BrainEnrich: Revealing Biological Insights for Imaging-Derived Phenotypes Through Transcriptomic Enrichment

While the field of imaging transcriptomics is evolving rapidly, several methodological challenges persist in functional enrichment analysis. Here, we introduce BrainEnrich, an R package that integrates whole-brain gene expression profiles from the Allen Human Brain Atlas (AHBA) with in vivo imaging-derived phenotypes (IDPs). By offering a suite of flexible association methods, aggregation strategies, comprehensive lists of predefined gene sets, and both competitive and self-contained null models, the package enables researchers to examine the spatial coupling between molecular profiles and IDPs at both group and individual levels. A novel feature of BrainEnrich is its individual-level enrichment analysis, which mapped individual IDPs onto a molecular coordinate framework, capturing the molecular signature of individual IDPs and enabling a deeper exploration of inter-individual variability. Its statistical power was examined through extensive simulation studies based on linear regression with different combinations of test statistics and null models. The results suggest that with appropriate null models, this approach effectively controlled Type 1 error while retaining sensitivity to detect associations between molecular profiles and phenotypic data. Two case studies were performed to demonstrate the utility of the package. In the group-level enrichment analysis, the effect size map of case-control comparison in cortical thickness of major depressive disorder patients was associated with molecular pathways such as synaptic signaling, lipid regulation, and steroid hormone balance, providing candidate molecular annotations for group-level IDPs. A separate case study found that synaptic gene set scores showed nominal associations with multiple cognitive measures, demonstrating its utility for individual-level molecular annotations to explore associations with phenotypic variables. Collectively, BrainEnrich provides a flexible framework for integrating macro-level IDPs with micro-level transcriptomic profiles for molecular contextualization of IDPs.