Reconstruction and visualization of large-scale volumetric models of neocortical circuits for physically-plausible in silico optical studies

Marwan Abdellah, Juan Hernando, Nicolas Antille, Stefan Eilemann, Henry Markram, Felix Schürmann

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Background: We present a software workflow capable of building large scale, highly detailed and realistic volumetric models of neocortical circuits from the morphological skeletons of their digitally reconstructed neurons. The limitations of the existing approaches for creating those models are explained, and then, a multi-stage pipeline is discussed to overcome those limitations. Starting from the neuronal morphologies, we create smooth piecewise watertight polygonal models that can be efficiently utilized to synthesize continuous and plausible volumetric models of the neurons with solid voxelization. The somata of the neurons are reconstructed on a physically-plausible basis relying on the physics engine in Blender. Results: Our pipeline is applied to create 55 exemplar neurons representing the various morphological types that are reconstructed from the somatsensory cortex of a juvenile rat. The pipeline is then used to reconstruct a volumetric slice of a cortical circuit model that contains 210,000 neurons. The applicability of our pipeline to create highly realistic volumetric models of neocortical circuits is demonstrated with an in silico imaging experiment that simulates tissue visualization with brightfield microscopy. The results were evaluated with a group of domain experts to address their demands and also to extend the workflow based on their feedback. Conclusion: A systematic workflow is presented to create large scale synthetic tissue models of the neocortical circuitry. This workflow is fundamental to enlarge the scale of in silico neuroscientific optical experiments from several tens of cubic micrometers to a few cubic millimeters. AMS Subject Classification: Modelling and Simulation
Original languageEnglish (US)
JournalBMC Bioinformatics
Issue numberS10
StatePublished - Sep 13 2017
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-08
Acknowledgements: Publication of this article was funded by competitive research funding from King Abdullah University of Science and Technology (KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications


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