Task-induced brain connectivity promotes the detection of individual differences in brain-behavior relationships
文献类型:期刊论文
作者 | Jiang, Rongtao8,9,10; Zuo, Nianming8,9; Ford, Judith M.11,12; Qi, Shile2; Zhi, Dongmei8,9,10; Zhuo, Chuanjun3,4; Xu, Yong5; Fu, Zening2; Bustillo, Juan6; Turner, Jessica A.1,2 |
刊名 | NEUROIMAGE |
出版日期 | 2020-02-15 |
卷号 | 207页码:11 |
ISSN号 | 1053-8119 |
关键词 | Individualized prediction Reading comprehension Task state Functional connectivity Cognitive demand |
DOI | 10.1016/j.neuroimage.2019.116370 |
通讯作者 | Calhoun, Vince D.(vcalhoun@gsu.edu) ; Sui, Jing(jing.sui@nlpr.ia.ac.cn) |
英文摘要 | 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. |
WOS关键词 | FUNCTIONAL CONNECTIVITY ; STATE ; CONNECTOME ; NETWORK ; CORTEX ; ARCHITECTURES ; ORGANIZATION ; PREDICTION ; REGRESSION ; COGNITION |
资助项目 | National Key Research and Development Program of China[2017YFC0112000] ; China Natural Science Foundation[61773380] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32040100] ; Brain Science and Brain-inspired Technology Plan of Beijing City[Z181100001518005] ; National Institutes of Health[R01EB020407] ; National Institutes of Health[1R01EB005846] ; National Institutes of Health[1R56MH117107] ; National Institutes of Health[1R01MH094524] ; National Institutes of Health[P20GM103472] ; National Institutes of Health[P30GM122734] ; National Science Foundation[1539067] ; 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 |
语种 | 英语 |
出版者 | ACADEMIC PRESS INC ELSEVIER SCIENCE |
WOS记录号 | WOS:000509662600049 |
资助机构 | National Key Research and Development Program of China ; China Natural Science Foundation ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Brain Science and Brain-inspired Technology Plan of Beijing City ; National Institutes of Health ; National Science Foundation ; 16 National Institutes of Health (NIH) Institutes and Centers ; McDonnell Center for Systems Neuroscience at Washington University |
源URL | [http://ir.ia.ac.cn/handle/173211/29517] |
专题 | 自动化研究所_脑网络组研究中心 |
通讯作者 | Calhoun, Vince D.; Sui, Jing |
作者单位 | 1.Georgia State Univ, Dept Psychol & Neurosci, Atlanta, GA 30302 USA 2.Emory Univ, Georgia State Univ, Triinst Ctr Translat Res Neuroimaging & Data Sci, Georgia Inst Technol, Atlanta, GA 30303 USA 3.Nankai Univ, Tianjin Mental Hlth Ctr, Dept Psychiat Neuroimaging Genet, Affiliated Anding Hosp, Tianjin 300222, Peoples R China 4.Nankai Univ, Tianjin Mental Hlth Ctr, Morbid Lab PNGC Lab, Affiliated Anding Hosp, Tianjin 300222, Peoples R China 5.Shanxi Med Univ, Dept Psychiat, Hosp 1, Taiyuan 030001, Peoples R China 6.Univ New Mexico, Dept Psychiat, Albuquerque, NM 87131 USA 7.Chinese Acad Sci, Inst Automat, Ctr Excellence Brain Sci, Beijing, Peoples R China 8.Chinese Acad Sci, Brainnetome Ctr, Inst Automat, Beijing 100190, Peoples R China 9.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 10.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
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,2020,207:11. |
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,207,11. |
MLA | Jiang, Rongtao,et al."Task-induced brain connectivity promotes the detection of individual differences in brain-behavior relationships".NEUROIMAGE 207(2020):11. |
入库方式: OAI收割
来源:自动化研究所
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