Development of a novel robust measure for interhemispheric synchrony in the neonatal EEG: activation synchrony index (ASI).

摘要

The degree of interhemispheric synchrony in the neonatal EEG assessment refers to the co-occurrence of activity bouts during quiet sleep or burst suppression, and it has been widely considered as a key component in assessing background activity. However, no objective measures have been published for measuring it, and all conventionally used visual criteria suffer from significant ambiguities. Our present study aimed to develop such a quantitative measure of (a)synchrony, called activation synchrony index (ASI). We developed the ASI paradigm based on the testing of statistical independence between two quantized amplitude envelopes of wideband-filtered signals where higher frequencies had been pre-emphasized. The core parameter settings of ASI paradigm were defined using a smaller EEG dataset, and the final ASI paradigm was tested using a visually classified dataset of EEG records from 33 fullterm and 25 preterm babies, which showed varying grades of asynchrony. Our findings show that ASI could distinguish all EEG recordings with normal synchrony from those with modest or severe asynchrony at individual level, and there was a highly significant correlation (p<0.001) between ASI and the visually assessed grade of asynchrony. In addition, we showed that i) ASI is stable in recordings over several hours in duration, such as the typical neonatal brain monitoring, that ii) ASI values are sensitive to sleep stage, and that iii) they correlate with age in the preterm babies. Comparison of ASI to other three potential paradigms demonstrated a significant competitive advantage. Finally, ASI was found to be remarkably resistant to common artefacts as tested by adding significant level of real EEG artefacts from noisy recordings. An objective and reliable measure of (a)synchrony may open novel avenues for using ASI as a putative early functional biomarker in the neonatal brain, as well as for building proper automated classifiers of neonatal EEG background. Notably, the signature of synchrony of this kind, temporal coincidence of activity bouts, is a common feature in biological signals, suggesting that ASI may also hold promise as a useful paradigm for assessing temporal synchrony in other biosignals as well, such as muscle activities or movements.