas [6]. The large-scale network model of brain activity is based on an informed by eSC Kuramoto model [8] and is simulated using The Virtual Brain (TVB) platform [7], with an optimized code from TVB-HPC adequate for high-performance clusters computing. We found that the two considered atlases are in good agreement with respect to the optimal parameters (e.g. global coupling strength K) and the corresponding values of the correlation coefficient of the best correspondence between sFC and eSC. Moreover, the considered model can demonstrate relatively strong correlations between eSC and sFC matrices whereas the correspondence between eFC and sFC matrices is, however, weaker for both atlases [9].References[1] Park H. J. and Friston K. J. (2013). Structural and functional brain networks: from connections to cognition. Science 342: 1238411.[2] Maier-Hein K. H., Neher P. F., Houde J.-C., Côté M.-A., Garyfallidis E., Zhong J., et al. (2017). The challenge of mapping the human connectome based on diffusion tractography. Nat. Commun. 8: 1349.[3] Popovych O. V., Manos T., Hoffstaedter F. and Eickhoff S. B. (2019). What can computational models contribute to neuroimaging data analytics? Frontiers in Systems Neuroscience (in press).[4] Deco G. and Kringelbach M. (2016). Metastability and Coherence: Extending the Communication through Coherence Hypothesis Using a Whole-Brain Computational Perspective. Trends in Neurosciences. 39(6):432[5] McNab J. A., Edlow B. L., Witzel T., Huang S. Y., Bhat H., Heberlein K., Feiweier T., Liu K., Keil B., Cohen-Adad J., Tisdall M. D., Folkerth R. D., Kinney H. C., Wald L. L. (2013). The Human Connectome Project and beyond: initial applications of 300 mT/m gradients. NeuroImage 80:234.[6] Schaefer A., Kong R., Gordon E. M., Laumann T. O., Zuo X. N., Holmes A. J., Eickhoff S. B., and Yeo B. T. T. (2017). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI, Cereb. Cortex, 28(9): 3095.[7] Sanz Leon P., Knock S. A., Woodman M. M., Domide L., Mersmann J., McIntosh A. R. and Jirsa V. (2013). The Virtual Brain: a simulator of primate brain network dynamics. Front. Neuroinform. 7:10 (TVB-HPC: https://gitlab.thevirtualbrain.org/tvb/hpc).[8] Kuramoto Y. (1984). Chemical oscillations, waves, and turbulence, Springer, Berlin.[9] Manos T., Diaz-Pier S., Hoffstaedter F., Schreiber J., Eickhoff S. B. and Popovych O. V. (in preparation).
000865117 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0
000865117 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x1
000865117 536__ $$0G:(EU-Grant)826421$$aVirtualBrainCloud - Personalized Recommendations for Neurodegenerative Disease (826421)$$c826421$$fH2020-SC1-DTH-2018-1$$x2
000865117 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x3
000865117 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x4
000865117 7001_ $$0P:(DE-Juel1)165859$$aDiaz, Sandra$$b1$$ufzj
000865117 7001_ $$0P:(DE-Juel1)131684$$aHoffstaedter, Felix$$b2$$ufzj
000865117 7001_ $$0P:(DE-Juel1)169295$$aSchreiber, Jan$$b3$$ufzj
000865117 7001_ $$0P:(DE-Juel1)161525$$aPeyser, Alexander$$b4$$ufzj
000865117 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b5$$ufzj
000865117 7001_ $$0P:(DE-Juel1)131880$$aPopovych, Oleksandr$$b6$$eCorresponding author$$ufzj
000865117 8564_ $$uhttps://juser.fz-juelich.de/record/865117/files/P251.pdf$$yOpenAccess
000865117 8564_ $$uhttps://juser.fz-juelich.de/record/865117/files/P251.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000865117 909CO $$ooai:juser.fz-juelich.de:865117$$pec_fundedresources$$pdriver$$pVDB$$popen_access$$popenaire
000865117 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)164577$$aForschungszentrum Jülich$$b0$$kFZJ
000865117 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165859$$aForschungszentrum Jülich$$b1$$kFZJ
000865117 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131684$$aForschungszentrum Jülich$$b2$$kFZJ
000865117 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)169295$$aForschungszentrum Jülich$$b3$$kFZJ
000865117 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)161525$$aForschungszentrum Jülich$$b4$$kFZJ
000865117 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131678$$aForschungszentrum Jülich$$b5$$kFZJ
000865117 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131880$$aForschungszentrum Jülich$$b6$$kFZJ
000865117 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0
000865117 9131_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x1
000865117 9141_ $$y2019
000865117 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000865117 920__ $$lyes
000865117 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000865117 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x1
000865117 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x2
000865117 980__ $$aposter
000865117 980__ $$aVDB
000865117 980__ $$aI:(DE-Juel1)JSC-20090406
000865117 980__ $$aI:(DE-Juel1)INM-7-20090406
000865117 980__ $$aI:(DE-Juel1)INM-1-20090406
000865117 980__ $$aUNRESTRICTED
000865117 9801_ $$aFullTexts