Does meditation lead to a stable mind? Synchronous stability and time-varying graphs in meditators

Perl

Uribe

et al. 2021

Preprint

Recently, significant advances have been made by identifying the levels of synchronicity of the underlying dynamics of a given brain state. This research has demonstrated that unconscious dynamics tend to be more synchronous than those found in conscious states, which are more asynchronous. Here we go beyond this dichotomy to demonstrate that the different brain states are always underpinned by spatiotemporal chaos but with dissociable turbulent dynamics. We investigated human neuroimaging data from different brain states (resting state, meditation, deep sleep, and disorders of consciousness after coma) and were able to distinguish between them using complementary model-free and model-based measures of turbulent information transmission. Our model-free approach used recent advances describing a measure of information cascade across spatial scales using tools from turbulence theory. Complementarily, our model-based approach used exhaustive in silico perturbations of whole-brain models fitted to the empirical neuroimaging data, which allowed us to study the information encoding capabilities of the brain states. Overall, the current framework demonstrates that different levels of turbulent dynamics are fundamental for describing and differentiating between brain states.

Section: Revealing Susceptibility and Information Encoding Capability Using Whole-brain Modellingsupporting

confidence: 93%

Unifying turbulent dynamics framework distinguishes different brain states

Perl

Uribe

et al. 2021

Preprint

“…The copyright holder for this preprint this version posted June 10, 2021. ; https://doi.org/10.1101/2021.06.10.447903 doi: bioRxiv preprint et al, 2019). Similar evidence comes from the work conducted by Toutain et al (2020), in which they investigated topological stability across relaxation state and meditation in experienced meditators using the electroencephalogram (EEG). The authors demonstrated an increase in stability of global topological patterns during meditation compared to relaxation state (Toutain et al, 2020).…”

Section: Introductionmentioning

confidence: 62%

“…Similar evidence comes from the work conducted by Toutain et al (2020), in which they investigated topological stability across relaxation state and meditation in experienced meditators using the electroencephalogram (EEG). The authors demonstrated an increase in stability of global topological patterns during meditation compared to relaxation state (Toutain et al, 2020). Nonetheless, these phenomenological studies cannot by nature provide a mechanistic explanation about how long-term meditation practice shapes the global spatio-temporal BOLD structure via the underlying interregional connections, which instead requires a model-based approach.…”

Section: Introductionmentioning

confidence: 62%

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

Filippi

Càmara

et al. 2021

Preprint

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.

Section: Introductionmentioning

confidence: 62%

“…At the same time, they found that meditation appears to be characterized as a state of reduced information processing, indicating a switch to a less complex regime compared to the resting state (Escrichs et al, 2019). Similar evidence comes from the work conducted by Toutain et al (2020), in which they investigated topological stability across relaxation state and meditation in experienced meditators using the electroencephalogram (EEG). The authors demonstrated an increase in stability of global topological patterns during meditation compared to relaxation state (Toutain et al, 2020).…”

Section: Introductionmentioning

confidence: 65%

Meditation-Induced Effects on Whole-Brain Structural and Effective Connectivity

Filippi

Càmara

et al. 2021

Preprint

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. 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.