Neural complexity in preterm infants is predicted by developmental variables

Neural complexity, measured as the entropy of noninvasively recorded electrophysiological signals, evolves with age in early infancy, differentiates between typical and atypical development, and likely serves as a surrogate marker of brain maturation. However, the reason for this evolution of neural entropy in early infant development remains unclear. To understand this evolution, we measured the proportion of time that the infant brain spent in a bursting pattern of activity and related this activity pattern to the neural complexity (i.e., entropy or entropy rate). Additionally, we sought to predict neural complexity using each infant’s gestational age and to replicate sex-related complexity differences previously reported in age-equivalent fetuses. Four distinct complexity estimator algorithms – Lempel-Ziv (LZ) complexity, multiscale entropy (MSE), complexity via state-space entropy rate (CSER), and context tree weighting (CTW) – were applied to 8-channel infant electroencephalogram (EEG) recordings in 28 preterm infants (27–34 weeks gestational age). To explore factors influencing signal complexity, we modeled relationships between complexity estimates, on the one hand, and spontaneous activity transients, gestational age, and sex, on the other hand. We calculated channel-averages for each complexity estimate separately, as derived either from entire EEG recordings or separately from burst and interburst periods. Our results suggest that increased EEG signal continuity with maturation may drive increases in neural complexity as quiescent periods subside. Additionally, our results largely recapitulate previous findings linking neural complexity to biological sex in third-trimester fetuses. We also observed unexpected differences between entropy rate results obtained using CSER (a newer algorithm) and older algorithms. These findings support further research into neural complexity as a potential predictor of clinical outcomes in infants at high risk for neurodevelopmental disorders.Author summary: There are currently two languages for describing the evolution of neural dynamics in the first weeks of human life: the qualitative language of tracé discontinu – electrophysiological patterns of bursting and quiescent activity – and the quantitative language of signal entropy, or the number of ways in which discrete states of a neural signal can be arranged. Here, we attempt to unify these two languages by measuring bursting activity in terms of spontaneous activity transients and using these measurements, alongside other developmental variables, to predict the neural entropy or entropy rate (collectively referred to as complexity) in premature infants. A further goal of our analysis was to understand why neural complexity changes week-by-week in the human infant brain. Our results suggest that this change is driven by qualitative changes in infant neural activity, which grows more continuous with age. Consistent with previous work, we also uncovered associations between biological sex and neural complexity in the earliest weeks of life. By revealing the developmental drivers of neural complexity in premature infants, we not only provide a useful context for understanding the evolution of infant neural complexity but, also, we lay the groundwork for future efforts toward practical, predictive biomarkers of developmental outcomes rooted in neural complexity features.