Early development of synchrony in cortical activations in the human

N Koolen, A Dereymaeker, O Räsänen, K Jansen, J Vervisch, V Matic, G Naulaers, M De Vos, S Van Huffel, S Vanhatalo, N Koolen, A Dereymaeker, O Räsänen, K Jansen, J Vervisch, V Matic, G Naulaers, M De Vos, S Van Huffel, S Vanhatalo

Abstract

Early intermittent cortical activity is thought to play a crucial role in the growth of neuronal network development, and large scale brain networks are known to provide the basis for higher brain functions. Yet, the early development of the large scale synchrony in cortical activations is unknown. Here, we tested the hypothesis that the early intermittent cortical activations seen in the human scalp EEG show a clear developmental course during the last trimester of pregnancy, the period of intensive growth of cortico-cortical connections. We recorded scalp EEG from altogether 22 premature infants at post-menstrual age between 30 and 44 weeks, and the early cortical synchrony was quantified using recently introduced activation synchrony index (ASI). The developmental correlations of ASI were computed for individual EEG signals as well as anatomically and mathematically defined spatial subgroups. We report two main findings. First, we observed a robust and statistically significant increase in ASI in all cortical areas. Second, there were significant spatial gradients in the synchrony in fronto-occipital and left-to-right directions. These findings provide evidence that early cortical activity is increasingly synchronized across the neocortex. The ASI-based metrics introduced in our work allow direct translational comparison to in vivo animal models, as well as hold promise for implementation as a functional developmental biomarker in future research on human neonates.

Keywords: biomarker; brain connectivity; brain monitoring; early development; neonatal EEG.

Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Figures

Fig. 1
Fig. 1
Comparison of ASI analysis settings with respect to developmental correlations. Left side graph shows correlation coefficient (r) between ASI and PMA in the same dataset when ASI is computed using different amount of data (x axis), different analysis windows (1 min vs 2.5 min), or different combinations of channels. A little increase in r-values is found when using longer EEG epochs. On the right side, PMA correlations are shown for 1 min and 2.5 min ASI windows. Both correlations are significant, however use of 2.5 min ASI windows gave clearly steeper developmental trends and higher correlation values.
Fig. 2
Fig. 2
Intraindividual ASI stability and its development. (A) Synchrony matrices derived from subsequent EEG epochs of 2.5 min including all 28 ASI values derived from the possible channel pair combinations, (B) temporal variability of global synchrony values defined as the iqr of 8 global synchrony values (from the subsequent synchrony matrices) (see also Fig.3A), (C) spatial variability derived from the 28 channel combinations as the iqr of each specific ASI over consecutive epochs, calculated for each individual without significant developmental trend (in function of PMA), (D) similar interquartile variability over all patients for each single channel pair combination.
Fig. 3
Fig. 3
Spatial ASI analysis and its development. (A) In addition to the temporal variability seen as the interquartile range of GS values in successive epochs, there was also an overall increase in GS values with increasing PMA, (B) graphs depicting the developmental change in the mean ASI of each EEG channel compared to the other 7 channels, which have all significant correlations, (C) graphs representing the developmental change in the mean ASI over the given spatial subgroup as schematically shown in the topoplots. Premature infants with early IVH grade III are shown in gray (for GS and interhemispheric synchrony). The right most plot depicts developmental change of the first component of principal component analysis (PCA). Significance of the correlation is depicted with an asterisk after correlation coefficient ‘r’. The value ‘a’ depicts the slope of linear regression computed for the given graph.
Fig. 4
Fig. 4
Developmental change of graph metrics, MST mean and algebraic connectivity, both of which showed a significant correlation with PMA.
Fig. 5
Fig. 5
ASI in bipolar derivations. (A) Developmental changes in ASI computed from bipolar derivations for both interhemispheric and intrahemispheric channel combinations. Note that the correlation is often not significant and its strength (r) is smaller as compared to monopolar derivations (Fig. 3). (B) Hemispheric and anterior–posterior comparisons reveal significant asymmetries. Comparison of frontal and posterior interhemispheric connections shows frontal dominance in 20 out of 22 cases, while the left side shows stronger ASI in 16 out of 22 cases.

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