Putting the “dynamic” back into dynamic functional connectivity

Heitmann, Stewart; Breakspear, Michael

doi:10.1101/181313

Cited by 13 publications

(19 citation statements)

References 115 publications

(148 reference statements)

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“…The occurrence of such bursts corresponds to a dynamical phenomenon known as metastability. In brief, for strong coupling, the system possesses a single, low-dimensional chaotic attractor that is embedded within the synchronization manifold: Although the dynamics of this chaotic attractor are reasonably complex (Supplementary Information I, Heitmann & Breakspear, 2018 ), both systems converge onto the same manifold, corresponding to stable (and stationary) generalized synchronization (Ashwin, 1995 ). The dynamics considered within the full (six-dimensional) space spanned by both systems become relatively simple.…”

Section: Resultsmentioning

confidence: 99%

“…The equation for the amplitude dynamics of a single node according to this simplified model are given by

where r is the amplitude, λ and β are parameters that control the size and depth of the fixed-point and limit-cycle attractor basins. The parameters b 1 and b 2 control the influence of the additive ζ 1 and multiplicative noise ζ 2 x , respectively (see Supplementary Information III for full details; Heitmann & Breakspear, 2018 ).…”

Section: Resultsmentioning

confidence: 99%

“…All simulations in this paper are performed using the Brain Dynamics Toolbox ( https://bdtoolbox.blogspot.com.au/ ), an open-source Matlab-based toolbox for interactive simulations of neuronal dynamics (as described in Supplementary Information II, Heitmann & Breakspear, 2018 ). The Brain Dynamics Toolbox allows scaling up to simulate large ensembles, the employment of other local node dynamics, the introduction of local stochastic influences, and the treatment of internode time delays.…”

Section: Methodsmentioning

confidence: 99%

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Putting the “dynamic” back into dynamic functional connectivity

Heitmann

Breakspear

2018

Network Neuroscience

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The study of fluctuations in time-resolved functional connectivity is a topic of substantial current interest. As the term “dynamic functional connectivity” implies, such fluctuations are believed to arise from dynamics in the neuronal systems generating these signals. While considerable activity currently attends to methodological and statistical issues regarding dynamic functional connectivity, less attention has been paid toward its candidate causes. Here, we review candidate scenarios for dynamic (functional) connectivity that arise in dynamical systems with two or more subsystems; generalized synchronization, itinerancy (a form of metastability), and multistability. Each of these scenarios arises under different configurations of local dynamics and intersystem coupling: We show how they generate time series data with nonlinear and/or nonstationary multivariate statistics. The key issue is that time series generated by coupled nonlinear systems contain a richer temporal structure than matched multivariate (linear) stochastic processes. In turn, this temporal structure yields many of the phenomena proposed as important to large-scale communication and computation in the brain, such as phase-amplitude coupling, complexity, and flexibility. The code for simulating these dynamics is available in a freeware software platform, the Brain Dynamics Toolbox.

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Section: Resultsmentioning

confidence: 99%

“…The equation for the amplitude dynamics of a single node according to this simplified model are given by

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Putting the “dynamic” back into dynamic functional connectivity

Heitmann

Breakspear

2018

Network Neuroscience

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“…This raises the question whether RSNs manifest only on longer time scales or whether they also 'exist' at shorter time scales. Recent studies of time-varying functional connectivity (tvFC; Heitmann and Breakspear, 2018;Lurie et al 2020;Kucyi et al 2018) have addressed the issue, approaching fine temporal structure and dynamics of FC through the use of shorter data samples, e.g. sliding windows or instantaneous co-activation patterns that result in temporally ordered sequences of functional networks and network states (Liu and Duyn 2013;Allen et al 2014;Shakil et al 2016;Preti et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Dynamic Expression of Brain Functional Systems Disclosed by Fine-Scale Analysis of Edge Time Series

Sporns

Faskowitz

Teixera

et al. 2020

Preprint

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Functional connectivity (FC) describes the statistical dependence between brain regions in resting-state fMRI studies and is usually estimated as the Pearson correlation of time courses. Clustering reveals densely coupled sets of regions constituting a set of resting-state networks or functional systems. These systems manifest most clearly when FC is sampled over longer epochs lasting many minutes but appear to fluctuate on shorter time scales. Here, we propose a new approach to track these temporal fluctuations. Un-wrapping FC signal correlations yields pairwise co-fluctuation time series, one for each node pair/edge, and reveals fine-scale dynamics across the network. Co-fluctuations partition the network, at each time step, into exactly two communities. Sampled over time, the overlay of these bipartitions, a binary decomposition of the original time series, very closely approximates functional connectivity. Bipartitions exhibit characteristic spatiotemporal patterns that are reproducible across participants and imaging sessions and disclose fine-scale profiles of the time-varying levels of expression of functional systems. Our findings document that functional systems appear transiently and intermittently, and that FC results from the overlay of many variable instances of system expression. Potential applications of this decomposition of functional connectivity into a set of binary patterns are discussed.

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“…For example, Vidaurre et al (2017) showed, using a hidden Markov model, that the transitions between networks (states) are non-random. Two recent studies (Heitmann and Breakspear, 2017;Liégeois et al, 2017) also made the important point of distinguishing the meaning of "dynamic" from the statistical point of view (i.e., non-uniformity in time) from the link of these fluctuations to the intrinsic dynamics of neural populations that generate the recordings under examination.…”

Section: Introductionmentioning

confidence: 99%

Dynamic causal modelling of fluctuating connectivity in resting-state EEG

Steen

Almgren

Razi

et al. 2018

Preprint

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Functional and effective connectivity are known to change systematically over time. These changes might be explained by several factors, including intrinsic fluctuations in activity-dependent neuronal coupling and contextual factors, like experimental condition and time. Furthermore, contextual effects may be subject-specific or conserved over subjects. To characterize fluctuations in effective connectivity, we used dynamic causal modelling (DCM) of cross spectral responses over 1 min of electroencephalogram (EEG) recordings during rest, divided into 1-sec windows. We focused on two intrinsic networks: the default mode and the saliency network. DCM was applied to estimate connectivity in each time-window for both networks. Fluctuations in DCM connectivity parameters were assessed using hierarchical parametric empirical Bayes (PEB). Within-subject, between-window effects were modelled with a second-level linear model with temporal basis functions as regressors.This procedure was conducted for every subject separately. Bayesian model reduction was then used to assess which (combination of) temporal basis functions best explain dynamic connectivity over windows. A third (between-subject) level model was used to infer which dynamic connectivity parameters are conserved over subjects. Our results indicate that connectivity fluctuations in the saliency network comprised both subject-specific components and a common component. For the default mode network, connectivity trajectories only showed a common component. For both networks, connections to higher order regions appear to monotonically increase during the one minute period. These results not only establish the predictive validity of dynamic connectivity . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/303933 doi: bioRxiv preprint first posted online Apr. 18, 2018; 2 estimates -in virtue of detecting systematic changes over subjects -they also suggest a networkspecific dissociation in the relative contribution of fluctuations in connectivity that depend upon experimental context. We envisage these procedures could be useful for characterizing brain state transitions that may be explained by their cognitive or neuropathological underpinnings.

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Putting the “dynamic” back into dynamic functional connectivity

Cited by 13 publications

References 115 publications

Putting the “dynamic” back into dynamic functional connectivity

Putting the “dynamic” back into dynamic functional connectivity

Dynamic Expression of Brain Functional Systems Disclosed by Fine-Scale Analysis of Edge Time Series

Dynamic causal modelling of fluctuating connectivity in resting-state EEG

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