Time Series Analysis of fMRI Data: Spatial Modelling and Bayesian Computation

TL;DR

This paper evaluates the accuracy of variational Bayes approximations in fMRI time series analysis with spatial modeling, comparing it to Hamiltonian Monte Carlo, and highlights its reliability at high SNR but limitations at low SNR.

Contribution

It provides a detailed comparison between VB and HMC for spatially-varying coefficient models in fMRI data, clarifying when VB is appropriate and when HMC is necessary.

Findings

VB is computationally faster than HMC.

VB performs well at high SNR, producing similar results to HMC.

VB shows larger errors and less accuracy at low SNR.

Abstract

Time series analysis of fMRI data is an important area of medical statistics for neuroimaging data. The neuroimaging community has embraced mean-field variational Bayes (VB) approximations, which are implemented in Statistical Parametric Mapping (SPM) software. While computationally efficient, the quality of VB approximations remains unclear even though they are commonly used in the analysis of neuroimaging data. For reliable statistical inference, it is important that these approximations be accurate and that users understand the scenarios under which they may not be accurate. We consider this issue for a particular model that includes spatially-varying coefficients. To examine the accuracy of the VB approximation we derive Hamiltonian Monte Carlo (HMC) for this model and conduct simulation studies to compare its performance with VB. As expected we find that the computation time required for VB is considerably less than that for HMC. In settings involving a high or moderate signal-to-noise ratio (SNR) we find that the two approaches produce very similar results suggesting that the VB approximation is useful in this setting. On the other hand, when one considers a low SNR, substantial differences are found, suggesting that the approximation may not be accurate in such cases and we demonstrate that VB produces Bayes estimators with larger mean squared error (MSE). A real application related to face perception is also carried out. Overall, our work clarifies the usefulness of VB for the spatiotemporal analysis of fMRI data, while also pointing out the limitation of VB when the SNR is low and the utility of HMC in this case.