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

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 structural and functional connectivities (SC and FC, respectively). 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, we performed a network-based analysis of anatomical connectivity. Then, 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 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. The whole-brain SC analysis revealed strengthened anatomical connectivity across large-scale networks for meditators compared to controls. We found that differences in SC were reflected in the functional domain as well. We demonstrated through a machine-learning approach that EC features were more informative than FC and SC solely. Using EC features we reached high performance for the condition-based classification within each group and moderately high accuracies when comparing the two groups in each condition. Moreover, we showed that the most informative EC links that discriminated between meditators and controls involved the same large-scale networks previously found to have increased anatomical connectivity. Overall, the results of our whole-brain model-based approach revealed a mechanism underlying meditation by providing causal relationships at the structure-function level.

Unifying turbulent dynamics framework distinguishes different brain states

“…We also found that changes in regions in visual- and default-mode- networks indexed differences between deep sleep and wakeful resting, consistent with other studies of the human wake-sleep cycle 67 – 69 . In contrast, comparing meditation with resting state in expert meditators revealed regions in limbic- and default-mode- networks, similar to other findings in meditation 70 – 74 .…”

Unifying turbulent dynamics framework distinguishes different brain states

“…We also found that changes in regions in visual-and default-mode-networks indexed differences between deep sleep and wakeful resting, consistent with other studies of the human wake-sleep cycle (Horovitz et al, 2009;Ipiña et al, 2020;Stevner et al, 2019). In contrast, comparing meditation with resting state in expert meditators revealed regions in limbic-and default-mode-networks, similar to other findings in meditation (Filippi et al, 2021;Tang et al, 2015;Taylor et al, 2013).…”

The effect of external stimulation on functional networks in the aging healthy human brain

“…For the whole-brain model, we used an average structural connectivity matrix (SC) from a sample of 38 unrelated healthy subjects previously described in De Filippi et al (2021). MRI images were acquired on a 3T whole-body Siemens TRIO scanner (Hospital Clínic, Barcelona) using a dual spin-echo difussion tensor imaging (DTI) sequence (TR = 680ms; TE = 92ms; FOV = 236mm; 60 contiguous axial slices; isotropic voxel size 2 × 2 × 2 mm; no gap, and 118 × 118 matrix sizes).…”