Emergence of Stable Functional Networks in Long-Term Human Electroencephalography

Abstract

Functional connectivity networks have become a central focus in neuroscience as they reveal key higher dimensional features of normal and abnormal nervous system physiology. Functional networks reflect activity-based coupling between brain regions that may be constrained by relatively static anatomical connections, yet these networks appear to support tremendously dynamic behaviors. Within this growing field, the stability and temporal characteristics of functional connectivity brain networks have not been well characterized. We evaluated the temporal stability of spontaneous functional connectivity networks derived from multi-day scalp encephalogram (EEG) recordings in five healthy human subjects. Topological stability and graph characteristics of networks derived from averaged data epochs ranging from one second to multiple hours across different states of consciousness were compared. We show that although functional networks are highly variable on the order of seconds, stable network templates emerge after as little as ~100s of recording and persist across different states and frequency bands (albeit with slightly different characteristics in different states and frequencies). Within these network templates, the most common edges are markedly consistent, constituting a network “core”. Although average network topologies persist across time, measures of global network connectivity, density and clustering coefficient, are state- and frequency- specific, with sparsest but most highly clustered networks seen during sleep and in the gamma frequency band. These findings support the notion that a core functional organization underlies spontaneous cortical processing and may provide a reference template upon which unstable, transient, and rapidly adaptive long-range assemblies are overlaid in a frequency dependent manner.

EEG functional connectivity is partially predicted by underlying white matter connectivity

“…A minimum of two minutes (range 122–199 seconds) of artifact-free recording from each patient was used for analysis. This epoch size has been previously demonstrated to be sufficient to produce high similarity between inferred functional networks within patients across different states of consciousness (Chu et al, 2012). …”

“…We note that all of our reported results were qualitatively similar when using distance correction off, but the number of long distance structural connections identified was negligible without distance correction. Because proximal nodes are known to be highly connected in both structural and functional networks due to true anatomical and physiological connectivity as well as spatial bias in measurement techniques (Chu et al, 2012; Li et al, 2012), we report our results with distance correction on in order to highlight correlations between long distance structural and functional connections. Using the number of streamlines launched from the seed ROI and the number of voxels in the target ROI allows for calculation of a Connectivity Index (adapted from McNab et al, 2013): $$\text{CI}=false[\text{Mean}\#\text{of}\text{distance}\text{weighted}\text{streamlines}\text{that}\text{reach}\text{the}\text{target}\text{ROI}/\text{total}\#\text{of}\text{streamlines}\text{propagated}\text{from}\text{seed}\text{ROI}false].$$…”

Opportunities and methodological challenges in EEG and MEG resting state functional brain network research

“…Different epoch lengths and number of epochs are currently used in resting state functional connectivity studies, ranging from one second (Knyazeva et al , 2010, Chu et al , 2012) to a few minutes (Tahaei et al , 2012) or even a day (Kuhnert et al , 2010) for epoch length; and from one epoch (Ahmadlou et al , 2011) to over 100 epochs containing the entire EEG recording (Knyazeva et al , 2010). Previous studies have investigated epoch length in relation to connectivity stability (David et al , 2004, Honey et al , 2007, Chu et al , 2012 and showed that the length of epochs to obtain stable connectivity measures is highly dependent on the type of connectivity measure.…”

“…Previous studies have investigated epoch length in relation to connectivity stability (David et al , 2004, Honey et al , 2007, Chu et al , 2012 and showed that the length of epochs to obtain stable connectivity measures is highly dependent on the type of connectivity measure.…”

Beyond the Connectome: The Dynome

“…Indeed, the functional connectome (as described in a graph) is known to be dynamic (Bassett et al , 2011; Chu et al , 2012). However, it largely remains to be understood how the rich diversity of observed functional network dynamics are regulated.…”