Pain-regulation circuitry as a predictor of chronic pain phenotypes

Abstract

Chronic pain involves sensory, emotional, and functional disruption, yet diagnostic labels rarely capture this variability, limiting individualized care. Multidimensional models, such as fear-avoidance and predictive coding, suggest that high emotional burden may disrupt expectation-based pain modulation and midbrain pain regulatory pathways, particularly within the periaqueductal gray (PAG). We tested whether chronic pain phenotypes defined by pain intensity, disability, and affective distress (PDA) differ in expectation-induced pain modulation, PAG connectivity, and behavioral markers including catastrophizing, hypervigilance, and medication use, and whether these features aid phenotype classification. We studied 159 patients with fibromyalgia or chronic low back pain and 72 controls. Our data-driven clustering approach identified high and low PDA groups. High PDA patients showed impaired modulation when positive expectations were violated and reported greater cognitive and behavioral burden (P<0.05). They also exhibited more negative connectivity between the dorsolateral/lateral PAG (dl/lPAG) and the dorsomedial prefrontal cortex (dmPFC) (corrected). Machine learning models classified PDA subtypes above chance, with accuracy improving when PAG connectivity was included. Findings highlight disrupted expectation-driven regulation and altered PAG pathways as markers for chronic pain stratification.