Nonparametric Inference for Neuroimaging Data

描述（由申请人提供）：在控制假阳性的同时识别激活的大脑区域是功能性神经成像中的中心问题。作为人脑项目（HBP）第一阶段的一部分，我们建议开发，评估和实施神经成像的非参数推理工具。非参数检验不需要任何假设或仅需要弱分布假设，它提供的推断具有与指定的假阳性率完全相同的假阳性率。此外，使用我们的SnPM软件的初步工作发现，非参数方法可以比参数随机场结果更强大，同时不需要随机场平滑和高阈值假设。 作为我们研究计划的一部分，我们将（1）评估现有的非参数和参数方法，（2）开发和评估新的非参数方法，（3）构建，分发和支持非参数软件工具。我们将使用非参数方法来验证参数随机场方法与真实的数据，也将评估和优化现有的非参数工具。我们将创建新的非参数工具，解决现有方法的缺点，例如，在异构平滑下有效的聚类大小测试，或可变阈值方法来集中或均匀化功率。最重要的是，我们将在我们的模态独立软件中实现所有提出的方法。我们将使我们的软件既可编写脚本，又更加用户友好，并创建基于网络的文档。此外，我们将使我们的软件与其他广泛使用的神经成像软件工具互操作，本着HBP和NIMH & NINDS NIfTI倡议的精神。 非参数方法曾经非常慢，现在对于拥有最低计算硬件的研究人员来说也是实用的。我们的软件将使非参数方法广泛可用且易于使用。我们的软件将通过允许使用任意的统计数据来促进方法的发展，而不仅仅是那些具有参数结果的统计数据，并通过为研究人员提供强大的非参数方法来促进神经科学的发展。

DESCRIPTION (provided by applicant): Identification of activated brain regions while controlling false positives is a central problem in functional neuroimaging. As part of Phase I of the Human Brain Project (HBP), we propose to develop, evaluate, and implement nonparametric inference tools for neuroimaging. Requiring no assumptions or only weak distributional assumptions, nonparametric tests offer inferences with false positive rates exactly as specified. Further, preliminary work using our SnPM software has found that nonparametric methods can be substantially more powerful than parametric random field results, while requiring none of the random field smoothness and high-threshold assumptions. As part of our research plan, we will (1) evaluate existing nonparametric and parametric methods, (2) develop and evaluate new nonparametric methods, and (3) build, distribute and support nonparametric software tools. We will use nonparametric methods to validate parametric random field methods with real data, and will also evaluate and optimize existing nonparametric tools. We will create new nonparametric tools, which address shortcomings of existing methodology, for example, cluster size tests valid under heterogeneous smoothness, or variable threshold methods to focus or homogenize power. And most significantly, we will implement all of the proposed methods in our modality-independent software. We will make our software both scriptable and more user-friendly, and create web-based documentation. In addition, we will make our software interoperable with other widely used neuroimaging software tools, in the spirit of both the HBP and the NIMH & NINDS NIfTI initiative. Once prohibitively slow, nonparametric methods are now practical for researchers with even modest computing hardware. Our software will make nonparametric methods widely available and easy to use. Our software will facilitate methodological developments by allowing use of the arbitrary statistics, instead of just those with parametric results, and facilitate neuroscientific developments by giving researchers access to powerful nonparametric methods.