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@ARTICLE{Nostro:845281,
      author       = {Nostro, Alessandra D. and Müller, Veronika and Varikuti,
                      Deepthi and Pläschke, Rachel and Hoffstaedter, Felix and
                      Langner, Robert and Patil, Kaustubh and Eickhoff, Simon},
      title        = {{P}redicting personality from network-based resting-state
                      functional connectivity},
      journal      = {Brain structure $\&$ function},
      volume       = {223},
      number       = {6},
      issn         = {1863-2661},
      address      = {Berlin},
      publisher    = {Springer},
      reportid     = {FZJ-2018-02562},
      pages        = {2699–2719},
      year         = {2018},
      abstract     = {Personality is associated with variation in all kinds of
                      mental faculties, including affective, social, executive,
                      and memory functioning. The intrinsic dynamics of neural
                      networks underlying these mental functions are reflected in
                      their functional connectivity at rest (RSFC). We, therefore,
                      aimed to probe whether connectivity in functional networks
                      allows predicting individual scores of the five-factor
                      personality model and potential gender differences thereof.
                      We assessed nine meta-analytically derived functional
                      networks, representing social, affective, executive, and
                      mnemonic systems. RSFC of all networks was computed in a
                      sample of 210 males and 210 well-matched females and in a
                      replication sample of 155 males and 155 females. Personality
                      scores were predicted using relevance vector machine in both
                      samples. Cross-validation prediction accuracy was defined as
                      the correlation between true and predicted scores. RSFC
                      within networks representing social, affective, mnemonic,
                      and executive systems significantly predicted self-reported
                      levels of Extraversion, Neuroticism, Agreeableness, and
                      Openness. RSFC patterns of most networks, however, predicted
                      personality traits only either in males or in females.
                      Personality traits can be predicted by patterns of RSFC in
                      specific functional brain networks, providing new insights
                      into the neurobiology of personality. However, as most
                      associations were gender-specific, RSFC–personality
                      relations should not be considered independently of gender.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {571 - Connectivity and Activity (POF3-571) / HBP SGA1 -
                      Human Brain Project Specific Grant Agreement 1 (720270)},
      pid          = {G:(DE-HGF)POF3-571 / G:(EU-Grant)720270},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29572625},
      UT           = {WOS:000434980400013},
      doi          = {10.1007/s00429-018-1651-z},
      url          = {https://juser.fz-juelich.de/record/845281},
}