RoboEM: automated 3D flight tracing for synaptic-resolution connectomics.

Mapping neuronal networks from three-dimensional electron microscopy (3D-EM) data still poses substantial reconstruction challenges, in particular for thin axons. Currently available automated image segmentation methods require manual proofreading for many types of connectomic analysis. Here we introduce RoboEM, an artificial intelligence-based self-steering 3D 'flight' system trained to navigate along neurites using only 3D-EM data as input.

High-contrast en bloc staining of mouse whole-brain and human brain samples for EM-based connectomics.

Connectomes of human cortical gray matter require high-contrast homogeneously stained samples sized at least 2 mm on a side, and a mouse whole-brain connectome requires samples sized at least 5-10 mm on a side. Here we report en bloc staining and embedding protocols for these and other applications, removing a key obstacle for connectomic analyses at the mammalian whole-brain level.

Connectomic analysis of thalamus-driven disinhibition in cortical layer 4.

Sensory signals are transmitted via the thalamus primarily to layer 4 (L4) of the primary sensory cortices. While information about average neuronal connectivity in L4 is available, its detailed higher-order circuit structure is not known. Here, we used three-dimensional electron microscopy for a connectomic analysis of the thalamus-driven inhibitory network in L4.

Connectomic comparison of mouse and human cortex.

The human cerebral cortex houses 1000 times more neurons than that of the cerebral cortex of a mouse, but the possible differences in synaptic circuits between these species are still poorly understood. We used three-dimensional electron microscopy of mouse, macaque, and human cortical samples to study their cell type composition and synaptic circuit architecture. The 2.

Neuropathic pain caused by miswiring and abnormal end organ targeting.

Nerve injury leads to chronic pain and exaggerated sensitivity to gentle touch (allodynia) as well as a loss of sensation in the areas in which injured and non-injured nerves come together. The mechanisms that disambiguate these mixed and paradoxical symptoms are unknown. Here we longitudinally and non-invasively imaged genetically labelled populations of fibres that sense noxious stimuli (nociceptors) and gentle touch (low-threshold afferents) peripherally in the skin for longer than 10 months after nerve injury, while simultaneously tracking pain-related behaviour in the same mice.

Cellular connectomes as arbiters of local circuit models in the cerebral cortex.

With the availability of cellular-resolution connectivity maps, connectomes, from the mammalian nervous system, it is in question how informative such massive connectomic data can be for the distinction of local circuit models in the mammalian cerebral cortex. Here, we investigated whether cellular-resolution connectomic data can in principle allow model discrimination for local circuit modules in layer 4 of mouse primary somatosensory cortex. We used approximate Bayesian model selection based on a set of simple connectome statistics to compute the posterior probability over proposed models given a to-be-measured connectome.

Postnatal connectomic development of inhibition in mouse barrel cortex.

Brain circuits in the neocortex develop from diverse types of neurons that migrate and form synapses. Here we quantify the circuit patterns of synaptogenesis for inhibitory interneurons in the developing mouse somatosensory cortex. We studied synaptic innervation of cell bodies, apical dendrites, and axon initial segments using three-dimensional electron microscopy focusing on the first 4 weeks postnatally (postnatal days P5 to P28).

The Mind of a Mouse.

Large scientific projects in genomics and astronomy are influential not because they answer any single question but because they enable investigation of continuously arising new questions from the same data-rich sources. Advances in automated mapping of the brain's synaptic connections (connectomics) suggest that the complicated circuits underlying brain function are ripe for analysis. We discuss benefits of mapping a mouse brain at the level of synapses.

A community-based transcriptomics classification and nomenclature of neocortical cell types.

To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent.

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

We investigated the synaptic innervation of apical dendrites of cortical pyramidal cells in a region between layers (L) 1 and 2 using 3-D electron microscopy applied to four cortical regions in mouse. We found the relative inhibitory input at the apical dendrite's main bifurcation to be more than 2-fold larger for L2 than L3 and L5 thick-tufted pyramidal cells. Towards the distal tuft dendrites in upper L1, the relative inhibitory input was at least about 2-fold larger for L5 pyramidal cells than for all others.

Dense connectomic reconstruction in layer 4 of the somatosensory cortex.

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.

Big data in nanoscale connectomics, and the greed for training labels.

The neurosciences have developed methods that outpace most other biomedical fields in terms of acquired bytes. We review how the information content and analysis challenge of such data indicates that electron microscopy (EM)-based connectomics is an especially hard problem. Here, as in many other current machine learning applications, the need for excessive amounts of labelled data while utilizing only a small fraction of available raw image data for algorithm training illustrates the still fundamental gap between artificial and biological intelligence.

