Noise during Rest Enables the Exploration of the Brain's Dynamic Repertoire

Abstract

Traditionally brain function is studied through measuring physiological responses in controlled sensory, motor, and cognitive paradigms. However, even at rest, in the absence of overt goal-directed behavior, collections of cortical regions consistently show temporally coherent activity. In humans, these resting state networks have been shown to greatly overlap with functional architectures present during consciously directed activity, which motivates the interpretation of rest activity as day dreaming, free association, stream of consciousness, and inner rehearsal. In monkeys, it has been shown though that similar coherent fluctuations are present during deep anesthesia when there is no consciousness. Here, we show that comparable resting state networks emerge from a stability analysis of the network dynamics using biologically realistic primate brain connectivity, although anatomical information alone does not identify the network. We specifically demonstrate that noise and time delays via propagation along connecting fibres are essential for the emergence of the coherent fluctuations of the default network. The spatiotemporal network dynamics evolves on multiple temporal scales and displays the intermittent neuroelectric oscillations in the fast frequency regimes, 1–100 Hz, commonly observed in electroencephalographic and magnetoencephalographic recordings, as well as the hemodynamic oscillations in the ultraslow regimes, <0.1 Hz, observed in functional magnetic resonance imaging. The combination of anatomical structure and time delays creates a space–time structure in which the neural noise enables the brain to explore various functional configurations representing its dynamic repertoire.

Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity

“…(Friston, 1997(Friston, , 2000), computational (e.g. (Breakspear et al, 2003, Honey et al, 2007, Ghosh et al, 2008, Deco et al, 2009 (Brookes et al, 2012b). Indeed this finding has been mirrored by other MEG studies (Hipp et al, 2012), and is in general agreement with findings from simultaneous electroencephalography (EEG) / fMRI.…”

“…(Friston, 1997(Friston, , 2000), computational (e.g. (Breakspear et al, 2003, Honey et al, 2007, Ghosh et al, 2008, Deco et al, 2009) and empirical (e.g. (Breakspear et al, 2004, Bassett et al, 2006) evidence suggests that complex and highly temporally variable neuronal dynamics underlie the coupling observed between spatially separate brain regions.…”

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

“…Based on this concept, a theory of "coordination dynamics" has been developed, which states that systems can flexibly switch from one coordination state to another (Fuchs and Jirsa, 2007;Kelso, 2010;Tognoli and Kelso, 2014). Transitions can be evoked by external stimulation (Jirsa et al, 1998;Schoner et al, 1986)) or by noise (during resting conditions) (Deco and Jirsa, 2012) (Ghosh et al, 2008). Importantly, metastable coordination dynamics enable a system to change itself even in the absence of input or noise.…”

MR connectomics: Principles and challenges

“…Towards this objective, a number of computational studies have attempted to combine interregional connectivity data with intra-regional circuit and cellular properties to produce large-scale models of cortical (Ghosh et al, 2008;Honey et al, 2007Honey et al, , 2009Knock et al, 2009) and corticothalamic function (Izhikevich and Edelman, 2008). The value of modeling studies is twofold.…”