Task-induced brain connectivity promotes the detection of individual differences in brain-behavior relationships | |
Jiang, Rongtao1,2,3; Zuo, Nianming1,2; Ford, Judith M.4,5; Qi, Shile6; Zhi, Dongmei1,2,3; Zhuo, Chuanjun7,8; Xu, Yong9; Fu, Zening6; Bustillo, Juan10; Turner, Jessica A.6,11; Calhoun, Vince D.6; Sui, Jing1,2,3,6,12 | |
发表期刊 | NEUROIMAGE |
ISSN | 1053-8119 |
2020-02-15 | |
卷号 | 207期号:116370页码:11 |
摘要 | 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 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 traits. Here, using a large dataset incorporating fMRI data from rest and 7 distinct task conditions, we replicated the original study by employing a different machine learning approach, and applying the method to predict two reading comprehension-related cognitive measures. Consistent with their findings, we found that task-based machine learning models often outperformed rest-based models. We also observed that combining multi-task fMRI improved prediction performance, yet, integrating the more fMRI conditions can not necessarily ensure better predictions. Compared with rest, the predictive FCs derived from language and working memory tasks were highlighted with more predictive power in predominantly default mode and frontoparietal networks. Moreover, prediction models demonstrated high stability to be generalizable across distinct cognitive states. Together, this replication study highlights the benefit of using task-based FCs to reveal brain-behavior relationships, which may confer more predictive power and promote the detection of individual differences of connectivity patterns underlying relevant cognitive traits, providing strong evidence for the validity and robustness of the original findings. |
关键词 | Individualized prediction Reading comprehension Task state Functional connectivity Cognitive demand |
DOI | 10.1016/j.neuroimage.2019.116370 |
关键词[WOS] | FUNCTIONAL CONNECTIVITY ; STATE ; CONNECTOME ; NETWORK ; CORTEX ; ARCHITECTURES ; ORGANIZATION ; PREDICTION ; REGRESSION ; COGNITION |
收录类别 | SCI |
语种 | 英语 |
资助项目 | McDonnell Center for Systems Neuroscience at Washington University ; 16 National Institutes of Health (NIH) Institutes and Centers ; National Institutes of Health[P30GM122734] ; National Institutes of Health[1R01MH094524] ; National Institutes of Health[1R56MH117107] ; National Institutes of Health[1R01EB005846] ; Brain Science and Brain-inspired Technology Plan of Beijing City[Z181100001518005] ; National Key Research and Development Program of China[2017YFC0112000] ; National Institutes of Health[R01EB020407] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32040100] ; National Science Foundation[1539067] ; China Natural Science Foundation[61773380] ; National Institutes of Health[P20GM103472] ; National Institutes of Health[P20GM103472] ; China Natural Science Foundation[61773380] ; National Science Foundation[1539067] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32040100] ; National Institutes of Health[R01EB020407] ; National Key Research and Development Program of China[2017YFC0112000] ; Brain Science and Brain-inspired Technology Plan of Beijing City[Z181100001518005] ; National Institutes of Health[1R01EB005846] ; National Institutes of Health[1R56MH117107] ; National Institutes of Health[1R01MH094524] ; National Institutes of Health[P30GM122734] ; 16 National Institutes of Health (NIH) Institutes and Centers ; McDonnell Center for Systems Neuroscience at Washington University |
WOS研究方向 | Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging |
WOS类目 | Neurosciences ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging |
WOS记录号 | WOS:000509662600049 |
出版者 | ACADEMIC PRESS INC ELSEVIER SCIENCE |
七大方向——子方向分类 | 脑网络分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/29518 |
专题 | 脑网络组研究 |
通讯作者 | Calhoun, Vince D.; Sui, Jing |
作者单位 | 1.Chinese Acad Sci, Brainnetome Ctr, Inst Automat, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Univ Calif San Francisco, Dept Psychiat, San Francisco, CA 94143 USA 5.San Francisco VA Med Ctr, San Francisco, CA 94143 USA 6.Emory Univ, Georgia State Univ, Triinst Ctr Translat Res Neuroimaging & Data Sci, Georgia Inst Technol, Atlanta, GA 30303 USA 7.Nankai Univ, Tianjin Mental Hlth Ctr, Dept Psychiat Neuroimaging Genet, Affiliated Anding Hosp, Tianjin 300222, Peoples R China 8.Nankai Univ, Tianjin Mental Hlth Ctr, Morbid Lab PNGC Lab, Affiliated Anding Hosp, Tianjin 300222, Peoples R China 9.Shanxi Med Univ, Dept Psychiat, Hosp 1, Taiyuan 030001, Peoples R China 10.Univ New Mexico, Dept Psychiat, Albuquerque, NM 87131 USA 11.Georgia State Univ, Dept Psychol & Neurosci, Atlanta, GA 30302 USA 12.Chinese Acad Sci, Inst Automat, Ctr Excellence Brain Sci, Beijing, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室; 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Jiang, Rongtao,Zuo, Nianming,Ford, Judith M.,et al. Task-induced brain connectivity promotes the detection of individual differences in brain-behavior relationships[J]. NEUROIMAGE,2020,207(116370):11. |
APA | Jiang, Rongtao.,Zuo, Nianming.,Ford, Judith M..,Qi, Shile.,Zhi, Dongmei.,...&Sui, Jing.(2020).Task-induced brain connectivity promotes the detection of individual differences in brain-behavior relationships.NEUROIMAGE,207(116370),11. |
MLA | Jiang, Rongtao,et al."Task-induced brain connectivity promotes the detection of individual differences in brain-behavior relationships".NEUROIMAGE 207.116370(2020):11. |
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