Abstract

Psychological and personality factors, socioeconomic status, and brain properties all contribute to chronic pain but have essentially been studied independently. Here, we administered a broad battery of questionnaires to patients with chronic back pain (CBP). Clustering and network analyses revealed four orthogonal dimensions accounting for 60% of the variance, and defining chronic pain traits. Two of these traits - Pain-trait and Emote-trait - were related to back pain characteristics and could be predicted from distinct distributed functional networks in a cross-validation procedure, identifying neurotraits. These neurotraits were relatively stable in time and segregated CBP patients into subtypes showing distinct traits, pain affect, pain qualities, and socioeconomic status (neuropsychotypes). The results unravel the trait space of chronic pain leading to reliable categorization of patients into distinct types. The approach provides metrics aiming at unifying the psychology and the neurophysiology of chronic pain across diverse clinical conditions, and promotes prognostics and individualized therapeutics.