MARM: multiscale adaptive regression models for neuroimaging data.
MARM: multiscale adaptive regression models for neuroimaging data.
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DOI：
10.1007/978-3-642-02498-6_26
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发表时间：
2009
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我们开发了一种新颖的统计模型，称为多尺度自适应回归模型（MARM），用于神经影像数据的空间和自适应分析。所提出方法的主要动机和应用是对各种神经影像研究的二维 (2D) 表面或 3D 体积中的成像数据进行统计分析。现有的体素方法对于成像数据的分析有几个主要限制，这凸显了方法论发展的巨大需求。体素方式本质上将所有体素视为独立单元，而神经影像数据本质上是空间相关的，并且通常预期具有相当尖锐边缘的空间连续激活区域。体素方法之前的初始平滑步骤通常会模糊激活区域边缘附近的图像数据，因此会显着增加误报和漏报的数量。 MARM 是为解决这些限制而开发的，在成像数据分析中具有三个关键特征：空间性、层次性和自适应性。 MARM 在每个位置（称为体素）构建一个小球体，并使用这些跨所有体素连续连接的球体来捕获成像观察之间的空间依赖性。然后，MARM 通过增加每个体素周围球形邻域的半径来构建分层嵌套球体，并将每个体素给定半径内的所有数据与适当的权重相结合，以自适应地计算参数估计和测试统计数据。从理论上讲，我们首先确定 MARM 优于经典的体素方法。模拟研究用于演示该方法并检查 MARM 的有限样本性能。我们将我们的方法应用于阿尔茨海默病的神经影像研究中脑萎缩的空间模式的检测。我们对已知地面事实的模拟研究证实，MARM 显着优于体素方法。
We develop a novel statistical model, called multiscale adaptive regression model (MARM), for spatial and adaptive analysis of neuroimaging data. The primary motivation and application of the proposed methodology is statistical analysis of imaging data on the two-dimensional (2D) surface or in the 3D volume for various neuroimaging studies. The existing voxel-wise approach has several major limitations for the analyses of imaging data, underscoring the great need for methodological development. The voxel-wise approach essentially treats all voxels as independent units, whereas neuroimaging data are spatially correlated in nature and spatially contiguous regions of activation with rather sharp edges are usually expected. The initial smoothing step before the voxel-wise approach often blurs the image data near the edges of activated regions and thus it can dramatically increase the numbers of false positives and false negatives. The MARM, which is developed for addressing these limitations, has three key features in the analysis of imaging data: being spatial, being hierarchical, and being adaptive. The MARM builds a small sphere at each location (called voxel) and use these consecutively connected spheres across all voxels to capture spatial dependence among imaging observations. Then, the MARM builds hierarchically nested spheres by increasing the radius of a spherical neighborhood around each voxel and combine all the data in a given radius of each voxel with appropriate weights to adaptively calculate parameter estimates and test statistics. Theoretically, we first establish that the MARM outperforms classical voxel-wise approach. Simulation studies are used to demonstrate the methodology and examine the finite sample performance of the MARM. We apply our methods to the detection of spatial patterns of brain atrophy in a neuroimaging study of Alzheimers disease. Our simulation studies with known ground truth confirm that the MARM significantly outperforms the voxel-wise methods.