Exploring the network dynamics underlying brain activity during rest

Abstract

Since the mid 1990s, the intriguing dynamics of the brain at rest has been attracting a growing body of research in neuroscience. Neuroimaging studies have revealed distinct functional networks that slowly activate and deactivate, pointing to the existence of an underlying network dynamics emerging spontaneously during rest, with specific spatial, temporal and spectral characteristics. Several theoretical scenarios have been proposed and tested with the use of large-scale computational models of coupled brain areas. However, a mechanistic explanation that encompasses all the phenomena observed in the brain during rest is still to come. In this review, we provide an overview of the key findings of resting-state activity covering a range of neuroimaging modalities including fMRI, EEG and MEG. We describe how to best define and analyze anatomical and functional brain networks and how unbalancing these networks may lead to problems with mental health. Finally, we review existing large-scale models of resting-state dynamics in health and disease. An important common feature of resting-state models is that the emergence of resting-state functional networks is obtained when the model parameters are such that the system operates at the edge of a bifurcation. At this critical working point, the global network dynamics reveals correlation patterns that are spatially shaped by the underlying anatomical structure, leading to an optimal fit with the empirical BOLD functional connectivity. However, new insights coming from recent studies, including faster oscillatory dynamics and non-stationary functional connectivity, must be taken into account in future models to fully understand the network mechanisms leading to the resting-state activity.

Synchronization, non-linear dynamics and low-frequency fluctuations: Analogy between spontaneous brain activity and networked single-transistor chaotic oscillators

“…Correspondence between synchronization, spectral content, and non-linear dynamics in resting-state brain activity While spatiotemporal coherencies underlying RSNs are knowingly driven by low-frequency activity <0.1 Hz, in itself, this does not imply that the intensity of these fluctuations is topographically related to "overall strength" of functional connectivity (activity synchronization), particularly given that using approaches like independent component or seed-based analysis discrete anti-correlated or even asynchronous RSNs are found. 7,11,12,22,58 Using a graph-based model of inter-regional synchronization, we were able to show strong coupling between node degree and relative amplitude of low-frequency activity, more explicitly than previous studies which considered individual RSNs rather than a holistic representation of the functional connectome. 59,60 Spontaneous brain activity during awake idleness generates BOLD signals that have approximately white noise-like spectrum for weakly connected (synchronized) regions and display a gradual shift towards low-frequency fluctuations (e.g., pink, red or brown noise), corresponding to increased temporal autocorrelation, in stronger-connected areas such as precuneus, lateral parietal, and medial frontal cortex.…”

“…This enables exploring the hypothesis that certain collective phenomena appear preferentially close to the point of criticality, as found for the emergence of functional from structural connectivity in some simulations of brain dynamics. [5][6][7]9,10 C. Network implementation…”

“…As such, it can rapidly explore different dynamical regimes, generating avalanches of activity that propagate across the entire network. [5][6][7] Based on realistic models of structural connectivity, numerical simulations predict the emergence of functional connectivity (activity synchronization between regions) in the form of discrete "resting-state" networks (RSNs), such as the "default-mode network" and "fronto-parietal network." [7][8][9][10] These networks have been detected by means of independent component analysis of blood oxygen level dependent (BOLD) time-series recorded using functional MRI during awake idleness (resting-state), whose fluctuations primarily represent spontaneous neural activity.…”

“…[5][6][7] Based on realistic models of structural connectivity, numerical simulations predict the emergence of functional connectivity (activity synchronization between regions) in the form of discrete "resting-state" networks (RSNs), such as the "default-mode network" and "fronto-parietal network." [7][8][9][10] These networks have been detected by means of independent component analysis of blood oxygen level dependent (BOLD) time-series recorded using functional MRI during awake idleness (resting-state), whose fluctuations primarily represent spontaneous neural activity. 11,12 Graph-based analyses of BOLD signal synchronization have also confirmed high node degree of functional connectivity (representing a measure of "synchronization density") in the aforementioned cortical hub regions, in broad agreement with the underlying structural connectivity.…”

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

“…This new view of how the brain processes information led to a vast amount of studies that investigated large-scale brain networks at rest: their spatial organization, temporal dynamics, associations with cognitive states, and alterations due to different cognitive disorders and neurological diseases (Cabral et al, 2014;Foster et al, 2016;Fox and Greicius, 2010;Mitra and Raichle, 2016). Various methods are used to reveal these networks, leading to different interpretations regarding their spatial and temporal organization.…”