001     1034879
005     20250305202200.0
024 7 _ |a 10.1002/hbm.70099
|2 doi
024 7 _ |a 1065-9471
|2 ISSN
024 7 _ |a 1097-0193
|2 ISSN
024 7 _ |a 10.34734/FZJ-2025-00002
|2 datacite_doi
024 7 _ |a 39705057
|2 pmid
024 7 _ |a WOS:001381365700001
|2 WOS
037 _ _ |a FZJ-2025-00002
082 _ _ |a 610
100 1 _ |a Maleki Balajoo, Somayeh
|0 P:(DE-Juel1)178767
|b 0
|e Corresponding author
245 _ _ |a Discovery, Replicability, and Generalizability of a Left Anterior Hippocampus' Morphological Network Linked to Self‐Regulation
260 _ _ |a New York, NY
|c 2024
|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 1735821846_31057
|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 human hippocampus is a key region in cognitive and emotional processing, but also a vulnerable and plastic region. Accordingly, there is a great interest in understanding how variability in the hippocampus' structure relates to variability in behavior in healthy and clinical populations. In this study, we aimed to link interindividual variability in subregional hippocampal networks (i.e., the brain grey matter networks of hippocampal subregions) to variability in behavioral phenotype. To do so, we used a multiblock multivariate approach mapping the association between grey matter volume in hippocampal subregions, grey matter volume in the whole brain regions, and behavioral variables in healthy adults. To ensure the robustness and generalizability of the findings, we implemented a cross-cohort discovery and validation framework. This framework utilized two independent cohorts: the Human Connectome Project Young Adult (HCP-YA) cohort and the Human Connectome Project Aging (HCP-A) cohort, enabling us to assess the replicability and generalizability of hippocampal-brain-behavior phenotypes across different age groups in the population. Our results highlighted a left anterior hippocampal morphological network including the left amygdala and the posterior midline structures whose expression relates to higher self-regulation, life satisfaction, and better performance at standard neuropsychological tests. The cross-cohort generalizability of the hippocampus-brain-behavior mapping demonstrates its relevance beyond a specific population sample. Our previous work in developmental populations showed that the hippocampus' head co-maturates with most of the brain during childhood. The current data-driven study further suggests that grey matter volume in the left hippocampal head network would be particularly relevant for self-regulation abilities in adults that influence a range of life outcomes. Future studies should thus investigate the factors influencing the development of this morphological network across childhood, as well as its relationship to neurocognitive phenotypes in various brain diseases.
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 Plachti, Anna
|0 P:(DE-Juel1)200163
|b 1
700 1 _ |a Nicolaisen, Eliana
|0 P:(DE-Juel1)180537
|b 2
700 1 _ |a Dong, Debo
|0 P:(DE-Juel1)190904
|b 3
700 1 _ |a Hoffstaedter, Felix
|0 P:(DE-Juel1)131684
|b 4
700 1 _ |a Meuth, Sven G.
|0 P:(DE-HGF)0
|b 5
700 1 _ |a Melzer, Nico
|0 P:(DE-HGF)0
|b 6
700 1 _ |a Eickhoff, Simon B.
|0 P:(DE-Juel1)131678
|b 7
700 1 _ |a Genon, Sarah
|0 P:(DE-Juel1)161225
|b 8
|e Corresponding author
773 _ _ |a 10.1002/hbm.70099
|g Vol. 45, no. 18, p. e70099
|0 PERI:(DE-600)1492703-2
|n 18
|p e70099
|t Human brain mapping
|v 45
|y 2024
|x 1065-9471
856 4 _ |u https://juser.fz-juelich.de/record/1034879/files/Human%20Brain%20Mapping%20-%202024%20-%20Maleki%20Balajoo%20-%20Discovery%20Replicability%20and%20Generalizability%20of%20a%20Left%20Anterior%20Hippocampus.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:1034879
|p openaire
|p open_access
|p OpenAPC
|p driver
|p VDB
|p openCost
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)178767
910 1 _ |a HHU Düsseldorf
|0 I:(DE-HGF)0
|b 0
|6 P:(DE-Juel1)178767
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)200163
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)180537
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)190904
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 4
|6 P:(DE-Juel1)131684
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 7
|6 P:(DE-Juel1)131678
910 1 _ |a HHU Düsseldorf
|0 I:(DE-HGF)0
|b 7
|6 P:(DE-Juel1)131678
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 8
|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)POF4-5252
|x 0
914 1 _ |y 2024
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2023-08-25
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2023-08-25
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2023-08-25
915 _ _ |a DEAL Wiley
|0 StatID:(DE-HGF)3001
|2 StatID
|d 2023-08-25
|w ger
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2023-08-25
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2023-08-25
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
|d 2024-12-19
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2024-12-19
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2024-12-19
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2024-08-08T17:07:28Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2024-08-08T17:07:28Z
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Anonymous peer review
|d 2024-08-08T17:07:28Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2024-12-19
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2024-12-19
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2024-12-19
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2024-12-19
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2024-12-19
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2024-12-19
915 p c |a APC keys set
|2 APC
|0 PC:(DE-HGF)0000
915 p c |a Local Funding
|2 APC
|0 PC:(DE-HGF)0001
915 p c |a DFG OA Publikationskosten
|2 APC
|0 PC:(DE-HGF)0002
915 p c |a DEAL: Wiley 2019
|2 APC
|0 PC:(DE-HGF)0120
915 p c |a DOAJ Journal
|2 APC
|0 PC:(DE-HGF)0003
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)INM-7-20090406
|k INM-7
|l Gehirn & Verhalten
|x 0
980 1 _ |a FullTexts
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)INM-7-20090406
980 _ _ |a APC


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21