Carbonell Felix, Charil Arnaud, Zijdenbos Alex P, Evans Alan C, Bedell Barry J
Biospective Inc., Montreal, QC, Canada.
1] Biospective Inc., Montreal, QC, Canada [2] Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
J Cereb Blood Flow Metab. 2014 Dec;34(12):1936-43. doi: 10.1038/jcbfm.2014.165. Epub 2014 Oct 8.
Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI).
传统的脑连接性分析通常基于区域间相关性的评估。鉴于相关系数是由协方差和方差推导而来，协方差中的组间差异可能会被方差项的差异所掩盖。为便于对连接性进行全面评估，我们提出了一个统一的统计框架，该框架对相关系数的各个项进行探究。我们使用来自阿尔茨海默病神经影像倡议（ADNI）研究的[18F]2-氟-2-脱氧葡萄糖（FDG）正电子发射断层扫描（PET）数据评估了该方法在代谢连接性分析中的效用。作为该方法效用的一个示例，我们检查了轻度认知障碍（MCI）患者中β-淀粉样蛋白负担低和高的角回和楔前叶种子区域的代谢连接性。这种新的多变量方法使我们能够识别代谢连接组中的改变，而使用经典的基于种子的相关性分析则无法检测到这些改变。最终，这种新方法应可扩展到脑网络分析，并广泛应用于其他成像模态，如功能磁共振成像（MRI）。
