Socioeconomic disparities are associated with differences in cognitive development. The extent to which this translates to disparities in brain structure is unclear. Here, we investigated relationships between socioeconomic factors and brain morphometry, independently of genetic ancestry, among a cohort of 1099 typically developing individuals between 3 and 20 years. Income was logarithmically associated with brain surface area. Specifically, among children from lower income families, small differences in income were associated with relatively large differences in surface area, whereas, among children from higher income families, similar income increments were associated with smaller differences in surface area. These relationships were most prominent in regions supporting language, reading, executive functions and spatial skills; surface area mediated socioeconomic differences in certain neurocognitive abilities. These data indicate that income relates most strongly to brain structure among the most disadvantaged children. Potential implications are discussed.

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Cerebral metabolite concentrations and water content were measured by means of localized proton magnetic resonance spectroscopy in 50 children, while metabolite peak ratios in short echo time spectra were evaluated in 173 examinations. Normative curves for normal development were established for two cerebral locations. The current report presents the first study of absolute metabolite concentrations and T1- and T2- relaxation as a function of age. Myo-inositol was found dominating the spectra at birth (12 mmoles/kg), while choline is responsible for the strongest peak in older infants (2.5 mmoles/kg). Creatine and N-acetyl groups are at significantly lower concentrations in the neonate than in the adult (Cr: 6, NA: 5 mmoles/kg). NA and Cr are determined by gestational age, whereas the concentration of ml correlates best with postnatal age. Quantitative 1H MRS is expected to be of particular value in diagnosis and monitoring of pathology in infants, since metabolite ratios are often misleading.

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MRI is a sensitive method for detecting subtle anatomic abnormalities in the neonatal brain. To optimize the usefulness for neonatal and pediatric care, systematic research, based on quantitative image analysis and functional correlation, is required. Normalization-based image analysis is one of the most effective methods for image quantification and statistical comparison. However, the application of this methodology to neonatal brain MRI scans is rare. Some of the difficulties are the rapid changes in T1 and T2 contrasts and the lack of contrast between brain structures, which prohibits accurate cross-subject image registration. Diffusion tensor imaging (DTI), which provides rich and quantitative anatomical contrast in neonate brains, is an ideal technology for normalization-based neonatal brain analysis. In this paper, we report the development of neonatal brain atlases with detailed anatomic information derived from DTI and co-registered anatomical MRI. Combined with a diffeomorphic transformation, we were able to normalize neonatal brain images to the atlas space and three-dimensionally parcellate images into 122 regions. The accuracy of the normalization was comparable to the reliability of human raters. This method was then applied to babies of 37 to 53 post-conceptional weeks to characterize developmental changes of the white matter, which indicated a posterior-to-anterior and a central-to-peripheral direction of maturation. We expect that future applications of this atlas will include investigations of the effect of prenatal events and the effects of preterm birth or low birth weights, as well as clinical applications, such as determining imaging biomarkers for various neurological disorders.

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