Bayesian Spatial Point Process Modeling of Neuroimage Data

描述（由申请人提供）：功能性神经成像已成为无创研究正常和临床人群大脑的重要工具。在过去的20年里，使用神经成像方法的研究数量急剧增长。这种爆炸性的研究得到了一种简单且计算效率高的方法的支持，即质量单变量方法（MUA）。然而，尽管MUA被广泛使用，但它有几个限制：1)无法推断出效应的确切位置；2)

DESCRIPTION (provided by applicant): Functional neuroimaging has become an essential tool for non-invasively studying the brain of normal and clinical populations. The volume of research using neuroimaging methods has been growing dramatically in the last 20 years. This explosion of research has been supported by a simple and computationally efficient method known as the mass univariate approach (MUA). Despite its common use, however, there are several limitation to the MUA: 1) the inability to infer on the exact location of an effect; 2) the inability to properly account for spatial heterogeneity amongst subjects and the spatial structure of the effect; and 3) it is not designed for point pattern data, such as that from a neuroimaging meta analysis study, nor the binary valued data from multiple sclerosis lesions. To overcome these limitation of the MUA, we are proposing the development of Bayesian statistical models that explicitly address these issues. Specially, we will develop hierarchical Bayesian spatial point process models to analyze neuroimaging coordinate-level data (e.g. when only the peak location of the activation centers are available such as is the case in neuroimaging meta analysis data), binary imaging data (such as that obtained from multiple sclerosis lesions) and hierarchical Bayesian spatial process/spatial point process models for neuroimage voxel-level data (e.g. when the entire contrast or t-statistic image is available on a group of subjects). More recently, neuroscientists have been collecting longitudinal data, as well as cross-sectional data, with the intent of studying progression of disease. There is little work done on the analysis of longitudinal neuroimaging data, so we further propose to extend our modeling to incorporate the longitudinal aspect in the data as well as the cross-sectional aspect. We will implement and optimize our methods and make the software available to the public. One notable feature of this work is that the models can be used to help predict/diagnose neuropsychiatric/neurodegenerative diseases and disorders. Thus our models will assist in understanding the development of neuropsychiatric and neurodegenerative disorders, as well as normal brain development, that cannot be answered by current methods/models. This, in turn, will aid in our understanding of the human brain in normal and diseased states.