Moon Seok Woo, Dinov Ivo D, Hobel Sam, Zamanyan Alen, Choi Young Chil, Shi Ran, Thompson Paul M, Toga Arthur W
Department of Psychiatry, Konkuk University School of Medicine, Seoul, 143-701, Korea.
Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, 90032.
J Neuroimaging. 2015 Sep-Oct;25(5):728-37. doi: 10.1111/jon.12252. Epub 2015 May 4.
This study investigates 36 subjects aged 55-65 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to expand our knowledge of early-onset (EO) Alzheimer's Disease (EO-AD) using neuroimaging biomarkers.
Nine of the subjects had EO-AD, and 27 had EO mild cognitive impairment (EO-MCI). The structural ADNI data were parcellated using BrainParser, and the 15 most discriminating neuroimaging markers between the two cohorts were extracted using the Global Shape Analysis (GSA) Pipeline workflow. Then the Local Shape Analysis (LSA) Pipeline workflow was used to conduct local (per-vertex) post-hoc statistical analyses of the shape differences based on the participants' diagnoses (EO-MCI+EO-AD). Tensor-based Morphometry (TBM) and multivariate regression models were used to identify the significance of the structural brain differences based on the participants' diagnoses.
The significant between-group regional differences using GSA were found in 15 neuroimaging markers. The results of the LSA analysis workflow were based on the subject diagnosis, age, years of education, apolipoprotein E (ε4), Mini-Mental State Examination, visiting times, and logical memory as regressors. All the variables had significant effects on the regional shape measures. Some of these effects survived the false discovery rate (FDR) correction. Similarly, the TBM analysis showed significant effects on the Jacobian displacement vector fields, but these effects were reduced after FDR correction.
These results may explain some of the differences between EO-AD and EO-MCI, and some of the characteristics of the EO cognitive impairment subjects.
本研究调查了阿尔茨海默病神经影像倡议（ADNI）数据库中36名年龄在55 - 65岁之间的受试者，以利用神经影像生物标志物扩展我们对早发型（EO）阿尔茨海默病（EO - AD）的认识。
其中9名受试者患有EO - AD，27名患有EO轻度认知障碍（EO - MCI）。使用BrainParser对ADNI的结构数据进行分割，并使用全局形状分析（GSA）管道工作流程提取两组之间最具区分性的15种神经影像标志物。然后使用局部形状分析（LSA）管道工作流程，基于参与者的诊断（EO - MCI + EO - AD）对形状差异进行局部（逐顶点）事后统计分析。基于张量的形态计量学（TBM）和多元回归模型用于根据参与者的诊断确定脑结构差异的显著性。
使用GSA在15种神经影像标志物中发现了显著的组间区域差异。LSA分析工作流程的结果基于受试者诊断、年龄、受教育年限、载脂蛋白E（ε4）、简易精神状态检查表、就诊次数和逻辑记忆作为回归变量。所有这些变量对区域形状测量均有显著影响。其中一些影响在错误发现率（FDR）校正后仍然显著。同样，TBM分析对雅可比位移矢量场显示出显著影响，但在FDR校正后这些影响有所降低。
这些结果可能解释了EO - AD和EO - MCI之间的一些差异，以及EO认知障碍受试者的一些特征。
