Objective. Glucose-based positron emission tomography (PET) imaging has been widely used to predict the progression of mild cognitive impairment (MCI) into Alzheimer’s disease (AD) clinically. However, existing discriminant methods are unsubtle to reveal pathophysiological changes. Therefore, we present a novel metabolic connectome-based predictive modeling to predict progression from MCI to AD accurately. Methods. In this study, we acquired fluorodeoxyglucose PET images and clinical assessments from 420 MCI patients with 36 months follow-up. Individual metabolic network based on connectome analysis was constructed, and the metabolic connectivity in this network was extracted as predictive features. Three different classification strategies were implemented to interrogate the predictive performance. To verify the effectivity of selected features, specific brain regions associated with MCI conversion were identified based on these features and compared with prior knowledge. Results. As a result, 4005 connectome features were obtained, and 153 in which were selected as efficient features. Our proposed feature extraction method had achieved 85.2% accuracy for MCI conversion prediction (sensitivity: 88.1%; specificity: 81.2%; and AUC: 0.933). The discriminative brain regions associated with MCI conversion were mainly located in the precentral gyrus, precuneus, lingual, and inferior frontal gyrus. Conclusion. Overall, the results suggest that our proposed individual metabolic connectome method has great potential to predict whether MCI patients will progress to AD. The metabolic connectome may help to identify brain metabolic dysfunction and build a clinically applicable biomarker to predict the MCI progression.

中文翻译：

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一种预测轻度认知障碍进展的新代谢连接组方法。

客观。基于葡萄糖的正电子发射断层扫描 (PET) 成像已广泛用于临床上预测轻度认知障碍 (MCI) 进展为阿尔茨海默病 (AD)。然而，现有的判别方法难以揭示病理生理变化。因此，我们提出了一种新的基于代谢连接组的预测模型，以准确预测从 MCI 到 AD 的进展。方法. 在这项研究中，我们获得了 420 名 MCI 患者的氟脱氧葡萄糖 PET 图像和临床评估，并进行了 36 个月的随访。构建基于连接组分析的个体代谢网络，并提取该网络中的代谢连接作为预测特征。实施了三种不同的分类策略来询问预测性能。为了验证所选特征的有效性，根据这些特征识别与 MCI 转换相关的特定大脑区域，并与先验知识进行比较。结果. 结果，获得了 4005 个连接组特征，其中 153 个被选为有效特征。我们提出的特征提取方法在 MCI 转换预测方面取得了 85.2% 的准确率（灵敏度：88.1%；特异性：81.2%；和 AUC：0.933）。与MCI转换相关的辨别脑区主要位于中央前回、楔前叶、舌和额下回。结论。总体而言，结果表明我们提出的个体代谢连接组方法具有预测 MCI 患者是否会进展为 AD 的巨大潜力。代谢连接组可能有助于识别大脑代谢功能障碍并建立临床适用的生物标志物来预测 MCI 进展。