Reduced Cortical Complexity in Cirrhotic Patients with Minimal Hepatic Encephalopathy.

Purpose: Gray matter volume loss, regional cortical thinning, and local gyrification index alteration have been documented in minimal hepatic encephalopathy (MHE). Fractal dimension (FD), another morphological parameter, has been widely used to describe structural complexity alterations in neurological or psychiatric disease. Here, we conducted the first study to investigate FD alterations in MHE. Methods and Materials: We performed high-resolution structural magnetic resonance imaging on cirrhotic patients with MHE (n = 20) and healthy controls (n = 21). We evaluated their cognitive performance using the psychometric hepatic encephalopathy score (PHES). The regional FD value was calculated by Computational Anatomy Toolbox (CAT12) and compared between groups. We further estimated the association between patients' cognitive performance and FD values. Results: MHE patients presented significantly decreased FD values in the left precuneus, left supramarginal gyrus, right caudal anterior cingulate cortex, right isthmus cingulate cortex, right insula, bilateral pericalcarine cortex, and bilateral paracentral cortex compared to normal controls. In addition, the FD values in the right isthmus cingulate cortex and right insula were shown to be positively correlated with patients' cognitive performance. Conclusion: Aberrant cortical complexity is an additional characteristic of MHE, and FD analysis may provide novel insight into the neurobiological basis of cognitive dysfunction in MHE.

Brain Microstructural Abnormalities in Patients With Cirrhosis Without Overt Hepatic Encephalopathy: A Voxel-Based Diffusion Kurtosis Imaging Study.

OBJECTIVE: This study aimed to investigate whole-brain microstructural abnormalities and their correlation with cognitive impairment in patients with cirrhosis using diffusion kurtosis imaging (DKI). SUBJECTS AND METHODS: Eighteen patients with cirrhosis and 17 healthy control subjects underwent DKI. Cognition was measured using psychometric hepatic encephalopathy (HE) scores. Whole-brain voxel-based analyses were performed to investigate between-group differences in DKI-derived parameters, including mean kurtosis, axial kurtosis, and radial kurtosis. RESULTS: Compared with control subjects, the patients with cirrhosis had lower psychometric HE scores, indicating cognitive impairments. The patients with cirrhosis had significantly lower global mean kurtosis, axial kurtosis, and radial kurtosis in gray matter (GM) and white matter (WM). Voxel-based analyses showed that patients with cirrhosis had decreased mean kurtosis, axial kurtosis, and radial kurtosis in diffuse GM regions (particularly in the cingulate cortex, precuneus, insular cortex, frontal areas, basal ganglia, hippocampus and parahippocampal gyrus, supramarginal gyrus and angular gyrus, postcentral and precentral gyrus, and cerebellum) and WM regions (particularly in the corpus callosum, internal capsule, frontal regions, parietal regions, occipital regions, and cerebellum). The DKI metrics were positively correlated with psychometric HE score among patients. CONCLUSION: Lower DKI parameters suggest decreased brain microstructural complexity in patients with cirrhosis, which may contribute to the neurobiologic basis of cognitive impairment.

Altered dynamic functional connectivity in the default mode network in patients with cirrhosis and minimal hepatic encephalopathy.

