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

Functional connectivity mapping using the ferromagnetic Potts spin model.

Larissa Stanberry1, Alejandro Murua, Dietmar Cordes

  • 1Department of Statistics, University of Washington, Seattle, Washington 98195-4322, USA. lstan@u.washington.edu

Human Brain Mapping
|May 15, 2007
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

Nonlinear KCCA in fMRI activation analysis: Self-supervised optimization and robust back-reconstruction.

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

Advancing biomarker development for chronic traumatic encephalopathy: Summary and recommendations from the 2025 Leon Thal Summit.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Dynamic neurocognitive adaptation: childhood and adult-midlife engagement associated with later-life brain structure and cognition in older adults with and without mild cognitive impairment.

Brain imaging and behavior·2026
Same author

Coronary Artery Calcium Scans by Area Deprivation Index in a Multicenter Health System.

JACC. Advances·2026
Same author

Cohort Study of Initial Diuretic Dosing and Outcomes Among Patients Hospitalized for Congestive Heart Failure: Insights From the Cardiovascular Quality Improvement and Care Innovation Consortium.

Journal of the American Heart Association·2026
Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same journal

Benchmarking fMRI Denoising Pipelines.

Human brain mapping·2026
Same journal

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

Human brain mapping·2026
Same journal

Intrinsic Functional Architecture Reflects Individual Differences in Passive Working Memory: An Exploratory Resting-State fMRI Study.

Human brain mapping·2026
Same journal

Symptom Overlap and Neurobiological Similarities Between Posttraumatic Stress Disorder and Tinnitus.

Human brain mapping·2026
Same journal

Test-Retest Reliability of Sensorimotor Activity Measured With Spinal Cord fMRI.

Human brain mapping·2026
Same journal

The Human Visual Claustrum Responses to Physical Stimulus Properties and Subjective Content During Movie Viewing.

Human brain mapping·2026
See all related articles

This study introduces an unsupervised clustering method using the Potts spin model to identify functionally connected brain regions. This approach effectively reveals hidden structures in complex neuroimaging data, like functional connectivity networks.

Area of Science:

  • Neuroscience
  • Computational Biology
  • Statistical Physics

Background:

  • Functional connectivity analysis is crucial for understanding brain organization.
  • Existing methods often require pre-defined parameters or assumptions about data distribution.
  • Developing robust, unsupervised methods for identifying brain networks is an ongoing challenge.

Purpose of the Study:

  • To introduce and evaluate an unsupervised stochastic clustering method based on the ferromagnetic Potts spin model for determining functionally connected brain regions.
  • To assess the method's performance and parameter dependencies using simulated and real fMRI data.
  • To explore the benefits of integrating Euclidean information into connectivity analysis.

Main Methods:

  • An unsupervised stochastic clustering algorithm utilizing the ferromagnetic Potts spin model.

Related Experiment Videos

  • Investigation of method performance on simulated datasets.
  • Application to functional MRI (fMRI) data from a finger-tapping task and resting-state scans.
  • Exploration of parameter influences, including neighborhood size and interaction terms.

    • Main Results:

    • The Potts model clustering effectively identifies functionally connected regions without prior assumptions on cluster number or data distribution.
    • The method demonstrated its ability to uncover hidden structures in complex fMRI data.
    • Analysis of resting-state data revealed functional connectivity networks of the anterior and posterior cingulate cortices in healthy subjects.

    Conclusions:

    • The Potts spin model offers a powerful and intuitive tool for unsupervised functional connectivity analysis in neuroscience.
    • The method's flexibility and lack of distributional assumptions make it suitable for complex neuroimaging datasets.
    • This approach enhances the ability to map functional brain networks, particularly in resting-state conditions.