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

Nonlinear estimation and modeling of fMRI data using spatio-temporal support vector regression.

Yongmei Michelle Wang1, Robert T Schultz, R Todd Constable

  • 1Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, CT 06520, USA. wang@noodle.med.yale.edu

Information Processing in Medical Imaging : Proceedings of the ... Conference
|September 4, 2004
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

Is the whole more than the sum of its parts? Considering global and local features of the connectome improves prediction of individuals and phenotypes.

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

SAR-Efficient Sub-Volume Imaging Using Nonlinear Gradient Magnetic Fields.

Journal of imaging·2026
Same author

Using connectome-based predictive models to reveal the systems standardized tests and clinical symptoms are reflecting.

Nature communications·2026
Same author

STNAGNN: Data-driven Spatio-temporal Brain Connectivity beyond FC.

Proceedings of machine learning research·2026
Same author

Optimizing functional connectivity scanning conditions for predicting autistic traits.

Nature. Mental health·2026
Same author

Feature overlap in transdiagnostic connectome-based models of sustained attention and autism symptoms.

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

Enhancing Alzheimer's Diagnosis: Leveraging Anatomical Landmarks in Graph Convolutional Neural Networks on Tetrahedral Meshes.

Information processing in medical imaging : proceedings of the ... conference·2026
Same journal

Cycle-Consistent Zero-Shot Through-Plane Super-Resolution for Anisotropic Head MRI.

Information processing in medical imaging : proceedings of the ... conference·2026
Same journal

Brightness-Invariant Tracking Estimation in Tagged MRI.

Information processing in medical imaging : proceedings of the ... conference·2025
Same journal

Multi-View and Multi-Scale Alignment for Contrastive Language-Image Pre-training in Mammography.

Information processing in medical imaging : proceedings of the ... conference·2025
Same journal

Using Multiple Instance Learning to Build Multimodal Representations.

Information processing in medical imaging : proceedings of the ... conference·2025
Same journal

mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds.

Information processing in medical imaging : proceedings of the ... conference·2024
See all related articles

This study introduces a novel nonlinear framework for functional magnetic resonance imaging (fMRI) data analysis using support vector machines. This approach enhances the analysis of neural activity and brain function by incorporating nonlinear dynamics.

Area of Science:

  • Neuroscience
  • Statistical Learning
  • Biomedical Signal Processing

Background:

  • Current functional magnetic resonance imaging (fMRI) analysis often relies on simplified linear models.
  • Recent findings highlight the importance of nonlinear dynamics in neural activity and hemodynamic responses.
  • Existing fMRI analysis methods are typically categorized as either model-driven or data-driven, limiting comprehensive analysis.

Purpose of the Study:

  • To present a new, general nonlinear framework for fMRI data analysis using statistical learning, specifically support vector machines.
  • To develop a unified approach that integrates model-driven and data-driven fMRI analysis methods.
  • To incorporate multiresolution signal analysis into the fMRI data processing pipeline.

Main Methods:

Related Experiment Videos

  • Utilizing spatio-temporal support vector regression (SVR) to capture intrinsic spatio-temporal autocorrelations in fMRI data.
  • Developing a novel problem formulation that merges model-driven and data-driven methodologies.
  • Implementing multiresolution signal analysis within the nonlinear framework.

    • Main Results:

    • The proposed nonlinear framework accurately reflects the complex dynamics of neural activity and hemodynamic physiology.
    • The unified approach successfully integrates previously separate model-driven and data-driven fMRI analysis techniques.
    • The method inherently handles multiresolution signal analysis and avoids interpolation artifacts post-motion estimation.

    Conclusions:

    • The developed nonlinear framework offers a more comprehensive and accurate analysis of fMRI data compared to traditional linear methods.
    • This approach provides a unified platform for fMRI analysis, enhancing its applicability to complex neuroscience research.
    • The framework's advantages include embedded noise reduction and seamless integration of multi-subject and multi-task studies.