Abstract
Hepatic encephalopathy (HE) is a neurocognitive dysfunction based on metabolic disorders caused by severe liver disease, which has a high one-year mortality. Mild hepatic encephalopathy (MHE) has a high risk of converting to overt HE, and thus the accurate identification of MHE from cirrhosis with no HE (noHE) is of great significance in reducing mortality. Previously, most studies focused on studying abnormality in the static brain networks of MHE to find biomarkers. In this study, we aimed to use multi-layer modular algorithm to study abnormality in dynamic graph properties of brain network in MHE patients and construct a machine learning model to identify individual MHE from noHE. Here, a time length of 500-second resting-state functional MRI data were collected from 41 healthy subjects, 32 noHE patients and 30 MHE patients. Multi-layer modular algorithm was performed on dynamic brain functional connectivity graph. The connection-stability score was used to characterize the loyalty in each brain network module. Nodal flexibility, cohesion and disjointness were calculated to describe how the node changes the network affiliation across time. Results show that significant differences between MHE and noHE were found merely in nodal disjointness in higher cognitive network modules (ventral attention, fronto-parietal, default mode networks) and these abnormalities were associated with the decline in patients’ attention and visual memory function evaluated by Digit Symbol Test. Finally, feature extraction from node disjointness with the support vector machine classifier showed an accuracy of 88.71% in discrimination of MHE from noHE, which was verified by different window sizes, modular partition parameters and machine learning parameters. All these results show that abnormal nodal disjointness in higher cognitive networks during brain network evolution can be seemed as a biomarker for identification of MHE, which help us understand the disease mechanism of MHE at a fine scale.
Highlights
Hepatic encephalopathy (HE) is a syndrome of central nervous system dysfunction based on metabolic disorders caused by severe liver disease (Agrawal et al, 2015; Zhang et al, 2017b)
Oneway ANOVA results indicated that average connection-stability score in sensory/somatomotor mouth network (SSMN), default mode network (DMN), cingulo-opercular task control network (CON), and frontro-parietal task control network (FPN) showed
Results showed that the average connection-stability scores of Mild hepatic encephalopathy (MHE) patients were significantly lower than that of healthy controls (HCs) in CON and FPN, and marginally significant in DMN after false discovery rate (FDR) correction, but no significant differences were observed between other groups
Summary
Hepatic encephalopathy (HE) is a syndrome of central nervous system dysfunction based on metabolic disorders caused by severe liver disease (Agrawal et al, 2015; Zhang et al, 2017b). Mild hepatic encephalopathy (MHE), as a mildest form of HE spectrum, has no recognizable clinical symptoms of HE, but is characterized by subtle neurocognitive and psychomotor deficits, such as psychomotor slowing, shortened attention concentration, dysfunctional executive abilities, and memory loss (Agrawal et al, 2015). It is of great significance to understand the dysfunction mechanism of MHE and to explore the biomarkers for precisely clinical diagnosis
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