001     910730
005     20230123110717.0
024 7 _ |a 10.1002/hbm.26041
|2 doi
024 7 _ |a 1065-9471
|2 ISSN
024 7 _ |a 1097-0193
|2 ISSN
024 7 _ |a 2128/32870
|2 Handle
024 7 _ |a 36441844
|2 pmid
024 7 _ |a WOS:000842244700001
|2 WOS
037 _ _ |a FZJ-2022-04100
082 _ _ |a 610
100 1 _ |a Wang, Mengmeng
|0 P:(DE-HGF)0
|b 0
245 _ _ |a Risk‐taking in the human brain: An activation likelihood estimation meta‐analysis of the balloon analog risk task (BART)
260 _ _ |a New York, NY
|c 2022
|b Wiley-Liss
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 1669714848_13478
|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
520 _ _ |a The Balloon Analog Risk Task (BART) is increasingly used to assess risk-taking behavior and brain function. However, the brain networks underlying risk-taking during the BART and its reliability remain controversial. Here, we combined the activation likelihood estimation (ALE) meta-analysis with both task-based and task-free functional connectivity (FC) analysis to quantitatively synthesize brain networks involved in risk-taking during the BART, and compared the differences between adults and adolescents studies. Based on 22 pooled publications, the ALE meta-analysis revealed multiple brain regions in the reward network, salience network, and executive control network underlying risk-taking during the BART. Compared with adult risk-taking, adolescent risk-taking showed greater activation in the insula, putamen, and prefrontal regions. The combination of meta-analytic connectivity modeling with task-free FC analysis further confirmed the involvement of the reward, salience, and cognitive control networks in the BART. These findings demonstrate the core brain networks for risk-taking during the BART and support the utility of the BART for future neuroimaging and developmental research.
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 Zhang, Shunmin
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Suo, Tao
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Mao, Tianxin
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Wang, Fenghua
|0 P:(DE-HGF)0
|b 4
700 1 _ |a Deng, Yao
|0 P:(DE-HGF)0
|b 5
700 1 _ |a Eickhoff, Simon
|0 P:(DE-Juel1)131678
|b 6
|u fzj
700 1 _ |a Pan, Yu
|0 P:(DE-HGF)0
|b 7
|e Corresponding author
700 1 _ |a Jiang, Caihong
|0 P:(DE-HGF)0
|b 8
700 1 _ |a Rao, Hengyi
|0 P:(DE-HGF)0
|b 9
|e Corresponding author
773 _ _ |a 10.1002/hbm.26041
|g p. hbm.26041
|0 PERI:(DE-600)1492703-2
|n 18
|p 5643-5657
|t Human brain mapping
|v 43
|y 2022
|x 1065-9471
856 4 _ |u https://juser.fz-juelich.de/record/910730/files/Human%20Brain%20Mapping%20-%202022%20-%20Wang%20-%20Risk%E2%80%90taking%20in%20the%20human%20brain%20An%20activation%20likelihood%20estimation%20meta%E2%80%90analysis%20of.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:910730
|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 6
|6 P:(DE-Juel1)131678
910 1 _ |a HHU Düsseldorf
|0 I:(DE-HGF)0
|b 6
|6 P:(DE-Juel1)131678
910 1 _ |a Yu Pan and Hengyi Rao, Center for Magnetic Resonance Imaging Research, Shanghai International Studies University, Shanghai, China.
|0 I:(DE-HGF)0
|b 7
|6 P:(DE-HGF)0
910 1 _ |a Yu Pan and Hengyi Rao, Center for Magnetic Resonance Imaging Research, Shanghai International Studies University, Shanghai, China.
|0 I:(DE-HGF)0
|b 9
|6 P:(DE-HGF)0
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 2022
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2021-01-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2021-01-27
915 _ _ |a Creative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0
|0 LIC:(DE-HGF)CCBYNCND4
|2 HGFVOC
915 _ _ |a DEAL Wiley
|0 StatID:(DE-HGF)3001
|2 StatID
|d 2021-01-27
|w ger
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2021-01-27
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
|d 2022-11-22
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2022-11-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2022-11-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2022-09-27T20:46:01Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2022-09-27T20:46:01Z
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Blind peer review
|d 2022-09-27T20:46:01Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2022-11-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2022-11-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2022-11-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2022-11-22
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b HUM BRAIN MAPP : 2021
|d 2022-11-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2022-11-22
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2022-11-22
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b HUM BRAIN MAPP : 2021
|d 2022-11-22
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