PDF(Pubmed)
Abstract：
Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities.
摘要：
从大脑功能连接（FC）模式预测个体行为有助于我们对人脑功能的理解。如果预测是基于从外接，先验定义的功能网络，这提高了可解释性。此外，一些证据表明,与静息状态的FC数据相比,基于任务的FC数据可能产生更成功的行为预测.这里,我们全面研究了功能网络先验和任务状态与行为目标域的对应关系在多大程度上影响了认知中个体表现的可预测性，社会,和情感任务。为此,我们使用HumanConnectome项目的数据对个人工作记忆能力(WM)进行大规模样本外预测，心理理论认知(社会)，和来自相应和非相应状态（WM/社会/EMO/静息状态）和网络（WM/社会/EMO/全脑连接体）的FC的情感处理（EMO）。使用均方根误差和确定系数来评估模型拟合表明，预测性能总体上相当差。在特定任务网络中，来自全脑FC的预测略好于来自FC的预测，并且在WM域中观察到基于任务与静息状态的FC预测的轻微益处。除此之外，我们没有发现网络通信的任何重大影响，任务状态,和性能域。一起,这些结果表明，在任务状态和静息状态下的多变量FC模式包含的关于个体表现水平的信息很少，呼吁重新考虑大脑如何调节心理能力的个体差异。
