Selective impairments of resting-state networks in minimal hepatic encephalopathy

Functional connectivity disruption of insular subregions in the cirrhotic patients with minimal hepatic encephalopathy

We investigated abnormal functional connectivity (FC) patterns of insular subregions in patients with minimal hepatic encephalopathy (MHE) and examined their relationships with cognitive dysfunction using resting-state functional magnetic resonance imaging (fMRI). We collected resting-state fMRI data in 54 patients with cirrhosis [20 with MHE and 34 without MHE (NHE)] and 25 healthy controls. After defining six subregions of insula, we mapped whole-brain FC of the insular subregions and identified FC differences through three groups. FC of the insular subregions was correlated against clinical parameters (including venous blood ammonia level, Child-Pugh score, and cognitive score). The discrimination performance between the MHE and NHE groups was evaluated by performing a classification analysis using the FC index. Across three groups, the observed FC differences involved four insular subregions, including the left-ventral anterior insula, left-dorsal anterior insula, right-dorsal anterior insula, and left-posterior insula (P < 0.05 with false discovery rate correction). Moreover, the FC of these four insular subregions progressively attenuated from NHE to MHE. In addition, hypoconnectivity of insular subregions was correlated with the poor neuropsychological performance and the evaluated blood ammonia levels in patients (P < 0.05 with Bonferroni correction). The FC of insular subregions yielded moderate discriminative value between the MHE and NHE groups (AUC = 0.696-0.809). FC disruption of insular subregions is related to worse cognitive performance in MHE. This study extended our understanding about the neurophysiology of MHE and may assist for its diagnosis.

Improved cognition after rifaximin treatment is associated with changes in intra- and inter-brain network functional connectivity

BACKGROUND

Rifaximin is a non-reabsorbable antibiotic which acts at gut level, and improves cognition and inflammatory parameters in minimal hepatic encephalopathy (MHE) patients, but not all patients show the same level of response. This study aims to assess brain activity, both within and between brain networks, following rifaximin treatment, considering the differences between response groups as well.

METHODS

Twenty-two healthy controls and 53 patients with cirrhosis (22 without and 31 with MHE, diagnosed by Psychometric Hepatic Encephalopathy Score, PHES) performed psychometric, attention and coordination tests, and blood inflammatory parameters were measured. Resting-state functional magnetic resonance imaging (fMRI) acquisitions were performed on controls and MHE patients. Eighteen MHE patients underwent a rifaximin treatment for 6 months, after which all measures were repeated. fMRI images were analysed and changes after treatment were assessed.

RESULTS

After rifaximin treatment, 13 patients improved their PHES score (Responder patients) while 5 did not (Non-responder patients). No significant decrease in blood ammonia was observed after rifaximin treatment, but there was a decrease in plasma inflammatory cytokines in responder patients. A global effect of rifaximin was detected on the sensorimotor and fronto-parietal networks. Responder patients showed a relative increase of thalamic network connectivity in comparison to non-responder patients. Before treatment, responder and non-responder patients showed connectivity differences in basal ganglia network. The connection of the sensorimotor and thalamic networks between them and with other networks suffered changes after treatment. These connections between networks mostly decreased after treatment. All changes and differences showed a significant level of correlation with the performance of psychometric tests and the blood levels of inflammatory biomarkers.

CONCLUSIONS

There was an improvement of the communication between executive, motor and attention-related brain areas, and their functional independence following rifaximin treatment. Patients who respond also show a less deteriorated connection involved in these functions before treatment. Results suggest that the improved inflammatory state of MHE patients, following rifaximin treatment would favour the observed changes in brain function and enhanced cognitive performance.

Identifying Minimal Hepatic Encephalopathy: A New Perspective from Magnetic Resonance Imaging

Type C hepatic encephalopathy (HE) is a condition characterized by brain dysfunction caused by liver insufficiency and/or portal-systemic blood shunting, which manifests as a broad spectrum of neurological or psychiatric abnormalities, ranging from minimal HE (MHE), detectable only by neuropsychological or neurophysiological assessment, to coma. Though MHE is the subclinical phase of HE, it is highly prevalent in cirrhotic patients and strongly associated with poor quality of life, high risk of overt HE, and mortality. It is, therefore, critical to identify MHE at the earliest and timely intervene, thereby minimizing the subsequent complications and costs. However, proper and sensitive diagnosis of MHE is hampered by its unnoticeable symptoms and the absence of standard diagnostic criteria. A variety of neuropsychological or neurophysiological tests have been performed to diagnose MHE. However, these tests are nonspecific and susceptible to multiple factors (eg, aging, education), thereby limiting their application in clinical practice. Thus, developing an objective, effective, and noninvasive method is imperative to help detect MHE. Magnetic resonance imaging (MRI), a noninvasive technique which can produce many objective biomarkers by different imaging sequences (eg, Magnetic resonance spectroscopy, DWI, rs-MRI, and arterial spin labeling), has recently shown the ability to screen MHE from NHE (non-HE) patients accurately. As advanced MRI techniques continue to emerge, more minor changes in the brain could be captured, providing new means for early diagnosis and quantitative assessment of MHE. In addition, the advancement of artificial intelligence in medical imaging also presents the potential to mine more effective diagnostic biomarkers and further improves the predictive efficiency of MHE. Taken together, advanced MRI techniques may provide a new perspective for us to identify MHE in the future. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Brain Gray Matter Alterations in Hepatic Encephalopathy: A Voxel-Based Meta-Analysis of Whole-Brain Studies

Background

Previous studies on voxel-based morphometry (VBM) have found that there were gray matter alterations in patients with hepatic encephalopathy (HE). However, the reported results were inconsistent and lack a quantitative review. Therefore, this study aims for a quantitative meta-analysis of VBM analysis on patients with HE.

Methods

The studies in our meta-analysis were collected from Pubmed, Web of Science, and Embase, which were published from January 1947 to October 2021. The seed-based d mapping (SDM) method was applied to quantitatively estimate the regional gray matter abnormalities in patients with HE. A meta-regression analysis was applied to evaluate the relationship between plasma ammonia and gray matter alteration.

Results

There were nine studies, with sixteen datasets consisting of 333 participants with HE and 429 healthy controls. The pooled and subgroup meta-analyses showed an increase in gray matter volume (GMV) in the bilateral thalamus and the calcarine fissure but a decrease in the GMV in the bilateral insula, the basal ganglia, the anterior cingulate gyrus, and the cerebellum. The meta-regression showed that plasma ammonia was positively associated with the GMV in the left thalamus but was negatively associated with the GMV in the cerebellum and the bilateral striatum.

