Big imaging data is becoming more prominent in brain sciences across spatiotemporal scales and phylogenies. We have developed a computational ecosystem that enables storage, visualization, and analysis of these data in the cloud, thusfar spanning 20+ publications and 100+ terabytes including nanoscale ultrastructure, microscale synaptogenetic diversity, and mesoscale whole brain connectivity, making NeuroData the largest and most diverse open repository of brain data.

Calcium imaging with fluorescent protein sensors is widely used to record activity in neuronal populations. The transform between neural activity and calcium-related fluorescence involves nonlinearities and low-pass filtering, but the effects of the transformation on analyses of neural populations are not well understood. We compared neuronal spikes and fluorescence in matched neural populations in behaving mice. We report multiple discrepancies between analyses performed on the two types of data, including changes in single-neuron selectivity and population decoding. These were only partially resolved by spike inference algorithms applied to fluorescence. To model the relation between spiking and fluorescence we simultaneously recorded spikes and fluorescence from individual neurons. Using these recordings we developed a model transforming spike trains to synthetic-imaging data. The model recapitulated the differences in analyses. Our analysis highlights challenges in relating electrophysiology and imaging data, and suggests forward modeling as an effective way to understand differences between these data.

Aphids exhibit divergent modes of embryogenesis during the sexual and asexual phases of the life cycle. To explore how a single genome can give rise to these alternative developmental modes, we have initiated embryological studies of the pea aphid, Acyrthosiphon pisum. Here we present a detailed description of parthenogenetic, viviparous embryonic development in the pea aphid. We compare and contrast development of the parthenogenetic embryo with that of the embryo resulting from sexual reproduction. The primary difference between the embryos is the scale on which development occurs: early parthenogenetic development occurs in a volume approximately three orders of magnitude smaller than the sexual egg, largely because of the apparent absence of yolk in the parthenogenetic egg. This results in a drastically different duration of syncytial energid cleavage and, presumably, patterning processes in the two embryos must act at scales that differ by orders of magnitude. The eggs also develop on time scales that differ approximately by an order of magnitude and the timing of the embryonic movements, collectively called blastokinesis, have temporally shifted relative to growth of the embryo. In addition, the endosymbiotic bacteria are transferred from mother to embryo in different ways in the two embryos. Finally, the function of the serosa has diverged greatly in the two embryos: in the sexual egg the serosa deposits a thick cuticle that protects the egg, whereas the serosa of the parthenogenetic embryo is greatly reduced and its function is unclear. The pea aphid is a useful model system for examining how a single genome has evolved to allow divergent modes of development.

Many animals rely on optic flow for navigation, using differences in eye image velocity to detect deviations from their intended direction of travel. However, asymmetries in image velocity between the eyes are often overshadowed by strong, symmetric translational optic flow during navigation. Yet, the brain efficiently extracts these asymmetries for course control. While optic flow sensitive-neurons have been found in many animal species, far less is known about the postsynaptic circuits that support such robust optic flow processing. In the fly Drosophila melanogaster, a group of neurons called the horizontal system (HS) are involved in course control during high-speed translation. To understand how HS cells facilitate robust optic flow processing, we identified central networks that connect to HS cells using full brain electron microscopy datasets. These networks comprise three layers: convergent inputs from different, optic flow-sensitive cells, a middle layer with reciprocal, and lateral inhibitory interactions among different interneuron classes, and divergent output projecting to both the ventral nerve cord (equivalent to the vertebrate spinal cord), and to deeper regions of the fly brain. By combining two-photon optical imaging to monitor free calcium dynamics, manipulating GABA receptors and modeling, we found that lateral disinhibition between brain hemispheres enhance the selectivity to rotational visual flow at the output layer of the network. Moreover, asymmetric manipulations of interneurons and their descending outputs induce drifts during high-speed walking, confirming their contribution to steering control. Together, these findings highlight the importance of competitive disinhibition as a critical circuit mechanism for robust processing of optic flow, which likely influences course control and heading perception, both critical functions supporting navigation.

Drosophila brains contain numerous neurons that form complex circuits. These neurons are derived in stereotyped patterns from a fixed number of progenitors, called neuroblasts, and identifying individual neurons made by a neuroblast facilitates the reconstruction of neural circuits. An improved MARCM (mosaic analysis with a repressible cell marker) technique, called twin-spot MARCM, allows one to label the sister clones derived from a common progenitor simultaneously in different colors. It enables identification of every single neuron in an extended neuronal lineage based on the order of neuron birth. Here we report the first example, to our knowledge, of complete lineage analysis among neurons derived from a common neuroblast that relay olfactory information from the antennal lobe (AL) to higher brain centers. By identifying the sequentially derived neurons, we found that the neuroblast serially makes 40 types of AL projection neurons (PNs). During embryogenesis, one PN with multi-glomerular innervation and 18 uniglomerular PNs targeting 17 glomeruli of the adult AL are born. Many more PNs of 22 additional types, including four types of polyglomerular PNs, derive after the neuroblast resumes dividing in early larvae. Although different offspring are generated in a rather arbitrary sequence, the birth order strictly dictates the fate of each post-mitotic neuron, including the fate of programmed cell death. Notably, the embryonic progenitor has an altered temporal identity following each self-renewing asymmetric cell division. After larval hatching, the same progenitor produces multiple neurons for each cell type, but the number of neurons for each type is tightly regulated. These observations substantiate the origin-dependent specification of neuron types. Sequencing neuronal lineages will not only unravel how a complex brain develops but also permit systematic identification of neuron types for detailed structure and function analysis of the brain.

