Search bioRxiv⌕ Search

bioRxiv · 10.1101/2023.11.29.569241

Connectomic reconstruction predicts the functional organization of visual inputs to the navigation center of the Drosophila brain

Abstract

Many animals, including humans, navigate their surroundings by visual input, yet we understand little about how visual information is transformed and integrated by the navigation system. In Drosophila melanogaster, compass neurons in the donut-shaped ellipsoid body of the central complex generate a sense of direction by integrating visual input from ring neurons, a part of the anterior visual pathway (AVP). Here, we densely reconstruct all neurons in the AVP using FlyWire, an AI-assisted tool for analyzing electron-microscopy data. The AVP comprises four neuropils, sequentially linked by three major classes of neurons: MeTu neurons, which connect the medulla in the optic lobe to the small unit of anterior optic tubercle (AOTUsu) in the central brain; TuBu neurons, which connect the anterior optic tubercle to the bulb neuropil; and ring neurons, which connect the bulb to the ellipsoid body. Based on neuronal morphologies, connectivity between different neural classes, and the locations of synapses, we identified non-overlapping channels originating from four types of MeTu neurons, which we further divided into ten subtypes based on the presynaptic connections in medulla and postsynaptic connections in AOTUsu. To gain an objective measure of the natural variation within the pathway, we quantified the differences between anterior visual pathways from both hemispheres and between two electron-microscopy datasets. Furthermore, we infer potential visual features and the visual area from which any given ring neuron receives input by combining the connectivity of the entire AVP, the MeTu neurons dendritic fields, and presynaptic connectivity in the optic lobes. These results provide a strong foundation for understanding how distinct visual features are extracted and transformed across multiple processing stages to provide critical information for computing the flys sense of direction.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Garner, D., Kind, E., Nern, A., Houghton, L., Zhao, A., Sancer, G., Rubin, G. M., Wernet, M. F., Kim, S. S.. 2023-11-30. Connectomic reconstruction predicts the functional organization of visual inputs to the navigation center of the Drosophila brain. https://doi.org/10.1101/2023.11.29.569241

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Spatial organization and mitigation of autofluorescence in multiplexed spatial proteomics of aged fresh-frozen human brain

Multiplexed imaging technologies are transforming the study of human tissue biology, but their application to the aged brain is hindered by autofluorescence, particularly in fresh-frozen specimens. Here, we characterized autofluorescence across four brain regions from 21 donors and found broad spectral emission, regional and gray-white matter differences, and an association with donor age. Photobleaching conditions adopted from formalin-fixed paraffin-embedded tissue caused marked region- and compartment-dependent damage in fresh-frozen sections. We therefore developed a Tris-EDTA-supplemented photobleaching workflow that reduced autofluorescence by 58-70% while preserving tissue architecture and cellular content. We established a custom 28-plex DNA-barcoded antibody panel targeting neuronal, glial, immune, and vascular markers, providing a resource for fresh-frozen human brain. Integration of the optimized photobleaching workflow with this panel enabled spatial proteomic analysis across fresh-frozen brain regions. By co-registering pre-photobleaching autofluorescence with multiplexed protein maps, we further established a cellular-resolution framework for spatial characterization of autofluorescence. This revealed region-dependent protein marker relationships and preferential enrichment of autofluorescent particles near nuclei and within microglial and CD68-positive regions. Together, this work establishes a practical workflow for multiplexed spatial proteomics in fresh-frozen brain and characterizes autofluorescence as both a technical confound and a spatially structured feature of the aged human brain.

neuroscience↗

A Novel Cortico-Striatal NREM Sleep Rhythm in Mice and Non-Human Primates

With practice, rapid early gains in performance are followed by a slower phase marked by kinematic refinement, automaticity and enhanced cortical and striatal interactions. While sleep is known to support early learning, its causal role in the slow phase of learning is not known. Here we recorded neuronal activity in primary motor cortex (M1) and the dorsolateral striatum (DLS) during long-term skill acquisition and interleaved sleep. Surprisingly, the slow phase of learning was marked by the emergence of a previously unrecognized 5-10 Hz oscillatory activity during NREM sleep that was coherent across M1 and DLS. This oscillation resulted in repeated joint reactivation of task-specific information in cortex and striatum. Strikingly, during later stages of training, such joint reactivation of task activity increased over the course of NREM sleep, suggesting that sleep-dependent processing strengthens cortico-striatal interactions. The strength of M1-DLS coherence was predictive of next-day performance gains and increased cortico-striatal coupling during task performance. Targeted closed-loop disruption of this oscillation during NREM sleep abolished performance gains, whereas switching to a dose matched random stimulation paradigm enabled performance improvements in the same animals. Importantly, the same cortico-striatal 5-10 Hz rhythm was also found in sleeping non-human primates, where it was selectively enhanced following learning. Together, we identify, across species, a novel NREM sleep oscillation that is important for sleep-dependent performance gains which depend on cortico-striatal processing.

neuroscience↗

Behavioral demands organize a decision process into distinct yet coordinated neural representations in parietal cortex

Perceptual decisions are widely modeled as the accumulation of evidence to a bound. In the lateral intraparietal area (LIP), this computation is thought to be implemented in a low-dimensional population representation organized around the single action used to report the choice, consistent with an intentional framework. The intentional framework, however, implies that changing the behavioral demands on the report should change the representation itself, raising a question about the generality of the low-dimensional decision representation described in LIP: is it a special case of decisions reported through a single action, or does it reflect a more general computational architecture that can support multiple behavioral outputs? We tested this by training monkeys to report the \textit{termination} of a motion-discrimination decision with a saccade to a choice-neutral target, and its \textit{content} only later, with a saccade to one of two choice targets. Even though the two reports were behaviorally separable, the timing of termination remained systematically linked to the accumulation of sensory evidence supporting the eventual choice in both monkeys, indicating that both reports continued to draw on a common underlying computation. Using high-density Neuropixels recordings from LIP, however, we found that decision termination and decision content were represented along orthogonal population coding directions supported by largely non-overlapping groups of neurons. Yet the two representations were not independent: trial-by-trial fluctuations in the population encoding content predicted subsequent fluctuations in the population encoding termination, with their coupling strengthening as the decision evolved. These results suggest that a single decision computation can be flexibly reformatted into distinct, action-specific representations, coordinated by selective transfer of information between neural populations.

neuroscience↗