To fully understand how the human brain works, knowledge of its structure at high resolution is needed. Presented here is a computationally intensive reconstruction of the ultrastructure of a cubic millimeter of human temporal cortex that was surgically removed to gain access to an underlying epileptic focus. It contains about 57,000 cells, about 230 millimeters of blood vessels, and about 150 million synapses and comprises 1.4 petabytes. Our analysis showed that glia outnumber neurons 2:1, oligodendrocytes were the most common cell, deep layer excitatory neurons could be classified on the basis of dendritic orientation, and among thousands of weak connections to each neuron, there exist rare powerful axonal inputs of up to 50 synapses. Further studies using this resource may bring valuable insights into the mysteries of the human brain.

Connectomics is a nascent neuroscience field to map and analyze neuronal networks. It provides a new way to investigate abnormalities in brain tissue, including in models of Alzheimer's disease (AD). This age-related disease is associated with alterations in amyloid-β (Aβ) and phosphorylated tau (pTau). These alterations correlate with AD's clinical manifestations, but causal links remain unclear. Therefore, studying these molecular alterations within the context of the local neuronal and glial milieu may provide insight into disease mechanisms. Volume electron microscopy (vEM) is an ideal tool for performing connectomics studies at the ultrastructural level, but localizing specific biomolecules within large-volume vEM data has been challenging. Here we report a volumetric correlated light and electron microscopy (vCLEM) approach using fluorescent nanobodies as immuno-probes to localize Alzheimer's disease-related molecules in a large vEM volume. Three molecules (pTau, Aβ, and a marker for activated microglia (CD11b)) were labeled without the need for detergents by three nanobody probes in a sample of the hippocampus of the 3xTg Alzheimer's disease model mouse. Confocal microscopy followed by vEM imaging of the same sample allowed for registration of the location of the molecules within the volume. This dataset revealed several ultrastructural abnormalities regarding the localizations of Aβ and pTau in novel locations. For example, two pTau-positive post-synaptic spine-like protrusions innervated by axon terminals were found projecting from the axon initial segment of a pyramidal cell. Three pyramidal neurons with intracellular Aβ or pTau were 3D reconstructed. Automatic synapse detection, which is necessary for connectomics analysis, revealed the changes in density and volume of synapses at different distances from an Aβ plaque. This vCLEM approach is useful to uncover molecular alterations within large-scale volume electron microscopy data, opening a new connectomics pathway to study Alzheimer's disease and other types of dementia.

Connectomic reconstruction of neural circuits relies on nanometer resolution microscopy which produces on the order of a petabyte of imagery for each cubic millimeter of brain tissue. The cost of storing such data is a significant barrier to broadening the use of connectomic approaches and scaling to even larger volumes. We present an image compression approach that uses machine learning-based denoising and standard image codecs to compress raw electron microscopy imagery of neuropil up to 17-fold with negligible loss of reconstruction accuracy.

We acquired a rapidly preserved human surgical sample from the temporal lobe of the cerebral cortex. We stained a 1 mm 3 volume with heavy metals, embedded it in resin, cut more than 5000 slices at ∼30 nm and imaged these sections using a high-speed multibeam scanning electron microscope. We used computational methods to render the three-dimensional structure containing 57,216 cells, hundreds of millions of neurites and 133.7 million synaptic connections. The 1.4 petabyte electron microscopy volume, the segmented cells, cell parts, blood vessels, myelin, inhibitory and excitatory synapses, and 104 manually proofread cells are available to peruse online . Many interesting and unusual features were evident in this dataset. Glia outnumbered neurons 2:1 and oligodendrocytes were the most common cell type in the volume. Excitatory spiny neurons comprised 69% of the neuronal population, and excitatory synapses also were in the majority (76%). The synaptic drive onto spiny neurons was biased more strongly toward excitation (70%) than was the case for inhibitory interneurons (48%). Despite incompleteness of the automated segmentation caused by split and merge errors, we could automatically generate (and then validate) connections between most of the excitatory and inhibitory neuron types both within and between layers. In studying these neurons we found that deep layer excitatory cell types can be classified into new subsets, based on structural and connectivity differences, and that chandelier interneurons not only innervate excitatory neuron initial segments as previously described, but also each other’s initial segments. Furthermore, among the thousands of weak connections established on each neuron, there exist rarer highly powerful axonal inputs that establish multi-synaptic contacts (up to ∼20 synapses) with target neurons. Our analysis indicates that these strong inputs are specific, and allow small numbers of axons to have an outsized role in the activity of some of their postsynaptic partners.

