Abstract
Twin imaging studies have been valuable for understanding the contribution of the environment and genes on brain structure and function. The conventional analyses are limited due to the same amount of smoothing throughout the whole image, the arbitrary choice of smoothing extent, and the decreased power in detecting environmental and genetic effects introduced by smoothing raw images. The goal of this article is to develop a two-stage multiscale adaptive regression method (TwinMARM) for spatial and adaptive analysis of twin neuroimaging and behavioral data. The first stage is to establish the relationship between twin imaging data and a set of covariates of interest, such as age and gender. The second stage is to disentangle the environmental and genetic influences on brain structure and function. Simulation studies and real data analysis show that TwinMARM significantly outperforms the conventional analyses.
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Li, Y., Gilmore, J.H., Wang, J., Styner, M., Lin, W., Zhu, H. (2011). Two-Stage Multiscale Adaptive Regression Methods for Twin Neuroimaging Data. In: Liu, T., Shen, D., Ibanez, L., Tao, X. (eds) Multimodal Brain Image Analysis. MBIA 2011. Lecture Notes in Computer Science, vol 7012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24446-9_13
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DOI: https://doi.org/10.1007/978-3-642-24446-9_13
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