| Literature DB >> 30048614 |
Philip A Kragel1, Leonie Koban2, Lisa Feldman Barrett3, Tor D Wager4.
Abstract
Human neuroimaging research has transitioned from mapping local effects to developing predictive models of mental events that integrate information distributed across multiple brain systems. Here we review work demonstrating how multivariate predictive models have been utilized to provide quantitative, falsifiable predictions; establish mappings between brain and mind with larger effects than traditional approaches; and help explain how the brain represents mental constructs and processes. Although there is increasing progress toward the first two of these goals, models are only beginning to address the latter objective. By explicitly identifying gaps in knowledge, research programs can move deliberately and programmatically toward the goal of identifying brain representations underlying mental states and processes.Entities:
Keywords: affect; brain signature; decoding; fMRI; machine learning; multivariate; pain; pattern recognition; population coding; representation
Mesh:
Year: 2018 PMID: 30048614 PMCID: PMC6296466 DOI: 10.1016/j.neuron.2018.06.009
Source DB: PubMed Journal: Neuron ISSN: 0896-6273 Impact factor: 17.173