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

You might also read

Related Articles

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

Sort by
Same author

Excessive Censoring Degrades Individual-Specific Cortical Parcellations and Personalized TMS Targets.

bioRxiv : the preprint server for biology·2026
Same author

Consensus recommendations for clinical functional MRI applied to language mapping.

Aperture neuro·2026
Same author

FEMA-Long: Modeling unstructured covariances for discovery of time-dependent effects in large-scale longitudinal datasets.

PLoS genetics·2026
Same author

Leveraging STRAW +10 criteria to evaluate menopause stage effects on sleep quality.

Climacteric : the journal of the International Menopause Society·2026
Same author

Widespread use of invalid statistical tests in biomedical machine learning.

bioRxiv : the preprint server for biology·2026
Same author

Scalable Bayesian Image-on-Scalar Regression for Population-Scale Neuroimaging Data Analysis.

Journal of the American Statistical Association·2026

Related Experiment Video

Updated: May 22, 2026

Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
09:38

Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Published on: November 14, 2017

A Bayesian non-parametric Potts model with application to pre-surgical FMRI data.

Timothy D Johnson1, Zhuqing Liu, Andreas J Bartsch

  • 1Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA. tdjtdj@umich.edu

Statistical Methods in Medical Research
|May 26, 2012
PubMed
Summary

This study introduces a novel non-parametric Potts model for segmenting functional magnetic resonance imaging (fMRI) data. The new model accurately assesses peritumoral brain activation, outperforming traditional methods when assumptions are not met.

Keywords:
Dirichlet processFMRIPotts modeldecision theoryhidden Markov random fieldnon-parametric Bayes

More Related Videos

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

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Related Experiment Videos

Last Updated: May 22, 2026

Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
09:38

Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Published on: November 14, 2017

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

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

  • Medical Imaging
  • Computational Neuroscience
  • Statistical Modeling

Background:

  • The Potts model is widely used for image segmentation.
  • Parametric distributions are typically assumed for data within classes.
  • Functional magnetic resonance imaging (fMRI) requires robust segmentation for clinical applications.

Purpose of the Study:

  • To present a non-parametric Potts model for image segmentation.
  • To apply this model to fMRI data for pre-surgical assessment of peritumoral brain activation.
  • To evaluate the model's performance against parametric approaches.

Main Methods:

  • Developed a non-parametric Potts model using Dirichlet process priors within a Bayesian framework.
  • Segmented Z-score images into activated, deactivated, and null classes.
  • Estimated model parameters using Markov chain Monte Carlo (MCMC) algorithms.
  • Utilized Bayesian decision theory for final classifications.

Main Results:

  • The non-parametric Potts model demonstrated performance on par with correctly specified parametric models.
  • The proposed model outperformed misspecified parametric models in simulation studies.
  • Joint estimation of spatial regularization and null state probability parameters was performed.

Conclusions:

  • The non-parametric Potts model offers a flexible and robust alternative for image segmentation, particularly in neuroimaging.
  • This approach enhances the pre-surgical assessment of brain activation by accommodating complex data distributions.
  • The model's adaptability makes it valuable when underlying data distributions are unknown or misspecified.