We develop an automatic method for synaptic partner identification in insect brains and use it to predict synaptic partners in a whole-brain electron microscopy dataset of the fruit fly. The predictions can be used to infer a connectivity graph with high accuracy, thus allowing fast identification of neural pathways. To facilitate circuit reconstruction using our results, we develop CIRCUITMAP, a user interface add-on for the circuit annotation tool CATMAID.
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The code used to train networks and predict synaptic partners is available in the synful repository (https://github.com/funkelab/synful)22. Code used to evaluate the results is available in the synful_fafb repository (https://github.com/funkelab/synful_fafb)20. A python library to query circuits in the FAFB volume using the predicted synaptic partners is available in the SynfulCircuit repository (https://github.com/funkelab/synfulcircuit)14. The CATMAID widget used to interactively query circuits is available in the CircuitMap repository (https://github.com/catmaid/circuitmap)15.
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We thank S. Lauritzen (HHMI, Janelia Research Campus) for helping with data acquisition; M. Nichols (HHMI, Janelia Research Campus) for importing CATMAID annotations; W. Patton (HHMI, Janelia Research Campus) for code contributions; N. Eckstein (HHMI, Janelia Research Campus) for helpful discussions; J. Maitin-Shepard (Google) for adding synapse visualization features to neuroglancer; V. Jayaraman (HHMI, Janelia Research Campus) for providing evaluation data; and the HHMI Janelia Connectome Annotation Team (R. Parekh, A. Suleiman, T. Paterson) for evaluation data. We also thank Z. Zheng, F. Li, C. Fisher, N. Sharifi and S. Calle-Schuler (HHMI, Janelia) for access to prepublication data used for evaluation. This work was supported by the HHMI and the Swiss National Science Foundation (grant 205321L 160133).
S.G. is the founder and CEO of UniDesign Solutions GmbH, which provides IT services.
Peer review information Nature Methods thanks Chung-Chuan Lo, Stephan Sigrist and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Precision and recall over 10 densely annotated cubes in different brain regions (dataset DenseCubes). Highlights show best f-score over the detection threshold. Top row: Black triangle marks show the inter-human variance between two annotations, with one annotation treated as ground-truth evaluated against the other one. Bottom right: Visualization of the cube locations within the FAFB dataset.
a, Outline of axon-dendrite splitting procedure. b, Exemplary well segregated (left) and unsegregated (right) PN. Boxplot shows segregation index (SI) for all PNs separated into uni- and multiglomerular PNs. c, Impact of completeness of neuronal reconstruction on recovery of synapses.
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Buhmann, J., Sheridan, A., Malin-Mayor, C. et al. Automatic detection of synaptic partners in a whole-brain Drosophila electron microscopy data set. Nat Methods 18, 771–774 (2021). https://doi.org/10.1038/s41592-021-01183-7
Nature Methods (2022)