Abstract
IntroductionFunctional Electroencephalography (EEG) networks in infants have been proposed as useful biomarkers for developmental brain disorders. However, the reliability of these networks and their characteristics has not been established. We evaluated the reliability of these networks and their characteristics in 10‐month‐old infants.MethodsData were obtained during two EEG sessions 1 week apart and was subsequently analyzed at delta (0.5–3 Hz), theta (3–6 Hz), alpha1 (6–9 Hz), alpha2 (9–12 Hz), beta (12–25 Hz), and low gamma (25–45 Hz) frequency bands. Connectivity matrices were created by calculating the phase lag index between all channel pairs at given frequency bands. To determine the reliability of these connectivity matrices, intra‐class correlations were calculated of global connectivity, local connectivity, and several graph characteristics.ResultsComparing both sessions, global connectivity, as well as global graph characteristics (characteristic path length and average clustering coefficient) are highly reliable across multiple frequency bands; the alpha1 and theta band having the highest reliability in general. In contrast, local connectivity characteristics were less reliable across all frequency bands.ConclusionsWe conclude that global connectivity measures are highly reliable over sessions. Local connectivity measures show lower reliability over sessions. This research therefore underlines the possibility of these global network characteristics to be used both as biomarkers of neurodevelopmental disorders, but also as important factors explaining development of typical behavior.
Highlights
Functional Electroencephalography (EEG) networks in infants have been proposed as useful biomarkers for developmental brain disorders
We showed for the first time that infant functional brain network characteristics can be reliable, by determining the test‐retest reliability and the inter‐subject variability of infant func‐ tional EEG connectivity across a 1‐week period
The reliability of EEG networks can be assessed on three levels, which coincide with three steps of network analysis: The reliability of (a) the complete connectivity matrices, (b) global and local functional connectivity measures gathered from these matri‐ ces and, (c) graph characteristics gathered from the graphs created from these matrices
Summary
The brain is a complex network consisting of highly interconnected regions. During early childhood, these networks develop at a rapid pace. Small‐worldness is calculated as the ratio between the normalized clustering coefficient and the normal‐ ized path length All of these characteristics have been connected to several neurodevelopmental disorders, like ASD (Peters et al, 2013; Rudie et al, 2013; Tsiaras et al, 2011) and ADHD (Ahmadlou, Adeli, & Adeli, 2012). While these connectivity and graph measures show potential as biomarkers to detect atypical development, biomarkers are only useful if they have a low inter‐subject variability and a high test‐re‐ test reliability (Hardmeier et al, 2014). In this study, we set out to determine the test‐ retest reliability and inter‐subject variability for functional EEG network measures, created by task‐dependent continuous EEG in infants
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