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@ARTICLE{Rauland:1033996,
      author       = {Rauland, Amelie and Jung, Kyesam and Satterthwaite,
                      Theodore and Cieslak, Matthew and Reetz, Kathrin and
                      Eickhoff, Simon and Popovych, Oleksandr},
      title        = {{W}eak and {U}nstable {P}rediction of {P}ersonality from
                      the {S}tructural {C}onnectome},
      journal      = {Imaging neuroscience},
      volume       = {3},
      issn         = {2837-6056},
      address      = {Cambridge, MA},
      publisher    = {MIT Press},
      reportid     = {FZJ-2024-06829},
      pages        = {$imag_a_00416$},
      year         = {2025},
      note         = {This work was funded by the German Research Foundation
                      (Deutsche Forschungsgemeinschaft, DFG)—grant number
                      269953372/GRK2150 (International Research Training Group
                      2150) and by the Portfolio Theme Supercomputing and Modeling
                      for the Human Brain by the Helmholtz association, the Human
                      Brain Project and the European Union’s Horizon 2020
                      Research and Innovation Programme under the Grant Agreements
                      945539 (HBP SGA3) and 826421 (VirtualBrainCloud). S.B.E.
                      acknowledges funding by the Deutsche Forschungsgemeinschaft
                      (SPP 2041, SFB 1451, IRTG 2150).},
      abstract     = {Personality neuroscience aims to discover links between
                      personality traits and features of the brain. Previous
                      neuroimaging studies have investigated the connection
                      between the brain structure, microstructural properties of
                      brain tissue, or the functional connectivity (FC) and these
                      personality traits. Analyses relating personality to
                      diffusion-weighted MRI measures were limited to
                      investigating the voxel-wise or tract-wise association of
                      microstructural properties with trait scores. The main goal
                      of our study was to determine whether there is an individual
                      predictive relationship between the structural connectome
                      (SC) and the big five personality traits. To that end, we
                      expanded past work in two ways: First, by focusing on the
                      entire structural connectome (SC) instead of separate voxels
                      and tracts; and second, by predicting personality trait
                      scores instead of performing a statistical correlation
                      analysis to assess an out-of-sample performance. Prediction
                      of personality from the SC is, however, not yet as
                      established as prediction of behavior from the FC, and
                      sparse studies in this field so far delivered rather
                      heterogeneous results. We, therefore, further dedicated our
                      study to investigate whether and how different pipeline
                      settings influence prediction performance. In a sample of
                      426 unrelated subjects with high-quality MRI acquisitions
                      from the Human Connectome Project, we analyzed 19 different
                      brain parcellations, 3 SC weightings, 3 groups of subjects,
                      and 4 feature classes for the prediction of the 5
                      personality traits using a ridge regression. From the large
                      number of evaluated pipelines, only very few lead to
                      promising results of prediction accuracyr> 0.2, while the
                      vast majority lead to a small prediction accuracy centered
                      around zero. A markedly better prediction was observed for a
                      cognition target confirming the chosen methods for SC
                      calculation and prediction and indicating limitations of the
                      personality trait scores and their relation to the SC. We
                      therefore report that, for methods evaluated here, the SC
                      cannot predict personality trait scores. Overall, we found
                      that all considered pipeline conditions influence the
                      predictive performance of both cognition and personality
                      trait scores. The strongest differences were found for the
                      trait openness and the SC weighting by number of streamlines
                      which outperformed the other traits and weightings,
                      respectively. As there is a substantial variation in
                      prediction accuracy across pipelines even for the same
                      subjects and the same target, these findings highlight the
                      crucial importance of pipeline settings for predicting
                      individual traits from the SC.Keywords: big five personality
                      traits; cognition; diffusion-weighted MRI; individual
                      differences; machine learning prediction analysis;
                      structural connectome.},
      cin          = {INM-7 / INM-11},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406 / I:(DE-Juel1)INM-11-20170113},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / 5253 - Neuroimaging (POF4-525) / DFG project
                      G:(GEPRIS)431549029 - SFB 1451: Schlüsselmechanismen
                      normaler und krankheitsbedingt gestörter motorischer
                      Kontrolle (431549029) / GRK 2150 - GRK 2150: Neuronale
                      Grundlagen der Modulation von Aggression und Impulsivität
                      im Rahmen von Psychopathologie (269953372) / HBP SGA3 -
                      Human Brain Project Specific Grant Agreement 3 (945539)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)POF4-5253 /
                      G:(GEPRIS)431549029 / G:(GEPRIS)269953372 /
                      G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1162/imag_a_00416},
      url          = {https://juser.fz-juelich.de/record/1033996},
}