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly's brain.

Article Figures and data Abstract eLife digest Introduction Appendix 1 Data availability References Decision letter Author response Article and author information Metrics Abstract The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly’s brain. eLife digest Animal brains of all sizes, from the smallest to the largest, work in broadly similar ways. Studying the brain of any one animal in depth can thus reveal the general principles behind the workings of all brains. The fruit fly Drosophila is a popular choice for such research. With about 100,000 neurons – compared to some 86 billion in humans – the fly brain is small enough to study at the level of individual cells. But it nevertheless supports a range of complex behaviors, including navigation, courtship and learning. Thanks to decades of research, scientists now have a good understanding of which parts of the fruit fly brain support particular behaviors. But exactly how they do this is often unclear. This is because previous studies showing the connections between cells only covered small areas of the brain. This is like trying to understand a novel when all you can see is a few isolated paragraphs. To solve this problem, Scheffer, Xu, Januszewski, Lu, Takemura, Hayworth, Huang, Shinomiya et al. prepared the first complete map of the entire central region of the fruit fly brain. The central brain consists of approximately 25,000 neurons and around 20 million connections. To prepare the map – or connectome – the brain was cut into very thin 8nm slices and photographed with an electron microscope. A three-dimensional map of the neurons and connections in the brain was then reconstructed from these images using machine learning algorithms. Finally, Scheffer et al. used the new connectome to obtain further insights into the circuits that support specific fruit fly behaviors. The central brain connectome is freely available online for anyone to access. When used in combination with existing methods, the map will make it easier to understand how the fly brain works, and how and why it can fail to work correctly. Many of these findings will likely apply to larger brains, including our own. In the long run, studying the fly connectome may therefore lead to a better understanding of the human brain and its disorders. Performing a similar analysis on the brain of a small mammal, by scaling up the methods here, will be a likely next step along this path. Introduction The connectome we present is a dense reconstruction of a portion of the central brain (referred to here as the hemibrain) of the fruit fly, Drosophila melanogaster, as shown in Figure 1. This region was chosen since it contains all the circuits of the central brain (assuming bilateral symmetry), and in particular contains circuits critical to unlocking mysteries involving associative learning in the mushroom body, navigation and sleep in the central complex, and circadian rhythms among clock circuits. The largest dense reconstruction to date, it contains around 25,000 neurons, most of which were rigorously clustered and named, with about 20 million chemical synapses between them, plus portions of many other neurons truncated by the boundary of the data set (details in Figure 1). Each neuron is documented at many levels - the detailed voxels that constitute it, a skeleton with segment diameters, its synaptic partners and the location of most of their synapses. Figure 1 Download asset Open asset The hemibrain and some basic statistics. The highlighted area shows the portion of the central brain that was imaged and reconstructed, superimposed on a grayscale representation of the entire Drosophila brain. For the table, a neuron is traced if all its main branches within the volume are reconstructed. A neuron is considered uncropped if most arbors (though perhaps not the soma) are contained in the volume. Others are considered cropped. Note: (1) our definition of cropped is somewhat subjective; (2) the usefulness of a cropped neuron depends on the application; and (3) some small fragments are known to be distinct neurons. For simplicity, we will often state that the hemibrain contains ≈25K neurons. Producing this data set required advances in sample preparation, imaging, image alignment, machine segmentation of cells, synapse detection, data storage, proofreading