Accelerating connectomic proofreading for larger brains and multiple individuals

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Technology for automating the segmentation of neurons from electron microscopic (EM) data has improved dramatically, making it now possible to obtain accurate reconstructions of neural circuits from large EM volumes. However, even the best automation still must be followed by human proofreading to attain high accuracy. Hundreds of neuroscientists are already using our ChunkedGraph system for proofreading neural circuits. The system is fully web- and cloud-ready, facilitating seamless collaboration. The software is open-source, and at this writing is being operated by three institutions (Princeton, Harvard Medical School, Allen Institute) to serve the proofreading of four large datasets (fly and mouse) by international communities. A notable example is the FlyWire community, which at this writing is engaging over 160 scientists from 40 labs to proofread a whole Drosophila brain. The ChunkedGraph is on its way to becoming a standard and indispensable tool for connectomics. The data structure was designed to permit scaling to arbitrarily large datasets in principle, even to the whole mouse brain connectome project that is currently being considered by the NIH. In practice, there are deficiencies in the current implementation that impede efficiency of proofreading of datasets on the scale of the fly brain, and are preventing further scaling to larger volumes and brains. To remove these barriers to scaling, we will make it possible to upgrade a ChunkedGraph system after proofreading has already started, to take advantage of new and improved automated reconstructions made possible by advances in AI. We will make it possible to visualize neurons in 3D with multi-resolution sharded meshes and skeletons that are rapidly updated after every proofreading edit. We further propose to build a subsequent processing step that rapidly derives morphological features and skeletons, an important prerequisite for downstream analysis and scientific discovery. One of the next frontiers in connectomics is the reconstruction of multiple brains of the same species. For nervous systems with sufficient stereotypy, comparing reconstructions of different individuals can guide the detection and correction of errors. We will develop software that speeds up proofreading by automatically matching a reconstructed neuron to a reference reconstruction, and computing and suggesting candidate corrections if necessary. We will pilot this software for the Drosophila brain, for which multiple EM datasets are now appearing. The same software will be extendable to other model organisms with relatively stereotyped nervous systems (e.g. bee, ant, etc.). In the long term, the approach could further be extended to mammalian nervous systems once the field has developed sufficiently accurate morphological models of cell types. Our proofreading software will continue to be open source and freely accessible. Proofreading generates accurate wiring diagrams of neural circuits, which are helpful for understanding brain function and dysfunction.