Effect of perinatal adversity on structural connectivity of the developing brain

Globally, preterm birth (defined as birth at <37 weeks of gestation) affects around 11% of deliveries and it is closely associated with cerebral palsy, cognitive impairments and neuropsychiatric diseases in later life. Magnetic Resonance Imaging (MRI) has utility for measuring different properties of the brain during the lifespan. Specially, diffusion MRI has been used in the neonatal period to quantify the effect of preterm birth on white matter structure, which enables inference about brain development and injury. By combining information from both structural and diffusion MRI, is it possible to calculate structural connectivity of the brain. This involves calculating a model of the brain as a network to extract features of interest. The process starts by defining a series of nodes (anatomical regions) and edges (connections between two anatomical regions). Once the network is created, different types of analysis can be performed to find features of interest, thereby allowing group wise comparisons. The main frameworks/tools designed to construct the brain connectome have been developed and tested in the adult human brain. There are several differences between the adult and the neonatal brain: marked variation in head size and shape, maturational processes leading to changes in signal intensity profiles, relatively lower spatial resolution, and lower contrast between tissue classes in the T1 weighted image. All of these issues make the standard processes to construct the brain connectome very challenging to apply in the neonatal population. Several groups have studied the neonatal structural connectivity proposing several alternatives to overcome these limitations. The aim of this thesis was to optimise the different steps involved in connectome analysis for neonatal data. First, to provide accurate parcellation of the cortex a new atlas was created based on a control population of term infants; this was achieved by propagating the atlas from an adult atlas through intermediate childhood spatio-temporal atlases using image registration. After this the advanced anatomically-constrained tractography framework was adapted for the neonatal population, refined using software tools for skull-stripping, tissue segmentation and parcellation specially designed and tested for the neonatal brain. Finally, the method was used to test the effect of early nutrition, specifically breast milk exposure, on structural connectivity in preterm infants. We found that infants with higher exposure to breastmilk in the weeks after preterm birth had improved structural connectivity of developing networks and greater fractional anisotropy in major white matter fasciculi. These data also show that the benefits are dose dependent with higher exposure correlating with increased white matter connectivity. In conclusion, structural connectivity is a robust method to investigate the developing human brain. We propose an optimised framework for the neonatal brain, designed for our data and using tools developed for the neonatal brain, and apply it to test the effect of breastmilk exposure on preterm infants

White matter connectomes at birth accurately predict cognitive abilities at age 2

Cognitive ability is an important predictor of mental health outcomes that is influenced by neurodevelopment. Evidence suggests that the foundational wiring of the human brain is in place by birth, and that the white matter (WM) connectome supports developing brain function. It is unknown, however, how the WM connectome at birth supports emergent cognition. In this study, a deep learning model was trained using cross-validation to classify full-term infants (n = 75) as scoring above or below the median at age 2 using WM connectomes generated from diffusion weighted magnetic resonance images at birth. Results from this model were used to predict individual cognitive scores. We additionally identified WM connections important for classification. The model was also evaluated in a separate set of preterm infants (n = 37) scanned at term-age equivalent. Findings revealed that WM connectomes at birth predicted 2-year cognitive score group with high accuracy in both full-term (89.5%) and preterm (83.8%) infants. Scores predicted by the model were strongly correlated with actual scores (r = 0.98 for full-term and r = 0.96 for preterm). Connections within the frontal lobe, and between the frontal lobe and other brain areas were found to be important for classification. This work suggests that WM connectomes at birth can accurately predict a child's 2-year cognitive group and individual score in full-term and preterm infants. The WM connectome at birth appears to be a useful neuroimaging biomarker of subsequent cognitive development that deserves further study

Mapping connections in the neonatal brain with magnetic resonance imaging

The neonatal brain undergoes rapid development after birth, including the growth and maturation of the white matter fibre bundles that connect brain regions. Diffusion MRI (dMRI) is a unique tool for mapping these bundles in vivo, providing insight into factors that impact the development of white matter and how its maturation influences other developmental processes. However, most studies of neonatal white matter do not use specialised analysis tools, instead using tools that have been developed for the adult brain. However, the neonatal brain is not simply a small adult brain, as differences in geometry and tissue decomposition cause considerable differences in dMRI contrast. In this thesis, methods are developed to map white matter connections during this early stage of neurodevelopment. First, two contrasting approaches are explored: ROI-constrained protocols for mapping individual tracts, and the generation of whole-brain connectomes that capture the developing brain's full connectivity profile. The impact of the gyral bias, a methodological confound of tractography, is quantified and compared with the equivalent measurements for adult data. These connectomes form the basis for a novel, data-driven framework, in which they are decomposed into white matter bundles and their corresponding grey matter terminations. Independent component analysis and non-negative matrix factorisation are compared for the decomposition, and are evaluated against in-silico simulations. Data-driven components of dMRI tractography data are compared with manual tractography, and networks obtained from resting-state functional MRI. The framework is further developed to provide corresponding components between groups and individuals. The data-driven components are used to generate cortical parcellations, which are stable across subjects. Finally, some future applications are outlined that extend the use of these methods beyond the context of neonatal imaging, in order to bridge the gap between functional and structural analysis paradigms, and to chart the development of white matter throughout the lifespan and across species

