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Gillet, V.

Publications and source records attributed to Gillet, V..

3 recordsLinked to original sources

Deep conservation of head direction circuits in bees, ants and flies

To navigate, animal brains must continuously estimate the bodys heading in space and compare it with internal goals to guide movement. In the fruit fly Drosophila melanogaster a neural circuit in the central complex of the brain serves as an internal compass, exploiting a network architecture called a ring attractor1, 2. While this region is highly conserved and involved in navigation in many insects3, 4, it remains unclear whether the fly circuit represents a general blueprint for head direction computation, or whether different ecologies have driven distinct circuit solutions. Using synaptic-resolution circuit mapping, we identified homologous head direction networks in bees and ants and compared them to the fly circuit. We show that the insect head direction network is conserved across at least 300 million years of evolution. At the level of cell types and projection patterns, all studied species share a nearly identical neural layout, both qualitatively and quantitatively. At the synaptic level, however, the fly and bee circuits differed fundamentally. The distinct wiring principles of homologous neurons expose highly evolvable elements within this otherwise stable circuit. Using these differences in circuit architecture to constrain computational models, we show that both the bee and fly circuits can effectively function as ring attractors with similar, yet distinct, properties. These results demonstrate that complex neural circuits can remain stable over many hundreds of millions of years, while still offering access points for evolution to flexibly adjust neural computations to changing ecological demands, illustrating how evolution balances stability and flexibility in brain circuits.

neuroscience↗

A multi-resolution imaging and analysis pipeline for comparative circuit reconstruction in insects

Connectomics has become essential for the study of brain function, yet for most research groups it remains prohibitively costly in imaging time, data storage, and analysis. Here, we present an imaging, processing, and analysis pipeline for multi-resolution image acquisition and circuit reconstruction. Applied to the central complex of six insect species, we were able to obtain global projectomes at cellular resolution (40-50 nm) with embedded local connectomes describing key computational compartments at synaptic resolution (8-12 nm). We provide standardized protocols for volume EM sample preparation, image acquisition and image alignment, combined with existing methods for {micro}CT block trimming, automatic segmentation, synapse detection, collaborative skeleton tracing with CATMAID, and segmentation proofreading via CAVE. We validated our workflow by reconstructing head direction cells across all six insect species, which revealed deep conservation at the level of cell types, cell numbers and projection patterns, while also revealing circuit level specializations. Overall, our pipeline democratizes comparative connectomics by making this method accessible for small research groups with modest resources.

neuroscience↗

CAVE: Connectome Annotation Versioning Engine

Advances in Electron Microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create new annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this constantly changing and expanding data landscape. Here, we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure for immediate and reproducible connectome analysis in up-to petascale datasets ([~]1mm3) while proofreading and annotating is ongoing. For segmentation, CAVE provides a distributed proofreading infrastructure for continuous versioning of large reconstructions. Annotations in CAVE are defined by locations such that they can be quickly assigned to the underlying segment which enables fast analysis queries of CAVEs data for arbitrary time points. CAVE supports schematized, extensible annotations, so that researchers can readily design novel annotation types. CAVE is already used for many connectomics datasets, including the largest datasets available to date.

neuroscience↗