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Studies have documented behavior differences between more versus less resilient adults with chronic pain (CP), but the presence and nature of underlying neurophysiological differences have received scant attention. In this study, we attempted to identify regions of interest (ROIs) in which resting state (Rs) brain activity discriminated more from less resilient CP subgroups based on multiple kernel learning (MKL). More and less resilient community-dwellers with chronic musculoskeletal pain (70 women, 39 men) engaged in structural and functional magnetic resonance imaging (MRI) scans, wherein MKL assessed Rs activity based on amplitude of low frequency fluctuations (ALFF), fractional amplitudes of low frequency fluctuations (fALFF), and regional homogeneity (ReHo) modalities to identify ROIs most salient for discriminating more versus less resilient subgroups. Compared to classification based on single modalities, multi-modal classification based on combined fALFF and ReHo features achieved a substantially higher classification accuracy rate (79%).Brain regions with the best discriminative power included those implicated in pain processing, reward, executive function, goal-directed action, emotion regulation and resilience to mood disorders though variation trends were not consistent between more and less resilient subgroups. Results revealed patterns of Rs activity that serve as possible biomarkers for resilience to chronic musculoskeletal pain.

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BackgroundExtrauterine growth restriction among the very-low birth weight preterm infants (VLBWPIs) is associated with poorer cognitive development outcome, while the rapid weight gain in infancy increases the long-term risk of obesity and noncommunicable disease among VLBWPIs. However, the results of research on the association between early postnatal growth velocity and neurodevelopmental outcomes in VLBWPIs are still limited and controversial.ObjectiveWe aimed to explore the association between the growth velocity in early postnatal and neurodevelopmental impairment (NDI) among VLBWPIs.MethodsThis study was a secondary analysis of a previously published prospective cohort. It was based on data on 1,791 premature infants with a birth weight of less than 1500 g, registered in the database of the Premature Baby Foundation of Taiwan between 2007 and 2011. A binary logistic regression model was used to evaluate the association between the weight gain velocity in different periods [from birth to 6 months corrected age (CA), 6 to 12 months CA, and 12 to 24 months CA] and NDI, respectively. The generalized additive model and the smooth curve fitting (penalized spline method) were used to address nonlinearity, and a two-piece-wise binary logistic regression model was added to explain the nonlinearity further.ResultsNonlinearities were observed between NDI and the weight gain velocity from birth to 6 months CA [inflection point 20.36, <inflection point: odds ratio (OR) = 0.75, 95% confidence interval (CI) 0.67–0.84, >inflection point: OR = 1.01, 95% CI 0.97–1.05], 6–12 months CA [inflection point 9.44, <inflection point: OR = 0.89, 95% CI 0.84–0.94, >inflection point: OR = 1.05, 95% CI 1.05–(1.00, 1.11)], and 12–24 months CA [inflection point 16.00, <inflection point: OR = 0.93, 95% CI 0.88–0.98, >inflection point: OR = 1.75, 95% CI 1.05–(0.96, 3.08)].ConclusionThe neurodevelopmental benefits from a rapid weight gain velocity from birth to 24 months CA might be limited once the growth pace reaches an optimum level. It would help find a pattern of growth that facilitates optimal neurodevelopment, yet minimizes negative health consequences associated with overnutrition further.