Mapping connections in the neonatal brain with magnetic resonance imaging

The neonatal brain undergoes rapid development after birth, including the growth and maturation of the white matter fibre bundles that connect brain regions. Diffusion MRI (dMRI) is a unique tool for mapping these bundles in vivo, providing insight into factors that impact the development of white matter and how its maturation influences other developmental processes. However, most studies of neonatal white matter do not use specialised analysis tools, instead using tools that have been developed for the adult brain. However, the neonatal brain is not simply a small adult brain, as differences in geometry and tissue decomposition cause considerable differences in dMRI contrast. In this thesis, methods are developed to map white matter connections during this early stage of neurodevelopment. First, two contrasting approaches are explored: ROI-constrained protocols for mapping individual tracts, and the generation of whole-brain connectomes that capture the developing brain's full connectivity profile. The impact of the gyral bias, a methodological confound of tractography, is quantified and compared with the equivalent measurements for adult data. These connectomes form the basis for a novel, data-driven framework, in which they are decomposed into white matter bundles and their corresponding grey matter terminations. Independent component analysis and non-negative matrix factorisation are compared for the decomposition, and are evaluated against in-silico simulations. Data-driven components of dMRI tractography data are compared with manual tractography, and networks obtained from resting-state functional MRI. The framework is further developed to provide corresponding components between groups and individuals. The data-driven components are used to generate cortical parcellations, which are stable across subjects. Finally, some future applications are outlined that extend the use of these methods beyond the context of neonatal imaging, in order to bridge the gap between functional and structural analysis paradigms, and to chart the development of white matter throughout the lifespan and across species

The Examination of White Matter Microstructure, Autism Traits, and Social Cognitive Abilities in Neurotypical Adults

The purpose of this study was to examine the relationships among mentalizing abilities, self-reported autism traits, and two white matter tracts, uncinate fasciculus (UF) and inferior longitudinal fasciculus (ILF), in neurotypical adults. UF and ILF were hypothesized to connect brain regions implicated in a neuroanatomical model of mentalizing. Data were available for 24 neurotypical adults (mean age = 21.92 (4.72) years; 15 women). Tract-based spatial statistics (TBSS) was used to conduct voxelwise cross-participant comparisons of fractional anisotropy (FA) values in UF and ILF as predicted by mentalizing abilities and self-reported autism traits. Self-reported autism traits were positively related to FA values in left ILF. Results suggest that microstructural differences in left ILF are specifically involved in the expression of subclinical autism traits in neurotypical individuals

Examining the development of brain structure in utero with fetal MRI, acquired as part of the Developing Human Connectome Project

