Effects of Healthy Aging Measured By Intracranial Compartment Volumes Using a Designed MR Brain Database

Mortamet, Bénédicte; Zeng, Donglin; Gerig, Guido; Prastawa, Marcel; Bullitt, Elizabeth

doi:10.1007/11566465_48

Cited by 33 publications

(35 citation statements)

References 16 publications

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“…Overall brain volume in the neonatal period is ϳ35% that of normal adults scanned on the same scanner (Mortamet et al, 2005), indicating that there is enormous overall growth of the brain between birth and adulthood. Early results from follow-up scans at 1 year of age suggest that total brain size increases 100% in the first year of life to ϳ73% of adult size (our unpublished results).…”

Section: Discussionmentioning

confidence: 99%

Regional Gray Matter Growth, Sexual Dimorphism, and Cerebral Asymmetry in the Neonatal Brain

Gilmore

Lin

Prastawa³

et al. 2007

J. Neurosci.

411

358

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Although there has been recent interest in the study of childhood and adolescent brain development, very little is known about normal brain development in the first few months of life. In older children, there are regional differences in cortical gray matter development, whereas cortical gray and white matter growth after birth has not been studied to a great extent. The adult human brain is also characterized by cerebral asymmetries and sexual dimorphisms, although very little is known about how these asymmetries and dimorphisms develop. We used magnetic resonance imaging and an automatic segmentation methodology to study brain structure in 74 neonates in the first few weeks after birth. We found robust cortical gray matter growth compared with white matter growth, with occipital regions growing much faster than prefrontal regions. Sexual dimorphism is present at birth, with males having larger total brain cortical gray and white matter volumes than females. In contrast to adults and older children, the left hemisphere is larger than the right hemisphere, and the normal pattern of fronto-occipital asymmetry described in older children and adults is not present. Regional differences in cortical gray matter growth are likely related to differential maturation of sensory and motor systems compared with prefrontal executive function after birth. These findings also indicate that whereas some adult patterns of sexual dimorphism and cerebral asymmetries are present at birth, others develop after birth.

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Section: Discussionmentioning

confidence: 99%

Regional Gray Matter Growth, Sexual Dimorphism, and Cerebral Asymmetry in the Neonatal Brain

Gilmore

Lin

Prastawa³

et al. 2007

J. Neurosci.

411

358

View full text Add to dashboard Cite

show abstract

“…The cohort comprised T1 MR images (1 mm 3 isotropic voxels) of 30 females (age 34.8 ± 9.6 years) and 27 males (age 36 ± 11 years), obtained after enforcing quality-assurance checks on every image in the dataset in [18]. Image analysis: (i) parcellate lobes, (ii) segment tissues [19], (iii) resample segmentations to 0.4 3 mm 3 isotropic voxels, (iv) fit a level set to the cortical graywhite interface M, (v) compute (C(m), S(m))∀m ∈ M, (vi) estimate P replication (Section 3.1) and P scale (Section 3.3) for all lobes, (vii) cross-sectional test for gender differences via SnPM (Section 3.2).…”

Section: Clinical Cohort Imaging and Image Analysismentioning

confidence: 99%

Gender Differences in Cerebral Cortical Folding: Multivariate Complexity-Shape Analysis with Insights into Handling Brain-Volume Differences

Awate

Yushkevich

Licht

et al. 2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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Abstract. This paper presents a study of gender differences in adult human cerebral cortical folding patterns. The study employs a new multivariate statistical descriptor for analyzing folding patterns in a region of interest (ROI) and a rigorous nonparametric permutation-based scheme for hypothesis testing. Unlike typical ROI-based methods that summarize folding complexity or shape by single/few numbers, the proposed descriptor systematically constructs a unified description of complexity and shape in a high-dimensional space (thousands of numbers/dimensions). Furthermore, this paper presents new mathematical insights into the relationship of intra-cranial volume (ICV) with cortical complexity and shows that conventional complexity descriptors implicitly handle ICV differences in different ways, thereby lending different meanings to "complexity". This paper describes two systematic methods for handling ICV changes in folding studies using the proposed descriptor. The clinical study in this paper exploits these theoretical insights to demonstrate that (i) the answer to which gender has higher/lower "complexity" depends on how a folding measure handles ICV differences and (ii) cortical folds in males and females differ significantly in shape as well.

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“…1 we apply kernel regression to measurements reported in a study by Mortamet et al (2005) on the effect of aging on gray matter and ventricle volume in the brain. The regression curves demonstrate the average volume, as a function of patient age, of these structures.…”

Section: Introductionmentioning

confidence: 99%

Population Shape Regression from Random Design Data

et al. 2010

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Regression analysis is a powerful tool for the study of changes in a dependent variable as a function of an independent regressor variable, and in particular it is applicable to the study of anatomical growth and shape change. When the underlying process can be modeled by parameters in a Euclidean space, classical regression techniques (Hardle, Applied Nonparametric Regression, 1990; Wand and Jones, Kernel Smoothing, 1995) are applicable and have been studied extensively. However, recent work suggests that attempts to describe anatomical shapes using flat Euclidean spaces undermines our ability to represent natural biological variability (Fletcher et al.In this paper we develop a method for regression analysis of general, manifold-valued data. Specifically, we extend Nadaraya-Watson kernel regression by recasting the regression problem in terms of Fréchet expectation. Although this method is quite general, our driving problem is the study anatomical shape change as a function of age from random design image data.We demonstrate our method by analyzing shape change in the brain from a random design dataset of MR images of 97 healthy adults ranging in age from 20 to 79 years.To study the small scale changes in anatomy, we use the infinite dimensional manifold of diffeomorphic transformations, with an associated metric. We regress a representative anatomical shape, as a function of age, from this population.

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Effects of Healthy Aging Measured By Intracranial Compartment Volumes Using a Designed MR Brain Database

Cited by 33 publications

References 16 publications

Regional Gray Matter Growth, Sexual Dimorphism, and Cerebral Asymmetry in the Neonatal Brain

Regional Gray Matter Growth, Sexual Dimorphism, and Cerebral Asymmetry in the Neonatal Brain

Gender Differences in Cerebral Cortical Folding: Multivariate Complexity-Shape Analysis with Insights into Handling Brain-Volume Differences

Population Shape Regression from Random Design Data

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