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

Vision01:24

Vision

55.8K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
55.8K

You might also read

Related Articles

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

Sort by
Same author

Clinicopathological features, management and outcome of patients with poorly-differentiated oral and oropharyngeal squamous cell carcinoma.

Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery·2017
Same author

Detection of Methanol with Fast Response by Monodispersed Indium Tungsten Oxide Ellipsoidal Nanospheres.

ACS sensors·2017
Same author

Randomized, controlled trial evaluating the effect of multi-strain probiotic on the mucosal microbiota in canine idiopathic inflammatory bowel disease.

Gut microbes·2017
Same author

Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search.

Biomedical optics express·2017
Same author

Sulfation of the Extracellular Polysaccharide Produced by the King Oyster Culinary-Medicinal Mushroom, Pleurotus eryngii (Agaricomycetes), and Its Antioxidant Properties In Vitro.

International journal of medicinal mushrooms·2017
Same author

Cryogenic 3D printing for producing hierarchical porous and rhBMP-2-loaded Ca-P/PLLA nanocomposite scaffolds for bone tissue engineering.

Biofabrication·2017

Related Experiment Video

Updated: Oct 5, 2025

Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.4K

Reconstructing rapid natural vision with fMRI-conditional video generative adversarial network.

Chong Wang1,2, Hongmei Yan1,2, Wei Huang1

  • 1The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.

Cerebral Cortex (New York, N.Y. : 1991)
|January 25, 2022
PubMed
Summary

Researchers developed a novel fMRI-conditional video generative adversarial network (f-CVGAN) to reconstruct rapid video stimuli from brain activity. This method successfully decodes visual perception from slow blood oxygen level-dependent signals.

Keywords:
conditional generative adversarial networksfMRIvisual reconstruction

More Related Videos

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

2.0K

Related Experiment Videos

Last Updated: Oct 5, 2025

Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.4K
Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

2.0K

Area of Science:

  • Neuroscience
  • Computer Vision
  • Machine Learning

Background:

  • Functional magnetic resonance imaging (fMRI) has advanced visual content reconstruction.
  • Reconstructing dynamic natural vision is challenging due to fMRI's limited temporal resolution.

Purpose of the Study:

  • To develop a novel fMRI-conditional video generative adversarial network (f-CVGAN) for reconstructing rapid video stimuli from fMRI responses.
  • To assess the model's ability to capture spatial and temporal information of original stimuli.

Main Methods:

  • Developed an f-CVGAN model with a generator for spatiotemporal reconstructions.
  • Employed separate spatial and temporal discriminators for assessment.
  • Trained and tested the model on two public video-fMRI datasets.

Main Results:

  • The f-CVGAN produced pixel-level reconstructions of 8 perceived video frames per fMRI volume.
  • Reconstructed videos were fMRI-related and captured key spatial and temporal details.
  • Cortical importance mapping revealed extensive visual cortex involvement, particularly in low-level areas (V1-V4).

Conclusions:

  • Slow blood oxygen level-dependent signals can represent neural representations of fast perceptual processes.
  • The developed f-CVGAN demonstrates practical decoding of visual perception from fMRI data.
  • This approach enhances understanding of visual mechanisms and brain activity decoding.