A Structural MRI Study of Human Brain Development from Birth to 2 Years

Knickmeyer, Rebecca; Gouttard, Sylvain; Kang, Chaeryon; Evans, Dianne D.; Wilber, Kathy; Ja, Smith; Hamer, Robert M.; Lin, Weili; Gerig, Guido; Gilmore, John H.

doi:10.1523/jneurosci.3479-08.2008

Cited by 941 publications

(868 citation statements)

References 57 publications

(67 reference statements)

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“…In developmental analyses, such as pediatric neurodevelopment, quantitative magnetic resonance imaging has significantly advanced our understanding of brain development during childhood and adolescence. Many clinical studies, however, still rely on traditional morphometrics, such as intracranial volume and the volumes of brain lobes and subcortical structures [69]. Computational shape analysis in this context therefore promises to give not only new basic insights into the process of development, but also potentially provide new diagnostic measures of individuals against normative models.…”

Section: Neurobiologymentioning

confidence: 99%

“…Courchesne, et al [22], for example, describe an imaging study that looks at di↵erences in growth patterns in autism compared to controls in children over four years in age. Data measured in infants from birth to 4 years, however, are mostly volumetric measurements, such as intracranial volume and volumes of brain lobes and subcortical structures [69], and there has been little work to date examining the change in shape with age in early childhood brain development.…”

Section: Introductionmentioning

confidence: 99%

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Shape Modeling and Analysis with Entropy-Based Particle Systems

Cates

Fletcher

Styner

et al. 2007

Lecture Notes in Computer Science

134

224

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Many important fields of basic research in medicine and biology routinely employ tools for the statistical analysis of collections of similar shapes. Biologists, for example, have long relied on homologous, anatomical landmarks as shape models to characterize the growth and development of species. Increasingly, however, researchers are exploring the use of more detailed models that are derived computationally from three-dimensional images and surface descriptions. While computationally-derived models of shape are promising new tools for biomedical research, they also present some significant engineering challenges, which existing modeling methods have only begun to address.In this dissertation, I propose a new computational framework for statistical shape modeling that significantly advances the state-of-the-art by overcoming many of the limitations of existing methods. The framework uses a particle-system representation of shape, with a fast correspondence-point optimization based on information content. The optimization balances the simplicity of the model (compactness) with the accuracy of the shape representations by using two commensurate entropy metrics and no free parameters. The idea is to maximize both the geometric accuracy and the statistical simplicity of the shape model, in accordance with the principle of parsimony in model selection. The nonparametric representation allows the method to be applied to a larger class of problems than existing methods, including nonspherical surfaces, open surfaces, and sets of multiple surfaces.The relative simplicity of the surface representation and the low number of free parameters results in a framework that is easy to use and can operate directly on image segmentations. In collaboration with scientists from several important areas of biomedicine, I have demonstrated that the proposed method is indeed an e↵ective tool for scientific research.The specific research contributions of this dissertation are as follows. First, I describe a mathematical framework and a robust numerical algorithm for computing optimized correspondence-point shape models using an entropy-based optimization and particle-system technology. Second, I develop a series of extensions of the framework to more general classes of shape analysis problems, including the analysis of multiple-object complexes, the generalization to correspondence based on generic functions of position, an extension to handle surfaces with open boundaries, and shape modeling with simple regression. Third, I describe the application of statistical hypothesis testing, regression analysis, and multiple-analysis of covariance to the proposed shape models. I also introduce new techniques for visualization and interpretation of these statistics. Finally, in cooperation with biomedical researchers, I present validation of the above research contributions by their successful application to real-world research problems.

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Section: Neurobiologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Shape Modeling and Analysis with Entropy-Based Particle Systems

Cates

Fletcher

Styner

et al. 2007

Lecture Notes in Computer Science

134

224

View full text Add to dashboard Cite

show abstract

“…The human brain undergoes profound global and regional changes throughout childhood (17)(18)(19). Overall brain volume achieves 80-90% of its lifetime maximum by 2 y of age (20), whereas in many regions gray matter volumes peak in infancy before undergoing variable rates of decline throughout adolescence (21)(22)(23). In contrast, myelination continues to progress from posterior to anterior throughout adolescence and well into young adulthood (24,25), and frontal myelination may continue beyond this age (19).…”

mentioning

confidence: 99%

Network-level structural covariance in the developing brain

Zielinski

Gennatas

Zhou

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

366

382

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Intrinsic or resting state functional connectivity MRI and structural covariance MRI have begun to reveal the adult human brain's multiple network architectures. How and when these networks emerge during development remains unclear, but understanding ontogeny could shed light on network function and dysfunction. In this study, we applied structural covariance MRI techniques to 300 children in four age categories (early childhood, 5-8 y; late childhood, 8.5-11 y; early adolescence, 12-14 y; late adolescence, 16-18 y) to characterize gray matter structural relationships between cortical nodes that make up large-scale functional networks. Network nodes identified from eight widely replicated functional intrinsic connectivity networks served as seed regions to map whole-brain structural covariance patterns in each age group. In general, structural covariance in the youngest age group was limited to seed and contralateral homologous regions. Networks derived using primary sensory and motor cortex seeds were already well-developed in early childhood but expanded in early adolescence before pruning to a more restricted topology resembling adult intrinsic connectivity network patterns. In contrast, language, social-emotional, and other cognitive networks were relatively undeveloped in younger age groups and showed increasingly distributed topology in older children. The so-called default-mode network provided a notable exception, following a developmental trajectory more similar to the primary sensorimotor systems. Relationships between functional maturation and structural covariance networks topology warrant future exploration.childhood | children | connectivity | development | neuroimaging

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“…Both patients had markedly elevated guanidino compounds pre-OLT. Perhaps the explanation for the different neurological outcome in these two patients is that vast brain growth with organizational changes takes place during the first two postnatal years (Knickmeyer et al 2008). Thus, any toxic factor acting on the brain during this period of time will be more harmful and leave the worst possible sequels.…”

Section: Discussionmentioning

confidence: 99%