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Mapping Mesoscale Axonal Projections in the Mouse Brain Using A 3D Convolutional Network

Drew Friedmann, Albert Pun, Eliza L Adams, Jan H Lui, Justus M Kebschull, Sophie M Grutzner, Caitlin Castagnola, Marc Tessier-Lavigne, Liqun Luo
doi: https://doi.org/10.1101/812644
Drew Friedmann
Department of Biology, Stanford University, Stanford, CA 94305, USAHoward Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
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Albert Pun
Department of Biology, Stanford University, Stanford, CA 94305, USAHoward Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
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Eliza L Adams
Department of Biology, Stanford University, Stanford, CA 94305, USANeurosciences Graduate Program, Stanford University, Stanford, CA 94305, USA
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Jan H Lui
Department of Biology, Stanford University, Stanford, CA 94305, USAHoward Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
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Justus M Kebschull
Department of Biology, Stanford University, Stanford, CA 94305, USAHoward Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
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Sophie M Grutzner
Department of Biology, Stanford University, Stanford, CA 94305, USAHoward Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
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Caitlin Castagnola
Department of Biology, Stanford University, Stanford, CA 94305, USA
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Marc Tessier-Lavigne
Department of Biology, Stanford University, Stanford, CA 94305, USA
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Liqun Luo
Department of Biology, Stanford University, Stanford, CA 94305, USAHoward Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
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  • For correspondence: lluo@stanford.edu
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Abstract

The projection targets of a neuronal population are a key feature of its anatomical characterization. Historically, tissue sectioning, confocal microscopy, and manual scoring of specific regions of interest have been used to generate coarse summaries of mesoscale projectomes. We present here TrailMap, a 3D convolutional network for extracting axonal projections from intact cleared mouse brains imaged by light-sheet microscopy. TrailMap allows region-based quantification of total axon content in large and complex 3D structures after registration to a standard reference atlas. The identification of axonal structures as thin as one voxel benefits from data augmentation but also requires a loss function that tolerates errors in annotation. A network trained with volumes of serotonergic axons in all major brain regions can be generalized to map and quantify axons from thalamocortical, deep cerebellar, and cortical projection neurons, validating transfer learning as a tool to adapt the model to novel categories of axonal morphology. Speed of training, ease of use, and accuracy improve over existing tools without a need for specialized computing hardware. Given the recent emphasis on genetically and functionally defining cell types in neural circuit analysis, TrailMap will facilitate automated extraction and quantification of axons from these specific cell types at the scale of the entire mouse brain, an essential component of deciphering their connectivity.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted October 21, 2019.
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Mapping Mesoscale Axonal Projections in the Mouse Brain Using A 3D Convolutional Network
Drew Friedmann, Albert Pun, Eliza L Adams, Jan H Lui, Justus M Kebschull, Sophie M Grutzner, Caitlin Castagnola, Marc Tessier-Lavigne, Liqun Luo
bioRxiv 812644; doi: https://doi.org/10.1101/812644
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Mapping Mesoscale Axonal Projections in the Mouse Brain Using A 3D Convolutional Network
Drew Friedmann, Albert Pun, Eliza L Adams, Jan H Lui, Justus M Kebschull, Sophie M Grutzner, Caitlin Castagnola, Marc Tessier-Lavigne, Liqun Luo
bioRxiv 812644; doi: https://doi.org/10.1101/812644

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