Generation of dense statistical connectomes from sparse morphological data

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.

The Impact of Structural Heterogeneity on Excitation-Inhibition Balance in Cortical Networks

“…As described previously (Egger et al., 2014) and reviewed in the Supplemental Information, a full-scale matrix of the probability of connections between all neurons in vS1 (“dense statistical connectome”) was generated on the basis of precisely measured 3D distributions of excitatory and inhibitory somata, which are non-uniform and cell-type specific even within L4 (Figure 5B), and 3D reconstructions of in vivo- and in vitro-labeled dendrite and axon morphologies, which are highly variable even within cell types (Figure 5C). Cell-type average connection probabilities have been validated (Egger et al., 2014) by comparison with studies that used paired recordings (Feldmeyer et al., 1999, Constantinople and Bruno, 2013) or correlated light and electron microscopy (Schoonover et al., 2014). …”

“…There is strong anatomical evidence that the L4 barrel comprises a relatively complete local network, with axons and dendrites of both excitatory and inhibitory neurons remaining largely restricted to the barrel, and thalamic ventral posteromedial nucleus (VPM) axons defining the barrel boundaries. Using a statistical connectivity model based on reconstructions of the detailed 3D anatomy of the barrel cortex (Egger et al., 2014), we provide realistic estimates of the heterogeneity in input connectivity in the local L4 circuits (i.e., within the barrel) and study the resulting dynamics in an anatomically constrained network of linear integrate-and-fire (LIF) point neurons (Gerstner and Kistler, 2002). We find that non-uniformities in the distributions of excitatory and inhibitory somata (Meyer et al., 2013), and morphological diversity within and across L4 cell types (Koelbl et al., 2015, Narayanan et al., 2015) yield substantial heterogeneity in input connectivity.…”

Robustness of sensory-evoked excitation is increased by inhibitory inputs to distal apical tuft dendrites

“…Using this combination, we estimated the synaptic inputs impinging onto L2 PNs in rat vS1. We generated synaptic input maps by calculating the structural overlap between the dendrites of a representative L2 PN morphology and a dense statistical model of the cell type-specific axon/bouton distributions in rat vS1 (9). The number of synaptic contacts was further multiplied with in vivo-recorded AP probabilities of the respective PN cell types (7).…”

“…These data, acquired under the same experimental conditions as previously used to determine whisker-evoked spiking and 3D morphologies for PN cell types (7), were used to inform and constrain simulation experiments. Specifically, we converted the 3D soma/dendrite morphology of an in vivo-labeled L2 PN into a biophysically detailed full-compartmental model (8) and integrated the neuron model into a recently reported detailed reconstruction of the excitatory circuitry in vS1 (9). This integration enabled us to statistically measure the number and subcellular distribution of cell type-specific synaptic contacts impinging onto the exemplary L2 PN from L1 INs and L2-5 PNs, respectively.…”

EM connectomics reveals axonal target variation in a sequence-generating network

“…The proportion of axonal pathlength both within HVC and within a 200 µm radius from the soma was computed in Amira for each neuron using the ZIB extension package (Egger et al, 2014). Axonal boutons and dendritic spines were annotated manually in Amira using high-resolution LM stacks.…”