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Gender Differences in Connectome-based Predictions of Individualized Intelligence Quotient and Sub-domain Scores

Published on Jul 29, 2019in Cerebral Cortex5.437
路 DOI :10.1093/cercor/bhz134
Rongtao Jiang6
Estimated H-index: 6
(CAS: Chinese Academy of Sciences),
Vince D. Calhoun93
Estimated H-index: 93
(Georgia Institute of Technology)
+ 10 AuthorsJing Sui31
Estimated H-index: 31
Abstract
  • References (72)
  • Citations (4)
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References72
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#1Angus Ho Ching Fong (Yale University)H-Index: 1
#2Kwangsun Yoo (Yale University)H-Index: 11
Last. Marvin M. Chun (Yale University)H-Index: 62
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Abstract Dynamic functional connectivity (DFC) aims to maximize resolvable information from functional brain scans by considering temporal changes in network structure. Recent work has demonstrated that static, i.e. time-invariant resting-state and task-based FC predicts individual differences in behavior, including attention. Here, we show that DFC predicts attention performance across individuals. Sliding-window FC matrices were generated from fMRI data collected during rest and attention task...
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#2Dustin ScheinostH-Index: 28
Last. Kathleen M. CarrollH-Index: 77
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Objective:The authors sought to identify a brain-based predictor of cocaine abstinence by using connectome-based predictive modeling (CPM), a recently developed machine learning approach. CPM is a ...
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#1Abigail S. Greene (Yale University)H-Index: 3
#2Siyuan Gao (Yale University)H-Index: 4
Last. R. Todd Constable (Yale University)H-Index: 84
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Recent work has begun to relate individual differences in brain functional organization to human behaviors and cognition, but the best brain state to reveal such relationships remains an open question. In two large, independent data sets, we here show that cognitive tasks amplify trait-relevant individual differences in patterns of functional connectivity, such that predictive models built from task fMRI data outperform models built from resting-state fMRI data. Further, certain tasks consistent...
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#1Jing SuiH-Index: 31
#2Shile Qi (CAS: Chinese Academy of Sciences)H-Index: 5
Last. Vince D. Calhoun (UNM: University of New Mexico)H-Index: 93
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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...
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#1Rongtao Jiang (CAS: Chinese Academy of Sciences)H-Index: 6
#2Vince D. Calhoun (UNM: University of New Mexico)H-Index: 93
Last. Jing Sui (CAS: Chinese Academy of Sciences)H-Index: 31
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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 rea...
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#1Erhan Gen莽 (RUB: Ruhr University Bochum)H-Index: 11
#2Christoph Fraenz (RUB: Ruhr University Bochum)H-Index: 3
Last. Rex E. Jung (UNM: University of New Mexico)H-Index: 2
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Previous research has demonstrated that individuals with higher intelligence are more likely to have larger gray matter volume in brain areas predominantly located in parieto-frontal regions. These findings were usually interpreted to mean that individuals with more cortical brain volume possess more neurons and thus exhibit more computational capacity during reasoning. In addition, neuroimaging studies have shown that intelligent individuals, despite their larger brains, tend to exhibit lower r...
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#1Julien Dubois (Cedars-Sinai Medical Center)H-Index: 13
#2Paola Galdi (Edin.: University of Edinburgh)H-Index: 5
Last. Ralph Adolphs (California Institute of Technology)H-Index: 94
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Individual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, because it is the single best predictor of long-term life success. The most replicated neural correlate of human intelligence to date is total brain...
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#1Chandra Sripada (UM: University of Michigan)H-Index: 26
#2Mike Angstadt (UM: University of Michigan)H-Index: 34
Last. Saige Rutherford (UM: University of Michigan)H-Index: 4
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Identifying brain-based markers of general cognitive ability, i.e., "intelligence", has been a longstanding goal of cognitive and clinical neuroscience. Previous studies focused on relatively static, enduring features of the brain such as gray matter volume and white matter structure. In this report, we investigate prediction of intelligence based on task activation patterns during the N-back working memory task as well as six other tasks in the Human Connectome Project dataset, encompassing 19 ...
