% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Weis:863624,
      author       = {Weis, Susanne and Patil, Kaustubh R and Hoffstaedter, Felix
                      and Nostro, Alessandra and Yeo, B T Thomas and Eickhoff,
                      Simon B},
      title        = {{S}ex {C}lassification by {R}esting {S}tate {B}rain
                      {C}onnectivity},
      journal      = {Cerebral cortex},
      volume       = {30},
      number       = {2},
      issn         = {1460-2199},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {FZJ-2019-03635},
      pages        = {824-835},
      year         = {2020},
      note         = {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},
      abstract     = {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.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {571 - Connectivity and Activity (POF3-571) / SMHB -
                      Supercomputing and Modelling for the Human Brain
                      (HGF-SMHB-2013-2017) / HBP SGA2 - Human Brain Project
                      Specific Grant Agreement 2 (785907)},
      pid          = {G:(DE-HGF)POF3-571 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      G:(EU-Grant)785907},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31251328},
      UT           = {WOS:000530440700031},
      doi          = {10.1093/cercor/bhz129},
      url          = {https://juser.fz-juelich.de/record/863624},
}