@article {807, title = {Bayesian scalar-on-image regression with application to association between intracranial DTI and cognitive outcomes.}, journal = {Neuroimage}, volume = {83}, year = {2013}, month = {2013 Dec}, pages = {210-23}, abstract = {

Diffusion tensor imaging (DTI) measures water diffusion within white matter, allowing for in vivo quantification of brain pathways. These pathways often subserve specific functions, and impairment of those functions is often associated with imaging abnormalities. As a method for predicting clinical disability from DTI images, we propose a hierarchical Bayesian "scalar-on-image" regression procedure. Our procedure introduces a latent binary map that estimates the locations of predictive voxels and penalizes the magnitude of effect sizes in these voxels, thereby resolving the ill-posed nature of the problem. By inducing a spatial prior structure, the procedure yields a sparse association map that also maintains spatial continuity of predictive regions. The method is demonstrated on a simulation study and on a study of association between fractional anisotropy and cognitive disability in a cross-sectional sample of 135 multiple sclerosis patients.

}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2013.06.020}, author = {Huang, Lei and Goldsmith, Jeff and Reiss, Philip T and Reich, Daniel S and Crainiceanu, Ciprian M} } @article {799, title = {Body composition and arsenic metabolism: a cross-sectional analysis in the Strong Heart Study.}, journal = {Environ Health}, volume = {12}, year = {2013}, month = {2013}, pages = {107}, abstract = {

OBJECTIVE: The objective of this study was to evaluate the association between measures of body composition and patterns of urine arsenic metabolites in the 1989-1991 baseline visit of the Strong Heart Study, a cardiovascular disease cohort of adults recruited from rural communities in Arizona, Oklahoma, North Dakota and South Dakota.

METHODS: We evaluated 3,663 Strong Heart Study participants with urine arsenic species above the limit of detection and no missing data on body mass index, \% body fat and fat free mass measured by bioelectrical impedance, waist circumference and other variables. We summarized urine arsenic species patterns as the relative contribution of inorganic (iAs), methylarsonate (MMA) and dimethylarsinate (DMA) species to their sum. We modeled the associations of \% arsenic species biomarkers with body mass index, \% body fat, fat free mass, and waist circumference categories in unadjusted regression models and in models including all measures of body composition. We also considered adjustment for arsenic exposure and demographics.

RESULTS: Increasing body mass index was associated with higher mean \% DMA and lower mean \% MMA before and after adjustment for sociodemographic variables, arsenic exposure, and for other measures of body composition. In unadjusted linear regression models, \% DMA was 2.4 (2.1, 2.6) \% higher per increase in body mass index category (< 25, >=25 \& <30, >=30 \& <35, >=35 kg/m2), and \% MMA was 1.6 (1.4, 1.7) \% lower. Similar patterns were observed for \% body fat, fat free mass, and waist circumference measures in unadjusted models and in models adjusted for potential confounders, but the associations were largely attenuated or disappeared when adjusted for body mass index.

CONCLUSION: Measures of body size, especially body mass index, are associated with arsenic metabolism biomarkers. The association may be related to adiposity, fat free mass or body size. Future epidemiologic studies of arsenic should consider body mass index as a potential modifier for arsenic-related health effects.

}, issn = {1476-069X}, doi = {10.1186/1476-069X-12-107}, author = {Gribble, Matthew O and Crainiceanu, Ciprian M and Howard, Barbara V and Umans, Jason G and Francesconi, Kevin A and Goessler, Walter and Zhang, Ying and Silbergeld, Ellen K and Guallar, Eliseo and Navas-Acien, Ana} }