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Codex: Connectome Data Explorer

Authors:
Arie Matsliah at Princeton University
Arie Matsliah
Amy R Sterling
Amy R Sterling
Amy R Sterling
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Sven Dorkenwald
Sven Dorkenwald
Sven Dorkenwald
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Kai Kuehner
Kai Kuehner
Kai Kuehner
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Abstract

Emerging connectomics resources of whole brains consist of large synapse graphs with millions of connections, making analysis of these rich assets challenging. To disseminate them widely we need web-based platforms that allow users to query, visualize, and explore interactively and without requiring advanced programming skills. Here we present Codex (codex.flywire.ai) , a new platform for exploring and analyzing large connectomics datasets. Compared to similar web-based platforms (Clements et al. 2020; Milyaev et al. 2012) , Codex provides a simplified interface that does not assume any domain-specific knowledge from the user, a tailored search index for faster query execution, unique visualization tools for multi-cell connectivity & pathways, and simple architecture for serving future connectomes with minimal operational complexity.
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Codex: Connectome Data Explorer
Arie Matsliah
1
, Amy R Sterling
1,3
, Sven Dorkenwald
1,2
, Kai Kuehner
1
, Ryan Morey
1
,
H. Sebastian Seung
1,2
* , Mala Murthy
1
*
1 Princeton Neuroscience Institute, Princeton University, Princeton, USA
2 Computer Science Department, Princeton University, Princeton, USA
3 Eyewire, Boston, USA
*Correspondence to mmurthy@pricneton.edu , sseung@princeton.edu
Emerging connectomics resources of whole brains consist of large synapse graphs with millions of
connections, making analysis of these rich assets challenging. To disseminate them widely we need
web-based platforms that allow users to query, visualize, and explore interactively and without
requiring advanced programming skills. Here we present Codex (codex.flywire.ai) , a new platform for
exploring and analyzing large connectomics datasets. Compared to similar web-based platforms
(Clements et al. 2020; Milyaev et al. 2012) , Codex provides a simplified interface that does not
assume any domain-specific knowledge from the user, a tailored search index for faster query
execution, unique visualization tools for multi-cell connectivity & pathways, and simple architecture for
serving future connectomes with minimal operational complexity. As of July 2023, Codex serves the
public FlyWire Drosophila adult brain connectome with its annotations (Dorkenwald et al. 2023;
Schlegel et al. 2023; Zheng et al. 2018) , and is used by 5000+ individuals from 100+ labs globally.
Codex key features include:
● Advanced Search: Tailored search engine with a simple query interface. Users can search for
specific neurons, annotations, brain regions, as well as connections and pathways.
● Visualization: Multi-cell connectivity network and pathway views with grouping and layout
options. Users can create interactive and shareable visualizations of large groups of neurons.
● Connectivity Analysis: Tools to quantify and analyze synaptic strengths, identify prominent
hubs or clusters within the network, and explore network motifs and organizational principles.
● Collaborative Annotation: Interactive features for annotation and labeling of neurons or regions
within the connectome. Enhances data sharing in a collaborative environment.
● Data catalog and access portal: Detailed description and schematics of connectomic data
resources, with versioned links for downloading raw data in standard formats (e.g., CSV, SWC).
Codex makes the FlyWire Connectome accessible to a wide range of audiences. As the field of
connectomics continues to evolve and additional connectomes come online in Codex, scientists can
accelerate their understanding of the fundamental principles underlying brain structures, paving the
way for novel discoveries and potential applications in neuroscience and beyond.
Bibliography
[1] Dorkenwald et al. (Jun. 2023). Neuronal wiring diagram of an adult brain. https://doi.org/10.1101/2023.06.27.546656
[2] Schlegel et al. (Jun. 2023). A consensus cell type atlas from multiple connectomes reveals principles of circuit stereotypy and
variation. https://doi.org/10.1101/2023.06.27.546055
[3] Zheng et al. (Cell, Jul. 2018). A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster.
https://doi.org/10.1016/j.cell.2018.06.019
[4] Clements et al. (Jan. 2020). neuPrint: Analysis Tools for EM Connectomics. https://doi.org/10.1101/2020.01.16.909465
[5] Milyaev et al. (Bioinformatics, Feb. 2012). “The Virtual Fly Brain browser and query interface”.
http://dx.doi.org/10.1093/bioinformatics/btr677
... Moreover, artificial activation of these neurons is sufficient to trigger fictive saccades. However, these two types of neurons do not receive direct input from visual interneurons [21][22][23][24] . In contrast, other descending neurons have arborizations within optic glomeruli and were implicated in the execution of behavioral responses elicited by visual looming, such as escape jumps and landing [25][26][27] . ...
... It receives a significant fraction of its input from looming-sensitive LPLC1 and LC4 neurons on its dendritic tree spanning multiple optical glomeruli 26,27 . It then crosses the midline and makes synapses with other descending neurons similar to DNaX and DNb01 22 . Within the VNC, it makes direct synapse with wing motor neurons involved in steering during flight 30 . ...
