The GAL4-UAS system has proven its versatility in studying the function and expression patterns of neurons the Drosophila central nervous system. Although the GAL4 system has been used for 25 years, recent genetic intersectional tools have enabled genetic targeting of very small numbers of neurons aiding in the understanding of their function. This split-GAL4 system is extremely powerful for studying neuronal morphology and the neural basis of animal behavior. However, choosing lines to intersect that have overlapping patterns restricted to one to a few neurons has been cumbersome. This challenge is now growing as the collections of GAL4 driver lines has increased. Here we present a new method and software plug-in for Fiji to dramatically improve the speed of querying large databases of potential lines to intersect and aid in the split-GAL4 creation. We also provide pre-computed datasets for the Janelia GAL4 (5,738 lines) and VT GAL4 (7,429 lines) of the Drosophila central nervous system (CNS). The tool reduced our split-GAL4 creation effort dramatically.

Large electron microscopy image datasets for connectomics are typically composed of thousands to millions of partially overlapping two-dimensional images (tiles), which must be registered into a coherent volume prior to further analysis. A common registration strategy is to find matching features between neighboring and overlapping image pairs, followed by a numerical estimation of optimal image deformation using a so-called solver program. 
Existing solvers are inadequate for large data volumes, and inefficient for small-scale image registration. 
In this work, an efficient and accurate matrix-based solver method is presented. A linear system is constructed that combines minimization of feature-pair square distances with explicit constraints in a regularization term. In absence of reliable priors for regularization, we show how to construct a rigid-model approximation to use as prior. The linear system is solved using available computer programs, whose performance on typical registration tasks we briefly compare, and to which future scale-up is delegated. Our method is applied to the joint alignment of 2.67 million images, with more than 200 million point-pairs and has been used for successfully aligning the first full adult fruit fly brain.

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.

Neuroscientists are now able to acquire data at staggering rates across spatiotemporal scales. However, our ability to capitalize on existing datasets, tools, and intellectual capacities is hampered by technical challenges. The key barriers to accelerating scientific discovery correspond to the FAIR data principles: findability, global access to data, software interoperability, and reproducibility/re-usability. We conducted a hackathon dedicated to making strides in those steps. This manuscript is a technical report summarizing these achievements, and we hope serves as an example of the effectiveness of focused, deliberate hackathons towards the advancement of our quickly-evolving field.

Mechanics plays a key role in the development of higher organisms. However, understanding this relationship is complicated by the difficulty of modeling the link between local forces generated at the subcellular level and deformations observed at the tissue and whole-embryo levels. Here we propose an approach first developed for lipid bilayers and cell membranes, in which force-generation by cytoskeletal elements enters a continuum mechanics formulation for the full system in the form of local changes in preferred curvature. This allows us to express and solve the system using only tissue strains. Locations of preferred curvature are simply related to products of gene expression. A solution, in that context, means relaxing the system’s mechanical energy to yield global morphogenetic predictions that accommodate a tendency toward the local preferred curvature, without a need to explicitly model force-generation mechanisms at the molecular level. Our computational framework, which we call SPHARM-MECH, extends a 3D spherical harmonics parameterization known as SPHARM to combine this level of abstraction with a sparse shape representation. The integration of these two principles allows computer simulations to be performed in three dimensions on highly complex shapes, gene expression patterns, and mechanical constraints. We demonstrate our approach by modeling mesoderm invagination in the fruit-fly embryo, where local forces generated by the acto-myosin meshwork in the region of the future mesoderm lead to formation of a ventral tissue fold. The process is accompanied by substantial changes in cell shape and long-range cell movements. Applying SPHARM-MECH to whole-embryo live imaging data acquired with light-sheet microscopy reveals significant correlation between calculated and observed tissue movements. Our analysis predicts the observed cell shape anisotropy on the ventral side of the embryo and suggests an active mechanical role of mesoderm invagination in supporting the onset of germ-band extension.

Insects and mammals share similarities of neural organization underlying the perception of odors, taste, vision, sound, and gravity. We observed that insect somatosensation also corresponds to that of mammals. In Drosophila, the projections of all the somatosensory neuron types to the insect's equivalent of the spinal cord segregated into modality-specific layers comparable to those in mammals. Some sensory neurons innervate the ventral brain directly to form modality-specific and topological somatosensory maps. Ascending interneurons with dendrites in matching layers of the nerve cord send axons that converge to respective brain regions. Pathways arising from leg somatosensory neurons encode distinct qualities of leg movement information and play different roles in ground detection. Establishment of the ground pattern and genetic tools for neuronal manipulation should provide the basis for elucidating the mechanisms underlying somatosensation.

From 1980 to 1992, a series of influential papers reported on the discovery, genetics, and evolution of a periodic cycling of the interval between Drosophila male courtship song pulses. The molecular mechanisms underlying this periodicity were never described. To reinitiate investigation of this phenomenon, we previously performed automated segmentation of songs but failed to detect the proposed rhythm [Arthur BJ, et al. (2013) BMC Biol 11:11; Stern DL (2014) BMC Biol 12:38]. Kyriacou et al. [Kyriacou CP, et al. (2017) Proc Natl Acad Sci USA 114:1970-1975] report that we failed to detect song rhythms because (i) our flies did not sing enough and (ii) our segmenter did not identify many of the song pulses. Kyriacou et al. manually annotated a subset of our recordings and reported that two strains displayed rhythms with genotype-specific periodicity, in agreement with their original reports. We cannot replicate this finding and show that the manually annotated data, the original automatically segmented data, and a new dataset provide no evidence for either the existence of song rhythms or song periodicity differences between genotypes. Furthermore, we have reexamined our methods and analysis and find that our automated segmentation method was not biased to prevent detection of putative song periodicity. We conclude that there is no evidence for the existence of Drosophila courtship song rhythms.

The hippocampus is critical for producing stable representations of familiar spaces. How these representations arise is poorly understood, largely because changes to hippocampal inputs have not been measured during spatial learning. Here, using intracellular recording, we monitored inputs and plasticity-inducing complex spikes (CSs) in CA1 neurons while mice explored novel and familiar virtual environments. Inputs driving place field spiking increased in amplitude - often suddenly - during novel environment exploration. However, these increases were not sustained in familiar environments. Rather, the spatial tuning of inputs became increasingly similar across repeated traversals of the environment with experience - both within fields and throughout the whole environment. In novel environments, CSs were not necessary for place field formation. Our findings support a model in which initial inhomogeneities in inputs are amplified to produce robust place field activity, then plasticity refines this representation into one with less strongly modulated, but more stable, inputs for long-term storage.

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 six percent 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.