Longitudinal measurement of the developing grey matter in preterm subjects using multi-modal MRI.

Preterm birth is a major public health concern, with the severity and occurrence of adverse outcome increasing with earlier delivery. Being born preterm disrupts a time of rapid brain development: in addition to volumetric growth, the cortex folds, myelination is occurring and there are changes on the cellular level. These neurological events have been imaged non-invasively using diffusion-weighted (DW) MRI. In this population, there has been a focus on examining diffusion in the white matter, but the grey matter is also critically important for neurological health. We acquired multi-shell high-resolution diffusion data on 12 infants born at ≤28weeks of gestational age at two time-points: once when stable after birth, and again at term-equivalent age. We used the Neurite Orientation Dispersion and Density Imaging model (NODDI) (Zhang et al., 2012) to analyse the changes in the cerebral cortex and the thalamus, both grey matter regions. We showed region-dependent changes in NODDI parameters over the preterm period, highlighting underlying changes specific to the microstructure. This work is the first time that NODDI parameters have been evaluated in both the cortical and the thalamic grey matter as a function of age in preterm infants, offering a unique insight into neuro-development in this at-risk population

Using compartment models of diffusion MRI to investigate the preterm brain

Preterm birth is the leading cause of neonatal mortality, with survivors experiencing motor, cognitive and other deficits at increased rates. In preterm infancy, the developing brain undergoes folding, myelination, and rapid cellular growth. Diffusion-Weighted Magnetic Resonance Imaging (DW MRI) is an imaging modality that allows noninvasive inference of cellular microstructure in living tissue, and its parameters reflect changes in brain tissue composition. In this thesis, we employ compartment models of DW MRI to investigate the microstructure in preterm-born subjects at different ages. Within infants, we have used the NODDI model to investigate longitudinal changes in neurite density and orientation dispersion within the white matter, cerebral cortex and thalamus, explaining known trends in diffusion tensor parameters with greater specificity. We then used a quantitative T2 sequence to develop and investigate a novel, multi-modal parameter known as the ‘g-ratio’. We have also investigated changing microstructural geometry within the cortex. Immediately after preterm birth, the highly-ordered underlying cellular structure makes diffusion in the cortex almost entirely radial. This undergoes a transition to a disordered and isotropic state over the first weeks of life, which we have used the DIAMOND model to quantify. This radiality decreases at a rate that depends on the cortical lobe. In a cohort of young adults born extremely preterm, we have quantified differences in brain microstructure compared to term-born controls. In preterm subjects, the brain structures are smaller than for controls, leading to increased partial volume in some regions of interest. We introduce a method to infer diffusion parameters in partial volume, even for regions which are smaller than the diffusion resolution. Overall, this thesis utilises and evaluates a variety of compartment models of DW MRI. By developing and applying principled and robust methodology, we present new insights into microstructure within the preterm-born brain

Longitudinal analysis of extreme prematurity: a neuroimage investigation of early brain development

Brain development is a complex process, and disruptions from its normal course may affect the later neurological outcome of an individual. Preterm infants are at higher risk of disability, since a substantial part of brain development happens outside the mother’s womb, making it vulnerable to a range of insults. Understanding the early brain development during the preterm period is of critical importance and magnetic resonance imaging (MRI) allows us to investigate this. Methodologically, this is a challenging task, as classical approaches of studying longitudinal development over this period do not cope with the large changes taking place. This thesis focuses on the development of tools to study the changes in cortical folding, shape of different brain structures and microstructural changes over the preterm period from longitudinal data of extremely preterm-born infants. It describes a tissue segmentation pipeline, optimised on a postmortem fetal dataset, and then focuses on finding longitudinal correspondences between the preterm and termequivalent brain regions and structures in extremely preterm-born infants using MRI. Three novel registration techniques are proposed for longitudinal registration of this challenging data. These are based on matching the spectral components associated with either the cortical surfaces, diffusion tensor images, or both. These allow us to quantify longitudinal changes in different brain regions and structures. We investigated changes in cortical folding of different lobes, microstructural changes and tracts in the white matter, cortical thickness and changes in cortical fractional anisotropy and mean diffusivity. We used cortical surface registration to look at shape differences between controls and extremely preterm-born young adults to gain an insight into the long-term impact of prematurity. This research may contribute to the development of early biomarkers for predicting the neurological outcome of preterm infants and illuminate our understanding of brain development during this crucial period

