Abstract

Connectivity data of the nervous system and subdivisions, such as the brainstem, cerebral cortex and subcortical nuclei, are necessary to understand connectional structures, predict effects of connectional disorders and simulate network dynamics. For that purpose, a database was built and analyzed which comprises all known directed and weighted connections within the rat brainstem. A longterm metastudy of original research publications describing tract tracing results form the foundation of the brainstem connectome (BC) database which can be analyzed directly in the framework neuroVIISAS. The BC database can be accessed directly by connectivity tables, a web-based tool and the framework. Analysis of global and local network properties, a motif analysis, and a community analysis of the brainstem connectome provides insight into its network organization. For example, we found that BC is a scale-free network with a small-world connectivity. The Louvain modularity and weighted stochastic block matching resulted in partially matching of functions and connectivity. BC modeling was performed to demonstrate signal propagation through the somatosensory pathway which is affected in Multiple sclerosis.

Measurement(s)

brainstem

Technology Type(s)

tract tracing metastudy

Factor Type(s)

brain region

Sample Characteristic - Organism

Rattus rattus

Sample Characteristic - Environment

Experimental setup

Sample Characteristic - Location

Germany

Details

Title
The brainstem connectome database
Author
Schmitt, Oliver 1 ; Eipert, Peter 2 ; Ruß Frauke 2 ; Beier, Julia 2 ; Kanar, Kadir 2 ; Horn, Anja 3 

 University of Rostock, Dep. of Anatomy, Rostock, Germany (GRID:grid.10493.3f) (ISNI:0000000121858338); University of Applied Sciences and Medical University, MSH Medical School Hamburg, Hamburg, Germany (GRID:grid.11500.35) (ISNI:0000 0000 8919 8412) 
 University of Rostock, Dep. of Anatomy, Rostock, Germany (GRID:grid.10493.3f) (ISNI:0000000121858338) 
 Ludwig-Maximilians-Universität München, Institute of Anatomy and Cell Biology I, Munich, Germany (GRID:grid.5252.0) (ISNI:0000 0004 1936 973X) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20524463
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2649432183
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.