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Deep learning analyses have offered sensitivity leaps in detection ofcognitive 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 ischallenging. Among common explanation approaches for deep fMRI classifiers,attribution methods show poor specificity and perturbation methods show limitedplausibility. While counterfactual generation promises to address theselimitations, previous methods use variational or adversarial priors that yieldsuboptimal sample fidelity. Here, we introduce the first diffusion-drivencounterfactual method, DreaMR, to enable fMRI interpretation with highspecificity, plausibility and fidelity. DreaMR performs diffusion-basedresampling of an input fMRI sample to alter the decision of a downstreamclassifier, and then computes the minimal difference between the original andcounterfactual samples for explanation. Unlike conventional diffusion methods,DreaMR leverages a novel fractional multi-phase-distilled diffusion prior toimprove sampling efficiency without compromising fidelity, and it employs atransformer architecture to account for long-range spatiotemporal context infMRI scans. Comprehensive experiments on neuroimaging datasets demonstrate thesuperior specificity, fidelity and efficiency of DreaMR in sample generationover state-of-the-art counterfactual methods for fMRI interpretation.

DreaMR: Diffusion-driven Counterfactual Explanation for Functional MRI