Consistency of Graph Theoretical Measurements of Alzheimer's Disease Fiber Density Connectomes Across Multiple Parcellation Scales.
Consistency of Graph Theoretical Measurements of Alzheimer's Disease Fiber Density Connectomes Across Multiple Parcellation Scales.
复制标题
阿尔茨海默病纤维密度连接体在多个分区尺度上的图论测量的一致性。
DOI：
10.1109/bibm55620.2022.9995657
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
ADNI
中科院分区：
文献类型：
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作者：
Xu,Frederick;Garai,Sumita;Duong-Tran,Duy;Saykin,AndrewJ;Zhao,Yize;Shen,Li;ADNI
图论测量经常被用来研究阿尔茨海默病人脑连接体中中断的连接。然而，先前的研究已经指出，图形创建方法的差异是混淆因素，可能会改变这些措施中发现的拓扑观察。在这项研究中，我们进行了一项新的调查，关于从扩散张量成像的纤维密度网络计算的图论测量的parcellation规模的影响。我们计算了平均聚类系数、传递性、特征路径长度和全局效率的4个网络范围的图形理论测量，并测试了这些测量是否能够在洛桑分组的5个尺度上一致地识别阿尔茨海默病神经成像倡议（ADNI）队列中健康对照（HC）、轻度认知障碍（MCI）和AD组之间的组差异。我们发现，隔离措施的传递性提供了最大的一致性，在区分健康和患病群体的规模，而其他措施的规模选择不同程度的影响。全球效率是我们测试的第二个最一致的指标，该指标可以在所有5个量表中区分HC和MCI，并在5个量表中的3个量表中区分HC和AD。特征路径长度是高度敏感的规模的变化，证实了以前的研究结果，并不能确定组的差异，在许多规模。平均聚类系数也受到规模的极大影响，因为它始终无法识别高分辨率包裹中的组差异。从这些结果中，我们得出结论，许多图形理论的措施是敏感的parcellation规模的选择，并需要进一步发展的方法，以提供一个更强大的表征AD的关系中断连接。
Graph theoretical measures have frequently been used to study disrupted connectivity in Alzheimer’s disease human brain connectomes. However, prior studies have noted that differences in graph creation methods are confounding factors that may alter the topological observations found in these measures. In this study, we conduct a novel investigation regarding the effect of parcellation scale on graph theoretical measures computed for fiber density networks derived from diffusion tensor imaging. We computed 4 network-wide graph theoretical measures of average clustering coefficient, transitivity, characteristic path length, and global efficiency, and we tested whether these measures are able to consistently identify group differences among healthy control (HC), mild cognitive impairment (MCI), and AD groups in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort across 5 scales of the Lausanne parcellation. We found that the segregative measure of transtivity offered the greatest consistency across scales in distinguishing between healthy and diseased groups, while the other measures were impacted by the selection of scale to varying degrees. Global efficiency was the second most consistent measure that we tested, where the measure could distinguish between HC and MCI in all 5 scales and between HC and AD in 3 out of 5 scales. Characteristic path length was highly sensitive to the variation in scale, corroborating previous findings, and could not identify group differences in many of the scales. Average clustering coefficient was also greatly impacted by scale, as it consistently failed to identify group differences in the higher resolution parcellations. From these results, we conclude that many graph theoretical measures are sensitive to the selection of parcellation scale, and further development in methodology is needed to offer a more robust characterization of AD’s relationship with disrupted connectivity.