Abstract:
:In a function-on-scalar regression framework, we present some modeling strategies for functional mixed models and also some approaches for making inference about various aspects of the fixed effects. This is presented in the context of modeling positron emission tomography (PET) data in order to explore the density of various proteins of interest throughout the human brain. For this application, information about the density of the target protein in a given brain region is encapsulated in the impulse response function (IRF) of the region. Previous work on nonparametric estimation of the IRF is limited in that it is only able to model a single brain region at a time. We propose an extension, based on principles of functional data analysis, that will allow modeling of multiple brain regions simultaneously. Applicable more broadly to functional mixed regression modeling, we discuss two general approaches for permutation testing and describe valid strategies for identifying exchangeable units within the model and building corresponding permutation tests. We illustrate our methods with an application to PET data and explore the effects of depression and sex on the IRF.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Shi B,Ogden RTdoi
10.1002/sim.9087keywords:
["PET brain imaging","function-on-scalar regression","functional mixed models","permutation testing"]subject
Has Abstractpub_date
2021-09-20 00:00:00pages
4640-4659issue
21eissn
0277-6715issn
1097-0258journal_volume
40pub_type
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