Resting-State Functional Connectivity Associated with Non-Judgmental Awareness Predicted Multiple Measures of Negative Affect

In recent years, an increasing number of studies have highlighted the usefulness of the Five Facet Mindfulness Questionnaire (FFMQ) in identifying varying levels of trait mindfulness. As higher trait mindfulness (as reflected by higher FFMQ scores) has been associated with fewer negative affective symptoms, a thorough understanding of the neural correlates associated with FFMQ scores would inform the development of more individualized mindfulness interventions. The current study investigated how individual differences in trait mindfulness are related to different resting-state functional connectivity patterns, and whether these patterns could predict negative affective symptoms.

We analyzed data from 71 adults (age range: 20–45 years) from the Max Planck Institute-Leipzig Mind-Brain-Body dataset. Participants completed the FFMQ, the Beck Depression Inventory-II, and the Hospital Anxiety and Depression Scale, underwent resting-state functional magnetic resonance imaging scans and reported the content of thought emerged during the scanning session. Network-based statistics were used to identify resting-state networks that were significantly associated with the FFMQ facets. The strengths of these networks were then used to predict negative affective symptoms.

Results indicated that higher scores on the facets of act with awareness and nonjudge were associated with fewer negative affective symptoms. The network-based statistics revealed networks of edges that were significantly associated with the facet of nonjudge. Moreover, this network significantly predicted multiple measures of negative affect. There were no networks that were significantly associated with other facets.

These findings provide evidence at the neural level to suggest that the facet of nonjudge is inversely linked to negative affective symptoms.

This study was not preregistered.