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Connectome-based individualized prediction of temperament trait scores

Published on Dec 1, 2018in NeuroImage5.81
· DOI :10.1016/j.neuroimage.2018.08.038
Rongtao Jiang4
Estimated H-index: 4
(CAS: Chinese Academy of Sciences),
Vince Daniel Calhoun87
Estimated H-index: 87
(UNM: University of New Mexico)
+ 9 AuthorsJing Sui28
Estimated H-index: 28
(CAS: Chinese Academy of Sciences)
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Abstract
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.
  • References (77)
  • Citations (2)
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References77
Newest
Published on Dec 1, 2018in Nature Communications11.88
Jing Sui28
Estimated H-index: 28
,
Shile Qi4
Estimated H-index: 4
(CAS: Chinese Academy of Sciences)
+ 22 AuthorsAndrew R. Mayer37
Estimated H-index: 37
(UNM: University of New Mexico)
Cognitive impairment is a feature of many psychiatric diseases, including schizophrenia. Here we aim to identify multimodal biomarkers for quantifying and predicting cognitive performance in individuals with schizophrenia and healthy controls. A supervised learning strategy is used to guide three-way multimodal magnetic resonance imaging (MRI) fusion in two independent cohorts including both healthy individuals and individuals with schizophrenia using multiple cognitive domain scores. Results hi...
Published on Jul 1, 2018in Brain Structure & Function3.62
Alessandra D. Nostro2
Estimated H-index: 2
(HHU: University of Düsseldorf),
Veronika I. Müller15
Estimated H-index: 15
(HHU: University of Düsseldorf)
+ 5 AuthorsSimon B. Eickhoff83
Estimated H-index: 83
(HHU: University of Düsseldorf)
Personality is associated with variation in all kinds of mental faculties, including affective, social, executive, and memory functioning. The intrinsic dynamics of neural networks underlying these mental functions are reflected in their functional connectivity at rest (RSFC). We, therefore, aimed to probe whether connectivity in functional networks allows predicting individual scores of the five-factor personality model and potential gender differences thereof. We assessed nine meta-analyticall...
Published on May 25, 2018in Polymer Engineering and Science1.92
Nicola Toschi25
Estimated H-index: 25
,
Roberta Riccelli8
Estimated H-index: 8
+ 2 AuthorsLuca Passamonti26
Estimated H-index: 26
Published on May 1, 2018in Cerebral Cortex5.44
Zaixu Cui7
Estimated H-index: 7
(McGovern Institute for Brain Research),
Mengmeng Su6
Estimated H-index: 6
(McGovern Institute for Brain Research)
+ 2 AuthorsGaolang Gong29
Estimated H-index: 29
(BNU: Beijing Normal University)
Reading comprehension is a crucial reading skill for learning and putatively contains 2 key components: reading decoding and linguistic comprehension. Current understanding of the neural mechanism underlying these reading comprehension components is lacking, and whether and how neuroanatomical features can be used to predict these 2 skills remain largely unexplored. In the present study, we analyzed a large sample from the Human Connectome Project (HCP) dataset and successfully built multivariat...
Published on Apr 1, 2018in Neuropsychopharmacology7.16
Rongtao Jiang4
Estimated H-index: 4
(CAS: Chinese Academy of Sciences),
Christopher C. Abbott1
Estimated H-index: 1
(CAS: Chinese Academy of Sciences)
+ 13 AuthorsMing Song (UCLA: University of California, Los Angeles)
SMRI Biomarkers Predict Electroconvulsive Treatment Outcomes: Accuracy with Independent Data Sets
Published on Feb 1, 2018in Journal of Cognitive Neuroscience3.03
Monica D. Rosenberg14
Estimated H-index: 14
,
Wei-Ting Hsu4
Estimated H-index: 4
+ 2 AuthorsMarvin M. Chun59
Estimated H-index: 59
Although we typically talk about attention as a single process, it comprises multiple independent components. But what are these components, and how are they represented in the functional organization of the brain? To investigate whether long-studied components of attention are reflected in the brain's intrinsic functional organization, here we apply connectome-based predictive modeling (CPM) to predict the components of Posner and Petersen's influential model of attention: alerting (preparing a...
Published on Feb 1, 2018in Social Cognitive and Affective Neuroscience3.66
Wei-Ting Hsu4
Estimated H-index: 4
(Yale University),
Monica D. Rosenberg14
Estimated H-index: 14
(Yale University)
+ 2 AuthorsMarvin M. Chun59
Estimated H-index: 59
(Yale University)
Roger E. Beaty22
Estimated H-index: 22
(Harvard University),
Yoed N. Kenett11
Estimated H-index: 11
(UPenn: University of Pennsylvania)
+ 8 AuthorsMichael J. Kane38
Estimated H-index: 38
(UNCG: University of North Carolina at Greensboro)
People’s ability to think creatively is a primary means of technological and cultural progress, yet the neural architecture of the highly creative brain remains largely undefined. Here, we employed a recently developed method in functional brain imaging analysis—connectome-based predictive modeling—to identify a brain network associated with high-creative ability, using functional magnetic resonance imaging (fMRI) data acquired from 163 participants engaged in a classic divergent thinking task. ...
