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Towards Differential Connectomics with NeuroVIISAS

Abstract

The comparison of connectomes is an essential step to identify changes in structural and functional neuronal networks. However, the connectomes themselves as well as the comparisons of connectomes could be manifold. In most applications, comparisons of connectomes are applied to specific sets of data. In many studies collections of scripts are applied optimized for certain species (non-generic approaches) or diseases (control versus disease group connectomes). These collections of scripts have a limited functionality which do not support functional and topographic mappings of connectomes (hemispherical asymmetries, peripheral nervous system). The platform-independent and generic neuroVIISAS framework is built to circumvent limitations that come with variants of nomenclatures, connectivity lists and connectional hierarchies as well as restrictions to structural connectome analyses. A new analytical module is introduced into the framework to compare different types of connectomes and different representations of the same connectome within a unique software environment. As an example a differential analysis of the partial connectome of the laboratory rat that is based on virus tract tracing with the same regions of non-virus tract tracing has been performed. A relatively large connectional coherence between the two different techniques was found. However, some detected connections are described by virus tract-tracing only.

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Acknowledgements

We would like to thank Heidi Schumann and Christian Tominski (Computer Graphics, Institute of Computer Science, University of Rostock) for their helpful advice on the manuscript.

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Correspondence to Oliver Schmitt.

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Schwanke, S., Jenssen, J., Eipert, P. et al. Towards Differential Connectomics with NeuroVIISAS. Neuroinform 17, 163–179 (2019). https://doi.org/10.1007/s12021-018-9389-6

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Keywords

  • Connectome
  • Differential connectomics
  • Neuronal networks
  • Multidimensional connectomes
  • Visualization
  • Graph analysis
  • Rat
  • Nervous system