Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA.
Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA.
Neuroimage. 2018 Dec;183:401-411. doi: 10.1016/j.neuroimage.2018.08.040. Epub 2018 Aug 18.
The main goal of this work is to investigate the feasibility of estimating an anatomical index that can be used as an Alzheimer's disease (AD) risk factor in the Women's Health Initiative Magnetic Resonance Imaging Study (WHIMS-MRI) using MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), a well-characterized imaging database of AD patients and cognitively normal subjects. We called this index AD Pattern Similarity (AD-PS) scores. To demonstrate the construct validity of the scores, we investigated their associations with several AD risk factors. The ADNI and WHIMS imaging databases were collected with different goals, populations and data acquisition protocols: it is important to demonstrate that the approach to estimating AD-PS scores can bridge these differences.
MRI data from both studies were processed using high-dimensional warping methods. High-dimensional classifiers were then estimated using the ADNI MRI data. Next, the classifiers were applied to baseline and follow-up WHIMS-MRI GM data to generate the GM AD-PS scores. To study the validity of the scores we investigated associations between GM AD-PS scores at baseline (Scan 1) and their longitudinal changes (Scan 2 -Scan 1) with: 1) age, cognitive scores, white matter small vessel ischemic disease (WM SVID) volume at baseline and 2) age, cognitive scores, WM SVID volume longitudinal changes respectively. In addition, we investigated their associations with time until classification of independently adjudicated status in WHIMS-MRI.
Higher GM AD-PS scores from WHIMS-MRI baseline data were associated with older age, lower cognitive scores, and higher WM SVID volume. Longitudinal changes in GM AD-PS scores (Scan 2 - Scan 1) were also associated with age and changes in WM SVID volumes and cognitive test scores. Increases in the GM AD-PS scores predicted decreases in cognitive scores and increases in WM SVID volume. GM AD-PS scores and their longitudinal changes also were associated with time until classification of cognitive impairment. Finally, receiver operating characteristic curves showed that baseline GM AD-PS scores of cognitively normal participants carried information about future cognitive status determined during follow-up.
We applied a high-dimensional machine learning approach to estimate a novel AD risk factor for WHIMS-MRI study participants using ADNI data. The GM AD-PS scores showed strong associations with incident cognitive impairment and cross-sectional and longitudinal associations with age, cognitive function, cognitive status and WM SVID volume lending support to the ongoing validation of the GM AD-PS score.
本研究的主要目标是利用阿尔茨海默病神经影像学倡议（ADNI）的 MRI 数据，探索一种在妇女健康倡议磁共振成像研究（WHIMS-MRI）中可作为阿尔茨海默病（AD）风险因素的解剖学指数的可行性，ADNI 是一个具有良好特征的 AD 患者和认知正常受试者的成像数据库。我们将这个指数称为 AD 模式相似性（AD-PS）评分。为了证明评分的构建有效性，我们研究了它们与几个 AD 风险因素的关联。ADNI 和 WHIMS 成像数据库的收集目的、人群和数据采集方案不同：证明估计 AD-PS 评分的方法可以弥合这些差异非常重要。
使用高维变形方法对两个研究的 MRI 数据进行处理。然后，使用 ADNI 的 MRI 数据来估计高维分类器。接下来，将分类器应用于基线和随访 WHIMS-MRI GM 数据，以生成 GM AD-PS 评分。为了研究评分的有效性，我们研究了基线（扫描 1）时 GM AD-PS 评分与其纵向变化（扫描 2-扫描 1）之间的关联：1）年龄、认知评分、基线时的白质小血管缺血性疾病（WM SVID）体积和 2）年龄、认知评分、WM SVID 体积的纵向变化。此外，我们还研究了它们与 WHIMS-MRI 中独立裁决状态的分类时间之间的关联。
来自 WHIMS-MRI 基线数据的较高 GM AD-PS 评分与年龄较大、认知评分较低和 WM SVID 体积较高有关。GM AD-PS 评分的纵向变化（扫描 2-扫描 1）也与年龄以及 WM SVID 体积和认知测试评分的变化有关。GM AD-PS 评分的增加预测了认知评分的降低和 WM SVID 体积的增加。GM AD-PS 评分及其纵向变化也与认知障碍分类的时间有关。最后，受试者工作特征曲线显示，认知正常参与者的基线 GM AD-PS 评分携带了在随访期间确定的未来认知状态的信息。
我们使用 ADNI 数据应用高维机器学习方法来估计 WHIMS-MRI 研究参与者的新型 AD 风险因素。GM AD-PS 评分与认知障碍的发生具有很强的关联，与年龄、认知功能、认知状态和 WM SVID 体积的横断面和纵向关联支持 GM AD-PS 评分的持续验证。
