Human brain mapping
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Human brain mapping · Feb 2015
Observational StudySmoking increases risk of pain chronification through shared corticostriatal circuitry.
Smoking is associated with increased incidence of chronic pain. However, the evidence is cross-sectional in nature, and underlying mechanisms remain unclear. In a longitudinal observational study, we examined the relationship between smoking, transition to chronic pain, and brain physiology. ⋯ Mediation analysis indicated the prediction of BP persistence by smoking was largely due to synchrony of fMRI activity between two brain areas (nucleus accumbens and medial prefrontal cortex, NAc-mPFC). In SBP or CBP who ceased smoking strength of NAc-mPFC decreased from precessation to postcessation of smoking. We conclude that smoking increases risk of transitioning to CBP, an effect mediated by corticostriatal circuitry involved in addictive behavior and motivated learning.
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Human brain mapping · Feb 2015
Multivariate decoding of cerebral blood flow measures in a clinical model of on-going postsurgical pain.
Recent reports of multivariate machine learning (ML) techniques have highlighted their potential use to detect prognostic and diagnostic markers of pain. However, applications to date have focussed on acute experimental nociceptive stimuli rather than clinically relevant pain states. These reports have coincided with others describing the application of arterial spin labeling (ASL) to detect changes in regional cerebral blood flow (rCBF) in patients with on-going clinical pain. ⋯ Using all data from all sessions, an independent Gaussian Process binary classifier successfully discriminated postsurgical from presurgical states with 94.73% accuracy; over 80% accuracy could be achieved using half of the data (equivalent to 15 min scan time). This work demonstrates the concept and feasibility of time-efficient, probabilistic prediction of clinically relevant pain at the individual level. We discuss the potential of ML techniques to impact on the search for novel approaches to diagnosis, management, and treatment to complement conventional patient self-reporting.