This investigation sought to examine the integration of cognitive and emotional communication across brainstem regions involved in pain modulation by comparing data from previous functional MRI studies of affective modulation of pain. Our psychological state greatly influences our perception of sensations and pain, both external and visceral, and is expected to contribute to individual pain sensitivity as well as chronic pain conditions. Finally, the learner generalization methods we employ provide a blueprint for evaluating the spatial scale of information in other domains. These findings show that multiple cortical and subcortical systems are needed to decode pain intensity, especially heat pain, and that representation of pain experience may not be circumscribed by any elementary region or canonical network.
#Toau appraisal full#
Full brain models showed no predictive advantage over multisystem models.
All spatial scales conveyed information about pain intensity, but distributed, multisystem models predicted pain 20% more accurately than any individual region or network and were more generalizable to multimodal pain (thermal, visceral, and mechanical) and specific to pain. We estimated model accuracy using leave-one-study-out cross-validation (CV 7 studies) and subsequently validated in 4 independent holdout studies. We compared models based on (a) a single most pain-predictive region or resting-state network (b) pain-associated cortical–subcortical systems developed from prior literature (“multisystem models”) and (c) a model spanning the full brain. In this multistudy analysis of 376 participants across 11 studies, we compared multivariate predictive models to investigate the spatial scale and location of evoked heat pain intensity representation. For pain, the scale of representation has not been formally tested, and quantitative comparisons of pain representations across regions and networks are lacking. Information is coded in the brain at multiple anatomical scales: locally, distributed across regions and networks, and globally.