Multivariate neural signatures for health neuroscience: assessing spontaneous regulation during food choice.

ABSTRACT: Establishing links between neural systems and health can be challenging since there is not a one-to-one mapping between brain regions and psychological states. Building sensitive and specific predictive models of health-relevant constructs using multivariate activation patterns of brain activation is a promising new direction. We illustrate the potential of this approach by building two 'neural signatures' of food craving regulation (CR) using multivariate machine learning and, for comparison, a univariate contrast. We applied the signatures to two large validation samples of overweight adults who completed tasks measuring CR ability and valuation during food choice. Across these samples, the machine learning signature was more reliable. This signature decoded CR from food viewing and high

SUBMITTER: Cosme D 

PROVIDER: S-EPMC7657386 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature