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Inducing a meditative state by artificial perturbations: A causal mechanistic understanding of brain dynamics underlying meditation

Paulina Clara Dagnino, Javier A. Galadí, Estela Càmara, Gustavo Deco, Anira Escrichs

bioRxiv Preprint Server July 27, 2023 preprint DOI: 10.1101/2023.07.27.550828 via bioRxiv

Summary

Meditation produces distinct whole-brain dynamics compared to rest, particularly in the triple-network model (executive control, salience, and default-mode networks). Using a causal mechanistic framework, researchers defined probabilistic metastable substates from dynamic brain patterns and adjusted a whole-brain model of the resting state to simulate transitions to meditation. They successfully induced the meditative state through localized artificial perturbations, primarily shifting areas in the somatomotor and dorsal attention networks. The work suggests meditation can be studied as a practice for health and as a potential therapy for brain disorders.

Study at a glance

Characteristics Observational and computational modeling study
Population Expert meditators
Citations 1
Key finding Meditation exhibits distinct whole-brain dynamics from rest, and the meditative state can be induced via localized perturbations of somatomotor and dorsal attention networks.

Abstract

Contemplative neuroscience has increasingly explored meditation using neuroimaging. However, the brain mechanisms underlying meditation remain elusive. Here, we implemented a causal mechanistic framework to explore the spatiotemporal dynamics of expert meditators during meditation and rest. We first applied a model-free approach by defining a probabilistic metastable substate (PMS) space for each state, consisting of different probabilities of occurrence from a repertoire of dynamic patterns. Different brain signatures were mainly found in the triple-network model (i.e., the executive control, salience, and default-mode networks). Moreover, we implemented a model-based approach by adjusting the PMS of the resting state to a whole-brain model, which enabled us to explore in silico perturbations to transition to the meditation state. Consequently, we assessed the sensitivity of different brain areas regarding their perturbability and their mechanistic local-global effects. Using a synchronous protocol, we successfully transitioned from the resting state to the meditative state by shifting areas mainly from the somatomotor and dorsal attention networks. Overall, our work reveals distinct whole-brain dynamics in meditation compared to rest, and how the meditation state can be induced with localized artificial perturbations. It motivates future work regarding meditation as a practice in health and as a potential therapy for brain disorders.

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