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Combining Multimodal Neuroimaging Biomarkers in the Diagnosis of Alzheimer's Disease and Mild Cognitive Impairment

机译：结合多模式神经影像生物标记物在阿尔茨海默氏病和轻度认知障碍的诊断中

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摘要

Effective diagnosis of Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI), a transitional state between normal and AD, is of great importance in dementia research. Different neuroimaging biomarkers for AD may be potentially different and complementary in the diagnosis of AD. It is necessaiy to combine multimodal neuroimaging biomarkers simultaneously for the higher classification accuracy and more effective diagnosis. In this study, Magnetic Resonance Imaging (MRI) and Fluorine-18 deoxyglucose (FDG) positron emission tomography (PET) were firstly analysed separately, then biomarkers from both MRI and FDG-PET were combined using logistic regression and fisher linear discrimination, widely used approaches to combine multiple variables for the simultaneous analysis. The results showed that MRI and FDG-PET biomarkers were all sensitive to the AD diagnosis, although some differences existed. Combining information from multiple brain regions or from multimodal neuroimaging data sets could increase the accuracy of distinguishing AD or MCI patients from NC group, instead of causing information redundancy.
机译：有效诊断阿尔茨海默氏病（AD）和轻度认知障碍（MCI），这是正常人与AD之间的过渡状态，在痴呆研究中非常重要。用于AD的不同神经影像生物标记可能在AD的诊断中可能有所不同和互补。为了更高的分类精度和更有效的诊断，有必要同时组合多模式神经影像生物标记。在这项研究中，首先分别对磁共振成像（MRI）和氟18脱氧葡萄糖（FDG）正电子发射断层扫描（PET）进行分析，然后通过逻辑回归和费舍尔线性判别法将MRI和FDG-PET的生物标记物结合起来，广泛使用多个变量进行同步分析的方法。结果表明，尽管存在一些差异，但MRI和FDG-PET生物标志物均对AD诊断敏感。合并来自多个大脑区域或来自多模式神经影像数据集的信息可以提高区分AD或MCI患者与NC组的准确性，而不会引起信息冗余。

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