New reconstruction techniques are generating connectomes of unprecedented size. These must be analyzed to generate human comprehensible results. The analyses being used fall into three general categories. The first is interactive tools used during reconstruction, to help guide the effort, look for possible errors, identify potential cell classes, and answer other preliminary questions. The second type of analysis is support for formal documents such as papers and theses. Scientific norms here require that the data be archived and accessible, and the analysis reproducible. In contrast to some other "omic" fields such as genomics, where a few specific analyses dominate usage, connectomics is rapidly evolving and the analyses used are often specific to the connectome being analyzed. These analyses are typically performed in a variety of conventional programming language, such as Matlab, R, Python, or C++, and read the connectomic data either from a file or through database queries, neither of which are standardized. In the short term we see no alternative to the use of specific analyses, so the best that can be done is to publish the analysis code, and the interface by which it reads connectomic data. A similar situation exists for archiving connectome data. Each group independently makes their data available, but there is no standardized format and long-term accessibility is neither enforced nor funded. In the long term, as connectomics becomes more common, a natural evolution would be a central facility for storing and querying connectomic data, playing a role similar to the National Center for Biotechnology Information for genomes. The final form of analysis is the import of connectome data into downstream tools such as neural simulation or machine learning. In this process, there are two main problems that need to be addressed. First, the reconstructed circuits contain huge amounts of detail, which must be intelligently reduced to a form the downstream tools can use. Second, much of the data needed for these downstream operations must be obtained by other methods (such as genetic or optical) and must be merged with the extracted connectome.

Automatic image segmentation is critical to scale up electron microscope (EM) connectome reconstruction. To this end, segmentation competitions, such as CREMI and SNEMI, exist to help researchers evaluate segmentation algorithms with the goal of improving them. Because generating ground truth is time-consuming, these competitions often fail to capture the challenges in segmenting larger datasets required in connectomics. More generally, the common metrics for EM image segmentation do not emphasize impact on downstream analysis and are often not very useful for isolating problem areas in the segmentation. For example, they do not capture connectivity information and often over-rate the quality of a segmentation as we demonstrate later. To address these issues, we introduce a novel strategy to enable evaluation of segmentation at large scales both in a supervised setting, where ground truth is available, or an unsupervised setting. To achieve this, we first introduce new metrics more closely aligned with the use of segmentation in downstream analysis and reconstruction. In particular, these include synapse connectivity and completeness metrics that provide both meaningful and intuitive interpretations of segmentation quality as it relates to the preservation of neuron connectivity. Also, we propose measures of segmentation correctness and completeness with respect to the percentage of "orphan" fragments and the concentrations of self-loops formed by segmentation failures, which are helpful in analysis and can be computed without ground truth. The introduction of new metrics intended to be used for practical applications involving large datasets necessitates a scalable software ecosystem, which is a critical contribution of this paper. To this end, we introduce a scalable, flexible software framework that enables integration of several different metrics and provides mechanisms to evaluate and debug differences between segmentations. We also introduce visualization software to help users to consume the various metrics collected. We evaluate our framework on two relatively large public groundtruth datasets providing novel insights on example segmentations.

Artificial activation of anatomically localized, genetically defined hypothalamic neuron populations is known to trigger distinct innate behaviors, suggesting a hypothalamic nucleus-centered organization of behavior control. To assess whether the encoding of behavior is similarly anatomically confined, we performed simultaneous neuron recordings across twenty hypothalamic regions in freely moving animals. Here we show that distinct but anatomically distributed neuron ensembles encode the social and fear behavior classes, primarily through mixed selectivity. While behavior class-encoding ensembles were spatially distributed, individual ensembles exhibited strong localization bias. Encoding models identified that behavior actions, but not motion-related variables, explained a large fraction of hypothalamic neuron activity variance. These results identify unexpected complexity in the hypothalamic encoding of instincts and provide a foundation for understanding the role of distributed neural representations in the expression of behaviors driven by hardwired circuits.

Vocal learning in songbirds requires an anatomically discrete and functionally dedicated circuit called the anterior forebrain pathway (AFP). The AFP is homologous to cortico-basal ganglia-thalamo-cortical loops in mammals. The basal ganglia portion of this pathway, Area X, shares many features characteristic of the mammalian striatum and pallidum, including cell types and connectivity. The AFP also deviates from mammalian basal ganglia circuits in fundamental ways. In addition, the microcircuitry, role of neuromodulators, and function of Area X are still unclear. Elucidating the mechanisms by which both mammalian-like and unique features of the AFP contribute to vocal learning may help lead to a broad understanding of the sensorimotor functions of basal ganglia circuits.

