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Distinct effects of prematurity on MRI metrics of brain functional connectivity, activity, and structure: Univariate
Antonio M Chiarelli1, Carlo Sestieri1, Riccardo Navarra1
1Department of Neuroscience, Imaging, and Clinical Sciences, University G. D'Annunzio of Chieti-Pescara; Institute for Advanced Biomedical Technologies, Chieti, Italy.
Insights
Premature birth alters brain development, affecting functional connectivity and regional volumes. Machine learning analysis revealed these changes, offering potential for early risk identification in infants.
Area of Science:
- Neuroscience
- Developmental Biology
- Medical Imaging
Background:
- Premature birth significantly impacts infant brain development during a critical maturation period.
- Understanding these effects is crucial for predicting long-term neurodevelopmental outcomes.
Purpose of the Study:
- To investigate the effects of prematurity on brain structure and function using advanced MRI techniques.
- To compare univariate and multivariate analytical approaches in detecting prematurity-related brain alterations.
Main Methods:
- Utilized blood-oxygen-level dependent (BOLD) and anatomical MRI at 40 weeks postmenstrual age in 88 preterm infants.
- Extracted resting-state functional connectivity, regional activity (fALFF), and regional volumes from 90 brain regions.
- Employed univariate and machine learning (multivariate) analyses to assess associations with gestational age at birth.
Main Results:
- Prematurity was linked to bidirectional changes in functional connectivity and regional brain volumes, with less pronounced effects on regional activity (fALFF).
- Functional connectivity alterations were diffuse and primarily cortical, while volume and fALFF changes were more focal and subcortical.
- Multivariate analysis demonstrated higher sensitivity in detecting complex prematurity-associated brain patterns compared to univariate methods.
Conclusions:
- Prematurity induces widespread alterations in brain connectivity and focal changes in structure.
- Machine learning approaches show promise for identifying preterm infants at risk for adverse neurodevelopmental outcomes.
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-oxygen-level 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.