software, and protocols to arbitrate each decision. A number of new tests for estimating the completeness and accuracy were required and therefore developed, in order to verify the correctness of the connectome. These data describe whole-brain properties and circuits, as well as contain new methods to classify cell types based on connectivity. Computational compartments are now more carefully defined, we conclusively identify synaptic circuits, and each neuron is annotated by name and putative cell type, making this the first complete census of neuropils, tracts, cells, and connections in this portion of the brain. We compare the statistics and structure of different brain regions, and for the brain as a whole, without the confounds introduced by studying different circuitry in different animals. All data are publicly available through web interfaces. This includes a browser interface, NeuPrint (Clements et al., 2020), designed so that any interested user can query the hemibrain connectome even without specific training. NeuPrint can query the connectivity, partners, connection strengths and morphologies of all specified neurons, thus making identification of upstream and downstream partners both orders of magnitude easier, and significantly more confident, compared to existing genetic methods. In addition, for those who are willing to program, the full data set - the gray scale voxels, the segmentation and proofreading results, skeletons, and graph model of connectivity, are also available through publicly accessible application program interfaces (APIs). This effort differs from previous EM reconstructions in its social and collaborative aspects. Previous reconstructions were either dense in much smaller EM volumes (such as Meinertzhagen and O'Neil, 1991; Helmstaedter et al., 2013; Takemura et al., 2017) or sparse in larger volumes (such as Eichler et al., 2017 or Zheng et al., 2018). All have concentrated on the reconstruction of specific circuits to answer specific questions. When the same EM volume is used for many such efforts, as has occurred in the Drosophila larva and the full adult fly brain, this leads to an overall reconstruction that is the union of many individual efforts (Saalfeld et al., 2009). The result is inconsistent coverage of the brain, with some regions well reconstructed and others missing entirely. In contrast, here we have analyzed the entire volume, not just the subsets of interest to specific groups of researchers with the expertise to tackle EM reconstruction. We are making these data available without restriction, with only the requirement to cite the source. This allows the benefits of known circuits and connectivity to accrue to the field as a whole, a much larger audience than those with expertise in EM reconstruction. This is analogous to progress in genomics, which transitioned from individual groups studying subsets of genes, to publicly available genomes that can be queried for information about genes of choice (Altschul et al., 1990). One major benefit to this effort is to facilitate research into the circuits of the fly’s brain. A common question among researchers, for example, is the identity of upstream and downstream (respectively input and output) partners of specific neurons. Previously, this could only be addressed by genetic trans-synaptic labeling, such as trans-Tango (Talay et al., 2017), or by sparse tracing in previously imaged EM volumes (Zheng et al., 2018). However, the genetic methods may give false positives and negatives, and both alternatives require specialized expertise and are time consuming, often taking months of effort. Now, for any circuits contained in our volume, a researcher can obtain the same answers in seconds by querying a publicly available database. Another major benefit of dense reconstruction is its exhaustive nature. Genetic methods such as stochastic labeling may miss certain cell types, and counts of cells of a given type are dependent on expression levels, which are always uncertain. Previous dense reconstructions have demonstrated that existing catalogs of cell types are incomplete, even in well-covered regions (Takemura et al., 2017). In our hemibrain sample, we have identified all the cells within the reconstructed volume, thus providing a complete and unbiased census of all cell types in the fly’s central brain (at least in this single female), and