Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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DOI：
10.3791/50319
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发表时间：
2013-06-01
影响因子：
1.2
通讯作者：
Eidelberg, David
中科院分区：
文献类型：
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作者：
Spetsieris, Phoebe;Ma, Yilong;Eidelberg, David
缩放子轮廓模型(SSM)(1-4)是一种基于多变量主元分析的算法，它识别患者和对照组脑图像数据的主要变异来源，同时剔除较小的成分(图1)。直接应用于稳态多模图像的逐个体素的协方差数据，整个组图像集可以被缩减为几个显著的线性独立的协方差模式和相应的主题分数。每个模式被称为组不变子轮廓(GIS)，是代表功能上相互关联的大脑区域的空间分布网络的正交主成分。通过固有的对数转换和数据(2，5，6)的平均居中，可以消除可能掩盖较小网络特定贡献的大的全球平均标量效应。受试者用一个简单的标量分数来表示这些模式的不同程度，该分数可以与独立的临床或心理测量学描述符(7，8)相关联。使用受试者得分的Logistic回归分析(即模式表达值)，可以得到线性系数，以将多个主成分组合成与疾病相关的单一空间协方差模式，即具有改进的患者与健康对照受试者的区分度的复合网络(5，6)。可以使用自举重采样技术(9)来执行派生集中的交叉验证。通过对预期数据集(10)中的派生模式的直接得分评估来容易地确认正向验证。一旦得到验证，疾病相关模式就可以用来相对于固定的参考样本对个别患者进行评分，参考样本通常是在原始模式推导中(与疾病组一起)使用的一组健康受试者(11)。这些标准值又可用于辅助鉴别诊断(12，13)，并在网络级别评估疾病进展和治疗效果(7，14-16)。我们给出了一个使用我们的内部软件将这种方法应用于帕金森氏病患者和正常对照的FDG PET数据的例子，以得出疾病的特征协方差模式生物标记物。
The scaled subprofile model (SSM)(1-4) is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data(2,5,6). Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors(7,8). Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects(5,6). Cross-validation within the derivation set can be performed using bootstrap resampling techniques(9). Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets(10). Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation(11). These standardized values can in turn be used to assist in differential diagnosis(12,13) and to assess disease progression and treatment effects at the network level(7,14-16). We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.