A cautionary tale on the effects of different covariance structures in linear mixed effects modeling of fMRI data

With the steadily increasing abundance of longitudinal neuroimaging studies with large sample sizes and multiple repeated measures, questions arise regarding the appropriate modeling of variance and covariance. The current study examined the influence of standard classes of variance–covariance struc...

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Bibliographic Details
Published in:Human brain mapping 2024-05, Vol.45 (7), p.e26699-n/a
Main Authors: Horn, Harm Jan, Erhardt, Erik B., Dodd, Andrew B., Nathaniel, Upasana, Wick, Tracey V., McQuaid, Jessica R., Ryman, Sephira G., Vakhtin, Andrei A., Meier, Timothy B., Mayer, Andrew R.
Format: Article
Language:English
Subjects:
Adolescent
Autoregressive processes
Brain
Brain - diagnostic imaging
Brain - physiology
Brain Concussion - diagnostic imaging
Brain Concussion - physiopathology
Brain mapping
Brain Mapping - methods
Child
cognitive neuroscience
covariance
Covariance matrix
Executive Function - physiology
Female
fMRI
Functional magnetic resonance imaging
Head injuries
Hemodynamic responses
Humans
Injury analysis
linear mixed models
Linear Models
Magnetic Resonance Imaging
Male
Medical imaging
Modelling
Monte Carlo simulation
Neuroimaging
Pediatrics
Response functions
Standard error
Traumatic brain injury
Variance
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Summary:With the steadily increasing abundance of longitudinal neuroimaging studies with large sample sizes and multiple repeated measures, questions arise regarding the appropriate modeling of variance and covariance. The current study examined the influence of standard classes of variance–covariance structures in linear mixed effects (LME) modeling of fMRI data from patients with pediatric mild traumatic brain injury (pmTBI; N = 181) and healthy controls (N = 162). During two visits, participants performed a cognitive control fMRI paradigm that compared congruent and incongruent stimuli. The hemodynamic response function was parsed into peak and late peak phases. Data were analyzed with a 4‐way (GROUP×VISIT×CONGRUENCY×PHASE) LME using AFNI's 3dLME and compound symmetry (CS), autoregressive process of order 1 (AR1), and unstructured (UN) variance–covariance matrices. Voxel‐wise results dramatically varied both within the cognitive control network (UN>CS for CONGRUENCY effect) and broader brain regions (CS>UN for GROUP:VISIT) depending on the variance–covariance matrix that was selected. Additional testing indicated that both model fit and estimated standard error were superior for the UN matrix, likely as a result of the modeling of individual terms. In summary, current findings suggest that the interpretation of results from complex designs is highly dependent on the selection of the variance–covariance structure using LME modeling. Differences in whole‐brain activation (at p 
ISSN:1065-9471
1097-0193
DOI:10.1002/hbm.26699