a precise count of the cells of each type. Another scientific benefit lies in an analysis without the uncertainty of pooling data obtained from different animals. The detailed circuitry of the fly’s brain is known to depend on nutritional history, age, and circadian rhythm. Here, these factors are held constant, as are the experimental methods, facilitating comparison between different fly brain regions in this single animal. Evaluating stereotypy across animals will of course eventually require additional connectomes. Previous reconstructions of compartmentalized brains have concentrated on particular regions and circuits. The mammalian retina (Helmstaedter et al., 2013) and cortex (Kasthuri et al., 2015), and insect mushroom bodies (Eichler et al., 2017; Takemura et al., 2017) and optic lobes (Takemura et al., 2015) have all been popular targets. Additional studies have examined circuits that cross regions, such as those for sensory integration (Ohyama et al., 2015) or motion vision (Shinomiya et al., 2019). So far lacking are systematic studies of the statistical properties of computational compartments and their connections. Neural circuit motifs have been studied (Song et al., 2005), but only those restricted to small motifs and at most a few cell types, usually in a single portion of the brain. Many of these results are in mammals, leading to questions of whether they also apply to invertebrates, and whether they extend to other regions of the brain. While there have been efforts to build reduced, but still accurate, electrical models of neurons (Marasco et al., 2012), none of these to our knowledge have used the compartment structure of the brain. What is included Table 1 shows the hierarchy of the named brain regions that are included in the hemibrain. Table 2 shows the primary regions that are at least 50% included in the hemibrain sample, their approximate size, and their completion percentage. Our names for brain regions follow the conventions of Ito et al., 2014 with the addition of ‘(L)’ or ‘(R)’ to indicate whether the region (most of which occur on both sides of the fly) has its cell bodies in the left or right, respectively. The mushroom body (Tanaka et al., 2008; Aso et al., 2014) and central complex (Wolff et al., 2015; Wolff and Rubin, 2018) are further divided into finer compartments. Table 1 Brain regions contained and defined in the hemibrain, following the naming conventions of Ito et al., 2014 with the addition of (R) and (L) to specify the side of the soma for that region. Italics indicate master regions not explicitly defined in the hemibrain. Region LA is not included in the volume. The regions are hierarchical, with the more indented regions forming subsets of the less indented. The only exceptions are dACA, lACA, and vACA which are considered part of the mushroom body but are not contained in the master region MB. OL(R)Optic lobeCXCentral complexLH(R)Lateral horn LAlamina FBFan-shaped body ME(R)Medula FBl1Fan-shaped body layer 1SNP(R)/(L)Superior neuropils AME(R)Accessory medulla FBl2Fan-shaped body layer 2 SLP(R)Superior lateral protocerebrum LO(R)Lobula FBl3Fan-shaped body layer 4 SIP(R)/(L)Superior intermediate protocerebrum LOP(R)Lobula plate FBl4Fan-shaped body layer 4 SMP(R)(L)Superior medial protocerebrum FBl5Fan-shaped body layer 5MB(R)/(L)Mushroom body FBl6Fan-shaped body layer 6INPInferior neuropils CA(R)/(L)Calyx FBl7Fan-shaped body layer 7 CRE(R)/(L)Crepine dACA(R)Dorsal accessory calyx FBl8Fan-shaped body layer 8 RUB(R)/(L)Rubu lACA(R)Lateral accessory calyx FBl9Fan-shaped body layer 9 ROB(R)Round body vACA(R)Ventral accessory calyx EBEllipsoid body SCL(R)/(L)Superior clamp PED(R)Pedunculus EBr1Ellipsoid body zone r1 ICL(R)/(L)Inferior clamp a’L(R)/(L)Alpha prime lobe EBr2r4Ellipsoid body zone r2r4 IBInferior bridge a’1(R)Alpha prime lobe compartment 1 EBr3amEllipsoid body zone r3am ATL(R)/(L)Antler a’2(R)Alpha prime lobe compartment 2 EBr3dEllipsoid body zone r3d a’3(R)Alpha prime lobe compartment 3 EBr3pwEllipsoid body zone r3pwAL(R)/(L)Antennal lobe aL(R)/(L)Alpha lobe EBr5Ellipsoid body zone r5 a1(R)Alpha lobe compartment 1 EBr6Ellipsoid body zone r6VMNPVentromedial neuropils a2(R)Alpha lobe compartment 2 AB(R)/(L)Asymmetrical body VES(R)/(L)Vest a3(R)Alpha lobe compartment 3 PBProtocerebral bridge EPA(R)/(L)Epaulette gL(R)/(L)Gamma lobe PB(R1)PB glomerulus R1 GOR(R)/(L)Gorget g1(R)Gamma