Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan; Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Hackettstown Medical Center, Atlantic Health System, Hackettstown, NJ, United States; Touro College of Osteopathic Medicine, New York, NY, United States; Rutgers Medical School, Newark, NJ, United States.
Prog Brain Res. 2024;290:179-190. doi: 10.1016/bs.pbr.2024.07.005. Epub 2024 Aug 31.
This research examined the distinctions in brain network characteristics among individuals with Alzheimer's disease (AD), mild cognitive impairment (MCI), and a control group. Magnetic resonance imaging (MRI) and mini-mental state examination (MMSE) data were retrieved from the Alzheimer's Disease Neuroimaging Initiative (ANDI) database, comprising 40 subjects in each group. Correlation maps for evaluating brain network connectivity were generated using fractal dimension (FD) analysis, a method capable of quantifying morphological changes in cortical and cerebral regions. Employing graph theory, each parcellated brain region was represented as a node, and edges between nodes were utilized to compute small-world network properties for each group. In the comparison between control and AD demonstrated the significantly lower FD values (P<0.05) in temporal lobe, motor cortex, part of occipital and parietal, hippocampus, amygdala, and entorhinal cortex, which present the atrophy. Similarly, comparing control group to MCIs, regions closely associated with memory, such as the hippocampus, showed significantly lower FD values. Furthermore, both AD and MCI groups displayed diminished connectivity and decreased network efficiency. In conclusion, fractal dimension (FD) analysis illustrate the progression of structural declination from mild cognitive impairment (MCI) to Alzheimer's disease (AD). Additionally, structural small-world network analysis presents itself as a potential method for assessing network efficiency and the progression of AD. Moving forward, further clinical assessments are warranted to validate the findings observed in this study.
本研究旨在探讨阿尔茨海默病（AD）、轻度认知障碍（MCI）患者与对照组之间脑网络特征的差异。研究数据来自阿尔茨海默病神经影像学倡议（ADNI）数据库，共包括 40 名 AD 患者、40 名 MCI 患者和 40 名对照组个体的磁共振成像（MRI）和简易精神状态检查（MMSE）数据。采用分形维数（FD）分析生成评估脑网络连通性的相关图谱，该方法可量化皮质和大脑区域的形态变化。利用图论，将每个分割的脑区表示为一个节点，通过节点之间的边来计算每个组的小世界网络属性。在 AD 与对照组的比较中，发现颞叶、运动皮层、部分枕叶和顶叶、海马体、杏仁核和内嗅皮层的 FD 值显著降低（P<0.05），这些区域均出现萎缩。同样，在将对照组与 MCI 进行比较时，发现与记忆密切相关的区域，如海马体，FD 值显著降低。此外，AD 和 MCI 组的连通性降低，网络效率下降。总之，分形维数（FD）分析表明，从轻度认知障碍（MCI）到阿尔茨海默病（AD）的结构衰退。此外，结构小世界网络分析可能成为评估网络效率和 AD 进展的一种方法。需要进一步的临床评估来验证本研究中的发现。
