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MoMo - Combining Neuron Morphology and Connectivity for Interactive Motif Analysis in Connectomes.

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    MoMo is a new tool for analyzing brain structure (connectomics). It visualizes neuron shapes and connections, enabling researchers to find fundamental processing units in brain maps.

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    Area of Science:

    • Neuroscience
    • Computational Neuroscience
    • Graph Theory

    Background:

    • Connectomics maps brain structure at synapse resolution.
    • Motif analysis identifies recurring patterns in brain networks.
    • Current tools often ignore neuron morphology, oversimplifying complex brain data.

    Purpose of the Study:

    • Introduce MoMo, an interactive visualization framework.
    • Enable analysis of morphology-aware motifs in large connectome graphs.
    • Address limitations of existing tools in representing neuronal complexity.

    Main Methods:

    • Developed an advanced graph data structure integrating neuronal morphology and synaptic connectivity.
    • Implemented efficient, parallel subgraph isomorphism searches for interactive queries.
    • Created a sketch-based interface for intuitive exploration of morphology-based motifs.

    Main Results:

    • MoMo allows interactive motif searches on large connectomes.
    • Results are visualized as interactive 3D renderings.
    • Demonstrated the framework's utility through case studies with domain experts.

    Conclusions:

    • MoMo offers a novel approach to analyzing neuron morphology-aware motifs.
    • The tool enhances the exploration of fundamental processing units in brain networks.
    • MoMo is effective and relevant for real-world neuroscience research.