Conclusion

Gray matter volume in patients with HE largely varied and could be affected by plasma ammonia. The findings of this study could help us to better understand the pathophysiology of cognitive dysfunction in patients with HE.

Progress in research of blood oxygen level dependent functional magnetic resonance imaging in cirrhotic patients with minimal hepatic encephalopathy

Hepatic encephalopathy (HE) is a neuropsychiatric syndrome that develops in patients with cirrhosis or other severe liver diseases. The incidence of minimal hepatic encephalopathy (MHE) is high, and MHE is often misdiagnosed because of its subclinical manifestations. MHE significantly reduces patients' quality of life and its prognosis is poor. Blood oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI) has been used to explore the brain functional changes and to further understand pathophysiological mechanisms of brain disease. This article focuses on the progress in the research of BOLD-fMRI in cirrhotic patients with MHE with regard to its principles, categories, and post-processing algorithms.

Progressive Disruption of Dynamic Functional Network Connectivity in Patients With Hepatitis B Virus-related cirrhosis

BACKGROUND

The diseased-related dynamic functional network connectivity (dFNC) disruption and its relationship with cognitive impairment in hepatitis B virus-related cirrhosis (HBV-RC) patients with minimal hepatic encephalopathy (MHE) and no MHE (NMHE) remain unknown. This knowledge would help identify MHE pathophysiology and monitor disease progression in HBV-RC patients.

PURPOSE

To investigate the dFNC in patients with NMHE and MHE and the relationship between dFNC indices with the psychometric hepatic encephalopathy score (PHES).

STUDY TYPE

Prospective.

POPULATION

Thirty HBV-RC patients (including 17 NMHE and 13 MHE) and 38 healthy controls (HC).

FIELD STRENGTH/SEQUENCE

A 1.5 T MRI and gradient-echo echo-planar imaging and fast field echo three-dimensional T1-weighted imaging.

ASSESSMENT

The independent components, dFNC matrix and dFNC indices (mean dwell times [DT], number of states, number of transitions, and fraction time in each state), were obtained through GIFT package. Cognitive measurement and patients grouping were based on PHES tests.

STATISTICAL TESTS

One-way ANOVA, chi-square test, two-sample t-test, Kruskal-Wallis test, spearman's correlation analysis and the false discovery rate. Significance level: P < 0.05.

RESULTS

Compared to HC (21.1 ± 4.02), the DT of state 1 decreased in NMHE (9.0 ± 3.04, P = 0.062, 95% confidence interval [CI] is -0.65 to 24.88) and significantly in MHE stage (1.2 ± 1.01) and was significantly correlated with PHES (r = 0.5) for all patients. The DT of state 2 increased gradually in NMHE (75.2 ± 13.10, P = 0.052, 95% CI, -54.23 to 0.28) and significantly in MHE stage (94.6 ± 15.61) when compared to HC (48.2 ± 6.97). Moreover, the connectivity between cognitive control network (CCN) and visual network (VIS) in state 1 (0.7 ± 0.79) and between default mode (DMN) and VIS in state 2 (-0.2 ± 0.29) decreased significantly in MHE when compared to HC (0.1 ± 0.68 for CCN-VIS in state 1 and 0.1 ± 0.17 for DMN-VIS for state 2). DATA CONCLUSION: dFNC exhibited progressive impairment as the disease advances in patients with HBV-RC.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Identification of minimal hepatic encephalopathy based on dynamic functional connectivity

To investigate whether dynamic functional connectivity (DFC) metrics can better identify minimal hepatic encephalopathy (MHE) patients from cirrhotic patients without any hepatic encephalopathy (noHE) and healthy controls (HCs). Resting-state functional MRI data were acquired from 62 patients with cirrhosis (MHE, n = 30; noHE, n = 32) and 41 HCs. We used the sliding time window approach and functional connectivity analysis to extract the time-varying properties of brain connectivity. Three DFC characteristics (i.e., strength, stability, and variability) were calculated. For comparison, we also calculated the static functional connectivity (SFC). A linear support vector machine was used to differentiate MHE patients from noHE and HCs using DFC and SFC metrics as classification features. The leave-one-out cross-validation method was used to estimate the classification performance. The strength of DFC (DFC-Dstrength) achieved the best accuracy (MHE vs. noHE, 72.5%; MHE vs. HCs, 84%; and noHE vs. HCs, 88%) compared to the other dynamic features. Compared to static features, the classification accuracies of the DFC-Dstrength feature were improved by 10.5%, 8%, and 14% for MHE vs. noHE, MHE vs. HC, and noHE vs. HCs, respectively. Based on the DFC-Dstrength, seven nodes were identified as the most discriminant features to classify MHE from noHE, including left inferior parietal lobule, left supramarginal gyrus, left calcarine, left superior frontal gyrus, left cerebellum, right postcentral gyrus, and right insula. In summary, DFC characteristics have a higher classification accuracy in identifying MHE from cirrhosis patients. Our findings suggest the usefulness of DFC in capturing neural processes and identifying disease-related biomarkers important for MHE identification.

Lights and Shadows in Hepatic Encephalopathy Diagnosis

Hepatic encephalopathy (HE) is a form of brain dysfunction that is caused by liver insufficiency and/or portal-systemic shunting. The exact nature of HE is debated; as such, conflicting uses of the term "HE" may cause inconsistencies in its detection and management. This review highlights the meaning of the term "HE" on the basis of its historical origins and current consensus. It also provides criteria for the diagnosis of the condition based on its phenotypes and risk factors for its occurrence. The procedure for differential diagnosis from other conditions which result in similar phenotypes is considered, together with precipitants and confounders. Finally, the current multidimensional approach for the correct clinical reporting of HE episodes is discussed.

