Li F F, Zhang X D, Lu Z N, Chen C, Xu J H, Fan L Z, Cheng Y
Department of Radiology, Tianjin First Central Hospital, Tianjin Institute of Imaging Medicine, Tianjin 300192, China.
College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin 300350, China.
Zhonghua Yi Xue Za Zhi. 2024 Jan 23;104(4):269-275. doi: 10.3760/cma.j.cn112137-20231011-00710.
To investigate the brain aging in patients with cirrhosis and hepatic encephalopathy(HE), constructed a prediction model of brain age based on deep learning and T high-resolution MRI, and try to reveal the specific regions where cirrhosis and HE accelerating brain aging. A cross-sectional study. A brain age prediction model based on the 3D full convolutional neural network was constructed through T high-resolution MRI data from 3 609 healthy individuals across eight global public datasets. The mean absolute error (MAE) between actual age and predicted brain age, Pearson correlation coefficient () and determination coefficient () were calculated to evaluate the accuracy of the model's predictions. A test set (=555) from the Human Connectome Project was used to assess the accuracy of the model. A total of 136 patients with cirrhosis were recruited from Tianjin First Central Hospital as the case group (79 patients with cirrhosis without HE and 57 patients with cirrhosis with HE), and 70 healthy individuals were recruited from the society as the healthy control group during the same period. Brain-predicted age difference (Brain-PAD), digital connection-A (NCT-A) and digital-symbol test (DST) scores of all subjects were calculated for all subjects to assess brain aging and cognitive function in the healthy control group, the cirrhosis without HE group, and the cirrhosis with HE group. The network occlusion sensitivity analysis method was employed to assess the importance of each brain region in predicting brain age. As for the prediction model, in the training set, MAE=2.85, =0.98, =0.96. In the test set, MAE=4.45, =0.96, =0.92. In the local data set of the healthy control group, MAE=3.77, =0.85, =0.73. The time of NCT-A in both cirrhosis groups was longer than healthy control group, while the DST scores were lower than healthy control group, and the differences were statistically significant (both <0.001); the Brain-PAD of healthy control group was (0.8±4.5) years, the Brain-PAD of no-HE group was (6.9±8.1) years, and the HE group was (10.2±7.7) years. The differences between the three groups were statistically significant (<0.001), and the differences between any two groups were statistically significant (all <0.05). The importance ratio of visual network in predicting brain age increased in cirrhosis patients, and the HE group was higher than no-HE group. In patients with cirrhosis, the cognitive function is reduced, brain aging is accelerated, and these changes are more obvious in patients with HE. The importance differences of each brain network in predicting brain aging provide a new direction for identifying the specific regions where cirrhosis and HE accelerate brain aging.
为研究肝硬化和肝性脑病（HE）患者的脑老化情况，构建基于深度学习和T2加权高分辨率MRI的脑龄预测模型，并试图揭示肝硬化和HE加速脑老化的特定区域。一项横断面研究。通过来自八个全球公共数据集的3609名健康个体的T2加权高分辨率MRI数据构建基于三维全卷积神经网络的脑龄预测模型。计算实际年龄与预测脑龄之间的平均绝对误差（MAE）、Pearson相关系数（r）和决定系数（R2），以评估模型预测的准确性。使用来自人类连接体项目的测试集（n = 555）评估模型的准确性。同期从天津市第一中心医院招募136例肝硬化患者作为病例组（79例无HE的肝硬化患者和57例有HE的肝硬化患者），从社会招募70名健康个体作为健康对照组。计算所有受试者的脑预测年龄差（Brain-PAD）、数字连接-A（NCT-A）和数字符号测试（DST）分数，以评估健康对照组、无HE的肝硬化组和有HE的肝硬化组的脑老化和认知功能。采用网络遮挡敏感性分析方法评估每个脑区在预测脑龄中的重要性。对于预测模型，在训练集中，MAE = 2.85，r = 0.98，R2 = 0.96。在测试集中MAE = 4.45，r = 0.96，R2 = 0.92。在健康对照组的本地数据集中，MAE = 3.77，r = 0.85，R2 = 0.73。两个肝硬化组的NCT-A时间均长于健康对照组，而DST分数低于健康对照组，差异有统计学意义（均P < 0.001）；健康对照组的Brain-PAD为（0.8±4.5）岁，无HE组为（6.9±8.1）岁，HE组为（10.2±7.7）岁。三组间差异有统计学意义（P < 0.001），任意两组间差异有统计学意义（均P < 0.05）。肝硬化患者中视觉网络在预测脑龄中的重要性比值增加，且HE组高于无HE组。在肝硬化患者中，认知功能降低，脑老化加速，且这些变化在HE患者中更明显。各脑网络在预测脑老化中的重要性差异为识别肝硬化和HE加速脑老化的特定区域提供了新方向。
