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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.

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.

The neural circuits responsible for behavior remain largely unknown. Previous efforts have reconstructed the complete circuits of small animals, with hundreds of neurons, and selected circuits for larger animals. Here we (the FlyEM project at Janelia and collaborators at Google) summarize new methods and present the complete circuitry of a large fraction of the brain of a much more complex animal, the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses, and proofread such large data sets; new methods that define cell types based on connectivity in addition to morphology; and new methods to simplify access to a large and evolving data set. From the resulting data we derive a better definition of computational compartments and their connections; an exhaustive atlas of cell examples and types, many of them novel; detailed circuits for most of the central brain; and exploration of the statistics and structure of different brain compartments, and the brain as a whole. We make the data public, with a web site and resources specifically designed to make it easy to explore, for all levels of expertise from the expert to the merely curious. The public availability of these data, and the simplified means to access it, dramatically reduces the effort needed to answer typical circuit questions, such as the identity of upstream and downstream neural partners, the circuitry of brain regions, and to link the neurons defined by our analysis with genetic reagents that can be used to study their functions.Note: In the next few weeks, we will release a series of papers with more involved discussions. One paper will detail the hemibrain reconstruction with more extensive analysis and interpretation made possible by this dense connectome. Another paper will explore the central complex, a brain region involved in navigation, motor control, and sleep. A final paper will present insights from the mushroom body, a center of multimodal associative learning in the fly brain.

Recent advances in automatic image segmentation and synapse prediction in electron microscopy (EM) datasets of the brain enable more efficient reconstruction of neural connectivity. In these datasets, a single neuron can span thousands of images containing complex tree-like arbors with thousands of synapses. While image segmentation algorithms excel within narrow fields of views, the algorithms sometimes struggle to correctly segment large neurons, which require large context given their size and complexity. Conversely, humans are comparatively good at reasoning with large objects. In this paper, we introduce several semi-automated strategies that combine 3D visualization and machine guidance to accelerate connectome reconstruction. In particular, we introduce a strategy to quickly correct a segmentation through merging and cleaving, or splitting a segment along supervoxel boundaries, with both operations driven by affinity scores in the underlying segmentation. We deploy these algorithms as streamlined workflows in a tool called Neu3 and demonstrate superior performance compared to prior work, thus enabling efficient reconstruction of much larger datasets. The insights into proofreading from our work clarify the trade-offs to consider when tuning the parameters of image segmentation algorithms.

Open-source software development has skyrocketed in part due to community tools like github.com, which allows publication of code as well as the ability to create branches and push accepted modifications back to the original repository. As the number and size of EM-based datasets increases, the connectomics community faces similar issues when we publish snapshot data corresponding to a publication. Ideally, there would be a mechanism where remote collaborators could modify branches of the data and then flexibly reintegrate results via moderated acceptance of changes. The DVID system provides a web-based connectomics API and the first steps toward such a distributed versioning approach to EM-based connectomics datasets. Through its use as the central data resource for Janelia's FlyEM team, we have integrated the concepts of distributed versioning into reconstruction workflows, allowing support for proofreader training and segmentation experiments through branched, versioned data. DVID also supports persistence to a variety of storage systems from high-speed local SSDs to cloud-based object stores, which allows its deployment on laptops as well as large servers. The tailoring of the backend storage to each type of connectomics data leads to efficient storage and fast queries. DVID is freely available as open-source software with an increasing number of supported storage options.

Understanding memory formation, storage and retrieval requires knowledge of the underlying neuronal circuits. In Drosophila, the mushroom body (MB) is the major site of associative learning. We reconstructed the morphologies and synaptic connections of all 983 neurons within the three functional units, or compartments, that compose the adult MB’s α lobe, using a dataset of isotropic 8 nm voxels collected by focused ion-beam milling scanning electron microscopy. We found that Kenyon cells (KCs), whose sparse activity encodes sensory information, each make multiple en passant synapses to MB output neurons (MBONs) in each compartment. Some MBONs have inputs from all KCs, while others differentially sample sensory modalities. Only 6% of KC>MBON synapses receive a direct synapse from a dopaminergic neuron (DAN). We identified two unanticipated classes of synapses, KC>DAN and DAN>MBON. DAN activation produces a slow depolarization of the MBON in these DAN>MBON synapses and can weaken memory recall. DOI: http://dx.doi.org/10.7554/eLife.26975.001