Identifying Mild Hepatic Encephalopathy Based on Multi-Layer Modular Algorithm and Machine Learning

Hepatic encephalopathy (HE) is a neurocognitive dysfunction based on metabolic disorders caused by severe liver disease, which has a high one-year mortality. Mild hepatic encephalopathy (MHE) has a high risk of converting to overt HE, and thus the accurate identification of MHE from cirrhosis with no HE (noHE) is of great significance in reducing mortality. Previously, most studies focused on studying abnormality in the static brain networks of MHE to find biomarkers. In this study, we aimed to use multi-layer modular algorithm to study abnormality in dynamic graph properties of brain network in MHE patients and construct a machine learning model to identify individual MHE from noHE. Here, a time length of 500-second resting-state functional MRI data were collected from 41 healthy subjects, 32 noHE patients and 30 MHE patients. Multi-layer modular algorithm was performed on dynamic brain functional connectivity graph. The connection-stability score was used to characterize the loyalty in each brain network module. Nodal flexibility, cohesion and disjointness were calculated to describe how the node changes the network affiliation across time. Results show that significant differences between MHE and noHE were found merely in nodal disjointness in higher cognitive network modules (ventral attention, fronto-parietal, default mode networks) and these abnormalities were associated with the decline in patients’ attention and visual memory function evaluated by Digit Symbol Test. Finally, feature extraction from node disjointness with the support vector machine classifier showed an accuracy of 88.71% in discrimination of MHE from noHE, which was verified by different window sizes, modular partition parameters and machine learning parameters. All these results show that abnormal nodal disjointness in higher cognitive networks during brain network evolution can be seemed as a biomarker for identification of MHE, which help us understand the disease mechanism of MHE at a fine scale.

Pitch Processing Can Indicate Cognitive Alterations in Chronic Liver Disease: An fNIRS Study

Early detection and evaluation of cognitive alteration in chronic liver disease is important for predicting the subsequent development of hepatic encephalopathy. While visuomotor tasks have been rigorously employed for cognitive evaluation in chronic liver disease, there is a paucity of auditory processing task. Here we focused on auditory perception and examined behavioral and haemodynamic responses to a melodic contour identification task (CIT) to compare cognitive abilities in patients with chronic liver disease (CLD, N = 30) and healthy controls (N = 25). Further, we used support vector machines to examine the optimal combination of channels of functional near-infrared spectroscopy that can classify cognitive alterations in CLD. Behavioral findings showed that CIT performance was significantly worse in the patient group and CIT significantly correlated with neurocognitive evaluation (i.e., number connection test, digit span test). The findings indicated that CIT can measure auditory cognitive capacity and its difference existing between patient group and healthy controls. Additionally, optimal subsets classified the 16-dimensional haemodynamic data with 78.35% classification accuracy, yielding markers of cognitive alterations in the prefrontal regions (CH6, CH7, CH10, CH13, CH14, and CH16). The results confirmed the potential use of behavioral as well as haemodynamic responses to music perception as an alternative or supplementary method for evaluating cognitive alterations in chronic liver disease.

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.

Aberrant inter-hemispheric coordination characterizes the progression of minimal hepatic encephalopathy in patients with HBV-related cirrhosis

Patients with hepatitis B virus (HBV)-related cirrhosis (HBV-RC) and minimal hepatic encephalopathy (MHE) exhibit alterations in homotopic inter-hemispheric functional connectivity (FC) and corpus callosum (CC) degeneration. However, the progression of inter-hemispheric dysconnectivity in cirrhotic patients from no MHE (NMHE) to MHE and its association with the progression of diseased-related cognitive impairment remain uncharacterized. We hypothesized that inter-hemispheric dysconnectivity exists in NMHE patients and further deteriorates at the MHE stage, which is associated with performance measured by psychometric hepatic encephalopathy scores (PHES) that can characterize cirrhotic patients with NMHE and MHE. Using inter-hemispheric homotopic FC and CC (and its subfields) volumetric measurements in 31 patients with HBV-RC (17 with NMHE and 14 with MHE) and 37 healthy controls, we verified that MHE patients had significant attenuated inter-hemispheric homotopic FC in the bilateral cuneus, post-central gyrus, inferior parietal lobule, and superior temporal gyms, as well as CC degeneration in total CC, CC2, CC3, and CC4 (each comparison had a corrected P < 0.05). In contrast, NMHE patients had relatively less severe inter-hemispheric homotopic FC and no CC degeneration. In addition, the degeneration of the CC and inter-hemispheric homotopic functional disconnections correlated with poor PHES performances in all cirrhotic patients (NMHE and MHE). Furthermore, impairment of inter-hemispheric homotopic FC partially mediated the association between CC degeneration and worse PHES performance. Notably, a combination of inter-hemispheric homotopic FC and CC volumes had higher discriminative values according to the area under the curve (AUC) score (AUC = 0.908, P < 0.001) to classify patients into MHE or NMHE groups when compared with either alone. Our findings shed light on the progression of inter-hemispheric dysconnectivity in relation to the progression of disease-related cognitive impairment in patients with HBV-RC.

Presurgical localization and spatial shift of resting state networks in patients with brain metastases

Brain metastases are the most prevalent cerebral tumors. Resting state networks (RSNs) are involved in multiple perceptual and cognitive functions. Therefore, precisely localizing multiple RSNs may be extremely valuable before surgical resection of metastases, to minimize neurocognitive impairments. Here we aimed to investigate the reliability of independent component analysis (ICA) for localizing multiple RSNs from resting-state functional MRI (rs-fMRI) data in individual patients, and further evaluate lesion-related spatial shifts of the RSNs. Twelve patients with brain metastases and 14 healthy controls were recruited. Using an improved automatic component identification method, we successfully identified seven common RSNs, including: the default mode network (DMN), executive control network (ECN), dorsal attention network (DAN), language network (LN), sensorimotor network (SMN), auditory network (AN) and visual network (VN), in both individual patients and controls. Moreover, the RSNs in the patients showed a visible spatial shift compared to those in the controls, and the spatial shift of some regions was related to the tumor location, which may reflect a complicated functional mechanism - functional disruptions and reorganizations - caused by metastases. Besides, higher cognitive networks (DMN, ECN, DAN and LN) showed significantly larger spatial shifts than perceptual networks (SMN, AN and VN), supporting a functional dichotomy between the two network groups even in pathologic alterations associated with metastases. Overall, our findings provide evidence that ICA is a promising approach for presurgical localization of multiple RSNs from rs-fMRI data in individual patients. More attention should be paid to the spatial shifts of the RSNs before surgical resection.

