Communications Biology
June 29, 2022
Anira Escrichs, Yonatan Sanz Perl, Carme Uribe et al.
67 citations
Different brain states—resting, meditating, deep sleep, and disorders of consciousness after coma—are underpinned by distinct spatiotemporal dynamics that can be characterized using turbulence theory. Non-conscious states tend to be more synchronous, while conscious states are more asynchronous, but the work goes beyond this simple dichotomy. A model-free analysis of human neuroimaging data applied Kuramoto's turbulence framework with coupled oscillators and measured information cascades across spatial scales. A complementary model-based approach used exhaustive computer simulations of whole-brain models fitted to those measures to study information encoding. The framework shows that turbulence theory provides excellent tools for describing and differentiating between brain states.
Brain Structure and Function
May 6, 2022
Eleonora de Filippi, Anira Escrichs, Estela Càmara et al.
23 citations
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.
Network neuroscience (Cambridge, Mass.)
January 1, 2024
Paulina Clara Dagnino, Javier A Galadí, Estela Càmara et al.
4 citations
Meditation produces distinct whole-brain dynamics compared to rest. Using fMRI data from expert meditators and controls, the authors defined probabilistic metastable substates (PMS) for each condition, capturing different probabilities of dynamic brain patterns. They then fit a whole-brain model to these substates and performed in silico perturbations to simulate transitions between resting-state and meditation. The results show that localized artificial perturbations can induce such transitions, and the sensitivity of different brain areas to perturbation varies. This mechanistic framework clarifies how meditation alters brain dynamics and suggests potential applications for health and therapy.
bioRxiv Preprint Server
July 27, 2023
Paulina Clara Dagnino, Javier A. Galadí, Estela Càmara et al.
1 citation
preprint
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.