Full reconstruction of large lobula plate tangential cells in Drosophila from a 3D EM dataset.

With the advent of neurogenetic methods, the neural basis of behavior is presently being analyzed in more and more detail. This is particularly true for visually driven behavior of Drosophila melanogaster where cell-specific driver lines exist that, depending on the combination with appropriate effector genes, allow for targeted recording, silencing and optogenetic stimulation of individual cell-types. Together with detailed connectomic data of large parts of the fly optic lobe, this has recently led to much progress in our understanding of the neural circuits underlying local motion detection.

FluoEM, virtual labeling of axons in three-dimensional electron microscopy data for long-range connectomics.

The labeling and identification of long-range axonal inputs from multiple sources within densely reconstructed electron microscopy (EM) datasets from mammalian brains has been notoriously difficult because of the limited color label space of EM. Here, we report FluoEM for the identification of multi-color fluorescently labeled axons in dense EM data without the need for artificial fiducial marks or chemical label conversion. The approach is based on correlated tissue imaging and computational matching of neurite reconstructions, amounting to a virtual color labeling of axons in dense EM circuit data.

Axonal synapse sorting in medial entorhinal cortex.

Research on neuronal connectivity in the cerebral cortex has focused on the existence and strength of synapses between neurons, and their location on the cell bodies and dendrites of postsynaptic neurons. The synaptic architecture of individual presynaptic axonal trees, however, remains largely unknown. Here we used dense reconstructions from three-dimensional electron microscopy in rats to study the synaptic organization of local presynaptic axons in layer 2 of the medial entorhinal cortex, the site of grid-like spatial representations.

SynEM, automated synapse detection for connectomics.

Nerve tissue contains a high density of chemical synapses, about 1 per µm 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.

webKnossos: efficient online 3D data annotation for connectomics.

We report webKnossos, an in-browser annotation tool for 3D electron microscopic data. webKnossos provides flight mode, a single-view egocentric reconstruction method enabling trained annotator crowds to reconstruct at a speed of 1.5 ± 0.

SegEM: Efficient Image Analysis for High-Resolution Connectomics.

Progress in electron microscopy-based high-resolution connectomics is limited by data analysis throughput. Here, we present SegEM, a toolset for efficient semi-automated analysis of large-scale fully stained 3D-EM datasets for the reconstruction of neuronal circuits. By combining skeleton reconstructions of neurons with automated volume segmentations, SegEM allows the reconstruction of neuronal circuits at a work hour consumption rate of about 100-fold less than manual analysis and about 10-fold less than existing segmentation tools.

Large-volume en-bloc staining for electron microscopy-based connectomics.

Large-scale connectomics requires dense staining of neuronal tissue blocks for electron microscopy (EM). Here we report a large-volume dense en-bloc EM staining protocol that overcomes the staining gradients, which so far substantially limited the reconstructable volumes in three-dimensional (3D) EM. Our protocol provides densely reconstructable tissue blocks from mouse neocortex sized at least 1 mm in diameter.

Common circuit design in fly and mammalian motion vision.

Motion-sensitive neurons have long been studied in both the mammalian retina and the insect optic lobe, yet striking similarities have become obvious only recently. Detailed studies at the circuit level revealed that, in both systems, (i) motion information is extracted from primary visual information in parallel ON and OFF pathways; (ii) in each pathway, the process of elementary motion detection involves the correlation of signals with different temporal dynamics; and (iii) primary motion information from both pathways converges at the next synapse, resulting in four groups of ON-OFF neurons, selective for the four cardinal directions. Given that the last common ancestor of insects and mammals lived about 550 million years ago, this general strategy seems to be a robust solution for how to compute the direction of visual motion with neural hardware.

The mutual inspirations of machine learning and neuroscience.

Neuroscientists are generating data sets of enormous size, which are matching the complexity of real-world classification tasks. Machine learning has helped data analysis enormously but is often not as accurate as human data analysis. Here, Helmstaedter discusses the challenges and promises of neuroscience-inspired machine learning that lie ahead.

Synaptic Conductance Estimates of the Connection Between Local Inhibitor Interneurons and Pyramidal Neurons in Layer 2/3 of a Cortical Column.

Stimulation of a principal whisker yields sparse action potential (AP) spiking in layer 2/3 (L2/3) pyramidal neurons in a cortical column of rat barrel cortex. The low AP rates in pyramidal neurons could be explained by activation of interneurons in L2/3 providing inhibition onto L2/3 pyramidal neurons. L2/3 interneurons classified as local inhibitors based on their axonal projection in the same column were reported to receive strong excitatory input from spiny neurons in L4, which are also the main source of the excitatory input to L2/3 pyramidal neurons.