Altered cognitive control network is related to psychometric and biochemical profiles in covert hepatic encephalopathy

The cognitive control network (CCN) is a network responsible for multiple executive functions, which are impaired in covert hepatic encephalopathy (CHE). We aimed to use functional connectivity (FC) magnetic resonance imaging to test the hypothesis that CHE manifested with disconnection within the CCN, which is associated with impaired neuropsychiatric and biochemical profiles. CHE was detected with abnormally low psychometric hepatic encephalopathy scores (PHES) (total cut-off score <−4). Two seeds in the dorsal anterior cingulate cortex (dACC) and the dorsolateral prefrontal cortex (DLPFC) were used to calculate the FC map within the CCN. Pearson correlation analysis was performed between the CCN and psychometric, biochemical profiles including ammonia, Interleukin (IL)-6, and tumor necrosis factor (TNF)-α. Eighteen CHE, 36 non-HE (NHE) cirrhotic patients and 36 controls were studied. Significant differences in FC were noted among groups, which revealed CHE patients had a lower FC in the bilateral lateral occipital cortex (seed in the bilateral dACC) and in the right lateral occipital and precuneus cortices (seed in the left DLPFC) (P < 0.05, corrected) compared with NHE. Progressively decreased FC in the left precentral gyrus within the CCN was noted from control, NHE to CHE. PHES positively and biochemistry negatively correlated with FC in the CCN. In conclusion, CHE patients showed aberrant FC within the CCN which is correlated with both cognitive dysfunction and biochemical profiles. Ammonia and pro-inflammatory cytokines may contribute to the occurrence of aberrant connectivity. Impaired FC within the CCN may serve as a complementary biomarker for CHE.

Subnetwork mining on functional connectivity network for classification of minimal hepatic encephalopathy

Hepatic encephalopathy (HE), as a complication of cirrhosis, is a serious brain disease, which may lead to death. Accurate diagnosis of HE and its intermediate stage, i.e., minimal HE (MHE), is very important for possibly early diagnosis and treatment. Brain connectivity network, as a simple representation of brain interaction, has been widely used for the brain disease (e.g., HE and MHE) analysis. However, those studies mainly focus on finding disease-related abnormal connectivity between brain regions, although a large number of studies have indicated that some brain diseases are usually related to local structure of brain connectivity network (i.e., subnetwork), rather than solely on some single brain regions or connectivities. Also, mining such disease-related subnetwork is a challenging task because of the complexity of brain network. To address this problem, we proposed a novel frequent-subnetwork-based method to mine disease-related subnetworks for MHE classification. Specifically, we first mine frequent subnetworks from both groups, i.e., MHE patients and non-HE (NHE) patients, respectively. Then we used the graph-kernel based method to select the most discriminative subnetworks for subsequent classification. We evaluate our proposed method on a MHE dataset with 77 cirrhosis patients, including 38 MHE patients and 39 NHE patients. The results demonstrate that our proposed method can not only obtain the improved classification performance in comparison with state-of-the-art network-based methods, but also identify disease-related subnetworks which can help us better understand the pathology of the brain diseases.

Cortical networks are disturbed in people with cirrhosis even in the absence of neuropsychometric impairment

OBJECTIVE

Hepatic encephalopathy is a common complication of cirrhosis; it is characterised by neuropsychometric/neurophysiological abnormalities. Its pathophysiology is complex but glial neuronal communication is likely to be disrupted and to impact on oscillatory networks and cortical connectivity. The aim of this study was to use multichannel electroencephalography (EEG) to investigate functional connectivity, as a surrogate for cortical networks, in patients with cirrhosis.

METHODS

Resting EEGs were recorded in 98 healthy controls and in 264 patients with cirrhosis characterised psychometrically using the Psychometric Hepatic Encephalopathy Score (PHES). Functional connectivity was calculated using the phase-lag index with stratification into standard EEG frequency bands. The findings were validated in a further cohort of 39 healthy controls and 106 patients with cirrhosis.

RESULTS

Widespread disruption in functional connectivity was observed in the patients compared with the controls; connectivity was increased in the theta (4-8 Hz) band and decreased in the delta (1-3.5 Hz), alpha (8.5-13 Hz) and beta (13.5-26.5 Hz) bands. Changes were apparent even in patients who were psychometrically unimpaired compared with healthy controls viz mean ± SEM theta 0.107 ± 0.001 vs. 0.103 ± 0.002 (p < 0.05) and alpha 0.139 ± 0.003 vs. 0.154 ± 0.003 (p < 0.01); more pronounced changes were observed with increasing neuropsychometric impairment. The findings were replicated in the second cohort.

CONCLUSIONS

Cortical networks are disturbed in patients with cirrhosis even in the absence of psychometric impairment.

SIGNIFICANCE

These findings will facilitate further exploration of the pathophysiology of this condition and provide a robust means for assessing treatment effects in research settings.

Altered Topological Properties of Gray Matter Structural Covariance Networks in Minimal Hepatic Encephalopathy

Background and Aims: Liver cirrhosis commonly induces brain structural impairments that are associated with neurological complications (e.g., minimal hepatic encephalopathy (MHE)), but the topological characteristics of the brain structural network are still less well understood in cirrhotic patients with MHE. This study aimed to conduct the first investigation on the topological alterations of brain structural covariance networks in MHE.

Methods: This study included 22 healthy controls (HCs) and 22 cirrhotic patients with MHE. We calculated the gray matter volume of 90 brain regions using an automated anatomical labeling (AAL) template, followed by construction of gray matter structural covariance networks by thresholding interregional structural correlation matrices as well as graph theoretical analysis.

Results: MHE patients showed abnormal small-world properties of the brain structural covariance network, i.e., decreased clustering coefficient and characteristic path length and lower small-worldness parameters, which indicated a tendency toward more random architecture. In addition, MHE patients lost hubs in the prefrontal and parietal regions, although they had new hubs in the temporal and occipital regions. Compared to HC, MHE patients had decreased regional degree/betweenness involving several regions, primarily the prefrontal and parietal lobes, motor region, insula and thalamus. In addition, the MHE group also showed increased degree/betweenness in the occipital lobe and hippocampus.

Conclusion: These results suggest that MHE leads to altered coordination patterns of gray matter morphology and provide structural evidence supporting the idea that MHE is a neurological complication related to disrupted neural networks.

Learning and Memory Impairments in Patients with Minimal Hepatic Encephalopathy are Associated with Structural and Functional Connectivity Alterations in Hippocampus

Patients with minimal hepatic encephalopathy (MHE) show mild cognitive impairment associated with alterations in attentional and executive networks. There are no studies evaluating the relationship between memory in MHE and structural and functional connectivity (FC) changes in the hippocampal system. This study aimed to evaluate verbal learning and long-term memory in cirrhotic patients with (C-MHE) and without MHE (C-NMHE) and healthy controls. We assessed the relationship between alterations in memory and the structural integrity and FC of the hippocampal system. C-MHE patients showed impairments in learning, long-term memory, and recognition, compared to C-NMHE patients and controls. Cirrhotic patients showed reduced fimbria volume compared to controls. Larger volumes in hippocampus subfields were related to better memory performance in C-NMHE patients and controls. C-MHE patients presented lower FC between the L-presubiculum and L-precuneus than C-NMHE patients. Compared to controls, C-MHE patients had reduced FC between L-presubiculum and subiculum seeds and bilateral precuneus, which correlated with cognitive impairment and memory performance. Alterations in the FC of the hippocampal system could contribute to learning and long-term memory impairments in C-MHE patients. This study demonstrates the association between alterations in learning and long-term memory and structural and FC disturbances in hippocampal structures in cirrhotic patients.

