Dataset Information

Corticostriatal circuits in the transition to chronic back pain: The predictive role of reward learning.

ABSTRACT: Connectivity between the nucleus accumbens (NAc) and ventromedial prefrontal cortex (vmPFC) and reward learning independently predict the transition from acute to chronic back pain (CBP). However, how these predictors are related remains unclear. Using functional magnetic resonance imaging, we investigate NAc- and vmPFC-dependent reward learning in 50 patients with subacute back pain (SABP) and follow them over 6 months. Additionally, we compare 29 patients with CBP and 29 pain-free controls to characterize mechanisms of reward learning in the chronic stage. We find that the learning-related updating of the value of reinforcement (prediction error) in the NAc predicts the transition to chronicity. In CBP, compared with controls, vmPFC responses to this prediction error signal are decreased, but increased during a discriminative stimulus. Distinct processes of reward learning in the vmPFC and NAc characterize the development and maintenance of CBP. These could be targeted for the prevention and treatment of chronic pain.

SUBMITTER: Loffler M

PROVIDER: S-EPMC9381385 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Corticostriatal circuits in the transition to chronic back pain: The predictive role of reward learning.

Löffler Martin M Levine Seth M SM Usai Katrin K Desch Simon S Kandić Mina M Nees Frauke F Flor Herta H

Cell reports. Medicine 20220706 7

Connectivity between the nucleus accumbens (NAc) and ventromedial prefrontal cortex (vmPFC) and reward learning independently predict the transition from acute to chronic back pain (CBP). However, how these predictors are related remains unclear. Using functional magnetic resonance imaging, we investigate NAc- and vmPFC-dependent reward learning in 50 patients with subacute back pain (SABP) and follow them over 6 months. Additionally, we compare 29 patients with CBP and 29 pain-free controls to ...[more]

PMID: 35798001

Similar Datasets

Project description:IntroductionLow back pain (LBP) leads to considerable impairment of quality of life worldwide and is often accompanied by psychosomatic symptoms.ObjectivesFirst, to assess the association between stress and chronic low back pain (CLBP) and its simultaneous appearance with fatigue and depression as a symptom triad. Second, to identify the most predictive stress-related pattern set for CLBP for a 1-year diagnosis.MethodsIn a 1-year observational study with four measurement points, a total of 140 volunteers (aged 18-45 years with intermittent pain) were recruited. The primary outcomes were pain [characteristic pain intensity (CPI), subjective pain disability (DISS)], fatigue, and depressive mood. Stress was assessed as chronic stress, perceived stress, effort reward imbalance, life events, and physiological markers [allostatic load index (ALI), hair cortisol concentration (HCC)]. Multiple linear regression models and selection procedures for model shrinkage and variable selection (least absolute shrinkage and selection operator) were applied. Prediction accuracy was calculated by root mean squared error (RMSE) and receiver-operating characteristic curves.ResultsThere were 110 participants completed the baseline assessments (28.2 ± 7.5 years, 38.1% female), including HCC, and a further of 46 participants agreed to ALI laboratory measurements. Different stress types were associated with LBP, CLBP, fatigue, and depressive mood and its joint occurrence as a symptom triad at baseline; mainly social-related stress types were of relevance. Work-related stress, such as "excessive demands at work"[b = 0.51 (95%CI -0.23, 1.25), p = 0.18] played a role for upcoming chronic pain disability. "Social overload" [b = 0.45 (95%CI -0.06, 0.96), p = 0.080] and "over-commitment at work" [b = 0.28 (95%CI -0.39, 0.95), p = 0.42] were associated with an upcoming depressive mood within 1-year. Finally, seven psychometric (CPI: RMSE = 12.63; DISS: RMSE = 9.81) and five biomarkers (CPI: RMSE = 12.21; DISS: RMSE = 8.94) could be derived as the most predictive pattern set for a 1-year prediction of CLBP. The biomarker set showed an apparent area under the curve of 0.88 for CPI and 0.99 for DISS.ConclusionStress disrupts allostasis and favors the development of chronic pain, fatigue, and depression and the emergence of a "hypocortisolemic symptom triad," whereby the social-related stressors play a significant role. For translational medicine, a predictive pattern set could be derived which enables to diagnose the individuals at higher risk for the upcoming pain disorders and can be used in practice.

Dataset Information

Corticostriatal circuits in the transition to chronic back pain: The predictive role of reward learning.

Publications

Corticostriatal circuits in the transition to chronic back pain: The predictive role of reward learning.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets