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

Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
Organization of the Brain01:30

Organization of the Brain

The brain is an integral component of the nervous system and serves as the center for processing sensory inputs, making decisions, and directing bodily actions. This complex organ is organized into three primary sections: the hindbrain, midbrain, and forebrain, each responsible for a range of vital functions.
Hindbrain
The hindbrain, located at the base of the brain, plays a vital role in regulating automatic processes that sustain life. It includes the medulla oblongata, which is essential for...

You might also read

Related Articles

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

Sort by
Same author

Geometric indicators of local plasticity in glasses measured by scanning small-beam diffraction.

Acta crystallographica. Section A, Foundations and advances·2025
Same author

Correcting Non-Uniform Milling in FIB-SEM Images with Unsupervised Cross-Plane Image-to-Image Translation.

bioRxiv : the preprint server for biology·2025
Same author

Confining thrombus morphospace through targeted inhibition of platelet mechanosensory signaling.

Journal of thrombosis and haemostasis : JTH·2025
Same author

The first complete 3D reconstruction and morphofunctional mapping of an insect eye.

eLife·2025
Same author

Sub-cellular population imaging tools reveal stable apical dendrites in hippocampal area CA3.

Nature communications·2025
Same author

The neuron as a direct data-driven controller.

Proceedings of the National Academy of Sciences of the United States of America·2024

Related Experiment Video

Updated: May 9, 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

Electron Microscopy Reconstruction of Brain Structure Using Sparse Representations Over Learned Dictionaries.

Tao Hu, Juan Nunez-Iglesias, Shiv Vitaladevuni

    IEEE Transactions on Medical Imaging
    |August 9, 2013
    PubMed
    Summary

    Researchers developed a new computational method using unsupervised learning to achieve high-depth-resolution electron microscopy (EM) images. This breakthrough enables high-throughput reconstruction of neural circuits at the synapse level without sacrificing image quality.

    More Related Videos

    Optical Clearing and Labeling for Light-sheet Fluorescence Microscopy in Large-scale Human Brain Imaging
    06:52

    Optical Clearing and Labeling for Light-sheet Fluorescence Microscopy in Large-scale Human Brain Imaging

    Published on: January 26, 2024

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    Related Experiment Videos

    Last Updated: May 9, 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

    Optical Clearing and Labeling for Light-sheet Fluorescence Microscopy in Large-scale Human Brain Imaging
    06:52

    Optical Clearing and Labeling for Light-sheet Fluorescence Microscopy in Large-scale Human Brain Imaging

    Published on: January 26, 2024

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    Area of Science:

    • Neuroscience
    • Computational Biology
    • Microscopy

    Background:

    • Reconstructing neuronal circuits at the synapse level is a central problem in neuroscience.
    • High-resolution and high-throughput imaging are crucial for mapping brain architecture.
    • Current electron microscopy (EM) techniques lack both high depth resolution and high throughput.

    Purpose of the Study:

    • To develop a computational method for obtaining high depth-resolution EM images without sacrificing throughput.
    • To enable high-throughput reconstruction of neural circuits on the synapse level.

    Main Methods:

    • Utilized unsupervised learning and signal processing to computationally enhance EM image depth resolution.
    • Represented brain tissue as a sparse linear combination of learned localized basis functions.
    • Applied compressive sensing-inspired techniques using few tomographic views (typically five) per section for reconstruction.

    Main Results:

    • Achieved high depth-resolution EM images computationally.
    • Enabled tracing of neuronal processes.
    • Facilitated high-throughput reconstruction of neural circuits at the synapse level.

    Conclusions:

    • The developed method overcomes limitations of existing EM techniques.
    • This approach offers a pathway to efficiently map complex neural circuits.
    • Advances in computational methods are key to understanding brain architecture.