Analytic Methods for Functional Neuroimaging Data

描述(申请人提供)：功能神经成像技术，包括功能磁共振成像(FMRI)和正电子发射断层扫描(PET)，是心理健康研究的强大的非侵入性工具。Fmr！的分析方法和PET数据对于确定可以解决的实质性研究问题和确保推断的有效性都是至关重要的。该项目致力于为功能性磁共振成像和正电子发射计算机断层扫描数据开发最先进的统计方法，这将对临床实践和研究产生重要的心理健康影响。长期目标是1)帮助医生为精神障碍患者做出治疗决定，这里的重点是精神分裂症和严重抑郁症，2)建立一个模型框架来描述与任务相关的大脑活动，这种活动解释了例如来自大脑区域之间复杂的神经生理联系的空间联系。为了对临床心理健康实践产生影响，一个目标是开发一种新的方法来预测个人对治疗的特定反应。具体地说，目标是根据治疗前扫描和其他患者特征预测特定患者治疗后与任务相关的脑活动模式，并预测最终症状对治疗的反应。计划中的发展需要为精神分裂症患者和从未接受治疗的抑郁症受试者构建和验证贝叶斯分层模型和准确的分类算法。第二个目标是构建一个贝叶斯分层模型，用于对大脑活动中与任务相关的变化做出推断，考虑到不同空间位置(体素)之间的功能关联。这种方法将产生与通常采用的方法类似的局部估计数，但将根据关键职能联系进行估计和调整。通过建立基于与数据非常匹配的假设的模型，拟议程序的一个主要优势是能够从相关体素中借用力量得出局部推断，通常会产生更准确的结果。第二个优势是，对扩大的解剖区域的测试可以包含对体素间相关性的估计。在目标2基础上发展的空间建模可能会导致对拟议的预测框架(目标1)的扩展。预测算法的成功开发将提供自然转化为临床环境的结果，以帮助医生做出关于精神治疗的决定。此外，提出的空间建模框架将是对现有功能神经成像数据分析方法的新贡献。这里的重点是与精神分裂症、抑郁症和可卡因依赖相关的功能磁共振成像和正电子发射计算机断层扫描数据，说明所建议的方法在一系列精神健康障碍中的潜在适用性和相关性。

DESCRIPTION (provided by applicant): Functional neuroimaging technologies, including functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), are powerful noninvasive tools for mental health research. Analytic methods for fMR! and PET data are critical both for determining substantive research questions that can be addressed and for ensuring the validity of inferences. This project seeks to develop state-of-the-art statistical methodology for fMRI and PET data that will have important mental health implications for clinical practice and research. The long-term goals are 1) to assist physicians in making treatment decisions for patients with psychiatric disorders, focusing here on schizophrenia and major depression and 2) to establish a modeling framework for characterizing task-related brain activity that accounts for spatial associations arising, for example, from complex neurophysiological links between brain regions. In an effort to make an impact on clinical mental health practices, one aim is to develop a novel approach to predict individual-specific responses to treatment. Specifically, the goal is to predict post- treatment patterns of task-related brain activity for a particular patient based on pre-treatment scans and other patient characteristics and to predict eventual symptom response to treatment. The planned developments entail constructing and validating a Bayesian hierarchical model and an accurate classification algorithm for schizophrenia patients and for never-treated depressed subjects. A second aim is to construct a Bayesian hierarchical model for making inferences regarding task-related changes in brain activity, accounting for functional associations between different spatial locations (voxels). This approach would yield localized estimates, similar to commonly applied methods, but would estimate and adjust for key functional linkages. By building a model based on assumptions that are well-suited to the data, a major advantage of the proposed procedure is the ability to draw localized inferences that borrow strength from related voxels, often yielding more accurate results. A second advantage is that tests about extended anatomical regions can incorporate estimates of between-voxel correlations. Spatial modeling developments from Aim 2 may give rise to extensions to the proposed prediction framework (Aim 1). Successful development of the predictive algorithms would provide results that translate naturally to a clinical setting to help inform physicians' decisions regarding psychiatric treatments. Furthermore, the proposed spatial modeling framework would be a novel contribution to existing analytic methods for functional neuroimaging data. The focus here on fMRI and PET data related to schizophrenia, depression, and cocaine-dependence illustrates the potential applicability and relevance of the proposed methods across a range of mental health disorders.