Machine learning-based prediction of clinical pain using multimodal neuroimaging and autonomic metrics.

ABSTRACT: Although self-report pain ratings are the gold standard in clinical pain assessment, they are inherently subjective in nature and significantly influenced by multidimensional contextual variables. Although objective biomarkers for pain could substantially aid pain diagnosis and development of novel therapies, reliable markers for clinical pain have been elusive. In this study, individualized physical maneuvers were used to exacerbate clinical pain in patients with chronic low back pain (N = 53), thereby experimentally producing lower and higher pain states. Multivariate machine-learning models were then built from brain imaging (resting-state blood-oxygenation-level-dependent and arterial spin labeling functional imaging) and autonomic activity (heart rate variability) features to predict

SUBMITTER: Lee J 

PROVIDER: S-EPMC6377310 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature