Connectomics across development:towards mapping brain structure from birth to childhood

The brain is probably the most complex system of the human body, composed of numerous neural units interconnected at dierent scales. This highly structured architecture provides the ability to communicate, synthesize information and perform the analytical tasks of human beings. Its development starts during the transition between the embryonic and fetal periods, from a simple tubular to a highly complex folded structure. It is globally organized as early as birth. This developing process is highly vulnerable to antenatal adverse conditions. Indeed, extreme prematurity and intra uterine growth restriction are major risk factors for long-term morbidities, including developmental ailments such as cerebral palsy, mental retardation and a wide spectrum of learning disabilities and behavior disorders. In this context, the characterization of the brainâs normative wiring pattern is crucial for our understanding of its architecture and workings, as the origin of many neurological and neurobehavioral disorders is found in early structural brain development. Diusion magnetic resonance imaging (dMRI) allows the in vivo assessment of biological tissues at the microstructural level. It has emerged as a powerful tool to study brain connectivity and analyse the underlying substrate of the human brain, comprising its structurally integrated and functionally specialized architecture. dMRI has been widely used in adult studies. Nevertheless, due to technical constraints, this mapping at earlier stages of development has not yet been accomplished. Yet, this time period is of extreme importance to comprehend the structural and functional integrity of the brain. This thesis is motivated by this shortfall, and intends to fill the gap between the clinical and neuroscience demands and the methodological developments needed to fulfill them. In our work, we comprehensibly study the brain structural connectivity of children born extremely prematurely and/or with additional prenatal restriction at school-age. We provide evidence that brain systems that mature early in development are the most vulnerable to antenatal insults. Interestingly, the alterations highlighted in these systems correlate with the neurobehavioral and cognitive impairments seen in these children at school-age. The overall brain organization appear also altered after preterm birth and prenatal restriction. Indeed, these children show dierent brain network modular topology, with a reduction in the overall network capacity. What remains unclear is whether the alterations seen at school age are already present at birth and, if yes, to what extent. In this thesis we set the technical basis to enable the connectome analysis as early as at birth. This task is challenging when dealing with neonatal data. Indeed, most of the assumptions used in adult data processing methods do not hold, due to the inverted image contrast and other MRI artefacts such as motion, partial volume and intensity inhomogeneities. Here, we propose a novel technique for surface reconstruction, and provide a fully-automatic procedure to delineate the newborn cortical surface, opening the way to establish the newborn connectome

Multimodality evaluation of the pediatric brain: DTI and its competitors

The development of the human brain, from the fetal period until childhood, happens in a series of intertwined neurogenetical and histogenetical events that are influenced by environment. Neuronal proliferation and migration, cell aggregation, axonal ingrowth and outgrowth, dendritic arborisation, synaptic pruning and myelinisation contribute to the ‘plasticity of the developing brain'. These events taken together contribute to the establishment of adult-like neuroarchitecture required for normal brain function. With the advances in technology today, mostly due to the development of non-invasive neuroimaging tools, it is possible to analyze these structural events not only in anatomical space but also longitudinally in time. In this review we have highlighted current ‘state of the art' neuroimaging tools. Development of the new MRI acquisition sequences (DTI, CHARMED and phase imaging) provides valuable insight into the changes of the microstructural environment of the cortex and white matter. Development of MRI imaging tools dedicated for analysis of the acquired images (i) TBSS and ROI fiber tractography, (ii) new tissue segmentation techniques and (iii) morphometric analysis of the cortical mantle (cortical thickness and convolutions) allows the researchers to map the longitudinal changes in the macrostructure of the developing brain that go hand-in-hand with the acquisition of cognitive skills during childhood. Finally, the latest and the newest technologies, like connectom analysis and resting state fMRI connectivity analysis, today, for the first time provide the opportunity to study the developing brain through the prism of maturation of the systems and networks beyond individual anatomical areas. Combining these methods in the future and modeling the hierarchical organization of the brain might ultimately help to understand the mechanisms underlying complex brain structure function relationships of normal development and of developmental disorder