lobe compartment 1 PB(R2)PB glomerulus R2 SPS(R)/(L)Superior posterior slope g2(R)Gamma lobe compartment 2 PB(R3)PB glomerulus R3 IPS(R)/(L)Inferior posterior slope g3(R)Gamma lobe compartment 3 PB(R4)PB glomerulus R4 g4(R)Gamma lobe compartment 4 PB(R5)PB glomerulus R5PENPPariesophageal neuropils g5(R)Gamma lobe compartment 5 PB(R6)PB glomerulus R6 SADSaddle b’L(R)/(L)Beta prime lobe PB(R7)PB glomerulus R7 AMMCAntennal mechanosensory and motor center b’1(R)Beta prime lobe compartment 1 PB(R8)PB glomerulus R8 FLA(R)Flange b’2(R)Beta prime lobe compartment 2 PB(R9)PB glomerulus R9 CAN(R)Cantle bL(R)/(L)Beta lobe PB(L1)PB glomerulus L1 PRWprow b1(R)Beta lobe compartment 1 PB(L2)PB glomerulus L2 b2(R)Beta lobe compartment 2 PB(L3)PB glomerulus L3GNGGnathal ganglia PB(L4)PB glomerulus L4LX(R)/(L)Lateral complex PB(L5)PB glomerulus L5Major Fiber bundles BU(R)/(L)Bulb PB(L6)PB glomerulus L6 AOT(R)Anterior optic tract LAL(R)/(L)Lateral accessory lobe PB(L7)PB glomerulus L7 GCGreat commissure GA(R)Gall PB(L8)PB glomerulus L8 GF(R)Giant Fiber (single neuron) PB(L9)PB glomerulus L9 mALT(R)/(L)Medial antennal lobe tractVLNP(R)Ventrolateral neuropils NONoduli POCPosterior optic commissure AOTU(R)Anterior optic tubercle NO1(R)/(L)Nodulus 1 AVLP(R)Anterior ventrolateral protocerebrum NO2(R)/(L)Nodulus 2 PVLP(R)Posterior ventrolateral protocerebrum NO3(R)/(L)Nodulus 3 PLP(R)Posterior lateral cerebrum WED(R)Wedge Table 2 Regions with ≥50% included in the hemibrain, sorted by completion percentage. The approximate percentage of the region included in the hemibrain volume is shown as ‘%inV’. ‘T-bars’ gives a rough estimate of the size of the region. ‘comp%’ is the fraction of the post-synaptic densities (PSDs) contained in the brain region for which both the PSD and the corresponding T-bar are in neurons marked ‘Traced’. Name%inVT-barscomp%Name%inVT-barscomp%PED(R)100%5480585%aL(R)100%9537584%b’L(R)100%6769583%bL(R)100%7111283%gL(R)100%17678583%a’L(R)100%3909182%EB100%16428681%bL(L)56%5879981%NO100%3672279%b’L(L)88%5780278%gL(L)55%13325676%CA(R)100%6951773%AB(R)100%273465%aL(L)51%4480362%FB100%45103162%AL(R)83%50100459%AB(L)100%57257%PB100%4655755%AME(R)100%604551%BU(R)100%938546%CRE(R)100%13794640%AOTU(R)100%9257838%LAL(R)100%23438838%SMP(R)100%51093734%PVLP(R)100%47521930%ATL(R)100%2547229%SPS(R)100%25381829%ATL(L)100%2815329%VES(R)84%15716829%IB100%20044728%CRE(L)90%13265628%SIP(R)100%18749326%BU(L)52%701426%GOR(R)100%2714026%WED(R)100%23289825%SMP(L)100%46078426%EPA(R)100%3143826%PLP(R)100%42994926%AVLP(R)100%63053823%ICL(R)100%20254923%SLP(R)100%48779523%LO(R)64%85525122%SCL(R)100%18956922%GOR(L)60%1955821%LH(R)100%23166219%CAN(R)68%651216% Appendix 1—table 6 provide the list of identified neuron types and their naming schemes. These include newly identified sensory inputs and motor outputs. The nature of the proofreading process allows us to improve the data even after their initial publication. Our initial data release was version v1.0 (Xu et al., 2020c). Version v1.1 is now available, including improvements such as better accuracy, more consistent cell naming and typing, and inclusion of anatomical names for central complex neurons. The old version(s) remain online and available, to allow reproducibility of older analyses, but we strongly recommend all new analyses use the latest version. The analyses in this article, and in the corresponding articles on the mushroom body and central complex, are based on version v1.1, unless otherwise noted. What is not included This research focused on the neurons of the brain and the chemical synapses between them. Every step in our process, from staining and sample preparation through segmentation and proofreading, has been optimized with this goal in mind. While neurons and their chemical synapses are critical to brain operation, they are far from the full story. Other contributors, known to be important, could not be included in our study, largely for technical reasons. Among these are gap junctions, glia, and structures internal to the cell such as mitochondria. Gap junctions, or electrical connections between neurons, are difficult to reliably detect by FIB-SEM under the best of circumstances and not detectable at the low (for EM) resolution needed to complete this study in a reasonable amount of time. Their contribution to the connectome will need to be established through other means - see