Yimei Li

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Twin imaging studies have been valuable for understanding the relative contribution of the environment and genes on brain structures and their functions. Conventional analyses of twin imaging data include three sequential steps: spatially smoothing imaging data, independently fitting a structural equation model at each voxel, and finally correcting for(More)
Many large-scale longitudinal imaging studies have been or are being widely conducted to better understand the progress of neuropsychiatric and neurodegenerative disorders and normal brain development. The goal of this article is to develop a multiscale adaptive generalized estimation equation (MAGEE) method for spatial and adaptive analysis of neuroimaging(More)
Drosophila dendritic arborization (da) neurons contain subclasses of neurons with distinct dendritic morphologies. We investigated calcium/calmodulin-dependent protein kinase II (CaMKII) regulation of dendritic structure and dynamics in vivo using optically transparent Drosophila larvae. CaMKII increases the dynamic nature and formation of dendritic(More)
We develop a novel statistical model, called multiscale adaptive regression model (MARM), for spatial and adaptive analysis of neuroimaging data. The primary motivation and application of the proposed methodology is statistical analysis of imaging data on the two-dimensional (2D) surface or in the 3D volume for various neuroimaging studies. The existing(More)
The aim of this paper is to develop a spatial Gaussian predictive process (SGPP) framework for accurately predicting neuroimaging data by using a set of covariates of interest, such as age and diagnostic status, and an existing neuroimaging data set. To achieve a better prediction, we not only delineate spatial association between neuroimaging data and(More)
Diffusion tensor imaging (DTI) is important for characterizing the structure of white matter fiber bundles as well as detailed tissue properties along these fiber bundles in vivo. There has been extensive interest in the analysis of diffusion properties measured along fiber tracts as a function of age, diagnostic status, and gender, while controlling for(More)
This paper explores the network performance and costs associated with the deployment, labor, and maintenance of a long-term outdoor multi-hop wireless sensor network (WSN) located at the Audubon Society of Western Pennsylvania (ASWP), which has been in operation for more than four years for environmental data collection. The WSN performance is studied over(More)
—Data compression is a useful technique in the deployments of resource-constrained wireless sensor networks (WSNs) for energy conservation. In this letter, we present a new lossless data compression algorithm in WSNs. Compared to existing WSN data compression algorithms, our proposed algorithm is not only efficient but also highly robust for diverse WSN(More)
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