Low-Dimensional Dynamics of Resting-State Cortical Activity

Abstract

Endogenous brain activity supports spontaneous human thought and shapes perception and behavior. Connectivity-based analyses of endogenous, or resting-state, functional magnetic resonance imaging (fMRI) data have revealed the existence of a small number of robust networks which have a rich spatial structure. Yet the temporal information within fMRI data is limited, motivating the complementary analysis of electrophysiological recordings such as electroencephalography (EEG). Here we provide a novel method based on multivariate time-frequency interdependence to reconstruct the principal resting-state network dynamics in human EEG data. The stability of network expression across subjects is assessed using resampling techniques. We report the presence of seven robust networks, with distinct topographic organizations and high frequency (∼ 5-45 Hz) fingerprints, nested within slow temporal sequences that build up and decay over several orders of magnitude. Interestingly, all seven networks are expressed concurrently during these slow dynamics, although there is a temporal asymmetry in the pattern of their formation and dissolution. These analyses uncover the complex temporal character of endogenous cortical fluctuations and, in particular, offer an opportunity to reconstruct the low dimensional linear subspace in which they unfold.

Intrinsic Coupling Modes in Source-Reconstructed Electroencephalography

“…These findings add to an increasing body of literature suggesting that neuronal interactions result in both in phase and amplitude coupling (Bayraktaroglu et al, 2013;Chawla et al, 1999Chawla et al, , 2000Daffertshofer and van Wijk, 2011;Mehrkanoon et al, 2014). On the other hand, amplitude and phase coupling are considered to have different causes and putative functions (Engel et al, 2013), and to operate at different time scales .…”

“…Similarly, envelope correlation of resting-state MEG revealed a broad range of carrier frequencies , with slight variations between cortical regions (Brookes et al, 2011;Hipp et al, 2012). These findings suggest that brain networks do not generally display pure rhythms within distinct frequency bands-mostly generated in restricted neuronal circuits-but a coalescence of rhythms (Mantini et al, 2007;Mehrkanoon et al, 2014). When comparing to connectivity with other voxels at the same physical distance, we found that bilateral coupling was significantly higher at specific frequencies, which was dependent on the cortical region.…”

“…PCA is a multivariate approach that decomposes the correlation matrix into orthogonal modes. In a previous study we used PCA to decompose time resolved connectivity matrix into the orthogonal subspace of resting-state connectivity (Mehrkanoon et al, 2014). PCA may also be applied to power envelopes to obtain a multivariate extension of the method proposed by (Hipp et al, 2012).…”

“…Computational models show that resting-state dynamics reflect the slow modulations of long-range synchronization of fast local neuronal activity (Breakspear et al, 2004;Deco et al, 2009;Freyer et al, 2011;Ghosh et al, 2008;Honey et al, 2007). Recent studies on resting-state dynamics have hence focused on resting-state electrophysiology-with its high temporal resolution-to capture the synchronization dynamics of these faster time scales (Britz et al, 2010;Brookes et al, 2011;He et al, 2008;Hipp et al, 2012;Mantini et al, 2007;Mehrkanoon et al, 2014;Siegel et al, 2012).…”

On spurious and real fluctuations of dynamic functional connectivity during rest

“…While this gives access to a rich amount of information, one remaining issue is how to combine scales into a concise and meaningful summarizing measure. Recent approaches in EEG analysis have employed a combination of windowed Fourier analysis and principal component analysis (PCA) for that purpose (Mehrkanoon et al, 2014).…”