PURPOSE: Abnormal brain intrinsic functional connectivity (FC) has been documented in minimal hepatic encephalopathy (MHE) by static connectivity analysis. However, changes in dynamic FC (dFC) remain unknown. We aimed to identify altered dFC within the default mode network (DMN) associated with MHE. METHODS: Resting-state functional MRI data were acquired from 20 cirrhotic patients with MHE and 24 healthy controls. DMN seed regions were defined using seed-based FC analysis (centered on the posterior cingulate cortex (PCC)). Dynamic FC architecture was calculated using a sliding time-window method. K-means clustering (number of clusters = 2-4) was applied to estimate FC states. RESULTS: When the number of clusters was 2, MHE patients presented weaker connectivity strengths compared with controls in states 1 and 2. In state 1, decreased FC strength was found between the PCC/precuneus (PCUN) and right medial temporal lobe (MTL)/bilateral lateral temporal cortex (LTC); left inferior parietal lobule (IPL) and right MTL/left LTC; right IPL and right MTL/bilateral LTC; right MTL and right LTC; and medial prefrontal cortex (MPFC) and right MTL/bilateral LTC. In state 2, reduced FC strength was observed between the PCC/PCUN and bilateral MTL/bilateral LTC; left IPL and left MTL/bilateral LTC/MPFC; and left LTC and right LTC. Altered connectivities from state 1 were correlated with patient cognitive performance. Similar findings were observed when the number of clusters was set to 3 or 4. CONCLUSION: Aberrant dynamic DMN connectivity is an additional characteristic of MHE. Dynamic connectivity analysis offers a novel paradigm for understanding MHE-related mechanisms.

Identifying minimal hepatic encephalopathy in cirrhotic patients by measuring spontaneous brain activity.

It has been demonstrated that minimal hepatic encephalopathy (MHE) is associated with aberrant regional intrinsic brain activity in cirrhotic patients. However, few studies have investigated whether altered intrinsic brain activity can be used as a biomarker of MHE among cirrhotic patients. In this study, 36 cirrhotic patients (with MHE, n = 16; without MHE [NHE], n = 20) underwent resting-state functional magnetic resonance imaging (fMRI). Spontaneous brain activity was measured by examining the amplitude of low-frequency fluctuations (ALFF) in the fMRI signal. MHE was diagnosed based on the Psychometric Hepatic Encephalopathy Score (PHES). A two-sample t-test was used to determine the regions of interest (ROIs) in which ALFF differed significantly between the two groups; then, ALFF values within ROIs were selected as classification features. A linear discriminative analysis was used to differentiate MHE patients from NHE patients. The leave-one-out cross-validation method was used to estimate the performance of the classifier. The classification analysis was 80.6 % accurate (81.3 % sensitivity and 80.0 % specificity) in terms of distinguishing between the two groups. Six ROIs were identified as the most discriminative features, including the bilateral medial frontal cortex/anterior cingulate cortex, posterior cingulate cortex/precuneus, left precentral and postcentral gyrus, right lingual gyrus, middle frontal gyrus, and inferior/superior parietal lobule. The ALFF values within ROIs were correlated with PHES in cirrhotic patients. Our findings suggest that altered regional brain spontaneous activity is a useful biomarker for MHE detection among cirrhotic patients.

Identification of patients with and without minimal hepatic encephalopathy based on gray matter volumetry using a support vector machine learning algorithm.

Minimal hepatic encephalopathy (MHE) is characterized by diffuse abnormalities in cerebral structure, such as reduced cortical thickness and altered brain parenchymal volume. This study tested the potential of gray matter (GM) volumetry to differentiate between cirrhotic patients with and without MHE using a support vector machine (SVM) learning method. High-resolution, T1-weighted magnetic resonance images were acquired from 24 cirrhotic patients with MHE and 29 cirrhotic patients without MHE (NHE). Voxel-based morphometry was conducted to evaluate the GM volume (GMV) for each subject. An SVM classifier was employed to explore the ability of the GMV measurement to diagnose MHE, and the leave-one-out cross-validation method was used to assess classification accuracy. The SVM algorithm based on GM volumetry achieved a classification accuracy of 83.02%, with a sensitivity of 83.33% and a specificity of 82.76%. The majority of the most discriminative GMVs were located in the bilateral frontal lobe, bilateral lentiform nucleus, bilateral thalamus, bilateral sensorimotor areas, bilateral visual regions, bilateral temporal lobe, bilateral cerebellum, left inferior parietal lobe, and right precuneus/posterior cingulate gyrus. Our results suggest that SVM analysis based on GM volumetry has the potential to help diagnose MHE in cirrhotic patients.

Identification of Amyotrophic Lateral Sclerosis Based on Diffusion Tensor Imaging and Support Vector Machine.