... Third, DNp03 also shows changes in activity associated with spontaneous saccades, similar to DNaX. Several DNa neurons are postsynaptic to DNp03 on its contralateral side 22 . The descending neuron DNaX has been shown previously to be active during both looming-elicited and spontaneous saccades 20,21 . ...
Sustained activity in a descending neuron is associated with flight saccades in Drosophila
Preprint
Full-text available
  • Aug 2024
Flies perform rapid turns termed saccades to change direction during flight. Evasive turns can be elicited by looming stimuli mimicking an approaching object such as a potential predator. Whereas projection neurons of the optic lobes responsive to looming stimuli have been well described, how this information is transmitted to the motor system to elicit a saccade, is not well understood. Here we describe activity of the descending neuron DNp03 in Drosophila, which receives direct input from looming-sensitive visual interneurons and projects to wing motor areas within the ventral nerve cord. Whole-cell patch-clamp recordings from this neuron during head-fixed flight confirm that DNp03 is responsive to looming stimuli on the side ipsilateral to its dendrites. In addition, activity of this neuron is state-dependent as looming stimuli only elicit spikes during flight and not rest. The behavior in response to the looming stimulus is variable, which allowed us to study how activity of DNp03 relates to the execution of a saccade. Our analysis revealed that sustained activity in DNp03, persisting even after the visual stimulus ended, was the strongest predictor of saccade execution, therefore reflecting the behavioral decision of the fly to respond to the stimulus.
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... This particular subnetwork is unlikely to be involved in the counter-saccadic behaviour described in our work, as DNp15 innervates the neck and the halters 69 . It is more likely that other descending pathways connected to HS or H2 cells (Supplementary Fig. 15d) 70 are required, such as those involved in saccadic turns 71 . Nonetheless, it highlights the challenges involved in understanding the full circuit involved in transforming optic-flow patterns into specific motor outputs. ...
Bilateral interactions of optic-flow sensitive neurons coordinate course control in flies
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  • Oct 2024
Animals rely on compensatory actions to maintain stability and navigate their environment efficiently. These actions depend on global visual motion cues known as optic-flow. While the optomotor response has been the traditional focus for studying optic-flow compensation in insects, its simplicity has been insufficient to determine the role of the intricate optic-flow processing network involved in visual course control. Here, we reveal a series of course control behaviours in Drosophila and link them to specific neural circuits. We show that bilateral electrical coupling of optic-flow-sensitive neurons in the fly’s lobula plate are required for a proper course control. This electrical interaction works alongside chemical synapses within the HS-H2 network to control the dynamics and direction of turning behaviours. Our findings reveal how insects use bilateral motion cues for navigation, assigning a new functional significance to the HS-H2 network and suggesting a previously unknown role for gap junctions in non-linear operations.
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... Dm12 neurites span three medulla columns, corresponding to~15°of visual space, making it a prime candidate for spatial pooling (Fig. 7b). In each column, Dm12 neurons receive major inputs from L3 neurons 50 . To probe functional connectivity, we expressed csChrimson 51 in Dm12, while recording GCaMP6f signals in Tm9 neurons (Fig. 7c). ...
Neural pathways and computations that achieve stable contrast processing tuned to natural scenes
Article
Full-text available
  • Oct 2024
Natural scenes are highly dynamic, challenging the reliability of visual processing. Yet, humans and many animals perform accurate visual behaviors, whereas computer vision devices struggle with rapidly changing background luminance. How does animal vision achieve this? Here, we reveal the algorithms and mechanisms of rapid luminance gain control in Drosophila, resulting in stable visual processing. We identify specific transmedullary neurons as the site of luminance gain control, which pass this property to direction-selective cells. The circuitry further involves wide-field neurons, matching computational predictions that local spatial pooling drive optimal contrast processing in natural scenes when light conditions change rapidly. Experiments and theory argue that a spatially pooled luminance signal achieves luminance gain control via divisive normalization. This process relies on shunting inhibition using the glutamate-gated chloride channel GluClα. Our work describes how the fly robustly processes visual information in dynamically changing natural scenes, a common challenge of all visual systems.
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... [4][5][6][7][8][9]. This is the same value as the default in Codex, the connectome data explorer provided by the FlyWire community 37, 75 . Analysis of connectivity across three brain hemispheres (two brain halves from the FAFB dataset 7 and one from the hemibrain dataset 77 ) revealed that connections "stronger than ten synapses or 1.1% of the target's inputs have a greater than 90% change to be preserved" 38 . ...
