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

Abstract

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.

Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience

“…ultra C ircuit2 V olume , takes an input microcircuit and a configuration file, and produces a volumetric tissue model that is tagged with multiple optical properties. The configuration file describes the annotation details of the circuit 10 .…”

“…We also extended U ltraliser to create annotated voxel-based tissue models from surface meshes of neuronal morphologies. Voxel-based models are becoming essential, not only for visual analytics, but also for performing in silico optical imaging experiments that simulate light interaction with brain tissue using physically plausible Monte Carlo visualization 10 . Simulation applicability and even its accuracy are subject to two factors.…”

“…In case of fluorescence, voxels corresponding to intracellular spaces are annotated with the spectral parameters of fluorescent dyes (Methods). The resulting volumes are used with recently developed in silico imaging simulators 10 to create synthetic optical sections of cortical tissue models, on physically plausible basis (shown in Fig. 7 ).…”

“…Watertight triangular meshes are used for (i) performing 3D particle simulations, (ii) mesh-based skeletonization, in which accurate morphologies of cellular structures are obtained for performing 1D compartmental simulations and (iii) tetrahedralization, where we can generate tetrahedral volume meshes for 3D reaction-diffusion simulations. Annotated volumetric tissue models are also used in in silico imaging studies, where we can simulate optical imaging experiments with brightfield or fluorescence microscopy 10 . U ltraliser ’s workflow is graphically illustrated in Figure 1 and a detailed schematic showing the ecosystem and relationship between its different modules is illustrated in Supplementary Figure S2 .…”