PURPOSE 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). METHODS 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. RESULTS 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. CONCLUSION 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.

中文翻译：

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基于深度学习的大脑FDG PET的认知特征：从阿尔茨海默氏病到帕金森氏病的域转移。

目的尽管功能性脑成像已用于认知功能障碍的早期和客观评估，但仍缺乏基于通用图像的生物标记物，该标记物可评估各种疾病中个人的认知功能障碍。为此，我们开发了适用于帕金森氏病（PD）和阿尔茨海默氏病（AD）的FDG脑PET的基于深度学习的认知特征。方法通过从阿尔茨海默氏病神经影像学倡议数据库中获得的636个FDG PET组成的训练集，建立了区分AD与正常对照（NC）的深度学习模型。该模型直接转移到轻度认知障碍（MCI）患者（n = 666）的图像中，以识别谁将迅速转变为AD，以及另一个由62名PD患者组成的独立队列，以区分患有痴呆的PD患者。通过接收器工作特性（ROC）分析的曲线下面积（AUC）来测量模型的准确性。所有图像之间的关系通过基于深度学习的特征的二维投影可视化。该模型还设计用于预测受试者的认知评分，并在PD患者中进行了验证。认知功能障碍相关区域通过深层CNN模型的特征图可视化。结果ROC用于区分AD和NC的AUC为0.94（95％CI 0.89-0.98）。该模型的转移可以区分将要转化为AD的MCI患者（AUC = 0。82）和PD与痴呆症（AUC = 0.81）。与正常大脑相比，二维投影映射可视化了认知功能障碍的程度，而与不同的疾病队列无关。预测的认知得分（该模型的输出）与小心理状态考试得分高度相关。个体与认知功能障碍相关的区域包括扣带状和高额顶叶皮质，而它们表现出个体差异。结论基于深度学习的认知功能评估模型可以成功地转移到多个疾病领域。我们建议这种方法可能扩展到客观的认知特征，为各种神经退行性疾病的认知功能障碍提供定量的生物标志物。与正常大脑相比，二维投影映射可视化了认知功能障碍的程度，而与不同的疾病队列无关。预测的认知得分（该模型的输出）与小心理状态考试得分高度相关。个体与认知功能障碍相关的区域包括扣带状和高额顶叶皮质，而它们表现出个体差异。结论基于深度学习的认知功能评估模型可以成功地转移到多个疾病领域。我们建议这种方法可能扩展到客观的认知特征，为各种神经退行性疾病的认知功能障碍提供定量的生物标志物。与正常大脑相比，二维投影映射可视化了认知功能障碍的程度，而与不同的疾病队列无关。预测的认知得分（该模型的输出）与小心理状态考试得分高度相关。个体与认知功能障碍相关的区域包括扣带状和高额顶叶皮质，而它们表现出个体差异。结论基于深度学习的认知功能评估模型可以成功地转移到多个疾病领域。我们建议这种方法可能扩展到客观的认知特征，为各种神经退行性疾病的认知功能障碍提供定量的生物标志物。预测的认知得分（该模型的输出）与小心理状态考试得分高度相关。个体与认知功能障碍相关的区域包括扣带状和高额顶叶皮质，而它们表现出个体差异。结论基于深度学习的认知功能评估模型可以成功地转移到多个疾病领域。我们建议这种方法可能扩展到客观的认知特征，为各种神经退行性疾病的认知功能障碍提供定量的生物标志物。预测的认知得分（该模型的输出）与小心理状态考试得分高度相关。个体与认知功能障碍相关的区域包括扣带状和高额顶叶皮质，而它们表现出个体差异。结论基于深度学习的认知功能评估模型可以成功地转移到多个疾病领域。我们建议这种方法可能扩展到客观的认知特征，为各种神经退行性疾病的认知功能障碍提供定量的生物标志物。结论基于深度学习的认知功能评估模型可以成功地转移到多个疾病领域。我们建议这种方法可能扩展到客观的认知特征，为各种神经退行性疾病的认知功能障碍提供定量的生物标志物。结论基于深度学习的认知功能评估模型可以成功地转移到多个疾病领域。我们建议这种方法可能扩展到客观的认知特征，为各种神经退行性疾病的认知功能障碍提供定量的生物标志物。