Hierarchical Complexity of the Macro-Scale Neonatal Brain.

Manuel Blesa,Alan J Quigley,David Q Stoye,Javier Escudero,Gemma Sullivan,Keith M Smith,Michael J Thrippleton,Mark E Bastin,James P Boardman,Simon R Cox,Paola Galdi,Gillian J Lamb

doi:10.1093/cercor/bhaa345

Abstract

The human adult structural connectome has a rich nodal hierarchy, with highly diverse connectivity patterns aligned to the diverse range of functional specializations in the brain. The emergence of this hierarchical complexity in human development is unknown. Here, we substantiate the hierarchical tiers and hierarchical complexity of brain networks in the newborn period, assess correspondences with hierarchical complexity in adulthood, and investigate the effect of preterm birth, a leading cause of atypical brain development and later neurocognitive impairment, on hierarchical complexity. We report that neonatal and adult structural connectomes are both composed of distinct hierarchical tiers and that hierarchical complexity is greater in term born neonates than in preterms. This is due to diversity of connectivity patterns of regions within the intermediate tiers, which consist of regions that underlie sensorimotor processing and its integration with cognitive information. For neonates and adults, the highest tier (hub regions) is ordered, rather than complex, with more homogeneous connectivity patterns in structural hubs. This suggests that the brain develops first a more rigid structure in hub regions allowing for the development of greater and more diverse functional specialization in lower level regions, while connectivity underpinning this diversity is dysmature in infants born preterm.

Highlights

The integrity of brain development during pregnancy and the newborn period is critical for life-long cognitive function and brain health
We report that neonatal and adult structural connectomes are both composed of distinct hierarchical tiers and that hierarchical complexity is greater in term born neonates than in preterms
The Gaussian Mixture Modelling (GMM) models with best fit were identified by minimizing the Bayesian Information Criterion (BIC), which finds the best trade-off of high accuracy and low model complexity

Summary

Introduction

The integrity of brain development during pregnancy and the newborn period is critical for life-long cognitive function and brain health. Findings indicate similar characteristics as found ubiquitously in real-world networks, including local and global efficiency, high clustering coefficient and short characteristic path length (i.e. small worldness), and a strong rich club coefficient (Ball et al, 2014; Brown et al, 2014; Van Den Heuvel et al, 2015; Batalle et al, 2017; Lee et al, 2019) Often they reveal remarkable structural and functional architectural facsimiles between the newborn and adult brain (Ball et al, 2014; Telford et al, 2017; Stoecklein et al, 2019). Studying the degree hierarchy has provided a tractable signature of brain network architecture in the adult connectome: four hierarchical tiers broadly comprise different categories of functional processing—cognitive, sensorimotor, integrative, and memory and emotion (Smith et al, 2019)

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