Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan; Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan.
Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan.
Neuroimage Clin. 2019;22:101680. doi: 10.1016/j.nicl.2019.101680. Epub 2019 Jan 25.
Alzheimer's disease (AD), an irreversible neurodegenerative disease, is the most common type of dementia in elderly people. This present study incorporated multiple structural and functional connectivity metrics into a graph theoretical analysis framework and investigated alterations in brain network topology in patients with mild cognitive impairment (MCI) and AD. By using this multiparametric analysis, we expected different connectivity metrics may reflect additional or complementary information regarding the topological changes in brain networks in MCI or AD. In our study, a total of 73 subjects participated in this study and underwent the magnetic resonance imaging scans. For the structural network, we compared commonly used connectivity metrics, including fractional anisotropy and normalized streamline count, with multiple diffusivity-based metrics. We compared Pearson correlation and covariance by investigating their sensitivities to functional network topology. Significant disruption of structural network topology in MCI and AD was found predominantly in regions within the limbic system, prefrontal and occipital regions, in addition to widespread alterations of local efficiency. At a global scale, our results showed that the disruption of the structural network was consistent across different edge definitions and global network metrics from the MCI to AD stages. Significant changes in connectivity and tract-specific diffusivity were also found in several limbic connections. Our findings suggest that tract-specific metrics (e.g., fractional anisotropy and diffusivity) provide more sensitive and interpretable measurements than does metrics based on streamline count. Besides, the use of inversed radial diffusivity provided additional information for understanding alterations in network topology caused by AD progression and its possible origins. Use of this proposed multiparametric network analysis framework may facilitate early MCI diagnosis and AD prevention.
阿尔茨海默病（AD）是一种不可逆转的神经退行性疾病，是老年人最常见的痴呆类型。本研究将多种结构和功能连接度量纳入图论分析框架，研究了轻度认知障碍（MCI）和 AD 患者大脑网络拓扑结构的变化。通过使用这种多参数分析，我们预计不同的连接度量可能反映了 MCI 或 AD 大脑网络拓扑变化的附加或补充信息。在我们的研究中，共有 73 名受试者参与了这项研究，并接受了磁共振成像扫描。对于结构网络，我们比较了常用的连接度量，包括分数各向异性和标准化流线计数，以及多种基于扩散的度量。通过研究它们对功能网络拓扑的敏感性，我们比较了 Pearson 相关和协方差。在 MCI 和 AD 中，结构网络拓扑的显著破坏主要发生在边缘系统、前额叶和枕叶区域内，以及局部效率的广泛改变。在全局范围内，我们的结果表明，从 MCI 到 AD 阶段，不同边缘定义和全局网络度量的结构网络破坏是一致的。在几个边缘连接中也发现了连接和束内特定扩散的显著变化。我们的研究结果表明，束内特定度量（例如分数各向异性和扩散性）比基于流线计数的度量提供了更敏感和可解释的测量。此外，使用反转径向扩散性为理解 AD 进展及其可能起源引起的网络拓扑变化提供了额外的信息。使用这个提出的多参数网络分析框架可能有助于早期 MCI 诊断和 AD 预防。
