Zia Ghina, Shahid Syed Salman, Yang Ho-Ching, Gao Sujuan, Risacher Shannon L, Saykin Andrew J, Wu Yu-Chien
Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
Neurobiol Aging. 2025 Nov;155:113-123. doi: 10.1016/j.neurobiolaging.2025.07.008. Epub 2025 Jul 12.
Human brains undergo considerable morphologic variation with age, a primary risk factor for neurodegenerative disorders. While aging often causes neurocognitive decline, its governing biological mechanisms remain unclear. These age-related brain microstructural changes may be quantified by advanced diffusion MRI (dMRI) with tissue-specific compartment modeling approach. This longitudinal study investigates age-related differences in hippocampal subfields vulnerable to early stages of Alzheimer's disease (AD). Thirty-seven cognitively normal (CN) older adults (70.6 ± 6.7 years) from the Indiana Alzheimer's Disease Research Center (IADRC) underwent baseline and follow-up MRI scans, within 24 ± 11.7 months. Grey matter-specific multi-compartment diffusion model, cortical-neurite orientation dispersion, and density imaging (cortical-NODDI) was used to derive diffusion microstructural metrics, namely orientation dispersion index (ODI) and neurite density index (NDI) in hippocampal-subfields (CA1-3, CA4DG, and subiculum). We investigated rate of change in diffusion metrics and its associations with age and baseline diffusion metrics in hippocampal subfields using linear regression analysis, after adjusting for confounding factors (i.e., sex, education, Apolipoprotein E (APOE) ε4, and baseline subfield volumes). CA1-3 and subiculum volumes significantly decreased between baseline and follow-up scans. ODI rate of change was significantly higher than zero in CA4DG, while rate of change in NDI was significantly lower than zero in CA1-3 and CA4DG. ODI rate of change in CA1-3 was significantly associated with baseline age of participants and initial microstructural value of ODI in CA1-3. Results showed that Cornu Ammonis is most sensitive to age-related changes with increased microstructural dispersion and decreased neurite density with age- and initial state-dependent changes.
人类大脑会随着年龄增长经历显著的形态学变化，这是神经退行性疾病的一个主要风险因素。虽然衰老通常会导致神经认知能力下降，但其主导的生物学机制仍不清楚。这些与年龄相关的脑微结构变化可以通过先进的扩散磁共振成像（dMRI）和组织特异性隔室建模方法进行量化。这项纵向研究调查了易患早期阿尔茨海默病（AD）的海马亚区与年龄相关的差异。来自印第安纳州阿尔茨海默病研究中心（IADRC）的37名认知正常（CN）的老年人（70.6±6.7岁）在24±11.7个月内接受了基线和随访磁共振成像扫描。使用灰质特异性多隔室扩散模型、皮质神经突方向离散度和密度成像（皮质-神经突方向离散度和密度成像，cortical-NODDI）来推导扩散微结构指标，即海马亚区（CA1-3、CA4DG和下托）的方向离散度指数（ODI）和神经突密度指数（NDI）。在调整混杂因素（即性别、教育程度、载脂蛋白E（APOE）ε4和基线亚区体积）后，我们使用线性回归分析研究了海马亚区扩散指标的变化率及其与年龄和基线扩散指标的关联。在基线扫描和随访扫描之间，CA1-3和下托的体积显著减小。CA4DG中ODI的变化率显著高于零，而CA1-3和CA4DG中NDI的变化率显著低于零。CA1-3中ODI的变化率与参与者的基线年龄以及CA1-3中ODI的初始微结构值显著相关。结果表明，海马结构对与年龄相关的变化最为敏感，随着年龄增长和初始状态依赖性变化，微结构离散度增加，神经突密度降低。
