Dense connectomic reconstruction in layer 4 of the somatosensory cortex

Abstract

The dense circuit structure of mammalian cerebral cortex is still unknown. With developments in three-dimensional electron microscopy, the imaging of sizable volumes of neuropil has become possible, but dense reconstruction of connectomes is the limiting step. We reconstructed a volume of ~500,000 cubic micrometers from layer 4 of mouse barrel cortex, ~300 times larger than previous dense reconstructions from the mammalian cerebral cortex. The connectomic data allowed the extraction of inhibitory and excitatory neuron subtypes that were not predictable from geometric information. We quantified connectomic imprints consistent with Hebbian synaptic weight adaptation, which yielded upper bounds for the fraction of the circuit consistent with saturated long-term potentiation. These data establish an approach for the locally dense connectomic phenotyping of neuronal circuitry in the mammalian cortex.

neuPrint: Analysis Tools for EM Connectomics

“…By representing the neurons as nodes and synapses as edges, the resulting connectivity graph provides scientists one tool to help understand neural mechanisms in brains. Technical hurdles in generating and reconstructing connectomes from EM data limited prior studies to either small brains like C. elegans [1] or smaller portions of larger brains [2], [3], [4]. Despite the relatively small size, typically 1000 or fewer neurons, compared to the 100, 000 neurons in the Drosophila or millions of neurons in a mouse brain, deciphering the circuits formed by these neurons is challenging.…”

Binary and analog variation of synapses between cortical pyramidal neurons

“…To probe whether these variables are modified by synaptic plasticity, we again examine dual connections. It was previously shown that synapse pairs at dual connections are correlated in size, and the correlations have been attributed to activity-dependent plasticity (Sorra and Harris, 1993;Koester and Johnston, 2005;Bartol et al , 2015;Kasthuri et al , 2015;Dvorkin and Ziv, 2016;Bloss et al , 2018;Motta et al , 2019) . We find that the binary variables are highly correlated, while the continuous variables are not.…”

“…Here we show that synapse size, when restricted to synapses between L2/3 pyramidal cells, is well-modeled by the sum of a binary variable and an analog variable drawn from a log-normal distribution. Two synapses sharing the same presynaptic and postsynaptic cells are known to be correlated in size (Sorra and Harris, 1993;Koester and Johnston, 2005;Bartol et al , 2015;Kasthuri et al , 2015;Dvorkin and Ziv, 2016;Bloss et al , 2018;Motta et al , 2019) . We show that the binary variables of the two synapses are highly correlated, while the analog variables are not.…”

Cell-type specific innervation of cortical pyramidal cells at their apical tufts

“…Thus, L2 ADs receive about 3-fold more relative inhibition at their main bifurcation compared to L3 and L5 pyramidal cells (9.9% vs 33.6%, Wilcoxon rank-sum test, p<10 -11 ). As controls, we asked whether this difference in inhibitory innervation could be confounded by the way we identified inhibitory vs excitatory synapses: we used the previously reported preferential innervation of dendritic shafts vs spines as criterion for axon identity (Kubota, Karube et al 2016, Motta, Berning et al 2018. We found that, in fact, the preference of axons to either innervate shafts or spines of ADs was almost binary (Fig.…”