Animal behaviour arises from computations in neuronal circuits, but our understanding of these computations has been frustrated by the lack of detailed synaptic connection maps, or connectomes. For example, despite intensive investigations over half a century, the neuronal implementation of local motion detection in the insect visual system remains elusive. Here we develop a semi-automated pipeline using electron microscopy to reconstruct a connectome, containing 379 neurons and 8,637 chemical synaptic contacts, within the Drosophila optic medulla. By matching reconstructed neurons to examples from light microscopy, we assigned neurons to cell types and assembled a connectome of the repeating module of the medulla. Within this module, we identified cell types constituting a motion detection circuit, and showed that the connections onto individual motion-sensitive neurons in this circuit were consistent with their direction selectivity. Our results identify cellular targets for future functional investigations, and demonstrate that connectomes can provide key insights into neuronal computations. Reconstruction of a connectome within the fruitfly visual medulla, containing more than 300 neurons and over 8,000 chemical synapses, reveals a candidate motion detection circuit; such a circuit operates by combining displaced visual inputs, an operation consistent with correlation based motion detection. Three papers in this issue of Nature use the retina as a model for mapping neuronal circuits from the level of individual synaptic contacts to the long-range scale of dendritic interactions. Helmstaedter et al. used electron microscopy to map a mammalian retinal circuit of close to a thousand neurons. The work reveals a new type of retinal bipolar neuron and suggests functional mechanisms for known visual computations. The other two groups study the detection of visual motion in the Drosophila visual system — a classic neural computation model. Takemura et al. used semi-automated electron microscopy to reconstruct the basic connectome (8,637 chemical synapses among 379 neurons) of Drosophila's optic medulla. Their results reveal a candidate motion detection circuit with a wiring plan consistent with direction selectivity. Maisak et al. used calcium imaging to show that T4 and T5 neurons are divided into specific subpopulations responding to motion in four cardinal directions, and are specific to 'ON' versus 'OFF' edges, respectively.

We reconstructed the synaptic circuits of seven columns in the second neuropil or medulla behind the fly's compound eye. These neurons embody some of the most stereotyped circuits in one of the most miniaturized of animal brains. The reconstructions allow us, for the first time to our knowledge, to study variations between circuits in the medulla's neighboring columns. This variation in the number of synapses and the types of their synaptic partners has previously been little addressed because methods that visualize multiple circuits have not resolved detailed connections, and existing connectomic studies, which can see such connections, have not so far examined multiple reconstructions of the same circuit. Here, we address the omission by comparing the circuits common to all seven columns to assess variation in their connection strengths and the resultant rates of several different and distinct types of connection error. Error rates reveal that, overall

The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM). Thus, a common approach to network reconstruction is to perform (error-prone) automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We have developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration), improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others). We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the current limitations of the gala library and how we intend to address them.

An artificial neural network can be trained to detect a structure of interest within medica1 images. The method entails the training of the neural net with hand-drawn masks using the backpropagation algorithm. Multiple neural nets, each a detector for a different structure, can be created in this fashion. In order to enhance the detection of a single target structure, a final neural net can be trained to incorporate the output of a primary target detector as well as related structure detectors. This paper describes a method for aorta detection using neural nets trained to detect the aorta, spinal cord, and inferior vena cava.

This paper describes the development of a Physician's Workstation (PWS) for the evaluation of soft tissue types utilizing MR, which includes the analysis, quantification and 3-D visualization of the pertinent tissues. A major theme underlying our work is that it is possible to develop robust image processinglpattem recognition algorithms for the evaluation of diseased tissue by focusing on specific diseases involving localized regions of the body obtained with consistent and well developed imaging protocols. We have implemented two specific image processinglpattem recognition and display systems for (1) the evaluation of atherosclerosis; and (2) the differentiation of breast lesions.

