Davatzikos Christos, Xu Feng, An Yang, Fan Yong, Resnick Susan M
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Brain. 2009 Aug;132(Pt 8):2026-35. doi: 10.1093/brain/awp091. Epub 2009 May 4.
A challenge in developing informative neuroimaging biomarkers for early diagnosis of Alzheimer's disease is the need to identify biomarkers that are evident before the onset of clinical symptoms, and which have sufficient sensitivity and specificity on an individual patient basis. Recent literature suggests that spatial patterns of brain atrophy discriminate amongst Alzheimer's disease, mild cognitive impairment (MCI) and cognitively normal (CN) older adults with high accuracy on an individual basis, thereby offering promise that subtle brain changes can be detected during prodromal Alzheimer's disease stages. Here, we investigate whether these spatial patterns of brain atrophy can be detected in CN and MCI individuals and whether they are associated with cognitive decline. Images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to construct a pattern classifier that recognizes spatial patterns of brain atrophy which best distinguish Alzheimer's disease patients from CN on an individual person basis. This classifier was subsequently applied to longitudinal magnetic resonance imaging scans of CN and MCI participants in the Baltimore Longitudinal Study of Aging (BLSA) neuroimaging study. The degree to which Alzheimer's disease-like patterns were present in CN and MCI subjects was evaluated longitudinally in relation to cognitive performance. The oldest BLSA CN individuals showed progressively increasing Alzheimer's disease-like patterns of atrophy, and individuals with these patterns had reduced cognitive performance. MCI was associated with steeper longitudinal increases of Alzheimer's disease-like patterns of atrophy, which separated them from CN (receiver operating characteristic area under the curve equal to 0.89). Our results suggest that imaging-based spatial patterns of brain atrophy of Alzheimer's disease, evaluated with sophisticated pattern analysis and recognition methods, may be useful in discriminating among CN individuals who are likely to be stable versus those who will show cognitive decline. Future prospective studies will elucidate the temporal dynamics of spatial atrophy patterns and the emergence of clinical symptoms.
开发用于阿尔茨海默病早期诊断的信息丰富的神经影像学生物标志物面临的一个挑战是，需要识别在临床症状出现之前就已明显，并且在个体患者层面具有足够敏感性和特异性的生物标志物。最近的文献表明，脑萎缩的空间模式能够在个体层面上以高精度区分阿尔茨海默病、轻度认知障碍（MCI）和认知正常（CN）的老年人，从而为在阿尔茨海默病前驱期检测到细微的脑变化带来了希望。在此，我们研究这些脑萎缩的空间模式是否能在CN和MCI个体中被检测到，以及它们是否与认知衰退相关。来自阿尔茨海默病神经影像倡议（ADNI）的图像被用于构建一个模式分类器，该分类器能够识别在个体层面上最能区分阿尔茨海默病患者和CN的脑萎缩空间模式。随后，这个分类器被应用于巴尔的摩老龄化纵向研究（BLSA）神经影像研究中CN和MCI参与者的纵向磁共振成像扫描。纵向评估CN和MCI受试者中阿尔茨海默病样模式的存在程度与认知表现的关系。年龄最大的BLSA CN个体显示出阿尔茨海默病样萎缩模式逐渐增加，并且具有这些模式的个体认知表现下降。MCI与阿尔茨海默病样萎缩模式更陡峭的纵向增加相关，这将它们与CN区分开来（曲线下面积的受试者操作特征等于0.89）。我们的结果表明，通过复杂的模式分析和识别方法评估的基于成像的阿尔茨海默病脑萎缩空间模式，可能有助于区分可能保持稳定的CN个体和那些将出现认知衰退的个体。未来的前瞻性研究将阐明空间萎缩模式的时间动态以及临床症状的出现。
