The Effects of Mindfulness on Brain Network Dynamics Following an Acute Stressor in a Population of Drinking Adults

Previous research has found that mindfulness-based techniques are beneficial for reducing stress in heavy-drinking individuals. However, the underlying neurobiology of these stress-reducing effects are unclear. Moreover, much of the research examining neurobiological correlates of mindfulness has used static functional connectivity, suggesting that brain activity goes unchanged for the entire length of an MRI scan.

In the current study, we used a state-based dynamic functional connectivity model to examine brain states during either a 10 min mindfulness session or resting control that followed an individually tailored stress imagery task. Using a hidden semi-Markov model (HSMM), six brain states and the associated dynamics of state traversal were estimated for a population of moderate-to-heavy drinkers (N = 32). We modeled the 36 Schaefer atlas regions spanning the salience and default mode networks, and the HSMM characterized each state by its distinct multivariate pattern of activity and covariance structure. Group differences in dwell times, transition behavior, and overall state dynamics were evaluated using permutation tests and mixed-effects models.

Participants that experienced the mindfulness session had more transitions and longer time spent in states in which the salience network was more active. Participants assigned to the control group had more transitions and increased time spent in states in which nodes of the default mode network were more active. Moreover, for control participants, increased occupancy time to SN-dominant states was associated with lower perceived stress.

Using HSMM provided a unique insight into network connectivity during mindful states; we believe it offers a novel approach to testing and optimizing mindful-based therapies.