Jiménez-Huete Adolfo, Estévez-Santé Susana
Department of Neurology, Hospital Ruber Internacional, C/La Masó, 38, 28034 Madrid, Spain.
Department of Neurology, Hospital Universitario Príncipe de Asturias, Alcalá de Henares, Spain.
J Neurol Sci. 2017 Jul 15;378:110-119. doi: 10.1016/j.jns.2017.04.046. Epub 2017 Apr 27.
BACKGROUND/AIMS: Alzheimer's disease (AD) shows a characteristic pattern of brain atrophy, with predominant involvement of posterior limbic structures, and relative preservation of rostral limbic and primary cortical regions. We aimed to investigate the diagnostic utility of two gray matter volume ratios based on this pattern, and to develop a fully automated method to calculate them from unprocessed MRI files.
Cross-sectional study of 118 subjects from the ADNI database, including normal controls and patients with mild cognitive impairment (MCI) and AD. Clinical variables and 3T T1-weighted MRI files were analyzed. Regional gray matter and total intracranial volumes were calculated with a shell script (gm_extractor) based on FSL. Anteroposterior and primary-to-posterior limbic ratios (APL and PPL) were calculated from these values. Diagnostic utility of variables was tested in logistic regression models using Bayesian model averaging for variable selection. External validity was evaluated with bootstrap sampling and a test set of 60 subjects.
gm_extractor showed high test-retest reliability and high concurrent validity with FSL's FIRST. Volumetric measurements agreed with the expected anatomical pattern associated with AD. APL and PPL ratios were significantly different between groups, and were selected instead of hippocampal and entorhinal volumes to differentiate normal from MCI or cognitively impaired (MCI plus AD) subjects.
APL and PPL ratios may be useful components of models aimed to differentiate normal subjects from patients with MCI or AD. These values, and other gray matter volumes, may be reliably calculated with gm_extractor.
背景/目的:阿尔茨海默病(AD)呈现出特征性的脑萎缩模式,后边缘结构受累为主,而前边缘和初级皮质区域相对保留。我们旨在研究基于这种模式的两个灰质体积比的诊断效用,并开发一种从未处理的MRI文件中自动计算它们的方法。
对来自ADNI数据库的118名受试者进行横断面研究,包括正常对照、轻度认知障碍(MCI)患者和AD患者。分析临床变量和3T T1加权MRI文件。使用基于FSL的外壳脚本(gm_extractor)计算区域灰质和总颅内体积。根据这些值计算前后边缘比(APL)和初级与后边缘比(PPL)。在逻辑回归模型中使用贝叶斯模型平均进行变量选择,测试变量的诊断效用。通过自助抽样和60名受试者的测试集评估外部有效性。
gm_extractor显示出高重测信度和与FSL的FIRST的高同时效度。体积测量结果与AD相关的预期解剖模式一致。APL和PPL比值在各组之间有显著差异,并且被选用来区分正常人与MCI或认知受损(MCI加AD)受试者,而不是海马和内嗅体积。
APL和PPL比值可能是旨在区分正常人与MCI或AD患者的模型的有用组成部分。这些值以及其他灰质体积可以用gm_extractor可靠地计算出来。