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

机译：组合多峰神经影像生物标志物在阿尔茨海默病的诊断中和轻度认知障碍

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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 necessary 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病（AD）和轻度认知障碍（MCI），正常和广告之间的过渡状态，对痴呆症研究具有重要意义。不同的神经影像生物标志物在广告的诊断中可能是可能的不同和互补性。有必要将多峰神经影像生物标志物同时用于较高的分类准确性和更有效的诊断。在该研究中，首先分别分别分析磁共振成像（MRI）和氟-18脱氧葡萄糖（FDG）正电子发射断层扫描（PET），然后使用Logistic回归和Fisher线性歧视，使用来自MRI和FDG-PET的生物标志物，广泛使用结合多个变量的方法同时分析。结果表明，MRI和FDG-PET生物标志物对广告诊断全部敏感，尽管存在一些差异。组合来自多个大脑区域或多模式神经影像数据集的信息可以提高区分AD或MCI患者的核心组的准确性，而不是导致信息冗余。

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