the section on future research. Glial cells were difficult to segment, due to both staining differences and convoluted morphologies. We identified the volumes where they exist (a glia ’mask’, which allows these regions to be color-coded when viewed in NeuroGlancer) but did not separate them into cells. Structures internal to the neurons, except for synapses, are not considered here even though many are visible in our EM preparation. The most obvious example is mitochondria. Again, we have identified many of them so we could evaluate their effect on segmentation, but they are not included in our connectome. Finally, autapses (synapses from a neuron onto itself) are known to exist in Drosophila, but are sufficiently rare that they fall well below the rate of false positives in our automated synapse detection. Therefore most of the putative autapses are false positives, and we do not include them in our connectivity data. Differences from connectomes of vertebrates Most accounts of neurobiology define the operation of the mammalian nervous system with, at most, only passing reference to invertebrate brains. Fly (or other insect) nervous systems differ from those of vertebrates in several aspects (Meinertzhagen, 2016b). Some main differences include: Most synapses are polyadic. Each synapse structure comprises a single presynaptic release site and, adjacent to this, several neurites expressing neurotransmitter receptors. An element, T-shaped and typically called a T-bar in flies, marks the site of transmitter release into the cleft between cells. This site typically abuts the neurites of several other cells, where a postsynaptic density (PSD) marks the receptor location. Most neurites are neither purely axonic nor dendritic, but have both pre- and postsynaptic partners, a feature that may be more prominent in mammalian brains than recognized (Morgan and Lichtman, 2020). Within a single brain region, however, neurites are frequently predominantly dendritic (postsynaptic) or axonic (presynaptic). Unlike some synapses in mammals, EM imagery (at least as we have acquired and analyzed it here) fails to reveal obvious information about whether a synapse is excitatory or inhibitory. The soma or cell body of each fly neuron resides in a rind (the cell body layer) on the periphery of the brain, mostly disjoint from the main neurites innervating the internal neuropil. As a result, unlike vertebrate neurons, no synapses form directly on the soma. The neuronal process between the soma and the first branch point is called the cell body fiber (CBF), which is likewise not involved in the synaptic transmission of information. Synapse sizes are much more uniform than those of mammals. Stronger connections are formed by increasing the number of synapses in parallel, not by forming larger synapses, as in vertebrates. In this paper, we will refer to the ‘strength’ of a connection as the synapse count, even though we acknowledge that we lack information on the relative activity and strength of the synapses, and thus a true measure of their coupling strength. The brain is small, about 250 μm per side, and has roughly the same size as the dendritic arbor of a single pyramidal neuron in the mammalian cortex. Axons of fly neurons are not myelinated. Some fly neurons rely on graded transmission (as opposed to spiking), without obvious anatomical distinction. Some neurons even switch between graded and spiking operation (Pimentel et al., 2016). Connectome reconstruction Producing a connectome comprising reconstructed neurons and the chemical synapses between them required several steps. The first step, preparing a fly brain and imaging half of its center, produced a dataset consisting of 26 teravoxels of data, each with 8 bits of grayscale information. We applied numerous machine-learning algorithms and over 50 person-years of proofreading effort over ≈2 calendar years to extract a variety of more compact and useful representations, such as neuron skeletons, synapse locations, and connectivity graphs. These are both more useful and much smaller than the raw grayscale data. For example, the connectivity could be reasonably summarized by a graph with ≈25,000 nodes and ≈3 million edges. Even when the connections were assigned to different brain regions, such a graph took only 26 MB, still large but roughly a million fold reduction in data size. Many of the supporting