A new framework for analyzing white matter maturation in early brain development

pre-printThe trajectory of early brain development is marked by rapid growth presented by volume but also by tissue property changes. Capturing regional characteristics of axonal structuring and myelination via neuroimaging requires analysis of longitudinal image data with multiple modalities. Complementary to earlier studies of volume and cortical folding analysis, this paper focuses on white matter tissue changes as seen in multimodal MRI and DTI. We propose a new framework for analyzing early maturation in white matter that generates a normative spatiotemporal model and provides 3D maps of absolute and relative indices of maturation. The method, using a continuous model of intensity changes using modified Legendre polynomials, has been applied to a multimodal dataset (T1W, T2W, PD, DTI) with 8 subjects that have been scanned at approximately 2 weeks, 1 year, and 2 years. We demonstrate that spatial maturation maps generated from different modalities capture different properties of white matter growth which might lead to a better understanding of the underlying neurobiology

Segmenting the Invisible : Processing 4D Image Data

poste

Modeling longitudinal MRI changes in populations using a localized, information-theoretic measure of contrast

pre-printLongitudinal MR imaging during early brain development provides important information about growth patterns and the development of neurological disorders. We propose a new framework for studying brain growth patterns within and across populations based on MRI contrast changes, measured at each time point of interest and at each voxel. Our method uses regression in the LogOdds space and an information-theoretic measure of distance between distributions to capture contrast in a manner that is robust to imaging parameters and without requiring intensity normalization. We apply our method to a clinical neuroimaging study on early brain development in autism, where we obtain a 4D spatiotemporal model of contrast changes in multimodal structural MRI

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

A NEW FRAMEWORK FOR ANALYZING WHITE MATTER MATURATION IN EARLY BRAIN DEVELOPMENT

The trajectory of early brain development is marked by rapid growth presented by volume but also by tissue property changes. Capturing regional characteristics of axonal structuring and myelination via neuroimaging requires analysis of longitudinal image data with multiple modalities. Complementary to earlier studies of volume and cortical folding analysis, this paper focuses on white matter tissue changes as seen in multimodal MRI and DTI. We propose a new framework for analyzing early maturation in white matter that generates a normative spatiotemporal model and provides 3D maps of absolute and relative indices of maturation. The method, using a continuous model of intensity changes using modified Legendre polynomials, has been applied to a multimodal dataset (T1W, T2W, PD, DTI) with 8 subjects that have been scanned at approximately 2 weeks, 1 year, and 2 years. We demonstrate that spatial maturation maps generated from different modalities capture different properties of white matter growth which might lead to a better understanding of the underlying neurobiology

Spatio-temporal Modeling and Analysis of Brain Development

The incidence of preterm birth is increasing and has emerged as a leading cause of neurodevelopmental impairment in childhood. In early development, defined here as the period before and around birth, the brain undergoes significant morphological, functional and appearance changes. The scope and rate of change is arguably greater than at any other time in life, but quantitative markers of this period of development are limited. Improved understanding of cerebral changes during this critical period is important for mapping normal growth, and for investigating mechanisms of injury associated with risk factors for maldevelopment such as premature birth. The objective of this thesis is the development of methods for spatio-temporal modeling and quantitative measures of brain development that can assist understanding the patterns of normal growth and can guide interventions designed to reduce the burden of preterm brain injury. An approach for constructing high-definition spatio-temporal atlases of the developing brain is introduced. A novelty in the proposed approach is the use of a time-varying kernel width, to overcome the variations in the distribution of subjects at different ages. This leads to an atlas that retains a consistent level of detail at every time-point. The resulting 4D fetal and neonatal average atlases have greater anatomic definition than currently available 4D atlases, an important factor in improving registrations between the atlas and individual subjects with clear anatomical structures and atlas-based automatic segmentation. The fetal atlas provides a natural benchmark for assessing preterm born neonates and gives some insight into differences between the groups. Also, a novel framework for longitudinal registration which can accommodate large intra-subject anatomical variations is introduced. The framework exploits previously developed spatio-temporal atlases, which can aid the longitudinal registration process as it provides prior information about the missing anatomical evolution between two scans taken over large time-interval. Finally, a voxel-wise analysis framework is proposed which complements the analysis of changes in brain morphology by the study of spatio-temporal signal intensity changes in multi-modal MRI, which can offer a useful marker of neurodevelopmental changes