Abnormal Regional Homogeneity and Functional Connectivity of Baseline Brain Activity in Hepatitis B Virus-Related Cirrhosis With and Without Minimal Hepatic Encephalopathy

Background and Aims: Abnormalities in neural activity have been reported in cirrhosis with minimal hepatic encephalopathy (MHE). However, little is known about the neurophysiological mechanisms in this disorder. We aimed to investigate the altered patterns of regional synchronization and functional connections in hepatitis B virus-related cirrhosis (HBV-RC) patients with and without MHE using both regional homogeneity (ReHo) and region of interest (ROI)-based functional connectivity (FC) computational methods.

Methods: Data of magnetic resonance imaging scans were collected from 30 HBV-RC patients with MHE, 32 HBV-RC patients without MHE (NMHE) and 64 well-matched controls. Several regions showing differences in ReHo after one-way analysis of variance (ANOVA) were defined as ROIs for FC analysis. Next, post hoc t-tests were applied to calculate the group differences in ReHo and FC (false discovery rate (FDR) correction, p < 0.05). Correlations between clinical variables and the altered ReHo and FC were then assessed in patient groups.

Results: Across three groups, significant ReHo differences were found in nine ROI regions mainly within the visual network (VN), dorsal attention network (DAN), somatomotor network (SMN), fronto parietal control (FPC) network and thalamus. Compared with healthy controls (HC), the MHE group exhibited abnormal FC mainly between the right calcarine (CAL.R) and middle frontal gyrus (MFG.L)/right thalamus. The MHE patients showed increased FC between the MFG.L and CAL.R compared to NMHE patients. Disease duration of MHE patients was positively correlated with increased mean ReHo values in the right fusiform gyrus (FFG); psychometric hepatic encephalopathy score (PHES) test scores were negatively correlated with increased FC between MFG.L and CAL.R and positively correlated with reduced FC between the CAL.R and THA.R. For NMHE patients, the mean ReHo values in the right frontal pole were positively correlated with disease duration and positively correlated with the PHES scores.

Conclusion: Our results exhibited that the functional brain modifications in patients with and without MHE are characterized by compound alterations in local coherence and functional connections in the VN, SMN, DAN, FPC networks and thalamus by using a combination of ReHo and ROI-based FC analysis. These functional imaging changes are correlated with disease duration/PHES. This study helped us gain a better understanding of the features of brain network modifications in cirrhosis.

Multimodal MR imaging in hepatic encephalopathy: state of the art

Hepatic encephalopathy (HE) is a neurological or neuropsychological complication due to liver failure or portosystemic shunting. The clinical manifestation is highly variable, which can exhibit mild cognitive or motor impairment initially, or gradually progress to a coma, even death, without treatment. Neuroimaging plays a critical role in uncovering the neural mechanism of HE. In particular, multimodality MR imaging is able to assess both structural and functional derangements of the brain with HE in focal or neural network perspectives. In recent years, there has been rapid development in novel MR technologies and applications to investigate the pathophysiological mechanism of HE. Therefore, it is necessary to update the latest MR findings regarding HE by use of multimodality MRI to refine and deepen our understanding of the neural traits in HE. Herein, this review highlights the latest MR imaging findings in HE to refresh our understanding of MRI application in HE.

Abnormalities of voxel-based whole-brain functional connectivity patterns predict the progression of hepatic encephalopathy

Resting state functional magnetic resonance imaging (fMRI) is an important tool for understanding the functional reorganization of the brain in cirrhotic patients. Previous studies revealed that functional integration failure were observed in the whole brain. However, the whole-brain functional connectivity analysis methods used in these studies have the limitation that the result relied on a priori definition of network nodes. Moreover, the utility of resting state functional connectivity in the diagnosis and prediction of hepatic encephalopathy (HE) is not well examined. In this study, we recruited 87 subjects consisting of patients without HE, with HE, and healthy controls. We employed a voxel-based, unbiased functional connectivity analysis and the functional connectivity density (FCD) metric to precisely study abnormalities in the intrinsic functional connectivity patterns of cirrhotic patients. FCD analyses showed that hub regions in the brain were less topologically important in cirrhotic patients, whereas non-hub regions became topologically important in the disease state. This trend was more apparent with the progression of cirrhosis severity. Most FCD abnormalities were associated with deficits in psychomotor function, executive control, or visual-spatial abilities (p < 0.05, AlphaSim corrected). FCD alterations in the left inferior parietal lobe and the right hippocampal gyrus/parahippocampal gyrus were significantly correlated with cognitive ability and blood ammonia level (p < 0.05, AlphaSim corrected). A pattern classification analysis indicated that whole-brain FCD differences distinguished cirrhotic patients from healthy controls and predicted disease severity with high accuracies. These findings suggest that voxel-based FCD analysis may be clinically important for the diagnosis and prediction of HE.

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.

Re-Establishing Brain Networks in Patients with ESRD after Successful Kidney Transplantation

BACKGROUND AND OBJECTIVES

Cognition in ESRD may be improved by kidney transplantation, but mechanisms are unclear. We explored patterns of resting-state networks with resting-state functional magnetic resonance imaging among patients with ESRD before and after kidney transplantation.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Thirty-seven patients with ESRD scheduled for kidney transplantation and 22 age-, sex-, and education-matched healthy subjects underwent resting-state functional magnetic resonance imaging. Patients were imaged before and 1 and 6 months after kidney transplantation. Functional connectivity of seven resting-state subnetworks was evaluated: default mode network, dorsal attention network, central executive network, self-referential network, sensorimotor network, visual network, and auditory network. Mixed effects models tested associations of ESRD, kidney transplantation, and neuropsychological measurements with functional connectivity.

