A connectome of the adult drosophila central 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

A connectome and analysis of the adult Drosophila central brain

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

A connectome and analysis of the adult Drosophila central brain

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

Is aspirin effective in women undergoing in vitro fertilization (IVF)? Results from an individual patient data meta-analysis (IPD MA)

BACKGROUND Aspirin is believed to improve the outcome of IVF, but previous conventional meta-analyses on the subject are conflicting. Therefore, we performed a meta-analysis with individual patient data (IPD MA) of randomized clinical trials (RCTs) on the subject. METHODS A systematic literature search was conducted to identify RCTs assessing the effectiveness of aspirin in IVF. Authors were asked to share their original data. In a one step meta-analytic approach, the treatment effect of aspirin was estimated with odds ratios (ORs) and 95% confidence intervals (CIs) using logistic regression, based on the intention to treat principle. RESULTS Ten studies fulfilled the inclusion criteria. Authors of six studies provided IPD, including 1119 patients (562 placebo and 557 aspirin). There were 160 clinical pregnancies in the aspirin (28.8%) and 179 (31.9%) in the placebo group [OR 0.86, 95% CI (0.69–1.1)]. There were 129 ongoing pregnancies in the aspirin (23.6%) and 147 in the placebo group (26.7%) [OR 0.85, 95% CI (0.65–1.1)]. Whereas the conventional meta-analysis limited to studies that could provide IPD showed an OR of 0.89 (95% CI 0.69–1.2), the conventional meta-analysis limited to the eight studies of which method of randomization could be confirmed showed an OR of 0.94 (95% CI 0.76–1.17) and the conventional meta-analysis including all 10 eligible RCTs identified with our search changed the OR to 1.07 (95% CI 0.81–1.41). This difference in direction of effect, derived from the studies not able to share IPD of which quality of randomization could not be confirmed. CONCLUSIONS Aspirin does not improve pregnancy rates after IVF.E. Groeneveld, K.A. Broeze, M.J. Lambers, M. Haapsamo, K. Dirckx, B.C. Schoot, B. Salle, C.I. Duvan, R. Schats, B.W. Mol, and P.G.A. Hompes, for the IPD MARIA study grou