ArticleCommunications biology2024
An externally validated resting-state brain connectivity signature of pain-related learning.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Brain Association Studies in 2024: A Systematic Review of Sample Sizes and Preregistrations.Psychophysiology · 2026Pooled it
- Cognitive impairment and pain-related disability in patients with chronic pain: a mediation analysis.Pain reports · 2025Article
- Neurobiology of Chronic Pain, Posttraumatic Stress Disorder, and Mild Traumatic Brain Injury.Biology · 2025Review
- External validation of machine learning models-registered models and adaptive sample splitting.GigaScience · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.