001043577 001__ 1043577
001043577 005__ 20250916202447.0
001043577 0247_ $$2doi$$a10.1038/s41598-025-04511-5
001043577 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-02934
001043577 0247_ $$2pmid$$a40542008
001043577 0247_ $$2WOS$$aWOS:001512790500022
001043577 037__ $$aFZJ-2025-02934
001043577 082__ $$a600
001043577 1001_ $$0P:(DE-Juel1)178611$$aJung, Kyesam$$b0$$ufzj
001043577 245__ $$aEffective workflow from multimodal MRI data to model-based prediction
001043577 260__ $$a[London]$$bSpringer Nature$$c2025
001043577 3367_ $$2DRIVER$$aarticle
001043577 3367_ $$2DataCite$$aOutput Types/Journal article
001043577 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1751385100_17235
001043577 3367_ $$2BibTeX$$aARTICLE
001043577 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001043577 3367_ $$00$$2EndNote$$aJournal Article
001043577 500__ $$aThis work was supported by the Portfolio Theme Supercomputing and Modeling for the Human Brain by the Helmholtz association, the Human Brain Project and the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreements 785907 (HBP SGA2), 945539 (HBP SGA3) and 826421 (VirtualBrainCloud). Open-access publication was funded by the Deutsche Forschungsgemeinschaft (German Research Foundation) − 491111487. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
001043577 520__ $$aPredicting human behavior from neuroimaging data remains a complex challenge in neuroscience. To address this, we propose a systematic and multi-faceted framework that incorporates a model-based workflow using dynamical brain models. This approach utilizes multi-modal MRI data for brain modeling and applies the optimized modeling outcome to machine learning. We demonstrate the performance of such an approach through several examples such as sex classification and prediction of cognition or personality traits. We in particular show that incorporating the simulated data into machine learning can significantly improve the prediction performance compared to using empirical features alone. These results suggest considering the output of the dynamical brain models as an additional neuroimaging data modality that complements empirical data by capturing brain features that are difficult to measure directly. The discussed model-based workflow can offer a promising avenue for investigating and understanding inter-individual variability in brain-behavior relationships and enhancing prediction performance in neuroimaging research.
001043577 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001043577 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x1
001043577 536__ $$0G:(EU-Grant)604102$$aHBP - The Human Brain Project (604102)$$c604102$$fFP7-ICT-2013-FET-F$$x2
001043577 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001043577 7001_ $$0P:(DE-Juel1)178756$$aWischnewski, Kevin J.$$b1$$ufzj
001043577 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b2$$ufzj
001043577 7001_ $$0P:(DE-Juel1)131880$$aPopovych, Oleksandr V.$$b3$$eCorresponding author$$ufzj
001043577 773__ $$0PERI:(DE-600)2615211-3$$a10.1038/s41598-025-04511-5$$gVol. 15, no. 1, p. 20126$$n1$$p20126$$tScientific reports$$v15$$x2045-2322$$y2025
001043577 8564_ $$uhttps://www.nature.com/articles/s41598-025-04511-5
001043577 8564_ $$uhttps://juser.fz-juelich.de/record/1043577/files/s41598-025-04511-5.pdf$$yOpenAccess
001043577 8767_ $$8SN-2025-00897-b$$92025-08-27$$a1200217075$$d2025-09-16$$eAPC$$jZahlung erfolgt
001043577 909CO $$ooai:juser.fz-juelich.de:1043577$$pec_fundedresources$$pVDB$$pdriver$$pOpenAPC$$popen_access$$popenaire$$popenCost$$pdnbdelivery
001043577 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178611$$aForschungszentrum Jülich$$b0$$kFZJ
001043577 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178756$$aForschungszentrum Jülich$$b1$$kFZJ
001043577 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)178756$$a HHU Düsseldorf$$b1
001043577 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131678$$aForschungszentrum Jülich$$b2$$kFZJ
001043577 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)131678$$a HHU Düsseldorf$$b2
001043577 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131880$$aForschungszentrum Jülich$$b3$$kFZJ
001043577 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5251$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
001043577 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$$x1
001043577 9141_ $$y2025
001043577 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2024-12-18
001043577 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2024-12-18
001043577 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2024-12-18
001043577 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2024-12-18
001043577 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2024-12-18
001043577 915__ $$0StatID:(DE-HGF)1040$$2StatID$$aDBCoverage$$bZoological Record$$d2024-12-18
001043577 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bSCI REP-UK : 2022$$d2024-12-18
001043577 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2024-07-29T15:28:26Z
001043577 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2024-07-29T15:28:26Z
001043577 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2024-12-18
001043577 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2024-12-18
001043577 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2024-12-18
001043577 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2024-12-18
001043577 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001043577 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2024-12-18
001043577 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2024-12-18
001043577 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences$$d2024-12-18
001043577 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2024-12-18
001043577 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
001043577 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2024-12-18
001043577 915pc $$0PC:(DE-HGF)0000$$2APC$$aAPC keys set
001043577 915pc $$0PC:(DE-HGF)0001$$2APC$$aLocal Funding
001043577 915pc $$0PC:(DE-HGF)0002$$2APC$$aDFG OA Publikationskosten
001043577 915pc $$0PC:(DE-HGF)0003$$2APC$$aDOAJ Journal
001043577 915pc $$0PC:(DE-HGF)0113$$2APC$$aDEAL: Springer Nature 2020
001043577 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
001043577 9801_ $$aFullTexts
001043577 980__ $$ajournal
001043577 980__ $$aVDB
001043577 980__ $$aUNRESTRICTED
001043577 980__ $$aI:(DE-Juel1)INM-7-20090406
001043577 980__ $$aAPC