Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
European Radiology
Published in: European Radiology 9/2023

04-04-2023 | Hepatic Encephalopathy | Neuro

Dynamic evolution of brain structural patterns in liver transplantation recipients: a longitudinal study based on 3D convolutional neuronal network model

Authors: Yue Cheng, Xiao-Dong Zhang, Cheng Chen, Ling-Fei He, Fang-Fei Li, Zi-Ning Lu, Wei-Qi Man, Yu-Jiao Zhao, Zhi-Xing Chang, Ying Wu, Wen Shen, Ling-Zhong Fan, Jun-Hai Xu

Published in: European Radiology | Issue 9/2023

Login to get access

Abstract

Objectives

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.

Methods

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.

Results

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.

Conclusions

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.

Key Points

• 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.
Appendix
Available only for authorised users
Literature
1.
Hopp AE, Dirks M, Petrusch C et al (2019) Hepatic encephalopathy is reversible in the long term after liver transplantation. Liver Transpl 25:1661–1672CrossRefPubMed
2.
Lin WC, Hsu TW, Chen CL et al (2014) Reestablishing brain networks in patients without overt hepatic encephalopathy after liver transplantation. J Cereb Blood Flow Metab 34:1877–1886CrossRefPubMedPubMedCentral
3.
Cole JH, Franke K (2017) Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends Neurosci 40:681–690CrossRefPubMed
4.
Baecker L, Garcia-Dias R, Vieira S, Scarpazza C, Mechelli A (2021) Machine learning for brain age prediction: Introduction to methods and clinical applications. EBioMedicine 72:103600CrossRefPubMedPubMedCentral
5.
Schnack HG, van Haren NE, Nieuwenhuis M, Hulshoff Pol HE, Cahn W, Kahn RS (2016) Accelerated brain aging in schizophrenia: a longitudinal pattern recognition study. Am J Psychiatry 173:607–616CrossRefPubMed
6.
Han S, Chen Y, Zheng R et al (2021) The stage-specifically accelerated brain aging in never-treated first-episode patients with depression. Hum Brain Mapp 42:3656–3666CrossRefPubMedPubMedCentral
7.
8.
Gonneaud J, Baria AT, PichetBinette A et al (2021) Accelerated functional brain aging in pre-clinical familial Alzheimer’s disease. Nat Commun 12:5346CrossRefPubMedPubMedCentral
9.
Cole JH, Raffel J, Friede T et al (2020) Longitudinal assessment of multiple sclerosis with the brain-age paradigm. Ann Neurol 88:93–105CrossRefPubMed
10.
Garcia-Martinez R, Rovira A, Alonso J et al (2011) Hepatic encephalopathy is associated with posttransplant cognitive function and brain volume. Liver Transpl 17:38–46CrossRefPubMed
11.
Zhang LJ, Zhong J, Lu GM (2013) Multimodality MR imaging findings of low-grade brain edema in hepatic encephalopathy. AJNR Am J Neuroradiol 34(4):707–715CrossRefPubMedPubMedCentral
12.
Yeo BT, Krienen FM, Sepulcre J et al (2011) The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 106:1125–1165CrossRefPubMed
13.
Cole JH (2020) Multimodality neuroimaging brain-age in UK biobank: relationship to biomedical, lifestyle, and cognitive factors. Neurobiol Aging 92:34–42CrossRefPubMedPubMedCentral
14.
McEvoy LK, Fennema-Notestine C, Roddey JC et al (2009) Alzheimer disease: quantitative structural neuroimaging for detection and prediction of clinical and structural changes in mild cognitive impairment. Radiol 251:195–205CrossRef
15.
Lampit A, Hallock H, Suo C, Naismith SL, Valenzuela M (2015) Cognitive training-induced short-term functional and long-term structural plastic change is related to gains in global cognition in healthy older adults: a pilot study. Front Aging Neurosci 9(7):14
16.
Lin W, Chen X, Gao YQ, Yang ZT, Yang W, Chen HJ (2019) Hippocampal atrophy and functional connectivity disruption in cirrhotic patients with minimal hepatic encephalopathy. Metab Brain Dis 34:1519–1529CrossRefPubMed
17.
Liu K, Chen G, Ren SY et al (2019) Regional gray matter abnormality in hepatic myelopathy patients after transjugular intrahepatic portosystemic shunt: a voxel-based morphometry study. Neural Regen Res 14:850–857CrossRefPubMedPubMedCentral
18.
Wang M, Cui J, Liu Y et al (2019) Structural and functional abnormalities of vision-related brain regions in cirrhotic patients: a MRI study. Neuroradiology 61:695–702CrossRefPubMedPubMedCentral
19.
Sepehrinezhad A, Zarifkar A, Namvar G, Shahbazi A, Williams R (2020) Astrocyte swelling in hepatic encephalopathy: molecular perspective of cytotoxic edema. Metab Brain Dis 35:559–578CrossRefPubMed
20.
Görg B, Karababa A, Shafigullina A, Bidmon HJ, Häussinger D (2015) Ammonia-induced senescence in cultured rat astrocytes and in human cerebral cortex in hepatic encephalopathy. Glia 63:37–50CrossRefPubMed
21.
Liotta EM, Kimberly WT (2020) Cerebral edema and liver disease: classic perspectives and contemporary hypotheses on mechanism. Neurosci Lett 721:134818CrossRefPubMedPubMedCentral
22.
Qi R, Zhang LJ, Zhong J et al (2013) Grey and white matter abnormalities in minimal hepatic encephalopathy: a study combining voxel-based morphometry and tract-based spatial statistics. Eur Radiol 23:3370–3378CrossRefPubMed
23.
