Development of neonatal brain functional centrality and alterations associated with preterm birth

Abstract

Formation of the functional connectome in early life underpins future learning and behavior. However, our understanding of how the functional organization of brain regions into interconnected hubs (centrality) matures in the early postnatal period is limited, especially in response to factors associated with adverse neurodevelopmental outcomes such as preterm birth. We characterized voxel-wise functional centrality (weighted degree) in 366 neonates from the Developing Human Connectome Project. We tested the hypothesis that functional centrality matures with age at scan in term-born babies and is disrupted by preterm birth. Finally, we asked whether neonatal functional centrality predicts general neurodevelopmental outcomes at 18 months. We report an age-related increase in functional centrality predominantly within visual regions and a decrease within the motor and auditory regions in term-born infants. Preterm-born infants scanned at term equivalent age had higher functional centrality predominantly within visual regions and lower measures in motor regions. Functional centrality was not related to outcome at 18 months old. Thus, preterm birth appears to affect functional centrality in regions undergoing substantial development during the perinatal period. Our work raises the question of whether these alterations are adaptive or disruptive and whether they predict neurodevelopmental characteristics that are more subtle or emerge later in life.

Parsing brain-behavior heterogeneity in very preterm born children using integrated similarity networks

“…Resting-state functional MRI (rs-fMRI) data were pre-processed as in our previous work; [55] for more details see Supplemental Information. Functional connectivity was evaluated using a measure of weighted degree centrality at the voxel-level (i.e., the sum of the correlations between the time-series of each voxel and all other voxels within a gray matter mask of the brain) [31,56]. Edges with a correlation coefficient below a threshold of 0.2 were excluded and the degree centrality values for each voxel in the gray matter mask were z-scored and used in subsequent between-subgroup analyses.…”

“…Edges with a correlation coefficient below a threshold of 0.2 were excluded and the degree centrality values for each voxel in the gray matter mask were z-scored and used in subsequent between-subgroup analyses. Whilst other functional network measures are available (i.e., participation coefficient and within module-z [57], we opted to study degree centrality as we recently showed this to be disrupted in preterm born neonates [31]. Furthermore, degree centrality is a good voxel-wise summary measure of connectivity strength, which is reliable and correlates with relevant phenotypes, such as age and sex [58].…”

“…Differences between latent subgroups of VPT children and infants have been previously studied in relation to qualitative measures of brain abnormalities and/or high grade brain injury based on neonatal Magnetic Resonance Imaging (MRI), as well as quantitative differences in brain tissue volumes [8,27,28]. However, it remains to be explored whether distinct multidimensional subgroups of VPT children could also be characterized by localized differences in early brain development using advanced quantitative measures of brain structure and function, such as log-Jacobians, tract based spatial statistics and degree centrality, which have previously been used in neonatal samples [29][30][31]. Conducting analyses at the whole-brain and voxel-wise level, allows for an enhanced spatial localization of potential structural and functional between-subgroup differences, thus extending previous research [8,27,28].…”

Exploring functional connectivity in clinical and data-driven groups of preterm and term adults

“…For each participant, rsFC matrices were calculated using Pearson’s correlations between pairs of all 374 regional time series. A threshold of 0.2 was used to eliminate weak correlations (i.e., weights of edges with r 2 0.2 were retained) and a Fisher Z-transformation was applied (Buckner et al, 2009; Fenn-Moltu et al, 2022; Zalesky et al, 2016).…”

Characterizing the temporal dynamics and maturation of resting-state activity: an EEG microstate study in preterm and full-term infants

“…However, the functional correlates of such anatomical alterations are still poorly understood despite the common impairments observed at the behavioral level in children born preterm. In the past decade, functional MRI (fMRI) has allowed identifying precursors of spatially organized functional brain networks in the resting-state activity of preterms, which show reduced maturation in terms of long-range connectivity compared to full-terms (Doria et al, 2010) and alterations of network-level properties (Fenn-Moltu et al, 2023) that persist until TEA (Gozdas et al, 2018) and childhood (Wehrle et al, 2018). Yet, understanding how these alterations affect the efficiency of brain networks for processing environmental information requires investigating the dynamic aspects of the brain activity that go beyond the temporal resolution of the Blood Oxygenation Level Dependent (BOLD) effect captured by fMRI.…”