FEMA: Fast and efficient mixed‐effects algorithm for large sample whole‐brain imaging data

• Published in: Human brain mapping 2024-02, Vol.45 (2), p.e26579-n/a
• Main Authors: Parekh, Pravesh, Fan, Chun Chieh, Frei, Oleksandr, Palmer, Clare E., Smith, Diana M., Makowski, Carolina, Iversen, John R., Pecheva, Diliana, Holland, Dominic, Loughnan, Robert, Nedelec, Pierre, Thompson, Wesley K., Hagler, Donald J., Andreassen, Ole A., Jernigan, Terry L., Nichols, Thomas E., Dale, Anders M.
• Format: Article
• Language: English
• Subjects: ABCD; Adolescent; Adolescents; Age factors; Algorithms; Brain; Brain - diagnostic imaging; Cognitive ability; Computational neuroscience; Connectome - methods; Cross-Sectional Studies; Functional magnetic resonance imaging; Humans; longitudinal analysis; Magnetic Resonance Imaging - methods; Maximum likelihood estimates; Medical imaging; mixed models; Neural networks; Neuroimaging; Neuroimaging - methods; Parameter estimation; Source code; Sparsity; Spatial analysis; Thickness; Variables; vertex‐wise; voxel‐wise; whole brain
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.26579

The linear mixed‐effects model (LME) is a versatile approach to account for dependence among observations. Many large‐scale neuroimaging datasets with complex designs have increased the need for LME; however LME has seldom been used in whole‐brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed‐effects algorithm (FEMA) that makes whole‐brain vertex‐wise, voxel‐wise, and connectome‐wide LME analyses in large samples possible. We validate FEMA with extensive simulations, showing that the estimates of the fixed effects are equivalent to standard maximum likelihood estimates but obtained with orders of magnitude improvement in computational speed. We demonstrate the applicability of FEMA by studying the cross‐sectional and longitudinal effects of age on region‐of‐interest level and vertex‐wise cortical thickness, as well as connectome‐wide functional connectivity values derived from resting state functional MRI, using longitudinal imaging data from the Adolescent Brain Cognitive DevelopmentSM Study release 4.0. Our analyses reveal distinct spatial patterns for the annualized changes in vertex‐wise cortical thickness and connectome‐wide connectivity values in early adolescence, highlighting a critical time of brain maturation. The simulations and application to real data show that FEMA enables advanced investigation of the relationships between large numbers of neuroimaging metrics and variables of interest while considering complex study designs, including repeated measures and family structures, in a fast and efficient manner. The source code for FEMA is available via: https://github.com/cmig-research-group/cmig_tools/. We present fast and efficient mixed‐effects algorithm (FEMA), a MATLAB‐based package for performing whole‐brain voxel‐wise, vertex‐wise, or connectome‐wide mixed‐effects analyses for imaging data or other tabular data in a fast and computationally efficient manner, enabling analyses of several thousand variables across large sample sizes in a matter of seconds to minutes.