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

Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Association Areas of the Cortex01:21

Association Areas of the Cortex

Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
Somatosensory, Motor, and Association Cortex01:23

Somatosensory, Motor, and Association Cortex

The somatosensory cortex in the parietal lobes is crucial for interpreting sensory data such as touch, temperature, and proprioception. The somatosensory cortex, situated in the parietal lobes, plays a vital role in interpreting sensory information like touch, temperature, and proprioception—awareness of body position. This specialized brain region features an organized structure wherein neurons at the top primarily process sensations originating from the lower body. In contrast, those at the...
Motor and Sensory Areas of the Cortex01:14

Motor and Sensory Areas of the Cortex

The cerebral cortex, the brain's outermost layer, is pivotal in processing complex cognitive tasks, emotions, and various sensory inputs and executing voluntary motor activities. This intricate structure is divided into three primary functional areas: the motor areas, sensory areas, and association areas.
Motor Areas
The motor areas located in the frontal lobe are central to controlling voluntary movements. This region is further subdivided into the primary motor cortex and the premotor cortex.
The Normal and Binormal Vectors01:27

The Normal and Binormal Vectors

A roller coaster spiraling upward along a helical track offers a vivid illustration of the geometry of space curves. As the car follows the track, its movement at each point can be described using a set of three mutually perpendicular unit vectors: the tangent, normal, and binormal vectors. Together, these vectors form the Frenet–Serret frame, a moving coordinate system that captures how a curve behaves in three-dimensional space.Tangent, Normal, and Binormal VectorsThe unit tangent vector...
Brain Imaging01:14

Brain Imaging

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 Stimulation (TMS).

You might also read

Related Articles

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

Sort by
Same author

Cerebral Amyloid Angiopathy and Risk of Dementia in Patients With Cognitive Complaint.

Neurology·2026
Same author

Evolutionary signatures in deep white matter architecture: A comparative study of humans and chimpanzees.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Ultra-high resolution multimodal MRI densely labelled holistic structural brain atlas.

Scientific reports·2026
Same author

Decoding cortical folding with deep learning: toward neurodevelopmental biomarkers of psychiatric disorders.

Journal of neural transmission (Vienna, Austria : 1996)·2026
Same author

The 'Rosetta Stone' of palaeoneurology: A detailed study of the link between the brain and the endocast on 75 volunteers.

Journal of anatomy·2026
Same author

A self-supervised learning framework for discovering cortical folding patterns under genetic influence: Application to the Anterior Cingulate Cortex.

Imaging neuroscience (Cambridge, Mass.)·2025
Same journal

Enhancing Alzheimer's Diagnosis: Leveraging Anatomical Landmarks in Graph Convolutional Neural Networks on Tetrahedral Meshes.

Information processing in medical imaging : proceedings of the ... conference·2026
Same journal

Cycle-Consistent Zero-Shot Through-Plane Super-Resolution for Anisotropic Head MRI.

Information processing in medical imaging : proceedings of the ... conference·2026
Same journal

Brightness-Invariant Tracking Estimation in Tagged MRI.

Information processing in medical imaging : proceedings of the ... conference·2025
Same journal

Multi-View and Multi-Scale Alignment for Contrastive Language-Image Pre-training in Mammography.

Information processing in medical imaging : proceedings of the ... conference·2025
Same journal

Using Multiple Instance Learning to Build Multimodal Representations.

Information processing in medical imaging : proceedings of the ... conference·2025
Same journal

mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds.

Information processing in medical imaging : proceedings of the ... conference·2024
See all related articles

Related Experiment Video

Updated: Jun 20, 2026

Visualization of Cortical Modules in Flattened Mammalian Cortices
08:49

Visualization of Cortical Modules in Flattened Mammalian Cortices

Published on: January 22, 2018

Joint Bayesian cortical sulci recognition and spatial normalization.

Matthieu Perrot1, Denis Rivière, Alan Tucholka

  • 1CEA, Neurospin, LNAO, Saclay, France. matthieu.perrot@cea.fr

Information Processing in Medical Imaging : Proceedings of the ... Conference
|August 22, 2009
PubMed
Summary
This summary is machine-generated.

This study refines brain sulci recognition using advanced registration techniques on T1 MRI scans. The new Bayesian framework improves sulci labeling accuracy to 86% and offers robust registration.

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Related Experiment Videos

Last Updated: Jun 20, 2026

Visualization of Cortical Modules in Flattened Mammalian Cortices
08:49

Visualization of Cortical Modules in Flattened Mammalian Cortices

Published on: January 22, 2018

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

  • Neuroimaging
  • Computational Anatomy
  • Medical Image Analysis

Background:

  • The Sulci Parcellation and Localization Model (SPAM) is sensitive to the common space used in group studies.
  • Previous work focused on sulci localization but required refinement of the common space.

Purpose of the Study:

  • To refine the common space for group studies by improving sulci registration and identification.
  • To enhance the accuracy of sulci labeling and develop a robust registration technique.

Main Methods:

  • A consistent Bayesian framework was developed to jointly identify and register sulci.
  • Two complementary normalization techniques were integrated: global rigid transformation and piecewise rigid registration, sulcus by sulcus.
  • Sulcuswise localization variability knowledge was used to constrain normalization.

Main Results:

  • Improved sulci labeling quality with a global recognition rate of 86%.
  • Achieved a basic yet robust registration technique for sulcal structures.
  • Demonstrated enhanced sulci recognition on a new T1 MRI database of 62 subjects.

Conclusions:

  • The proposed Bayesian framework effectively integrates identification and registration of sulci.
  • The refined normalization techniques significantly improve sulci labeling accuracy and registration robustness.
  • This work provides a foundation for more accurate and consistent neuroimaging analysis of cortical structures.