Selective and replicable neuroimaging-based indicators of pain discriminability

Abstract

Neural indicators of pain discriminability have far-reaching theoretical and clinical implications but have been largely overlooked previously. Here, to directly identify the neural basis of pain discriminability, we apply signal detection theory to three EEG (Datasets 1-3, total N= 366) and two fMRI (Datasets 4-5, total N=399) datasets where participants receive transient stimuli of four sensory modalities (pain, touch, audition, and vision) and two intensities (high and low) and report perceptual ratings. Datasets 1 and 4 are used for exploration and others for validation. We find that most pain-evoked EEG and fMRI brain responses robustly encode pain discriminability, which is well replicated in validation datasets. The neural indicators are also pain selective since they cannot track tactile, auditory, or visual discriminability, even though perceptual ratings and sensory discriminability are well matched between modalities. Overall, we provide compelling evidence that pain-evoked brain responses can serve as replicable and selective neural indicators of pain discriminability.