Development of Microstructural and Morphological Cortical Profiles in the Neonatal Brain

Abstract

Interruptions to neurodevelopment during the perinatal period may have long-lasting consequences. However, to be able to investigate deviations in the foundation of proper connectivity and functional circuits, we need a measure of how this architecture evolves in the typically developing brain. To this end, in a cohort of 241 term-born infants, we used magnetic resonance imaging to estimate cortical profiles based on morphometry and microstructure over the perinatal period (37-44 weeks postmenstrual age, PMA). Using the covariance of these profiles as a measure of inter-areal network similarity (morphometric similarity networks; MSN), we clustered these networks into distinct modules. The resulting modules were consistent and symmetric, and corresponded to known functional distinctions, including sensory-motor, limbic, and association regions, and were spatially mapped onto known cytoarchitectonic tissue classes. Posterior regions became more morphometrically similar with increasing age, while peri-cingulate and medial temporal regions became more dissimilar. Network strength was associated with age: Within-network similarity increased over age suggesting emerging network distinction. These changes in cortical network architecture over an 8-week period are consistent with, and likely underpin, the highly dynamic processes occurring during this critical period. The resulting cortical profiles might provide normative reference to investigate atypical early brain development.

Keywords: developing brain; morphometric similarity networks; multimodal MRI; perinatal; structural covariance.

Figure 1

Pipeline for clustering MSNs in the neonatal brain: (a) Cortical regions are defined using Voronoi tessellation of the cortical surface. (b) Each individual region is characterized by an eight-feature vector including averaged normalized values of cortical thickness (CT), mean curvature (MC), myelin index (MI), surface area (SA), fractional anisotropy (FA), mean diffusivity (MD), neurite density index (NDI), and orientation dispersion index (ODI). (c) Each pair of regions is correlated using Pearson’s r, resulting in a subject-specific similarity matrix with size n regions × n regions, which are then averaged to create a group mean similarity matrix, and the resulting group matrix is clustered using affinity propagation algorithm to examine network modularity.

Figure 2

Association between single-feature maps (n = 150 parcels) and age at scan: Positive correlations are marked in red and negative correlations in blue. NDI, neurite density index; ODI, orientation dispersion index.

Figure 3

Age and sex associations with MSNs: (a) Sum of positive and negative correlations between internode edge-strength and age at scan. (b) Spearman’s correlation between age and mean nodal edge-strength. (c) Sum of stronger internode edges in males (orange) and in females (green). (d) Significant sex differences between mean nodal edge-strength.

Figure 4

Clustering solution for MSNs created using eight features: Affinity propagation clustering based on a fixed preference value (median similarity) is shown on the left and for a fixed number of clusters on the right.

Figure 5

The spatial overlap (Dice coefficients) between MSN clusters and von Economo tissue classes: Line thickness indicates the Dice coefficient per pair of related regions and a Dice of <0.2 is not shown.

Figure 6

Agreement between clustering solutions using all features with single- and multimeasure covariance: results shown for left hemisphere for ease of interpretation. Clustering solutions are ordered from left (most similar) to right (least similar). (a) Level of agreement between modules derived from a single-feature covariance matrix and the eight-feature MSN. (b) Level of agreement between clustering solution for seven-feature MSN (leave-one-out analysis) and eight-feature MSN. In all cases the normalized variation of information value (z) is shown below (lower is better). CT, cortical thickness; MC, mean curvature; MI, myelin index; SA, surface area; FA, fractional anisotropy; MD, mean diffusivity; NDI, neurite density index; ODI, orientation dispersion index.

Figure 7

Inter- and intramodular similarity changes with age at scan: association between clusters’ edge-strength and age. Width of line is indicative of the strength of significant association.