Data-driven reconstruction of a point neuron mouse brain

In this work, we present a semi-automatic method to reconstruct a mouse whole-brain model at the point-neuron level by integrating a wide array of biological data. Our process has three parts: cell position and type assignment, connectivity mapping, and simulation. Additional validation is performed at every step of the workflow. We obtain cell positions from a voxelized data sets derived from high-resolution Nissl stained microscope image stacks in the Allen Mouse Brain reference atlas (Lein, et al. 2007), and global constraint numbers for the whole-brain (Herculano-Houzel, Ribeiro, et al. 2011). We then assign the type of each cell using In Situ Hybridization (ISH) image data from the same atlas. Cells are classified as glia subtypes, excitatory neurons, or inhibitory neurons, leaving the possibility to further expand the diversity of assigned cell types by using more genes in this step. We furthermore integrate region-specific cell densities from literature into our model. We then study cell type correlations throughout the brain and compare the resulting numbers to literature data in order to validate the process. In the second step, we use two-photon tomography images of recombinant Adeno-Associated Virus (rAAV) labeled axonal projections from the Allen Mouse Connectivity Atlas (Oh, et al. 2014) to determine the mesoscale connectivity between the neurons in different brain regions. For this step a comprehensive comparison to experimental data is difficult due to lack of similar connectivity data. We then obtain a network configuration that can be simulated with state-of the art software like Nest. We show results from a simulated whisker stimulation experiment and compare the evoked activity patterns to imaging data from Voltage Sensitive Dye (VSD) experiments (Ferezou, Haiss, et al. 2007). Finally, we build a glossary of possible whole-brain behaviors of our model, and briefly explore the possibility of a biologically plausible mapping of its input and output pathways.