001     1009296
005     20230929112540.0
024 7 _ |a 10.1093/braincomms/fcad200
|2 doi
024 7 _ |a 10.34734/FZJ-2023-02746
|2 datacite_doi
024 7 _ |a 37492488
|2 pmid
024 7 _ |a WOS:001036190500001
|2 WOS
037 _ _ |a FZJ-2023-02746
082 _ _ |a 610
100 1 _ |a Schiel, Julian E
|0 P:(DE-HGF)0
|b 0
|e Corresponding author
245 _ _ |a Associations between sleep health and grey matter volume in the UK Biobank cohort ( N = 33,356)
260 _ _ |a [Großbritannien]
|c 2023
|b Guarantors of Brain
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 1693303869_18991
|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 As suggested by previous research, sleep health is assumed to be a key determinant of future morbidity and mortality. In line with this, recent studies have found that poor sleep is associated with impaired cognitive function. However, to date, little is known about brain structural abnormalities underlying this association. Although recent findings link sleep health deficits to specific alterations in grey matter volume, evidence remains inconsistent and reliant on small sample sizes.Addressing this problem, the current preregistered study investigated associations between sleep health and grey matter volume (139 imaging-derived phenotypes) in the UK Biobank cohort (33,356 participants). Drawing on a large sample size and consistent data acquisition, sleep duration, insomnia symptoms, daytime sleepiness, chronotype, sleep medication, and sleep apnoea were examined.Our main analyses revealed that long sleep duration was systematically associated with larger grey matter volume of basal ganglia substructures. Insomnia symptoms, sleep medication and sleep apnoea were not associated with any of the 139 imaging-derived phenotypes. Short sleep duration, daytime sleepiness as well as late and early chronotype were associated with solitary imaging-derived phenotypes (no recognizable pattern, small effect sizes).To our knowledge, this is the largest study to test associations between sleep health and grey matter volume. Clinical implications of the association between long sleep duration and larger grey matter volume of basal ganglia are discussed. Insomnia symptoms as operationalised in the UK Biobank do not translate into grey matter volume findings.
536 _ _ |a 5252 - Brain Dysfunction and Plasticity (POF4-525)
|0 G:(DE-HGF)POF4-5252
|c POF4-525
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |a Tamm, Sandra
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Holub, Florian
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Petri, Roxana
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Dashti, Hassan S
|0 P:(DE-HGF)0
|b 4
700 1 _ |a Domschke, Katharina
|0 P:(DE-HGF)0
|b 5
700 1 _ |a Feige, Bernd
|0 P:(DE-HGF)0
|b 6
700 1 _ |a Goodman, Matthew O
|0 P:(DE-HGF)0
|b 7
700 1 _ |a Jones, Samuel E
|0 P:(DE-HGF)0
|b 8
700 1 _ |a Lane, Jacqueline M
|0 P:(DE-HGF)0
|b 9
700 1 _ |a Ratti, Pietro-Luca
|0 P:(DE-HGF)0
|b 10
700 1 _ |a Ray, David W
|0 P:(DE-HGF)0
|b 11
700 1 _ |a Redline, Susan
|0 P:(DE-HGF)0
|b 12
700 1 _ |a Riemann, Dieter
|0 P:(DE-HGF)0
|b 13
700 1 _ |a Rutter, Martin K
|0 P:(DE-HGF)0
|b 14
700 1 _ |a Saxena, Richa
|0 P:(DE-HGF)0
|b 15
700 1 _ |a Sexton, Claire E
|0 P:(DE-HGF)0
|b 16
700 1 _ |a Tahmasian, Masoud
|0 P:(DE-Juel1)188400
|b 17
|u fzj
700 1 _ |a Wang, Heming
|0 P:(DE-HGF)0
|b 18
700 1 _ |a Weedon, Michael N
|0 P:(DE-HGF)0
|b 19
700 1 _ |a Weihs, Antoine
|0 P:(DE-HGF)0
|b 20
700 1 _ |a Kyle, Simon D
|0 P:(DE-HGF)0
|b 21
700 1 _ |a Spiegelhalder, Kai
|0 P:(DE-HGF)0
|b 22
773 _ _ |a 10.1093/braincomms/fcad200
|g p. fcad200
|0 PERI:(DE-600)3020013-1
|n 4
|p fcad200
|t Brain communications
|v 5
|y 2023
|x 2632-1297
856 4 _ |u https://juser.fz-juelich.de/record/1009296/files/fcad200.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:1009296
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |a University of Freiburg
|0 I:(DE-HGF)0
|b 0
|6 P:(DE-HGF)0
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 17
|6 P:(DE-Juel1)188400
910 1 _ |a HHU Düsseldorf
|0 I:(DE-HGF)0
|b 17
|6 P:(DE-Juel1)188400
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)POF4-5252
|x 0
914 1 _ |y 2023
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2022-02-21T13:34:18Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2022-02-21T13:34:18Z
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2022-11-24
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2022-11-24
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b BRAIN COMMUN : 2022
|d 2023-08-24
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2023-08-24
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2023-08-24
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
|d 2023-08-24
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Anonymous peer review
|d 2022-02-21T13:34:18Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2023-08-24
915 _ _ |a WoS
|0 StatID:(DE-HGF)0112
|2 StatID
|b Emerging Sources Citation Index
|d 2023-08-24
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2023-08-24
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2023-08-24
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