methods for this reconstruction have been recently published. Here, we briefly survey each major area, with more details reported in the companion papers. Major advances include: New methods to fix and stain the sample, preparing a whole fly brain with well-preserved subcellular detail particularly suitable for machine analysis. Methods that have enabled us to collect the largest EM dataset yet using Focused Ion Beam Scanning Electron Microscopy (FIB-SEM), resulting in isotropic data with few artifacts, features that significantly sped up reconstruction. A coarse-to-fine, automated flood-filling network segmentation pipeline applied to image data normalized with cycle-consistent generative adversarial networks, and an aggressive automated agglomeration regime enabled by advances in proofreading. A new hybrid synapse prediction method, using two differing underlying techniques, for accurate synapse prediction throughout the volume. New top-down proofreading methods that utilize visualization and machine learning to achieve orders of magnitude faster reconstruction compared with previous approaches in the fly’s brain. Each of these is explained in more detail in the following sections and, where necessary, in the appendix. The companion papers are ‘The connectome of the Drosophila melanogaster mushroom body: implications for function’ (Li et al., 2020) and ‘A complete synaptic-resolution connectome of the Drosophila melanogaster central complex’ by Jayaraman, et al. Image stack collection The first steps, fixing and staining the specimen, have been accomplished taking advantage of three new developments. These improved methods allow us to fix and stain a full fly’s brain but nevertheless recover neurons as round profiles with darkly stained synapses, suitable for machine segmentation and automatic synapse detection. We started with a 5-day-old female of wild-type Canton S strain G1 x w1118, raised on a 12 hr day/night cycle. 1.5 hr after lights-on, we used a custom-made jig to microdissect the brain, which was then fixed and embedded in Epon, an epoxy resin. We then enhanced the electron contrast by staining with heavy metals, and progressively lowered the temperature during dehydration of the sample. Collectively, these methods optimize morphological preservation, allow full-brain preparation without distortion (unlike fast freezing methods), and provide increased staining intensity that speeds the rate of FIB-SEM imaging (Lu et al., 2019). The hemibrain sample is roughly 250 × 250 × 250 μm, larger than we can FIB-SEM without introducing milling artifacts. Therefore, we subdivided our epoxy-embedded samples into 20-μm-thick slabs, both to avoid artifacts and allow imaging in parallel (each slab can be imaged in a different FIB machine) for increased throughput. To be effective, the cut surfaces of the slabs must be smooth at the ultrastructural level and have only minimal material loss. Specifically, for connectomic research, all long-distance processes must remain traceable across sequential slabs. We used an improved version of our previously published ‘hot-knife’ ultrathick sectioning procedure (Hayworth et al., 2015) which uses a heated, oil-lubricated diamond knife, to section the Drosophila brain into 37 sagittal slabs of 20 μm thickness with an estimated material loss between consecutive slabs of only ∼30 nm – sufficiently small to allow tracing of long-distance neurites. Each slab was re-embedded, mounted, and trimmed, then examined in 3D with X-ray tomography to check for sample quality and establish a scale factor for Z-axis cutting by FIB. The resulting slabs were FIB-SEM imaged separately (often in parallel, in different FIB-SEM machines), and the resulting volume datasets were stitched together computationally. Connectome studies come with clearly defined resolution requirements – the finest neurites must be traceable by humans and should be reliably segmented by automated algorithms (Januszewski et al., 2018). In Drosophila, the very finest neural processes are usually 50 nm but can be as little as 15 nm (Meinertzhagen, 2016a). This fundamental biological dimension determines the minimum isotropic resolution requirements for tracing neural circuits. To meet the demand for high isotropic resolution and large volume imaging, we chose the FIB-SEM imaging platform, which offers high isotropic resolution (<10 nm in