001     878064
005     20210118134536.0
024 7 _ |a 10.1016/j.neubiorev.2020.07.001
|2 doi
024 7 _ |a 0149-7634
|2 ISSN
024 7 _ |a 1873-7528
|2 ISSN
024 7 _ |a 2128/26701
|2 Handle
024 7 _ |a altmetric:85758852
|2 altmetric
024 7 _ |a 32659287
|2 pmid
024 7 _ |a WOS:000557868200028
|2 WOS
037 _ _ |a FZJ-2020-02608
082 _ _ |a 610
100 1 _ |a Morawetz, Carmen
|0 P:(DE-HGF)0
|b 0
|e Corresponding author
245 _ _ |a Multiple large-scale neural networks underlying emotion regulation
260 _ _ |a Amsterdam [u.a.]
|c 2020
|b Elsevier Science
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 1610368557_13913
|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 Recent models suggest emotion generation, perception, and regulation rely on multiple, interacting large-scale brain networks. Despite the wealth of research in this field, the exact functional nature and different topological features of these neural networks remain elusive. Here, we addressed both using a well-established data-driven meta-analytic grouping approach. We applied k-means clustering to a large set of previously published experiments investigating emotion regulation (independent of strategy, goal and stimulus type) to segregate the results of these experiments into large-scale networks. To elucidate the functional nature of these distinct networks, we used functional decoding of metadata terms (i.e. task-level descriptions and behavioral domains). We identified four large-scale brain networks. The first two were related to regulation and functionally characterized by a stronger focus on response inhibition or executive control versus appraisal or language processing. In contrast, the second two networks were primarily related to emotion generation, appraisal, and physiological processes. We discuss how our findings corroborate and inform contemporary models of emotion regulation and thereby significantly add to the literature.Keywords: Distraction; Emotion regulation strategies; Neuroimaging; Reappraisal; Suppression; fMRI.
536 _ _ |a 571 - Connectivity and Activity (POF3-571)
|0 G:(DE-HGF)POF3-571
|c POF3-571
|f POF III
|x 0
588 _ _ |a Dataset connected to CrossRef
700 1 _ |a Riedel, Michael C.
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Salo, Taylor
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Berboth, Stella
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Eickhoff, Simon B.
|0 P:(DE-Juel1)131678
|b 4
700 1 _ |a Laird, Angela R.
|0 P:(DE-HGF)0
|b 5
700 1 _ |a Kohn, Nils
|0 P:(DE-Juel1)162407
|b 6
773 _ _ |a 10.1016/j.neubiorev.2020.07.001
|g Vol. 116, p. 382 - 395
|0 PERI:(DE-600)1498433-7
|p 382 - 395
|t Neuroscience & biobehavioral reviews
|v 116
|y 2020
|x 0149-7634
856 4 _ |y Published on 2020-07-11. Available in OpenAccess from 2021-07-11.
|z StatID:(DE-HGF)0510
|u https://juser.fz-juelich.de/record/878064/files/ER_Networks_Ver14Morawetz%20oW.pdf
856 4 _ |y Restricted
|u https://juser.fz-juelich.de/record/878064/files/Morawetz.pdf
909 C O |o oai:juser.fz-juelich.de:878064
|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 4
|6 P:(DE-Juel1)131678
913 1 _ |a DE-HGF
|b Key Technologies
|l Decoding the Human Brain
|1 G:(DE-HGF)POF3-570
|0 G:(DE-HGF)POF3-571
|3 G:(DE-HGF)POF3
|2 G:(DE-HGF)POF3-500
|4 G:(DE-HGF)POF
|v Connectivity and Activity
|x 0
914 1 _ |y 2020
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2020-08-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2020-08-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2020-08-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2020-08-26
915 _ _ |a Creative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0
|0 LIC:(DE-HGF)CCBYNCND4
|2 HGFVOC
915 _ _ |a Embargoed OpenAccess
|0 StatID:(DE-HGF)0530
|2 StatID
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b NEUROSCI BIOBEHAV R : 2018
|d 2020-08-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2020-08-26
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2020-08-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2020-08-26
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2020-08-26
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b NEUROSCI BIOBEHAV R : 2018
|d 2020-08-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1120
|2 StatID
|b BIOSIS Reviews Reports And Meetings
|d 2020-08-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2020-08-26
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
|d 2020-08-26
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2020-08-26
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