Putting the “dynamic” back into dynamic functional connectivity

Abstract

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.

Dynamic Cortical Connectivity during General Anesthesia in Healthy Volunteers

“…For example, an array of synchronization dynamics that can emerge in neuronal ensembles (such as generalized synchronization, metastability, and multistability) has been proposed as a source of the spontaneous fluctuations in empirical observations. 45 In this study of dynamic cortical connectivity during surgical levels of anesthesia, unique methodologic strengths are worth highlighting. First, the anesthetic protocol is clinically relevant but without the confound of surgical intervention.…”

On the nature of time-varying functional connectivity in resting fMRI

“…There are a wide variety of multi-scale models of interconnected pools of neurons, including neural mass and neural field models (Bojak, et al 2010;Breakspear 2017;Deco, et al 2008). These have been shown to produce neurobiologically plausible behaviors such as generalized synchronization, metastability, and multistability (Breakspear 2017;Deco, et al 2008;Golos, et al 2015;Heitmann and Breakspear 2018). Exploratory computational work involves adjusting the model structure and tuning parameters in order to obtain, through simulation, synthetic BOLD data that exhibits similar dependence and dynamics to empirical observations (Kashyap and Keilholz).…”

“…Such processes can be represented by reference to a suitable surrogate "null" distribution, typically generated through simulation or non-parametric resampling (Breakspear, et al 2004;Prichard and Theiler 1994). Multiple methods have been developed to simulate surrogate data, including methods that represent a null model based on a specific system (Hindriks, et al 2016), biophysical models which simulate different classes of dynamics in the brain (Heitmann and Breakspear 2018), and techniques that are designed to test the properties of specific methods used to estimate TVC (Allen, et al 2014;Shakil, et al 2016).…”

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“…In many application scenarios, graphs are assumed to be generated by a stationary process, implying that the topology and graph attributes are drawn from a fixed, albeit unknown, distribution [8]. However, the stationarity assumption does not always hold true, with relevant examples including cyberphysical systems [9], functional networks associated with brain imaging (where neural activity changes over time autonomously, or by reaction to stimuli) [10], and many others, e.g., see [11], [12], [13], [14].…”