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

Cerebellum: Anatomical Regions01:17

Cerebellum: Anatomical Regions

2.2K
The cerebellum, also known as the "little brain," is located in the posterior cranial fossa, inferior to the tentorium cerebelli and dorsal to the brainstem. It plays a significant role in motor control, coordination, and proprioception.
Cerebellar Structure
Externally, the cerebellum features a highly convoluted surface with numerous folia (narrow ridges) separated by shallow sulci (grooves). The cerebellum is divided into two hemispheres by a thin median structure known as the vermis. The...
2.2K
Anatomy of the Brain: Major Regions01:20

Anatomy of the Brain: Major Regions

6.3K
The brain is the most complex organ in the human body. It consists of four main parts: the cerebrum, diencephalon, cerebellum, and brainstem.
The cerebrum is the largest section of the brain and divides into left and right hemispheres, separated by a deep fissure. The cerebral outer layer of grey matter — the cerebral cortex — comprises elevations called gyri and shallow groves called sulci. The inner portion of white matter includes long nerve fibers known as axons, which connect...
6.3K

You might also read

Related Articles

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

Sort by
Same author

Synergistic Decoupling of Distance and Velocity in a Triboelectric Touchless Sensor for Intelligent Identity Recognition.

ACS applied materials & interfaces·2026
Same author

Generating R2<sup>*</sup> maps from T1W and T2W images using image-to-image translation for Parkinson's disease.

Medical physics·2026
Same author

Deep-learning based electroencephalogram denoising: A literature review.

Journal of neural engineering·2026
Same author

The discovery of novel 3-nitropyridin-2-amine-based 2,3-dihydroimidazo[1,2-a]pyridin-5(1H)-one insecticides.

Pest management science·2026
Same author

The Discovery of HNPC-A0073: A Novel Thiazol-5-ylmethyl-Based 3-Nitropyridin-2-amine Fungicide.

Journal of agricultural and food chemistry·2026
Same author

Lineage tracing of soma-to-primordial germ cell-like conversion in human tumor cell line.

iScience·2026

Related Experiment Video

Updated: Sep 2, 2025

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

1.2K

A Joint Constrained CCA Model for Network-Dependent Brain Subregion Parcellation.

Qinrui Ling, Aiping Liu, Yu Li

    IEEE Journal of Biomedical and Health Informatics
    |August 5, 2022
    PubMed
    Summary

    This study introduces a novel group-guided functional brain parcellation model using joint constrained canonical correlation analysis (JC-CCA). The method effectively identifies brain subregions with consistent connectivity, outperforming classical approaches and showing promise for clinical applications in Parkinson's disease research.

    More Related Videos

    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

    5.7K
    Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
    08:43

    Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

    Published on: August 7, 2017

    8.0K

    Related Experiment Videos

    Last Updated: Sep 2, 2025

    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

    1.2K
    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

    5.7K
    Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
    08:43

    Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

    Published on: August 7, 2017

    8.0K

    Area of Science:

    • Neuroimaging
    • Computational Neuroscience
    • Brain Mapping

    Background:

    • Functional magnetic resonance imaging (fMRI) based brain region parcellation faces challenges with heterogeneous subject populations (aged, diseased) and spatial normalization.
    • Existing methods struggle with inter-subject variability, especially when fMRI data is transformed into a common space.

    Purpose of the Study:

    • To develop a group-guided functional brain region parcellation model that maintains subject-specific native fMRI spaces.
    • To enable consistent identification of subregions with similar connectivity profiles across multiple subjects.
    • To assess the model's performance and clinical utility, particularly in distinguishing between Parkinson's disease (PD) patients and healthy controls.

    Main Methods:

    • Proposed a novel group-guided functional parcellation model utilizing joint constrained canonical correlation analysis (JC-CCA).
    • The JC-CCA method allows for group-guided parcellation while accommodating varying data dimensions for parcellated regions per subject.
    • Employed extensive experiments on both synthetic and real fMRI datasets, including data from subjects with and without Parkinson's disease.

    Main Results:

    • The proposed JC-CCA model demonstrated superior performance compared to classical parcellation methods in extensive experiments.
    • Application to Parkinson's disease (PD) fMRI data revealed significant differences in Putamen subregions and their connectivity patterns between patient and control groups.
    • The model achieved superior classification and regression outcomes, highlighting its potential for clinical diagnostic and prognostic applications.

    Conclusions:

    • The developed group-guided functional parcellation model effectively addresses heterogeneity in fMRI data by preserving native spaces.
    • The JC-CCA approach provides a robust method for identifying functionally distinct brain subregions and their connectivity.
    • The model shows significant potential for advancing neuroimaging research in neurological disorders like Parkinson's disease and improving clinical practice.