  Functional Magnetic Resonance Imaging (fMRI) maps cerebral activation in
response to stimuli but this activation is often difficult to detect,
especially in low-signal contexts and single-subject studies. Accurate
activation detection can be guided by the fact that very few voxels are, in
reality, truly activated and that activated voxels are spatially localized, but
it is challenging to incorporate both these facts. We provide a computationally
feasible and methodologically sound model-based approach, implemented in the R
package MixfMRI, that bounds the a priori expected proportion of activated
voxels while also incorporating spatial context. Results on simulation
experiments for different levels of activation detection difficulty are
uniformly encouraging. The value of the methodology in low-signal and
single-subject fMRI studies is illustrated on a sports imagination experiment.
Concurrently, we also extend the potential use of fMRI as a clinical tool to,
for example, detect awareness and improve treatment in individual patients in
persistent vegetative state, such as traumatic brain injury survivors.
