Parcels and particles: Markov blankets in the brain

Network Neuroscience ◽

10.1162/netn_a_00175 ◽

2020 ◽

pp. 1-76

Author(s):

Karl J. Friston ◽

Erik D. Fagerholm ◽

Tahereh S. Zarghami ◽

Thomas Parr ◽

Inês Hipólito ◽

...

Keyword(s):

Message Passing ◽

Effective Connectivity ◽

Directed Graphs ◽

Functional Integration ◽

Brain Networks ◽

Functional Anatomy ◽

Phenomenological Approach ◽

Dynamical Instability ◽

Self Organised Criticality ◽

Markov Blankets

At the inception of human brain mapping, two principles of functional anatomy underwrote most conceptions – and analyses – of distributed brain responses: namely functional segregation and integration. There are currently two main approaches to characterising functional integration. The first is a mechanistic modelling of connectomics in terms of directed effective connectivity that mediates neuronal message passing and dynamics on neuronal circuits. The second phenomenological approach usually characterises undirected functional connectivity (i.e., measurable correlations), in terms of intrinsic brain networks, self-organised criticality, dynamical instability, etc. This paper describes a treatment of effective connectivity that speaks to the emergence of intrinsic brain networks and critical dynamics. It is predicated on the notion of Markov blankets that play a fundamental role in the self-organisation of far from equilibrium systems. Using the apparatus of the renormalisation group, we show that much of the phenomenology found in network neuroscience is an emergent property of a particular partition of neuronal states, over progressively coarser scales. As such, it offers a way of linking dynamics on directed graphs to the phenomenology of intrinsic brain networks.

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Modules or Mean-Fields?

Entropy ◽

10.3390/e22050552 ◽

2020 ◽

Vol 22 (5) ◽

pp. 552 ◽

Cited By ~ 2

Author(s):

Thomas Parr ◽

Noor Sajid ◽

Karl J. Friston

Keyword(s):

Steady State ◽

Message Passing ◽

Mean Field Theory ◽

Functional Anatomy ◽

Mean Field ◽

State Density ◽

Stochastic Dynamical Systems ◽

Conditional Independency ◽

The Brain ◽

Non Equilibrium

The segregation of neural processing into distinct streams has been interpreted by some as evidence in favour of a modular view of brain function. This implies a set of specialised ‘modules’, each of which performs a specific kind of computation in isolation of other brain systems, before sharing the result of this operation with other modules. In light of a modern understanding of stochastic non-equilibrium systems, like the brain, a simpler and more parsimonious explanation presents itself. Formulating the evolution of a non-equilibrium steady state system in terms of its density dynamics reveals that such systems appear on average to perform a gradient ascent on their steady state density. If this steady state implies a sufficiently sparse conditional independency structure, this endorses a mean-field dynamical formulation. This decomposes the density over all states in a system into the product of marginal probabilities for those states. This factorisation lends the system a modular appearance, in the sense that we can interpret the dynamics of each factor independently. However, the argument here is that it is factorisation, as opposed to modularisation, that gives rise to the functional anatomy of the brain or, indeed, any sentient system. In the following, we briefly overview mean-field theory and its applications to stochastic dynamical systems. We then unpack the consequences of this factorisation through simple numerical simulations and highlight the implications for neuronal message passing and the computational architecture of sentience.

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Effective connectivity decreases in specific brain networks with postparalysis facial synkinesis: a dynamic causal modeling study

Brain Imaging and Behavior ◽

10.1007/s11682-021-00547-z ◽

2021 ◽

Author(s):

Zhen-Zhen Ma ◽

Ye-Chen Lu ◽

Jia-Jia Wu ◽

Xu-Yun Hua ◽

Si-Si Li ◽

...

Keyword(s):

Effective Connectivity ◽

Brain Networks ◽

Causal Modeling ◽

Dynamic Causal Modeling ◽

Facial Synkinesis ◽

Modeling Study

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Large-scale DCMs for resting-state fMRI

Network Neuroscience ◽

10.1162/netn_a_00015 ◽

2017 ◽

Vol 1 (3) ◽

pp. 222-241 ◽

Cited By ~ 63

Author(s):

