A data-driven framework for mapping domains of human neurobiology

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. Beam et al. created a data-driven mapping of human brain function, drawing on full texts and coordinate data reported in neuroimaging studies. This validated framework outperformed leading and widely used knowledge frameworks, namely Research Domain Criteria (RDoC) and the Diagnostic and Statistical Manual of Mental Disorders (DSM).

Automated summary

A data-driven framework for understanding human brain circuits was developed using computational methods applied to a large number of neuroimaging articles. This framework outperformed traditional knowledge frameworks in explaining the relationships between brain structure and function. The approach divided the literature into modular subfields, providing insights into key structure-function patterns.