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

Brain Imaging01:14

Brain Imaging

235
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
235

You might also read

Related Articles

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

Sort by
Same author

The intrinsic cortical geometry of reading.

bioRxiv : the preprint server for biology·2026
Same author

Human hippocampal ripples tune cortical responses based on predicted uncertainty.

Nature neuroscience·2026
Same author

Lip-reading and eye-gaze discrimination are functionally lateralized across the left and right posterior superior temporal sulci.

Cerebral cortex (New York, N.Y. : 1991)·2026
Same author

Patterns of Ongoing Thought Are Associated With Emotion Regulation and Negative Affect in Daily Life.

Affective science·2026
Same author

Testing and tracking in the UK: A dynamic causal modelling study.

Wellcome open research·2026
Same author

The evolutionary and organizational bases of active affordance.

Cerebral cortex (New York, N.Y. : 1991)·2026

Related Experiment Video

Updated: Jul 13, 2025

Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation
00:08

Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation

Published on: August 20, 2019

14.4K

Establishing brain states in neuroimaging data.

Zalina Dezhina1, Jonathan Smallwood2, Ting Xu3

  • 1Department of Neuroimaging, King's College London, United Kingdom.

Plos Computational Biology
|October 16, 2023
PubMed
Summary
This summary is machine-generated.

Researchers propose a new definition for brain states using dynamical systems theory and fMRI data. Findings suggest first-order models describe resting brain states, while second-order models better fit task states, challenging current computational neuroscience models.

More Related Videos

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.2K
Deep Brain Stimulation with Simultaneous fMRI in Rodents
11:09

Deep Brain Stimulation with Simultaneous fMRI in Rodents

Published on: February 15, 2014

14.1K

Related Experiment Videos

Last Updated: Jul 13, 2025

Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation
00:08

Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation

Published on: August 20, 2019

14.4K
Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.2K
Deep Brain Stimulation with Simultaneous fMRI in Rodents
11:09

Deep Brain Stimulation with Simultaneous fMRI in Rodents

Published on: February 15, 2014

14.1K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Dynamical Systems Theory

Background:

  • The definition of a 'brain state' lacks consensus across neuroscience subfields.
  • This ambiguity hinders the development of accurate neural dynamics models.
  • Dynamical systems theory offers a framework for defining system states.

Purpose of the Study:

  • To adopt a dynamical systems definition of state for neuroimaging data.
  • To establish brain states in functional magnetic resonance imaging (fMRI) time series.
  • To investigate the order of models describing brain states.

Main Methods:

  • Applied Dynamic Causal Modelling (DCM) to low-dimensional embeddings of fMRI data.
  • Analyzed both resting and task condition fMRI datasets.
  • Compared first-order and second-order dynamical models.

Main Results:

  • Approximately 90% of subjects in resting states were better described by first-order models.
  • Approximately 55% of subjects in task states were better described by second-order models.
  • Identified distinct model orders for resting versus task brain states.

Conclusions:

  • The study proposes a novel method for defining brain states in neuroimaging.
  • Findings challenge the predominant use of first-order equations in computational neuroscience.
  • Introduces phase space representations for brain states in fMRI data.