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Neuroradiologists and neurosurgeons increasingly opt to use functionalmagnetic resonance imaging (fMRI) to map functionally relevant brain regionsfor noninvasive presurgical planning and intraoperative neuronavigation. Thisapplication requires a high degree of spatial accuracy, but the fMRIsignal-to-noise ratio (SNR) decreases as spatial resolution increases. Inpractice, fMRI scans can be collected at multiple spatial resolutions, and itis of interest to make more accurate inference on brain activity by combiningdata with different resolutions. To this end, we develop a new Bayesian modelto leverage both better anatomical precision in high resolution fMRI and higherSNR in standard resolution fMRI. We assign a Gaussian process prior to the meanintensity function and develop an efficient, scalable posterior computationalgorithm to integrate both sources of data. We draw posterior samples using analgorithm analogous to Riemann manifold Hamiltonian Monte Carlo in an expandedparameter space. We illustrate our method in analysis of presurgical fMRI data,and show in simulation that it infers the mean intensity more accurately thanalternatives that use either the high or standard resolution fMRI data alone.

Bayesian Inference for Brain Activity from Functional Magnetic Resonance Imaging Collected at Two Spatial Resolutions