000908132 001__ 908132
000908132 005__ 20220722190615.0
000908132 0247_ $$2doi$$a10.25493/CBE0-EQV
000908132 037__ $$aFZJ-2022-02396
000908132 041__ $$aEnglish
000908132 1001_ $$0P:(DE-Juel1)179582$$aDomhof, Justin W. M.$$b0$$ufzj
000908132 245__ $$aParcellation-based functional connectivity simulated by personalized whole-brain dynamical models (1.0)
000908132 260__ $$bEBRAINS$$c2022
000908132 3367_ $$2BibTeX$$aMISC
000908132 3367_ $$0PUB:(DE-HGF)32$$2PUB:(DE-HGF)$$aDataset$$bdataset$$mdataset$$s1658470274_2712
000908132 3367_ $$026$$2EndNote$$aChart or Table
000908132 3367_ $$2DataCite$$aDataset
000908132 3367_ $$2ORCID$$aDATA_SET
000908132 3367_ $$2DINI$$aResearchData
000908132 520__ $$aThis dataset contains functional connectomes generated by whole-brain dynamical models for a healthy cohort and 19 brain parcellations. The models were derived from and validated against the parcellation-based empirical structural (SC) and functional connectivities (FC) of individuals, respectively, which have been published as a separate dataset ([DOI: 10.25493/81EV-ZVT](https://doi.org/10.25493/81EV-ZVT)). In the current dataset, two particular models for local dynamics were considered for modeling the mean-field activities of the brain regions, in particular, the resting-state electrical and ultra-slow blood-oxygen-level-dependent dynamics of neuronal populations. Subsequently, the constructed models were simulated, which yielded the simulated activity time series for each brain region. From these time series, the corresponding simulated FC was calculated and compared with the empirical FC of the subject. Finally, the model parameters were optimized via a grid search so that the similarity between the empirical and simulated FC was maximized. The procedure was repeated for 200 subjects, the two models and 19 parcellations, and this dataset includes the corresponding optimal model parameter settings as well as the respective simulated FCs.
000908132 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x0
000908132 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x1
000908132 588__ $$aDataset connected to DataCite
000908132 650_7 $$2Other$$aNeuroscience
000908132 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b1$$ufzj
000908132 7001_ $$0P:(DE-Juel1)131880$$aPopovych, Oleksandr V.$$b2$$eCorresponding author$$ufzj
000908132 773__ $$a10.25493/CBE0-EQV
000908132 8564_ $$uhttps://search.kg.ebrains.eu/instances/1c0c904d-eff0-4149-9c95-85993ce5e587
000908132 909CO $$ooai:juser.fz-juelich.de:908132$$pVDB
000908132 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)179582$$aForschungszentrum Jülich$$b0$$kFZJ
000908132 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131678$$aForschungszentrum Jülich$$b1$$kFZJ
000908132 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131880$$aForschungszentrum Jülich$$b2$$kFZJ
000908132 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$$x0
000908132 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5254$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x1
000908132 9141_ $$y2022
000908132 920__ $$lyes
000908132 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
000908132 980__ $$adataset
000908132 980__ $$aVDB
000908132 980__ $$aI:(DE-Juel1)INM-7-20090406
000908132 980__ $$aUNRESTRICTED