Long-Term and Meditation-Specific Modulations of Brain Connectivity Revealed Through Multivariate Pattern Analysis

Area of Science:

• Neuroscience
• Cognitive Science
• Psychology

Background:

• Neuroimaging reveals meditation alters brain function and structure, affecting large-scale network interactions.
• The specific impact of distinct meditation styles on these brain networks remains largely unexplored.

Purpose of the Study:

• To investigate how focused attention and open monitoring meditation styles modulate large-scale brain networks using machine learning and fMRI.
• To differentiate meditation styles based on functional connectivity patterns.

Main Methods:

• Functional magnetic resonance imaging (fMRI) was used to measure brain activity.
• Machine learning classifiers were trained to distinguish between focused attention and open monitoring meditation styles.
• Analysis focused on functional connectivity within and between large-scale brain networks in expert and novice meditators.

Main Results:

• A machine learning classifier successfully differentiated meditation styles only in expert meditators.
• The Anterior Salience Network and Default Mode Network were key in discriminating meditation styles.
• Specific functional couplings related to attention, self-awareness, and somatosensory processing were identified.
• Increased left inter-hemispheric connectivity was observed in style classification.

Conclusions:

• Extensive meditation practice significantly modulates large-scale brain networks.
• Different meditation styles differentially impact brain network connectivity, supporting style-specific functional roles.
• Findings highlight the role of salience and default mode networks in meditation-induced brain changes.