RESULTS

Compared with controls, pretransplant patients showed abnormal functional connectivity in six subnetworks. Compared with pretransplant patients, increased functional connectivity was observed in the default mode network, the dorsal attention network, the central executive network, the sensorimotor network, the auditory network, and the visual network 1 and 6 months after kidney transplantation (P=0.01). Six months after kidney transplantation, no significant difference in functional connectivity was observed for the dorsal attention network, the central executive network, the auditory network, or the visual network between patients and controls. Default mode network and sensorimotor network remained significantly different from those in controls when assessed 6 months after kidney transplantation. A relationship between functional connectivity and neuropsychological measurements was found in specific brain regions of some brain networks.

CONCLUSIONS

The recovery patterns of resting-state subnetworks vary after kidney transplantation. The dorsal attention network, the central executive network, the auditory network, and the visual network recovered to normal levels, whereas the default mode network and the sensorimotor network did not recover completely 6 months after kidney transplantation. Neural resting-state functional connectivity was lower among patients with ESRD compared with control subjects, but it significantly improved with kidney transplantation. Resting-state subnetworks exhibited variable recovery, in some cases to levels that were no longer significantly different from those of normal controls.

Reduced resting state connectivity and gray matter volume correlate with cognitive impairment in minimal hepatic encephalopathy

BACKGROUND AND AIMS

Minimal hepatic encephalopathy (MHE) is associated with cognitive alterations and changes in connectivity. We assessed the relationship of the abnormalities of resting-state functional connectivity (rs-FC) and gray matter (GM) volume with different cognitive alterations and biochemical parameters associated to MHE.

METHODS

Thirty-nine cirrhotic patients (26 without and 13 with MHE) and 24 controls were widely cognitive assessed with a battery of psychometric tests. Atrophy was determined using Voxel-Based Morphometry and rs-FC was assessed by independent component analysis. Receiver operating characteristic (ROC) curves was performed to assess the diagnostic utility of rs-FC and GM reduction for the discrimination of patients with and without MHE. Blood ammonia, cGMP, and levels of pro-inflammatory interleukins were measured.

RESULTS

MHE patients showed significant decrease of GM volume and lesser degree of rs-FC in different networks related to attention and executive functions as compared to controls and patients without MHE. There is a progressive reduction in rs-FC in the default mode network with the progression of cognitive impairment. MHE patients showed GM reduction in the right frontal lobe, right insula and right cerebellum compared to patients without MHE. Alterations in GM volume and rs-FC correlated with the scores of different cognitive tests.

CONCLUSIONS

Decreased cognitive performance is associated by reduced rs-FC and GM atrophy in MHE patients. These changes could have predictive value for detecting MHE.

Predictive models of minimal hepatic encephalopathy for cirrhotic patients based on large-scale brain intrinsic connectivity networks

We aimed to find the most representative connectivity patterns for minimal hepatic encephalopathy (MHE) using large-scale intrinsic connectivity networks (ICNs) and machine learning methods. Resting-state fMRI was administered to 33 cirrhotic patients with MHE and 43 cirrhotic patients without MHE (NMHE). The connectivity maps of 20 ICNs for each participant were obtained by dual regression. A Bayesian machine learning technique, called Graphical Model-based Multivariate Analysis, was applied to determine ICN regions that characterized group differences. The most representative ICNs were evaluated by the performance of three machine learning methods (support vector machines (SVMs), multilayer perceptrons (MLP), and C4.5). The clinical significance of these potential biomarkers was further tested. The temporal lobe network (TLN), and subcortical network (SCN), and sensorimotor network (SMN) were selected as representative ICNs. The distinct functional integration patterns of the representative ICNs were significantly correlated with behavior criteria and Child-Pugh scores. Our findings suggest the representative ICNs based on GAMMA can distinguish MHE from NMHE and provide supplementary information to current MHE diagnostic criteria.

Combining arterial-spin labeling with functional magnetic resonance imaging measurement for characterizing patients with minimal hepatic encephalopathy

AIM

Our objective is to explore key changes in brain functions in relation to minimal hepatic encephalopathy (MHE). We incorporated both resting-state functional magnetic resonance imaging (fMRI) and arterial spin labeling (ASL) to enhance the detection of MHE.

METHODS

We undertook fMRI scanning for 56 MHE patients and 66 healthy controls. Region functional connectivity was carried out to assess the connectivity status between pairs of regions among 90 brain regions. Additionally, blood flow (BF) status was measured by ASL for all subjects. Spearman's correlation test was implemented to identify any correlation among z-values, results from number connection test type A, and digit symbol tests. Finally, the receiver operating characteristic curve was generated for assessing the accuracy of BF in MHE diagnosis.

RESULTS

The corresponding functional connectivity was significantly different between MHE and control groups in 15 regions. For MHE patients, BF showed an increasing pattern in regions of interest. Blood flood in the putamen was positively correlated with number connection test type A neuropsychological performance, whereas it was negatively correlated with the digit symbol test. Blood flood in the right putamen showed the highest value of area under the curve with a sensitivity of 85.7% and specificity of 89.4%.

CONCLUSION

Connectivity impairment resulting from ganglia-thalamo-cortical circuits may play important roles in mediating the development of MHE patients. An increase in the BF, particularly in the right putamen, may be considered as evidence for the presence of MHE.

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.

Functional connectivity alterations in patients with chronic hepatitis C virus infection: A multimodal MRI study

Chronic hepatitis C virus (HCV) infection is associated with fatigue and depression. Cognitive impairments are also reported in a smaller number of HCV-positive patients. Recent studies linked HCV to low-grade inflammation in brain. Here, we test the hypothesis that chronic HCV is associated with 3T-neuroimaging-derived grey matter volume (GMV) and functional connectivity alterations in a sample of chronic HCV (1b), without severe liver disease. Regional GMV and resting-state fMRI-derived eigenvector centrality (EC) were compared between 19 HCV-positive patients and 23 healthy controls (all females, 50-69 and 52-64 years, respectively), controlling for white matter hyperintensities and age. Standard tests were used to assess fatigue, depression and cognitive performance. Also, liver fibrosis stage and viral load were quantified among patients. In comparison with controls, HCV-positive patients had higher scores in fatigue and depression, and worse alertness scores. The groups performed similarly in other cognitive domains. We report higher EC in a cluster in the right anterior superior parietal lobule in patients, while no differences are found in GMV. Post hoc functional connectivity analysis showed increased connectivity of this cluster with primary and secondary somatosensory cortex, and temporal and occipital lobes in patients. Higher mean EC in the superior parietal cluster, adjusted for mean framewise displacement, was associated with better memory and attention performance, but not with fatigue, depression, viral load or level of liver fibrosis, among patients. These results suggest a compensatory mechanism in chronic hepatitis C and explain equivocal results in the literature about cognitive deficits in infected persons. Further studies should define the relation of these connectivity changes to the brain's inflammatory activity.

