FEMA

FEMA enables whole-brain vertex-wise, voxel-wise, and connectome-wide linear mixed-effects (LME) analyses in large-scale neuroimaging datasets with complex designs, including repeated measures and family structures.

Key Features:

• Fast and efficient mixed-effects algorithm: FEMA achieves computational speed improvements of orders of magnitude over standard maximum likelihood estimation while producing equivalent fixed effects estimates.
• Whole-brain analysis capabilities: FEMA supports vertex-wise cortical thickness analysis, voxel-wise analysis, and connectome-wide functional connectivity analysis.
• Complex design accommodation: FEMA handles repeated measures and family structures in large samples.
• Validated estimation: Extensive simulations confirm that fixed effects estimates are equivalent to standard maximum likelihood estimates.

Scientific Applications:

• Cross-sectional and longitudinal age effects: FEMA identifies distinct spatial patterns for annualized changes in vertex-wise cortical thickness and connectome-wide connectivity values in early adolescence.
• Resting state functional MRI analysis: FEMA analyzes connectome-wide functional connectivity values derived from resting state fMRI data.
• Large-scale neuroimaging cohort studies: FEMA enables advanced investigation of relationships between large numbers of neuroimaging metrics and variables of interest, as demonstrated with the Adolescent Brain Cognitive Development Study release 4.0.

Methodology:

FEMA implements a fast and efficient mixed-effects algorithm for linear mixed-effects model estimation. The method was validated through extensive simulations comparing estimates to standard maximum likelihood estimates, and applied to longitudinal imaging data including region-of-interest level and vertex-wise cortical thickness measurements and connectome-wide functional connectivity values from resting state functional MRI.