Network dynamics in the healthy and epileptic developing brain

Abstract

Electroencephalography (EEG) allows recording of cortical activity at high temporal resolution. EEG recordings can be summarized along different dimensions using network-level quantitative measures, such as channel-to-channel correlation, or band power distributions across channels. These reveal network patterns that unfold over a range of different timescales and can be tracked dynamically. Here we describe the dynamics of network state transitions in EEG recordings of spontaneous brain activity in normally developing infants and infants with severe early infantile epileptic encephalopathies (n = 8, age: 1–8 months). We describe differences in measures of EEG dynamics derived from band power, and correlation-based summaries of network-wide brain activity. We further show that EEGs from different patient groups and controls may be distinguishable on a small set of the novel quantitative measures introduced here, which describe dynamic network state switching. Quantitative measures related to the sharpness of switching from one correlation pattern to another show the largest differences between groups. These findings reveal that the early epileptic encephalopathies are associated with characteristic dynamic features at the network level. Quantitative network-based analyses like the one presented here may in the future inform the clinical use of quantitative EEG for diagnosis.

Scalp high-frequency oscillation rates are higher in younger children

“…We perceive this change within the context of brain maturation, as previously shown for the classical cortical rhythms of sleep EEG in early brain development. 21 , 32 , 38 , 39 …”

Detecting sequences of system states in temporal networks

“…For those data, networks are correlational, so-called the functional networks, and are composed of brain regions of interest used as nodes and the correlation value (or its thresholded version) conventionally used as edges [59][60][61] . Chronnectome analysis has revealed, for example, different patterns in system state dynamics between patients and controls 57,62 . The present framework can be regarded as chronnectome for general temporal networks including non-correlational ones, with general graph distance measures.…”

Recurrence quantification analysis of dynamic brain networks

“…Common approaches include tracking certain network measures over time (Sizemore & Bassett, 2018), using hidden Markov models (Eavani et al, 2013;Sourty et al, 2016;Vidaurre et al, 2018), and considering dynamic networks as multilayer networks (De Domenico et al, 2013;Kivelä et al, 2014;Sizemore & Bassett, 2018). Other recent approaches have used distance matrices to evaluate dFNs from fMRI (Cabral et al, 2017) and dynamic correlation matrices from scalp EEG (Rosch, Baldeweg, Moeller, & Baier, 2018). Future work should compare these different methods to our framework to find which one better characterizes dFNs in epilepsy and other contexts, and assess whether these approaches complement each other.…”