Network-based characterization of brain functional connectivity in Zen practitioners.

Area of Science:

• Neuroscience
• Cognitive Science
• Psychology

Background:

• Neuroimaging studies increasingly explore contemplative practices' effects.
• Previous research noted meditation-related changes in resting-state functional connectivity (rsFC), especially in default mode, attentional, saliency, and sensory networks.

Purpose of the Study:

• To investigate neurophysiological differences in experienced Zen meditators versus meditation-naïve controls using functional magnetic resonance imaging (fMRI).
• To identify specific resting-state functional connectivity (rsFC) patterns associated with meditation experience and their correlation with cognitive performance.

Main Methods:

• Collected fMRI data during a breathing attention task from 12 Zen meditators and 12 matched controls.
• Utilized a 209-node network system classified into nine functional modules for multi-stage rsFC analysis.
• Employed multivariate pattern analysis (MVPA) with support vector machine (SVM) to classify groups based on functional connectivity and neuropsychological test scores.

Main Results:

• Modulewise functional connectivity (FC) analysis revealed significant differences between meditators and controls, particularly involving frontoparietal circuits, executive control, and visual areas.
• SVM classification accurately distinguished meditators from controls with 79% accuracy based on modulewise FC.
• A strong correlation was found between the strength of frontoparietal, executive, and visual network connections and performance on a sustained attention task.

Conclusions:

• Network analysis techniques are effective in revealing neural correlates of contemplative practices.
• Meditation experience is associated with distinct patterns of brain connectivity, particularly within frontoparietal and visual networks.
• These connectivity differences correlate with enhanced sustained attention capabilities.