Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China.
J Alzheimers Dis. 2023;94(4):1487-1502. doi: 10.3233/JAD-230341.
Dementia presents a significant burden to patients and healthcare systems worldwide. Early and accurate diagnosis, as well as differential diagnosis of various types of dementia, are crucial for timely intervention and management. However, there is currently a lack of clinical tools for accurately distinguishing between these types.
This study aimed to investigate the differences in the structural white matter (WM) network among different types of cognitive impairment/dementia using diffusion tensor imaging, and to explore the clinical relevance of the structural network.
A total of 21 normal control, 13 subjective cognitive decline (SCD), 40 mild cognitive impairment (MCI), 22 Alzheimer's disease (AD), 13 mixed dementia (MixD), and 17 vascular dementia (VaD) participants were recruited. Graph theory was utilized to construct the brain network.
Our findings revealed a monotonic trend of disruption in the brain WM network (VaD > MixD > AD > MCI > SCD) in terms of decreased global efficiency, local efficiency, and average clustering coefficient, as well as increased characteristic path length. These network measurements were significantly associated with the clinical cognition index in each disease group separately.
These findings suggest that structural WM network measurements can be utilized to differentiate between different types of cognitive impairment/dementia, and these measurements can provide valuable cognition-related information.
痴呆症给全球的患者和医疗系统带来了重大负担。早期、准确的诊断，以及各种类型痴呆症的鉴别诊断，对于及时干预和管理至关重要。然而，目前缺乏用于准确区分这些类型的临床工具。
本研究旨在使用弥散张量成像研究不同类型认知障碍/痴呆症之间结构白质（WM）网络的差异，并探讨结构网络的临床相关性。
共纳入 21 名正常对照、13 名主观认知下降（SCD）、40 名轻度认知障碍（MCI）、22 名阿尔茨海默病（AD）、13 名混合性痴呆（MixD）和 17 名血管性痴呆（VaD）患者。采用图论构建脑网络。
我们的研究结果表明，WM 网络的破坏呈单调趋势（VaD＞MixD＞AD＞MCI＞SCD），表现在全局效率、局部效率和平均聚类系数降低，特征路径长度增加。这些网络测量与每个疾病组的临床认知指数显著相关。
这些发现表明，结构 WM 网络测量可用于区分不同类型的认知障碍/痴呆症，这些测量可提供有价值的认知相关信息。
