Dynamic functional connectivity markers of objective trait mindfulness.
Neuroimage
; 176: 193-202, 2018 08 01.
Article
in En
| MEDLINE
| ID: mdl-29709625
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; Nâ¯=â¯21) and low trait mindfulness (LTM; Nâ¯=â¯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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Brain
/
Mindfulness
Limits:
Adult
/
Female
/
Humans
/
Male
Language:
En
Journal:
Neuroimage
Journal subject:
DIAGNOSTICO POR IMAGEM
Year:
2018
Document type:
Article
Country of publication:
United States