Whole Brain Functional Connectivity Using Multi-scale Spatio-Spectral Random Effects Model

Functional brain networks produce connected low frequency patterns of activity when the brain is at rest which can be analyzed with resting state functional MRI (rs-fMRI) by fitting general linear models for signals acquired at a pre-defined seed region and other regions of interest (ROIs). However, typical rs-fMRI analysis tends to ignore spatial correlations in rs-fMRI data, hence biases the standard errors of estimated parameters and leads to incorrect inference. Spatio-temporal or spatio-spectral models can incorporate the spatial correlations in fMRI data. To date, these models have not targeted rs-fMRI connectivity analysis. Herein, we expand a spatio-spectral model from fMRI analysis based on several ROIs to whole brain rs-fMRI connectivity analysis. Our model captures distance-dependent local correlation (within an ROI), distance-independent global correlation (between ROIs), and temporal correlations for whole brain rs-fMRI connectivity analysis with or without confounders. Simulated and empirical experiments demonstrate that this spatio-spectral model yields valid inference for whole brain rs-fMRI connectivity analysis.