A functional mixed model for scalar on function regression with application to a functional MRI study

Wanying Ma, Luo Xiao, Bowen Liu, Martin A. Lindquist

Research output: Contribution to journalArticlepeer-review

Abstract

Motivated by a functional magnetic resonance imaging (fMRI) study, we propose a new functional mixed model for scalar on function regression. The model extends the standard scalar on function regression for repeated outcomes by incorporating subject-specific random functional effects. Using functional principal component analysis, the new model can be reformulated as a mixed effects model and thus easily fit. A test is also proposed to assess the existence of the subject-specific random functional effects. We evaluate the performance of the model and test via a simulation study, as well as on data from the motivating fMRI study of thermal pain. The data application indicates significant subject-specific effects of the human brain hemodynamics related to pain and provides insights on how the effects might differ across subjects.

Original languageEnglish (US)
Pages (from-to)439-454
Number of pages16
JournalBiostatistics (Oxford, England)
Volume22
Issue number3
DOIs
StatePublished - Jul 17 2021

Keywords

  • fMRI
  • Functional data analysis
  • Functional mixed model
  • Functional principal component
  • Repeated measurements
  • Variance component testing

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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