Fixed and random effect analysis of multi-subject fMRI data using wavelet transform

We propose a new method to estimate the random effect variance in group analysis of fMRI data. In the first level of analysis, general linear model (GLM) is used to estimate a parameter map (“effect”) for each subject. After applying discrete wavelet transform to the “effect” maps, noise is reduced through a vertical energy thresholding (VET). The fixed effect component in each coefficient is derived by averaging the wavelet coefficients along all subjects. Then, the wavelet coefficients containing significant random effect are identified by their higher sample variance along the subjects. Wavelet coefficients containing random effect component in each subject are used to reconstruct the random effect maps through an inverse wavelet transform. Random effect variance is obtained from random effect maps for use in random effect analysis. The proposed method and other methods like GLM group analysis and variance ratio smoothing are applied to both experimental and artificial fMRI data. ROC curves, obtained from the simulated data, show improved group activation detection compared to existing random effect analysis methods. For the experimental data, the proposed method shows its high sensitivity by detecting multiple activation regions, namely visual cortex, cuneus, precuneus, thalamus, and cerebellum. From these regions, precuneus and cerebellum are not detected by majority of the previously published methods.