An externally validated resting-state brain connectivity signature of pain-related learning
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, w...
| Main Authors: | , , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Nature Research
2024
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| _version_ | 1826313698494382080 |
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| author | Kincses, B Forkmann, K Schlitt, F Jan Pawlik, R Schmidt, K Timmann, D Elsenbruch, S Wiech, K Bingel, U Spisak, T |
| author_facet | Kincses, B Forkmann, K Schlitt, F Jan Pawlik, R Schmidt, K Timmann, D Elsenbruch, S Wiech, K Bingel, U Spisak, T |
| author_sort | Kincses, B |
| collection | OXFORD |
| 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. |
| first_indexed | 2024-09-25T04:19:06Z |
| format | Journal article |
| id | oxford-uuid:901d5ba3-41e1-4f51-8462-37d7557058e7 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:19:06Z |
| publishDate | 2024 |
| publisher | Nature Research |
| record_format | dspace |
| spelling | oxford-uuid:901d5ba3-41e1-4f51-8462-37d7557058e72024-07-27T19:33:42ZAn externally validated resting-state brain connectivity signature of pain-related learningJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:901d5ba3-41e1-4f51-8462-37d7557058e7EnglishJisc Publications RouterNature Research2024Kincses, BForkmann, KSchlitt, FJan Pawlik, RSchmidt, KTimmann, DElsenbruch, SWiech, KBingel, USpisak, TPain 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. |
| spellingShingle | Kincses, B Forkmann, K Schlitt, F Jan Pawlik, R Schmidt, K Timmann, D Elsenbruch, S Wiech, K Bingel, U Spisak, T An externally validated resting-state brain connectivity signature of pain-related learning |
| title | An externally validated resting-state brain connectivity signature of pain-related learning |
| title_full | An externally validated resting-state brain connectivity signature of pain-related learning |
| title_fullStr | An externally validated resting-state brain connectivity signature of pain-related learning |
| title_full_unstemmed | An externally validated resting-state brain connectivity signature of pain-related learning |
| title_short | An externally validated resting-state brain connectivity signature of pain-related learning |
| title_sort | externally validated resting state brain connectivity signature of pain related learning |
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