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

The genetic architecture of multimodal human brain age.

Nature communications·2024
Same author

Distance-weighted Sinkhorn loss for Alzheimer's disease classification.

iScience·2024
Same author

Integrating imaging and genomic data for the discovery of distinct glioblastoma subtypes: a joint learning approach.

Scientific reports·2024
Same author

Plasma Biomarkers as Predictors of Progression to Dementia in Individuals with Mild Cognitive Impairment.

Journal of Alzheimer's disease : JAD·2024
Same author

Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals.

JAMA psychiatry·2024
Same author

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning.

ArXiv·2024

Related Experiment Video

Updated: May 3, 2026

Resting-State Connectivity and Neuroimaging of Prefrontal Cortex Activity During a Block-Design Yoga Asana Practice Using fNIRS
07:56

Resting-State Connectivity and Neuroimaging of Prefrontal Cortex Activity During a Block-Design Yoga Asana Practice Using fNIRS

Published on: June 24, 2025

988

IDENTIFYING PATTERNS IN TEMPORAL VARIATION OF FUNCTIONAL CONNECTIVITY USING RESTING STATE FMRI.

Harini Eavani1, Theodore D Satterthwaite2, Raquel E Gur2

  • 1Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania.

Proceedings. IEEE International Symposium on Biomedical Imaging
|January 21, 2014
PubMed
Summary

This study introduces a new method for analyzing brain functional networks using functional MRI (fMRI) data. The technique reveals dynamic brain connectivity patterns by identifying fundamental network components and reducing data complexity.

Keywords:
functional connectivityresting state fMRItemporal network dynamics

More Related Videos

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

17.3K
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

8.3K

Related Experiment Videos

Last Updated: May 3, 2026

Resting-State Connectivity and Neuroimaging of Prefrontal Cortex Activity During a Block-Design Yoga Asana Practice Using fNIRS
07:56

Resting-State Connectivity and Neuroimaging of Prefrontal Cortex Activity During a Block-Design Yoga Asana Practice Using fNIRS

Published on: June 24, 2025

988
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

17.3K
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

8.3K

Area of Science:

  • Neuroscience
  • Brain Imaging
  • Network Science

Background:

  • Estimating functional brain networks from functional MRI (fMRI) data is crucial for understanding brain function.
  • Traditional methods often result in temporally averaged connectivity matrices, limiting insights into dynamic functional connectivity due to low sample sizes and high dimensionality.
  • The dynamic nature of functional connectivity remains largely unexplored.

Purpose of the Study:

  • To propose a novel constrained matrix factorization method for analyzing time-varying functional brain networks from fMRI data.
  • To address limitations of current methods by identifying basis networks and enabling dimensionality reduction for dynamic analysis.
  • To investigate the composition of whole-brain functional networks as combinations of underlying basis networks.

Main Methods:

  • Developed a constrained matrix factorization technique to identify a set of basis networks representing semantic parts of whole-brain networks.
  • Modeled time-varying whole-brain networks as non-negative combinations of these basis networks.
  • Achieved significant dimensionality reduction by projecting fMRI data onto the identified basis, facilitating temporal dynamic analysis.

Main Results:

  • Simulated fMRI data demonstrated the method's effectiveness in recovering underlying basis networks.
  • Application to resting-state fMRI scans revealed that functional connectivity at any given time is composed of combinations of overlapping task-positive/negative sub-networks.
  • The method successfully captures the dynamic interplay of brain sub-networks.

Conclusions:

  • The proposed constrained matrix factorization method effectively estimates dynamic functional brain networks from fMRI data.
  • It provides a framework for understanding whole-brain connectivity as a combination of fundamental, overlapping sub-networks.
  • This approach facilitates the study of brain network dynamics, offering new insights into brain function.