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Full-Text Articles in Physical Sciences and Mathematics
Bayesian Inferences For Beta Semiparametric Mixed Models To Analyze Longitudinal Neuroimaging Data, Xiaofeng Wang, Yingxing Li
Bayesian Inferences For Beta Semiparametric Mixed Models To Analyze Longitudinal Neuroimaging Data, Xiaofeng Wang, Yingxing Li
Xiaofeng Wang
Diffusion tensor imaging (DTI) is a quantitative magnetic resonance imaging technique that measures the three-dimensional diffusion of water molecules within tissue through the application of multiple diffusion gradients. This technique is rapidly increasing in popularity for studying white matter properties and structural connectivity in the living human brain. The major measure derived from the DTI process is known as fractional anisotropy, a continuous measure restricted on the interval (0,1). Motivated from a DTI study of multiple sclerosis, we use a beta semiparametric mixed-effect regression model for the longitudinal neuroimaging data. This work extends the generalized additive model methodology with beta …
Joint Generalized Models For Multi-Dimensional Outcomes: A Case Study Of Neuroscience Data From Multi-Modalities, Xiao-Feng Wang
Joint Generalized Models For Multi-Dimensional Outcomes: A Case Study Of Neuroscience Data From Multi-Modalities, Xiao-Feng Wang
Xiaofeng Wang
This paper is motivated from the analysis of neuroscience data in a study of neural and muscular mechanisms of muscle fatigue. Multidimensional outcomes of different natures were obtained simultaneously from multiple modalities, including handgrip force, electromyography (EMG), and functional magnetic resonance imaging (fMRI). We first study individual modeling of the univariate response depending on its nature. A mixed-effects beta model and a mixed-effects simplex model are compared for modeling the force/EMG percentages. A mixed-effects negative-binomial model is proposed for modeling the fMRI counts. Then, I present a joint modeling approach to model the multidimensional outcomes together, which allows us to …
Modeling Heterogeneity And Dependence For Analysis Of Neuronal Data, Xiao-Feng Wang, Jiayang Sun, Kenneth J. Gustafson, Guang H. Yue
Modeling Heterogeneity And Dependence For Analysis Of Neuronal Data, Xiao-Feng Wang, Jiayang Sun, Kenneth J. Gustafson, Guang H. Yue
Xiaofeng Wang
In this paper, we describe two types of neuroscience problems which challenge the typical statistical models assumed for analyzing neuronal data. This offers an opportunity for new modeling and statistical inference. In the first problem, the data are spatial neural counts which are often over-dispersed and spatially correlated so that a standard Poisson regression model is inadequate. In the second problem, the data are averaged electroencephalograph signals recorded during muscle fatigue, where a time series AR(1) regression model cannot fully capture all the variation and correlation structure in the data. It is shown that an additional parameter has to be …
Spatial-Temporal Data Mining Procedure: Lasr, Xiao-Feng Wang, Jiayang Sun, Kath Bogie
Spatial-Temporal Data Mining Procedure: Lasr, Xiao-Feng Wang, Jiayang Sun, Kath Bogie
Xiaofeng Wang
This paper is concerned with the statistical development of our spatial-temporal data mining procedure, LASR (pronounced "laser"). LASR is the abbreviation for Longitudinal Analysis with Self-Registration of largep-small-n data. It was motivated by a study of "Neuromuscular Electrical Stimulation" experiments, where the data are noisy and heterogeneous, might not align from one session to another, and involve a large number of multiple comparisons. The three main components of LASR are: (1) data segmentation for separating heterogeneous data and for distinguishing outliers, (2) automatic approaches for spatial and temporal data registration, and (3) statistical smoothing mapping for identifying "activated" regions based …