Effective connectivity of LSD-induced ego dissolution

AbstractClassic psychedelic-induced ego dissolution involves a shift in the sense of self and blurring of boundary between the self and the world. A similar phenomenon is identified in psychopathology and is associated to the balance of anticorrelated activity between the default mode network (DMN) – which directs attention inwards – and the salience network (SN) – which recruits the dorsal attention network (DAN) to direct attention outward. To test whether change in anticorrelated networks underlie the peak effects of LSD, we applied dynamic causal modeling to infer effective connectivity of resting state functional MRI scans from a study of 25 healthy adults who were administered 100mg of LSD, or placebo. We found that change in inhibitory effective connectivity from the SN to DMN became excitatory, and inhibitory effective connectivity from DMN to DAN decreased under the peak effect of LSD. These changes in connectivity reflect diminution of the anticorrelation between resting state networks that may be a key neural mechanism of LSD-induced ego dissolution. Our findings suggest the hierarchically organised balance of resting state networks is a central feature in the construct of self.SignificanceThe findings can inform the parallel between the maintenance of subject-object boundary and changes to anticorrelated canonical resting state brain networks. Effective connectivity informs the hierarchical organisation of brain networks underlying modes of perception. Moreover, the anticorrelation of brain networks is an important measure of mental function. Understanding the neural mechanisms of anticorrelation change under psychedelics help identify its relationship to psychosis and its association to psychedelic assisted therapeutic outcomes.

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Regression dynamic causal modeling for resting-state fMRI

10.1101/2020.08.12.247536 ◽

2020 ◽

Cited By ~ 1

Author(s):

Stefan Frässle ◽

Samuel J. Harrison ◽

Jakob Heinzle ◽

Brett A. Clementz ◽

Carol A. Tamminga ◽

...

Keyword(s):

Resting State ◽

Brain Connectivity ◽

Effective Connectivity ◽

Brain Networks ◽

Resting State Fmri ◽

Causal Modeling ◽

Computationally Efficient ◽

Dynamic Causal Modeling ◽

Whole Brain ◽

Wide Range

Abstract“Resting-state” functional magnetic resonance imaging (rs-fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks.Here, we show that a method recently developed for task-fMRI – regression dynamic causal modeling (rDCM) – extends to rs-fMRI and offers both directional estimates and scalability to whole-brain networks. First, simulations demonstrate that rDCM faithfully recovers parameter values over a wide range of signal-to-noise ratios and repetition times. Second, we test construct validity of rDCM in relation to an established model of effective connectivity, spectral DCM. Using rs-fMRI data from nearly 200 healthy participants, rDCM produces biologically plausible results consistent with estimates by spectral DCM. Importantly, rDCM is computationally highly efficient, reconstructing whole-brain networks (>200 areas) within minutes on standard hardware. This opens promising new avenues for connectomics.

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Within and between subject variability of effective connectivity in (small) resting state networks: A spectral dynamic causal modeling study

Frontiers in Neuroscience ◽

10.3389/conf.fnins.2017.94.00010 ◽

2017 ◽

Vol 11 ◽

Author(s):

Hannes Almgren ◽

Frederik Van De Steen ◽

Simone Kühn ◽

Adeel Razi ◽

Daniele Marinazzo

Keyword(s):

Resting State ◽

Effective Connectivity ◽

Causal Modeling ◽

Resting State Networks ◽

Dynamic Causal Modeling ◽

Subject Variability ◽

Modeling Study

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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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The effect of global signal regression on DCM estimates of noise and effective connectivity from resting state fMRI

10.1101/634063 ◽

2019 ◽

Author(s):

Hannes Almgren ◽

Frederik Van de Steen ◽

Adeel Razi ◽

Karl Friston ◽

Daniele Marinazzo

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Effective Connectivity ◽

Network Connectivity ◽

Resting State Fmri ◽

Measurement Noise ◽

Resting State Networks ◽

Cross Sectional ◽

Global Signal ◽

Leibler Divergence

AbstractThe influence of the global BOLD signal on resting state functional connectivity in fMRI data remains a topic of debate, with little consensus. In this study, we assessed the effects of global signal regression (GSR) on effective connectivity within and between resting-state networks – as estimated with dynamic causal modelling (DCM) for resting state fMRI (rsfMRI). DCM incorporates a forward (generative) model that quantifies the contribution of different types of noise (including global measurement noise), effective connectivity, and (neuro)vascular processes to functional connectivity measurements. DCM analyses were applied to two different designs; namely, longitudinal and cross-sectional designs. In the modelling of longitudinal designs, we included four extensive longitudinal resting state fMRI datasets with a total number of 20 subjects. In the analysis of cross-sectional designs, we used rsfMRI data from 361 subjects from the Human Connectome Project. We hypothesized that (1) GSR would have no discernible impact on effective connectivity estimated with DCM, and (2) GSR would be reflected in the parameters representing global measurement noise. Additionally, we performed comparative analyses of the informative value of data with and without GSR. Our results showed negligible to small effects of GSR on connectivity within small (separately estimated) RSNs. For between-network connectivity, we found two important effects: the effect of GSR on between-network connectivity (averaged over all connections) was negligible to small, while the effect of GSR on individual connections was non-negligible. Contrary to our expectations, we found either no effect (in the longitudinal designs) or a non-specific (cross-sectional design) effect of GSR on parameters representing (global) measurement noise. Data without GSR were found to be more informative than data with GSR; however, in small resting state networks the precision of posterior estimates was greater using data after GSR. In conclusion, GSR is a minor concern in DCM studies; however, individual between-network connections (as opposed to average between-network connectivity) and noise parameters should be interpreted quantitatively with some caution. The Kullback-Leibler divergence of the posterior from the prior, together with the precision of posterior estimates, might offer a useful measure to assess the appropriateness of GSR, when nuancing data features in resting state fMRI.

