Tang Xiaoying, Holland Dominic, Dale Anders M, Younes Laurent, Miller Michael I
Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA.
Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA.
J Alzheimers Dis. 2015;44(2):599-611. doi: 10.3233/JAD-141605.
In this paper, we propose a novel predictor for the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD). This predictor is based on the shape diffeomorphometry patterns of subcortical and ventricular structures (left and right amygdala, hippocampus, thalamus, caudate, putamen, globus pallidus, and lateral ventricle) of 607 baseline scans from the Alzheimer's Disease Neuroimaging Initiative database, including a total of 210 healthy control subjects, 222 MCI subjects, and 175 AD subjects. The optimal predictor is obtained via a feature selection procedure applied to all of the 14 sets of shape features via linear discriminant analysis, resulting in a combination of the shape diffeomorphometry patterns of the left hippocampus, the left lateral ventricle, the right thalamus, the right caudate, and the bilateral putamen. Via 10-fold cross-validation, we substantiate our method by successfully differentiating 77.04% (104/135) of the MCI subjects who converted to AD within 36 months and 71.26% (62/87) of the non-converters. To be specific, for the MCI-converters, we are capable of correctly predicting 82.35% (14/17) of subjects converting in 6 months, 77.5% (31/40) of subjects converting in 12 months, 74.07% (20/27) of subjects converting in 18 months, 78.13% (25/32) of subjects converting in 24 months, and 73.68% (14/19) of subject converting in 36 months. Statistically significant correlation maps were observed between the shape diffeomorphometry features of each of the 14 structures, especially the bilateral amygdala, hippocampus, lateral ventricle, and two neuropsychological test scores--the Alzheimer's Disease Assessment Scale-Cognitive Behavior Section and the Mini-Mental State Examination.
在本文中,我们提出了一种用于预测从轻度认知障碍(MCI)转变为阿尔茨海默病(AD)的新型预测器。该预测器基于阿尔茨海默病神经影像倡议数据库中607次基线扫描的皮质下和脑室结构(左、右杏仁核、海马体、丘脑、尾状核、壳核、苍白球和侧脑室)的形状微分同胚测量模式,其中包括总共210名健康对照受试者、222名MCI受试者和175名AD受试者。通过应用线性判别分析对所有14组形状特征进行特征选择过程,获得了最优预测器,结果得到了左海马体、左侧脑室、右丘脑、右尾状核和双侧壳核的形状微分同胚测量模式的组合。通过10倍交叉验证,我们成功区分了在36个月内转变为AD的77.04%(104/135)的MCI受试者和未转变者中的71.26%(62/87),从而证实了我们的方法。具体而言,对于MCI转变者,我们能够正确预测6个月内转变的受试者中的82.35%(14/17)、12个月内转变的受试者中的77.5%(31/40)、18个月内转变的受试者中的74.07%(20/27)、24个月内转变的受试者中的78.13%(25/32)以及36个月内转变的受试者中的73.68%(14/19)。在14个结构中每个结构的形状微分同胚测量特征之间观察到了具有统计学意义的相关图,特别是双侧杏仁核、海马体、侧脑室以及两个神经心理学测试分数——阿尔茨海默病评估量表 - 认知行为部分和简易精神状态检查表。