In this paper, algorithms for the segmentation of head structures within three-dimensional magnetic resonance images are described. The process can be described as passing the raw images through a series of image processing programs to form afinal image with voxel values corresponding to tissue type. This resulting image can be loaded into a commercial graphics package in order to produce near photorealistic pictures and spinning animations of head structures.

Three experience-based learning techniques are explored using the game Go-Moku (Connect-5). The first method, an exception tree, is used to prevent poor lines of play by recording critical moves in a move tree. The second method simply records all games in a large experience tree, backtracking the eventual outcomes in traditional minimax fashion. Both techniques allow a computer player to modify its behavior based on past experience, and, therefore, typically defeat static game programs. The last method explored is the use of a multilayer feedforward artificial neural network for strategy calculation. The network was trained on selected interior nodes of the experience tree using the backpropagation algorithm. The neural network strategy algorithm compares favorably with a fine-tuned hand-crafted strategy algorithm. >

We have designed and implemented a computer-based system for three-dimensional stereotactic planning of minimally invasive neurosurgical procedures. The system integrates rapid acquisition of digital medical images, segmentation, multi-modality registration, and three-dimensional planning capabilities. Emphasis on real-time planning is central to our system: imaging, pre-processing and planning are performed on the morning of surgery in clinically useful times. We have tested the system on procedures such as needle biopsies, depth electrode placements, pallidotomies, thalamotomies and craniectomies for arteriovenous malformations, aneurysms and tumors. We describe in this paper the core algorithms of our system, and discuss issues related to implementation, validation and user acceptance.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

With the advent of fast 3D magnetic resonance imaging (MRI) sequences, truly 3D volumes of data can be routinely acquired. While images produced by modalities such as positron emission tomography (PET;) and single photon emission computed tomography (SPECT) depict functional information, newer MRI techniques like magnetization-prepared rapid gradient-echo (MP-RAGE) capture a great deal of ana tomical detail [3,10]. Such 3D images are used by clinicians in two types of tasks, visualization and quLantifi cation. Visualization, in its most basic form, permits a user to see structures of interest within a volume of data. The "structures of interest" may not correspond to any physically visible phenomenon (e.g. the quan tity of blood flow to neural tissue in a functional image) but the process of visualization transforms the data into images which may then be displayed using computer graphics. On the other hand, quantification, while often depicted graphically, attempts to reduce the mass of data into numbers useful as clinical indicators. A major obstacle to both of these tasks is the prerequisite image segmentation. In order for the brain to be visualized beneath the overlying head, a segmentation step must determine which volume elements, or voxels, in the 3D head image correspond to the brain. Similarly, quantification requires the distinguishing of background voxels from voxels corresponding to the VOl. A growing number of clinical studies depend on volume measurements after segmentation. Examples include: tracking the progression/remission of disease processes (e.g. the size of intracranial tumors); evaluating the neuroanatomical abnormalities associated with schizophrenia (e.g. ventricular volumes); and determining atrophy associated with Alzheimer-type dementia and temporal lobe epilepsy (as in the hippocampal formation).

An automated procedure for segmentation of the brain from 3-D MR images of the head is described. This process combines some heuristics with a number of three-dimensional image processing and computer vision techniques including seed-based volume growing, DOG convolution and zero-crossing detection, convolution with the Zucker-Hummel operator, and watershed anaylsis. There are two broad steps: (1) rough estimation of brain voxels, and (2) refinement of the first step through a reverse-gravity watershed analysis. All operations are performed in three-dimensions in order to fully utilize the information present in the voxels generated by the 3-D MP-RAGE sequence.

This paper describes the rationale and importance of utilizing a priori, model based information to guide segmentation and classification in complex medical images represented by Magnetic Resonance Imaging (MRI). The incorporation of such a priori, model based information requires the development of a "top down" model driven system, rather than the more traditional "bottom up" data driven system. Two different examples which incorporate such model based a priori information are discussed: (1) The segmentation and classification of tissues involved in atherosclerosis; and (2) The segmentation and classification of brain tissue for neurosurgical applications.