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

Kernel-based manifold learning for statistical analysis of diffusion tensor images.

Parmeshwar Khurd1, Ragini Verma, Christos Davatzikos

  • 1Section of Biomedical Image Analysis, Dept. of Radiology, University of Pennsylvania, Philadelphia, USA. khurdp@uphs.upenn.edu

Information Processing in Medical Imaging : Proceedings of the ... Conference
|July 19, 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

The genetic architecture of multimodal human brain age.

Nature communications·2024
Same author

Distance-weighted Sinkhorn loss for Alzheimer's disease classification.

iScience·2024
Same author

Integrating imaging and genomic data for the discovery of distinct glioblastoma subtypes: a joint learning approach.

Scientific reports·2024
Same author

Plasma Biomarkers as Predictors of Progression to Dementia in Individuals with Mild Cognitive Impairment.

Journal of Alzheimer's disease : JAD·2024
Same author

Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals.

JAMA psychiatry·2024
Same author

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning.

ArXiv·2024
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 kernel-based method for analyzing diffusion tensor imaging (DTI) data. Kernel Fisher Discriminant Analysis (kFDA) effectively identifies statistical differences in brain white matter structure between groups.

Area of Science:

  • Neuroimaging
  • Biomedical data analysis
  • Statistical modeling

Background:

  • Diffusion Tensor Imaging (DTI) is crucial for studying white matter structure.
  • Voxel-based statistical analysis of DTI is essential for biomedical applications.
  • Current methods face challenges in characterizing tensor distributions and identifying group differences.

Purpose of the Study:

  • To present a kernel-based approach for voxel-wise statistical analysis of DTI data.
  • To address limitations in characterizing tensor distributions and group comparisons.
  • To improve statistical analysis of white matter structure using manifold learning.

Main Methods:

  • Utilized kernel principal component analysis (kPCA) to learn tensor probability densities.

Related Experiment Videos

  • Employed kernel Fisher discriminant analysis (kFDA) for optimal group discrimination.
  • Applied methods to both simulated and real DTI data.

    • Main Results:

    • kPCA effectively characterized the probability density of diffusion tensors.
    • kFDA identified optimal features for discriminating between groups.
    • Demonstrated successful application of kFDA in a schizophrenia clinical study.

    Conclusions:

    • Kernel-based methods offer a robust approach for voxel-wise DTI statistical analysis.
    • kPCA and kFDA satisfy key desiderata for DTI analysis.
    • This approach enhances the ability to detect group differences in brain structure.