Tools for multi-scale, multi-modal annotation of brain networks

Imaging technologies are increasingly used to generate high-resolution reference maps of brain structure and function. Modern scientific discovery relies on making comparisons between new maps (e.g. task activations, group structural differences) and these reference maps.  Although recent data sharing initiatives have increased the accessibility of such brain maps, data are often shared in disparate coordinate systems (or ``spaces''), precluding systematic and accurate comparisons among them. Here I will describe the main challenges and proposed solutions for a more integrative approach to interpreting brain maps. I will introduce new methods for accessing, transforming, and analyzing structural and functional brain annotations. Our initial efforts have yielded multiple curated reference maps and biological ontologies of the human brain, including maps of gene expression, neurotransmitter receptors, metabolism, neurophysiological oscillations, developmental and evolutionary expansion, functional hierarchy, individual functional variability, and cognitive specialization. We have also implemented multiple methods to generate high-quality transformations between four standard coordinate systems commonly used in neuroimaging research. Finally, we provide robust quantitative assessment of map-to-map similarity via a suite of spatial autocorrelation-preserving null models. Altogether, these methods combine open-access data with transparent functionality for standardizing and comparing brain maps, providing a systematic workflow for comprehensive structural and functional annotation enrichment analysis of the human brain.