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.

21The dense circuit structure of the mammalian cerebral cortex is still unknown. With 22 developments in 3-dimensional (3D) electron microscopy, the imaging of sizeable 23 volumes of neuropil has become possible, but dense reconstruction of connectomes 24 from such image data is the limiting step. Here, we report the dense reconstruction 25 of a volume of about 500,000 µm 3 from layer 4 of mouse barrel cortex, about 300 26 times larger than previous dense reconstructions from the mammalian cerebral 27 cortex. Using a novel reconstruction technique, FocusEM, we were able to 28 reconstruct a total of 0.9 meters of dendrites and about 1.8 meters of axons investing 29 only about 4,000 human work hours, about 10-25 times more efficient than previous 30 dense circuit reconstructions. We find that connectomic data alone allows the 31 definition of inhibitory axon types that show established principles of synaptic 32 specificity for subcellular postsynaptic compartments. We find that also a fraction of 33 excitatory axons exhibit such subcellular target specificity. Only about 34 35% of inhibitory and 55% of excitatory synaptic subcellular innervation can be 35 predicted from the geometrical availability of membrane surface, revoking coarser 36 models of random wiring for synaptic connections in cortical layer 4. We furthermore 37 find evidence for enhanced variability of synaptic input composition between neurons 38 at the level of primary dendrites in cortical layer 4. Finally, we obtain evidence for 39 Hebbian synaptic weight adaptation in at least 24% of connections; at least 35% of 40 connections show no sign of such previous plasticity. Together, these results 41 establish an approach to connectomic phenotyping of local dense neuronal circuitry 42 in the mammalian cortex. 43 44 109First, we reconstructed those neurons, which had their cell bodies in the tissue 110 volume ( Fig. 1h,i, Supplementary Material 1, n=125 cell bodies, of these 97 111 neuronal, of these 89 reconstructed with dendrites in the dataset) using a set of 112 simple growth rules for automatically connecting neurite pieces based on the 113 segment-to-segment neighborhood graph and the connectivity and neurite type 114 classifiers ( Fig. 1f, see Methods). As a result, we obtained fully automated 115 reconstructions of the neuron's soma and dendritic processes. Notably with a 116 minimal additional manual correction investment of 9.7 hours for 89 cells (54.5 mm 117 6 dendritic and 2.1 mm axonal path length), the dendritic shafts of these neurons could 118 be reconstructed without merge errors, but 37 remaining split errors, at 87.3% 119 dendritic length recall ( Fig. 1h,i, Supplementary Material 1, see Methods). This 120 reconstruction efficiency compares favorably to recent reports of automated 121 segmentation of neurons in 3D EM data from the bird brain obtained at about 2-fold 122 higher imaging resolution (Januszewski et al., 2018), which reports soma-based 123 neuron reconstruction at an error rate of beyond 100 errors per 66 mm dendritic 124 ...

Nerve tissue contains a high density of chemical synapses, about 1 per µm3 in the mammalian cerebral cortex. Thus, even for small blocks of nerve tissue, dense connectomic mapping requires the identification of millions to billions of synapses. While the focus of connectomic data analysis has been on neurite reconstruction, synapse detection becomes limiting when datasets grow in size and dense mapping is required. Here, we report SynEM, a method for automated detection of synapses from conventionally en-bloc stained 3D electron microscopy image stacks. The approach is based on a segmentation of the image data and focuses on classifying borders between neuronal processes as synaptic or non-synaptic. SynEM yields 97% precision and recall in binary cortical connectomes with no user interaction. It scales to large volumes of cortical neuropil, plausibly even whole-brain datasets. SynEM removes the burden of manual synapse annotation for large densely mapped connectomes.DOI: http://dx.doi.org/10.7554/eLife.26414.001

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ABSTRACT

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Synaptic connectivity detection is a critical task for neural reconstruction from Electron Microscopy (EM) data. Most of the existing algorithms for synapse detection do not identify the cleft location and direction of connectivity simultaneously. The few methods that computes direction along with contact location have only been demonstrated to work on either dyadic (most common in vertebrate brain) or polyadic (found in fruit fly brain) synapses, but not on both types. In this paper, we present an algorithm to automatically predict the location as well as the direction of both dyadic and polyadic synapses. The proposed algorithm first generates candidate synaptic connections from voxelwise predictions of signed proximity generated by a 3D U-net. A second 3D CNN then prunes the set of candidates to produce the final detection of cleft and connectivity orientation. Experimental results demonstrate that the proposed method outperforms the existing methods for determining synapses in both rodent and fruit fly brain 1 .

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