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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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

Related Experiment Video

Updated: May 18, 2026

Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy
07:13

Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy

Published on: May 27, 2020

Decoding lifespan changes of the human brain using resting-state functional connectivity MRI.

Lubin Wang1, Longfei Su, Hui Shen

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

Plos One
|September 7, 2012
PubMed
Summary

Brain network development across the lifespan shows distinct patterns. Resting-state functional connectivity MRI reveals age-related changes in emotion, sensorimotor, and cognitive systems, aiding in understanding brain maturation and aging.

More Related Videos

Acquisition of Resting-State Functional Magnetic Resonance Imaging Data in the Rat
12:41

Acquisition of Resting-State Functional Magnetic Resonance Imaging Data in the Rat

Published on: August 28, 2021

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

Related Experiment Videos

Last Updated: May 18, 2026

Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy
07:13

Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy

Published on: May 27, 2020

Acquisition of Resting-State Functional Magnetic Resonance Imaging Data in the Rat
12:41

Acquisition of Resting-State Functional Magnetic Resonance Imaging Data in the Rat

Published on: August 28, 2021

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

Area of Science:

  • Neuroscience
  • Developmental Neuroscience
  • Brain Imaging

Background:

  • Lifelong brain development involves complex functional network changes.
  • Resting-state functional connectivity MRI (rs-fcMRI) is a key tool for studying these dynamics.
  • Understanding age-related brain changes is crucial for cognitive and behavioral insights.

Purpose of the Study:

  • To decode the developmental trajectory of whole-brain functional networks across seven decades of human life (ages 8-79).
  • To investigate linear and nonlinear age effects on resting-state functional connectivity.
  • To predict individual "brain ages" using rs-fcMRI data.

Main Methods:

  • Parametric curve fitting to analyze age-related changes in functional connectivity.
  • Manifold learning combined with support vector machine for "brain age" prediction.
  • Analysis of rs-fcMRI data across a wide age range (8-79 years).

Main Results:

  • Age-related changes in functional connectivity are spatially and temporally specific.
  • Functional connections linearly increase in emotion systems and decrease in sensorimotor systems with age.
  • Quadratic age trajectories were observed in higher-order cognitive function networks.
  • A low-dimensional manifold effectively captured complex age-related network patterns.
  • Manifold coordinates accurately predicted individuals' functional development levels.

Conclusions:

  • Brain maturation and aging exhibit inherent structural patterns within functional connectivity.
  • rs-fcMRI provides a quantitative method to describe typical and atypical brain development.
  • Findings offer insights into neural substrates of age-related cognitive and behavioral changes.