RETRACTED ARTICLE: A mechanistic model of the neural entropy increase elicited by psychedelic drugs

Herzog, Rubén; Mediano, Pedro A. M.; Rosas, Fernando; Carhart‐Harris, Robin; Perl, Yonatan Sanz; Tagliazucchi, Enzo; Cofré, Rodrigo

doi:10.1038/s41598-020-74060-6

Cited by 59 publications

(70 citation statements)

References 57 publications

(84 reference statements)

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“…Speculatively, this pattern could be taken as indicating predictive processing in which higher-level coherent neural activity successfully suppresses bottom-up information flows (Bastos et al, 2020;Walsh et al, 2020). While disjoint flexibility could be viewed as a neural entropy measure in terms of independent node switching, entropy has also been associated with intelligent processing (Carhart-Harris et al, 2014;Chang et al, 2018;Cieri et al, 2021;Herzog et al, 2020;Vivot et al, 2020), and so this measure may also sometimes exhibit an inverted U relationship with psychological functioning. Qualitatively speaking, low-to-moderate levels of disjointed migration may be better than stasis/rigidity, but the non-coherent nature of node migration may tend to indicate a breakdown of adaptive functioning.…”

Section: Different Forms Of Flexibility: Disjointedness Vs Cohesivenessmentioning

confidence: 99%

On the importance of being flexible: dynamic brain networks and their potential functional significances

Safron¹,

Klimaj²,

Hipólito³

2021

Preprint

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In this theoretical review, we begin by discussing brains and minds from a dynamical systems perspective, and then go on to describe methods for characterizing the flexibility of dynamic networks. We discuss how varying degrees and kinds of flexibility may be adaptive (or maladaptive) in different contexts, specifically focusing on measures related to either more disjoint or cohesive dynamics. While disjointed flexibility may be useful for assessing neural entropy, cohesive flexibility may potentially serve as a proxy for self-organized criticality as a fundamental property enabling adaptive behavior in complex systems. Particular attention is given to recent studies in which flexibility methods have been used to investigate neurological and cognitive maturation, as well as the breakdown of conscious processing under varying levels of anesthesia. We further discuss how these findings and methods might be contextualized within the Free Energy Principle as a fundamental principle of brain organization, and describe potential methodological advances from this paradigm. Finally, with relevance to computational psychiatry, we propose a research program for obtaining a better understanding of ways that dynamic networks may relate to different forms of psychological flexibility, which may be the single most important factor for ensuring human flourishing.

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Section: Different Forms Of Flexibility: Disjointedness Vs Cohesivenessmentioning

confidence: 99%

On the importance of being flexible: dynamic brain networks and their potential functional significances

Safron¹,

Klimaj²,

Hipólito³

2021

Preprint

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“…They have been mostly used to study the resting-state as well as cognitive functions, including ”consciousness” and alterations thereof. These studies have focused on generating empirical FC with empirical anatomical SC, for psychedelic states (Deco et al, 2018; Herzog et al, 2020; Kringelbach et al, 2020), sleep stages (Jobst et al, 2017; Ipiña et al, 2020) and consciousness disorders (Lopez-Gonzalez et al, 2020; Cofré et al, 2020). In contrast with this work, another line of research has focused on the estimation of the ”effective connectivity”, which describes the directional influence that one region exerts on another in a dynamic model (Friston, 2011).…”

Section: Introductionmentioning

confidence: 99%

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

Filippi

Escrichs

Càmara

et al. 2021

Preprint

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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.

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“…However, there are more biophysically realistic models that require space-dependent parameters. For example, some recent neural mass network models incorporate neuronal receptors and their densities, which are known to vary across the cortex [54][55][56]. The CHAOSS method can in principle be extended to account for space-dependent model parameters, and numerical simulations of graph neural fields can also be carried out with space-dependent parameters, but both would become significantly more computationally demanding than their counterparts with space-independent parameters.…”

Section: Discussionmentioning

confidence: 99%

Graph neural fields: A framework for spatiotemporal dynamical models on the human connectome

et al. 2021

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Tools from the field of graph signal processing, in particular the graph Laplacian operator, have recently been successfully applied to the investigation of structure-function relationships in the human brain. The eigenvectors of the human connectome graph Laplacian, dubbed “connectome harmonics”, have been shown to relate to the functionally relevant resting-state networks. Whole-brain modelling of brain activity combines structural connectivity with local dynamical models to provide insight into the large-scale functional organization of the human brain. In this study, we employ the graph Laplacian and its properties to define and implement a large class of neural activity models directly on the human connectome. These models, consisting of systems of stochastic integrodifferential equations on graphs, are dubbed graph neural fields, in analogy with the well-established continuous neural fields. We obtain analytic predictions for harmonic and temporal power spectra, as well as functional connectivity and coherence matrices, of graph neural fields, with a technique dubbed CHAOSS (shorthand for Connectome-Harmonic Analysis Of Spatiotemporal Spectra). Combining graph neural fields with appropriate observation models allows for estimating model parameters from experimental data as obtained from electroencephalography (EEG), magnetoencephalography (MEG), or functional magnetic resonance imaging (fMRI). As an example application, we study a stochastic Wilson-Cowan graph neural field model on a high-resolution connectome graph constructed from diffusion tensor imaging (DTI) and structural MRI data. We show that the model equilibrium fluctuations can reproduce the empirically observed harmonic power spectrum of resting-state fMRI data, and predict its functional connectivity, with a high level of detail. Graph neural fields natively allow the inclusion of important features of cortical anatomy and fast computations of observable quantities for comparison with multimodal empirical data. They thus appear particularly suitable for modelling whole-brain activity at mesoscopic scales, and opening new potential avenues for connectome-graph-based investigations of structure-function relationships.

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RETRACTED ARTICLE: A mechanistic model of the neural entropy increase elicited by psychedelic drugs

Cited by 59 publications

References 57 publications

On the importance of being flexible: dynamic brain networks and their potential functional significances

On the importance of being flexible: dynamic brain networks and their potential functional significances

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

Graph neural fields: A framework for spatiotemporal dynamical models on the human connectome

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