PDF(Pubmed)
Abstract：
Multivariate approaches have recently gained in popularity to address the physiological unspecificity of neuroimaging metrics and to better characterize the complexity of biological processes underlying behavior. However, commonly used approaches are biased by the intrinsic associations between variables, or they are computationally expensive and may be more complicated to implement than standard univariate approaches. Here, we propose using the Mahalanobis distance (D2), an individual-level measure of deviation relative to a reference distribution that accounts for covariance between metrics. To facilitate its use, we introduce an open-source python-based tool for computing D2 relative to a reference group or within a single individual: the MultiVariate Comparison (MVComp) toolbox. The toolbox allows different levels of analysis (i.e., group- or subject-level), resolutions (e.g., voxel-wise, ROI-wise) and dimensions considered (e.g., combining MRI metrics or WM tracts). Several example cases are presented to showcase the wide range of possible applications of MVComp and to demonstrate the functionality of the toolbox. The D2 framework was applied to the assessment of white matter (WM) microstructure at 1) the group-level, where D2 can be computed between a subject and a reference group to yield an individualized measure of deviation. We observed that clustering applied to D2 in the corpus callosum yields parcellations that highly resemble known topography based on neuroanatomy, suggesting that D2 provides an integrative index that meaningfully reflects the underlying microstructure. 2) At the subject level, D2 was computed between voxels to obtain a measure of (dis)similarity. The loadings of each MRI metric (i.e., its relative contribution to D2) were then extracted in voxels of interest to showcase a useful option of the MVComp toolbox. These relative contributions can provide important insights into the physiological underpinnings of differences observed. Integrative multivariate models are crucial to expand our understanding of the complex brain-behavior relationships and the multiple factors underlying disease development and progression. Our toolbox facilitates the implementation of a useful multivariate method, making it more widely accessible.
摘要：
最近，多变量方法已得到普及，以解决神经成像指标的生理不特异性，并更好地表征潜在行为的生物过程的复杂性。然而,常用的方法受到变量之间的内在关联的偏差，或者它们计算昂贵，并且可能比标准单变量方法实现起来更复杂。这里,我们建议使用马氏距离(D2)，相对于参考分布的偏差的个体水平度量，用于说明度量之间的协方差。为了便于使用，我们介绍了一个基于python的开源工具，用于计算相对于参考组或单个个体的D2：多变量比较（MVComp）工具箱。工具箱允许不同级别的分析(即，团体或学科级别)，决议(例如，逐体素,ROI方面)和考虑的维度(例如，结合MRI指标或WM束)。提供了几个示例案例，以展示MVComp的广泛可能应用并演示工具箱的功能。D2框架应用于1)组水平的白质(WM)微结构评估，其中D2可以在受试者和参考组之间计算，以产生个性化的偏差度量。我们观察到，在call体中应用于D2的聚类会产生与基于神经解剖学的已知地形非常相似的切片，这表明D2提供了一个有意义地反映底层微观结构的综合指数。2）在学科层面，在体素之间计算D2以获得(dis)相似性的度量。每个MRI度量的载荷(即，然后在感兴趣的体素中提取其对D2的相对贡献），以展示MVComp工具箱的有用选项。这些相对贡献可以提供对观察到的差异的生理基础的重要见解。综合多变量模型对于扩大我们对复杂的大脑行为关系以及疾病发展和进展的多种因素的理解至关重要。我们的工具箱有助于实现有用的多变量方法，使其更广泛地获得。
