| 001 | 863624 | ||
| 005 | 20220930130214.0 | ||
| 024 | 7 | _ | |a 10.1093/cercor/bhz129 |2 doi |
| 024 | 7 | _ | |a 1047-3211 |2 ISSN |
| 024 | 7 | _ | |a 1460-2199 |2 ISSN |
| 024 | 7 | _ | |a 2128/24591 |2 Handle |
| 024 | 7 | _ | |a altmetric:62849496 |2 altmetric |
| 024 | 7 | _ | |a pmid:31251328 |2 pmid |
| 024 | 7 | _ | |a WOS:000530440700031 |2 WOS |
| 037 | _ | _ | |a FZJ-2019-03635 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Weis, Susanne |0 P:(DE-Juel1)172811 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Sex Classification by Resting State Brain Connectivity |
| 260 | _ | _ | |a Oxford |c 2020 |b Oxford Univ. Press |
| 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 1585044339_4655 |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 The Deutsche Forschungsgemeinschaft (EI 816/11-1), TheNational Institute of Mental Health (R01-MH074457); TheHelmholtz Portfolio Theme “Supercomputing and Modeling forthe Human Brain”; The European Union [Horizon 2020 Researchand Innovation Programme under grant agreement no. 720270(HBP SGA1) 785907 (HBP SGA2)]; Singapore National ResearchFoundation [fellowship (class of 2017) to B.T.T.Y.].APC & Rechnung ergänzt 10.07.19 |
| 520 | _ | _ | |a A large amount of brain imaging research has focused on group studies delineating differences between males and females with respect to both cognitive performance as well as structural and functional brain organization. To supplement existing findings, the present study employed a machine learning approach to assess how accurately participants' sex can be classified based on spatially specific resting state (RS) brain connectivity, using 2 samples from the Human Connectome Project (n1 = 434, n2 = 310) and 1 fully independent sample from the 1000BRAINS study (n = 941). The classifier, which was trained on 1 sample and tested on the other 2, was able to reliably classify sex, both within sample and across independent samples, differing both with respect to imaging parameters and sample characteristics. Brain regions displaying highest sex classification accuracies were mainly located along the cingulate cortex, medial and lateral frontal cortex, temporoparietal regions, insula, and precuneus. These areas were stable across samples and match well with previously described sex differences in functional brain organization. While our data show a clear link between sex and regionally specific brain connectivity, they do not support a clear-cut dimorphism in functional brain organization that is driven by sex alone. |
| 536 | _ | _ | |a 571 - Connectivity and Activity (POF3-571) |0 G:(DE-HGF)POF3-571 |c POF3-571 |f POF III |x 0 |
| 536 | _ | _ | |a SMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017) |0 G:(DE-Juel1)HGF-SMHB-2013-2017 |c HGF-SMHB-2013-2017 |f SMHB |x 1 |
| 536 | _ | _ | |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) |0 G:(EU-Grant)785907 |c 785907 |f H2020-SGA-FETFLAG-HBP-2017 |x 2 |
| 588 | _ | _ | |a Dataset connected to CrossRef |
| 700 | 1 | _ | |a Patil, Kaustubh R |0 P:(DE-Juel1)172843 |b 1 |u fzj |
| 700 | 1 | _ | |a Hoffstaedter, Felix |0 P:(DE-Juel1)131684 |b 2 |u fzj |
| 700 | 1 | _ | |a Nostro, Alessandra |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Yeo, B T Thomas |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Eickhoff, Simon B |0 P:(DE-Juel1)131678 |b 5 |u fzj |
| 773 | _ | _ | |a 10.1093/cercor/bhz129 |g p. bhz129 |0 PERI:(DE-600)1483485-6 |n 2 |p 824-835 |t Cerebral cortex |v 30 |y 2020 |x 1460-2199 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/863624/files/20190710134319842.pdf |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/863624/files/20191016104440022.pdf |
| 856 | 4 | _ | |x pdfa |u https://juser.fz-juelich.de/record/863624/files/20190710134319842.pdf?subformat=pdfa |
| 856 | 4 | _ | |x pdfa |u https://juser.fz-juelich.de/record/863624/files/20191016104440022.pdf?subformat=pdfa |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/863624/files/bhz129.pdf |
| 856 | 4 | _ | |y OpenAccess |x pdfa |u https://juser.fz-juelich.de/record/863624/files/bhz129.pdf?subformat=pdfa |
| 909 | C | O | |o oai:juser.fz-juelich.de:863624 |p openaire |p open_access |p OpenAPC |p driver |p VDB |p ec_fundedresources |p openCost |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)172811 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)172843 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)131684 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 5 |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 |2 G:(DE-HGF)POF3-500 |v Connectivity and Activity |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |
| 914 | 1 | _ | |y 2020 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1030 |2 StatID |b Current Contents - Life Sciences |
| 915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b CEREB CORTEX : 2017 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0110 |2 StatID |b Science Citation Index |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0111 |2 StatID |b Science Citation Index Expanded |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a IF >= 5 |0 StatID:(DE-HGF)9905 |2 StatID |b CEREB CORTEX : 2017 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0310 |2 StatID |b NCBI Molecular Biology Database |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0320 |2 StatID |b PubMed Central |
| 915 | _ | _ | |a Nationallizenz |0 StatID:(DE-HGF)0420 |2 StatID |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |
| 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 | _ | _ | |a APC |
| 980 | 1 | _ | |a APC |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|