Choi Hongyoon, Kim Yu Kyeong, Yoon Eun Jin, Lee Jee-Young, Lee Dong Soo
Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Eur J Nucl Med Mol Imaging. 2020 Feb;47(2):403-412. doi: 10.1007/s00259-019-04538-7. Epub 2019 Nov 25.
Although functional brain imaging has been used for the early and objective assessment of cognitive dysfunction, there is a lack of generalized image-based biomarker which can evaluate individual's cognitive dysfunction in various disorders. To this end, we developed a deep learning-based cognitive signature of FDG brain PET adaptable for Parkinson's disease (PD) as well as Alzheimer's disease (AD).
A deep learning model for discriminating AD from normal controls (NCs) was built by a training set consisting of 636 FDG PET obtained from Alzheimer's Disease Neuroimaging Initiative database. The model was directly transferred to images of mild cognitive impairment (MCI) patients (n = 666) for identifying who would rapidly convert to AD and another independent cohort consisting of 62 PD patients to differentiate PD patients with dementia. The model accuracy was measured by area under curve (AUC) of receiver operating characteristic (ROC) analysis. The relationship between all images was visualized by two-dimensional projection of the deep learning-based features. The model was also designed to predict cognitive score of the subjects and validated in PD patients. Cognitive dysfunction-related regions were visualized by feature maps of the deep CNN model.
AUC of ROC for differentiating AD from NC was 0.94 (95% CI 0.89-0.98). The transfer of the model could differentiate MCI patients who would convert to AD (AUC = 0.82) and PD with dementia (AUC = 0.81). The two-dimensional projection mapping visualized the degree of cognitive dysfunction compared with normal brains regardless of different disease cohorts. Predicted cognitive score, an output of the model, was highly correlated with the mini-mental status exam scores. Individual cognitive dysfunction-related regions included cingulate and high frontoparietal cortices, while they showed individual variability.
The deep learning-based cognitive function evaluation model could be successfully transferred to multiple disease domains. We suggest that this approach might be extended to an objective cognitive signature that provides quantitative biomarker for cognitive dysfunction across various neurodegenerative disorders.
尽管功能性脑成像已被用于认知功能障碍的早期和客观评估，但缺乏一种可在各种疾病中评估个体认知功能障碍的通用的基于图像的生物标志物。为此，我们开发了一种基于深度学习的适用于帕金森病（PD）和阿尔茨海默病（AD）的FDG脑PET认知特征。
通过一个由从阿尔茨海默病神经影像倡议数据库获得的636例FDG PET组成的训练集，构建了一个用于区分AD与正常对照（NC）的深度学习模型。该模型被直接应用于轻度认知障碍（MCI）患者（n = 666）的图像，以识别哪些患者会迅速转变为AD，以及另一个由62例PD患者组成的独立队列，以区分患有痴呆症的PD患者。模型准确性通过受试者操作特征（ROC）分析的曲线下面积（AUC）来衡量。通过基于深度学习的特征的二维投影来可视化所有图像之间的关系。该模型还被设计用于预测受试者的认知分数，并在PD患者中进行验证。通过深度卷积神经网络模型的特征图来可视化与认知功能障碍相关的区域。
区分AD与NC的ROC的AUC为0.94（95%可信区间0.89 - 0.98）。该模型的转移能够区分会转变为AD的MCI患者（AUC = 0.82）和患有痴呆症的PD患者（AUC = 0.81）。二维投影映射可视化了与正常大脑相比的认知功能障碍程度，而不考虑不同的疾病队列。模型的输出——预测的认知分数与简易精神状态检查分数高度相关。个体与认知功能障碍相关的区域包括扣带回和额顶叶上部皮质，不过它们表现出个体差异。
基于深度学习的认知功能评估模型能够成功转移到多个疾病领域。我们建议这种方法可能会扩展为一种客观的认知特征，为各种神经退行性疾病中的认知功能障碍提供定量生物标志物。
