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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},
}