Scaled subprofile modeling of resting state imaging data in Parkinson's disease: methodological issues.
Scaled subprofile modeling of resting state imaging data in Parkinson's disease: methodological issues.
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DOI：
10.1016/j.neuroimage.2010.10.025
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发表时间：
2011-02-14
期刊：
影响因子：
5.7
通讯作者：
Eidelberg D
中科院分区：
文献类型：
--
作者：
Spetsieris PG;Eidelberg D
帕金森病（PD）的持续脑功能异常很难确定，因为与正常状态的差异往往很微妙。在这方面，多元分析方法的应用是成功的，但并非没有误解和争议。缩放子剖面模型（SSM）是一种基于主成分分析（PCA）的空间协方差方法，已经获得了关于PD和其他神经退行性疾病背后的功能性脑组织特征异常的关键信息。然而，疾病相关空间协方差模式（代谢脑网络）的相关性及其最有效的推导方法一直是争论的主题。我们在这里讨论这些问题，并讨论正确应用该方法的固有优势以及错误应用该方法的影响。我们表明，在SSM中，使用平均全球代谢率（GMR）或来自“参考”大脑区域（如小脑）的区域值进行比率预归一化可能需要单变量分析方法。我们讨论可能产生错误或混淆因素的方法偏差。
Consistent functional brain abnormalities in Parkinson's disease (PD) are difficult to pinpoint because differences from the normal state are often subtle. In this regard, the application of multivariate methods of analysis has been successful but not devoid of misinterpretation and controversy. The Scaled Subprofile Model (SSM), a principal components analysis (PCA)-based spatial covariance method, has yielded critical information regarding the characteristic abnormalities of functional brain organization that underlie PD and other neurodegenerative disorders. However, the relevance of disease-related spatial covariance patterns (metabolic brain networks) and the most effective methods for their derivation has been a subject of debate. We address these issues here and discuss the inherent advantages of proper application as well as the effects of the misapplication of this methodology. We show that ratio pre-normalization using the mean global metabolic rate (GMR) or regional values from a “reference” brain region (e.g. cerebellum) that may be required in univariate analytical approaches is obviated in SSM. We discuss deviations of the methodology that may yield erroneous or confounding factors.
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