Model-Informed Machine Learning for Multi-component T2 Relaxometry

Recovering the T2 distribution from multi-echo T2 magnetic resonance (MR) signals is challenging but has high potential as it provides biomarkers characterizing the tissue micro-structure, such as the myelin water fraction (MWF). In this work, we propose to combine machine learning and aspects of parametric (fitting from the MRI signal using biophysical models) and non-parametric (model-free fitting of the T2 distribution from the signal) approaches to T2 relaxometry in brain tissue by using a multi-layer perceptron (MLP) for the distribution reconstruction. For training our network, we construct an extensive synthetic dataset derived from biophysical models in order to constrain the outputs with \textit{a priori} knowledge of \textit{in vivo} distributions. The proposed approach, called Model-Informed Machine Learning (MIML), takes as input the MR signal and directly outputs the associated T2 distribution. We evaluate MIML in comparison to non-parametric and parametric approaches on synthetic data, an ex vivo scan, and high-resolution scans of healthy subjects and a subject with Multiple Sclerosis. In synthetic data, MIML provides more accurate and noise-robust distributions. In real data, MWF maps derived from MIML exhibit the greatest conformity to anatomical scans, have the highest correlation to a histological map of myelin volume, and the best unambiguous lesion visualization and localization, with superior contrast between lesions and normal appearing tissue. In whole-brain analysis, MIML is 22 to 4980 times faster than non-parametric and parametric methods, respectively.Comment: Preprint submitted to Medical Image Analysis (July 14, 2020

Structural brain network reorganization and social cognition related to adverse perinatal condition from infancy to early adolescence

Adverse conditions during fetal life have been associated to both structural and functional changes in neurodevelopment from the neonatal period to adolescence. In this study, connectomics was used to assess the evolution of brain networks from infancy to early adolescence. Brain network reorganization over time in subjects who had suffered adverse perinatal conditions is characterized and related to neurodevelopment and cognition. Three cohorts of prematurely born infants and children (between 28 and 35 weeks of gestational age), including individuals with a birth weight appropriated for gestational age and with intrauterine growth restriction (IUGR), were evaluated at 1, 6, and 10 years of age, respectively. A common developmental trajectory of brain networks was identified in both control and IUGR groups: network efficiencies of the fractional anisotropy (FA)-weighted and normalized connectomes increase with age, which can be related to maturation and myelination of fiber connections while the number of connections decreases, which can be associated to an axonal pruning process and reorganization. Comparing subjects with or without IUGR, a similar pattern of network differences between groups was observed in the three developmental stages, mainly characterized by IUGR group having reduced brain network efficiencies in binary and FA-weighted connectomes and increased efficiencies in the connectome normalized by its total connection strength (FA). Associations between brain networks and neurobehavioral impairments were also evaluated showing a relationship between different network metrics and specific social cognition-related scores, as well as a higher risk of inattention/hyperactivity and/or executive functional disorders in IUGR children

Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions

Quantitative magnetic resonance imaging (qMRI) can increase the specificity and sensitivity of conventional weighted MRI to underlying pathology by comparing meaningful physical or chemical parameters, measured in physical units, with normative values acquired in a healthy population. This study focuses on multi-echo T2 relaxometry, a qMRI technique that probes the complex tissue microstructure by differentiating compartment-specific T2 relaxation times. However, estimation methods are still limited by their sensitivity to the underlying noise. Moreover, estimating the model's parameters is challenging because the resulting inverse problem is ill-posed, requiring advanced numerical regularization techniques. As a result, the estimates from distinct regularization strategies are different. In this work, we aimed to investigate the variability and reproducibility of different techniques for estimating the transverse relaxation time of the intra- and extra-cellular space (T2IE) in gray (GM) and white matter (WM) tissue in a clinical setting, using a multi-site, multi-session, and multi-run T2 relaxometry dataset. To this end, we evaluated three different techniques for estimating the T2 spectra (two regularized non-negative least squares methods and a machine learning approach). Two independent analyses were performed to study the effect of using raw and denoised data. For both the GM and WM regions, and the raw and denoised data, our results suggest that the principal source of variance is the inter-subject variability, showing a higher coefficient of variation (CoV) than those estimated for the inter-site, inter-session, and inter-run, respectively. For all reconstruction methods studied, the CoV ranged between 0.32 and 1.64%. Interestingly, the inter-session variability was close to the inter-scanner variability with no statistical differences, suggesting that T2IE is a robust parameter that could be employed in multi-site neuroimaging studies. Furthermore, the three tested methods showed consistent results and similar intra-class correlation (ICC), with values superior to 0.7 for most regions. Results from raw data were slightly more reproducible than those from denoised data. The regularized non-negative least squares method based on the L-curve technique produced the best results, with ICC values ranging from 0.72 to 0.92