6 CitationsSource
#1Zhaowen Liu (Xidian University)H-Index: 5
#2Jie Zhang (Fudan University)H-Index: 24
Last. Jianfeng FengH-Index: 37
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Abstract Creative thinking plays a vital role in almost all aspects of human life. However, little is known about the neural and genetic mechanisms underlying creative thinking. Based on a cross-validation based predictive framework, we searched from the whole-brain connectome (34,716 functional connectivities) and whole genome data (309,996 SNPs) in two datasets (all collected by Southwest University, Chongqing) consisting of altogether 236 subjects, for a better understanding of the brain and ...
18 CitationsSource
#1Rongtao Jiang (CAS: Chinese Academy of Sciences)H-Index: 6
#2Christopher C. Abbott (UNM: University of New Mexico)H-Index: 13
Last. Vince D. Calhoun (UNM: University of New Mexico)H-Index: 93
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SMRI Biomarkers Predict Electroconvulsive Treatment Outcomes: Accuracy with Independent Data Sets
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#1Jing SuiH-Index: 31
#2Rongtao JiangH-Index: 6
Last. Vince D. CalhounH-Index: 93
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Abstract The neuroimaging community has witnessed a paradigm shift in biomarker discovery from using traditional univariate brain mapping approaches to multivariate predictive models, allowing the field to move towards a translational neuroscience era. Regression-based multivariate models (hereafter "predictive modeling") provide a powerful and widely-used approach to predict human behavior with neuroimaging features. These studies maintain a focus on decoding individual differences in a continu...
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#1Jing Sui (CAS: Chinese Academy of Sciences)H-Index: 31
#2Rongtao Jiang (CAS: Chinese Academy of Sciences)H-Index: 6
Last. Vince D. Calhoun (Georgia Institute of Technology)H-Index: 93
view all 4 authors...
The neuroimaging community has witnessed a paradigm shift in biomarker discovery from using traditional univariate brain mapping approaches to multivariate predictive models, allowing the field to move towards a translational neuroscience era. Regression-based multivariate models (hereafter "predictive modeling") provide a powerful and widely-used approach to predict human behavior with neuroimaging features. These studies maintain a focus on decoding individual differences in a continuously beh...
Source
#1Rongtao Jiang (CAS: Chinese Academy of Sciences)H-Index: 6
#2Nianming Zuo (CAS: Chinese Academy of Sciences)H-Index: 7
Last. Jing SuiH-Index: 31
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Abstract Although both resting and task-induced functional connectivity (FC) have been used to characterize the human brain and cognitive abilities, the potential of task-induced FCs in individualized prediction for out-of-scanner cognitive traits remains largely unexplored. A recent study Greene et聽al. (2018) predicted the fluid intelligence scores using FCs derived from rest and multiple task conditions, suggesting that task-induced brain state manipulation improved prediction of individual tr...
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#1Hailun Sun (CAS: Chinese Academy of Sciences)H-Index: 1
#2Rongtao Jiang (CAS: Chinese Academy of Sciences)H-Index: 6
Last. Jing SuiH-Index: 31
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Abstract Electroconvulsive therapy (ECT) works rapidly and has been widely used to treat depressive disorders (DEP). However, identifying biomarkers predictive of response to ECT remains a priority to individually tailor treatment and understand treatment mechanisms. This study used a connectome-based predictive modeling (CPM) approach in 122 patients with DEP to determine if pre-ECT whole-brain functional connectivity (FC) predicts depressive rating changes and remission status after ECT (47 of...
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#1Luigi A. Maglanoc (University of Oslo)H-Index: 3
#2Tobias Kaufmann (Oslo University Hospital)H-Index: 23
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Abstract Background Mental disorders and individual characteristics such as intelligence and personality are complex traits sharing a largely unknown neuronal basis. Their genetic architectures are highly polygenic and overlapping, which is supported by heterogeneous phenotypic expression and substantial clinical overlap. Brain network analysis provides a non-invasive means of dissecting biological heterogeneity yet its sensitivity, specificity and validity in assessing individual characteristic...
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#1Lijiang Wei (Capital Medical University)
#1Lijiang Wei (Capital Medical University)
Last. Haiyun Li (Capital Medical University)
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Resting state functional connectivity records enormous functional interaction information between any pair of brain nodes, which enriches the prediction of individual phenotypes. To reduce the high dimensional features in prediction, correlation analysis is a common way for feature selection. However, rs-fMRI signal exhibits typically low signal-to-noise ratio and correlation analysis is sensitive to outliers and data distribution, which may bring unstable and uninformative features to subsequen...
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