Computational Cortical Surface Analysis for Study of Early Brain Development

The study of morphological attributes of the cerebral cortex and their development is very important in understanding the dynamic and critical early brain development. Comparing with conventional studies in the image space, cortical surface-based analysis provides a better way to display, observe, and quantify the attributes of the cerebral cortex. The goal of this dissertation is to develop novel cortical surface-based methods for better studying the attributes of the cerebral cortex during early brain development. Specifically, this dissertation aims to develop methods for 1) estimating the development of morphological attributes of the cerebral cortex and 2) discovering the major cortical folding patterns. Estimation of the Development of Cortical Attributes. The early development of cortical attributes is highly correlated to the brain cognitive functionality and some neurodevelopmental disorders. Hence, accurately modeling the early development of cortical attributes is crucial for better understanding the mysterious normal and abnormal brain development. This task is very challenging, because infant cortical attributes change dramatically, complicatedly and regionally-heterogeneously during the first year of life. To address these problems, this dissertation proposes a Dynamically-Assembled Regression Forest (DARF). DARF first trains a single decision tree at each vertex on the cortical surface, and then groups nearby decision trees around each vertex as a vertex-specific forest to predict the cortical attribute. Since the vertex-specific forest can better capture regional details than the conventional regression forest trained for the whole brain, the prediction result is more precise. Moreover, because nearby forests share a large portion of decision trees, the prediction result is spatially smooth. On the other hand, missing cortical attribute maps in the longitudinal datasets often lead to insufficient data for unbiased analysis or training of accurate prediction models. To address this issue, a missing data estimation strategy based on DARF is further proposed. Experiments show that DARF outperforms the existing popular regression methods, and the proposed missing data estimation strategy based on DARF can effectively recover the missing cortical attribute maps. Discovery of Major Cortical Folding Patterns. The folding patterns of the cerebral cortex are highly variable across subjects. Exploring major cortical folding patterns in neonates is of great importance in neuroscience. Conventional geometric measurements of the cortex have limited capability in distinguishing major folding patterns. Although the recent sulcal pits-based analysis provides a better way for comparing sulcal patterns across individuals of adults or older children, whether and how sulcal pits are suitable for discovering major sulcal patterns in infants remain unknown. This dissertation adapts a sulcal pits extraction method from adults to infants, and validates the spatial consistency of sulcal pits in infants, so that they can be used as reliable landmarks for exploring major sulcal patterns. This dissertation further proposes a sulcal graph-based method for discovering major sulcal patterns, which is then applied to studying three primary cortical regions in 677 neonatal cortical surfaces. The experiments show that the proposed method is able to identify the previously unreported major sulcal patterns. Finally, this dissertation investigates and verifies that the sulcal pattern information could be utilized to help DARF for better estimating cortical attribute maps.Doctor of Philosoph

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

Morphogenetic Principles of Brain Organisation in Health and Disease

Non-invasive neuroimaging methods, such as MRI, provide a window into the structure of the mammalian brain. However, despite the ubiquity of these methods, the biological interpretation of the information obtained using these tools remains elusive. In order to accurately link this macroscale data to microscale measurements, it is critical that the construct validity is high. This thesis provides novel analyses, pipelines and methods to: i) generate and validate maps of brain organisation obtained via MRI, and ii) demonstrate the utility of these methods in capturing elements of cognition and psychopathology. First, in Chapter 1, I review some of the neuroscientific context for the new methods presented, from cytoarchitecture to gene expression to connectomes. Chapters 2-4 introduce a new method, “Morphometric Similarity Mapping”, which captures the brain organisation of an individual by mapping the relationships of multiple features of the cerebral cortex. Chapter 2 focuses on the development of the analysis pipeline and the graph theoretical features of the resulting morphometric similarity networks (MSNs), with an emphasis on reproducibility. Chapter 3 highlights the generalisability of MSNs to the macaque monkey, linking MSNs to ex vivo tract tracing experiments and presenting new tools for processing non-human imaging data; as well as evidence that MSN topography is organised by cytoarchitectonic features. Chapter 4 is focused on determining the transcriptomic correlates of MSNs using publicly available gene expression maps, and on applying MSNs to examine the relationship between brain organisation and intelligence. Chapter 5 is dedicated to rigorous evaluation of the applicability of MSNs to measure specific disease-relevant phenotypes in 8 rare genetic disorder cohorts. This includes the validation of novel methods for utilising data from both single-cell sequencing technologies and differential gene expression experiments (in multiple tissue types) in analysing neuroimaging and bulk transcriptomic brain maps. Chapter 6 provides a brief summary and presents some ongoing and future projects expanding on this original work. It also importantly discusses a general framework of comparing brain maps, including MSNs and gene expression, as well as other canonical maps of brain structure and function. Altogether, this thesis presents and evaluates novel methods and applications for integrating multimodal neuroimaging data with genetic data derived from multiple tissue types and through various acquisition strategies. It also includes tools for performing these analyses in non-human primates, and pipelines for statistically comparing brain maps. These results not only provide insight into the manifestation of brain-related changes due to various components of human variation, but also provides a framework for evaluating this variation at multiple biological scales purely from non-invasive neuroimaging data

