1Aberdeen Biomedical Imaging Center, University of Aberdeen, Aberdeen, AB25 2ZD, UK.
2Imaging Physics, National Health Service Grampian, Aberdeen, AB25 2ZD, UK.
Int J Neural Syst. 2019 Aug;29(6):1850055. doi: 10.1142/S0129065718500557. Epub 2018 Nov 14.
Models of the human brain as a complex network of inter-connected sub-units are important in helping to understand the structural basis of the clinical features of neurodegenerative disorders. The aim of this study was to characterize in a systematic manner the differences in the structural correlation networks in cortical thickness (CT) and surface area (SA) in Alzheimer's disease (AD) and behavioral variant Fronto-Temporal Dementia (bvFTD). We have used the baseline magnetic resonance imaging (MRI) data available from a large population of patients from three clinical trials in mild to moderate AD and mild bvFTD and compared this to a well-characterized healthy aging cohort. The study population comprised 202 healthy elderly subjects, 213 with bvFTD and 213 with AD. We report that both CT and SA network architecture can be described in terms of highly correlated networks whose positive and inverse links map onto the intrinsic modular organization of the four cortical lobes. The topology of the disturbance in structural network is different in the two disease conditions, and both are different from normal aging. The changes from normal are global in character and are not restricted to fronto-temporal and temporo-parietal lobes, respectively, in bvFTD and AD, and indicate an increase in both global correlational strength and in particular nonhomologous inter-lobar connectivity defined by inverse correlations. These inverse correlations appear to be adaptive in character, reflecting coordinated increases in CT and SA that may compensate for corresponding impairment in functionally linked nodes. The effects were more pronounced in the cortical thickness atrophy network in bvFTD and in the surface area network in AD. Although lobar modularity is preserved in the context of neurodegenerative disease, the hub-like organization of networks differs both from normal and between the two forms of dementia. This implies that hubs may be secondary features of the connectivity adaptation to neurodegeneration and may not be an intrinsic property of the brain. However, analysis of the topological differences in hub-like organization CT and SA networks, and their underlying positive and negative correlations, may provide a basis for assisting in the differential diagnosis of bvFTD and AD.
作为相互连接的子单元组成的复杂网络的人脑模型，对于帮助理解神经退行性疾病的临床特征的结构基础非常重要。本研究的目的是系统地描述阿尔茨海默病（AD）和行为变异额颞叶痴呆（bvFTD）在皮质厚度（CT）和表面积（SA）的结构相关性网络中的差异。我们使用了来自三项轻度至中度 AD 和轻度 bvFTD 临床试验的大量患者的基线磁共振成像（MRI）数据，并将其与经过充分描述的健康衰老队列进行了比较。研究人群包括 202 名健康老年人，213 名 bvFTD 患者和 213 名 AD 患者。我们报告说，CT 和 SA 网络结构都可以用高度相关的网络来描述，这些网络的正相关和负相关链接映射到四个皮质叶的固有模块组织。在两种疾病状态下，结构网络的干扰拓扑是不同的，并且都与正常衰老不同。从正常到异常的变化是全局性的，并且不仅限于 bvFTD 和 AD 中的额颞叶和颞顶叶，分别表明全局相关性强度增加以及通过负相关定义的特定非同源叶间连接性增加。这些负相关似乎具有适应性特征，反映了 CT 和 SA 的协调性增加，这可能补偿了功能相关节点的相应损伤。这些效应在 bvFTD 的皮质厚度萎缩网络和 AD 的表面积网络中更为明显。尽管在神经退行性疾病的背景下保留了叶模块性，但网络的枢纽组织方式既与正常情况不同，也与两种形式的痴呆症不同。这意味着枢纽可能是对神经退行性变的连接适应的次要特征，而不是大脑的固有特性。然而，分析 CT 和 SA 网络的枢纽组织以及它们潜在的正相关和负相关的拓扑差异，可能为协助 bvFTD 和 AD 的鉴别诊断提供基础。
