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

Towards the construction of a virtual yeast.

Nature·2026
Same author

Study on the Properties of Foamed Mixture Lightweight Soil Prepared from Waste Dredged Soil for Ecological Floating Landscapes.

Materials (Basel, Switzerland)·2026
Same author

Localizing the Epileptogenic Zone Using SEEG-Based Excitation-Inhibition Dynamics and Spectral Features in Drug-Resistant Epilepsy: A Multicenter Retrospective Study.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

Computational neuroelectrophysiology and artificial intelligence for drug-resistant epilepsy: recent advances, current challenges, and future directions.

Journal of neural engineering·2026
Same author

GUTK induces apoptosis in reactivating quiescent prostate cancer cells <i>via</i> Aurora A-mediated stabilization of SOD2.

iScience·2026
Same author

Comparative Evaluation of the Nutrient Composition and Lipidomic Profile of Different Parts of Muscle in the Chaka Sheep.

Food science of animal resources·2026
Same journal

LEARNABLE HIERARCHICAL VISUAL CONTEXTS FOR TUMOR SEGMENTATION IN COMPUTED TOMOGRAPHY IMAGES.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

DUAL CROSS-ATTENTION SIAMESE TRANSFORMER FOR RECTAL TUMOR REGROWTH ASSESSMENT IN WATCH-AND-WAIT ENDOSCOPY.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

LUMEN: LONGITUDINAL MULTI-MODAL RADIOLOGY MODEL FOR PROGNOSIS AND DIAGNOSIS.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

OVERVIEW OF THE CXR-LT 2026 CHALLENGE: MULTI-CENTER LONG-TAILED AND ZERO SHOT CHEST X-RAY CLASSIFICATION.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

CROSS-MODAL FINE-TUNING OF 3D CONVOLUTIONAL FOUNDATION MODELS FOR ADHD CLASSIFICATION WITH LOW-RANK ADAPTATION.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

AN IN SILICO STUDY OF LOW-INTENSITY FOCUSED ULTRASOUND DISPLACEMENT MAPPING WITH A 220 KHZ CLINICAL PHASED-ARRAY TRANSDUCER.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
See all related articles

Related Experiment Video

Updated: Dec 12, 2025

Author Spotlight: Advancements in In Vivo and Ex Vivo Retinal Imaging for Improved Glaucoma Diagnosis and Treatment
07:02

Author Spotlight: Advancements in In Vivo and Ex Vivo Retinal Imaging for Improved Glaucoma Diagnosis and Treatment

Published on: June 30, 2023

2.0K

DIFFEOMORPHIC SMOOTHING FOR RETINOTOPIC MAPPING.

Yanshuai Tu1, Duyan Ta1, Zhong-Lin Lu2,3

  • 1School of Computing, Informatics, Decision Systems Engineering, Arizona State Univ., Tempe, AZ.

Proceedings. IEEE International Symposium on Biomedical Imaging
|August 9, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new mathematical model to create accurate, diffeomorphic retinotopic maps from fMRI data. The novel approach improves upon conventional methods for vision science research.

Keywords:
Beltrami CoefficientDiffeomorphic SmoothingRetinotopic Mapping

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
Where You Cut Matters: A Dissection and Analysis Guide for the Spatial Orientation of the Mouse Retina from Ocular Landmarks
08:42

Where You Cut Matters: A Dissection and Analysis Guide for the Spatial Orientation of the Mouse Retina from Ocular Landmarks

Published on: August 4, 2018

14.8K

Related Experiment Videos

Last Updated: Dec 12, 2025

Author Spotlight: Advancements in In Vivo and Ex Vivo Retinal Imaging for Improved Glaucoma Diagnosis and Treatment
07:02

Author Spotlight: Advancements in In Vivo and Ex Vivo Retinal Imaging for Improved Glaucoma Diagnosis and Treatment

Published on: June 30, 2023

2.0K
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
Where You Cut Matters: A Dissection and Analysis Guide for the Spatial Orientation of the Mouse Retina from Ocular Landmarks
08:42

Where You Cut Matters: A Dissection and Analysis Guide for the Spatial Orientation of the Mouse Retina from Ocular Landmarks

Published on: August 4, 2018

14.8K

Area of Science:

  • Neuroscience
  • Vision Science
  • Computational Neuroscience

Background:

  • Retinotopic mapping links retinal visual input to cortical neurons.
  • Functional magnetic resonance imaging (fMRI) is typically used for this mapping.
  • fMRI-derived retinotopic maps often lack diffeomorphism, especially near the fovea, due to low signal-to-noise ratios.

Purpose of the Study:

  • To develop and solve a mathematical model for producing diffeomorphic retinotopic maps from fMRI data.
  • To address the non-diffeomorphic nature of current fMRI-based retinotopic maps.

Main Methods:

  • Utilized the Beltrami coefficient, a concept from geometry, to define diffeomorphism.
  • Formulated the problem within an optimization framework.
  • Developed efficient numerical methods for solving the mathematical model.

Main Results:

  • The proposed mathematical model successfully generates diffeomorphic retinotopic maps.
  • Experimental results with synthetic and real data show superiority over conventional smoothing techniques.
  • Demonstrated improved accuracy and smoothness in retinotopic mapping.

Conclusions:

  • The novel optimization framework effectively produces diffeomorphic retinotopic maps from fMRI data.
  • This method offers a significant advancement over existing techniques for retinotopic mapping.
  • Enhances the reliability of fMRI in vision science studies.