Objectives: White matter (WM) impairments involving both motor and extra-motor areas have been well-documented in amyotrophic lateral sclerosis (ALS). This study tested the potential of diffusion measurements in WM for identifying ALS based on support vector machine (SVM). Methods: Voxel-wise fractional anisotropy (FA) values of diffusion tensor images (DTI) were extracted from 22 ALS patients and 26 healthy controls and served as discrimination features. The revised ALS Functional Rating Scale (ALSFRS-R) was employed to assess ALS severity. Feature ranking and selection were based on Fisher scores. A linear kernel SVM algorithm was applied to build the classification model, from which the classification performance was evaluated. To promote classifier generalization ability, a leave-one-out cross-validation (LOOCV) method was adopted. Results: By using the 2,400~3,400 ranked features as optimal features, the highest classification accuracy of 83.33% (sensitivity = 77.27% and specificity = 88.46%, P = 0.0001) was achieved, with an area under receiver operating characteristic curve of 0.862. The predicted function value was positively correlated with patient ALSFRS-R scores (r = 0.493, P = 0.020). In the optimized SVM model, FA values from several regions mostly contributed to classification, primarily involving the corticospinal tract pathway, postcentral gyrus, and frontal and parietal areas. Conclusions: Our results suggest the feasibility of ALS diagnosis based on SVM analysis and diffusion measurements of WM. Additional investigations using a larger cohort is recommended in order to validate the results of this study.

Aberrant topological organization of the functional brain network associated with prior overt hepatic encephalopathy in cirrhotic patients.

A higher risk of cognitive impairments has been found after an overt hepatic encephalopathy (OHE) episode in cirrhotic patients. We investigated the effect of prior OHE episodes on the topological organization of the functional brain network and its association with the relevant cognitive impairments. Resting-state functional MRI data were acquired from 41 cirrhotic patients (19 with prior OHE (Prior-OHE) and 22 without (Non-Prior-OHE)) and 21 healthy controls (HC). A Psychometric Hepatic Encephalopathy Score (PHES) assessed cognition. The whole-brain functional network was constructed by thresholding functional correlation matrices of 90 brain regions (derived from the Automated Anatomic Labeling atlas). The topological properties of the brain network, including small-worldness, network efficiency, and nodal efficiency, were examined using graph theory-based analysis. Globally, the Prior-OHE group had a significantly decreased clustering coefficient and local efficiency, compared with the controls. Locally, the nodal efficiency in the bilateral medial superior frontal gyrus and the right postcentral gyrus decreased in the Prior-OHE group, while the nodal efficiency in the bilateral anterior cingulate/paracingulate gyri and right superior parietal gyrus increased in the Prior-OHE group. The alterations of global and regional network parameters progressed from Non-Prior-OHE to Prior-OHE and the clustering coefficient and local efficiency values were significantly correlated with PHES results. In conclusion, cirrhosis leads to the reduction of brain functional network efficiency, which could be aggravated by a prior OHE episode. Aberrant topological organization of the functional brain network may contribute to a higher risk of cognitive impairments in Prior-OHE patients.

Disrupted Brain Intrinsic Networks and Executive Dysfunction in Cirrhotic Patients without Overt Hepatic Encephalopathy.

OBJECTIVE: Patients with cirrhosis often exhibit cognitive deficits, particularly executive dysfunction, which is considered a predictor of overt hepatic encephalopathy (OHE). We examined brain intrinsic networks associated with executive function to investigate the neural basis of this cognitive deficiency in cirrhosis. METHODS: Resting-state functional MRI data were acquired from 20 cirrhotic patients and 18 healthy controls. Seed-based correlation analysis was used to identify the three well-known networks associated with executive function, including executive control (ECN), default mode (DMN), and salience (SN) networks. Functional connectivity (FC) within each network was compared between groups and correlated with patient executive performance (assessed by the Stroop task). RESULTS: Patients showed decreased FC between the ECN seed (right dorsolateral prefrontal cortex) and several regions (including right middle/inferior frontal gyrus, left inferior frontal gyrus, bilateral inferior/superior parietal lobules, bilateral middle/inferior temporal gyrus, and right medial frontal gyrus), between the DMN seed [posterior cingulate cortex (PCC)] and several regions (including bilateral medial frontal gyrus, bilateral anterior cingulate cortex, bilateral superior frontal gyrus, bilateral precuneus/PCC, left supramarginal gyrus, and left middle temporal gyrus), and between the SN seed (right anterior insula) and right supramarginal gyrus. FC strength in the ECN and SN was negatively correlated with patient performance during the Stroop task. CONCLUSION: Disrupted functional integration in the core brain cognitive networks, which is reflected by reductions in FC, occurs before OHE bouts and may play an important role in the neural mechanism of executive dysfunction associated with cirrhosis.