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Hierarchical Dynamic Causal Modeling of Resting-State fMRI Reveals Longitudinal Changes in Effective Connectivity in the Motor System after Thalamotomy for Essential Tremor

Frontiers in Neurology ◽

10.3389/fneur.2017.00346 ◽

2017 ◽

Vol 8 ◽

Cited By ~ 10

Author(s):

Hae-Jeong Park ◽

Chongwon Pae ◽

Karl Friston ◽

Changwon Jang ◽

Adeel Razi ◽

...

Keyword(s):

Essential Tremor ◽

Resting State ◽

Motor System ◽

Effective Connectivity ◽

Resting State Fmri ◽

Causal Modeling ◽

Dynamic Causal Modeling ◽

Longitudinal Changes

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Behavioral Interpretations of Intrinsic Connectivity Networks

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_00077 ◽

2011 ◽

Vol 23 (12) ◽

pp. 4022-4037 ◽

Cited By ~ 594

Author(s):

Angela R. Laird ◽

P. Mickle Fox ◽

Simon B. Eickhoff ◽

Jessica A. Turner ◽

Kimberly L. Ray ◽

...

Keyword(s):

Cognitive Processes ◽

Resting State ◽

Brain Networks ◽

Data Driven ◽

Resting State Networks ◽

Independent State ◽

Intrinsic Connectivity ◽

Mental Operations ◽

Intrinsic Connectivity Networks ◽

Insight Into

An increasingly large number of neuroimaging studies have investigated functionally connected networks during rest, providing insight into human brain architecture. Assessment of the functional qualities of resting state networks has been limited by the task-independent state, which results in an inability to relate these networks to specific mental functions. However, it was recently demonstrated that similar brain networks can be extracted from resting state data and data extracted from thousands of task-based neuroimaging experiments archived in the BrainMap database. Here, we present a full functional explication of these intrinsic connectivity networks at a standard low order decomposition using a neuroinformatics approach based on the BrainMap behavioral taxonomy as well as a stratified, data-driven ordering of cognitive processes. Our results serve as a resource for functional interpretations of brain networks in resting state studies and future investigations into mental operations and the tasks that drive them.

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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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State-Dependent Effective Connectivity in Resting-State fMRI

Frontiers in Neural Circuits ◽

10.3389/fncir.2021.719364 ◽

2021 ◽

Vol 15 ◽

Author(s):

Hae-Jeong Park ◽

Jinseok Eo ◽

Chongwon Pae ◽

Junho Son ◽

Sung Min Park ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Empirical Bayes ◽

Time Course ◽

Effective Connectivity ◽

The State ◽

Causal Modeling ◽

Occupation Times ◽

State Dependent ◽

Mental Diseases

The human brain at rest exhibits intrinsic dynamics transitioning among the multiple metastable states of the inter-regional functional connectivity. Accordingly, the demand for exploring the state-specific functional connectivity increases for a deeper understanding of mental diseases. Functional connectivity, however, lacks information about the directed causal influences among the brain regions, called effective connectivity. This study presents the dynamic causal modeling (DCM) framework to explore the state-dependent effective connectivity using spectral DCM for the resting-state functional MRI (rsfMRI). We established the sequence of brain states using the hidden Markov model with the multivariate autoregressive coefficients of rsfMRI, summarizing the functional connectivity. We decomposed the state-dependent effective connectivity using a parametric empirical Bayes scheme that models the effective connectivity of consecutive windows with the time course of the discrete states as regressors. We showed the plausibility of the state-dependent effective connectivity analysis in a simulation setting. To test the clinical applicability, we applied the proposed method to characterize the state- and subtype-dependent effective connectivity of the default mode network in children with combined-type attention deficit hyperactivity disorder (ADHD-C) compared with age-matched, typically developed children (TDC). All 88 children were subtyped according to the occupation times (i.e., dwell times) of the three dominant functional connectivity states, independently of clinical diagnosis. The state-dependent effective connectivity differences between ADHD-C and TDC according to the subtypes and those between the subtypes of ADHD-C were expressed mainly in self-inhibition, magnifying the importance of excitation inhibition balance in the subtyping. These findings provide a clear motivation for decomposing the state-dependent dynamic effective connectivity and state-dependent analysis of the directed coupling in exploring mental diseases.

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