An externally validated resting-state brain connectivity signature of pain-related learning

Journal article

Pain can be conceptualized as a precision signal for reinforcement learning in the brain and alterations in these processes are a hallmark of chronic pain conditions. Investigating individual differences in pain-related learning therefore holds important clinical and translational relevance. Here, we developed and externally validated a novel resting-state brain connectivity-based predictive model of pain-related learning. The pre-registered external validation indicates that the proposed model explains 8-12% of the inter-individual variance in pain-related learning. Model predictions are driven by connections of the amygdala, posterior insula, sensorimotor, frontoparietal, and cerebellar regions, outlining a network commonly described in aversive learning and pain. We propose the resulting model as a robust and highly accessible biomarker candidate for clinical and translational pain research, with promising implications for personalized treatment approaches and with a high potential to advance our understanding of the neural mechanisms of pain-related learning.

Authors: Kincses, B; Forkmann, K; Schlitt, F; Jan Pawlik, R; Schmidt, K

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: Communications Biology
• Volume: 7
• Issue: 1
• Article number: 875
• Publication date: 2024-07-17
• Acceptance date: 2024-07-10
• DOI: 10.1038/s42003-024-06574-y
• EISSN: 2399-3642
• ISSN: 2399-3642
• Language: English