001014929 001__ 1014929
001014929 005__ 20231027114415.0
001014929 0247_ $$2doi$$a10.1038/s41562-023-01670-1
001014929 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-03484
001014929 0247_ $$2pmid$$a37524932
001014929 0247_ $$2WOS$$aWOS:001040224100003
001014929 037__ $$aFZJ-2023-03484
001014929 082__ $$a150
001014929 1001_ $$0P:(DE-Juel1)177058$$aWu, Jianxiao$$b0$$eCorresponding author
001014929 245__ $$aThe challenges and prospects of brain-based prediction of behaviour
001014929 260__ $$aLondon$$bNature Research$$c2023
001014929 3367_ $$2DRIVER$$aarticle
001014929 3367_ $$2DataCite$$aOutput Types/Journal article
001014929 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1695710116_12767
001014929 3367_ $$2BibTeX$$aARTICLE
001014929 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001014929 3367_ $$00$$2EndNote$$aJournal Article
001014929 500__ $$aThis work was supported by the Deutsche Forschungsgemeinschaft (GE 2835/2–1, EI 816/ 4–1), the Helmholtz Portfolio Theme ‘Supercomputing and Modelling for the Human Brain’ and the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 720270 (HBP SGA1) and grant agreement no. 785907 (HBP SGA2).
001014929 520__ $$aRelating individual brain patterns to behaviour is fundamental in systemneuroscience. Recently, the predictive modelling approach has becomeincreasingly popular, largely due to the recent availability of large opendatasets and access to computational resources. This means that we can usemachine learning models and interindividual differences at the brain levelrepresented by neuroimaging features to predict interindividual differencesin behavioural measures. By doing so, we could identify biomarkers andneural correlates in a data-driven fashion. Nevertheless, this budding fieldof neuroimaging-based predictive modelling is facing issues that may limitits potential applications. Here we review these existing challenges, as wellas those that we anticipate as the field develops. We focus on the impactsof these challenges on brain-based predictions. We suggest potentialsolutions to address the resolvable challenges, while keeping in mind thatsome general and conceptual limitations may also underlie the predictivemodelling approach.
001014929 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001014929 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001014929 7001_ $$0P:(DE-Juel1)164828$$aLi, Jingwei$$b1
001014929 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b2
001014929 7001_ $$0P:(DE-HGF)0$$aScheinost, Dustin$$b3
001014929 7001_ $$0P:(DE-Juel1)161225$$aGenon, Sarah$$b4
001014929 773__ $$0PERI:(DE-600)2885046-4$$a10.1038/s41562-023-01670-1$$gVol. 7, no. 8, p. 1255 - 1264$$n8$$p1255 - 1264$$tNature human behaviour$$v7$$x2397-3374$$y2023
001014929 8564_ $$uhttps://juser.fz-juelich.de/record/1014929/files/Wu%20Jianxiao%20CBPP_Challenges_Paper_preprint.docx$$yOpenAccess
001014929 8564_ $$uhttps://juser.fz-juelich.de/record/1014929/files/s41562-023-01670-1.pdf$$yRestricted
001014929 8564_ $$uhttps://juser.fz-juelich.de/record/1014929/files/Wu%20Jianxiao%20CBPP_Challenges_Paper.pdf$$yOpenAccess
001014929 909CO $$ooai:juser.fz-juelich.de:1014929$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
001014929 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)177058$$aForschungszentrum Jülich$$b0$$kFZJ
001014929 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)177058$$a HHU Düsseldorf$$b0
001014929 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)164828$$aForschungszentrum Jülich$$b1$$kFZJ
001014929 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)164828$$a HHU Düsseldorf$$b1
001014929 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131678$$aForschungszentrum Jülich$$b2$$kFZJ
001014929 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)131678$$a HHU Düsseldorf$$b2
001014929 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Yale School of Medicine, New Haven, CT, USA$$b3
001014929 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)161225$$aForschungszentrum Jülich$$b4$$kFZJ
001014929 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)161225$$a HHU Düsseldorf$$b4
001014929 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$$x0
001014929 9141_ $$y2023
001014929 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2022-11-30
001014929 915__ $$0StatID:(DE-HGF)3003$$2StatID$$aDEAL Nature$$d2022-11-30$$wger
001014929 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001014929 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2022-11-30
001014929 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bNAT HUM BEHAV : 2022$$d2023-10-27
001014929 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2023-10-27
001014929 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2023-10-27
001014929 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2023-10-27
001014929 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2023-10-27
001014929 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2023-10-27
001014929 915__ $$0StatID:(DE-HGF)1180$$2StatID$$aDBCoverage$$bCurrent Contents - Social and Behavioral Sciences$$d2023-10-27
001014929 915__ $$0StatID:(DE-HGF)0130$$2StatID$$aDBCoverage$$bSocial Sciences Citation Index$$d2023-10-27
001014929 915__ $$0StatID:(DE-HGF)9925$$2StatID$$aIF >= 25$$bNAT HUM BEHAV : 2022$$d2023-10-27
001014929 920__ $$lyes
001014929 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
001014929 980__ $$ajournal
001014929 980__ $$aVDB
001014929 980__ $$aUNRESTRICTED
001014929 980__ $$aI:(DE-Juel1)INM-7-20090406
001014929 9801_ $$aFullTexts