Functional neuroimaging has been a mainstay of human neuroscience for the past 25 years. Interpretation of functional magnetic resonance imaging (fMRI) data has often occurred within knowledge frameworks crafted by experts, which have the potential to amplify biases that limit the replicability of findings. Here, we use a computational approach to derive a data-driven framework for neurobiological domains that synthesizes the texts and data of nearly 20,000 human neuroimaging articles. Across multiple levels of domain specificity, the structure–function links within domains better replicate in held-out articles than those mapped from dominant frameworks in neuroscience and psychiatry. We further show that the data-driven framework partitions the literature into modular subfields, for which domains serve as generalizable prototypes of structure–function patterns in single articles. The approach to computational ontology we present here is the most comprehensive characterization of human brain circuits quantifiable with fMRI and may be extended to synthesize other scientific literatures.

在过去的 25 年中，功能性神经影像学一直是人类神经科学的支柱。功能磁共振成像 (fMRI) 数据的解释通常发生在专家制定的知识框架内，这有可能放大限制研究结果可复制性的偏见。在这里，我们使用计算方法推导出神经生物学领域的数据驱动框架，该框架综合了近 20,000 篇人类神经影像学文章的文本和数据。在多层次的领域特异性中，领域内的结构-功能链接在保留文章中的复制比从神经科学和精神病学中的主要框架映射的更好。我们进一步表明，数据驱动的框架将文献划分为模块化的子领域，对于哪些领域作为单篇文章中结构-功能模式的可推广原型。我们在此介绍的计算本体方法是可使用 fMRI 量化的人脑回路的最全面表征，并且可以扩展到综合其他科学文献。