Browsing by Subject "Structural neuroimaging"

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    Altered Cortical Brain Structure and Increased Risk for Disease Seen Decades After Perinatal Exposure to Maternal Smoking: A Study of 9000 Adults in the UK Biobank
    (Oxford Academic, 2019-12-17) Salminen, Lauren E.; Wilcox, Rand R.; Zhu, Alyssa H.; Riedel, Brandalyn C.; Ching, Christopher R.K.; Rashid, Faisal; Thomopoulos, Sophia I.; Saremi, Arvin; Harrison, Marc B.; Ragothaman, Anjanibhargavi; Knight, Victoria; Boyle, Christina P.; Medland, Sarah E.; Thompson, Paul M.; Jahanshad, Neda; Radiology and Imaging Sciences, School of Medicine
    Secondhand smoke exposure is a major public health risk that is especially harmful to the developing brain, but it is unclear if early exposure affects brain structure during middle age and older adulthood. Here we analyzed brain MRI data from the UK Biobank in a population-based sample of individuals (ages 44–80) who were exposed (n = 2510) or unexposed (n = 6079) to smoking around birth. We used robust statistical models, including quantile regressions, to test the effect of perinatal smoke exposure (PSE) on cortical surface area (SA), thickness, and subcortical volumes. We hypothesized that PSE would be associated with cortical disruption in primary sensory areas compared to unexposed (PSE−) adults. After adjusting for multiple comparisons, SA was significantly lower in the pericalcarine (PCAL), inferior parietal (IPL), and regions of the temporal and frontal cortex of PSE+ adults; these abnormalities were associated with increased risk for several diseases, including circulatory and endocrine conditions. Sensitivity analyses conducted in a hold-out group of healthy participants (exposed, n = 109, unexposed, n = 315) replicated the effect of PSE on SA in the PCAL and IPL. Collectively our results show a negative, long term effect of PSE on sensory cortices that may increase risk for disease later in life.
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    Identifying brain hierarchical structures associated with Alzheimer’s disease using a regularized regression method with tree predictors
    (Oxford University Press, 2023) Zhao, Yi; Wang, Bingkai; Liu, Chin-Fu; Faria, Andreia V.; Miller, Michael I.; Caffo, Brian S.; Luo, Xi; Biostatistics and Health Data Science, School of Medicine
    Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheimer’s disease outcomes. In this study, we propose an ℓ1-type regularization for predictors that follow a hierarchical tree structure. Considering a tree as a directed acyclic graph, we interpret the model parameters from a path analysis perspective. Under this concept, the proposed penalty regulates the total effect of each predictor on the outcome. With regularity conditions, it is shown that under the proposed regularization, the estimator of the model coefficient is consistent in ℓ2-norm and the model selection is also consistent. When applied to a brain sMRI dataset acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the proposed approach identifies brain regions where atrophy in these regions demonstrates the declination in memory. With regularization on the total effects, the findings suggest that the impact of atrophy on memory deficits is localized from small brain regions, but at various levels of brain segmentation. Data used in preparation of this paper were obtained from the ADNI database.