Functional Network-Based Statistics Reveal Abnormal Resting-State Functional Connectivity in Minimal Hepatic Encephalopathy.

Abstract:

:Purpose: Whole-brain functional network analysis is an emerging methodology for exploring the mechanisms underlying hepatic encephalopathy (HE). This study aimed to identify the brain subnetwork that is significantly altered within the functional connectome in minimal HE (MHE), the earliest stage of HE. Materials and Methods: The study enrolled 19 cirrhotic patients with MHE and 19 controls who underwent the resting-state functional magnetic resonance imaging and cognitive assessment based on the Psychometric Hepatic Encephalopathy Score (PHES). A whole-brain functional connectivity (FC) matrix was calculated for each subject. Then, network-based statistical analyses of the functional connectome were used to perform group comparisons, and correlation analyses were conducted to identify the relationships between FC alterations and cognitive performance. Results: MHE patients showed significant reduction of positive FC within a subnetwork that predominantly involved the regions of the default-mode network, such as the bilateral posterior cingulate gyrus, bilateral medial prefrontal cortex, bilateral hippocampus and parahippocampal gyrus, bilateral angular gyrus, and left lateral temporal cortex. Meanwhile, MHE patients showed significant reduction of negative FC between default-mode network regions (such as the bilateral posterior cingulate gyrus, medial prefrontal cortex, and angular gyrus) and the regions involved in the somatosensory network (i.e., bilateral precentral and postcentral gyri) and the language network (i.e., the bilateral Rolandic operculum). The correlations of FC within the default-mode subnetwork and PHES results were noted. Conclusion: Default-mode network dysfunction may be one of the core issues in the pathophysiology of MHE. Our findings support the notion that HE is a neurological disease related to intrinsic brain network disruption.

journal_name

Front Neurol

journal_title

Frontiers in neurology

authors

Zhan C,Chen HJ,Gao YQ,Zou TX

doi

10.3389/fneur.2019.00033

subject

Has Abstract

pub_date

2019-01-29 00:00:00

pages

33

issn

1664-2295

journal_volume

10

pub_type

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