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 Experiment Videos

Scale-free brain functional networks.

Victor M Eguíluz1, Dante R Chialvo, Guillermo A Cecchi

  • 1Instituto Mediterráneo de Estudios Avanzados, IMEDEA, E07122 Palma de Mallorca, Spain.

Physical Review Letters
|February 9, 2005
PubMed
Summary
This summary is machine-generated.

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

Predicting transition to psychiatric disorders in clinical high-risk for psychosis subjects using machine learning and facial features.

Schizophrenia research·2026
Same author

Abnormal speech production in veterans with bipolar disorder: Relationship to the arcuate fasciculus.

Journal of affective disorders·2026
Same author

Alzheimer's Disease Brain Phenotypes are Age-dependent.

bioRxiv : the preprint server for biology·2026
Same author

Revisiting Amplitude of Low-Frequency Fluctuations (ALFF) in Resting-State fMRI: Clarifications and Improvements.

Human brain mapping·2026
Same author

Susceptibility for extremely low external fluctuations and critical behavior of Greenberg-Hastings neuronal model.

Physical review. E·2026
Same author

Advancing the Prediction and Understanding of Placebo Responses in Chronic Back Pain Using Large Language Models.

European journal of pain (London, England)·2025

Functional magnetic resonance imaging reveals human brain networks exhibit scale-free small-world properties. These network characteristics provide key insights into brain states during various tasks.

Area of Science:

  • Neuroscience
  • Network Science
  • Computational Biology

Background:

  • Functional magnetic resonance imaging (fMRI) is a key tool for mapping human brain activity.
  • Understanding functional brain networks is crucial for deciphering cognitive processes.
  • Previous studies have explored brain connectivity, but network properties require deeper analysis.

Purpose of the Study:

  • To analyze the network properties of functional human brain connections derived from fMRI data.
  • To determine if these networks exhibit characteristics of scale-free small-world networks.
  • To assess the functional significance of observed network properties.

Main Methods:

  • Utilizing functional magnetic resonance imaging (fMRI) to identify correlated brain sites.

Related Experiment Videos

  • Analyzing the distribution of functional connections and their relationship with distance.
  • Calculating characteristic path length and clustering coefficient for the brain networks.

    • Main Results:

    • Functional brain networks demonstrate a scale-free distribution of connections.
    • The probability of a connection versus distance follows a scale-free pattern.
    • Networks exhibit a small characteristic path length and a high clustering coefficient, indicative of small-world architecture.

    Conclusions:

    • Human brain functional networks possess scale-free small-world properties.
    • These topological features reflect significant functional information about brain states.
    • The findings contribute to understanding brain organization and information processing.