Intracellular recording allows precise measurement and manipulation of individual neurons, but it requires stable mechanical contact between the electrode and the cell membrane, and thus it has remained challenging to perform in behaving animals. Whole-cell recordings in freely moving animals can be obtained by rigidly fixing ('anchoring') the pipette electrode to the head; however, previous anchoring procedures were slow and often caused substantial pipette movement, resulting in loss of the recording or of recording quality. We describe a UV-transparent collar and UV-cured adhesive technique that rapidly (within 15 s) anchors pipettes in place with virtually no movement, thus substantially improving the reliability, yield and quality of freely moving whole-cell recordings. Recordings are first obtained from anesthetized or awake head-fixed rats. UV light cures the thin adhesive layers linking pipette to collar to head. Then, the animals are rapidly and smoothly released for recording during unrestrained behavior. The anesthetized-patched version can be completed in ∼4-7 h (excluding histology) and the awake-patched version requires ∼1-4 h per day for ∼2 weeks. These advances should greatly facilitate studies of neuronal integration and plasticity in identified cells during natural behaviors.

Many animals maintain an internal representation of their heading as they move through their surroundings. Such a compass representation was recently discovered in a neural population in the Drosophila melanogaster central complex, a brain region implicated in spatial navigation. Here, we use two-photon calcium imaging and electrophysiology in head-fixed walking flies to identify a different neural population that conjunctively encodes heading and angular velocity, and is excited selectively by turns in either the clockwise or counterclockwise direction. We show how these mirror-symmetric turn responses combine with the neurons' connectivity to the compass neurons to create an elegant mechanism for updating the fly's heading representation when the animal turns in darkness. This mechanism, which employs recurrent loops with an angular shift, bears a resemblance to those proposed in theoretical models for rodent head direction cells. Our results provide a striking example of structure matching function for a broadly relevant computation.

The field of connectomics has recently produced neuron wiring diagrams from relatively large brain regions from multiple animals. Most of these neural reconstructions were computed from isotropic (e.g., FIBSEM) or near isotropic (e.g., SBEM) data. In spite of the remarkable progress on algorithms in recent years, automatic dense reconstruction from anisotropic data remains a challenge for the connectomics community. One significant hurdle in the segmentation of anisotropic data is the difficulty in generating a suitable initial over-segmentation. In this study, we present a segmentation method for anisotropic EM data that agglomerates a 3D over-segmentation computed from the 3D affinity prediction. A 3D U-net is trained to predict 3D affinities by the MALIS approach. Experiments on multiple datasets demonstrates the strength and robustness of the proposed method for anisotropic EM segmentation.

Full reconstruction of neuron morphology is of fundamental interest for the analysis and understanding of their functioning. We have developed a novel method capable of automatically tracing neurons in three-dimensional microscopy data. In contrast to template-based methods, the proposed approach makes no assumptions about the shape or appearance of neurite structure. Instead, an efficient seeding approach is applied to capture complex neuronal structures and the tracing problem is solved by computing the optimal reconstruction with a weighted graph. The optimality is determined by the cost function designed for the path between each pair of seeds and by topological constraints defining the component interrelations and completeness. In addition, an automated neuron comparison method is introduced for performance evaluation and structure analysis. The proposed algorithm is computationally efficient and has been validated using different types of microscopy data sets including Drosophila’s projection neurons and fly neurons with presynaptic sites. In all cases, the approach yielded promising results.

How metazoan mechanotransduction channels sense mechanical stimuli is not well understood. The NOMPC channel in the transient receptor potential (TRP) family, a mechanotransduction channel for Drosophila touch sensation and hearing, contains 29 Ankyrin repeats (ARs) that associate with microtubules. These ARs have been postulated to act as a tether that conveys force to the channel. Here, we report that these N-terminal ARs form a cytoplasmic domain essential for NOMPC mechanogating in vitro, mechanosensitivity of touch receptor neurons in vivo, and touch-induced behaviors of Drosophila larvae. Duplicating the ARs elongates the filaments that tether NOMPC to microtubules in mechanosensory neurons. Moreover, microtubule association is required for NOMPC mechanogating. Importantly, transferring the NOMPC ARs to mechanoinsensitive voltage-gated potassium channels confers mechanosensitivity to the chimeric channels. These experiments strongly support a tether mechanism of mechanogating for the NOMPC channel, providing insights into the basis of mechanosensitivity of mechanotransduction channels.