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001033996 1001_ $$0P:(DE-Juel1)195856$$aRauland, Amelie$$b0
001033996 245__ $$aWeak and Unstable Prediction of Personality from the Structural Connectome
001033996 260__ $$aCambridge, MA$$bMIT Press$$c2025
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001033996 500__ $$aThis 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).
001033996 520__ $$aPersonality 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.
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001033996 7001_ $$0P:(DE-Juel1)178611$$aJung, Kyesam$$b1
001033996 7001_ $$0P:(DE-HGF)0$$aSatterthwaite, Theodore$$b2
001033996 7001_ $$0P:(DE-HGF)0$$aCieslak, Matthew$$b3
001033996 7001_ $$0P:(DE-Juel1)177889$$aReetz, Kathrin$$b4
001033996 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b5
001033996 7001_ $$0P:(DE-Juel1)131880$$aPopovych, Oleksandr$$b6$$eCorresponding author
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