A Bayesian Variable Selection Approach Yields Improved Detection of Brain Activation From Complex-Valued fMRI

ABSTRACT

Voxel functional magnetic resonance imaging (fMRI) time courses are complex-valued signals giving rise to magnitude and phase data. Nevertheless, most studies use only the magnitude signals and thus discard half of the data that could potentially contain important information. Methods that make use of complex-valued fMRI (CV-fMRI) data have been shown to lead to superior power in detecting active voxels when compared to magnitude-only methods, particularly for small signal-to-noise ratios (SNRs). We present a new Bayesian variable selection approach for detecting brain activation at the voxel level from CV-fMRI data. We develop models with complex-valued spike-and-slab priors on the activation parameters that are able to combine the magnitude and phase information. We present a complex-valued EM variable selection algorithm that leads to fast detection at the voxel level in CV-fMRI slices and also consider full posterior inference via Markov chain Monte Carlo (MCMC). Model performance is illustrated through extensive simulation studies, including the analysis of physically based simulated CV-fMRI slices. Finally, we use the complex-valued Bayesian approach to detect active voxels in human CV-fMRI from a healthy individual who performed unilateral finger tapping in a designed experiment. The proposed approach leads to improved detection of activation in the expected motor-related brain regions and produces fewer false positive results than other methods for CV-fMRI. Supplementary materials for this article are available online.

Keywords:

• Bayesian modeling
• Complex-valued time series
• CV-fMRI
• Variable selection

Supplementary Materials

The online supplementary materials contain the appendices, and the associated R and C++ code for the C-EMVS algorithm. The simulated data used in Section 3.2 can be downloaded at https://github.com/chenghanyu/CEMVS_simulation

Additional information

Funding

Cheng-Han Yu and Raquel Prado were partially supported by NSF award SES-1461497. Prado also received partial support from NSF award DMS-1407838. Daniel Rowe was partially supported by the National Institutes of Health (NIH) award #R21NS087450 and NSF award DMS-1127914 to SAMSI.