Descending networks transform command signals into population motor control
Article
Full-text available
  • Jun 2024
  • NATURE
To convert intentions into actions, movement instructions must pass from the brain to downstream motor circuits through descending neurons (DNs). These include small sets of command-like neurons that are sufficient to drive behaviours¹—the circuit mechanisms for which remain unclear. Here we show that command-like DNs in Drosophila directly recruit networks of additional DNs to orchestrate behaviours that require the active control of numerous body parts. Specifically, we found that command-like DNs previously thought to drive behaviours alone2–4 in fact co-activate larger populations of DNs. Connectome analyses and experimental manipulations revealed that this functional recruitment can be explained by direct excitatory connections between command-like DNs and networks of interconnected DNs in the brain. Descending population recruitment is necessary for behavioural control: DNs with many downstream descending partners require network co-activation to drive complete behaviours and drive only simple stereotyped movements in their absence. These DN networks reside within behaviour-specific clusters that inhibit one another. These results support a mechanism for command-like descending control in which behaviours are generated through the recruitment of increasingly large DN networks that compose behaviours by combining multiple motor subroutines.
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Neuronal wiring diagram of an adult brain
Preprint
Full-text available
  • Jun 2023
Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5x10^7 chemical synapses between ~130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.
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A consensus cell type atlas from multiple connectomes reveals principles of circuit stereotypy and variation
Preprint
Full-text available
  • Jun 2023
The fruit fly Drosophila melanogaster combines surprisingly sophisticated behaviour with a highly tractable nervous system. A large part of the fly's success as a model organism in modern neuroscience stems from the concentration of collaboratively generated molecular genetic and digital resources. As presented in our FlyWire companion paper ¹ , this now includes the first full brain connectome of an adult animal. Here we report the systematic and hierarchical annotation of this ~130,000-neuron connectome including neuronal classes, cell types and developmental units (hemilineages). This enables any researcher to navigate this huge dataset and find systems and neurons of interest, linked to the literature through the Virtual Fly Brain database ² . Crucially, this resource includes 4,179 cell types of which 3,166 consensus cell types are robustly defined by comparison with a second dataset, the "hemibrain" connectome ³ . Comparative analysis showed that cell type counts and strong connections were largely stable, but connection weights were surprisingly variable within and across animals. Further analysis defined simple heuristics for connectome interpretation: connections stronger than 10 unitary synapses or providing >1% of the input to a target cell are highly conserved. Some cell types showed increased variability across connectomes: the most common cell type in the mushroom body, required for learning and memory, is almost twice as numerous in FlyWire than in the hemibrain. We find evidence for functional homeostasis through adjustments of the absolute amount of excitatory input while maintaining the excitation-inhibition ratio. Finally, and surprisingly, about one third of the cell types recorded in the hemibrain connectome could not be robustly identified in the FlyWire connectome, cautioning against defining cell types based on single connectomes. We propose that a cell type should be robust to inter-individual variation, and therefore defined as a group of cells that are more similar to cells in a different brain than to any other cell in the same brain. We show that this new definition can be consistently applied to whole connectome datasets. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open source toolchain for brain-scale comparative connectomics.
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NeuPrint: Analysis Tools for EM Connectomics
Preprint
Full-text available
  • Jan 2020
Due to technological advances in electron microscopy (EM) and deep learning, it is now practical to reconstruct a connectome, a description of neurons and the connections between them, for significant volumes of neural tissue. The limited scope of past reconstructions meant they were primarily used by domain experts, and performance was not a serious problem. But the new reconstructions, of common laboratory creatures such as the fruit fly Drosophila melanogaster, upend these assumptions. These natural neural networks now contain tens of thousands of neurons and tens of millions of connections between them, with yet larger reconstructions pending, and are of interest to a large community of non-specialists. This requires new tools that are easy to use and efficiently handle large data. We introduce neuPrint to address these data analysis challenges. neuPrint is a database and analysis ecosystem that organizes connectome data in a manner conducive to biological discovery. In particular, we propose a data model that allows users to access the connectome at different levels of abstraction primarily through a graph database, neo4j, and its powerfully expressive query language Cypher. neuPrint is compatible with modern connectome reconstruction workflows, providing tools for assessing reconstruction quality, and offering both batch and incremental updates to match modern connectome reconstruction flows. Finally, we introduce a web interface and programmer API that targets a diverse user skill set. We demonstrate the effectiveness and efficiency of neuPrint through example database queries.
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A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster
Article
Full-text available
  • Jul 2018
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. Video Abstract
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The Virtual Fly Brain Browser and Query Interface
Article
  • Dec 2011
  • BIOINFORMATICS
Sources of neuroscience data in Drosophila are diverse and disparate making integrated search and retrieval difficult. A major obstacle to this is the lack of a comprehensive and logically structured anatomical framework and an intuitive interface. We present an online resource that provides a convenient way to study and query fly brain anatomy, expression and genetic data. We extended the newly developed BrainName nomenclature for the adult fly brain into a logically structured ontology that relates a comprehensive set of published neuron classes to the brain regions they innervate. The Virtual Fly Brain interface allows users to explore the structure of the Drosophila brain by browsing 3D images of a brain with subregions displayed as coloured overlays. An integrated query mechanism allows complex searches of underlying anatomy, cells, expression and other data from community databases. Virtual Fly Brain is freely available online at www.virtualflybrain.org jda@inf.ed.ac.uk.
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