Connectome subgraph isomorphisms and graph queries with DotMotif

As connectomics datasets continue to grow in size and complexity, methods to search for and identify interesting graph patterns offer a promising approach to quickly reduce data dimensionality and enable discovery. Recent advances in neuroscience have enabled brain structure exploration at the level of individual synaptic connections. These graphs are often too large to be analyzed manually, presenting significant barriers to searching for structure and testing hypotheses. We combine graph database and analysis libraries with an easy-to-use neuroscience grammar suitable for rapidly constructing queries and searching for subgraphs and patterns of interest. This abstracts many of the computer science and graph theory challenges associated with nanoscale brain network analysis and allows scientists to quickly achieve reproducible findings at scale. We demonstrate these tools to search for motifs on simulated data and real public connectomics datasets, and share simple and complex structures relevant to the neuroscience community. We contextualize these results and provide case studies to motivate future neuroscience questions. All of our tools are released open source to empower other scientists to use and extend these methods.