Decoding Mindfulness With Multivariate Predictive Models.

Using multivariate predictive models to identify brain mechanisms underlying the benefits of mindfulness meditation is a promising methodology that departs from conventional brain mapping. Two strategies—state induction and neuromarker identification—are highlighted, with examples distinguishing focused attention from mind wandering and showing effects of mindfulness interventions on somatic pain and drug-related cravings. Future research must address tradeoffs between personalized and population-based predictive modeling.

Study at a glance

Abstract

Identifying the brain mechanisms that underlie the salutary effects of mindfulness meditation and related practices is a critical goal of contemplative neuroscience. Here, we suggest that the use of multivariate predictive models represents a promising and powerful methodology that could be better leveraged to pursue this goal. This approach incorporates key principles of multivariate decoding, predictive classification, and model-based analyses, all of which represent a strong departure from conventional brain mapping approaches. We highlight 2 such research strategies-state induction and neuromarker identification-and provide illustrative examples of how these approaches have been used to examine central questions in mindfulness, such as the distinction between internally directed focused attention and mind wandering and the effects of mindfulness interventions on somatic pain and drug-related cravings. We conclude by discussing important issues to be addressed with future research, including key tradeoffs between using a personalized versus population-based approach to predictive modeling.