  Deep learning analyses have offered sensitivity leaps in detection of
cognitive states from functional MRI (fMRI) measurements across the brain. Yet,
as deep models perform hierarchical nonlinear transformations on their input,
interpreting the association between brain responses and cognitive states is
challenging. Among common explanation approaches for deep fMRI classifiers,
attribution methods show poor specificity and perturbation methods show limited
plausibility. While counterfactual generation promises to address these
limitations, previous methods use variational or adversarial priors that yield
suboptimal sample fidelity. Here, we introduce the first diffusion-driven
counterfactual method, DreaMR, to enable fMRI interpretation with high
specificity, plausibility and fidelity. DreaMR performs diffusion-based
resampling of an input fMRI sample to alter the decision of a downstream
classifier, and then computes the minimal difference between the original and
counterfactual samples for explanation. Unlike conventional diffusion methods,
DreaMR leverages a novel fractional multi-phase-distilled diffusion prior to
improve sampling efficiency without compromising fidelity, and it employs a
transformer architecture to account for long-range spatiotemporal context in
fMRI scans. Comprehensive experiments on neuroimaging datasets demonstrate the
superior specificity, fidelity and efficiency of DreaMR in sample generation
over state-of-the-art counterfactual methods for fMRI interpretation.