Drosophila melanogaster has a rich repertoire of innate and learned behaviors. Its 100,000-neuron brain is a large but tractable target for comprehensive neural circuit mapping. Only electron microscopy (EM) enables complete, unbiased mapping of synaptic connectivity; however, the fly brain is too large for conventional EM. We developed a custom high-throughput EM platform and imaged the entire brain of an adult female fly at synaptic resolution. To validate the dataset, we traced brain-spanning circuitry involving the mushroom body (MB), which has been extensively studied for its role in learning. All inputs to Kenyon cells (KCs), the intrinsic neurons of the MB, were mapped, revealing a previously unknown cell type, postsynaptic partners of KC dendrites, and unexpected clustering of olfactory projection neurons. These reconstructions show that this freely available EM volume supports mapping of brain-spanning circuits, which will significantly accelerate Drosophila neuroscience..

For most model organisms in neuroscience, research into visual processing in the brain is difficult because of a lack of high-resolution maps that capture complex neuronal circuitry. The microinsect Megaphragma viggianii, because of its small size and non-trivial behavior, provides a unique opportunity for tractable whole-organism connectomics. We image its whole head using serial electron microscopy. We reconstruct its compound eye and analyze the optical properties of the ommatidia as well as the connectome of the first visual neuropil-the lamina. Compared with the fruit fly and the honeybee, Megaphragma visual system is highly simplified: it has 29 ommatidia per eye and 6 lamina neuron types. We report features that are both stereotypical among most ommatidia and specialized to some. By identifying the "barebones" circuits critical for flying insects, our results will facilitate constructing computational models of visual processing in insects.

Flying insects exhibit remarkable navigational abilities controlled by their compact nervous systems. Optic flow, the pattern of changes in the visual scene induced by locomotion, is a crucial sensory cue for robust self-motion estimation, especially during rapid flight. Neurons that respond to specific, large-field optic flow patterns have been studied for decades, primarily in large flies, such as houseflies, blowflies, and hover flies. The best-known optic-flow sensitive neurons are the large tangential cells of the dipteran lobula plate, whose visual-motion responses, and to a lesser extent, their morphology, have been explored using single-neuron neurophysiology. Most of these studies have focused on the large, Horizontal and Vertical System neurons, yet the lobula plate houses a much larger set of 'optic-flow' sensitive neurons, many of which have been challenging to unambiguously identify or to reliably target for functional studies. Here we report the comprehensive reconstruction and identification of the Lobula Plate Tangential Neurons in an Electron Microscopy (EM) volume of a whole Drosophila brain. This catalog of 58 LPT neurons (per brain hemisphere) contains many neurons that are described here for the first time and provides a basis for systematic investigation of the circuitry linking self-motion to locomotion control. Leveraging computational anatomy methods, we estimated the visual motion receptive fields of these neurons and compared their tuning to the visual consequence of body rotations and translational movements. We also matched these neurons, in most cases on a one-for-one basis, to stochastically labeled cells in genetic driver lines, to the mirror-symmetric neurons in the same EM brain volume, and to neurons in an additional EM data set. Using cell matches across data sets, we analyzed the integration of optic flow patterns by neurons downstream of the LPTs and find that most central brain neurons establish sharper selectivity for global optic flow patterns than their input neurons. Furthermore, we found that self-motion information extracted from optic flow is processed in distinct regions of the central brain, pointing to diverse foci for the generation of visual behaviors.

How the brain perceives sensory information and generates meaningful behavior depends critically on its underlying circuitry. The protocerebral bridge (PB) is a major part of the insect central complex (CX), a premotor center that may be analogous to the human basal ganglia. Here, by deconstructing hundreds of PB single neurons and reconstructing them into a common three-dimensional framework, we have constructed a comprehensive map of PB circuits with labeled polarity and predicted directions of information flow. Our analysis reveals a highly ordered information processing system that involves directed information flow among CX subunits through 194 distinct PB neuron types. Circuitry properties such as mirroring, convergence, divergence, tiling, reverberation, and parallel signal propagation were observed; their functional and evolutional significance is discussed. This layout of PB neuronal circuitry may provide guidelines for further investigations on transformation of sensory (e.g., visual) input into locomotor commands in fly brains.