Abnormal spontaneous brain activity in minimal hepatic encephalopathy: resting-state fMRI study

PURPOSE

We aimed to assess the abnormality of baseline spontaneous brain activity in minimal hepatic encephalopathy (MHE) by amplitude of low frequency fluctuation (ALFF) and fraction ALFF (fALFF).

METHODS

A total of 14 MHE patients and 14 healthy controls were included in our study. Both ALFF and fALFF of functional magnetic resonance imaging were calculated for statistical analysis.

RESULTS

Compared with healthy controls, patients with MHE had significantly decreased ALFF in the bilateral medial prefrontal cortex (MPFC), left superior frontal gyrus, right precentral gyrus, left opercular part of inferior frontal gyrus, left gyrus rectus, bilateral precuneus, and the posterior lobe of right cerebellum; and they had significantly decreased fALFF in the bilateral MPFC, right middle frontal gyrus, right superior temporal gyrus, and the posterior lobe of left cerebellum.

CONCLUSION

ALFF and fALFF changes in many brain regions demonstrate abnormality of the spontaneous neuronal activity in MHE. Especially the impairment of right precuneus and left MPFC may play a critical role in manifestation of MHE. Changes of ALFF and fALFF in the precuneus and the MPFC can be used as a potential marker for MHE.

Advancing the Neurophysiological Understanding of Delirium

Delirium is a common problem associated with substantial morbidity and increased mortality. However, the brain dysfunction that leads some individuals to develop delirium in response to stressors is unclear. In this article, we briefly review the neurophysiologic literature characterizing the changes in brain function that occur in delirium, and in other cognitive disorders such as Alzheimer's disease. Based on this literature, we propose a conceptual model for delirium. We propose that delirium results from a breakdown of brain function in individuals with impairments in brain connectivity and brain plasticity exposed to a stressor. The validity of this conceptual model can be tested using Transcranial Magnetic Stimulation in combination with Electroencephalography, and, if accurate, could lead to the development of biomarkers for delirium risk in individual patients. This model could also be used to guide interventions to decrease the risk of cerebral dysfunction in patients preoperatively, and facilitate recovery in patients during or after an episode of delirium.

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.

Aberrant Resting-State Functional Connectivity Density in Patients with Hepatitis B Virus-Related Cirrhosis

There is increasing evidence that cirrhosis may affect functional connectivity among various brain regions in patients prior to onset of overt hepatic encephalopathy (HE). However, most investigators have focused mainly on alterations in functional connectivity strengths, and the changes in functional connectivity density (FCD) are largely unknown. Here, we investigated alterations in resting-state FCD in patients with hepatitis B virus-related cirrhosis (HBV-RC) without overt HE. Totally, 31 patients with HBV-RC without overt HE and 30 age- and sex-matched healthy controls underwent resting-state functional MRI examinations. FCD mapping was employed to compute local and global FCD maps. Then, short-range and long-range FCD values were calculated and voxel-based comparisons were performed between the two groups. The HBV-RC group showed significant decreases in FCD, including decreased short-range FCDs in the bilateral middle cingulum gyrus/precuneus, the bilateral cuneus, and the left lingual gyrus/inferior occipital gyrus and decreased long-range FCD in the bilateral cuneus/precuneus. In addition, the decreased long-range FCD in the bilateral cuneus/precuneus in the HBV-RC group was related to performance on the psychometric hepatic encephalopathy score (PHES) test. These findings suggest aberrant functional connectivity density in cirrhotic patients prior to overt HE onset, which may provide better insight into understanding the pathophysiological mechanisms underlying the cirrhotic-related cognitive impairment.

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.

Anomalous gray matter structural networks in patients with hepatitis B virus-related cirrhosis without overt hepatic encephalopathy

Background and Purpose

Increasing evidence suggests that cirrhosis may affect the connectivity among different brain regions in patients before overt hepatic encephalopathy (OHE) occurs. However, there has been no study investigating the structural reorganization of these altered connections at the network level. The primary focus of this study was to investigate the abnormal topological organization of the structural network in patients with hepatitis B virus-related cirrhosis (HBV-RC) without OHE using structural MRI.

Methods

Using graph theoretical analysis, we compared the global and regional topological properties of gray matter structural networks between 28 patients with HBV-RC without OHE and 30 age-, sex- and education-matched healthy controls. The structural correlation networks were constructed for the two groups based on measures of gray matter volume.

Results

The brain network of the HBV-RC group exhibited a significant decrease in the clustering coefficient and reduced small-worldness at the global level across a range of network densities. Regionally, brain areas with altered nodal degree/betweenness centrality were observed predominantly in association cortices (frontal and temporal regions) (p < 0.05, uncorrected), including a significantly decreased nodal degree in the inferior temporal gyrus (p < 0.001, uncorrected). Furthermore, the HBV-RC group exhibited a loss of association hubs and the emergence of an increased number of non-association hubs compared with the healthy controls.

Conclusion

The results of this large-scale gray matter structural network study suggest reduced topological organization efficiency in patients with HBV-RC without OHE. Our findings provide new insight concerning the mechanisms of neurobiological reorganization in the HBV-RC brain from a network perspective.

Quantitative measurements of brain iron deposition in cirrhotic patients using susceptibility mapping

BACKGROUND

Susceptibility-weighted imaging (SWI) has been used to detect micro-bleeds and iron deposits in the brain. However, no reports have been published on the application of SWI in studying iron changes in the brain of cirrhotic patients.

PURPOSE

To compare the susceptibility of different brain structures in cirrhotic patients with that in healthy controls and to evaluate susceptibility as a potential biomarker and correlate the measured susceptibility and cadaveric brain iron concentration for a variety of brain structures.

MATERIAL AND METHODS

Forty-three cirrhotic patients (27 men, 16 women; mean age, 50 ± 9 years) and 34 age- and sex-matched healthy controls (22 men, 12 women; mean age, 47 ± 7 years) were included in this retrospective study. Susceptibility was measured in the frontal white matter, basal ganglia, midbrain, and dentate nucleus and compared with results gathered from two postmortem brain studies. Correlation between susceptibility and clinical biomarkers and neuropsychiatric tests scores was calculated.

