Teacher: In this task, you are given an abstract of article. Your task is to generate title for this article. Preferred titles are under thirty words.
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Alterations of the architecture of cerebral white matter in the developing human brain can affect cortical development and result in functional disabilities. A line scan diffusion-weighted magnetic resonance imaging (MRI) sequence with diffusion tensor analysis was applied to measure the apparent diffusion coefficient, to calculate relative anisotropy, and to delineate three-dimensional fiber architecture in cerebral white matter in preterm (n = 17) and full-term infants (n = 7). To assess effects of prematurity on cerebral white matter development, early gestation preterm infants (n = 10) were studied a second time at term. In the central white matter the mean apparent diffusion coefficient at 28 wk was high, 1.8 microm2/ms, and decreased toward term to 1.2 microm2/ms. In the posterior limb of the internal capsule, the mean apparent diffusion coefficients at both times were similar (1.2 versus 1.1 microm2/ms). Relative anisotropy was higher the closer birth was to term with greater absolute values in the internal capsule than in the central white matter. Preterm infants at term showed higher mean diffusion coefficients in the central white matter (1.4 +/- 0.24 versus 1.15 +/- 0.09 microm2/ms, p = 0.016) and lower relative anisotropy in both areas compared with full-term infants (white matter, 10.9 +/- 0.6 versus 22.9 +/- 3.0%, p = 0.001; internal capsule, 24.0 +/- 4.44 versus 33.1 +/- 0.6% p = 0.006). Nonmyelinated fibers in the corpus callosum were visible by diffusion tensor MRI as early as 28 wk; full-term and preterm infants at term showed marked differences in white matter fiber organization. The data indicate that quantitative assessment of water diffusion by diffusion tensor MRI provides insight into microstructural development in cerebral white matter in living infants.
Solution: Microstructural development of human newborn cerebral white matter assessed in vivo by diffusion tensor magnetic resonance imaging.
Reason: This title is appropriate for the given abstract because the focus of this abstract is cerebral white matter assesment.

Now, solve this instance: Understanding which factors influence mammographically dense and nondense areas is important because percent mammographic density adjusted for age is a strong, continuously distributed risk factor for breast cancer, especially when adjusted for weight or body mass index. Using computer-assisted methods, we measured mammographically dense areas for 571 monozygotic and 380 dizygotic Australian and North American twin pairs ages 40 to 70 years. We used a novel regression modeling approach in which each twin's measure of dense and nondense area was regressed against one or both of the twin's and co-twin's covariates. The nature of changes to regression estimates with the inclusion of the twin and/or co-twin's covariates can be evaluated for consistency with causal and/or other models. By causal, we mean that if it were possible to vary a covariate experimentally then the expected value of the outcome measure would change. After adjusting for the individual's weight, the co-twin associations with weight were attenuated, consistent with a causal effect of weight on mammographic measures, which in absolute log cm(2)/kg was thrice stronger for nondense than dense area. After adjusting for weight, later age at menarche, and greater height were associated with greater dense and lesser nondense areas in a manner inconsistent with causality. The associations of dense and nondense areas with parity are consistent with a causal effect and/or within-person confounding. The associations between mammographic density measures and height are consistent with shared early life environmental factors that predispose to both height and percent mammographic density and possibly breast cancer risk.
Student:
Predictors of mammographic density: insights gained from a novel regression analysis of a twin study.