The mechanisms linking the brain’s network structure to cognitively relevant activation patterns remain largely unknown. Here, by leveraging principles of network control, we show how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic database. Specifically, we systematically integrated large-scale multimodal neuroimaging data from functional magnetic resonance imaging, diffusion tractography, cortical morphometry and positron emission tomography to simulate how anatomically guided transitions between cognitive states can be reshaped by neurotransmitter engagement or by changes in cortical thickness. Our model incorporates neurotransmitter-receptor density maps (18 receptors and transporters) and maps of cortical thickness pertaining to a wide range of mental health, neurodegenerative, psychiatric and neurodevelopmental diagnostic categories (17,000 patients and 22,000 controls). The results provide a comprehensive look-up table charting how brain network organization and chemoarchitecture interact to manifest different cognitive topographies, and establish a principled foundation for the systematic identification of ways to promote selective transitions between cognitive topographies.

将大脑网络结构与认知相关激活模式联系起来的机制在很大程度上仍然未知。在这里，通过利用网络控制原理，我们展示了人类连接组的架构如何在来自 NeuroSynth 元分析数据库的 123 个实验定义的认知激活图（认知拓扑）之间进行转换。具体来说，我们系统地整合了来自功能磁共振成像、扩散纤维束成像、皮质形态测量和正电子发射断层扫描的大规模多模态神经影像数据，以模拟如何通过神经递质参与或皮质厚度的变化来重塑解剖学引导的认知状态之间的转变。我们的模型包含与广泛的心理健康、神经退行性、精神病学和神经发育诊断类别（17,000 名患者和 22,000 名对照）相关的神经递质-受体密度图（18 种受体和转运蛋白）和皮质厚度图。结果提供了一个全面的查找表，描绘了大脑网络组织和化学结构如何相互作用以表现不同的认知拓扑，并为系统识别促进认知拓扑之间选择性转换的方法奠定了原则基础。