Published on Jan 1, 2018in Polymer Engineering and Science1.92
Sebastian Markett22
Estimated H-index: 22
,
Christian Montag3
Estimated H-index: 3
,
Martin Reuter53
Estimated H-index: 53
Published on Sep 1, 2017 in MLSP (International Workshop on Machine Learning for Signal Processing)
Rongtao Jiang4
Estimated H-index: 4
(CAS: Chinese Academy of Sciences),
Shile Qi4
Estimated H-index: 4
(CAS: Chinese Academy of Sciences)
+ 4 AuthorsJing Sui28
Estimated H-index: 28
(CAS: Chinese Academy of Sciences)
Variation in several brain regions and neural parameters is associated with intelligence. In this study, we adopted functional connectivity (FC) based on Brainnetome-atlas to predict the intelligence quotient (IQ) scores quantitatively with a prediction framework incorporating advanced feature selection and regression methods. We compared prediction performance of five regression models and evaluated the effectiveness of feature selection. The best prediction performance was achieved by ReliefF+...
Cited By2
Newest
Published on 2019in Psychiatry Research-neuroimaging2.21
Fiona Weathersby (UofU: University of Utah), Jace B. King6
Estimated H-index: 6
(UofU: University of Utah)
+ 2 AuthorsJeffrey S. Anderson28
Estimated H-index: 28
(UofU: University of Utah)
Abstract Functional MRI connectivity has identified neurophysiology relevant to cognition and personality, motivating a search for relationships between brain architecture and emotional health and well-being. Two approaches were used to asses functional connectivity correlates of emotional health and well-being. The first approach used principal component analysis to evaluate resting-state functional magnetic resonance imaging data from the Human Connectome Project 1200 Subjects Data Release. Pa...
Published on Jun 1, 2019in Hearing Research2.95
Xuesong Li (BIT: Beijing Institute of Technology), Yufei Qiao (Peking Union Medical College Hospital)+ 3 AuthorsHua Guo11
Estimated H-index: 11
(THU: Tsinghua University)
Abstract Growing evidence shows that partial auditory deprivation leads to extensive neural functional plasticity, which occurs not only in the auditory cortex but also in other sensory regions and cognitive areas. However, studies in structural topological properties are still limited, especially those investigating the relationship between structural connectome alterations and auditory abilities. To clarify this, we investigated white matter structural connectivity changes and the relationship...
Published on 2019in Cerebral Cortex5.44
Rongtao Jiang4
Estimated H-index: 4
(CAS: Chinese Academy of Sciences),
Vince D. Calhoun3
Estimated H-index: 3
(Georgia Institute of Technology)
+ 7 AuthorsMing Song16
Estimated H-index: 16
(CAS: Chinese Academy of Sciences)
Published on Jul 5, 2019in Brain Imaging and Behavior3.42
Rongtao Jiang4
Estimated H-index: 4
(CAS: Chinese Academy of Sciences),
Vince Daniel Calhoun1
Estimated H-index: 1
(Georgia Institute of Technology)
+ 8 AuthorsTianzi Jiang63
Estimated H-index: 63
Intelligence is a socially and scientifically interesting topic because of its prominence in human behavior, yet there is little clarity on how the neuroimaging and neurobiological correlates of intelligence differ between males and females, with most investigations limited to using either mass-univariate techniques or a single neuroimaging modality. Here we employed connectome-based predictive modeling (CPM) to predict the intelligence quotient (IQ) scores for 166 males and 160 females separate...
Published on Jan 1, 2019in Frontiers in Neurology2.63
Xuesong Li (BIT: Beijing Institute of Technology), Yuhui Xiong1
Estimated H-index: 1
(THU: Tsinghua University)
+ 7 AuthorsHua Guo11
Estimated H-index: 11
(THU: Tsinghua University)
Parkinson’s disease (PD) is a multi-systemic disease in the brain arising from the dysfunction of several neural networks. The diagnosis and treatment of PD have gained more attention for clinical researchers. While there have been many fMRI studies about functional topological changes of PD patients, whether the dynamic changes of functional connectivity can predict the drug therapy effect is still unclear. The primary objective of this study was to assess whether large-scale functional efficie...
Published on Jun 28, 2019in bioRxiv
Wei Li37
Estimated H-index: 37
(THU: Tsinghua University),
Cathy H. Wu58
Estimated H-index: 58
+ 4 AuthorsDai Zhang25
Estimated H-index: 25
The assessment of personality is crucial not only for scientific inquiries but also for real-world applications. However, most existing ways to quantify personality traits rely on self-reported scales, which are susceptible to biases such as self-presentational concerns, limiting their usefulness in application scenarios such as personnel selection. In this study, we propose and evaluate a novel implicit measure of personality that uses machine learning (ML) algorithms to predict an individual's...
Published on 2019in bioRxiv
Shile Qi4
Estimated H-index: 4
(The Mind Research Network),
Jing Sui28
Estimated H-index: 28
(CAS: Chinese Academy of Sciences)
+ 7 AuthorsDongdong Lin (The Mind Research Network)
ABSTRACT There is growing evidence that rather than using a single brain imaging modality to study its association with physiological or symptomatic features, the field is paying more attention to fusion of multimodal information. However, most current multimodal fusion approaches that incorporate functional magnetic resonance imaging (fMRI) are restricted to second-level 3D features, rather than the original 4D fMRI data. This trade-off is that the valuable temporal information is not utilized ...
View next paperIntrinsic connectivity networks and personality: The temperament dimension harm avoidance moderates functional connectivity in the resting brain