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

10.0K
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...
10.0K

You might also read

Related Articles

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

Sort by
Same author

Excessive Censoring Degrades Individual-Specific Cortical Parcellations and Personalized TMS Targets.

bioRxiv : the preprint server for biology·2026
Same author

Atrophy in preclinical Alzheimer's disease maps to a network that predicts longitudinal decline.

Molecular psychiatry·2026
Same author

Brain activity is not only for thinking.

Current opinion in behavioral sciences·2026
Same author

Brain resting state functional connectivity changes with aerobic exercise, and mindfulness: A narrative review.

Sports medicine and health science·2026
Same author

Individual-specific resting-state networks predict language dominance in drug-resistant epilepsy.

Epilepsia·2026
Same author

Widespread use of invalid statistical tests in biomedical machine learning.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: Feb 22, 2026

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

4.9K

Interpreting temporal fluctuations in resting-state functional connectivity MRI.

Raphaël Liégeois1, Timothy O Laumann2, Abraham Z Snyder3

  • 1Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore.

Neuroimage
|September 17, 2017
PubMed
Summary

Dynamic functional connectivity (dFC) research is debated, with some suggesting it

Keywords:
Autoregressive modelBrain statesDynamic FCLinear dynamical systemsStationaritySurrogate data

More Related Videos

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

7.2K
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

7.7K

Related Experiment Videos

Last Updated: Feb 22, 2026

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

4.9K
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

7.2K
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

7.7K

Area of Science:

  • Neuroscience
  • Statistics
  • Brain Network Analysis

Background:

  • Resting-state functional connectivity (FC) studies human brain networks.
  • Dynamic functional connectivity (dFC) may reflect brain state changes or sampling variability.
  • Statistical rigor in dFC analysis, particularly regarding stationarity, is underdeveloped.

Purpose of the Study:

  • To explore statistical issues of stationarity and hypothesis testing in dFC.
  • To clarify the relationship between stationary signals and discrete brain states.
  • To evaluate common statistical testing frameworks for dFC.

Main Methods:

  • Reviewed the statistical definition of stationarity and its relation to sample vs. ensemble statistics.
  • Analyzed assumptions of phase randomization (PR) and autoregressive randomization (ARR) for dFC null hypothesis testing.
  • Applied PR and multivariate ARR to Human Connectome Project data; compared autoregressive (AR) and hidden Markov models (HMM) for temporal FC fluctuations.

Main Results:

  • Stationarity encompasses a broader range of signals than often assumed, including those with discrete states.
  • Popular dFC testing frameworks (PR, ARR) generate stationary, linear, Gaussian null data, making rejection sensitive to non-stationarity, nonlinearity, or non-Gaussianity.
  • Most participants' data did not reject the null hypothesis with PR and multivariate ARR.
  • First-order AR models explained temporal FC fluctuations better than static FC models, indicating dynamical information beyond static FC.
  • AR models outperformed HMMs in explaining temporal FC fluctuations, even when the null hypothesis was rejected, suggesting limited evidence for discrete brain states.

Conclusions:

  • The stationary, linear, Gaussian null hypothesis is often not rejected in resting-state fMRI dFC analysis.
  • Autoregressive models are valuable for generating null data and analyzing dynamical properties of resting-state fMRI.
  • Findings suggest AR models may be more suitable than HMMs for capturing temporal FC dynamics, questioning the prevalence of discrete brain states in current fMRI data.