Distinct effects of prematurity on MRI metrics of brain functional connectivity, activity, and structure: Univariate and multivariate analyses

Abstract

Premature birth affects the developmental trajectory of the brain during a period of intense maturation with possible lifelong consequences. To better understand the effect of prematurity on brain structure and function, we performed blood-oxygenlevel dependent (BOLD) and anatomical magnetic resonance imaging (MRI) at 40 weeks of postmenstrual age on 88 newborns with variable gestational age (GA) at birth and no evident radiological alterations. We extracted measures of resting-state functional connectivity and activity in a set of 90 cortical and subcortical brain regions through the evaluation of BOLD correlations between regions and of fractional amplitude of low-frequency fluctuation (fALFF) within regions, respectively. Anatomical information was acquired through the assessment of regional volumes. We performed univariate analyses on each metric to examine the association with GA at birth, the spatial distribution of the effects, and the consistency across metrics. Moreover, a data-driven multivariate analysis (i.e., Machine Learning) framework exploited the high dimensionality of the data to assess the sensitivity of each metric to the effect of premature birth. Prematurity was associated with bidirectional alterations of functional connectivity and regional volume and, to a lesser extent, of fALFF. Notably, the effects of prematurity on functional connectivity were spatially diffuse, mainly within cortical regions, whereas effects on regional volume and fALFF were more focal, involving subcortical structures. While the two analytical approaches delivered consistent results, the multivariate analysis was more sensitive in capturing the complex pattern of prematurity effects. Future studies might apply multivariate frameworks to identify premature infants at risk of a negative neurodevelopmental outcome.

Interaction of the salience network, ventral attention network, dorsal attention network and default mode network in neonates and early development of the bottom-up attention system

“…Recent studies adopting machine learning methods, have shown that the functional connectivity data (expressed by different metrics) and volume data are able to predict prematurity both in cohorts of newborns (Smyser et al 2016;G et al 2016;Chiarelli et al 2021) and adults (Shang et al 2019) and to classify age in infants aged between 6 and 12 months (Pruett et al 2015).These studies support the view that a functional architecture of the brain exists before birth and constantly evolves, especially during the rst year of life. Changes in functional connectivity of prematurity also manifest in adults, which supports the hypothesis that neurocognitive disorders associated with preterm birth might represent a disease of brain connectivity (J et al 2011).…”

“…In a recent study by Chiarelli et al (Chiarelli et al 2021) the authors examined the effect of prematurity on measures of rs-FC, resting state functional connectivity nodal strength (rs-FCNS), fractional amplitude of low frequency uctuations (fALFF) and regional volume in 90 ROIs covering the whole brain by performing region-based univariate analysis of each metric to explore the association with GA at birth and the spatial consistency across metrics. To this aim they implemented a Machine learning framework using partial least square regression (PLS).…”

“…We analyzed the same data-set of the recent study from Chiarelli et al (Chiarelli et al 2021) and adopted the same Machine Learning multi-variate data-driven framework using partial least square regression (PLS), which allows to consider all the connections within the predicting network at once without any apriori assumptions. However, the goal of our study was fairly different from that of Chiarelli et al.…”

“…Our study was based on the same data-set of the recent study from Chiarelli et al (Chiarelli et al 2021) and adopted the same Machine Learning multi-variate data-driven framework which allows to consider all the connections within the predicting network at once without any a-priori assumptions. However, we focused on pre-selected higher order networks rather than on exploring the effect on prematurity of different functional connectivity metrics extracted from a set of regions of interest (ROIs) covering the whole brain (Shi et al 2011).…”

Multifaceted Atlases of the Human Brain in its Infancy

“…Atlases are commonly used to spatially normalize MRI data for quantification of anatomical volumes and investigation of structural/functional connectivity and cortical macrostructure/ microstructure [45][46][47][48][49] . However, existing infant atlases (Supplementary Table 5 and Supplementary Fig.…”