Dr. Yasheng Chen Profile

Dr. Yasheng Chen

at Univ of North Carolina at Chapel Hill

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Publications (3)

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Proceedings Article | 27 March 2009 Paper

RADTI: regression analyses of diffusion tensor images

Yimei Li, Hongtu Zhu, Yasheng Chen, Joseph Ibrahim, Hongyu An, Weili Lin, Colin Hall, Dinggang Shen

Proceedings Volume 7259, 72591C (2009) https://doi.org/10.1117/12.812328

KEYWORDS: Diffusion, Statistical analysis, Diffusion tensor imaging, Brain, Statistical methods, Diagnostics, Analytical research, Algorithm development, Anisotropy, Error analysis

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Diffusion tensor image (DTI) is a powerful tool for quantitatively assessing the integrity of anatomical connectivity in white matter in clinical populations. The prevalent methods for group-level analysis of DTI are statistical analyses of invariant measures (e.g., fractional anisotropy) and principal directions across groups. The invariant measures and principal directions, however, do not capture all information in full diffusion tensor, which can decrease the statistical power of DTI in detecting subtle changes of white matters. Thus, it is very desirable to develop new statistical methods for analyzing full diffusion tensors. In this paper, we develop a set of toolbox, called RADTI, for the analysis of the full diffusion tensors as responses and establish their association with a set of covariates. The key idea is to use the recent development of log-Euclidean metric and then transform diffusion tensors in a nonlinear space into their matrix logarithms in a Euclidean space. Our regression model is a semiparametric model, which avoids any specific parametric assumptions. We develop an estimation procedure and a test procedure based on score statistics and a resampling method to simultaneously assess the statistical significance of linear hypotheses across a large region of interest. Monte Carlo simulations are used to examine the finite sample performance of the test procedure for controlling the family-wise error rate. We apply our methods to the detection of statistical significance of diagnostic and age effects on the integrity of white matter in a diffusion tensor study of human immunodeficiency virus.

Proceedings Article | 27 March 2009 Paper

Hierarchical unbiased group-wise registration for atlas construction and population comparison

Yasheng Chen, Dinggang Shen, Hongtu Zhu, Hongyu An, John Gilmore, Weili Lin

Proceedings Volume 7259, 72592V (2009) https://doi.org/10.1117/12.812039

KEYWORDS: Image registration, Brain, Anisotropy, Image storage, Neuroimaging, Statistical analysis, Silicon, Diffusion tensor imaging, Expectation maximization algorithms, Image processing

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A novel hierarchical unbiased group-wise registration is developed to robustly transform each individual image towards a common space for atlas based analysis. This hierarchical group-wise registration approach consists of two main components, (1) data clustering to group similar images together and (2) unbiased group-wise registration to generate a mean image for each cluster. The mean images generated in the lower hierarchical level are regarded as the input images for the higher hierarchy. In the higher hierarchical level, these input images will be further clustered and then registered by using the same two components mentioned. This hierarchical bottom-up clustering and within-cluster group-wise registration is repeated until a final mean image for the whole population is formed. This final mean image represents the common space for all the subjects to be warped to in order for the atlas based analysis. Each individual image at the bottom of the constructed hierarchy is transformed towards the root node through concatenating all the intermediate displacement fields. In order to evaluate the performance of the proposed hierarchical registration in atlas based statistical analysis, comparisons were made with the conventional group-wise registration in detecting simulated brain atrophy as well as fractional anisotropy differences between neonates and 1-year-olds. In both cases, the proposed approach demonstrated improved sensitivity (higher t-scores) than the conventional unbiased registration approach.

Proceedings Article | 27 March 2009 Paper

LSTGEE: longitudinal analysis of neuroimaging data

Yimei Li, Hongtu Zhu, Yasheng Chen, Hongyu An, John Gilmore, Weili Lin, Dinggang Shen

Proceedings Volume 7259, 72590F (2009) https://doi.org/10.1117/12.812432

KEYWORDS: Neuroimaging, Brain, Statistical analysis, Image processing, Diffusion, Anisotropy, Data modeling, Analytical research, Statistical methods, Diagnostics

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Longitudinal imaging studies are essential to understanding the neural development of neuropsychiatric disorders, substance use disorders, and normal brain. Using appropriate image processing and statistical tools to analyze the imaging, behavioral, and clinical data is critical for optimally exploring and interpreting the findings from those imaging studies. However, the existing imaging processing and statistical methods for analyzing imaging longitudinal measures are primarily developed for cross-sectional neuroimaging studies. The simple use of these cross-sectional tools to longitudinal imaging studies will significantly decrease the statistical power of longitudinal studies in detecting subtle changes of imaging measures and the causal role of time-dependent covariate in disease process. The main objective of this paper is to develop longitudinal statistics toolbox, called LSTGEE, for the analysis of neuroimaging data from longitudinal studies. We develop generalized estimating equations for jointly modeling imaging measures with behavioral and clinical variables from longitudinal studies. We develop a test procedure based on a score test statistic and a resampling method to test linear hypotheses of unknown parameters, such as associations between brain structure and function and covariates of interest, such as IQ, age, gene, diagnostic groups, and severity of disease. We demonstrate the application of our statistical methods to the detection of the changes of the fractional anisotropy across time in a longitudinal neonate study. Particularly, our results demonstrate that the use of longitudinal statistics can dramatically increase the statistical power in detecting the changes of neuroimaging measures. The proposed approach can be applied to longitudinal data with multiple outcomes and accommodate incomplete and unbalanced data, i.e., subjects with different number of measurements.

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