Vaihe- ja amplitudikorrelaatiot vastasyntyneen EEG:ssa

In this thesis, the aim was to study the phase and amplitude correlations within neonatal electroencephalography (EEG) measurements. Premature birth can cause hindrances and changes in the functional connectivity of the brain, which can lead to serious neurological disorders later in life. The dataset consists of measurements from 113 babies: 67 born fullterm and 46 born preterm. The EEG measurements were done at the term-equivalent age in order to compare the phase and amplitude correlations between the two groups. Three different synchrony features were calculated from the data, in order to represent the different types of phase and amplitude correlations within the newborn brain. The main focus of this thesis is on the differences in functional connectivity between the two groups, but it is also studied how functional connectivity is located in the brain within the groups, and how the synchrony features are correlated with conceptional age, with each other and with the neurological scores C1 and C2. The neurological scores evaluate the motor and sensory development of a baby at birth. As a result of all these tests there are clear differences between the two groups, which show that the connections in the brain do not all develop at the same time and the development in the preterms might be slightly delayed. The results of this thesis also show that synchrony features might have some predictive value in regards to cognitive outcome.Tässä diplomityössä tutkittiin vastasyntyneen elektroenkefalogrammin (EEG) vaihe- ja amplitudikorrelaatioita. Ennenaikainen syntymä voi aiheuttaa komplikaatioita ja muutoksia aivojen funktionaalisessa konnektiivisuudessa, mikä voi johtaa vakaviin neurologisiin häiriöihin myöhemmin elämässä. Tutkimuksessa käytetty aineisto koostuu 113:n vauvan EEG-mittauksista: 67 täysiaikaisena syntynyttä ja 46 ennenaikaisesti syntynyttä. EEG-mittaukset suoritettiin täysiaikaisuutta vastaavassa iässä, jotta vaihe- ja amplitudikorrelaatiot ovat vertailukelpoisia ryhmien välillä. Aineistosta laskettiin kolme erilaista synkroniamittaria, jotka kuvaavat vastasyntyneen aivoissa esiintyviä erilaisia vaihe- ja amplitudikorrelaatioita. Diplomityön ensisijaisena tavoitteena oli tutkia edellä mainittujen kahden ryhmän välisiä eroja aivojen funktionaalisessa konnektiivisuudessa. Työssä tutkitaan myös, kuinka funktionaalinen konnektiivisuus on jakautunut aivoissa ryhmien sisäisesti sekä kuinka synkroniamittarit ovat korreloituneet iän kanssa, keskenään sekä neurologisten mittarien C1 ja C2 kanssa. Mittarit C1 ja C2 arvioivat vauvan motorista ja sensorista kehitystä syntymän hetkellä. Kaikki nämä tutkimukset osoittavat, että ryhmien välillä on selkeitä eroja, mistä havaitaan, että yhteydet aivoissa eivät kehity kaikkialla samaan aikaan ja että keskosina syntyneiden aivojen kehitys on mahdollisesti hieman myöhästynyt. Tuloksista havaitaan myös, että synkroniamittareista saattaa olla hyötyä vauvan kognitiivisen kehityksen ennustamisessa

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

Doctor of Philosophy

dissertationMagnetic Resonance (MR) is a relatively risk-free and flexible imaging modality that is widely used for studying the brain. Biophysical and chemical properties of brain tissue are captured by intensity measurements in T1W (T1-Weighted) and T2W (T2-Weighted) MR scans. Rapid maturational processes taking place in the infant brain manifest as changes in co{\tiny }ntrast between white matter and gray matter tissue classes in these scans. However, studies based on MR image appearance face severe limitations due to the uncalibrated nature of MR intensity and its variability with respect to changing conditions of scan. In this work, we develop a method for studying the intensity variations between brain white matter and gray matter that are observed during infant brain development. This method is referred to by the acronym WIVID (White-gray Intensity Variation in Infant Development). WIVID is computed by measuring the Hellinger Distance of separation between intensity distributions of WM (White Matter) and GM (Gray Matter) tissue classes. The WIVID measure is shown to be relatively stable to interscan variations compared with raw signal intensity and does not require intensity normalization. In addition to quantification of tissue appearance changes using the WIVID measure, we test and implement a statistical framework for modeling temporal changes in this measure. WIVID contrast values are extracted from MR scans belonging to large-scale, longitudinal, infant brain imaging studies and modeled using the NLME (Nonlinear Mixed Effects) method. This framework generates a normative model of WIVID contrast changes with time, which captures brain appearance changes during neurodevelopment. Parameters from the estimated trajectories of WIVID contrast change are analyzed across brain lobes and image modalities. Parameters associated with the normative model of WIVID contrast change reflect established patterns of region-specific and modality-specific maturational sequences. We also detect differences in WIVID contrast change trajectories between distinct population groups. These groups are categorized based on sex and risk/diagnosis for ASD (Autism Spectrum Disorder). As a result of this work, the usage of the proposed WIVID contrast measure as a novel neuroimaging biomarker for characterizing tissue appearance is validated, and the clinical potential of the developed framework is demonstrated