![]() ![]() J Cereb Blood Flow Metab 34:1877–1886Ĭole JH, Franke K (2017) Predicting age using neuroimaging: innovative brain ageing biomarkers. Lin WC, Hsu TW, Chen CL et al (2014) Reestablishing brain networks in patients without overt hepatic encephalopathy after liver transplantation. Hopp AE, Dirks M, Petrusch C et al (2019) Hepatic encephalopathy is reversible in the long term after liver transplantation. The change of primary sensory networks is the main contributor to the change in brain structural patterns.The patients’ brain aging aggravated within 1 month after surgery, and the subset of patients with a history of OHE was particularly affected.The recipients’ brain structural pattern showed an inverted U-shaped dynamic change after 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. 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 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. After that, the brain age began to decrease gradually, but it was still higher than the chronological age. 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). 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. ![]() 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. Methodsīecause of the ability to capture patterns across all voxels from a brain scan, the brain age prediction method was adopted. 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. ![]()
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