Connectome predictive modeling of trait mindfulness.

Area of Science:

• Neuroscience
• Psychology
• Cognitive Science

Background:

• Trait mindfulness, the tendency towards present-moment, non-judgmental awareness, is linked to positive mental health.
• Neural underpinnings of trait mindfulness are not well understood.
• Previous fMRI studies identified associations with default-mode (DMN), fronto-parietal (FPN), and salience networks, but generalizability and specificity are unclear.

Purpose of the Study:

• To investigate the neural correlates of trait mindfulness using the largest resting-state fMRI dataset to date.
• To identify generalizable brain connectivity models for trait mindfulness and its components.
• To explore the relationship between mindfulness neural models and mind-wandering.

Main Methods:

• Utilized resting-state functional magnetic resonance imaging (fMRI) in 367 adults across three independent samples.
• Employed pre-registered connectome predictive modeling (CPM) to identify neural predictors of trait mindfulness and its subscales.
• Tested the generalizability of identified models across different sites and scanners.

Main Results:

• No neural connections predicted overall trait mindfulness.
• Distinct positive network models were identified for "Acting with Awareness" (involving FPN) and "Non-judging" (involving DMN).
• Negative network models, overlapping across subscales, involved widespread connections (somatomotor, visual, DMN) and generalized better out-of-sample than positive models. Predictions were inversely correlated with mind-wandering models.

Conclusions:

• Preliminary evidence suggests distinct neural connectivity models for specific facets of trait mindfulness.
• Incomplete generalization across sites and limited model stability highlight challenges in identifying robust brain markers for mindfulness.
• Future research should focus on refining models for specific affective and cognitive components of mindfulness.