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

Concurrent mapping of brain ontogeny and phylogeny within a common connectivity space

Developmental and evolutionary effects on brain organisation are complex, yet linked, as evidenced by the striking correspondence in cortical expansion changes. However, it is still not possible to study concurrently the ontogeny and phylogeny of cortical areal connections, which is arguably more relevant to brain function than allometric changes. Here, we propose a novel framework that allows the integration of connectivity maps from humans (adults and neonates) and non-human primates (macaques) onto a common space. We use white matter bundles to anchor the definition of the common space and employ the uniqueness of the areal connection patterns to these bundles to probe areal specialisation. This enables us to quantitatively study divergences and similarities in cortical connectivity over both evolutionary and developmental scales. It further allows us to map brain maturation trajectories, including the effect of premature birth, and to translate cortical atlases between diverse brains

Microstructural alterations in association tracts and language abilities in schoolchildren born very preterm and with poor fetal growth

Background Prematurity and perinatal risk factors may influence white matter microstructure. In turn, these maturational changes may influence language development in this high-risk population of children.Objective To evaluate differences in the microstructure of association tracts between preterm and term children and between preterm children with appropriate growth and those with fetal growth restriction and to study whether the diffusion tensor metrics of these tracts correlate with language abilities in schoolchildren with no severe neurological impairment.Materials and methods This study prospectively followed 56 very preterm children (mean gestational age: 28.7 weeks) and 21 age- and gender-matched term children who underwent diffusion tensor imaging at a mean age of 9 years. We used automated probabilistic tractography and measured fractional anisotropy in seven bilateral association tracts known to belong to the white matter language network. Both groups participated in language assessment using five standardised tests at the same age.Results Preterm children had lower fractional anisotropy in the right superior longitudinal fasciculus 1 compared to term children (P P (P Conclusion There were some microstructural differences in language-related tracts between preterm and term children and between preterm children with appropriate and those with restricted fetal growth. Children with better language abilities had a higher fractional anisotropy in distinct white matter tracts.</p

Diffusion tensor imaging metrics associated with future disability in multiple sclerosis

The relationship between brain diffusion microstructural changes and disability in multiple sclerosis (MS) remains poorly understood. We aimed to explore the predictive value of microstructural properties in white (WM) and grey matter (GM), and identify areas associated with mid-term disability in MS patients. We studied 185 patients (71% female; 86% RRMS) with the Expanded Disability Status Scale (EDSS), timed 25-foot walk (T25FW), nine-hole peg test (9HPT), and Symbol Digit Modalities Test (SDMT) at two time-points. We used Lasso regression to analyse the predictive value of baseline WM fractional anisotropy and GM mean diffusivity, and to identify areas related to each outcome at 4.1 years follow-up. Motor performance was associated with WM (T25FW: RMSE = 0.524, R2 = 0.304; 9HPT dominant hand: RMSE = 0.662, R2 = 0.062; 9HPT non-dominant hand: RMSE = 0.649, R2 = 0.139), and SDMT with GM diffusion metrics (RMSE = 0.772, R2 = 0.186). Cingulum, longitudinal fasciculus, optic radiation, forceps minor and frontal aslant were the WM tracts most closely linked to motor dysfunction, and temporal and frontal cortex were relevant for cognition. Regional specificity related to clinical outcomes provide valuable information that can be used to develop more accurate predictive models that could improve therapeutic strategies

Lesion covariance networks reveal proposed origins and pathways of diffuse gliomas.

Diffuse gliomas have been hypothesized to originate from neural stem cells in the subventricular zone and develop along previously healthy brain networks. Here, we evaluated these hypotheses by mapping independent sources of glioma localization and determining their relationships with neurogenic niches, genetic markers and large-scale connectivity networks. By applying independent component analysis to lesion data from 242 adult patients with high- and low-grade glioma, we identified three lesion covariance networks, which reflect clusters of frequent glioma localization. Replicability of the lesion covariance networks was assessed in an independent sample of 168 glioma patients. We related the lesion covariance networks to important clinical variables, including tumour grade and patient survival, as well as genomic information such as molecular genetic subtype and bulk transcriptomic profiles. Finally, we systematically cross-correlated the lesion covariance networks with structural and functional connectivity networks derived from neuroimaging data of over 4000 healthy UK BioBank participants to uncover intrinsic brain networks that may that underlie tumour development. The three lesion covariance networks overlapped with the anterior, posterior and inferior horns of the lateral ventricles respectively, extending into the frontal, parietal and temporal cortices. These locations were independently replicated. The first lesion covariance network, which overlapped with the anterior horn, was associated with low-grade, isocitrate dehydrogenase -mutated/1p19q-codeleted tumours, as well as a neural transcriptomic signature and improved overall survival. Each lesion covariance network significantly coincided with multiple structural and functional connectivity networks, with the first bearing an especially strong relationship with brain connectivity, consistent with its neural transcriptomic profile. Finally, we identified subcortical, periventricular structures with functional connectivity patterns to the cortex that significantly matched each lesion covariance network. In conclusion, we demonstrated replicable patterns of glioma localization with clinical relevance and spatial correspondence with large-scale functional and structural connectivity networks. These results are consistent with prior reports of glioma growth along white matter pathways, as well as evidence for the coordination of glioma stem cell proliferation by neuronal activity. Our findings describe how the locations of gliomas relate to their proposed subventricular origins, suggesting a model wherein periventricular brain connectivity guides tumour development

Lesion covariance networks reveal proposed origins and pathways of diffuse gliomas.

Diffuse gliomas have been hypothesized to originate from neural stem cells in the subventricular zone and develop along previously healthy brain networks. Here, we evaluated these hypotheses by mapping independent sources of glioma localization and determining their relationships with neurogenic niches, genetic markers and large-scale connectivity networks. By applying independent component analysis to lesion data from 242 adult patients with high- and low-grade glioma, we identified three lesion covariance networks, which reflect clusters of frequent glioma localization. Replicability of the lesion covariance networks was assessed in an independent sample of 168 glioma patients. We related the lesion covariance networks to important clinical variables, including tumour grade and patient survival, as well as genomic information such as molecular genetic subtype and bulk transcriptomic profiles. Finally, we systematically cross-correlated the lesion covariance networks with structural and functional connectivity networks derived from neuroimaging data of over 4000 healthy UK BioBank participants to uncover intrinsic brain networks that may that underlie tumour development. The three lesion covariance networks overlapped with the anterior, posterior and inferior horns of the lateral ventricles respectively, extending into the frontal, parietal and temporal cortices. These locations were independently replicated. The first lesion covariance network, which overlapped with the anterior horn, was associated with low-grade, isocitrate dehydrogenase -mutated/1p19q-codeleted tumours, as well as a neural transcriptomic signature and improved overall survival. Each lesion covariance network significantly coincided with multiple structural and functional connectivity networks, with the first bearing an especially strong relationship with brain connectivity, consistent with its neural transcriptomic profile. Finally, we identified subcortical, periventricular structures with functional connectivity patterns to the cortex that significantly matched each lesion covariance network. In conclusion, we demonstrated replicable patterns of glioma localization with clinical relevance and spatial correspondence with large-scale functional and structural connectivity networks. These results are consistent with prior reports of glioma growth along white matter pathways, as well as evidence for the coordination of glioma stem cell proliferation by neuronal activity. Our findings describe how the locations of gliomas relate to their proposed subventricular origins, suggesting a model wherein periventricular brain connectivity guides tumour development