Sathe Aditi, Yang Yisu, Schilling Kurt G, Shashikumar Niranjana, Moore Elizabeth, Dumitrescu Logan, Pechman Kimberly R, Landman Bennett A, Gifford Katherine A, Hohman Timothy J, Jefferson Angela L, Archer Derek B
Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, United States.
Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States.
Imaging Neurosci (Camb). 2024 Sep 18;2:1-16. doi: 10.1162/imag_a_00293. eCollection 2024 Sep 1.
Diffusion MRI derived free-water (FW) metrics show promise in predicting cognitive impairment and decline in aging and Alzheimer's disease (AD). FW is sensitive to subtle changes in brain microstructure, so it is possible these measures may be more sensitive than traditional structural neuroimaging biomarkers. In this study, we examined the associations among FW metrics (measured in the hippocampus and two AD signature meta-ROIs) with cognitive performance, and compared FW findings to those from more traditional neuroimaging biomarkers of AD. We leveraged data from a longitudinal cohort (n = 296, n = 870, age at baseline: 73 ± 7 years, 40% mild cognitive impairment [MCI]) of older adults who underwent serial neuropsychological assessment (episodic memory, information processing speed, executive function, language, and visuospatial skills) and brain MRI over a maximum of four time points, including baseline (n = 284), 18-month (n = 246), 3-year (n = 215), and 5-year (n = 125) visits. The mean follow-up period was 2.8 ± 1.3 years. Structural MRI was used to quantify hippocampal volume, in addition to Schwarz and McEvoy AD Signatures. FW and FW-corrected fractional anisotropy (FAFWcorr) were quantified in the hippocampus (hippocampal FW) and the AD signature areas (Schwarz, McEvoy) from diffusion-weighted (dMRI) images using bi-tensor modeling (FW elimination and mapping method). Linear regression assessed the association of each biomarker with baseline cognitive performance. Additionally, linear mixed-effects regression assessed the association between baseline biomarker values and longitudinal cognitive performance. A subsequent competitive model analysis was conducted on both baseline and longitudinal data to determine how much additional variance in cognitive performance was explained by each biomarker compared to the covariate only model, which included age, sex, race/ethnicity, apolipoprotein-ε4 status, cognitive status, and modified Framingham Stroke Risk Profile scores. All analyses were corrected for multiple comparisons using an FDR procedure. Cross-sectional results indicate that hippocampal volume, hippocampal FW, Schwarz and McEvoy AD Signatures, and the Schwarz and McEvoy metrics are all significantly associated with memory performance. Baseline competitive model analyses showed that the McEvoy AD Signature and Schwarz explain the most unique variance beyond covariates for memory (ΔR = 3.47 ± 1.65%) and executive function (ΔR = 2.43 ± 1.63%), respectively. Longitudinal models revealed that hippocampal FW explained substantial unique variance for memory performance (ΔR = 8.13 ± 1.25%), and outperformed all other biomarkers examined in predicting memory decline (p = 1.95 x 10). This study shows that hippocampal FW is a sensitive biomarker for cognitive impairment and decline, and provides strong evidence for further exploration of this measure in aging and AD.
基于扩散磁共振成像(Diffusion MRI)得出的自由水(FW)指标在预测衰老和阿尔茨海默病(AD)中的认知障碍及衰退方面显示出前景。自由水对脑微结构的细微变化敏感,因此这些指标可能比传统的结构性神经影像生物标志物更敏感。在本研究中,我们检查了FW指标(在海马体和两个AD特征性元感兴趣区测量)与认知表现之间的关联,并将FW的研究结果与AD更传统的神经影像生物标志物的结果进行比较。我们利用了一个纵向队列(n = 296,n = 870,基线年龄:73±7岁,40%为轻度认知障碍[MCI])的老年人数据,这些老年人接受了一系列神经心理学评估(情景记忆、信息处理速度、执行功能、语言和视觉空间技能)以及最多四个时间点的脑部MRI检查,包括基线(n = 284)、18个月(n = 246)、3年(n = 215)和5年(n = 125)随访。平均随访期为2.8±1.3年。除了施瓦茨(Schwarz)和麦克沃伊(McEvoy)AD特征外,还使用结构MRI对海马体体积进行了量化。使用双张量模型(自由水消除和映射方法)从扩散加权(dMRI)图像中对海马体(海马体自由水)和AD特征区域(施瓦茨、麦克沃伊)的自由水和自由水校正分数各向异性(FAFWcorr)进行了量化。线性回归评估了每个生物标志物与基线认知表现的关联。此外,线性混合效应回归评估了基线生物标志物值与纵向认知表现之间的关联。随后对基线和纵向数据进行了竞争性模型分析,以确定与仅包含年龄、性别、种族/民族、载脂蛋白ε4状态、认知状态和改良弗明汉姆中风风险概况评分的协变量模型相比,每个生物标志物能解释多少额外的认知表现方差。所有分析均使用错误发现率(FDR)程序进行多重比较校正。横断面结果表明,海马体体积、海马体自由水、施瓦茨和麦克沃伊AD特征以及施瓦茨和麦克沃伊指标均与记忆表现显著相关。基线竞争性模型分析表明,麦克沃伊AD特征和施瓦茨分别解释了记忆(ΔR = 3.47±1.65%)和执行功能(ΔR = 2.43±1.63%)协变量之外的最大独特方差。纵向模型显示,海马体自由水解释了记忆表现的大量独特方差(ΔR = 8.13±1.25%),并且在预测记忆衰退方面优于所有其他检查的生物标志物(p = 1.95×10)。本研究表明,海马体自由水是认知障碍和衰退的敏感生物标志物,并为在衰老和AD中进一步探索该指标提供了有力证据。