Myelination of Preterm Brain Networks at Adolescence

Prematurity and preterm stressors severely affect the development of infants born before 37 weeks of gestation, with increasing effects seen at earlier gestations. Although preterm mortality rates have declined due to the advances in neonatal care, disability rates, especially in middle-income settings, continue to grow. With the advances in MRI imaging technology, there has been a focus on safely imaging the preterm brain to better understand its development and discover the brain regions and networks affected by prematurity. Such studies aim to support interventions and improve the neurodevelopment of preterm infants and deliver accurate prognoses. Few studies, however, have focused on the fully developed brain of preterm born infants, especially in extremely preterm subjects. To assess the long-term effect of prematurity on the adult brain, myelin related biomarkers such as myelin water fraction and g-ratio are measured for a cohort of 19-year-old extremely preterm subjects. Using multi-modal imaging techniques that combine T2 relaxometry and neurite density information, the results show that specific regions of the brain associated with white matter injuries due to preterm birth, such as the Posterior Limb of the Internal Capsule and Corpus Callosum, are still less myelinated in adulthood. Such findings might imply reduced connectivity in the adult preterm brain and explain the poor cognitive outcome

Developmental synchrony of thalamocortical circuits in the neonatal brain

10.1016/j.neuroimage.2015.03.039Neuroimage116168-176GUSTO (Growing up towards Healthy Outcomes

Univariate and multivariate pattern analysis of preterm subjects: a multimodal neuroimaging study

Background: Widespread lasting functional connectivity (FC) and brain volume changes in cortices and subcortices after premature birth have been researched in recent studies. However, the relationship remains unclear between spontaneously slow blood oxygen dependent level (BOLD) fluctuations and gray matter volume (GMV) changes in specific brain areas, such as temporal insular cortices, and whether classification methods based on MRI could be successfully applied to the identification of preterm individuals. In this thesis I hypothesized that in prematurely born adults 1. Ongoing neural excitability and brain activity, as estimated by regional functional connectivity of resting state functional MRI (rs-fMRI) is accompanied with altered low-frequency fluctuations and neonatal complications; 2. Altered regional functional connectivity is connected with superimposed cerebral structural reductions; and 3. multivariate neuroanatomical and functional brain patterns could be treated as features to identify preterm subjects from term subjects individually. Methods: To investigate these hypotheses, the principal results of structural alterations were measured with voxel-based morphometry (VBM), while rs-fMRI outcomes were estimated with amplitude of low-frequency fluctuations (ALFF) in analysis with ninety-four very preterm/very low birth weight (VP/VLBW) and ninety-two full-term (FT) born young adults. Results: The results of the thesis support the hypotheses by showing that, in univariate results, first in VP/VLBW grownups, ALFF was decreased in the left lateral temporal cortices no matter with global signal regression, and this reduction was closely associated with neonatal complications and cognitive variables. Second overlapped brain regions were found between reduced ALFF and reduced brain volumes in the left temporal cortices, and positively associated with each other, demonstrating a potential relationship between VBM and ALFF in this brain area. In multimodal multivariate pattern recognition analysis (MVPA), the gray matter volume (GMV) classifier displayed a higher accuracy (80.7%) contrast with the ALFF classifier (77.4%). The late fusion of GMV and ALFF did not outperform single GMV modality classification by reaching 80.4% accuracy. Moderator analysis from both rs-fMRI and structural MRI (sMRI) uncovered that the neuro-prematurity performance was predominantly determined by neonatal complications. Conclusions: In conclusion, these outcomes exhibit the long term effects of premature labour on lateral temporal cortices, which changed in both ongoing BOLD fluctuations and decreased cerebral structural volumes. This thesis further provided evidence that multivariate pattern analysis such as support vector machine (SVM) may identify imaging-based biomarkers and reliably detect signatures of preterm birth

