Decoding mindfulness with multivariate predictive models

Multivariate predictive models—such as multivariate decoding, predictive classification, and model-based analyses—offer a powerful alternative to conventional brain mapping for studying the neural mechanisms behind mindfulness and meditation. These approaches can distinguish internally directed focused attention from mind wandering and reveal how mindfulness interventions affect somatic pain and drug-related cravings. Future research must weigh 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. We describe the primary principles that underlie this approach, including multivariate decoding, predictive classification, and model-based analyses, all of which represent a strong departure from conventional brain mapping approaches. We highlight two 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 role 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.