From Stability to Flexibility: Neural Network Dynamics Associated with Trait Mindfulness

Mindfulness has been associated with large-scale brain network plasticity, yet prior neuroimaging work has predominantly relied on static functional network connectivity (sFNC), offering only a limited view of the temporal dynamics essential to mindfulness. The present study combined static and dynamic functional network connectivity (dFNC) analyses with machine learning to identify neural correlates associated with mindfulness-related traits. Forty adults (20 experienced meditators, 20 novices) underwent resting-state fMRI under eyes-open and eyes-closed conditions. Independent component analysis was used to extract mindfulness-related networks, followed by group comparisons of both sFNC and dFNC indices. Partial correlations examined associations between connectivity features and FFMQ subscales, and Bayesian logistic regression with cross-validation evaluated their classification performance. The experienced meditators exhibited stronger sFNC between the left frontoparietal network (LFPN) and the anterior default mode network (aDMN), with this connectivity under eyes-closed conditions positively correlated with the Acting with Awareness subscale of the Five Facet Mindfulness Questionnaire (FFMQ). At the dynamic level, experienced meditators demonstrated longer dwell time in a highly integrated network state and shorter dwell time in a partially segregated state, both linked to Acting with Awareness. Classification analyses showed that functional network connectivity-based models achieved good discriminative performance, with sFNC features constituting the primary discriminative feature (accuracy = 0.73, AUC = 0.81), as adding dFNC features and FFMQ did not meaningfully improve performance beyond sFNC alone. These findings highlight the distinct contributions of static and dynamic connectivity to understanding the neural correlates of mindfulness and underscore the potential of connectivity-based markers for characterizing mindfulness-related traits and informing individualized interventions. This study was conducted as part of a prospectively preregistered research project at ClinicalTrials.gov (Identifier: NCT05020301; available at: https://clinicaltrials.gov/study/NCT05020301 ). The present manuscript reports analyses of the preregistered resting-state MRI data, focusing on functional connectivity and the Five Facet Mindfulness Questionnaire (FFMQ), which were prespecified outcome measures relevant to the current research question. Other preregistered outcome measures were outside the scope of the present study and are therefore not reported here.