Meditation-induced effects on whole-brain structural and effective connectivity

Summary

Meditation-related changes in brain dynamics and structure were investigated by scanning experienced meditators and naive controls with MRI during rest and focused-attention meditation. A machine-learning approach showed that effective connectivity (causal relationships between brain regions) was more informative than functional or structural connectivity alone for distinguishing meditators from controls. The most informative effective-connectivity links involved several large-scale networks, predominantly in the left hemisphere. Anatomical differences were smaller but present: meditators had stronger structural connectivity between four left-hemisphere areas belonging to somatomotor, dorsal attention, subcortical, and visual networks. The findings suggest a mechanism linking brain structure and function underlying meditation.

Study at a glance

Abstract

In the past decades, there has been a growing scientific interest in characterizing neural correlates of meditation training. Nonetheless, the mechanisms underlying meditation remain elusive. In the present work, we investigated meditation-related changes in functional dynamics and structural connectivity (SC). For this purpose, we scanned experienced meditators and control (naive) subjects using magnetic resonance imaging (MRI) to acquire structural and functional data during two conditions, resting-state and meditation (focused attention on breathing). In this way, we aimed to characterize and distinguish both short-term and long-term modifications in the brain's structure and function. First, to analyze the fMRI data, we calculated whole-brain effective connectivity (EC) estimates, relying on a dynamical network model to replicate BOLD signals' spatio-temporal structure, akin to functional connectivity (FC) with lagged correlations. We compared the estimated EC, FC, and SC links as features to train classifiers to predict behavioral conditions and group identity. Then, we performed a network-based analysis of anatomical connectivity. We demonstrated through a machine-learning approach that EC features were more informative than FC and SC solely. We showed that the most informative EC links that discriminated between meditators and controls involved several large-scale networks mainly within the left hemisphere. Moreover, we found that differences in the functional domain were reflected to a smaller extent in changes at the anatomical level as well. The network-based analysis of anatomical pathways revealed strengthened connectivity for meditators compared to controls between four areas in the left hemisphere belonging to the somatomotor, dorsal attention, subcortical and visual networks. Overall, the results of our whole-brain model-based approach revealed a mechanism underlying meditation by providing causal relationships at the structure-function level.