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

Matelsky, Jordan; Reilly, Elizabeth P.; Johnson, Erik C.; Wester, Brock A.; Gray-Roncal, William

doi:10.1101/2020.06.08.140533

Cited by 4 publications

(4 citation statements)

References 50 publications

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“…The field of neuroscience has long sought a complete "wiring diagram" of the brain. One representation that has grown in popularity is a graph representation, where neurons are represented by nodes, and synapses are represented as directed edges [12], [13]. A dense reconstruction of even a modestly sized 3D volume of neural tissue comprises an enormous amount of manual labor if segmented exclusively by humans [14], [15], and so connectomics researchers generally leverage automated (often machine-learning based) tools to perform many of the key steps of connectome generation [1], [7], [16], [17].…”

Section: A Backgroundmentioning

confidence: 99%

CONFIRMS: A Toolkit for Scalable, Black Box Connectome Assessment and Investigation

Bishop

Matelsky

Wilt

et al. 2021

2021 43rd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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The nanoscale connectomics community has recently generated automated and semi-automated "wiring diagrams" of brain subregions from terabytes and petabytes of dense 3D neuroimagery. This process involves many challenging and imperfect technical steps, including dense 3D image segmentation, anisotropic nonrigid image alignment and coregistration, and pixel classification of each neuron and their individual synaptic connections. As data volumes continue to grow in size, and connectome generation becomes increasingly commonplace, it is important that the scientific community is able to rapidly assess the quality and accuracy of a connectome product to promote dataset analysis and reuse. In this work, we share our scalable toolkit for assessing the quality of a connectome reconstruction via targeted inquiry and large-scale graph analysis, and to provide insights into how such connectome proofreading processes may be improved and optimized in the future. We illustrate the applications and ecosystem on a recent reference dataset.Clinical relevance-Large-scale electron microscopy (EM) data offers a novel opportunity to characterize etiologies and neurological diseases and conditions at an unprecedented scale. EM is useful for low-level analyses such as biopsies; this increased scale offers new possibilities for research into areas such as neural networks if certain bottlenecks and problems are overcome.

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Section: A Backgroundmentioning

confidence: 99%

CONFIRMS: A Toolkit for Scalable, Black Box Connectome Assessment and Investigation

Bishop

Matelsky

Wilt

et al. 2021

2021 43rd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

Self Cite

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“…We replicated the motif atlas-scan methods previously applied to partial mouse and invertebrate connectomes (7). This atlas-scan procedure involves searching for and counting all undirected subgraphs up to a certain size (here, subgraphs with six vertices or fewer).…”

Section: Undirected Motif Atlas Scans Of the Growing C Elegans Connectome Reveal A Changing Local Structurementioning

confidence: 99%

Circuit motifs and graph properties of connectome development in C. elegans

Matelsky¹,

Norman-Tenazas²,

Davenport³

et al. 2021

Preprint

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Network science is a powerful tool that can be used to better explore the complex structure of brain networks. Leveraging graph and motif analysis tools, we interrogate C. elegans connectomes across multiple developmental time points and compare the resulting graph characteristics and substructures over time. We show the evolution of the networks and highlight stable invariants and patterns as well as those that grow or decay unexpectedly, providing a substrate for additional analysis.

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“…DrosoBOT is built to be modular and enables different types of feedback loops to be integrated for such queries. Other types of circuit motifs [20] can also be added to our programmable ontology to facilitate the construction of brain circuits that have functional significance.…”

Section: Exploration Of the Feedback Circuits Of The Fruit Fly Early Olfactory System With Neuronlp++mentioning

confidence: 99%

“…Concluding, DrosoBOT is built to be modular and enables different types of feedback loops to be added to our programmable ontology to facilitate the construction and visualization of circuit motifs [20]. NeuroNLP++ represents a step towards a more intuitive and natural way of extracting information from large connectome/synaptome datasets that are relevant for the in-depth study of the functional logic of brain circuits.…”

Section: Exploring the Morphology Of Cell-types And Feedback Circuitsmentioning

confidence: 99%

A Programmable Ontology Encompassing the Functional Logic of the Drosophila Brain

Lazar

Türkcan

Zhou

2021

Preprint

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The Drosophila brain has only a fraction of the number of neurons of higher organisms such as mice. Yet the sheer complexity of its neural circuits recently revealed by large connectomics datasets suggests that computationally modeling the function of fruit fly brain at this scale posits significant challenges. To address these challenges, we present here a programmable ontology that expands the scope of the current Drosophila brain anatomy ontologies to encompass the functional logic of the fly brain. The programmable ontology provides a language not only for defining functional circuit motifs but also for programmatically exploring their functional logic. To achieve this goal, we tightly integrated the programmable ontology with the workflow of the interactive FlyBrainLab computing platform. As part of the programmable ontology, we developed NeuroNLP++, a web application that supports free-form English queries for constructing functional brain circuits fully anchored on the available connectome/synaptome datasets, and the published worldwide literature. In addition, we present a methodology for including a model of the space of odorants into the programmable ontology, and for modeling olfactory sensory circuits of the antenna of the fruit fly brain that detect odorant sources. Furthermore, we describe a methodology for modeling the functional logic of the antennal lobe circuit consisting of massive local feedback loops, a characteristic feature observed across Drosophila brain regions. Finally, using a circuit library, we demonstrate the power of our methodology for interactively exploring the functional logic of the massive number of feedback loops in the antennal lobe.

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

Cited by 4 publications

References 50 publications

CONFIRMS: A Toolkit for Scalable, Black Box Connectome Assessment and Investigation

CONFIRMS: A Toolkit for Scalable, Black Box Connectome Assessment and Investigation

Circuit motifs and graph properties of connectome development in C. elegans

A Programmable Ontology Encompassing the Functional Logic of the Drosophila Brain

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