TY - JOUR
T1 - Task-induced brain connectivity promotes the detection of individual differences in brain-behavior relationships
AU - Jiang, Rongtao
AU - Zuo, Nianming
AU - Ford, Judith M.
AU - Qi, Shile
AU - Zhi, Dongmei
AU - Zhuo, Chuanjun
AU - Xu, Yong
AU - Fu, Zening
AU - Bustillo, Juan
AU - Turner, Jessica A.
AU - Calhoun, Vince D.
AU - Sui, Jing
N1 - Publisher Copyright:
© 2019
PY - 2020/2/15
Y1 - 2020/2/15
N2 - 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.
AB - 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.
KW - Cognitive demand
KW - Functional connectivity
KW - Individualized prediction
KW - Reading comprehension
KW - Task state
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U2 - 10.1016/j.neuroimage.2019.116370
DO - 10.1016/j.neuroimage.2019.116370
M3 - Article
C2 - 31751666
AN - SCOPUS:85075830542
SN - 1053-8119
VL - 207
JO - NeuroImage
JF - NeuroImage
M1 - 116370
ER -