Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
Neuroimage. 2011 Oct 1;58(3):818-28. doi: 10.1016/j.neuroimage.2011.06.065. Epub 2011 Jul 1.
The European Union AddNeuroMed program and the US-based Alzheimer Disease Neuroimaging Initiative (ADNI) are two large multi-center initiatives designed to collect and validate biomarker data for Alzheimer's disease (AD). Both initiatives use the same MRI data acquisition scheme. The current study aims to compare and combine magnetic resonance imaging (MRI) data from the two study cohorts using an automated image analysis pipeline and a multivariate data analysis approach. We hypothesized that the two cohorts would show similar patterns of atrophy, despite demographic differences and could therefore be combined. MRI scans were analyzed from a total of 1074 subjects (AD=295, MCI=444 and controls=335) using Freesurfer, an automated segmentation scheme which generates regional volume and regional cortical thickness measures which were subsequently used for multivariate analysis (orthogonal partial least squares to latent structures (OPLS)). OPLS models were created for the individual cohorts and for the combined cohort to discriminate between AD patients and controls. The ADNI cohort was used as a replication dataset to validate the model created for the AddNeuroMed cohort and vice versa. The combined cohort model was used to predict conversion to AD at baseline of MCI subjects at 1 year follow-up. The AddNeuroMed, the ADNI and the combined cohort showed similar patterns of atrophy and the predictive power was similar (between 80 and 90%). The combined model also showed potential in predicting conversion from MCI to AD, resulting in 71% of the MCI converters (MCI-c) from both cohorts classified as AD-like and 60% of the stable MCI subjects (MCI-s) classified as control-like. This demonstrates that the methods used are robust and that large data sets can be combined if MRI imaging protocols are carefully aligned.
欧盟 AddNeuroMed 计划和美国的阿尔茨海默病神经影像学倡议（ADNI）是两个旨在收集和验证阿尔茨海默病（AD）生物标志物数据的大型多中心计划。这两个计划都使用相同的 MRI 数据采集方案。本研究旨在使用自动图像分析管道和多变量数据分析方法比较和合并来自两个研究队列的 MRI 数据。我们假设两个队列尽管存在人口统计学差异，但会显示出相似的萎缩模式，因此可以合并。使用 Freesurfer 对来自总共 1074 名受试者（AD=295，MCI=444 和对照组=335）的 MRI 扫描进行了分析，Freesurfer 是一种自动分割方案，可生成区域体积和区域皮质厚度测量值，随后用于多变量分析（正交偏最小二乘到潜在结构（OPLS））。为个体队列和合并队列创建了 OPLS 模型，以区分 AD 患者和对照组。ADNI 队列被用作验证 AddNeuroMed 队列模型的复制数据集，反之亦然。使用合并队列模型预测 MCI 受试者在基线时 1 年后的 AD 转化。AddNeuroMed、ADNI 和合并队列显示出相似的萎缩模式，预测能力相似（80%至 90%）。合并模型在预测 MCI 向 AD 的转化方面也具有潜力，导致来自两个队列的 71%的 MCI 转化者（MCI-c）被归类为 AD 样，60%的稳定 MCI 受试者（MCI-s）被归类为对照组。这表明所使用的方法是稳健的，如果 MRI 成像方案得到仔细调整，则可以合并大型数据集。