Adeel Razi ◽

Mohamed L. Seghier ◽

Yuan Zhou ◽

Peter McColgan ◽

Peter Zeidman ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Large Scale ◽

Effective Connectivity ◽

Directed Graphs ◽

Resting State Fmri ◽

Causal Modeling ◽

Large Graphs ◽

Model Inversion ◽

Graph Theoretic

This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity. This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fMRI). We use spectral dynamic causal modeling (DCM) to invert large graphs comprising dozens of nodes or regions. The ensuing graphs are directed and weighted, hence providing a neurobiologically plausible characterization of connectivity in terms of excitatory and inhibitory coupling. Furthermore, we show that the use of Bayesian model reduction to discover the most likely sparse graph (or model) from a parent (e.g., fully connected) graph eschews the arbitrary thresholding often applied to large symmetric (functional connectivity) graphs. Using empirical fMRI data, we show that spectral DCM furnishes connectivity estimates on large graphs that correlate strongly with the estimates provided by stochastic DCM. Furthermore, we increase the efficiency of model inversion using functional connectivity modes to place prior constraints on effective connectivity. In other words, we use a small number of modes to finesse the potentially redundant parameterization of large DCMs. We show that spectral DCM—with functional connectivity priors—is ideally suited for directed graph theoretic analyses of resting-state fMRI. We envision that directed graphs will prove useful in understanding the psychopathology and pathophysiology of neurodegenerative and neurodevelopmental disorders. We will demonstrate the utility of large directed graphs in clinical populations in subsequent reports, using the procedures described in this paper.

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Multivariate Granger Causality Analysis of Acupuncture Effects in Mild Cognitive Impairment Patients: An fMRI Study

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2013/127271 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 5

Author(s):

Shangjie Chen ◽

Lijun Bai ◽

Maosheng Xu ◽

Fang Wang ◽

Liang Yin ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Granger Causality ◽

Effective Connectivity ◽

Brain Networks ◽

Brain Regions ◽

Causality Analysis ◽

Granger Causality Analysis ◽

Cognitive States ◽

Dorsolateral Prefrontal

Evidence from clinical reports has indicated that acupuncture has a promising effect on mild cognitive impairment (MCI). However, it is still unknown that by what way acupuncture can modulate brain networks involving the MCI. In the current study, multivariate Granger causality analysis (mGCA) was adopted to compare the interregional effective connectivity of brain networks by varying needling depths (deep acupuncture, DA; superficial acupuncture, SA) and at different cognitive states, which were the MCI and healthy control (HC). Results from DA at KI3 in MCI showed that the dorsolateral prefrontal cortex and hippocampus emerged as central hubs and had significant causal influences with each other, but significant in HC for DA. Moreover, only several brain regions had remarkable causal interactions following SA in MCI and even few brain regions following SA in HC. Our results indicated that acupuncture at KI3 at different cognitive states and with varying needling depths may induce distinct reorganizations of effective connectivities of brain networks, and DA at KI3 in MCI can induce the strongest and more extensive effective connectivities related to the therapeutic effect of acupuncture in MCI. The study demonstrated the relatively functional specificity of acupuncture at KI3 in MCI, and needling depths play an important role in acupuncture treatments.

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Effective connectivity of brain networks during self-initiated movement in Parkinson's disease

NeuroImage ◽

10.1016/j.neuroimage.2010.11.074 ◽

2011 ◽

Vol 55 (1) ◽

pp. 204-215 ◽

Cited By ~ 135

Author(s):

Tao Wu ◽

Liang Wang ◽

Mark Hallett ◽

Yi Chen ◽

Kuncheng Li ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Effective Connectivity ◽

Brain Networks

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Task-related effective connectivity reveals that the cortical rich club gates cortex-wide communication

10.1101/185603 ◽

2017 ◽

Cited By ~ 1

Author(s):

Mario Senden ◽

Niels Reuter ◽

Martijn P. van den Heuvel ◽

Rainer Goebel ◽

Gustavo Deco ◽

...

Keyword(s):

Information Exchange ◽

Effective Connectivity ◽

Functional Integration ◽

Brain Regions ◽

Rich Club ◽

State Dependent ◽

Gating Mechanism ◽

The Rich ◽

Cortical Regions ◽

Peripheral Regions

AbstractHigher cognition may require the globally coordinated integration of specialized brain regions into functional networks. A collection of structural cortical hubs - referred to as the rich club - has been hypothesized to support task-specific functional integration. In the present paper, we use a whole-cortex model to estimate directed interactions between 68 cortical regions from fMRI activity for four different tasks (reflecting different cognitive domains) and resting state. We analyze the state-dependent input and output effective connectivity of the structural rich club and relate these to whole-cortex dynamics and network reconfigurations. We find that the cortical rich club exhibits an increase in outgoing effective connectivity during task performance as compared to rest while incoming connectivity remains constant. Increased outgoing connectivity targets a sparse set of peripheral regions with specific regions strongly overlapping between tasks. At the same time, community detection analyses reveal massive reorganizations of interactions among peripheral regions, including those serving as target of increased rich club output. This suggests that while peripheral regions may play a role in several tasks, their concrete interplay might nonetheless be task-specific. Furthermore, we observe that whole-cortex dynamics are faster during task as compared to rest. The decoupling effects usually accompanying faster dynamics appear to be counteracted by the increased rich club outgoing effective connectivity. Together our findings speak to a gating mechanism of the rich club that supports fast-paced information exchange among relevant peripheral regions in a task-specific and goal-directed fashion, while constantly listening to the whole network.

