Bio-physically plausible visualization of highly scattering fluorescent neocortical models for in silico experimentation

Marwan Abdellah, Ahmet Bilgili, Stefan Eilemann, Julian Shillcock, Henry Markram, Felix Schürmann

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Background We present a visualization pipeline capable of accurate rendering of highly scattering fluorescent neocortical neuronal models. The pipeline is mainly developed to serve the computational neurobiology community. It allows the scientists to visualize the results of their virtual experiments that are performed in computer simulations, or in silico. The impact of the presented pipeline opens novel avenues for assisting the neuroscientists to build biologically accurate models of the brain. These models result from computer simulations of physical experiments that use fluorescence imaging to understand the structural and functional aspects of the brain. Due to the limited capabilities of the current visualization workflows to handle fluorescent volumetric datasets, we propose a physically-based optical model that can accurately simulate light interaction with fluorescent-tagged scattering media based on the basic principles of geometric optics and Monte Carlo path tracing. We also develop an automated and efficient framework for generating dense fluorescent tissue blocks from a neocortical column model that is composed of approximately 31000 neurons. Results Our pipeline is used to visualize a virtual fluorescent tissue block of 50 μm3 that is reconstructed from the somatosensory cortex of juvenile rat. The fluorescence optical model is qualitatively analyzed and validated against experimental emission spectra of different fluorescent dyes from the Alexa Fluor family. Conclusion We discussed a scientific visualization pipeline for creating images of synthetic neocortical neuronal models that are tagged virtually with fluorescent labels on a physically-plausible basis. The pipeline is applied to analyze and validate simulation data generated from neuroscientific in silico experiments.
Original languageEnglish (US)
JournalBMC Bioinformatics
Volume18
Issue numberS2
DOIs
StatePublished - Feb 15 2017
Externally publishedYes

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