x, y, and z), minimal artifacts, and robust image alignment. The high-resolution and isotropic dataset possible with FIB-SEM has substantially expedited the Drosophila connectome pipeline. Compared to serial-section imaging, with its sectioning artifacts and inferior Z-axis resolution, FIB-SEM offers high-quality image alignment, a smaller number of artifacts, and isotropic resolution. This allows higher quality automated segmentation and makes manual proofreading and correction easier and faster. At the beginning, deficiencies in imaging speed and system reliability of any commercial FIB-SEM system capped the maximum possible image volume to less than 0.01% of a full fly brain, problems that persist even now. To remedy them, we redesigned the entire control system, improved the imaging speed more than 10x, and created innovative solutions addressing all known failure modes, which thereby expanded the practical imaging volume of conventional FIB-SEM by more than four orders of magnitude from 103μm3 to 3⋅107 μm3, while maintaining an isotropic resolution of 8 × 8 × 8 nm voxels (Xu et al., 2017; Xu et al., 2020a). In order to overcome the aberration of a large field of view (up to 300 μm wide), we developed a novel tiling approach without sample stage movement, in which the imaging parameters of each tile are individually optimized through an in-line auto focus routine without overhead (Xu et al., 2020b). After numerous improvements, we have transformed the conventional FIB-SEM from a laboratory tool that is unreliable for more than a few days of imaging to a robust volume EM platform with effective long-term reliability, able to perform years of continuous imaging without defects in the final image stack. Imaging time, rather than FIB-SEM reliability, is now the main impediment to obtaining even larger volumes. In our study here, the Drosophila 'hemibrain', 13 consecutive hot-knifed slabs were imaged using two customized enhanced FIB-SEM systems, in which an FEI Magnum FIB column was mounted at 90° upon a Zeiss Merlin SEM. After data collection, streaking artifacts generated by secondary electrons along the FIB milling direction were computationally removed using a mask in the frequency domain. The image stacks were then aligned using a customized version of the software platform developed for serial section transmission electron microscopy (Zheng et al., 2018; Khairy et al., 2018), followed by binning

Reconstruction of neural circuitry at single-synapse resolution is a key target for improving understanding of the nervous system in health and disease. Serial section transmission electron microscopy (ssTEM) is among the most prolific imaging methods employed in pursuit of such reconstructions. We demonstrate how Flood-Filling Networks (FFNs) can be used to computationally segment a forty-teravoxel whole-brain Drosophila ssTEM volume. To compensate for data irregularities and imperfect global alignment, FFNs were combined with procedures that locally re-align serial sections as well as dynamically adjust and synthesize image content. The proposed approach produced a largely merger-free segmentation of the entire ssTEM Drosophila brain, which we make freely available. As compared to manual tracing using an efficient skeletonization strategy, the segmentation enabled circuit reconstruction and analysis workflows that were an order of magnitude faster.

Reconstruction of neural circuits from volume electron microscopy data requires the tracing of cells in their entirety, including all their neurites. Automated approaches have been developed for tracing, but their error rates are too high to generate reliable circuit diagrams without extensive human proofreading. We present flood-filling networks, a method for automated segmentation that, similar to most previous efforts, uses convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of individual neuronal processes. We used flood-filling networks to trace neurons in a dataset obtained by serial block-face electron microscopy of a zebra finch brain. Using our method, we achieved a mean error-free neurite path length of 1.1 mm, and we observed only four mergers in a test set with a path length of 97 mm. The performance of flood-filling networks was an order of magnitude better than that of previous approaches applied to this dataset, although with substantially increased computational costs.