A Dynamic Core Network and Global Efficiency in the Resting Human Brain

Abstract

Spontaneous brain activity is spatially and temporally organized in the absence of any stimulation or task in networks of cortical and subcortical regions that appear largely segregated when imaged at slow temporal resolution with functional magnetic resonance imaging (fMRI). When imaged at high temporal resolution with magneto-encephalography (MEG), these resting-state networks (RSNs) show correlated fluctuations of band-limited power in the beta frequency band (14-25 Hz) that alternate between epochs of strong and weak internal coupling. This study presents 2 novel findings on the fundamental issue of how different brain regions or networks interact in the resting state. First, we demonstrate the existence of multiple dynamic hubs that allow for across-network coupling. Second, dynamic network coupling and related variations in hub centrality correspond to increased global efficiency. These findings suggest that the dynamic organization of across-network interactions represents a property of the brain aimed at optimizing the efficiency of communication between distinct functional domains (memory, sensory-attention, motor). They also support the hypothesis of a dynamic core network model in which a set of network hubs alternating over time ensure efficient global communication in the whole brain.

The anatomical scaffold underlying the functional centrality of known cortical hubs

“…Several structural and functional hub locations have been reported [Bullmore and Sporns, ; Hagmann et al, ; Power et al, ] typically located in medial posterior cingulate cortex (pCC) and lateral (angular gyrus) parietal regions of the default mode network (DMN), and in anterior cingulate and anterior insula, part of the cingulo‐opercular, and lateral frontal and parietal cortex, part of the fronto‐parietal control networks [Buckner et al, ; Cole et al, ; de Pasquale et al, ; Tomasi and Volkow, ). In line with these results, other imaging modalities such as MEG identified a set of regions belonging to the DMN as highly central followed by nodes from the dorsal attention (DAN) and somato‐sensory (SMN) networks [de Pasquale et al, ].…”

Dissociable Changes of Frontal and Parietal Cortices in Inherent Functional Flexibility across the Human Life Span

“…The brain has been described as intrinsically active, rather than passively stimulus-driven (Engel et al, 2013). Extensive research suggests that dynamical organization of ongoing brain activity is endowed with meaningful spatiotemporal structure, enabling the expression of a rich, flexible repertoire of functional configurations (Deco et al, 2011(Deco et al, , 2013Park and Friston, 2013;Barttfeld et al, 2015;Hansen et al, 2015;Shen et al, 2015). Further understanding of this functional organization requires an integrated framework linking brain connectivity to brain dynamics (Liu and Duyn, 2013;Kopell et al, 2014).…”

“…Further understanding of this functional organization requires an integrated framework linking brain connectivity to brain dynamics (Liu and Duyn, 2013;Kopell et al, 2014). Therefore, dynamic functional connectivity, which emphasizes the spatiotemporal behavior of intrinsic brain activity, has recently garnered increased attention (Chang and Glover, 2010;Kiviniemi et al, 2011;Jones et al, 2012;Hutchison et al, 2013a;Zalesky et al, 2014;Karahanoglu and Van De Ville, 2015;de Pasquale et al, 2015; for review, see Hutchison et al, 2013b;. The sliding window is commonly used to identify discrete, reproducible functional states Barttfeld et al, 2015;Hansen et al, 2015;Shen et al, 2015) or quantify timevarying topological properties of brain functional networks (Zalesky et al, 2014;Liao et al, 2015) over the duration of the scan, and is currently one of the most popular approaches.…”

The Role of Local and Large-Scale Neuronal Synchronization in Human Cognition

“…4c). Topological properties have also been assessed for resting-state MEG networks (de Pasquale et al 2015;Rutter et al 2013), which also exhibit truncated power-law degree distributions but with several shared network hubs across frequencies (Rutter et al 2013). These results together suggest that task performance changes the network topology, which can be correlated with the efficiency of information processing.…”