Department of Radiology, Tianjin First Central Hospital, Tianjin, China.
College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China.
Eur Radiol. 2023 Sep;33(9):6134-6144. doi: 10.1007/s00330-023-09604-1. Epub 2023 Apr 4.
To evaluate the dynamic evolution process of overall brain health in liver transplantation (LT) recipients, we employed a deep learning-based neuroanatomic biomarker to measure longitudinal changes of brain structural patterns before and 1, 3, and 6 months after surgery.
Because of the ability to capture patterns across all voxels from a brain scan, the brain age prediction method was adopted. We constructed a 3D-CNN model through T1-weighted MRI of 3609 healthy individuals from 8 public datasets and further applied it to a local dataset of 60 LT recipients and 134 controls. The predicted age difference (PAD) was calculated to estimate brain changes before and after LT, and the network occlusion sensitivity analysis was used to determine the importance of each network in age prediction.
The PAD of patients with cirrhosis increased markedly at baseline (+ 5.74 years) and continued to increase within one month after LT (+ 9.18 years). After that, the brain age began to decrease gradually, but it was still higher than the chronological age. The PAD values of the OHE subgroup were higher than those of the no-OHE, and the discrepancy was more obvious at 1-month post-LT. High-level cognition-related networks were more important in predicting the brain age of patients with cirrhosis at baseline, while the importance of primary sensory networks increased temporarily within 6-month post-LT.
The brain structural patterns of LT recipients showed inverted U-shaped dynamic change in the early stage after transplantation, and the change in primary sensory networks may be the main contributor.
• The recipients' brain structural pattern showed an inverted U-shaped dynamic change after LT. • The patients' brain aging aggravated within 1 month after surgery, and the subset of patients with a history of OHE was particularly affected. • The change of primary sensory networks is the main contributor to the change in brain structural patterns.
为了评估肝移植（LT）受者整体大脑健康的动态演变过程，我们采用基于深度学习的神经解剖学生物标志物来测量手术前后 1、3 和 6 个月的脑结构模式的纵向变化。
由于脑年龄预测方法能够从大脑扫描的所有体素中捕捉模式，因此我们构建了一个 3D-CNN 模型，该模型通过 8 个公共数据集的 3609 名健康个体的 T1 加权 MRI 构建，并进一步将其应用于 60 名 LT 受者和 134 名对照者的本地数据集。计算预测年龄差异（PAD）以估计 LT 前后的脑变化，并使用网络遮挡敏感性分析来确定每个网络在年龄预测中的重要性。
肝硬化患者的 PAD 在基线时显著增加（+5.74 岁），并在 LT 后一个月内持续增加（+9.18 岁）。此后，大脑年龄开始逐渐下降，但仍高于实际年龄。OHE 亚组的 PAD 值高于无 OHE 亚组，并且在 LT 后 1 个月差异更明显。在基线时，高水平认知相关网络对于预测肝硬化患者的脑年龄更为重要，而在 LT 后 6 个月内，初级感觉网络的重要性暂时增加。
LT 受者的脑结构模式在移植后早期呈现出倒 U 形的动态变化，初级感觉网络的变化可能是主要贡献者。
• LT 受者的大脑结构模式在手术后早期呈现出倒 U 形的动态变化。• 手术后 1 个月内，患者的大脑老化加剧，有 OHE 病史的患者受影响尤为严重。• 初级感觉网络的变化是大脑结构模式变化的主要贡献者。
