DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queries

Abstract

Recent advances in neuroscience have enabled the exploration of brain structure at the level of individual synaptic connections. These 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. 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. Our approach abstracts many of the computer science and graph theory challenges associated with nanoscale brain network analysis and allows scientists to quickly conduct research at scale. We demonstrate the utility of these tools by searching for motifs on simulated data and real public connectomics datasets, and we share simple and complex structures relevant to the neuroscience community. We contextualize our findings and provide case studies and software to motivate future neuroscience exploration.

Scalable graph analysis tools for the connectomics community

“…The Gran-dIso algorithm isolates this memory cost in a single, onedimensional queue data structure. In the original GrandIso tool (written for the NetworkX Python library ( 19)), the queue resides in memory (7). This makes it extremely fast, but it also means that the total size of the motif search task is limited by the RAM of the machine.…”

“…Motif Studio has two primary windows — a query design window, and a query running window. In the editing view ( Figure 3 ), the user can edit a motif description on the left using the DotMotif language (7).…”

Connectomics Annotation Metadata Standardization for Increased Accessibility and Queryability

“…As such, new metadata standards must be built around the core knowledge products extracted from neurons, synapses, and their relationships (e.g., connectivity). Further, because user needs for data processing are diverse, standards must be conducive to common nanoscale connectomics research questions, such as those pertaining to location, topology, morphology, and cell types (LaGrow et al, 2018 ) as well as those surrounding connectivity at a local or circuit level (Matelsky et al, 2021 ) and even at higher-levels pertaining to brain regions and white matter tracts (Sporns et al, 2004 ; Bassett and Bullmore, 2006 ).…”

“…One benefit of annotation standards is the potential for mitigation of this challenge through design and modification of software tools to build upon annotation standards. Visualization and querying software such as Neuroglancer (Maitin-Shepard, 2020 ), Neuromorpho (Ascoli et al, 2007 ), DotMotif (Matelsky et al, 2021 ), Webknossos (Boergens et al, 2017 ), NeuPrint (Clements et al, 2020 ), and others (Yatsenko et al, 2015 ) can be modified to support community-developed annotation standards and even integrated into a standards-supported, centralized discovery portal geared toward users without extensive computational backgrounds. Such a centralized connectomics discovery platform that allows exploration of datasets across imaging modalities, organisms, and institutions, is an exciting prospect, and is most feasible once metadata standardization is adopted.…”

Ramsey’s Theory Meets The Human Brain Connectome

“…These networks are described at different coarse-grained scales, from single neurons to neuronal populations and nervous tissue's macroscale, corresponding to the various levels of spatial resolution of the currently available imaging techniques. Although the nervous networks' graphs are often too large to be manually analysed, advanced methods such as graph database and analysis libraries have been combined to search for and identify interesting subgraph patterns and motifs inside the ever-increasing amount of connectomics datasets (Matelsky et al, 2021). Given these premises, we will at first provide a gentle introduction to RAM and its theorems, then we will describe how RAM can be used to look for unknown, orderly substructures with regular properties hidden in the human connectome.…”