Heterogeneous, temporally consistent, and plastic brain development after preterm birth
Poster No:
323
Submission Type:
Abstract Submission
Authors:
Melissa Thalhammer1, Jakob Seidlitz2, Antonia Neubauer3, Aurore Menegaux4, Benita Schmitz-Koep4, Maria Di Biase5, Julia Schulz6, Lena Dorfschmidt7, Richard Bethlehem8, Aaron Alexander-Bloch9, Christopher Adamson10, Gareth Ball11, Claus Zimmer12, Marcel Daamen13, Henning Boecker14, Peter Bartmann15, Dieter Wolke16, Dennis Hedderich1, Christian Sorg17
Institutions:
1Technical University of Munich, Munich, Bavaria, 2Children's Hospital of Philadelphia, Philadelphia, PA, 3Ludwig-Maximilians-Universität Munich, Munich, Bavaria, 4TUM University Hospital, Technical University of Munich, School of Medicine and Health, Munich, Bavaria, 5The University of Melbourne, Melbourne, VIC, 6TU Munich, Munich, Bavaria, 7The Children’s Hospital of Philadelphia, Pennsylvania, PA, 8Department of Psychology, University of Cambridge, Cambridge, Cambridge, 9University of Pennsylvania, Philadelphia, PA, 10Monash University, Clayton, Victoria, 11Murdoch Children's Research Institute, Melbourne, VIC, 12Institute for Neuroradiology, TUM University Hospital, Munich, Bavaria, 13University Hospital Bonn, Bonn, NRW, 14Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, NRW, 15Department of Neonatology, University Hospital Bonn, Bonn, NRW, 16Department of Psychology, University of Warwick, Warwick, CV4 7AL, 17Department of Psychiatry, Klinikum Rechts der Isar, Technische Universität München, Munich, Bavaria
First Author:
Co-Author(s):
Aurore Menegaux
TUM University Hospital, Technical University of Munich, School of Medicine and Health
Munich, Bavaria
TUM University Hospital, Technical University of Munich, School of Medicine and Health
Munich, Bavaria
Benita Schmitz-Koep
TUM University Hospital, Technical University of Munich, School of Medicine and Health
Munich, Bavaria
TUM University Hospital, Technical University of Munich, School of Medicine and Health
Munich, Bavaria
Henning Boecker
Department of Diagnostic and Interventional Radiology, University Hospital Bonn
Bonn, NRW
Department of Diagnostic and Interventional Radiology, University Hospital Bonn
Bonn, NRW
Christian Sorg
Department of Psychiatry, Klinikum Rechts der Isar, Technische Universität München
Munich, Bavaria
Department of Psychiatry, Klinikum Rechts der Isar, Technische Universität München
Munich, Bavaria
Introduction:
The neurodevelopmental trajectory following preterm birth presents a significant paradox: while group-level magnetic resonance imaging (MRI) studies focusing on average alterations in brain development indicate largely uniform aberrations (Inder et al., 2023), individuals show diverse neurocognitive outcomes suggesting heterogeneous development. To address this contradiction, we investigated heterogeneity of brain development after preterm birth and its underlying biological mechanisms of injury and plasticity across different developmental stages.
Methods:
We analyzed structural T1- or T2-weighted MRI data from three distinct cohorts: (i) neonates scanned at term-equivalent age (92 preterm, 375 full-term) from the developing Human Connectome Project (dHCP) (Hughes et al., 2017), (ii) children scanned longitudinally at ages 10 and 12 years (191 preterm, 5,762 full-term) from the Adolescent Brain Cognitive Development Study (ABCD) (Casey et al., 2018), and (iii) adults scanned at age 26 years (95 preterm, 107 full-term) with a subgroup scanned again at age 38 years (52 preterm, 53 full-term) from the ongoing Bavarian Longitudinal Study (BLS). Using a GAMLSS-based normative reference framework previously established based on 100,000 subjects (Bethlehem et al., 2022), we assessed individual brain abnormality patterns (IBAPs) of regional cortical thickness (CTh) and surface area (SA). Thereby, brain measurement values below the 5th or above the 95th percentile were designated as infra- or supranormal, respectively. Furthermore, we investigated the association between IBAPs and regional cellular distributions (Hawrylycz et al., 2015; Seidlitz et al., 2020), perinatal, and early social-environmental factors to study biological mechanisms behind developmental heterogeneity.
Results:
Our analysis revealed that brain development after preterm birth is highly heterogeneous in terms of both, the severity and the spatial patterning of deviations. Across all cohorts, no more than 27 % of preterm subjects shared an extranormal deviation in any region. Despite this heterogeneity, we found remarkable consistency in the extent, location, and cellular underpinnings of IBAPs throughout development: first, in both preterm neonates (r = -0.198, p = 0.059) and adults (r = 0.313, p = 0.002), earlier birth was associated with a higher number of extranormal regions. Second, locations of extranormal deviations remained largely consistent along development in longitudinal assessments. Third, in earlier preterm born adults, IBAPs showed significant associations with glial cell types, which have been shown to be involved in disease pathology in preterm neonates (Volpe, 2019). Lastly, a worse socio-economic status (r = -0.217, p = 0.033) and worse mother-infant relationship (r = -0.211, p = 0.044) during early development were linked to more severe CTh IBAPs in adulthood.
Conclusions:
These findings substantially extend our understanding of preterm neurodevelopment, revealing a complex landscape of individual variation with underlying commonalities between subjects. The temporal consistency of IBAPs as well as their plasticity to the child's early social environment suggests the potential for more targeted intervention strategies (Fig. 1). Our results indicate the potential value of integrating brain charts and neuroimaging with social interventions during development (Wolke et al., 2019).
·Conceptual transition from average dysmaturation outcome-based view to individual brain abnormality pattern centered model of prematurity
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Genetics:
Transcriptomics
Lifespan Development:
Lifespan Development Other
Modeling and Analysis Methods:
Other Methods 2
Keywords:
ADULTS
Data analysis
Development
Modeling
MRI
Pediatric Disorders
Preprint
STRUCTURAL MRI
Other - preterm birth, normative modeling
1|2Indicates the priority used for review
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2. Casey, B. J., et. al., (2018). The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Developmental Cognitive Neuroscience, 32, 43–54. https://doi.org/10.1016/j.dcn.2018.03.001
3. Hawrylycz, M., et al., (2015). Canonical genetic signatures of the adult human brain. Nature Neuroscience, 18(12), 1832–1844. https://doi.org/10.1038/nn.4171
4. Hughes, E. J., et al., (2017). A dedicated neonatal brain imaging system. Magnetic Resonance in Medicine, 78(2), 794–804. https://doi.org/10.1002/mrm.26462
5. Inder, T. E., et al., (2023). Defining the Neurologic Consequences of Preterm Birth. New England Journal of Medicine, 389(5), 441–453. https://doi.org/10.1056/NEJMra2303347
6. Seidlitz, J., et al., (2020). Transcriptomic and cellular decoding of regional brain vulnerability to neurogenetic disorders. Nature Communications, 11(1), 3358. https://doi.org/10.1038/s41467-020-17051-5
7. Volpe, J. J. (2019). Dysmaturation of Premature Brain: Importance, Cellular Mechanisms, and Potential Interventions. Pediatric Neurology, 95, 42–66. https://doi.org/10.1016/j.pediatrneurol.2019.02.016
8. Wolke, D., et al., (2019). The Life Course Consequences of Very Preterm Birth. Annual Review of Developmental Psychology, 1(1), 69–92. https://doi.org/10.1146/annurev-devpsych-121318-084804