Task-induced brain connectivity promotes the detection of individual differences in brain-behavior relationships
Task-induced brain connectivity promotes the detection of individual differences in brain-behavior relationships
复制标题
任务引起的大脑连接促进了大脑行为关系中个体差异的检测。
DOI：
10.1016/j.neuroimage.2019.116370
复制
发表时间：
2020-02-15
期刊：
影响因子：
5.7
通讯作者：
Sui, Jing
中科院分区：
文献类型：
--
作者：
Jiang, Rongtao;Zuo, Nianming;Sui, Jing
尽管休息和任务诱导的功能连接（FC）都已被用来表征人类大脑和认知能力，但任务诱导的功能连接在扫描仪外认知特征的个性化预测中的潜力仍然很大程度上尚未被探索。格林等人最近的一项研究。 (2018) 使用来自休息和多个任务条件的 FC 预测了流体智力分数，这表明任务诱导的大脑状态操纵改善了对个体特征的预测。在这里，我们使用包含来自休息和 7 个不同任务条件的 fMRI 数据的大型数据集，通过采用不同的机器学习方法来复制原始研究，并应用该方法来预测两种与阅读理解相关的认知测量。与他们的发现一致，我们发现基于任务的机器学习模型通常优于基于休息的模型。我们还观察到，结合多任务功能磁共振成像可以提高预测性能，但是，结合更多的功能磁共振成像条件并不一定能确保更好的预测。与休息相比，来自语言和工作记忆任务的预测 FC 在主要默认模式和额顶网络中具有更强的预测能力。此外，预测模型表现出高度稳定性，可以在不同的认知状态下推广。总之，这项复制研究强调了使用基于任务的 FC 来揭示大脑行为关系的好处，这可能会赋予更多的预测能力，并促进检测相关认知特征背后的连接模式的个体差异，为原始研究结果的有效性和稳健性提供有力的证据。
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.