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

Brain Imaging01:14

Brain Imaging

208
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...
208

You might also read

Related Articles

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

Sort by
Same author

Beyond gray matter: unveiling the critical role of white matter in Alzheimer's disease.

Progress in neuro-psychopharmacology & biological psychiatry·2026
Same author

Domain-General Decoupling and Context-Specific Buffering: Transdiagnostic Eye-Tracking Biomarkers of ASD and ADHD During Naturalistic Viewing.

bioRxiv : the preprint server for biology·2026
Same author

Unique Amygdala Signatures and Shared Prefrontal Deficits in Autism: Mapping Social Heterogeneity via Naturalistic functional Magnetic Resonance Imaging.

bioRxiv : the preprint server for biology·2026
Same author

Glymphatic Dysfunction, Brain Damage, and Clinical Disability in Spinocerebellar Ataxia Type 3.

Movement disorders : official journal of the Movement Disorder Society·2026
Same author

Dynamic Alterations of Functional Systems in Alzheimer's Disease: A Co-Activation Pattern Analysis.

Human brain mapping·2026
Same author

Dynamic brain connectivity patterns induced by oxytocin: An fMRI Co-Activation pattern analysis study.

Molecular psychiatry·2026

Related Experiment Video

Updated: Jun 5, 2025

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

26.2K

Comparing Intra- and Inter-individual Correlational Brain Connectivity from Functional and Structural Neuroimaging

Xin Di1, Bharat B Biswal1,

  • 1Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA.

Biorxiv : the Preprint Server for Biology
|December 16, 2024
PubMed
Summary

This study reveals that brain connectivity patterns change over time within individuals, influenced by aging and temporary states. Understanding these intra-individual changes enhances our interpretation of brain structure and function.

Keywords:
Brain connectivityCovariance networkFunctional ConnectivityInter-individualMolecular connectivity

More Related Videos

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

7.2K
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.1K

Related Experiment Videos

Last Updated: Jun 5, 2025

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

26.2K
Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

7.2K
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.1K

Area of Science:

  • Neuroscience
  • Brain Imaging
  • Human Physiology

Background:

  • Inter-individual correlations in neuroimaging (PET, MRI) are influenced by genetics, experience, and aging.
  • Existing research often overlooks within-individual brain connectivity changes over time.

Purpose of the Study:

  • To investigate intra-individual correlations of structural and functional brain measures longitudinally.
  • To differentiate aging effects from state-like variability in brain connectivity.
  • To compare intra-individual correlations with traditional inter-individual correlations.

Main Methods:

  • Utilized two longitudinal datasets with repeated neuroimaging scans (MRI, PET) over extended periods.
  • Analyzed regional homogeneity (ReHo) from resting-state fMRI and gray matter volume (GMV) from structural MRI.
  • Examined Fludeoxyglucose (18F) FDG-PET data in a second cohort to validate findings.

Main Results:

  • Intra-individual correlations of ReHo and GMV mirrored resting-state functional connectivity.
  • ReHo correlations were driven by state-like variability, while GMV correlations reflected aging.
  • Functional measures (ReHo, FDG-PET) showed stronger associations with resting-state connectivity than structural measures.

Conclusions:

  • Intra-individual brain connectivity analysis, controlling for individual differences, enhances interpretability.
  • Both aging and state-like factors dynamically shape brain connectivity patterns.
  • Understanding intra-individual changes is crucial for a comprehensive view of brain connectivity, complementing inter-individual findings.