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Connectome-based individualized prediction of temperament trait scores.
Neuroimage 2018; 183:366-374N

Abstract

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.

Authors+Show Affiliations

Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China.The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, 87106, USA; Dept. of Psychiatry and Neurosciences, University of New Mexico, Albuquerque, NM, 87131, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87131, USA.Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, 87106, USA.Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China; The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, 87106, USA.Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China.The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, 87106, USA.Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China; University of Electronic Science and Technology of China, Chengdu, 610054, China; Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, China. Electronic address: jiangtz@nlpr.ia.ac.cn.Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, China. Electronic address: jing.sui@nlpr.ia.ac.cn.

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

30125712

Citation

Jiang, Rongtao, et al. "Connectome-based Individualized Prediction of Temperament Trait Scores." NeuroImage, vol. 183, 2018, pp. 366-374.
Jiang R, Calhoun VD, Zuo N, et al. Connectome-based individualized prediction of temperament trait scores. Neuroimage. 2018;183:366-374.
Jiang, R., Calhoun, V. D., Zuo, N., Lin, D., Li, J., Fan, L., ... Sui, J. (2018). Connectome-based individualized prediction of temperament trait scores. NeuroImage, 183, pp. 366-374. doi:10.1016/j.neuroimage.2018.08.038.
Jiang R, et al. Connectome-based Individualized Prediction of Temperament Trait Scores. Neuroimage. 2018;183:366-374. PubMed PMID: 30125712.
* Article titles in AMA citation format should be in sentence-case
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, Y1 - 2018/08/17/ PY - 2018/04/21/received PY - 2018/08/13/revised PY - 2018/08/16/accepted PY - 2018/8/21/pubmed PY - 2019/2/7/medline PY - 2018/8/21/entrez KW - Functional connectivity KW - Individualized prediction KW - Reward processing KW - Temperament and character inventory (TCI) KW - Temperament traits SP - 366 EP - 374 JF - NeuroImage JO - Neuroimage VL - 183 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. SN - 1095-9572 UR - https://www.unboundmedicine.com/medline/citation/30125712/Connectome_based_individualized_prediction_of_temperament_trait_scores_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S1053-8119(18)30736-5 DB - PRIME DP - Unbound Medicine ER -