Kim Hee-Jong, Shin Jeong-Hyeon, Han Cheol E, Kim Hee Jin, Na Duk L, Seo Sang Won, Seong Joon-Kyung
School of Biomedical Engineering, Korea UniversitySeoul, South Korea; Department of Bio-convergence Engineering, Korea UniversitySeoul, South Korea.
Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of MedicineSeoul, South Korea; Neuroscience Center, Samsung Medical CenterSeoul, South Korea.
Front Neurosci. 2016 Sep 1;10:394. doi: 10.3389/fnins.2016.00394. eCollection 2016.
Cortical thinning patterns in Alzheimer's disease (AD) have been widely reported through conventional regional analysis. In addition, the coordinated variance of cortical thickness in different brain regions has been investigated both at the individual and group network levels. In this study, we aim to investigate network architectural characteristics of a structural covariance network (SCN) in AD, and further to show that the structural covariance connectivity becomes disorganized across the brain regions in AD, while the normal control (NC) subjects maintain more clustered and consistent coordination in cortical atrophy variations. We generated SCNs directly from T1-weighted MR images of individual patients using surface-based cortical thickness data, with structural connectivity defined as similarity in cortical thickness within different brain regions. Individual SCNs were constructed using morphometric data from the Samsung Medical Center (SMC) dataset. The structural covariance connectivity showed higher clustering than randomly generated networks, as well as similar minimum path lengths, indicating that the SCNs are "small world." There were significant difference between NC and AD group in characteristic path lengths (z = -2.97, p < 0.01) and small-worldness values (z = 4.05, p < 0.01). Clustering coefficients in AD was smaller than that of NC but there was no significant difference (z = 1.81, not significant). We further observed that the AD patients had significantly disrupted structural connectivity. We also show that the coordinated variance of cortical thickness is distributed more randomly from one region to other regions in AD patients when compared to NC subjects. Our proposed SCN may provide surface-based measures for understanding interaction between two brain regions with co-atrophy of the cerebral cortex due to normal aging or AD. We applied our method to the AD Neuroimaging Initiative (ADNI) data to show consistency in results with the SMC dataset.
通过传统的区域分析，阿尔茨海默病（AD）中的皮质变薄模式已被广泛报道。此外，不同脑区皮质厚度的协同变化已在个体和群体网络层面进行了研究。在本研究中，我们旨在研究AD中结构协方差网络（SCN）的网络架构特征，并进一步表明，AD患者大脑区域间的结构协方差连接变得紊乱，而正常对照（NC）受试者在皮质萎缩变化中保持更聚集和一致的协调性。我们使用基于表面的皮质厚度数据，直接从个体患者的T1加权磁共振图像生成SCN，将结构连接定义为不同脑区内皮质厚度的相似性。使用三星医疗中心（SMC）数据集的形态学数据构建个体SCN。结构协方差连接显示出比随机生成的网络更高的聚类性，以及相似的最小路径长度，表明SCN是“小世界”网络。NC组和AD组在特征路径长度（z = -2.97，p < 0.01）和小世界值（z = 4.05，p < 0.01）上存在显著差异。AD组的聚类系数小于NC组，但无显著差异（z = 1.81，无显著性）。我们进一步观察到AD患者的结构连接明显紊乱。我们还表明，与NC受试者相比，AD患者皮质厚度的协同变化在从一个区域到其他区域的分布上更加随机。我们提出的SCN可能为理解由于正常衰老或AD导致的大脑皮质共同萎缩的两个脑区之间的相互作用提供基于表面的测量方法。我们将我们的方法应用于阿尔茨海默病神经影像学倡议（ADNI）数据，以显示与SMC数据集结果的一致性。
