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Abstract

Introduction

Cirrhosis is an abnormal liver condition, mainly caused by viral hepatitis B or C, fatty liver diseases and alcoholism. Ascites is common complication of cirrhosis, associated with poor quality of life, abnormal cognitive functions, increased work disability and increased risk of infection, consequently development of hepatic encephalopathy1. Studies have suggested that inflammation caused by secondary infection with hyper ammonia act as synergistic factor responsible for hepatic encephalopathy in cirrhotic patients2. Magnetic resonance imaging (MRI) is the most commonly used method to observe brain abnormalities in in cirrhotic patients. These patients showed hyperintensities in basal ganglion on T1-weighted MRI and abnormal brain metabolites such as increased Glx, decreased myo-inositol and choline level on MR spectroscopy (MRS), decreased magnetization transfer ratio, and increased mean diffusivity on diffusion tensor imaging MRI 3,4,5,6,7,8. Though different neuroimaging studies investigated structural, diffusion, functional and metabolic brain changes in adult cirrhotic, the regional changes in gray matter and structural brain connectivity are not yet studied in pediatric cirrhotic patients. In this study, we evaluated the gray matter changes and global and regional topological properties of structural brain networks in pediatric cirrhotic compared to pediatric controls.

Materials and methods

Institutional regulatory board and ethics committee approved the current study protocol. 22 pediatric cirrhotic (mean age 11.6 ± 3.4 years, no prior HE), and 17 age and sex matched heathy controls were included in this study. Written informed consent was obtained from each individual prior study. Cirrhosis was diagnosed by the presence of a combination of high serum-ascites albumin gradient ascites, splenomegaly, large varices without EHPVO, irregular liver surface, portal vein ≥  13 mm and collaterals. Magnetic resonance imaging (MRI) was performed at 3-T clinical MR Scanner (GE Healthcare Technologies, Milwaukee, WI, United States) using a standard quadrature head coil. Conventional T2-, T1-weighted imaging and high-resolution T1-weighted structural imaging using a fast spoiled gradient echo BRAVO pulse sequence (TR =  8.4 ms; TE =  3.32 ms; inversion time =  400 ms; FA =  13°; matrix size =  512′512; FOV =  240′240 mm2; slice-thickness =  1.0 mm), were performed on each subject. T2-, T1-weighted images were examined for any gross brain pathology, such as cysts, tumors, or any other mass lesions, and presence of such anomaly was used as an exclusion criteria. We used high-resolution T1-weighted structural images for measuring regional gray matter changes and construction of structural network. Brain imaging data were processed using the statistical parametric mapping package (SPM8, http://www.fil.ion.ucl.ac.uk/spm/), MRIcroN, and MATLAB-based (The Math Works Inc, Natick, MA) custom software. High-resolution T1-weighted images from all subjects were visually examined for the presence of tumors and cysts. High-resolution T1-weighted images corrected for any bias and inhomogeneity-corrected images were partitioned into gray, white, and cerebrospinal fluid tissue types using a unified segmentation approach9,10. Gray matter tissues maps were normalized to the Montreal Neurological Institute (MNI) space and were modulated and smoothed using a Gaussian filter (FWHM, 10 mm). For the strctural networks construction we used graph theory based analysis using GAT software by using gray matter maps as described in details elsewhere11. In brief we generated 90 cortical and subcortical regions of interest (ROIs), excluding the cerebellum, from the Automated Anatomical Labeling (AAL) atlas using the WFU PickAtlas Toolbox. The extracted residual volumes of all 90 anatomical ROIs were used for construction of structural correlation networks.

Statistical analysis

All statistical computations were performed using the Statistical Package for Social Sciences (SPSS) version 16.0 (SPSS Inc., Chicago, USA). The normalized and smoothed gray matter tissue probability maps were compared between groups using analysis of covariance (ANCOVA; uncorrected threshold, p =  0.01; extended threshold, 100 voxels), with age and gender included as covariates. A p value of less than 0.05 was considered to be statistically significant.

Results

Glass brain images in Fig. 1 are showing significantly lower gray matter volumes (GMV) in multiple brain sites with few brain areas showing significantly higher GMV in pediatric cirrhotic compared to those of controls (Fig. 1). The correlation matrix of the cirrhotic group showed overall lower correlation strength than the control group (Fig. 2). In pediatric cirrhotic reduced brain network characteristic across a range of network densities was observed compared to control (Fig. 3). Pediatric cirrhotic also showed altered structural connectivity networks and hubs (Fig. 4).

Discussion

Cirrhotic patients showed altered gray matter volumes suggesting the brain tissue injury and decreased regional connectivity (clustering coefficient), while reduced global network organization (small worldness) and integration (hubs) suggesting decreased robustness and efficiency of the brain network. These results contribute to novel insights regarding the neurobiological mechanisms underlying cognitive deficits in these patients. The pathophysiological mechanism of brain tissue injury may include hyper ammonia secondary to inflammation resulting neuronal tissue injury2. This structural analysis using voxel based and graph theory might provide a more appropriate paradigm for understanding complicated neurobiological mechanism of cirrhotic patients, and may help to improve the clinical managements of these patients 12.

References

1. Ferenci P, Lockwood A, Mullen K, et al. Hepatic encephalopathy: definition, nomenclature, diagnosis, and quantification-final report of the working party at the 11th World Congresses of Gastroenterology, Vienna, 1998.Hepatology 2002;35:716–21.

2. Butterworth RF. Pathogenesis of hepatic encephalopathy in cirrhosis: the concept of synergism revisited. Metab Brain Dis. 2015.

3. Lai PH, Chen C, Liang HL, et al. Hyperintense basal ganglia on T1-weighted MR imaging. AJR Am J Roentgenol 1999;172:1109–15.

4. Geissler A, Lock G, Fründ R, et al. Cerebral abnormalities in patients with cirrhosis detected by proton magnetic resonance spectroscopy and magnetic resonance imaging. Hepatology 1997;25:48–54.

5. Rovira A, Grivé E, Pedraza S, et al. Magnetization transfer ratio values and proton MR spectroscopy of normal-appearing cerebral white matter in patients with liver cirrhosis. AJNR Am J Neuroradiol 2001;22:1137–42.

6. Laubenberger J, Häussinger D, Bayer S, et al. Proton magnetic resonance spectroscopy of the brain in symptomatic and asymptomatic patients with liver cirrhosis. Gastroenterology 1997;112:1610–16.

7. Miese F, Kircheis G, Wittsack HJ, et al. 1H-MR spectroscopy, magnetization transfer, and diffusion-weighted imaging in alcoholic and nonalcoholic patients with cirrhosis with hepatic encephalopathy. AJNR Am J Neuroradiol 2006;27:1019–26.

8. Kale RA, Gupta RK, Saraswat VA, et al. Demonstration of interstitial cerebral edema with diffusion tensor MR imaging in type C hepatic encephalopathy. Hepatology 2006;43:698–706.

9. Ashburner J., Friston K. Multimodal image coregistration and partitioning—a unified framework. Neuroimage. 1997;6:209–217

10. Friston K.J., Holmes A., Worsley K.J., Poline J.B., Frith C.D., Frackowiak R.S. Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp.1995;2:189–210.

11. Hosseini SM, Hoeft F, Kesler SR GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks. PLoS One. 2012;7:e40709.

12. Petrella JR: Use of graph theory to evaluate brain networks: a clinical tool for a small world? Radiology 2011, 259:317–320.

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/content/papers/10.5339/qfarc.2016.HBPP2536
2016-03-21
2019-10-21
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