Most previous imaging studies have used traditional Pearson correlation analysis to construct brain networks. This approach fails to adequately and completely account for the interaction between adjacent brain regions. In this study, we used the L1-norm linear regression model to test the small-world attributes of the brain networks of three groups of patients, namely, those with mild cognitive impairment (MCI), Alzheimer’s disease (AD), and healthy controls (HCs); we attempted to identify the method that may detect minor differences in MCI and AD patients. Twenty-four AD patients, 33 MCI patients, and 27 HC elderly subjects were subjected to functional MRI (fMRI). We applied traditional Pearson correlation and the L1-norm to construct the brain networks and then tested the small-world attributes by calculating the following parameters: clustering coefficient (Cp), path length (Lp), global efficiency (Eg), and local efficiency (Eloc). As expected, L1 could detect slight changes, mainly in MCI patients expressing higher Cp and Eloc; however, no statistical differences were found between MCI patients and HCs in terms of Cp, Lp, Eg, and Eloc, using Pearson correlation. Compared with HCs, AD patients expressed a lower Cp, Eloc, and Lp and an increased Eg using both connectivity metrics. The statistical differences between the groups indicated the brain networks constructed by the L1-norm were more sensitive to detect slight small-world network changes in early stages of AD.

中文翻译：

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基于L1范数回归模型构建的脑网络在检测早期AD的小世界网络变化方面更为敏感。

以前的大多数影像学研究都使用传统的Pearson相关分析来构建大脑网络。这种方法不能充分和完全说明相邻大脑区域之间的相互作用。在这项研究中，我们使用L1-norm线性回归模型来测试三组患者的大脑网络的小世界属性，即患有轻度认知障碍（MCI），阿尔茨海默氏病（AD）和健康对照组的患者（HCs）；我们试图确定可以检测MCI和AD患者微小差异的方法。24例AD患者，33例MCI患者和27例HC老年受试者接受了功能性MRI（fMRI）检查。我们应用传统的Pearson相关和L1范数来构建大脑网络，然后通过计算以下参数来测试小世界属性：聚类系数（Cp），路径长度（Lp），全局效率（Eg）和局部效率（Eloc）。正如预期的那样，L1可以检测到轻微的变化，主要在表达较高Cp和Eloc的MCI患者中；然而，使用皮尔森相关性，在MCI患者和HCs之间，在Cp，Lp，Eg和Eloc方面未发现统计学差异。与HC相比，使用这两种连通性指标，AD患者的Cp，Eloc和Lp较低，而Eg则较高。两组之间的统计差异表明，由L1-norm构造的大脑网络对检测AD早期小世界网络的细微变化更为敏感。使用皮尔森相关性，在CCI，Lp，Eg和Eloc方面，在MCI患者和HC之间没有发现统计学差异。与HC相比，使用这两种连通性指标，AD患者的Cp，Eloc和Lp较低，而Eg则较高。两组之间的统计差异表明，由L1-norm构造的大脑网络对检测AD早期小世界网络的轻微变化更为敏感。使用皮尔森相关性，在CCI，Lp，Eg和Eloc方面，在MCI患者和HC之间没有发现统计学差异。与HC相比，使用这两种连通性指标，AD患者的Cp，Eloc和Lp较低，而Eg则较高。两组之间的统计差异表明，由L1-norm构造的大脑网络对检测AD早期小世界网络的轻微变化更为敏感。