Teipel Stefan J, Born Christine, Ewers Michael, Bokde Arun L W, Reiser Maximilian F, Möller Hans-Jürgen, Hampel Harald
Alzheimer Memorial Center, Dementia and Neuroimaging Section, Department of Psychiatry, Ludwig-Maximilian University, Nussbaumstr 7, 80336, Munich, Germany.
Neuroimage. 2007 Oct 15;38(1):13-24. doi: 10.1016/j.neuroimage.2007.07.008. Epub 2007 Jul 18.
Automated deformation-based analysis of MRI scans can be used to detect specific pattern of brain atrophy in Alzheimer's disease (AD), but it still lacks an established model to derive the individual risk of AD in at-risk subjects, such as patients with mild cognitive impairment (MCI). We applied principal component analysis to deformation maps derived from MRI scans of 32 AD patients, 18 elderly healthy controls and 24 MCI patients. Principal component scores were used to discriminate between AD patients and controls and between MCI converters and MCI non-converters. We found a significant regional pattern of atrophy (p<0.001) in medial temporal lobes, neocortical association areas, thalamus and basal ganglia and corresponding widening of cerebrospinal fluid (CSF) spaces (p<0.001) in AD patients compared to controls. Accuracy was 81% for CSF- and 83% for brain-based deformation maps to separate AD patients from controls. Nine out of 24 MCI patients converted to AD during clinical follow-up. Discrimination between MCI converters and non-converters reached 80% accuracy based on CSF maps and 73% accuracy based on brain maps. In a logistic regression model, principal component scores based on CSF maps predicted clinical outcome in MCI patients even after controlling for age, gender, MMSE score and time of follow-up. Our findings indicate that multivariate network analysis of deformation maps detects typical features of AD pathology and provides a powerful tool to predict conversion into AD in non-demented at risk patients.
基于MRI扫描的自动变形分析可用于检测阿尔茨海默病（AD）中特定的脑萎缩模式，但仍缺乏一个既定模型来推导高危人群（如轻度认知障碍（MCI）患者）患AD的个体风险。我们将主成分分析应用于32例AD患者、18例老年健康对照者和24例MCI患者的MRI扫描变形图。主成分得分用于区分AD患者与对照者以及MCI转化者与MCI非转化者。我们发现，与对照者相比，AD患者的内侧颞叶、新皮质联合区、丘脑和基底神经节存在显著的萎缩区域模式（p<0.001），相应的脑脊液（CSF）间隙增宽（p<0.001）。基于CSF的变形图将AD患者与对照者分开的准确率为81%，基于脑的变形图为83%。24例MCI患者中有9例在临床随访期间转化为AD。基于CSF图，MCI转化者与非转化者之间的辨别准确率达到80%，基于脑图的准确率为73%。在逻辑回归模型中，即使在控制了年龄、性别、MMSE评分和随访时间后，基于CSF图的主成分得分仍能预测MCI患者的临床结局。我们的研究结果表明，变形图的多变量网络分析可检测AD病理的典型特征，并为预测非痴呆高危患者转化为AD提供了一个强大的工具。
