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

MsGCN: a multi-stream graph convolutional network for multiband PLV graph fusion in EEG-based biometric identification.

Frontiers in computational neuroscience·2026
Same author

Reinforcement learning in linear embedding space unlocks generalizable control across soft robot configurations.

Nature communications·2026
Same author

Dual controllability de-differentiation of functional brain networks in major depressive disorder: Insights from large-scale neuroimaging and transcriptomic integration.

Journal of affective disorders·2026
Same author

Functional connectivity-based classification and subtyping of major depression for precision mental health: An ensemble graph neural network approach.

PLOS digital health·2026
Same author

Multi-Site Transfer Classification of Major Depressive Disorder: An fMRI Study in 3335 Subjects.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Transfer learning from 2D natural images to 4D fMRI brain images via geometric mapping.

Medical image analysis·2026
Same journal

The causal efficacy of consciousness: a neuroscientific analysis and explanation.

Frontiers in human neuroscience·2026
Same journal

Temporal-oscillatory entrainment: a multi-timescale framework for rhythmic coordination from neural to social frequencies.

Frontiers in human neuroscience·2026
Same journal

Role of AQP4 in ameliorating heat stress-induced cellular injury in a cell line model through active heat acclimation.

Frontiers in human neuroscience·2026
Same journal

Correction: Cognitive state monitoring for neuroadaptive information visualization.

Frontiers in human neuroscience·2026
Same journal

The synthetic self-hypothesis: dopaminergic redirection through self-face recognition in stuttering therapy.

Frontiers in human neuroscience·2026
Same journal

A randomised, placebo-controlled, triple-blind clinical trial to investigate the efficacy of <i>Ginkgo biloba</i> extract EGb 761<sup>®</sup> in cognitive impairment associated with post COVID-19 syndrome-the EGb COCOS protocol.

Frontiers in human neuroscience·2026
See all related articles

Related Experiment Video

Updated: Apr 6, 2026

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

10.6K

Predicting individual brain maturity using dynamic functional connectivity.

Jian Qin1, Shan-Guang Chen2, Dewen Hu1

  • 1College of Mechatronics and Automation, National University of Defense Technology, Changsha China.

Frontiers in Human Neuroscience
|August 4, 2015
PubMed
Summary
This summary is machine-generated.

Brain functional connectivity dynamics change with age, accurately predicting brain maturity. These dynamic changes in brain networks offer new insights into typical brain development.

Keywords:
developmentfMRIfunctional connectivitylow-frequency fluctuationmultivariate pattern analysis

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

7.8K
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

1.7K

Related Experiment Videos

Last Updated: Apr 6, 2026

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

10.6K
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

7.8K
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

1.7K

Area of Science:

  • Neuroscience
  • Developmental Neuroscience
  • Cognitive Neuroscience

Background:

  • Functional connectivity (FC) analyses reveal developmental trends in brain networks.
  • Understanding the link between resting-state FC dynamics and brain maturation is limited.

Purpose of the Study:

  • To investigate age-related differences in the temporal variability of functional connectivity dynamics.
  • To determine if dynamic FC can predict brain maturity across development.

Main Methods:

  • Utilized neuroimaging data from the Nathan Kline Institute (NKI) cohort (n=183, ages 7-30).
  • Analyzed age-related changes in the temporal variability of functional connectivity dynamics within and between intrinsic connectivity networks.

Main Results:

  • Dynamic inter-region interactions accurately predicted individual brain maturity across development.
  • Identified age-dependent trends in dynamic inter-network FC, including increased variability within the default mode network (DMN) and cerebellum, and between the visual network, DMN, and cerebellum.
  • Observed decreased variability within the cerebellum and between the cerebellum, DMN, and cingulo-opercular network.

Conclusions:

  • Significant developmental changes occur in dynamic inter-network interactions.
  • Dynamic functional connectivity patterns provide insights into the functional organization of developing brains.