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

Multi-View Stenosis Classification Leveraging Transformer-Based Multiple-Instance Learning Using Real-World Clinical Data.

IEEE transactions on medical imaging·2026
Same author

Molecular signatures of cortical expansion in the human foetal brain.

Nature communications·2024
Same author

UNITY: A low-field magnetic resonance neuroimaging initiative to characterize neurodevelopment in low and middle-income settings.

Developmental cognitive neuroscience·2024
Same author

Molecular signatures of cortical expansion in the human fetal brain.

bioRxiv : the preprint server for biology·2024
Same author

Interaction between clinicians and artificial intelligence to detect fetal atrioventricular septal defects on ultrasound: how can we optimize collaborative performance?

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology·2024
Same author

Sonographer interaction with artificial intelligence: collaboration or conflict?

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology·2023

Related Experiment Video

Updated: May 3, 2026

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index
09:57

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index

Published on: January 2, 2012

27.4K

Automatic quantification of normal cortical folding patterns from fetal brain MRI.

R Wright1, V Kyriakopoulou2, C Ledig1

  • 1Biomedical Image Analysis Group, Department of Computing, Imperial College London, SW7 2AZ, UK.

Neuroimage
|January 30, 2014
PubMed
Summary

We developed an automated method to measure fetal brain folding using MRI scans. This technique accurately tracks normal cortical development and can help detect abnormalities for earlier diagnosis.

Keywords:
Brain developmentCortical foldingFetal MRIGompertz function

More Related Videos

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy
08:49

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Published on: August 1, 2022

3.3K
Author Spotlight: High-Resolution Imaging of Mouse Neonate Brains – A Micro-CT Protocol with Lugol's Solution Contrast Agent
06:36

Author Spotlight: High-Resolution Imaging of Mouse Neonate Brains – A Micro-CT Protocol with Lugol's Solution Contrast Agent

Published on: May 19, 2023

2.6K

Related Experiment Videos

Last Updated: May 3, 2026

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index
09:57

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index

Published on: January 2, 2012

27.4K
Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy
08:49

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Published on: August 1, 2022

3.3K
Author Spotlight: High-Resolution Imaging of Mouse Neonate Brains – A Micro-CT Protocol with Lugol's Solution Contrast Agent
06:36

Author Spotlight: High-Resolution Imaging of Mouse Neonate Brains – A Micro-CT Protocol with Lugol's Solution Contrast Agent

Published on: May 19, 2023

2.6K

Area of Science:

  • Neuroimaging
  • Developmental Neuroscience
  • Medical Image Analysis

Background:

  • Accurate quantification of fetal brain development is crucial for early detection of neurological abnormalities.
  • Understanding normal cortical folding patterns provides a baseline for identifying deviations potentially linked to pathology.

Purpose of the Study:

  • To develop and validate an automated method for quantifying normal fetal cortical folding patterns from in utero MRI.
  • To establish an age-dependent model of cortical folding and analyze regional developmental differences.

Main Methods:

  • Automated segmentation of fetal brain MR images using a spatio-temporal atlas and expectation-maximization with MRF regularization.
  • Computation of eight curvature-based folding measures using an implicit high-resolution surface.
  • Validation of automated segmentation against manual delineations (average discrepancy ~1mm).

Main Results:

  • Strong correlation (R²=0.99) between gestational age (GA) and computed folding measures, confirming the link between development and convolution.
  • An accurate age-dependent non-linear model of cortical folding was fitted, showing rapid increases between 25-30 weeks GA.
  • Regional analysis revealed differential growth rates, with parietal and posterior temporal lobes developing fastest.

Conclusions:

  • The automated method accurately quantifies fetal cortical folding and its development across a wide GA range.
  • The developed model enables precise fetal age prediction from folding measures and supports visual observations of folding progression.
  • Identified regional variations in cortical development highlight specific areas of rapid growth during gestation.