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001017429 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-04125
001017429 037__ $$aFZJ-2023-04125
001017429 1001_ $$0P:(DE-Juel1)131882$$aSilchenko, Alexander$$b0$$eCorresponding author$$ufzj
001017429 1112_ $$aINM Retreat$$cJülich$$d2023-10-17 - 2023-10-18$$wGermany
001017429 245__ $$aDynamic causal modeling computational pipeline for resting-state fMRI data: the structure and main modules for DCM and PEB analysis
001017429 260__ $$c2023
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001017429 500__ $$aAcknowledgments: This work was supported by the Deutsche Forschungsgemeinschaft (DFG, EI 816/11-1), the National Institute of Mental Health (R01-MH074457), the Helmholtz Portfolio Theme Supercomputing and Modeling for the Human Brain, and the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreements 720270 (HBP SGA1), 785907 (HBP SGA2), and 826421 (VirtualBrainCloud).
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001017429 7001_ $$0P:(DE-Juel1)195969$$aBettazzi, Edoardo$$b1$$ufzj
001017429 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b2$$ufzj
001017429 7001_ $$0P:(DE-Juel1)188400$$aTahmasian, Masoud$$b3$$ufzj
001017429 7001_ $$0P:(DE-Juel1)131684$$aHoffstaedter, Felix$$b4$$ufzj
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