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Pierangelo Veltri

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Open access Jul 2026

The roles of neuroticism, genetic susceptibility, and amygdala structure in chronic musculoskeletal pain onset and recovery.

BACKGROUND Chronic musculoskeletal pain (CMP) represents a significant global health burden, yet the neurobiological mechanisms underlying its development remain poorly understood. This study investigates the complex interplay between neuroticism, genetic risk, and amygdala morphology in CMP onset, recovery, and pain characteristics using UK Biobank data. METHODS We conducted longitudinal analyses on (1) 9382 pain-free individuals followed for CMP development and (2) 3460 CMP patients tracked for recovery outcomes. Neuroticism was quantified via a validated 12-item questionnaire assessing psychological distress. To assess chronic pain trajectories, we examined polygenic risk scores (PRS) for CMP and neuroticism, along with amygdala grey matter volumes, as predictors. Multivariate logistic and linear regression models were employed, followed by mediation analyses to elucidate underlying relationships between variables. Analyses were restricted to individuals of European ancestry. RESULTS We found that higher neuroticism scores, neuroticism PRS, and CMP PRS were significantly associated with increased risk of CMP onset and reduced likelihood of recovery across cohorts (all Pcorrected < 0.05). Both neuroticism scores and PRS were positively correlated with pain intensity, whereas CMP PRS were not. Additionally, amygdala grey matter volumes were significantly associated with neuroticism in the CMP onset cohort but not in the recovery group. Neuroticism mediated the relationship between CMP PRS and subsequent CMP onset with a small effect size, while no mediation was observed for CMP recovery. CONCLUSIONS This study demonstrated significant associations between neuroticism, genetic factors, and amygdala morphology in CMP trajectories, highlighting a potential neurobiological mechanism linking psychological vulnerability to chronic pain. These results underscore the importance of integrating psychological assessment and targeted interventions into comprehensive pain management strategies.

M. Cannistra, Yu Liu, V. Sacca et al. · 0 citations
Jul 2026

Comparing Mpox epidemic controls through simulations and explainable graph convolution networks: A case study in the Republic of the Congo and Nigeria

The study supports the potential usefulness of combining contact-based models with explainable graph neural networks for scenario-based epidemic analysis and suggests that the k-GCN model captures relevant temporal and structural dependencies in the simulated graph-organized data.

Francesco Branda, G. Ceccarelli, Massimo Ciccozzi et al. · 0 citations