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

Glutamatergic signaling underlies brain structural organization for mathematical and reading abilities in children.

Nature communications·2026
Same author

Attention and social problems are uniquely associated with academic achievement beyond overall psychopathology: multicohort replication in 3,800 participants.

Journal of child psychology and psychiatry, and allied disciplines·2026
Same author

In-situ engineered RuTiO<sub>x</sub> nanorods for synergistic AEM-LOM pathways enabling overall water splitting.

Science bulletin·2026
Same author

Temporally-resolved deep learning reveals autism symptom-specific neural signatures during naturalistic social experiences.

Research square·2026
Same author

Nonergodicity and Simpson's paradox in neurocognitive dynamics of cognitive control.

Nature communications·2026
Same author

Latent brain state dynamics predict early amyloid accumulation and cognitive impairment.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: Mar 6, 2026

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

7.7K

Bayesian switching factor analysis for estimating time-varying functional connectivity in fMRI.

Jalil Taghia1, Srikanth Ryali1, Tianwen Chen1

  • 1Department of Psychiatry & Behavioral Sciences Stanford University, School of Medicine, Stanford, CA 94305, USA.

Neuroimage
|March 8, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces Bayesian switching factor analysis (BSFA), a novel generative model for analyzing dynamic brain connectivity using functional magnetic resonance imaging (fMRI). BSFA enhances understanding of time-varying functional interactions between brain regions, improving individual participant identification.

Keywords:
Bayesian inferenceDynamic functional networksFactor analysisHidden Markov modelResting-state fMRI

More Related Videos

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
06:50

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

10.0K
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

Related Experiment Videos

Last Updated: Mar 6, 2026

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

7.7K
Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
06:50

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

10.0K
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

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Statistical Modeling

Background:

  • Understanding dynamic functional interactions between brain regions is crucial but challenging with current fMRI analysis methods.
  • Existing multivariate techniques struggle to robustly estimate time-varying functional connectivity.
  • Limitations exist in modeling dynamic interactions between multiple brain areas from fMRI data.

Purpose of the Study:

  • To develop a novel Bayesian generative model for analyzing dynamic functional magnetic resonance imaging (fMRI) time-series.
  • To introduce Bayesian switching factor analysis (BSFA) for modeling time-varying brain functional networks.
  • To enhance the estimation of temporal dynamics in brain connectivity.

Main Methods:

  • Developed a Bayesian generative model within the hidden Markov model (HMM) framework, a dynamic variant of the static factor analysis model.
  • Integrated factor analysis into a generative HMM, termed Bayesian switching factor analysis (BSFA).
  • Utilized Bayesian model selection for automatic determination of latent states and estimated temporal evolution of brain states and transition probabilities.

Main Results:

  • Validated BSFA using extensive simulations on synthetic data and fingerprint analysis of Human Connectome Project (HCP) resting-state fMRI data.
  • Demonstrated improved individual participant identification (fingerprinting) by modeling temporal dependencies.
  • Revealed dynamic interactions among salience, central-executive, and default mode networks, with the salience network showing highest temporal flexibility.

Conclusions:

  • BSFA provides a powerful and novel generative model for investigating dynamic brain connectivity.
  • The model effectively captures temporal evolution of brain states and their transitions.
  • BSFA enhances the analysis of time-varying functional interactions in neuroimaging data.