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

• Published in: BMC bioinformatics Vol. 18; no. Suppl 10; pp. 402 - 50
• Main Authors: Abdellah, Marwan, Hernando, Juan, Antille, Nicolas, Eilemann, Stefan, Markram, Henry, Schürmann, Felix
• Format: Journal Article
• Language: English
• Published: London BioMed Central 13.09.2017; BioMed Central Ltd; BMC
• Subjects: 3-D graphics; Algorithms; Animal models; Animals; Bioinformatics; Biomedical and Life Sciences; Brain research; Brain slice preparation; Circuits; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer graphics; Computer Simulation; Cortex; Experiments; Feedback; Image Processing, Computer-Assisted; In silico neuroscience; Life Sciences; Light; Microarrays; Micrometers; Microscopy; Modeling and simulation; Models, Neurological; Morphology; Neocortex; Neocortex - physiology; Neocortical brain models; Nerve Net - physiology; Neuroimaging; Neurons; Neurons - physiology; Neurosciences; Optical Phenomena; Physiological aspects; Physiology; Polygonal and volumetric models; Rats; Scale (ratio); Technology application; Visualization; Volumetric analysis; Workflow; Polygonal and volumetric models; Neocortical brain models; Modeling and simulation; In silico neuroscience
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-017-1788-4

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