Zavaliangos-Petropulu Artemis, Nir Talia M, Thomopoulos Sophia I, Reid Robert I, Bernstein Matt A, Borowski Bret, Jack Clifford R, Weiner Michael W, Jahanshad Neda, Thompson Paul M
Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States.
Department of Information Technology, Mayo Clinic and Foundation, Rochester, MN, United States.
Front Neuroinform. 2019 Feb 19;13:2. doi: 10.3389/fninf.2019.00002. eCollection 2019.
Brain imaging with diffusion-weighted MRI (dMRI) is sensitive to microstructural white matter (WM) changes associated with brain aging and neurodegeneration. In its third phase, the Alzheimer's Disease Neuroimaging Initiative (ADNI3) is collecting data across multiple sites and scanners using different dMRI acquisition protocols, to better understand disease effects. It is vital to understand when data can be pooled across scanners, and how the choice of dMRI protocol affects the sensitivity of extracted measures to differences in clinical impairment. Here, we analyzed ADNI3 data from 317 participants (mean age: 75.4 ± 7.9 years; 143 men/174 women), who were each scanned at one of 47 sites with one of six dMRI protocols using scanners from three different manufacturers. We computed four standard diffusion tensor imaging (DTI) indices including fractional anisotropy (FA) and mean, radial, and axial diffusivity, and one FA index based on the tensor distribution function (FA), in 24 bilaterally averaged WM regions of interest. We found that protocol differences significantly affected dMRI indices, in particular FA. We ranked the diffusion indices for their strength of association with four clinical assessments. In addition to diagnosis, we evaluated cognitive impairment as indexed by three commonly used screening tools for detecting dementia and AD: the AD Assessment Scale (ADAS-cog), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating scale sum-of-boxes (CDR-sob). Using a nested random-effects regression model to account for protocol and site, we found that across all dMRI indices and clinical measures, the hippocampal-cingulum and fornix regions most consistently showed strong associations with clinical impairment. Overall, the greatest effect sizes were detected in the hippocampal-cingulum (CGH) and uncinate fasciculus (UNC) for associations between axial or mean diffusivity and CDR-sob. FA detected robust widespread associations with clinical measures, while FA was the weakest of the five indices for detecting associations. Ultimately, we were able to successfully pool dMRI data from multiple acquisition protocols from ADNI3 and detect consistent and robust associations with clinical impairment and age.
使用扩散加权磁共振成像（dMRI）进行脑成像对与脑老化和神经退行性变相关的微观结构白质（WM）变化敏感。在其第三阶段，阿尔茨海默病神经成像计划（ADNI3）正在使用不同的dMRI采集协议在多个地点和扫描仪上收集数据，以更好地了解疾病影响。了解何时可以跨扫描仪汇总数据，以及dMRI协议的选择如何影响提取指标对临床损伤差异的敏感性至关重要。在这里，我们分析了来自317名参与者（平均年龄：75.4±7.9岁；143名男性/174名女性）的ADNI3数据，这些参与者分别在47个地点之一，使用来自三个不同制造商的扫描仪，采用六种dMRI协议之一进行扫描。我们在24个双侧平均的WM感兴趣区域计算了四个标准扩散张量成像（DTI）指标，包括分数各向异性（FA）以及平均、径向和轴向扩散率，以及一个基于张量分布函数的FA指标（FA）。我们发现协议差异显著影响dMRI指标，尤其是FA。我们根据与四项临床评估的关联强度对扩散指标进行了排名。除了诊断外，我们还使用三种常用的痴呆和AD检测筛查工具来评估认知障碍：AD评估量表（ADAS-cog）、简易精神状态检查表（MMSE）和临床痴呆评定量表总和（CDR-sob）。使用嵌套随机效应回归模型来考虑协议和地点，我们发现，在所有dMRI指标和临床测量中，海马扣带回和穹窿区域与临床损伤的关联最为一致且强烈。总体而言，在海马扣带回（CGH）和钩束（UNC）中检测到轴向或平均扩散率与CDR-sob之间关联的效应量最大。FA检测到与临床测量的广泛且稳健的关联，而FA是五个指标中检测关联最弱的。最终，我们能够成功汇总来自ADNI3多个采集协议的dMRI数据，并检测到与临床损伤和年龄一致且稳健的关联。
