Home > Publications database > The challenges and prospects of brain-based prediction of behaviour > print

001     1014929
005     20231027114415.0
024 7 _ |a 10.1038/s41562-023-01670-1
|2 doi
024 7 _ |a 10.34734/FZJ-2023-03484
|2 datacite_doi
024 7 _ |a 37524932
|2 pmid
024 7 _ |a WOS:001040224100003
|2 WOS
037 _ _ |a FZJ-2023-03484
082 _ _ |a 150
100 1 _ |a Wu, Jianxiao
|0 P:(DE-Juel1)177058
|b 0
|e Corresponding author
245 _ _ |a The challenges and prospects of brain-based prediction of behaviour
260 _ _ |a London
|c 2023
|b Nature Research
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1695710116_12767
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
500 _ _ |a This 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).
520 _ _ |a Relating 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.
536 _ _ |a 5251 - Multilevel Brain Organization and Variability (POF4-525)
|0 G:(DE-HGF)POF4-5251
|c POF4-525
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |a Li, Jingwei
|0 P:(DE-Juel1)164828
|b 1
700 1 _ |a Eickhoff, Simon B.
|0 P:(DE-Juel1)131678
|b 2
700 1 _ |a Scheinost, Dustin
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Genon, Sarah
|0 P:(DE-Juel1)161225
|b 4
773 _ _ |a 10.1038/s41562-023-01670-1
|g Vol. 7, no. 8, p. 1255 - 1264
|0 PERI:(DE-600)2885046-4
|n 8
|p 1255 - 1264
|t Nature human behaviour
|v 7
|y 2023
|x 2397-3374
856 4 _ |y OpenAccess
|u https://juser.fz-juelich.de/record/1014929/files/Wu%20Jianxiao%20CBPP_Challenges_Paper_preprint.docx
856 4 _ |y Restricted
|u https://juser.fz-juelich.de/record/1014929/files/s41562-023-01670-1.pdf
856 4 _ |y OpenAccess
|u https://juser.fz-juelich.de/record/1014929/files/Wu%20Jianxiao%20CBPP_Challenges_Paper.pdf
909 C O |o oai:juser.fz-juelich.de:1014929
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)177058
910 1 _ |a HHU Düsseldorf
|0 I:(DE-HGF)0
|b 0
|6 P:(DE-Juel1)177058
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)164828
910 1 _ |a HHU Düsseldorf
|0 I:(DE-HGF)0
|b 1
|6 P:(DE-Juel1)164828
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)131678
910 1 _ |a HHU Düsseldorf
|0 I:(DE-HGF)0
|b 2
|6 P:(DE-Juel1)131678
910 1 _ |a Yale School of Medicine, New Haven, CT, USA
|0 I:(DE-HGF)0
|b 3
|6 P:(DE-HGF)0
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 4
|6 P:(DE-Juel1)161225
910 1 _ |a HHU Düsseldorf
|0 I:(DE-HGF)0
|b 4
|6 P:(DE-Juel1)161225
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-525
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Decoding Brain Organization and Dysfunction
|9 G:(DE-HGF)POF4-5251
|x 0
914 1 _ |y 2023
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2022-11-30
915 _ _ |a DEAL Nature
|0 StatID:(DE-HGF)3003
|2 StatID
|d 2022-11-30
|w ger
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2022-11-30
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b NAT HUM BEHAV : 2022
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1180
|2 StatID
|b Current Contents - Social and Behavioral Sciences
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0130
|2 StatID
|b Social Sciences Citation Index
|d 2023-10-27
915 _ _ |a IF >= 25
|0 StatID:(DE-HGF)9925
|2 StatID
|b NAT HUM BEHAV : 2022
|d 2023-10-27
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)INM-7-20090406
|k INM-7
|l Gehirn & Verhalten
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)INM-7-20090406
980 1 _ |a FullTexts

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21