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

Locally regularized spatiotemporal modeling and model comparison for functional MRI.

P L Purdon1, V Solo, R M Weisskoff

  • 1Massachusetts General Hospital NMR Center, Charlestown, Massachusetts, USA.

Neuroimage
|September 14, 2001
PubMed
Summary

This study introduces a new spatiotemporal modeling approach for functional MRI (fMRI) data analysis. Locally Regularized Spatiotemporal (LRST) modeling improves statistical model identification in fMRI studies.

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

Corrigendum to 'Role of electroencephalogram oscillations and the spectrogram in monitoring anaesthesia' [BJA Education 20 (2020) 166-172].

BJA education·2021
Same author

Role of electroencephalogram oscillations and the spectrogram in monitoring anaesthesia.

BJA education·2021
Same author

Using EEG markers to make inferences about anaesthetic-induced altered states of arousal.

British journal of anaesthesia·2018
Same author

Electroencephalographic markers of brain development during sevoflurane anaesthesia in children up to 3 years old.

British journal of anaesthesia·2018
Same author

On compression and damage evolution in two thermoplastics.

Proceedings. Mathematical, physical, and engineering sciences·2017
Same author

The Ageing Brain: Age-dependent changes in the electroencephalogram during propofol and sevoflurane general anaesthesia.

British journal of anaesthesia·2015

Area of Science:

  • Neuroimaging
  • Systems Biology
  • Statistical Modeling

Background:

  • Functional magnetic resonance imaging (fMRI) data analysis presents challenges in model formulation and estimation.
  • Existing methods like SPM-GLM may not fully capture the complex spatiotemporal dynamics of fMRI signals.
  • Accurate statistical modeling is crucial for reliable interpretation of fMRI results.

Purpose of the Study:

  • To develop and validate a novel spatiotemporal system identification framework for fMRI data analysis.
  • To introduce a new model incorporating a physiologically informed hemodynamic response and an empirically derived noise model.
  • To compare the performance of the proposed method against established techniques like SPM-GLM.

Main Methods:

  • fMRI data analysis framed as a spatiotemporal system identification problem.
Keywords:
Non-programmatic

Related Experiment Videos

  • Development of a new model featuring a physiologically based hemodynamic response and a low-frequency noise model.
  • Introduction of an estimation method using spatial regularization for precise noise estimates, termed locally regularized spatiotemporal (LRST) modeling.
  • Creation of a new model selection criterion for comparing statistical models.

    • Main Results:

    • The proposed LRST modeling approach enhances the precision of spatially varying noise estimates.
    • LRST modeling demonstrates improved identification of appropriate statistical models for fMRI data compared to SPM-GLM.
    • The new model selection criterion aids in choosing superior statistical models for fMRI studies.

    Conclusions:

    • Locally Regularized Spatiotemporal (LRST) modeling offers a more effective approach for fMRI data analysis.
    • The physiologically based hemodynamic response and spatial regularization contribute to improved model performance.
    • This work advances the field of neuroimaging by providing a robust framework for statistical modeling in fMRI studies.