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

278
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...
278

You might also read

Related Articles

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

Sort by
Same author

Modeled Long-Term Effects of Psilocybin on Dynamic Activity and Effective Connectivity of Fronto-Striatal-Thalamic Circuits.

Human brain mapping·2026
Same author

A canary in the mind: A single baseline brain scan predicts adolescent depression and anxiety one year later.

medRxiv : the preprint server for health sciences·2026
Same author

Large scale functional and effective connectivity alterations cross the Huntington's disease integrated staging system.

NeuroImage. Clinical·2026
Same author

Pre-stimulus brain states predict and control variability in stimulation responses.

Brain stimulation·2026
Same author

Brain function in language and associated networks in non- or minimally verbal children.

Brain communications·2026
Same author

Schooling Trajectories and the Development of Brain Dynamics: A Comparative Study of Montessori and Traditional Education.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Related Experiment Video

Updated: Aug 18, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K

Data-driven discovery of canonical large-scale brain dynamics.

Juan Piccinini1,2, Gustavo Deco3,4,5,6,7, Morten Kringelbach8,9,10,11

  • 1Department of Physics, University of Buenos Aires, Intendente Guiraldes 2160 Ciudad Universitaria, CABA, Argentina.

Cerebral Cortex Communications
|December 8, 2022
PubMed
Summary

Computational models of brain activity reveal that dynamics near a bifurcation point, particularly stable spiral attractors, best replicate human brain recordings. This suggests noise-driven dynamics near bifurcations are key to endogenous brain activity.

Keywords:
brain dynamicscomputational modelingfMRIresting statesleep

More Related Videos

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.0K
Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

26.3K

Related Experiment Videos

Last Updated: Aug 18, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.0K
Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

26.3K

Area of Science:

  • Computational neuroscience
  • Systems neuroscience
  • Neuroimaging analysis

Background:

  • Human behavior and cognition are linked to complex spatio-temporal brain dynamics.
  • Computational models offer a way to simulate these dynamics with varying biophysical realism.

Purpose of the Study:

  • To identify local dynamics in computational models that best reproduce functional magnetic resonance imaging (fMRI) data.
  • To classify the types of dynamics that enable accurate simulation of brain activity.

Main Methods:

  • Employed a data-driven optimization algorithm to tune computational models.
  • Utilized phase space and eigenvalue analyses to characterize model dynamics.
  • Compared model outputs to empirical fMRI data, focusing on synchronization, metastability, and functional connectivity.

Main Results:

  • Stable spiral attractors predominated in models that best matched empirical data.
  • Deviations from harmonic oscillations in limit cycles improved functional connectivity simulation.
  • Simulations for wakefulness benefited from dynamics near a bifurcation, while deep sleep correlated with increased stability.

Conclusions:

  • The study identifies specific dynamical properties (stable spirals, proximity to bifurcation) crucial for realistic brain simulations.
  • Findings support noise-driven dynamics near bifurcations as a fundamental mechanism for endogenous brain activity.
  • Results offer testable predictions for refining biophysical models of brain function.