Aberrant salience network and its functional coupling with default and executive networks in minimal hepatic encephalopathy: a resting-state fMRI study.

The purposes of this study are to explore functional alterations in salience network (SN) and its functional coupling with default mode (DMN) and central executive (CEN) networks in minimal hepatic encephalopathy (MHE). Twenty cirrhotic patients with MHE, 23 cirrhotic patients without MHE (NHE), and 18 controls underwent resting-state fMRI and psychometric hepatic encephalopathy score (PHES) test. Independent component analysis was performed to obtain DMN (including three subsystems: anterior, inferior-posterior, and superior-posterior DMN [a/ip/spDMN]), SN, and CEN (including three subsystems: left-ventral, right-ventral, and dorsal CEN [lv/rv/dCEN]). The intrinsic functional connectivity (iFC) within (intra-iFC) and between (inter-iFC and time-lagged inter-iFC) networks was measured. MHE patients showed decreased intra-iFC within aDMN, SN, lvCEN, and rvCEN; and decreased inter-iFC and time-lagged inter-iFC between SN and ipDMN/spDMN/lvCEN and increased inter-iFC and time-lagged inter-iFC between SN and aDMN, compared with controls. A progressive trend in connectivity alterations was found as the disease developed from NHE to MHE. The inter-iFC between ipDMN/spDMN and SN was significantly correlated with PHES score. In conclusion, an aberrant SN and its functional interaction with the DMN/CEN are core features of MHE that are associated with disease progression and may play an important role in neurocognitive dysfunction in MHE.

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

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.

Resting-state functional connectivity abnormalities correlate with psychometric hepatic encephalopathy score in cirrhosis.

BACKGROUND & AIMS: Neurocognitive impairment is a common complication of cirrhosis and regarded as the important characteristic for early stage of hepatic encephalopathy (HE). This study aimed to investigate the changes in brain network centrality of functional connectivity among cirrhotic patients and uncover the mechanisms about early HE. METHODS: Twenty-four cirrhotic patients without overt HE and 21 healthy controls were enrolled and underwent resting-state fMRI and Psychometric Hepatic Encephalopathy Score (PHES) tests. Whole-brain functional network was constructed by measuring the temporal correlations of every pairs of brain gray matter voxels; and then voxel-wise degree centrality (DC), an index reflecting importance of a node in functional integration, was calculated and compared between two groups. A seed-based resting-state functional connectivity (RSFC) analysis was further performed to investigate abnormal functional connectivity pattern of those regions with changed DC. RESULTS: Compared with controls, the cirrhotic patients had worse performances in all neurocognitive tests and lower PHES score. Meanwhile, patients showed decreased DC in bilateral medial prefrontal gyrus and anterior cingulate cortex, left middle frontal gyrus, and bilateral thalamus; while increased DC in right middle occipital gyrus and parahippocampal gyrus/inferior temporal gyrus. The seed-based RSFC analyses revealed that the relevant functional networks, such as default-mode and attention networks, visual network, and thalamo-cortical circuits, were disturbed in cirrhotic patients. The DC changes were correlated with PHES score in patient group. CONCLUSIONS: Our findings further confirm brain network disorganization in cirrhotic patients with neurocognitive impairments and may provide a new perspective for understanding HE-related mechanisms.