The human brain is an incredibly complex organ, and the study of it traverses many scales across space and time. The development of the brain is a protracted process that begins embryonically but continues into adulthood. Although neural circuits have the capacity to adapt and are modulated throughout life, the major structural foundations are laid in utero during the fetal period, through a series of rapid but precisely timed, dynamic processes. These include neuronal proliferation, migration, differentiation, axonal pathfinding, and myelination, to name a few. The fetal origins of disease hypothesis proposed that a variety of non-communicable diseases emerging in childhood and adulthood could be traced back to a series of risk factors effecting neurodevelopment in utero (Barker 1995). Since this publication, many studies have shown that the structural scaffolding of the brain is vulnerable to external environmental influences and the perinatal developmental window is a crucial determinant of neurological health later in life. However, there remain many fundamental gaps in our understanding of it. The study of human brain development is riddled with biophysical, ethical, and technical challenges. The Developing Human Connectome Project (dHCP) was designed to tackle these specific challenges and produce high quality open-access perinatal MRI data, to enable researchers to investigate normal and abnormal neurodevelopment (Edwards et al., 2022). This thesis will focus on investigating the diffusion-weighted and anatomical (T2) imaging data acquired in the fetal period, between the second to third trimester (22 – 37 gestational weeks). The limitations of fetal MR data are ill-defined due to a lack of literature and therefore this thesis aims to explore the data through a series of critical and strategic analysis approaches that are mindful of the biophysical challenges associated with fetal imaging. A variety of analysis approaches are optimised to quantify structural brain development in utero, exploring avenues to relate the changes in MR signal to possible neurobiological correlates. In doing so, the work in this thesis aims to improve mechanistic understanding about how the human brain develops in utero, providing the clinical and medical imaging community with a normative reference point. To this aim, this thesis investigates fetal neurodevelopment with advanced in utero MRI methods, with a particular emphasis on diffusion MRI. Initially, the first chapter outlines a descriptive, average trajectory of diffusion metrics in different white matter fiber bundles across the second to third trimester. This work identified unique polynomial trajectories in diffusion metrics that characterise white matter development (Wilson et al., 2021). Guided by previous literature on the sensitivity of DWI to cellular processes, I formulated a hypothesis about the biophysical correlates of diffusion signal components that might underpin this trend in transitioning microstructure. This hypothesis accounted for the high sensitivity of the diffusion signal to a multitude of simultaneously occurring processes, such as the dissipating radial glial scaffold, commencement of pre-myelination and arborization of dendritic trees. In the next chapter, the methods were adapted to address this hypothesis by introducing another dimension, and charting changes in diffusion properties along developing fiber pathways. With this approach it was possible to identify compartment-specific microstructural maturation, refining the spatial and temporal specificity (Wilson et al., 2023). The results reveal that the dynamic fluctuations in the components of the diffusion signal correlate with observations from previous histological work. Overall, this work allowed me to consolidate my interpretation of the changing diffusion signal from the first chapter. It also serves to improve understanding about how diffusion signal properties are affected by processes in transient compartments of the fetal brain. The third chapter of this thesis addresses the hypothesis that cortical gyrification is influenced by both underlying fiber connectivity and cytoarchitecture. Using the same fetal imaging dataset, I analyse the tissue microstructural change underlying the formation of cortical folds. I investigate correlations between macrostructural surface features (curvature, sulcal depth) and tissue microstructural measures (diffusion tensor metrics, and multi-shell multi-tissue decomposition) in the subplate and cortical plate across gestational age, exploring this relationship both at the population level and within subjects. This study provides empirical evidence to support the hypotheses that microstructural properties in the subplate and cortical plate are altered with the development of sulci. The final chapter explores the data without anatomical priors, using a data-driven method to extract components that represent coordinated structural maturation. This analysis aims to examine if brain regions with coherent patterns of growth over the fetal period converge on neonatal functional networks. I extract spatially independent features from the anatomical imaging data and quantify the spatial overlap with pre-defined neonatal resting state networks. I hypothesised that coherent spatial patterns of anatomical development over the fetal period would map onto the functional networks observed in the neonatal period. Overall, this thesis provides new insight about the developmental contrast over the second to third trimester of human development, and the biophysical correlates affecting T2 and diffusion MR signal. The results highlight the utility of fetal MRI to research critical mechanisms of structural brain maturation in utero, including white matter development and cortical gyrification, bridging scales from neurobiological processes to whole brain macrostructure. their gendered constructions relating to women

Characterisation of the optic radiations in children in health and disease

The normal and abnormal development of the optic radiations through childhood was examined in terms of their anatomical development, using MRI tractography, and their functional development, using visual evoked potentials (VEPs). Neurosurgical applications of these imaging techniques were assessed. Control cohorts of 74 children and 13 adults were recruited from Great Ormond Street Hospital. The anatomical development of the optic radiations in children from birth was described using tractography. A novel method to improve tractography analysis using VEP data was developed. VEP-enhanced tractography showed a more defined optic radiation in the gathering of the visual cortex, which caused a significant reduction in the mean FA in the adult cohort. Paediatric patients diagnosed with optic nerve hypoplasia (ONH) were recruited and 23 were compared with a matched control cohort using tractography. ONH patients presented reduced mean FA in the left optic radiation. TBSS analysis of the DTI scans showed that white matter FA was also lower in other areas of the brain outside of the visual system. Two paediatric seizure patient cohorts were recruited: 21 patients with a single episode of prolonged febrile convulsions and 20 regular users of anti-epileptic medicines. Both cohorts were compared with matched control cohorts using DTI tractography. The anti-epileptic user cohort presented lower mean FA at the front of both optic radiations, but the prolonged febrile convulsions cohort had no statistically-significant differences in mean FA, compared to controls. Two brain tumour case studies demonstrated that tractography is a valuable surgical tool in complicated paediatric neurosurgical cases where detailed description of white matter tracts can improve the surgical outcome and assist with counselling patients. Two hydrocephalus case studies demonstrated that VEP-enhanced tractography offers a novel method to identify white matter tracts in cases where conventional imaging techniques provide very limited information due to highly-distorted anatomies