Speech production relies on the orchestrated control of multiple brain regions. The specific, directional influences within these networks remain poorly understood. We used regression dynamic causal modelling to infer the whole-brain directed (effective) connectivity from functional magnetic resonance imaging data of 36 healthy individuals during the production of meaningful English sentences and meaningless syllables. We identified that the two dynamic connectomes have distinct architectures that are dependent on the complexity of task production. The speech was regulated by a dynamic neural network, the most influential nodes of which were centred around superior and inferior parietal areas and influenced the whole-brain network activity via long-ranging coupling with primary sensorimotor, prefrontal, temporal and insular regions. By contrast, syllable production was controlled by a more compressed, cost-efficient network structure, involving sensorimotor cortico-subcortical integration via superior parietal and cerebellar network hubs. These data demonstrate the mechanisms by which the neural network reorganizes the connectivity of its influential regions, from supporting the fundamental aspects of simple syllabic vocal motor output to multimodal information processing of speech motor output.

This article is part of the theme issue ‘Vocal learning in animals and humans’.

语音产生依赖于多个大脑区域的协调控制。这些网络中具体的、方向性的影响仍然知之甚少。我们使用回归动态因果模型从 36 名健康个体的功能磁共振成像数据中推断出在有意义的英语句子和无意义的音节产生过程中的全脑定向（有效）连接。我们发现这两个动态连接组具有不同的架构，取决于任务生产的复杂性。语音由一个动态神经网络调节，其最有影响的节点集中在上顶叶和下顶叶区域，并通过与初级感觉运动、前额叶、颞叶和岛叶区域的长程耦合影响全脑网络活动。相比之下，音节的产生由更压缩、更具成本效益的网络结构控制，包括通过优越的顶叶和小脑网络枢纽进行感觉运动皮层 - 皮层下的整合。这些数据展示了神经网络重组其影响区域的连通性的机制，从支持简单音节发声运动输出的基本方面到语音运动输出的多模态信息处理。

这篇文章是主题问题“动物和人类的声乐学习”的一部分。