title: Machine Learning Classification of Cirrhotic Patients with and without Minimal Hepatic Encephalopathy Based on Regional Homogeneity of Intrinsic Brain Activity.
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.
authors.affiliation: School of Information Science and Engineering, Central South University, Changsha 410083, China.
authors.forename: Qiu-Feng
authors.initials: QF
authors.lastname: Chen
authors.affiliation: Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
authors.forename: Hua-Jun
authors.initials: HJ
authors.lastname: Chen
authors.affiliation: Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Liu
authors.affiliation: Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
authors.forename: Tao
authors.initials: T
authors.lastname: Sun
authors.affiliation: School of Information Science and Engineering, Central South University, Changsha 410083, China.
authors.forename: Qun-Tai
authors.initials: QT
authors.lastname: Shen
mesh_terms: D000328:Adult; D001921:Brain / Q000503:physiopathology*; D005260:Female; D006501:Hepatic Encephalopathy / Q000175:diagnosis* / Q000209:etiology / Q000503:physiopathology; D006801:Humans; D008103:Liver Cirrhosis / Q000150:complications / Q000503:physiopathology*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009415:Nerve Net / Q000503:physiopathology*; D060388:Support Vector Machine