Univariate and multivariate pattern analysis of preterm subjects: a multimodal neuroimaging study

Background: Widespread lasting functional connectivity (FC) and brain volume changes in cortices and subcortices after premature birth have been researched in recent studies. However, the relationship remains unclear between spontaneously slow blood oxygen dependent level (BOLD) fluctuations and gray matter volume (GMV) changes in specific brain areas, such as temporal insular cortices, and whether classification methods based on MRI could be successfully applied to the identification of preterm individuals. In this thesis I hypothesized that in prematurely born adults 1. Ongoing neural excitability and brain activity, as estimated by regional functional connectivity of resting state functional MRI (rs-fMRI) is accompanied with altered low-frequency fluctuations and neonatal complications; 2. Altered regional functional connectivity is connected with superimposed cerebral structural reductions; and 3. multivariate neuroanatomical and functional brain patterns could be treated as features to identify preterm subjects from term subjects individually. Methods: To investigate these hypotheses, the principal results of structural alterations were measured with voxel-based morphometry (VBM), while rs-fMRI outcomes were estimated with amplitude of low-frequency fluctuations (ALFF) in analysis with ninety-four very preterm/very low birth weight (VP/VLBW) and ninety-two full-term (FT) born young adults. Results: The results of the thesis support the hypotheses by showing that, in univariate results, first in VP/VLBW grownups, ALFF was decreased in the left lateral temporal cortices no matter with global signal regression, and this reduction was closely associated with neonatal complications and cognitive variables. Second overlapped brain regions were found between reduced ALFF and reduced brain volumes in the left temporal cortices, and positively associated with each other, demonstrating a potential relationship between VBM and ALFF in this brain area. In multimodal multivariate pattern recognition analysis (MVPA), the gray matter volume (GMV) classifier displayed a higher accuracy (80.7%) contrast with the ALFF classifier (77.4%). The late fusion of GMV and ALFF did not outperform single GMV modality classification by reaching 80.4% accuracy. Moderator analysis from both rs-fMRI and structural MRI (sMRI) uncovered that the neuro-prematurity performance was predominantly determined by neonatal complications. Conclusions: In conclusion, these outcomes exhibit the long term effects of premature labour on lateral temporal cortices, which changed in both ongoing BOLD fluctuations and decreased cerebral structural volumes. This thesis further provided evidence that multivariate pattern analysis such as support vector machine (SVM) may identify imaging-based biomarkers and reliably detect signatures of preterm birth

Using neuro-cognitive modelling to link attention deficits to structural and functional brain changes

