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

Generalizable patterns in neuroimaging: how many principal components?

L K Hansen1, J Larsen, F A Nielsen

  • 1Department of Mathematical Modeling, Technical University of Denmark, Building 321, Lyngby, DK-2800, Denmark. lkhansen@imm.dtu.dk

Neuroimage
|May 18, 1999
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

Black Hole Spectroscopy and Tests of General Relativity with GW250114.

Physical review letters·2026
Same author

GW250114: Testing Hawking's Area Law and the Kerr Nature of Black Holes.

Physical review letters·2025
Same author

Offshore solar farms as habitats for Mytilus edulis: A preliminary modelling study on mussel growth, distribution, chlorophyll-a uptake and bio-deposition in the North Sea.

Marine environmental research·2025
Same author

No association between peripheral serotonin-gene-related DNA methylation and brain serotonin neurotransmission in the healthy and depressed state.

Clinical epigenetics·2024
Same author

Benomyl inhibits phosphorus transport but not fungal alkaline phosphatase activity in a Glomus-cucumber symbiosis.

The New phytologist·2021
Same author

Use of Crotalidae equine immune F(ab')<sub>2</sub> antivenom for treatment of an <i>Agkistrodon</i> envenomation.

Clinical toxicology (Philadelphia, Pa.)·2021
Same journal

Spatial frequency channels implement a mental ruler in spatial vision.

NeuroImage·2026
Same journal

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

NeuroImage·2026
Same journal

Closed-loop adaptation of transcranial magnetic stimulation intensity with electroencephalography feedback.

NeuroImage·2026
Same journal

Volumetric postmortem MRI of the medial temporal lobe in Alzheimer's disease and related disorders: methodological advances and implications for in vivo biomarker development.

NeuroImage·2026
Same journal

Neural responses to equity and inequity when receiving vicarious rewards for self and charity during adolescence.

NeuroImage·2026
Same journal

Cognitive Strategy-based neuromodulation optimizes neural communication to improve working memory.

NeuroImage·2026
See all related articles

This study quantifies generalization for principal component analysis (PCA). We show how generalization error helps select the optimal number of components for functional magnetic resonance imaging data.

Area of Science:

  • Neuroscience
  • Statistics
  • Machine Learning

Background:

  • Principal Component Analysis (PCA) is a widely used dimensionality reduction technique.
  • Assessing the generalization performance of PCA is crucial for reliable data analysis.
  • The number of principal components retained significantly impacts PCA's generalizability.

Purpose of the Study:

  • To quantitatively define and assess the generalization of PCA.
  • To demonstrate the utility of generalization error in selecting the optimal number of principal components.
  • To apply these methods to functional magnetic resonance imaging (fMRI) data analysis.

Main Methods:

  • Development of analytic and test set estimates for generalization error.
  • Quantitative definition of generalization for PCA.

Related Experiment Videos

  • Application of generalization error to select principal components in fMRI activation datasets.

    • Main Results:

    • Generalization error provides a quantitative measure of PCA performance.
    • The number of principal components directly influences the generalizability of PCA.
    • Selected principal components using generalization error improved analysis of fMRI data.

    Conclusions:

    • Generalization error is a valuable metric for evaluating PCA.
    • The proposed method aids in optimal principal component selection for fMRI studies.
    • This approach enhances the reliability and interpretability of PCA in neuroimaging.