Computer Science and Engineering, Michigan State University, East Lansing, MI, USA.
Department of Mathematics, Statistics and Computer Science, University of Wisconsin-Stout, Menomonie, WI, USA.
J Alzheimers Dis. 2018;64(1):149-169. doi: 10.3233/JAD-171048.
T1-weighted MRI has been extensively used to extract imaging biomarkers and build classification models for differentiating Alzheimer's disease (AD) patients from healthy controls, but only recently have brain connectome networks derived from diffusion-weighted MRI been used to model AD progression and various stages of disease such as mild cognitive impairment (MCI). MCI, as a possible prodromal stage of AD, has gained intense interest recently, since it may be used to assess risk factors for AD. Little work has been done to combine information from both white matter and gray matter, and it is unknown how much classification power the diffusion-weighted MRI-derived structural connectome could provide beyond information available from T1-weighted MRI. In this paper, we focused on investigating whether diffusion-weighted MRI-derived structural connectome can improve differentiating healthy controls subjects from those with MCI. Specifically, we proposed a novel feature-ranking method to build classification models using the most highly ranked feature variables to classify MCI with healthy controls. We verified our method on two independent cohorts including the second stage of Alzheimer's Disease Neuroimaging Initiative (ADNI2) database and the National Alzheimer's Coordinating Center (NACC) database. Our results indicated that 1) diffusion-weighted MRI-derived structural connectome can complement T1-weighted MRI in the classification task; 2) the feature-rank method is effective because of the identified consistent T1-weighted MRI and network feature variables on ADNI2 and NACC. Furthermore, by comparing the top-ranked feature variables from ADNI2, NACC, and combined dataset, we concluded that cross-validation using independent cohorts is necessary and highly recommended.
T1 加权磁共振成像已被广泛用于提取成像生物标志物并构建分类模型，以区分阿尔茨海默病（AD）患者和健康对照者，但直到最近，基于弥散加权磁共振成像的脑连接网络才被用于模拟 AD 进展和各种疾病阶段，如轻度认知障碍（MCI）。MCI 作为 AD 的可能前驱阶段，最近引起了极大的兴趣，因为它可能用于评估 AD 的危险因素。很少有工作将白质和灰质的信息结合起来，并且尚不清楚弥散加权 MRI 衍生的结构连接网络可以提供多少分类能力，而这些能力超出了 T1 加权 MRI 提供的信息。在本文中，我们专注于研究弥散加权 MRI 衍生的结构连接网络是否可以改善区分健康对照者和 MCI 患者的能力。具体来说，我们提出了一种新的特征排序方法，使用排名最高的特征变量构建分类模型，以对 MCI 和健康对照者进行分类。我们在包括阿尔茨海默病神经影像学倡议（ADNI2）数据库第二阶段和国家阿尔茨海默病协调中心（NACC）数据库的两个独立队列上验证了我们的方法。我们的结果表明：1）弥散加权 MRI 衍生的结构连接网络可以在分类任务中补充 T1 加权 MRI；2）特征排序方法是有效的，因为在 ADNI2 和 NACC 上确定了一致的 T1 加权 MRI 和网络特征变量。此外，通过比较 ADNI2、NACC 和组合数据集的排名最高的特征变量，我们得出结论，使用独立队列进行交叉验证是必要的，并且强烈建议使用。
