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

Eleonora de Filippi, Anira Escrichs, Estela Càmara, César Garrido, Theo Marins, Martí Sánchez-Fibla, Matthieu Gilson, Gustavo Deco

Brain Structure and Function May 6, 2022 DOI: 10.1007/s00429-022-02496-9 (opens in new tab)

Study at a glance

AI-extracted from the abstract
Characteristics Observational cohort Peer reviewed
Population Experienced meditators and naive control subjects
Intervention focused attention on breathing
Topics Default mode network Meditation
Keywords Functional connectivity Replicate Resting State FMRI Brain mapping Neuroimaging Cognitive psychology Artificial intelligence Pattern recognition psychology Electroencephalography
Citations 23
Key findings Effective connectivity features were more informative than functional or structural connectivity for distinguishing meditators from controls, with the most informative links involving left-hemisphere networks.

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.

Comparable studies

Other observational and cohort studies on meditation, most cited first.

Study Year Design Participants
Using Self-Report Assessment Methods to Explore Facets of Mindfulness Undergraduate students 2006 Observational cohort
Effectiveness of a meditation-based stress reduction program in the treatment of anxiety disorders Patients with generalized anxiety disorder or panic disorder with or without agoraphobia 1992 Observational cohort n = 22
Meditation experience is associated with increased cortical thickness Participants with extensive Insight meditation experience and matched controls 2005 Observational cohort n = 20
Meditation experience is associated with differences in default mode network activity and connectivity Experienced meditators and matched meditation-naive controls 2011 Observational cohort
Long-term meditators self-induce high-amplitude gamma synchrony during mental practice Long-term Buddhist practitioners and controls 2004 Observational cohort

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