Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Statistically valid explainable black-box machine learning: applications in sex classification across species using brain imaging.

PloS one·2026
Same author

Graph Neural Networks Powered by Encoder Embedding for Improved Node Learning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Accurate and efficient data-driven psychiatric assessment using machine learning.

BMC medical informatics and decision making·2026
Same author

Toward a science of prospective learning.

Neuron·2025
Same author

Is Pearson's correlation coefficient enough for functional connectivity in fMRI?

Imaging neuroscience (Cambridge, Mass.)·2025
Same author

Real-Time 3-D Video Reconstruction for Guidance of Transventricular Neurosurgery.

IEEE transactions on medical robotics and bionics·2025

Related Experiment Video

Updated: Apr 4, 2026

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
12:49

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

13.5K

An automated images-to-graphs framework for high resolution connectomics.

William R Gray Roncal1, Dean M Kleissas2, Joshua T Vogelstein3

  • 1Department of Computer Science, Johns Hopkins University Baltimore, MD, USA ; Applied Physics Laboratory, Research and Exploratory Development Department, Johns Hopkins University Laurel, MD, USA.

Frontiers in Neuroinformatics
|September 1, 2015
PubMed
Summary

This study introduces the first fully-automated pipeline to reconstruct neuronal connectivity maps from electron microscopy images. This breakthrough enables scalable brain graph generation for neuroscience research.

Keywords:
big datacomputer visionconnectomicselectron microscopyframeworkgraph errorimages to graphspipeline

More Related Videos

Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo
08:32

Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo

Published on: May 4, 2018

6.8K
High-resolution Confocal Imaging of the Blood-brain Barrier: Imaging, 3D Reconstruction, and Quantification of Transcytosis
10:30

High-resolution Confocal Imaging of the Blood-brain Barrier: Imaging, 3D Reconstruction, and Quantification of Transcytosis

Published on: November 16, 2017

12.5K

Related Experiment Videos

Last Updated: Apr 4, 2026

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
12:49

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

13.5K
Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo
08:32

Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo

Published on: May 4, 2018

6.8K
High-resolution Confocal Imaging of the Blood-brain Barrier: Imaging, 3D Reconstruction, and Quantification of Transcytosis
10:30

High-resolution Confocal Imaging of the Blood-brain Barrier: Imaging, 3D Reconstruction, and Quantification of Transcytosis

Published on: November 16, 2017

12.5K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Bioinformatics

Background:

  • Mapping neuronal connectivity is crucial for understanding brain function.
  • High-throughput electron microscopy (EM) generates large 3D brain tissue datasets.
  • Current automated methods address specific tasks, not end-to-end reconstruction.

Purpose of the Study:

  • To present the first fully-automated images-to-graphs pipeline for reconstructing neuronal networks.
  • To develop a metric for evaluating the quality of reconstructed brain graphs.
  • To establish a baseline for end-to-end pipeline performance on public data.

Main Methods:

  • Developed a fully-automated pipeline from 3D EM image volumes to brain graphs.
  • Created a novel metric to assess the quality of output brain graphs.
  • Evaluated various algorithms and parameters to optimize pipeline performance.

Main Results:

  • Successfully generated brain graphs from EM data without human intervention.
  • Identified optimal algorithms and parameters using the developed quality metric.
  • Deployed a reference end-to-end pipeline on a large public dataset.

Conclusions:

  • The presented pipeline offers a scalable solution for reconstructing neuronal connectivity.
  • The developed metric and baseline results facilitate community-driven advancements.
  • Publicly released code and data support future research in connectomics and neuropathology.