Häussinger D, Schliess F (2008) Pathogenetic mechanisms of hepatic encephalopathy. Gut 57(1156):1165
24.
De Stefano N, Giorgio A, Gentile G et al (2021) Dynamics of pseudo-atrophy in RRMS reveals predominant gray matter compartmentalization. Ann Clin Transl Neurol 8:623–630CrossRefPubMedPubMedCentral
25.
Schwarz AJ (2021) The use, standardization, and interpretation of brain imaging data in clinical trials of neurodegenerative disorders. Neurotherapeutics 18:686–708CrossRefPubMedPubMedCentral
26.
Rompianesi G, Montalti R, Cautero N et al (2015) Neurological complications after liver transplantation as a consequence of immunosuppression: univariate and multivariate analysis of risk factors. Transpl Int 28:864–869CrossRefPubMed
27.
Bernhardt M, Pflugrad H, Goldbecker A et al (2015) Central nervous system complications after liver transplantation: common but mostly transient phenomena. Liver Transpl 21:224–232CrossRefPubMed
28.
Serkova N, Jacobsen W, Niemann CU et al (2001) Sirolimus, but not the structurally related RAD (everolimus), enhances the negative effects of cyclosporine on mitochondrial metabolism in the rat brain. Br J Pharmacol 133:875–885CrossRefPubMedPubMedCentral
29.
Klawitter J, Gottschalk S, Hainz C, Leibfritz D, Christians U, Serkova NJ (2010) Immunosuppressant neurotoxicity in rat brain models: oxidative stress and cellular metabolism. Chem Res Toxicol 23:608–619CrossRefPubMedPubMedCentral
30.
Schmitz B, Pflugrad H, Tryc AB et al (2019) Brain metabolic alterations in patients with long-term calcineurin inhibitor therapy after liver transplantation. Aliment Pharmacol Ther 49:1431–1441CrossRefPubMed
31.
Bellwon P, Culot M, Wilmes A et al (2015) Cyclosporine A kinetics in brain cell cultures and its potential of crossing the blood-brain barrier. Toxicol In Vitro 30:166–175CrossRefPubMed
32.
Rose CF, Amodio P, Bajaj JS et al (2020) Hepatic encephalopathy: Novel insights into classification, pathophysiology and therapy. J Hepatol 73:1526–1547CrossRefPubMed
33.
Lin WC, Chou KH, Chen CL et al (2014) Longitudinal brain white matter alterations in minimal hepatic encephalopathy before and after liver transplantation. PLoS One 9:e105887CrossRefPubMedPubMedCentral
34.
Chavarria L, Alonso J, García-Martínez R et al (2011) Biexponential analysis of diffusion-tensor imaging of the brain in patients with cirrhosis before and after liver transplantation. AJNR Am J Neuroradiol 32:1510–1517CrossRefPubMedPubMedCentral
35.
Ahluwalia V, Wade JB, Moeller FG et al (2015) The etiology of cirrhosis is a strong determinant of brain reserve: a multimodal magnetic resonance imaging study. Liver Transpl 21:1123–1132CrossRefPubMedPubMedCentral
36.
Acharya C, Bajaj JS (2021) Hepatic encephalopathy and liver transplantation: the past, present, and future toward equitable access. Liver Transpl 27:1830–1843CrossRefPubMed
37.
Serper M, Asrani S, VanWagner L, Reese PP, Kim M, Wolf MS (2022) Redefining success after liver transplantation: from mortality toward function and fulfillment. Liver Transpl 28:304–313CrossRefPubMed
38.
Zhang G, Cheng Y, Liu B (2017) Abnormalities of voxel-based whole-brain functional connectivity patterns predict the progression of hepatic encephalopathy. Brain Imaging Behav 11:784–796CrossRefPubMed
39.
Zhang G, Li Y, Zhang X, Huang L, Cheng Y, Shen W (2020) Identifying mild hepatic encephalopathy based on multi-layer modular algorithm and machine learning. Front Neurosci 14:627062CrossRefPubMed
40.
Buckner RL, Andrews-Hanna JR, Schacter DL (2008) The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci 1124:1–38CrossRefPubMed
Metadata
Title
Dynamic evolution of brain structural patterns in liver transplantation recipients: a longitudinal study based on 3D convolutional neuronal network model
Authors
Yue Cheng
Xiao-Dong Zhang
Cheng Chen
Ling-Fei He
Fang-Fei Li
Zi-Ning Lu
Wei-Qi Man
Yu-Jiao Zhao
Zhi-Xing Chang
Ying Wu
Wen Shen
Ling-Zhong Fan
Jun-Hai Xu
Publication date
04-04-2023
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 9/2023
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-023-09604-1

Other articles of this Issue 9/2023

European Radiology 9/2023 Go to the issue

Ultrasound

Comparison of non-radiomics imaging features and radiomics models based on contrast-enhanced ultrasound and Gd-EOB-DTPA-enhanced MRI for predicting microvascular invasion in hepatocellular carcinoma within 5 cm

Magnetic Resonance

Correlation of R2* with fat fraction and bone mineral density and its role in quantitative assessment of osteoporosis

Oncology

Hybrid simultaneous whole-body 2-[18F]FDG-PET/MRI imaging in newly diagnosed multiple myeloma: first diagnostic performance and clinical added value results

Musculoskeletal

Ulnar neuropathy at the elbow: associations of pre-operative DTI parameters with clinical outcomes after cubital tunnel decompression

Neuro

Evaluation of glymphatic-meningeal lymphatic system with intravenous gadolinium-based contrast-enhancement in cerebral small-vessel disease

Magnetic Resonance

Prognostic value of LI-RADS category on MRI in patients with primary hepatic lymphoepithelioma-like carcinoma