Multimodality evaluation of the pediatric brain: DTI and its competitors

The development of the human brain, from the fetal period until childhood, happens in a series of intertwined neurogenetical and histogenetical events that are influenced by environment. Neuronal proliferation and migration, cell aggregation, axonal ingrowth and outgrowth, dendritic arborisation, synaptic pruning and myelinisation contribute to the 'plasticity of the developing brain'. These events taken together contribute to the establishment of adult-like neuroarchitecture required for normal brain function. With the advances in technology today, mostly due to the development of non-invasive neuroimaging tools, it is possible to analyze these structural events not only in anatomical space but also longitudinally in time. In this review we have highlighted current 'state of the art' neuroimaging tools. Development of the new MRI acquisition sequences (DTI, CHARMED and phase imaging) provides valuable insight into the changes of the microstructural environment of the cortex and white matter. Development of MRI imaging tools dedicated for analysis of the acquired images (i) TBSS and ROI fiber tractography, (ii) new tissue segmentation techniques and (iii) morphometric analysis of the cortical mantle (cortical thickness and convolutions) allows the researchers to map the longitudinal changes in the macrostructure of the developing brain that go hand-in-hand with the acquisition of cognitive skills during childhood. Finally, the latest and the newest technologies, like connectom analysis and resting state fMRI connectivity analysis, today, for the first time provide the opportunity to study the developing brain through the prism of maturation of the systems and networks beyond individual anatomical areas. Combining these methods in the future and modeling the hierarchical organization of the brain might ultimately help to understand the mechanisms underlying complex brain structure function relationships of normal development and of developmental disorders

Ultrahigh field in vivo characterization of microstructural abnormalities in the orbitofrontal cortex and amygdala in autism

There are currently no biomarkers for autism spectrum disorder (ASD). This neurodevelopmental condition has previously been associated with histopathological findings, including increased neuronal packing density in the amygdala, abnormal laminar cytoarchitecture and increased average neuronal density in the prefrontal cortex. The present study examined whether new brain imaging technologies could reveal in vivo, in adults with ASD, the manifestation of previously described histopathological changes. Using quantitative mapping at ultrahigh field (7 Tesla), we show that we can observe microstructural alterations in the right lateral orbitofrontal cortex and the bilateral amygdala in adult individuals with ASD in vivo. These imaging alterations point to an abnormal laminar cytoarchitecture and to an increased neuronal density, similar to what has been previously described in post-mortem data in ASD. Our data demonstrate that it is possible to visualize, in vivo and at the individual level, alterations of cortical and subcortical microstructure in ASD. Future studies will be needed to extend these findings to a larger group of individuals and evaluate their association with symptomatology as well as their specificity among the different neurodevelopmental disorders

Gender-specific attention system subnetwork vulnerability in prematurely born children

Children born prematurely are at high risk for long-term abnormal development, including deficits in attentional abilities that persist throughout adolescence. The severity of these cognitive difficulties varies significantly among preterm-born children, with both the level of prematurity and sex being the most relevant factors for the determination of their neurodevelopmental outcomes. In order to understand the neurostructural correlate of these disabilities, we used a brain connectome approach to analyze the effects of gender and degree of prematurity on the attentional system network related to executive control in school age preterm children. Our results suggest that being born extremely premature, as well as being born male, differentially affects brain connectivity and development

Prematurity and prenatal growth restriction differently affects brain connectivity at age 6 years.

Survival of children born prematurely or with very low birth weight has increased dramatically but the long term developmental outcome remains a concern with many children having deficits in their cognitive capacities, in particular involving executive domains. The origins of these disabilities are largely unknown but are likely to involve an overriding central nervous system deficit. In prior studies we and others had shown that both prematurity and IUGR affect newborn brain structures (cite Inder TE 2005, Borradori-Tolsa Ped Res 04 Dubois brain 08). To understand the neurostructural origin of this disabilities, we used DTI to study brain connectivity at the age of 6 years. Structurally segregated and functionally specialized regions of the cerebral cortex are interconnected by a dense network of axonal pathways. We noninvasively map these pathways across cortical hemispheres and construct normalized structural connection matrices derived from diffusion tensor MR tractography. Statistical group comparisons of brain connectivity reveal significant changes in fibers density in children who were born moderately premature but with poor intrauterine growth, in extremely premature (EP) children (less than 28 weeks of gestational age at birth) compared to control subjects, who were born moderately premature but with normal growth. Moderately premature babies with normal growth generally compare to children born fullterm in their neurodevelopmental outcome. These localized changes in connectivity suggest a direct link between cortico-axonal pathways and the central nervous system deficits observed in these children