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

Brain Imaging01:14

Brain Imaging

537
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
537
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

164
Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
164

You might also read

Related Articles

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

Sort by
Same author

Multiscale structural connectome eigenmodes constrain human brain functional dynamics.

Communications biology·2026
Same author

Mapping the functional connectome between grey matter and white matter.

Communications biology·2026
Same author

Brain energetic landscapes shape state dysregulation in major depressive disorder: a morphological network controllability perspective.

Translational psychiatry·2026
Same author

Risperidone reduces individualized morphometric similarity deviation in schizophrenia and associates with cortical transcriptomic patterns.

Schizophrenia (Heidelberg, Germany)·2026
Same author

NLRP3-mediated trained immunity of microglia is involved in the recurrence-like episode of depressive disorders.

Molecular psychiatry·2025
Same author

Diffusion trajectory of atypical morphological development in autism spectrum disorder.

Communications biology·2025

Related Experiment Video

Updated: Dec 9, 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

9.6K

'When' and 'what' did you see? A novel fMRI-based visual decoding framework.

Chong Wang1,2, Hongmei Yan1, 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, People's Republic of China.

Journal of Neural Engineering
|September 9, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework using functional magnetic resonance imaging (fMRI) to decode visual stimuli. The approach accurately identifies visual content and timing, advancing our understanding of visual perception.

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

2.2K
Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
08:19

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

1.4K

Related Experiment Videos

Last Updated: Dec 9, 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

9.6K
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

2.2K
Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
08:19

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

1.4K

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computer Vision

Background:

  • Visual perception decoding is crucial for understanding the human visual system.
  • Functional magnetic resonance imaging (fMRI) enables prediction of visual content from brain activity.
  • Previous studies focused on decoding single stimulus content, leaving temporal and category information less explored.

Purpose of the Study:

  • To develop a novel framework for simultaneously decoding temporal and category information of visual stimuli from fMRI data.
  • To extend existing visual perception decoding capabilities by integrating temporal dynamics and semantic understanding.
  • To investigate the roles of early and high-level visual cortices in processing visual information.

Main Methods:

  • Acquired 3 Tesla fMRI data from five volunteers viewing diverse natural images.
  • Developed two classification-based decoding modules using recurrent neural networks (RNNs) for temporal dynamics analysis.
  • Integrated temporal occurrence and semantic category decoding modules into a unified framework.

Main Results:

  • The proposed framework achieved decoding accuracy over 19 times the chance level across subjects.
  • Both early visual cortex (eVC) and high-level visual cortex (hVC) are involved in visual processing.
  • Semantic information of visual stimuli is predominantly represented in the high-level visual cortex (hVC).

Conclusions:

  • The novel framework significantly advances the decoding of visual experiences from fMRI data.
  • The findings enhance our understanding of how the brain processes visual stimuli, including timing and meaning.
  • This research provides a foundation for future investigations into visual perception and neural representations.