Recent task fMRI studies suggest that individual differences in trait empathy and empathic concern are mediated by patterns of connectivity between self-other resonance and top-down control networks that are stable across task demands. An untested implication of this hypothesis is that these stable patterns of connectivity should be visible even in the absence of empathy tasks. Using machine learning, we demonstrate that patterns of resting state fMRI connectivity (i.e. the degree of synchronous BOLD activity across multiple cortical areas in the absence of explicit task demands) of resonance and control networks predict trait empathic concern (n = 58). Empathic concern was also predicted by connectivity patterns within the somatomotor network. These findings further support the role of resonance-control network interactions and of somatomotor function in our vicariously driven concern for others. Furthermore, a practical implication of these results is that it is possible to assess empathic predispositions in individuals without needing to perform conventional empathy assessments.

中文翻译：

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从静息状态大脑连接预测同理心：一种多元方法。

最近的任务功能磁共振成像研究表明，特质共情和共情关注的个体差异是由自我他人共振和自上而下的控制网络之间的连接模式介导的，这些连接模式在任务需求中保持稳定。该假设的一个未经检验的含义是，即使在没有同理心任务的情况下，这些稳定的连接模式也应该是可见的。使用机器学习，我们证明了共振和控制网络的静息态 fMRI 连接模式（即在没有明确任务要求的情况下跨多个皮质区域的同步 BOLD 活动的程度）可以预测特质共情关注（n = 58）。体感关注也可以通过躯体运动网络内的连接模式来预测。这些发现进一步支持了共振控制网络相互作用和躯体运动功能在我们间接驱动的对他人的关心中的作用。此外，这些结果的实际意义是，可以评估个体的共情倾向，而无需进行传统的共情评估。