Ho Tsung-Ying, Huang Shu-Hua, Huang Chi-Wei, Lin Kun-Ju, Hsu Jung-Lung, Huang Kuo-Lun, Chen Ko-Ting, Chang Chiung-Chih, Hsiao Ing-Tsung, Huang Sheng-Yao
Department of Nuclear Medicine, Linkou Chang Gung Memorial Hospital, Chang Gung University, Taoyuan, Taiwan.
Department of Nuclear Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan.
J Imaging Inform Med. 2025 Apr;38(2):681-693. doi: 10.1007/s10278-024-01230-7. Epub 2024 Sep 4.
Amyloid plaques, implicated in Alzheimer's disease, exhibit a spatial propagation pattern through interconnected brain regions, suggesting network-driven dissemination. This study utilizes PET imaging to investigate these brain connections and introduces an innovative method for analyzing the amyloid network. A modified version of a previously established method is applied to explore distinctive patterns of connectivity alterations across cognitive performance domains. PET images illustrate differences in amyloid accumulation, complemented by quantitative network indices. The normal control group shows minimal amyloid accumulation and preserved network connectivity. The MCI group displays intermediate amyloid deposits and partial similarity to normal controls and AD patients, reflecting the evolving nature of cognitive decline. Alzheimer's disease patients exhibit high amyloid levels and pronounced disruptions in network connectivity, which are reflected in low levels of global efficiency (Eg) and local efficiency (Eloc). It is mostly in the temporal lobe where connectivity alterations are found, particularly in regions related to memory and cognition. Network connectivity alterations, combined with amyloid PET imaging, show potential as discriminative markers for different cognitive states. Dataset-specific variations must be considered when interpreting connectivity patterns. The variability in MCI and AD overlap emphasizes the heterogeneity in cognitive decline progression, suggesting personalized approaches for neurodegenerative disorders. This study contributes to understanding the evolving network characteristics associated with normal cognition, MCI, and AD, offering valuable insights for developing diagnostic and prognostic markers.
与阿尔茨海默病相关的淀粉样斑块在相互连接的脑区呈现出空间传播模式,提示其传播受网络驱动。本研究利用正电子发射断层扫描(PET)成像来研究这些脑连接,并引入了一种分析淀粉样蛋白网络的创新方法。应用先前建立方法的改良版本来探索跨认知表现领域的独特连接改变模式。PET图像展示了淀粉样蛋白积累的差异,并辅以定量网络指标。正常对照组显示出最小程度的淀粉样蛋白积累且网络连接保持完好。轻度认知障碍(MCI)组表现出中等程度的淀粉样蛋白沉积,与正常对照组和阿尔茨海默病患者部分相似,反映了认知衰退的渐进性。阿尔茨海默病患者表现出高淀粉样蛋白水平和明显的网络连接中断,这体现在全局效率(Eg)和局部效率(Eloc)水平较低。连接改变主要出现在颞叶,特别是与记忆和认知相关的区域。网络连接改变与淀粉样蛋白PET成像相结合,显示出作为不同认知状态鉴别标志物的潜力。在解释连接模式时必须考虑特定数据集的变化。MCI和阿尔茨海默病重叠部分的变异性强调了认知衰退进展的异质性,提示针对神经退行性疾病的个性化方法。本研究有助于理解与正常认知、MCI和阿尔茨海默病相关的不断演变的网络特征,为开发诊断和预后标志物提供了有价值的见解。