title: Painful Issues in Pain Prediction.
abstract: How perception of pain emerges from neural activity is largely unknown. Identifying a neural 'pain signature' and deriving a way to predict perceived pain from brain activity would have enormous basic and clinical implications. Researchers are increasingly turning to functional brain imaging, often applying machine-learning algorithms to infer that pain perception occurred. Yet, such sophisticated analyses are fraught with interpretive difficulties. Here, we highlight some common and troublesome problems in the literature, and suggest methods to ensure researchers draw accurate conclusions from their results. Since functional brain imaging is increasingly finding practical applications with real-world consequences, it is critical to interpret brain scans accurately, because decisions based on neural data will only be as good as the science behind them.
authors.affiliation: Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK; Key Laboratory of Cognition and Personality (Ministry of Education) and Faculty of Psychology, Southwest University, Chongqing, China. Electronic address: hulitju@gmail.com.
authors.forename: Li
authors.initials: L
authors.lastname: Hu
authors.affiliation: Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK. Electronic address: g.iannetti@ucl.ac.uk.
authors.forename: Gian Domenico
authors.initials: GD
authors.lastname: Iannetti
mesh_terms: D000818:Animals; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D058748:Pain Perception / Q000502:physiology*
keywords: functional magnetic resonance imaging (fMRI); machine learning; multivariate pattern analysis (MVPA); pain; pain signature; prediction