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High resolution data-driven model of the mouse connectome

Joseph E. Knox, View ORCID ProfileKameron Decker Harris, Nile Graddis, Jennifer D. Whitesell, View ORCID ProfileHongkui Zeng, Julie A. Harris, View ORCID ProfileEric Shea-Brown, Stefan Mihalas
doi: https://doi.org/10.1101/293019
Joseph E. Knox
1Allen Institute for Brain Science, Seattle, Washington, USA
2Applied Mathematics, U. of Washington, Seattle, Washington, USA
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Kameron Decker Harris
2Applied Mathematics, U. of Washington, Seattle, Washington, USA
3Computer Science and Engineering, U. of Washington, Seattle, Washington, USA
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  • ORCID record for Kameron Decker Harris
Nile Graddis
1Allen Institute for Brain Science, Seattle, Washington, USA
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Jennifer D. Whitesell
1Allen Institute for Brain Science, Seattle, Washington, USA
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Hongkui Zeng
1Allen Institute for Brain Science, Seattle, Washington, USA
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Julie A. Harris
1Allen Institute for Brain Science, Seattle, Washington, USA
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Eric Shea-Brown
1Allen Institute for Brain Science, Seattle, Washington, USA
2Applied Mathematics, U. of Washington, Seattle, Washington, USA
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Stefan Mihalas
1Allen Institute for Brain Science, Seattle, Washington, USA
2Applied Mathematics, U. of Washington, Seattle, Washington, USA
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Abstract

Knowledge of mesoscopic brain connectivity is important for understanding inter- and intra-region information processing. Models of structural connectivity are typically constructed and analyzed with the assumption that regions are homogeneous. We instead use the Allen Mouse Brain Connectivity Atlas to construct a model of whole brain connectivity at the scale of 100 µm voxels. The dataset used consists of 366 anterograde tracing experiments in wild type C7BL/6 mice, mapping fluorescently-labeled neuronal projections brain-wide. Inferring spatial connectivity with this dataset remains underdetermined, since the approximately 2 × 105 source voxels outnumber the number of experiments. To address this, we assume that connection patterns and strengths vary smoothly across major brain divisions. We model the connectivity at each voxel as a radial basis kernel-weighted average of the projection patterns of nearby injections. The voxel model outperforms a previous regional model in predicting held-out experiments and compared to a human-curated dataset. This voxel-scale model of the mouse connectome permits researchers to extend their previous analyses of structural connectivity to unprecedented levels of resolution, and allows for comparison with functional imaging and other datasets.

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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 April 01, 2018.
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High resolution data-driven model of the mouse connectome
Joseph E. Knox, Kameron Decker Harris, Nile Graddis, Jennifer D. Whitesell, Hongkui Zeng, Julie A. Harris, Eric Shea-Brown, Stefan Mihalas
bioRxiv 293019; doi: https://doi.org/10.1101/293019
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High resolution data-driven model of the mouse connectome
Joseph E. Knox, Kameron Decker Harris, Nile Graddis, Jennifer D. Whitesell, Hongkui Zeng, Julie A. Harris, Eric Shea-Brown, Stefan Mihalas
bioRxiv 293019; doi: https://doi.org/10.1101/293019

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