Key result
Resting-state attention-related brain network analysis yielded 94.6% classification accuracy between minimal hepatic encephalopathy patients and healthy controls, and 85.4% between MHE and NMHE.
Why the study?
Does resting-state attention-related brain network analysis accurately classify cirrhotic patients with minimal hepatic encephalopathy compared to those without and healthy controls?
Cross-Sectional (n=58)
No
Does resting-state attention-related brain network analysis accurately classify cirrhotic patients with minimal hepatic encephalopathy compared to those without and healthy controls?
Resting-state attention-related brain network analysis using fMRI can accurately classify cirrhotic patients with minimal hepatic encephalopathy, offering a potential new diagnostic biomarker.
May aid MHE classification in cirrhosis; hypothesis-generating and requires prospective validation before practice change.
BACKGROUND: Attention deficit is an early and key characteristic of minimal hepatic encephalopathy (MHE) and has been used as indicator for MHE detection. The aim of this study is to classify the cirrhotic patients with or without MHE (NMHE) and healthy controls (HC) using the resting-state attention-related brain network analysis. METHODS AND FINDINGS: Resting-state fMRI was administrated to 20 MHE patients, 21 NMHE patients, and 17 HCs. Three attention-related networks, including dorsal attention network (DAN), ventral attention network (VAN), and default mode network (DMN), were obtained by independent component analysis. One-way analysis of covariance was performed to determine the regions of interest (ROIs) showing significant functional connectivity (FC) change. With FC strength of ROIs as indicators, Linear Discriminant Analysis (LDA) was conducted to differentiate MHE from HC or NMHE. Across three groups, significant FC differences were found within DAN (left superior/inferior parietal lobule and right inferior parietal lobule), VAN (right superior parietal lobule), and DMN (bilateral posterior cingulate gyrus and precuneus, and left inferior parietal lobule). With FC strength of ROIs from three networks as indicators, LDA yielded 94.6% classification accuracy between MHE and HC (100% sensitivity and 88.2% specificity) and 85.4% classification accuracy between MHE and NMHE (90.0% sensitivity and 81.0% specificity). CONCLUSIONS: Our results suggest that the resting-state attention-related brain network analysis can be useful in classification of subjects with MHE, NMHE, and HC and may provide a new insight into MHE detection.
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Chen et al. (2014) conducted a cross-sectional in Minimal hepatic encephalopathy in liver cirrhosis (n=58). Resting-state attention-related brain network analysis vs. Healthy controls and cirrhotic patients without MHE was evaluated on Classification accuracy between MHE and HC. Resting-state attention-related brain network analysis yielded 94.6% classification accuracy between minimal hepatic encephalopathy patients and healthy controls, and 85.4% between MHE and NMHE.
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