| Title: |
An externally validated resting-state brain connectivity signature of pain-related learning |
| Authors: |
Kincses, B; Forkmann, K; Schlitt, F; Jan Pawlik, R; Schmidt, K; Timmann, D; Elsenbruch, S; Wiech, K; Bingel, U; Spisak, T |
| Publisher Information: |
Nature Research |
| Publication Year: |
2024 |
| Collection: |
Oxford University Research Archive (ORA) |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1038/s42003-024-06574-y |
| Availability: |
https://doi.org/10.1038/s42003-024-06574-y; https://ora.ox.ac.uk/objects/uuid:901d5ba3-41e1-4f51-8462-37d7557058e7 |
| Rights: |
info:eu-repo/semantics/openAccess ; CC Attribution (CC BY) |
| Accession Number: |
edsbas.D09BFDFB |
| Database: |
BASE |