RT Journal Article
SR Electronic
T1 Network dynamics in the healthy and epileptic developing brain
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 133488
DO 10.1101/133488
A1 RE Rosch
A1 T Baldeweg
A1 F Moeller
A1 G Baier
YR 2017
UL http://biorxiv.org/content/early/2017/05/02/133488.abstract
AB Electroencephalography (EEG) allows recording of cortical activity at high temporal resolution. EEG recordings can be summarised along different dimensions using network-level quantitative measures, e.g. channel-to-channel correlation, or band power distributions across channels. These reveal network patterns that unfold over a range of different time scales 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 can be distinguished based on a small set of the novel quantitative measures introduced here, which describe dynamic network state switching. Quantitative measures related to the smoothness 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 future inform the clinical use of quantitative EEG for diagnosis.