Machine Learning Classification of Cirrhotic Patients with and without Minimal Hepatic Encephalopathy Based on Regional Homogeneity of Intrinsic Brain Activity.

Qiu-Feng Chen,Tao Sun,Qun-Tai Shen,Hua-Jun Chen,Jun Liu,Han-Chieh Lin

doi:10.1371/journal.pone.0151263

Abstract

Machine learning-based approaches play an important role in examining functional magnetic resonance imaging (fMRI) data in a multivariate manner and extracting features predictive of group membership. This study was performed to assess the potential for measuring brain intrinsic activity to identify minimal hepatic encephalopathy (MHE) in cirrhotic patients, using the support vector machine (SVM) method. Resting-state fMRI data were acquired in 16 cirrhotic patients with MHE and 19 cirrhotic patients without MHE. The regional homogeneity (ReHo) method was used to investigate the local synchrony of intrinsic brain activity. Psychometric Hepatic Encephalopathy Score (PHES) was used to define MHE condition. SVM-classifier was then applied using leave-one-out cross-validation, to determine the discriminative ReHo-map for MHE. The discrimination map highlights a set of regions, including the prefrontal cortex, anterior cingulate cortex, anterior insular cortex, inferior parietal lobule, precentral and postcentral gyri, superior and medial temporal cortices, and middle and inferior occipital gyri. The optimized discriminative model showed total accuracy of 82.9% and sensitivity of 81.3%. Our results suggested that a combination of the SVM approach and brain intrinsic activity measurement could be helpful for detection of MHE in cirrhotic patients.

Highlights

Minimal hepatic encephalopathy (MHE) is a neurocognitive complication of cirrhosis, which has been reported in 30%–80% of tested patients [1, 2]
Changes in amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo) are correlated with the cognitive impairments seen in MHE and progress with advancement of HE [15, 16]. These findings suggest the potential of resting-state functional magnetic resonance imaging (fMRI) to provide biomarkers for identification of MHE
No significant differences were found with regard to age, gender, or education level between the two Accuracy, sensitivity, and specificity of ReHo-based classification

Summary

Introduction

Minimal hepatic encephalopathy (MHE) is a neurocognitive complication of cirrhosis, which has been reported in 30%–80% of tested patients [1, 2]. As the mildest form of hepatic encephalopathy (HE), MHE is defined as a condition in which cirrhotic patients have neuropsychiatric and neurophysiological defects, despite normal mental status. Classification of Patients with and without MHE in Cirrhosis by SVM executive abilities [3]. These neurocognitive deficits are subtle, and cannot be detected by routine clinical examinations [1], resulting in a relatively high rate of missed diagnosis and patients going untreated. As it remains challenging to detect MHE in cirrhotic patients, identification of new biomarkers or the development of novel screening methods for MHE diagnosis would be helpful for treatment and improving prognosis

Methods

Results

Conclusion