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Network analysis of mesoscale mouse brain structural connectome yields modular structure that aligns with anatomical regions and sensory pathways

View ORCID ProfileBernard A. Pailthorpe
doi: https://doi.org/10.1101/755041
Bernard A. Pailthorpe
Brain Dynamics Group, School of Physics. University of Sydney. NSW. 2006. Australia.
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  • ORCID record for Bernard A. Pailthorpe
  • For correspondence: bernard.pailthorpe@sydney.edu.au
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Abstract

The Allen mesoscale mouse brain structural connectome is analysed using standard network methods combined with 3D visualizations. The full region-to-region connectivity data is used, with a focus on the strongest structural links. The spatial embedding of links and time evolution of signalling is incorporated, with two-step links included. Modular decomposition using the Infomap method produces 8 network modules that correspond approximately to major brain anatomical regions and system functions. These modules align with the anterior and posterior primary sensory systems and association areas. 3D visualization of network links is facilitated by using a set of simplified schematic coordinates that reduces visual complexity. Selection of key nodes and links, such as sensory pathways and cortical association areas together reveal structural features of the mouse structural connectome consistent with biological functions in the sensory-motor systems, and selective roles of the anterior and posterior cortical association areas of the mouse brain. Time progression of signals along sensory pathways reveals that close links are to local cortical association areas and cross modal, while longer links provide anterior-posterior coordination and inputs to non cortical regions. The fabric of weaker links generally are longer range with some having brain-wide reach. Cortical gradients are evident along sensory pathways within the structural network.

Author’s Summary Network models incorporating spatial embedding and signalling delays are used to investigate the mouse structural connectome. Network models that include time respecting paths are used to trace signaling pathways and reveal separate roles of shorter vs. longer links. Here computational methods work like experimental probes to uncover biologically relevant features. I use the Infomap method, which follows random walks on the network, to decompose the directed, weighted network into 8 modules that align with classical brain anatomical regions and system functions. Primary sensory pathways and cortical association areas are separated into individual modules. Strong, short range links form the sensory-motor paths while weaker links spread brain-wide, possibly coordinating many regions.

Footnotes

  • The main text has been significantly shortened, to aid readability; with detailed text relegated to supplementary text (S1).

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 02, 2019.
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Network analysis of mesoscale mouse brain structural connectome yields modular structure that aligns with anatomical regions and sensory pathways
Bernard A. Pailthorpe
bioRxiv 755041; doi: https://doi.org/10.1101/755041
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Network analysis of mesoscale mouse brain structural connectome yields modular structure that aligns with anatomical regions and sensory pathways
Bernard A. Pailthorpe
bioRxiv 755041; doi: https://doi.org/10.1101/755041

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