The Added Value of Diffusion-Weighted MRI-Derived Structural Connectome in Evaluating Mild Cognitive Impairment: A Multi-Cohort Validation1.
The Added Value of Diffusion-Weighted MRI-Derived Structural Connectome in Evaluating Mild Cognitive Impairment: A Multi-Cohort Validation1.
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DOI：
10.3233/jad-171048
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Alzheimer’s Disease Neuroimaging Initiative and National Alzheimer’s Coordinating Center
中科院分区：
文献类型：
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作者：
Wang Q;Guo L;Thompson PM;Jack CR;Dodge H;Zhan L;Zhou J;Alzheimer’s Disease Neuroimaging Initiative and National Alzheimer’s Coordinating Center
T1加权磁共振已被广泛用于提取成像生物标志物和建立区分AD和健康对照的分类模型，但直到最近，扩散加权MRI衍生的脑连接网络才被用于模拟AD的进展和疾病的不同阶段，如轻度认知障碍。轻度认知障碍作为AD的一个可能的前驱阶段，由于可以用来评估AD的危险因素，近年来受到了极大的关注。将白质和灰质的信息结合起来的工作很少，而且目前还不清楚扩散加权MRI衍生的结构连接体可以提供多大的分类能力，而不是T1加权MRI所能提供的信息。在这篇文章中，我们重点研究扩散加权MRI衍生的结构连接体是否可以改善区分健康对照组受试者和轻度认知障碍受试者。具体地说，我们提出了一种新的特征排名方法，利用排名最高的特征变量建立分类模型，对轻度认知障碍和健康对照进行分类。我们在两个独立的队列上验证了我们的方法，包括阿尔茨海默病第二阶段神经成像倡议(ADNI2)数据库和国家阿尔茨海默病协调中心(NACC)数据库。我们的结果表明：1)扩散加权MRI衍生的结构连接体可以在分类任务中补充T1加权MRI；2)特征排名方法是有效的，因为在adni2和NACC上识别出一致的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靠前的特征变量，我们得出结论，使用独立队列进行交叉验证是必要的，并强烈推荐。
T1-weighted MRI has been extensively used to extract imaging biomarkers and build classification models for differentiating AD 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. Mild cognitive impairment, 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 mild cognitive impairment. Specifically, we proposed a novel feature-ranking method to build classification models using the most highly ranked feature variables to classify mild cognitive impairment 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.