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Basics of Multivariate Analysis in Neuroimaging Data
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A functional mixed model for scalar on function regression with application to a functional MRI study.

Wanying Ma1, Luo Xiao1, Bowen Liu1

  • 1Department of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, NC 27606, USA.

Biostatistics (Oxford, England)
|October 22, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new functional mixed model for analyzing repeated brain imaging data, revealing significant individual differences in brain responses to pain. The model enhances understanding of subject-specific brain hemodynamics.

Keywords:
Functional data analysisFunctional mixed modelFunctional principal componentRepeated measurementsVariance component testingfMRI

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Area of Science:

  • Statistics
  • Neuroscience
  • Functional Magnetic Resonance Imaging (fMRI)

Background:

  • Scalar on function regression is a statistical method for modeling relationships between scalar outcomes and functional predictors.
  • Standard models do not adequately account for repeated measurements and subject-specific variations in functional data.
  • Functional magnetic resonance imaging (fMRI) studies generate complex functional data requiring advanced analytical approaches.

Purpose of the Study:

  • To propose a novel functional mixed model for scalar on function regression that incorporates subject-specific random functional effects.
  • To develop a statistical test for detecting the presence of these subject-specific effects.
  • To apply the model and test to fMRI data from a thermal pain study.

Main Methods:

  • Development of a functional mixed model extending standard scalar on function regression.
  • Utilizing functional principal component analysis to reformulate the model for easier fitting.
  • Proposing a hypothesis test to assess the significance of random functional effects.
  • Simulation studies and application to real fMRI data for performance evaluation.

Main Results:

  • The proposed functional mixed model can be effectively fitted using functional principal component analysis.
  • A significant subject-specific effect was detected in the fMRI data related to thermal pain.
  • The model successfully identified variations in brain hemodynamics across subjects.

Conclusions:

  • The new functional mixed model provides a powerful tool for analyzing repeated functional data, particularly in neuroimaging.
  • Subject-specific effects are significant in brain responses to pain, highlighting inter-individual variability.
  • The findings offer insights into how brain hemodynamics related to pain differ among individuals.