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

Association Areas of the Cortex01:21

Association Areas of the Cortex

10.4K
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,...
10.4K
Somatosensory, Motor, and Association Cortex01:23

Somatosensory, Motor, and Association Cortex

3.9K
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...
3.9K

You might also read

Related Articles

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

Sort by
Same author

Community-level modeling of gyral folding patterns for robust and anatomically informed individualized brain mapping.

NeuroImage·2026
Same author

AD-GPT: large language models in Alzheimer's disease.

BMC medical informatics and decision making·2026
Same author

Large language models for bioinformatics.

Quantitative biology (Beijing, China)·2026
Same author

Biophysical modeling of anatomically realistic prenatal cortical folding development.

Research square·2026
Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

A Unified Continuous Staging Framework for Alzheimer's Disease and Lewy Body Dementia via Hierarchical Anatomical Features.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2025

Related Experiment Video

Updated: Mar 23, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.1K

Group-wise consistent cortical parcellation based on connectional profiles.

Tuo Zhang1, Dajiang Zhu2, Xi Jiang2

  • 1School of Automation and Brain Decoding Research Center, Northwestern Polytechnical University, Xi'an 710072, China; Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, GA 30605, USA.

Medical Image Analysis
|April 8, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel hierarchical method for consistent brain cortical parcellation. It uses Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOLs) to map brain regions across individuals, improving anatomical and functional consistency.

Keywords:
ConnectivityCortical parcellationGroup-wisedMRI

More Related Videos

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging
11:50

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

Published on: February 4, 2022

4.7K
Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

27.2K

Related Experiment Videos

Last Updated: Mar 23, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.1K
A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging
11:50

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

Published on: February 4, 2022

4.7K
Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

27.2K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Brain Imaging Analysis

Background:

  • Establishing consistent anatomical and functional brain regions across individuals is a significant challenge in neuroscience.
  • Existing methods struggle with defining meaningful cortical boundaries and establishing correspondences between parcellated regions.
  • The Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL) system offers consistent landmarks but doesn't cover the entire cortex or define clear boundaries.

Purpose of the Study:

  • To develop a novel hierarchical approach for group-wise consistent cortical parcellation.
  • To leverage DICCCOLs to overcome limitations of existing parcellation methods.
  • To achieve fine-granularity parcellation with intrinsically established structural correspondences across individual brains.

Main Methods:

  • A hierarchical parcellation scheme utilizing DICCCOLs as corresponding samples.
  • Automatic determination of cluster numbers at each hierarchical level.
  • Iterative classification of cortical surface vertices into corresponding clusters within a group-wise framework.

Main Results:

  • The proposed method achieves consistent, fine-granularity cortical parcellation across individual brains.
  • Intrinsically established structural correspondences are identified.
  • Group-wise parcellation boundaries effectively segregate functionally homogeneous areas, validated with fMRI data.

Conclusions:

  • The novel hierarchical approach successfully addresses the challenge of group-wise consistent cortical parcellation.
  • This method provides a robust framework for mapping and comparing brain structures and functions across individuals.
  • The findings have implications for understanding brain variability and developing standardized neuroimaging analysis techniques.