Fast transient networks in spontaneous human brain activity

Abstract

To provide an effective substrate for cognitive processes, functional brain networks should be able to reorganize and coordinate on a sub-second temporal scale. We used magnetoencephalography recordings of spontaneous activity to characterize whole-brain functional connectivity dynamics at high temporal resolution. Using a novel approach that identifies the points in time at which unique patterns of activity recur, we reveal transient (100–200 ms) brain states with spatial topographies similar to those of well-known resting state networks. By assessing temporal changes in the occurrence of these states, we demonstrate that within-network functional connectivity is underpinned by coordinated neuronal dynamics that fluctuate much more rapidly than has previously been shown. We further evaluate cross-network interactions, and show that anticorrelation between the default mode network and parietal regions of the dorsal attention network is consistent with an inability of the system to transition directly between two transient brain states.DOI: http://dx.doi.org/10.7554/eLife.01867.001

Measuring electrophysiological connectivity by power envelope correlation: a technical review on MEG methods

“…Taken together, these studies begin to demonstrate that within-network functional connectivity is underpinned by coordinated dynamics that fluctuate in time. Importantly, these fluctuations occur at a much more rapid timescale than has previously been envisaged (Baker et al, 2014).…”

“…In fact, the modified timecourse retains an element of leakage from 2 . Only the magnitude of that leakage is altered, in such a way as to ensure orthogonality between ̂1 M and ̂2 (Brookes et al, 2014). Second, as noted above, the method also means the removal of true zero-phase connections; this is significant, particularly given that invasive recordings show significant genuine zero-phase-lag effects in the brain (Singer, 1999).…”

“…However the link between neural oscillations and co-ordination of brain activity led a number of groups to hypothesise that neural oscillations are an intrinsic mode of electrophysiological coupling between regions (Schnitzler and Gross, 2005;Schoffelen and Gross, 2009;Engel et al, 2013). Indeed, MEG based assessment of functional connectivity has been achieved by a number of groups, based upon measurement of oscillations (Tass et al, 1998;Ioannides et al, 2000;Gross et al, 2001;Gross et al, 2002;Jerbi et al, 2007;Gow et al, 2008;Brookes et al, 2011b;Hipp et al, 2012;Brookes et al, 2012a;Luckhoo et al, 2012;Marzetti et al, 2013;Tewarie et al, 2013;Baker et al, 2014;O'Neill et al, 2015). The high information content of MEG signals means that functional connectivity can be derived in many different ways (see Scholvinck et al, 2013 for a review) and whilst a number of types of coupling have become prominent, two in particular have become popular.…”

Quantifying the performance of MEG source reconstruction using resting state data

“…In order to parcellate the data into self-similar periods that capture the resting state network 136 transitions (100-200 ms), a Hidden Markov Model (HMM) was used that could identify the rapid 137 formation and dissolution of recurring resting state networks. With this, a 'statepath' was estimated 138 for each 10 minute resting state block, which tracks the fine spatiotemporal dynamics and allocates 139 each point in time to one of eight dominant network states (Baker et al, 2014). For this statepath 140 determination, a copy of each subject data was dimensionally reduced using principle component 141 not peer-reviewed) is the author/funder.…”

Disruption of Neurosynaptic Physiology and Neuron Network Dysfunction in Brain Disorders: An Environmental and Occupational Health Perspective

“…Decreased clustering coefficient indicates loss of local connections. The alzheimer's patients show relative lower connectivity in parietal and occipital but higher in the frontal cortical area [103]. Loss of functional connectivity in alzheimer's dementia correlates both with structural changes as well as cognitive impairements that might improve the prediction of progression from MCI (Minimal Cognition Impairement) to AD [104,105].…”