Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
Department of Psychology, Stanford University, Stanford, CA, USA.
Nat Neurosci. 2021 Dec;24(12):1733-1744. doi: 10.1038/s41593-021-00948-9. Epub 2021 Nov 11.
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.
功能神经影像学在过去的 25 年里一直是人类神经科学的主要手段。功能磁共振成像（fMRI）数据的解释通常是在专家制定的知识框架内进行的，这些框架有可能放大限制发现可重复性的偏见。在这里，我们使用一种计算方法来为神经生物学领域推导一个数据驱动的框架，该框架综合了近 20000 篇人类神经影像学文章的文本和数据。在多个领域特异性层次上，领域内的结构-功能联系在保留文章中的复制效果要好于那些从神经科学和精神病学的主导框架映射出来的联系。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，对于这些子领域，领域本身就是在单篇文章中可推广的结构-功能模式的原型。我们在这里提出的计算本体论方法是最全面的 fMRI 可量化人类大脑回路的特征描述，并且可以扩展到其他科学文献的综合。
