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

In-scanner thoughts contribute to resting-state functional connectivity.

Nature communications·2026
Same author

A new fMRI quality metric using multi-echo information: Theory, validation and implications.

bioRxiv : the preprint server for biology·2026
Same author

Mapping high-amplitude fMRI edge time series events across space and time.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Challenges and opportunities of mesoscopic brain mapping with fMRI.

Current opinion in behavioral sciences·2026
Same author

Eye metrics often reflect visual conscious awareness, conscious content, and neural processing in cerebral blindness.

Communications biology·2025
Same author

Brief Encounters with Real Objects Modulate the Medial Parietal But Not Occipitotemporal Cortex.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2025
Same journal

Investigating the Neural Origins of Ear-EEG: A Correlation Study Using Scalp EEG Source Reconstruction.

NeuroImage·2026
Same journal

Hysteresis effects in visual and auditory perception and the comparison of underlying neural mechanisms - an EEG study.

NeuroImage·2026
Same journal

Short-term audio-tactile training affects cortical auditory speech-envelope tracking for incongruent but not congruent stimuli.

NeuroImage·2026
Same journal

Dissociable Neurocognitive Mechanisms of State and Trait Anxiety in Working Memory: Threat-Induced Alterations in Decision Dynamics and Attenuation of Large-Scale Network Reconfiguration.

NeuroImage·2026
Same journal

Neuro-Ocular Amyloid Characterization in Alzheimer's Disease via Cross-Site PET-MRI and Hierarchical Cross-Attention Driven Multimodal Representation Learning.

NeuroImage·2026
Same journal

Whole-brain network dynamics underlying intolerance of uncertainty.

NeuroImage·2026
See all related articles

Related Experiment Video

Updated: May 20, 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

Periodic changes in fMRI connectivity.

Daniel A Handwerker1, Vinai Roopchansingh, Javier Gonzalez-Castillo

  • 1Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bldg 10, Rm 1D80, 10 Center Dr MSC 1148, Bethesda, MD 20892-1148, USA. handwerkerd@mail.nih.gov

Neuroimage
|July 17, 2012
PubMed
Summary
This summary is machine-generated.

Functional MRI (fMRI) resting-state correlations fluctuate over time, exhibiting periodic patterns. These fluctuations can differentiate brain regions, even when neural connectivity dynamics are absent.

More Related Videos

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

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

Related Experiment Videos

Last Updated: May 20, 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

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

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

Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Systems Neuroscience

Background:

  • Functional magnetic resonance imaging (fMRI) research has historically focused on task-evoked brain activity.
  • Resting-state fMRI (rs-fMRI) studies increasingly analyze spontaneous brain signal correlations.
  • Current rs-fMRI analyses typically assess temporal correlations over 5-10 minute periods.

Purpose of the Study:

  • To investigate the temporal dynamics of brain correlations on shorter time scales within rs-fMRI data.
  • To examine how correlations with the posterior cingulate cortex (PCC) change over a 10-minute scan.
  • To determine if correlation fluctuations can be used to differentiate brain regions.

Main Methods:

  • Analysis of temporal fluctuations in fMRI signal correlations over a 10-minute resting state scan.
  • Examination of correlations specifically with the posterior cingulate cortex (PCC).
  • Use of synthetic time series data with randomized phase to control for amplitude spectra effects.

Main Results:

  • fMRI correlations exhibit significant temporal fluctuations during resting state.
  • These fluctuations demonstrate periodic characteristics.
  • Correlations between the PCC and other brain regions fluctuate at distinct frequencies, allowing for region parsing.
  • Synthetic data analysis suggests observed fluctuations may not solely reflect dynamic neural connectivity.

Conclusions:

  • fMRI correlation fluctuations are inherent to the method and can occur independently of dynamic neural connectivity changes.
  • Distinct fluctuation frequencies can differentiate brain regions and their functional networks.
  • Understanding these intrinsic fluctuations is crucial for accurately interpreting brain connectivity dynamics from fMRI data.
  • Power spectra of fMRI data contain valuable information for parsing brain regions based on connectivity patterns.