Dynamic functional connectivity markers of objective trait mindfulness

Abstract

While mindfulness is commonly viewed as a skill to be cultivated through practice, untrained individuals can also vary widely in dispositional mindfulness. Prior research has identified static neural connectivity correlates of this trait. Here, we use dynamic functional connectivity (DFC) analysis of resting-state fMRI to study time-varying connectivity patterns associated with naturally varying and objectively measured trait mindfulness. Participants were selected from the top and bottom tertiles of performers on a breath-counting task to form high trait mindfulness (HTM; N = 21) and low trait mindfulness (LTM; N = 18) groups. DFC analysis of resting state fMRI data revealed that the HTM group spent significantly more time in a brain state associated with task-readiness - a state characterized by high within-network connectivity and greater anti-correlations between task-positive networks and the default-mode network (DMN). The HTM group transitioned between brain states more frequently, but the dwell time in each episode of the task-ready state was equivalent between groups. These results persisted even after controlling for vigilance. Across individuals, certain connectivity metrics were weakly correlated with self-reported mindfulness as measured by the Five Facet Mindfulness Questionnaire, though these did not survive multiple comparisons correction. In the static connectivity maps, HTM individuals had greater within-network connectivity in the DMN and the salience network, and greater anti-correlations between the DMN and task-positive networks. In sum, DFC features robustly distinguish HTM and LTM individuals, and may be useful biological markers for the measurement of dispositional mindfulness.

Inter-relationships between changes in stress, mindfulness, and dynamic functional connectivity in response to a social stressor

“…In the centroid correlation matrix ( Supplementary Figure 1a), we observed that there was not a clear one-to-one statistical correspondence between the states obtained in Lim et al 2018, and those found in this experiment. As a further analysis to determine the distinctness of the connectivity states in our current dataset, we performed dynamic functional connectivity analysis using the pipeline described in this paper on a larger dataset of N = 173 resting-state of fMRI scans collected across our research center.…”

“…While results from these experiments are mixed, there is some agreement that mindfulness is associated with increased connectivity within the default mode network, and in particular between the posterior cingulate cortex and ventromedial prefrontal cortex, greater anti-correlations between the default mode network and attentional/control areas, and an increase in connectivity between regions of the ventral attentional network (particularly the insula) and executive control areas (see Mooneyham et al, 2016 for a review). Furthermore, studies have also found variation in dynamic connectivity that corresponds with trait mindfulness (Mooneyham et al, 2017;Lim et al, 2018;Marusak et al, 2018). In a recent study, we identified a mindfulness-related connectivity state (which we named the "taskready" state) that recapitulates some of the most robust features of findings from static connectivity; high levels of intra-network connectivity in the default mode network and ventral attention network, and strong anti-correlations between these same networks .…”

Characterizing the Dynamical Complexity underlying Meditation

“…In addition, the increase of metastability in meditators during resting-state is congruent with the increase of the temporal complexity of oscillations during rest in meditators observed in the previously mentioned study [34]. Moreover, studies applying 140 a dynamical functional connectivity approach found that individuals with high trait mindfulness, transitioned more frequently between brain states at rest [13,36].…”

Cognitive vulnerability to sleep deprivation is robustly associated with two dynamic connectivity states

“…Lim et al (2018); r s-TRS = 0.89, r s-LAS = 0.90, r s-HAS = 0.91) and moderately correlated with centroids obtained using the sliding-window approach): r s-TRS = 0.77, r s-LAS = 0.60, r s-HAS = 0.74). The characteristics of the named states are as follows: A) The LAS features lower within-network correlations and relatively small anticorrelations between task-positive networks (dorsal attention network (DAN), ventral attention/salience network (VAN), executive control network (ECN)) and the default-mode network (DMN).…”