SynEM, automated synapse detection for connectomics

  1. Benedikt Staffler
  2. Manuel Berning
  3. Kevin M Boergens
  4. Anjali Gour
  5. Patrick van der Smagt
  6. Moritz Helmstaedter  Is a corresponding author
  1. Max Planck Institute for Brain Research, Germany
  2. Volkswagen Group, Germany

Abstract

Nerve tissue contains a high density of chemical synapses, about 1 per µm3 in the mammalian cerebral cortex. Thus, even for small blocks of nerve tissue, dense connectomic mapping requires the identification of millions to billions of synapses. While the focus of connectomic data analysis has been on neurite reconstruction, synapse detection becomes limiting when datasets grow in size and dense mapping is required. Here, we report SynEM, a method for automated detection of synapses from conventionally en-bloc stained 3D electron microscopy image stacks. The approach is based on a segmentation of the image data and focuses on classifying borders between neuronal processes as synaptic or non-synaptic. SynEM yields 97% precision and recall in binary cortical connectomes with no user interaction. It scales to large volumes of cortical neuropil, plausibly even whole-brain datasets. SynEM removes the burden of manual synapse annotation for large densely mapped connectomes.

Data availability

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Benedikt Staffler

    Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
    Competing interests
    The authors declare that no competing interests exist.
  2. Manuel Berning

    Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3679-8363
  3. Kevin M Boergens

    Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
    Competing interests
    The authors declare that no competing interests exist.
  4. Anjali Gour

    Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
    Competing interests
    The authors declare that no competing interests exist.
  5. Patrick van der Smagt

    Data Lab, Volkswagen Group, Munich, Germany
    Competing interests
    The authors declare that no competing interests exist.
  6. Moritz Helmstaedter

    Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
    For correspondence
    mh@brain.mpg.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7973-0767

Funding

Max-Planck Society (Open access funding)

  • Benedikt Staffler
  • Manuel Berning
  • Kevin M Boergens
  • Anjali Gour
  • Moritz Helmstaedter

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: All animal experiments were performed in accordance with the guidelines for the Use of Laboratory Animals of the Max Planck Society and approved by the local authorities Regierungspräsidium Oberbayern, AZ 55.2-1-54-2532.3-103-12.

Copyright

© 2017, Staffler et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 3,912
    views
  • 607
    downloads
  • 59
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Benedikt Staffler
  2. Manuel Berning
  3. Kevin M Boergens
  4. Anjali Gour
  5. Patrick van der Smagt
  6. Moritz Helmstaedter
(2017)
SynEM, automated synapse detection for connectomics
eLife 6:e26414.
https://doi.org/10.7554/eLife.26414

Share this article

https://doi.org/10.7554/eLife.26414

Further reading

    1. Neuroscience
    Mirela Zaneva, Tao Coll-Martín ... Alyssa Hillary Zisk
    Feature Article

    Since its inception, the concept of neurodiversity has been defined in a number of different ways, which can cause confusion among those hoping to educate themselves about the topic. Learning about neurodiversity can also be challenging because there is a lack of well-curated, appropriately contextualized information on the topic. To address such barriers, we present an annotated reading list that was developed collaboratively by a neurodiverse group of researchers. The nine themes covered in the reading list are: the history of neurodiversity; ways of thinking about neurodiversity; the importance of lived experience; a neurodiversity paradigm for autism science; beyond deficit views of ADHD; expanding the scope of neurodiversity; anti-ableism; the need for robust theory and methods; and integration with open and participatory work. We hope this resource can support readers in understanding some of the key ideas and topics within neurodiversity, and that it can further orient researchers towards more rigorous, destigmatizing, accessible, and inclusive scientific practices.

    1. Neuroscience
    Meera Chikermane, Liz Weerdmeester ... Wolf Julian Neumann
    Research Article

    Brain rhythms can facilitate neural communication for the maintenance of brain function. Beta rhythms (13–35 Hz) have been proposed to serve multiple domains of human ability, including motor control, cognition, memory, and emotion, but the overarching organisational principles remain unknown. To uncover the circuit architecture of beta oscillations, we leverage normative brain data, analysing over 30 hr of invasive brain signals from 1772 channels from cortical areas in epilepsy patients, to demonstrate that beta is the most distributed cortical brain rhythm. Next, we identify a shared brain network from beta-dominant areas with deeper brain structures, like the basal ganglia, by mapping parametrised oscillatory peaks to whole-brain functional and structural MRI connectomes. Finally, we show that these networks share significant overlap with dopamine uptake as indicated by positron emission tomography. Our study suggests that beta oscillations emerge in cortico-subcortical brain networks that are modulated by dopamine. It provides the foundation for a unifying circuit-based conceptualisation of the functional role of beta activity beyond the motor domain and may inspire an extended investigation of beta activity as a feedback signal for closed-loop neurotherapies for dopaminergic disorders.