Connectome predictive modeling of trait mindfulness

Summary

Trait mindfulness—the tendency to attend to present-moment experience non-judgmentally—is linked to better mental health, but its neural basis remains unclear. In the largest resting-state fMRI study of trait mindfulness to date, involving 367 adults across three samples, researchers used connectome predictive modeling to test whether brain connectivity patterns could predict mindfulness scores. No connections predicted overall trait mindfulness, but models for two subscales—Acting with Awareness and Non-judging—were identified. Positive networks for these subscales involved fronto-parietal and default-mode networks, respectively. Negative networks, which overlapped across subscales, included somatomotor, visual, and default-mode connections. Only negative networks generalized to predict subscale scores in some out-of-sample datasets, and predictions correlated negatively with a mind-wandering model. The incomplete generalization and model overlap highlight the challenge of identifying robust brain markers for mindfulness facets.

Study at a glance

Abstract

Abstract Introduction Trait mindfulness refers to one’s disposition or tendency to pay attention to their experiences in the present moment, in a non-judgmental and accepting way. Trait mindfulness has been robustly associated with positive mental health outcomes, but its neural underpinnings are poorly understood. Prior resting-state fMRI studies have associated trait mindfulness with within- and between-network connectivity of the default-mode (DMN), fronto-parietal (FPN), and salience networks. However, it is unclear how generalizable the findings are, how they relate to different components of trait mindfulness, and how other networks and brain areas may be involved. Methods To address these gaps, we conducted the largest resting-state fMRI study of trait mindfulness to-date, consisting of a pre-registered connectome predictive modeling analysis in 367 adults across three samples collected at different sites. Results In the model-training dataset, we did not find connections that predicted overall trait mindfulness, but we identified neural models of two mindfulness subscales, Acting with Awareness and Non-judging . Models included both positive networks (sets of pairwise connections that positively predicted mindfulness with increasing connectivity) and negative networks, which showed the inverse relationship. The Acting with Awareness and Non-judging positive network models showed distinct network representations involving FPN and DMN, respectively. The negative network models, which overlapped significantly across subscales, involved connections across the whole brain with prominent involvement of somatomotor, visual and DMN networks. Only the negative networks generalized to predict subscale scores out-of-sample, and not across both test datasets. Predictions from both models were also negatively correlated with predictions from a well-established mind-wandering connectome model. Conclusions We present preliminary neural evidence for a generalizable connectivity models of trait mindfulness based on specific affective and cognitive facets. However, the incomplete generalization of the models across all sites and scanners, limited stability of the models, as well as the substantial overlap between the models, underscores the difficulty of finding robust brain markers of mindfulness facets.