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FC07-06 - Differences in the modulatory role of escitalopram and citalopram revealed by effective connectivity analysis

European Psychiatry ◽

10.1016/s0924-9338(11)73555-6 ◽

2011 ◽

Vol 26 (S2) ◽

pp. 1851-1851

Author(s):

C. Windischberger ◽

C. Kasess ◽

R. Sladky ◽

E. Moser ◽

S. Kasper ◽

...

Keyword(s):

Bayesian Model Averaging ◽

Effective Connectivity ◽

Functional Integration ◽

Pharmacological Study ◽

Model Averaging ◽

Brain Regions ◽

Antidepressant Effect ◽

Racemic Mixture ◽

Double Blind ◽

Fmri Study

IntroductionCitalopram is a widely applied SSRI in patients suffering from affective disorder. It is a racemic mixture of the S- and R-enantiomer of citalopram, consisting of equal parts of S-citalopram and R-citalopram, respectively. It has been shown that the inhibitory potency in serotonin reuptake of S-citalopram is much higher compared to R-citalopram, and it is assumed that S-citalopram is the main carrier of the antidepressant effect.ObjectivesHere we investigated the effects of the two SSRIs Citalopram (50% S-, 50% R-citalopram) and Escitalopram (100% S-citalopram) on brain networks during emotion processing using pharmacological functional magnetic resonance imaging (fMRI) and dynamic causal modelling (DCM), an advanced tool to investigate functional integration between different brain regions.MethodsOur results are based on a placebo-controlled, randomized, double-blind, cross-over pharmacological study in 16 healthy subjects during three fMRI scanning sessions performing a facial emotional discrimination paradigm (Windischberger, Neuroimage, 2010). 32 models of pharmacological modulation within the amygdalar-parahippocampal-orbitofrontal network were analysed using Bayesian Model Averaging (BMA) as implemented in SPM8.ResultsS-citalopram showed statistically significant modulatory effects on forward amygdala-orbitofrontal and bidirectional amygdala-parahippocampal connections. No significant modulatory effects of R-citalopram were found.ConclusionsThis is the first fMRI study that showed stimulus-specific differential effects of the two enantiomeres R- and S-citalopram at the neural connectivity level. Our results corroborate studies in rats where escitalopram-induced increases in extracellular serotonin levels were found attenuated when R-citalopram was coinjected. Taken together this might explain the response differences between study drugs as demonstrated in previous clinical trials.

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Structural and effective brain connectivity underlying biological motion detection

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1812859115 ◽

2018 ◽

Vol 115 (51) ◽

pp. E12034-E12042 ◽

Cited By ~ 18

Author(s):

Arseny A. Sokolov ◽

Peter Zeidman ◽

Michael Erb ◽

Philippe Ryvlin ◽

Karl J. Friston ◽

...

Keyword(s):

Biological Motion ◽

Effective Connectivity ◽

Inferior Frontal Gyrus ◽

Functional Integration ◽

Temporal Cortex ◽

Causal Modeling ◽

High Angular Resolution ◽

Wide Range ◽

Crus I ◽

The Right

The perception of actions underwrites a wide range of socio-cognitive functions. Previous neuroimaging and lesion studies identified several components of the brain network for visual biological motion (BM) processing, but interactions among these components and their relationship to behavior remain little understood. Here, using a recently developed integrative analysis of structural and effective connectivity derived from high angular resolution diffusion imaging (HARDI) and functional magnetic resonance imaging (fMRI), we assess the cerebro-cerebellar network for processing of camouflaged point-light BM. Dynamic causal modeling (DCM) informed by probabilistic tractography indicates that the right superior temporal sulcus (STS) serves as an integrator within the temporal module. However, the STS does not appear to be a “gatekeeper” in the functional integration of the occipito-temporal and frontal regions: The fusiform gyrus (FFG) and middle temporal cortex (MTC) are also connected to the right inferior frontal gyrus (IFG) and insula, indicating multiple parallel pathways. BM-specific loops of effective connectivity are seen between the left lateral cerebellar lobule Crus I and right STS, as well as between the left Crus I and right insula. The prevalence of a structural pathway between the FFG and STS is associated with better BM detection. Moreover, a canonical variate analysis shows that the visual sensitivity to BM is best predicted by BM-specific effective connectivity from the FFG to STS and from the IFG, insula, and STS to the early visual cortex. Overall, the study characterizes the architecture of the cerebro-cerebellar network for BM processing and offers prospects for assessing the social brain.

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