TY - JOUR
T1 - Connectome-based individualized prediction of temperament trait scores
AU - Jiang, Rongtao
AU - Calhoun, Vince D.
AU - Zuo, Nianming
AU - Lin, Dongdong
AU - Li, Jin
AU - Fan, Lingzhong
AU - Qi, Shile
AU - Sun, Hailun
AU - Fu, Zening
AU - Song, Ming
AU - Jiang, Tianzi
AU - Sui, Jing
N1 - Publisher Copyright:
© 2018 Elsevier Inc.
PY - 2018/12
Y1 - 2018/12
N2 - Temperament consists of multi-dimensional traits that affect various domains of human life. Evidence has shown functional connectome-based predictive models are powerful predictors of cognitive abilities. Putatively, individuals’ innate temperament traits may be predictable by unique patterns of brain functional connectivity (FC) as well. However, quantitative prediction for multiple temperament traits at the individual level has not yet been studied. Therefore, we were motivated to realize the individualized prediction of four temperament traits (novelty seeking [NS], harm avoidance [HA], reward dependence [RD] and persistence [PS]) using whole-brain FC. Specifically, a multivariate prediction framework integrating feature selection and sparse regression was applied to resting-state fMRI data from 360 college students, resulting in 4 connectome-based predictive models that enabled prediction of temperament scores for unseen subjects in cross-validation. More importantly, predictive models for HA and NS could be successfully generalized to two relevant personality traits for unseen individuals, i.e., neuroticism and extraversion, in an independent dataset. In four temperament trait predictions, brain connectivities that show top contributing power commonly concentrated on the hippocampus, prefrontal cortex, basal ganglia, amygdala, and cingulate gyrus. Finally, across independent datasets and multiple traits, we show person's temperament traits can be reliably predicted using functional connectivity strength within frontal-subcortical circuits, indicating that human social and behavioral performance can be characterized by specific brain connectivity profile.
AB - Temperament consists of multi-dimensional traits that affect various domains of human life. Evidence has shown functional connectome-based predictive models are powerful predictors of cognitive abilities. Putatively, individuals’ innate temperament traits may be predictable by unique patterns of brain functional connectivity (FC) as well. However, quantitative prediction for multiple temperament traits at the individual level has not yet been studied. Therefore, we were motivated to realize the individualized prediction of four temperament traits (novelty seeking [NS], harm avoidance [HA], reward dependence [RD] and persistence [PS]) using whole-brain FC. Specifically, a multivariate prediction framework integrating feature selection and sparse regression was applied to resting-state fMRI data from 360 college students, resulting in 4 connectome-based predictive models that enabled prediction of temperament scores for unseen subjects in cross-validation. More importantly, predictive models for HA and NS could be successfully generalized to two relevant personality traits for unseen individuals, i.e., neuroticism and extraversion, in an independent dataset. In four temperament trait predictions, brain connectivities that show top contributing power commonly concentrated on the hippocampus, prefrontal cortex, basal ganglia, amygdala, and cingulate gyrus. Finally, across independent datasets and multiple traits, we show person's temperament traits can be reliably predicted using functional connectivity strength within frontal-subcortical circuits, indicating that human social and behavioral performance can be characterized by specific brain connectivity profile.
KW - Functional connectivity
KW - Individualized prediction
KW - Reward processing
KW - Temperament and character inventory (TCI)
KW - Temperament traits
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UR - http://www.scopus.com/inward/citedby.url?scp=85051988141&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2018.08.038
DO - 10.1016/j.neuroimage.2018.08.038
M3 - Article
C2 - 30125712
AN - SCOPUS:85051988141
SN - 1053-8119
VL - 183
SP - 366
EP - 374
JO - NeuroImage
JF - NeuroImage
ER -