Statistical Methods for Neuroimaging Data

神经影像数据的统计方法

• 批准号: 6659909
• 负责人: F. DuBois Bowman
• 金额: $ 9.92万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-10 至 2007-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6659909
• 关键词: anxiety disorders; bioimaging /biomedical imaging; brain imaging /visualization /scanning; clinical research; computational neuroscience; computer program /software; computer system design /evaluation; drug addiction; functional magnetic resonance imaging; human data; image enhancement; image processing; mathematical model; method development; model design /development; neural information processing; neuroimaging; positron emission tomography; schizophrenia; statistics /biometry

DESCRIPTION: (provided by applicant) The long-term objective of this application is to establish the candidate as a leading member of multi-disciplinary research teams and as an independent researcher in statistical methodology for neuroimaging studies. The training plan complements the candidate?s strong statistical background by providing foundations in neuroscience, biomedical imaging, and mental health. The research plan focuses on new statistical methods for neuroimaging studies of mental health, in particular, schizophrenia, anxiety disorder, and addiction. One aim is to develop state-of-the-art statistical methodology to identify brain regions exhibiting similar functional magnetic resonance imaging (fMRI) profiles. Displays of colored brain images will distinguish clusters, and dynamic images will depict cluster changes across times, study conditions, or distance metrics. Several clustering methods will be compared and approximate approaches will be developed to increase computational efficiency. The clustering methodology will allow evaluation of the use of multiple brain regions by subjects when performing tasks, experiencing emotional states, or exhibiting certain behaviors. Other specific aims include developing statistical models for positron emission tomography (PET) and fMRI data incorporating intra-subject correlation. One method will use linear models with correlated errors and random effects. A second method will use Bayesian hierarchical models directly accounting for spatial correlation between and temporal correlation within brain voxels through various covariance models. Another approach will model temporal correlation directly and spatial correlation indirectly through prior distributions of voxel-specific parameters, e.g., pnors inducing similarity for neighboring voxels. Also, spatial networks will be extended to include voxels that are ?close? according to anatomical or physiological connections. Computer software for these research developments will be made accessible to neuroimaging scientists.

描述：（由申请人提供）本申请的长期目标是使候选人成为多学科研究团队的主要成员以及神经影像研究统计方法学的独立研究员。培训计划补充 候选人拥有强大的统计背景，为神经科学、生物医学成像和心理健康奠定了基础。该研究计划侧重于心理健康神经影像学研究的新统计方法，特别是精神分裂症、焦虑症和成瘾。 目标之一是开发最先进的统计方法来识别表现出相似功能磁共振成像 (fMRI) 特征的大脑区域。彩色大脑图像的显示将区分聚类，动态图像将描述聚类随时间、研究条件或距离度量的变化。对几种聚类方法进行比较和近似 将开发方法以提高计算效率。聚类方法将允许评估受试者在执行任务、经历情绪状态或表现出某些行为时对多个大脑区域的使用情况。 其他具体目标包括开发包含受试者内相关性的正电子发射断层扫描 (PET) 和功能磁共振成像数据的统计模型。一种方法将使用具有相关误差和随机效应的线性模型。第二种方法将使用贝叶斯分层模型直接解释大脑体素之间的空间相关性和脑体素内的时间相关性 通过各种协方差模型。另一种方法将直接建模时间相关性，并通过体素特定参数的先验分布间接建模空间相关性，例如，pnors 诱导相邻体素的相似性。此外，空间网络将扩展到包括“接近”的体素。根据解剖学或生理学联系。电脑 用于这些研究进展的软件将可供神经影像科学家使用。