Generation of dense statistical connectomes from sparse morphological data.

Robert Egger,Hans-Christian Hege,Vincent J Dercksen,Marcel Oberlaender,Daniel Udvary

doi:10.3389/fnana.2014.00129

Abstract

Sensory-evoked signal flow, at cellular and network levels, is primarily determined by the synaptic wiring of the underlying neuronal circuitry. Measurements of synaptic innervation, connection probabilities and subcellular organization of synaptic inputs are thus among the most active fields of research in contemporary neuroscience. Methods to measure these quantities range from electrophysiological recordings over reconstructions of dendrite-axon overlap at light-microscopic levels to dense circuit reconstructions of small volumes at electron-microscopic resolution. However, quantitative and complete measurements at subcellular resolution and mesoscopic scales to obtain all local and long-range synaptic in/outputs for any neuron within an entire brain region are beyond present methodological limits. Here, we present a novel concept, implemented within an interactive software environment called NeuroNet, which allows (i) integration of sparsely sampled (sub)cellular morphological data into an accurate anatomical reference frame of the brain region(s) of interest, (ii) up-scaling to generate an average dense model of the neuronal circuitry within the respective brain region(s) and (iii) statistical measurements of synaptic innervation between all neurons within the model. We illustrate our approach by generating a dense average model of the entire rat vibrissal cortex, providing the required anatomical data, and illustrate how to measure synaptic innervation statistically. Comparing our results with data from paired recordings in vitro and in vivo, as well as with reconstructions of synaptic contact sites at light- and electron-microscopic levels, we find that our in silico measurements are in line with previous results.

Highlights

One of the major challenges in neuroscience is to relate results from structural and functional measurements across multiple spatial scales
The average 3D distributions of excitatory and inhibitory somata were registered to the reference frame and somata were placed and assigned to cell types (Figure 3B) and anatomical substructures as described above
NN replaced each soma by appropriate 3D soma/dendrite/axon morphologies, using the upscaling routines specified in the Method section (Figures 3C–E)

Summary

Introduction

One of the major challenges in neuroscience is to relate results from structural and functional measurements across multiple spatial scales. Current anatomical approaches either provide information of synaptic connectivity at macroscopic, i.e., between brain regions (e.g., using bulk injections of retro/anterograde agents, Oh et al, 2014), mesoscopic, i.e., between cell types (e.g., using transgenic animal models, Wickersham et al, 2007), microscopic, i.e., between small numbers of individual neurons (e.g., using multi-electrode recordings in acute brain slices in vitro, Feldmeyer et al, 1999; Perin et al, 2011) or nanoscopic scales, i.e., reconstructing synaptic contact sites within small volumes (e.g., using electron microscopy in dense, Briggman et al, 2011, or sparsely labeled tissue, Schoonover et al, 2014) While all of these approaches provided important structural information about the neuronal circuitry, results obtained at different scales (and often even at the same scale when obtained by different methods) are largely incompatible. Paired recording/reconstruction approaches are limited to acute brain slices in vitro, where slice

Methods

Results

Discussion

Conclusion