‘Visual attention’ is an emerging property of interconnected neural networks, in which the interconnections are biased to promote targets over distracting stimuli. It has been shown that efficiency of the attention system is lost after many kinds of brain damage, with each presumably effecting different aspects of basic visual attention functions. Yet, our understanding of these processes is limited by the methodological shortcomings of classical neuropsychological assessment. The overarching goal of the current thesis was to overcome these constrains and thereby extend the link between attention deficits and underlying brain changes. The here used approach incorporates parametric measurement of visual attention derived from the computational Theory of Visual Attention (TVA, Bundesen, 1990) and modern magnetic resonance imaging techniques. Project 1 of the current thesis applied a combined TVA–neuroimaging analysis in a neurodevelopmental model (preterm birth) to relate attention deficits with changes in functional connectivity networks. We found that pre- versus full-term born adults show a selective reduction of visual short-term memory capacity. The remarkable changes we observed in attention-related large-scale brain networks of the occipital and posterior parietal cortices were most pronounced in those preterm born individuals with the most preserved attention functions. This finding was interpreted as evidence for a compensatory reorganization of functional connectivity in order to ameliorate the advert consequences of preterm birth on visual short-term memory. Project 2 of this thesis applied a combined TVA-neuroimaging analysis in a neurodegenerative model (posterior cortical atrophy) to relate attention deficits with structural changes in grey and white matter morphometry. Compared to healthy control participants, patients with posterior cortical atrophy suffered from a selective disturbance of visual processing speed. The individual rate of processing speed slowing was a valid predictor for the severity of simultanagnosia, the core symptom in this clinical condition. We further found wide-spread atrophy in occipital as well as parietal and to a smaller degree in temporal brain areas. White matter degeneration in the superior parietal lobe, rather than atrophy of any grey matter cluster, was significantly associated with patients’ impaired processing speed. Based on these results we propose that disruption of white matter pathways especially within the superior parietal lobe leads to reduced processing speed which then results in the overt clinical symptoms of simultanagnosia. Altogether, projects of the current thesis expanded the link between specific attention deficits and underlying brain damage by using neuro-cognitive modelling. We demonstrated that parametric measurements of attention facilitate, in the role of intermediate cognitive constructs, the mapping between etiological factors and behavioral outcomes. Identifying predictable behavior-brain relationships in attention disorders may offer new perspectives for diagnosis and treatment. The clinical application of an integrated TVA-neuroimaging analysis could additionally compliment insights from healthy participants toward understanding the principles of normal visual attention as well as identifying their neuronal basis

Using neuro-cognitive modelling to link attention deficits to structural and functional brain changes

‘Visual attention’ is an emerging property of interconnected neural networks, in which the interconnections are biased to promote targets over distracting stimuli. It has been shown that efficiency of the attention system is lost after many kinds of brain damage, with each presumably effecting different aspects of basic visual attention functions. Yet, our understanding of these processes is limited by the methodological shortcomings of classical neuropsychological assessment. The overarching goal of the current thesis was to overcome these constrains and thereby extend the link between attention deficits and underlying brain changes. The here used approach incorporates parametric measurement of visual attention derived from the computational Theory of Visual Attention (TVA, Bundesen, 1990) and modern magnetic resonance imaging techniques. Project 1 of the current thesis applied a combined TVA–neuroimaging analysis in a neurodevelopmental model (preterm birth) to relate attention deficits with changes in functional connectivity networks. We found that pre- versus full-term born adults show a selective reduction of visual short-term memory capacity. The remarkable changes we observed in attention-related large-scale brain networks of the occipital and posterior parietal cortices were most pronounced in those preterm born individuals with the most preserved attention functions. This finding was interpreted as evidence for a compensatory reorganization of functional connectivity in order to ameliorate the advert consequences of preterm birth on visual short-term memory. Project 2 of this thesis applied a combined TVA-neuroimaging analysis in a neurodegenerative model (posterior cortical atrophy) to relate attention deficits with structural changes in grey and white matter morphometry. Compared to healthy control participants, patients with posterior cortical atrophy suffered from a selective disturbance of visual processing speed. The individual rate of processing speed slowing was a valid predictor for the severity of simultanagnosia, the core symptom in this clinical condition. We further found wide-spread atrophy in occipital as well as parietal and to a smaller degree in temporal brain areas. White matter degeneration in the superior parietal lobe, rather than atrophy of any grey matter cluster, was significantly associated with patients’ impaired processing speed. Based on these results we propose that disruption of white matter pathways especially within the superior parietal lobe leads to reduced processing speed which then results in the overt clinical symptoms of simultanagnosia. Altogether, projects of the current thesis expanded the link between specific attention deficits and underlying brain damage by using neuro-cognitive modelling. We demonstrated that parametric measurements of attention facilitate, in the role of intermediate cognitive constructs, the mapping between etiological factors and behavioral outcomes. Identifying predictable behavior-brain relationships in attention disorders may offer new perspectives for diagnosis and treatment. The clinical application of an integrated TVA-neuroimaging analysis could additionally compliment insights from healthy participants toward understanding the principles of normal visual attention as well as identifying their neuronal basis