RESULTS

In cirrhotic patients, the susceptibility of left frontal white matter, bilateral caudate head, and right substantia nigra was higher than that in healthy controls (P < 0.05). There was a positive correlation between susceptibility and iron concentration from one postmortem brain study (r = 0.835, P = 0.01) in eight deep grey matter structures and another in five brain structures (r = 0.900, P = 0.03). The susceptibility of right caudate head (r = 0.402) and left caudate head (r = 0.408) correlated with neuropsychological test scores (both P < 0.05).

CONCLUSION

Abnormal iron deposits occur in cirrhotic patients and abnormal susceptibility of some brain regions appears to reflect neurocognitive changes.

Brain magnetic resonance in hepatic encephalopathy

The term hepatic encephalopathy (HE) covers a wide spectrum of neuropsychiatric abnormalities caused by portal-systemic shunting. The diagnosis requires demonstration of liver dysfunction or portal-systemic shunts and exclusion of other neurologic disorders. Most patients with this condition have liver dysfunction caused by cirrhosis, but it also occurs in patients with acute liver failure and less commonly, in patients with portal-systemic shunts that are not associated with hepatocellular disease. Various magnetic resonance (MR) techniques have improved our knowledge about the pathophysiology of HE. Proton MR spectroscopy and T1-weighted imaging can detect and quantify accumulations of brain products that are normally metabolized or eliminated such as glutamine and manganese. Other MR techniques such as T2-weighted and diffusion-weighted imaging can identify white matter abnormalities resulting from disturbances in cell volume homeostasis secondary to brain hyperammonemia. Partial or complete recovery of these abnormalities has been observed with normalization of liver function or after successful liver transplantation. MR studies have undoubtedly improved our understanding of the mechanisms involved in the pathogenesis of HE, and some findings can be considered biomarkers for monitoring the effects of therapeutic measures focused on correcting this condition.

Reestablishing brain networks in patients without overt hepatic encephalopathy after liver transplantation

Cirrhotic patients without overt hepatic encephalopathy (HE) have associated with widespread neuro-psychological impairment. Liver transplantation can restore metabolic abnormalities but the mechanisms are unclear. We investigate brain functional networks after transplantation using resting-state funtional magnetic resonance imaging (MRI). Twenty-six cirrhotic patients without overt HE completed neuro-psychological assessment before and 6 to 12 months after transplantation, and compared with 35 healthy controls. Five major functional brain networks, default mode (DMN), dorsal attention (DAN), executive control (ECN), salience (SN), and primary networks (PN), were assessed. Nodal efficiency and strength in different functional networks were weighed and their interaction metrics displayed. Granger causal analysis between pretransplantation and posttransplantation was performed. Before transplantation, the intrafunctional connectivity was decreased in DMN, DAN, ECN, and SN. After transplantation, cognitive functions improved with increased functional connectivity. The interaction metrics among large-scale networks in patients became similar to healthy controls. The increase in PN affected the decrease in SN, while the increase in DAN forced a decrease in DMN. There was a bidirectional balance between DMN and SN. Dynamic disruptions and reconstruction in intrinsic large-scale networks are associated with parallel patterns of cognitive information processing deficits and recovery. Remapping of SN, DMN, and DAN is essential for restoring cognition after transplantation.

Altered intrinsic connectivity networks in frontal lobe epilepsy: a resting-state fMRI study

Examining the resting-state networks (RSNs) may help us to understand the neural mechanism of the frontal lobe epilepsy (FLE). Resting-state functional MRI (fMRI) data were acquired from 46 patients with FLE (study group) and 46 age- and gender-matched healthy subjects (control group). The independent component analysis (ICA) method was used to identify RSNs from each group. Compared with the healthy subjects, decreased functional connectivity was observed in all the networks; however, in some areas of RSNs, functional connectivity was increased in patients with FLE. The duration of epilepsy and the seizure frequency were used to analyze correlation with the regions of interest (ROIs) in the nine RSNs to determine their influence on FLE. The functional network connectivity (FNC) was used to study the impact on the disturbance and reorganization of FLE. The results of this study may offer new insight into the neuropathophysiological mechanisms of FLE.

Resting-state functional magnetic resonance imaging in hepatic encephalopathy: current status and perspectives

Hepatic encephalopathy (HE) is a neuropsychiatric syndrome which develops in patients with severe liver diseases and/or portal-systemic shunting. Minimal HE, the earliest manifestation of HE, has drawn increasing attention in the last decade. Minimal HE is associated with a series of brain functional changes, such as attention, working memory, and so on. Blood oxygen level dependent (BOLD) functional MRI (fMRI), especially resting-state fMRI has been used to explore the brain functional changes of HE, yielding important insights for understanding pathophysiological mechanisms and functional reorganization of HE. This paper briefly reviews the principles of BOLD fMRI, potential applications of resting-state fMRI with advanced post-processing algorithms such as regional homogeneity, amplitude of low frequency fluctuation, functional connectivity and future research perspective in this field.

Aberrant interhemispheric functional coordination in patients with HBV-related cirrhosis and minimal hepatic encephalopathy

Aberrant brain functional connectivity has been considered as the important mechanism underlying minimal hepatic encephalopathy (MHE); however, little is known about the change in interhemispheric connection in MHE patients. Twenty patients with HBV-related cirrhosis and MHE and 15 healthy controls were included in this study and underwent the resting-state fMRI scanning and diffusion tensor imaging. The functional connectivity between symmetric interhemispheric voxels was computed by a technique called voxel-mirrored homotopic connectivity (VMHC), in which the time series for each voxel in one hemisphere was correlated with that of its homotopic voxel. Diffusion tensor imaging was conducted to measure the mean diffusivity (MD) and fractional anisotropy (FA) values in corpus callosum (CC). Compared with controls, MHE patients showed decreased regional VMHC in medial frontal gyrus, superior frontal gryus, anterior cingulate gyrus, inferior parietal lobule, postcentral gyrus, lingual gyrus, and middle occipital gyrus. MHE patients had significant decreased FA value in CC genu and CC splenium and increased MD value in CC genu. Pearson correlation analyses showed that the VMHC in anterior cingulate gyrus/medial frontal gyrus was correlated with FA/MD values of CC genu. These findings may suggest aberrant interhemispheric coordination in MHE and may provide new insight into the disease-related mechanisms.

Disrupted small world networks in patients without overt hepatic encephalopathy: a resting state fMRI study

PURPOSE

To explore changes in functional connectivity and topological organization of brain functional networks in cirrhotic patients with minimal hepatic encephalopathy (MHE) and non hepatic encephalopathy (nonHE) and their relationship with clinical markers.

MATERIALS AND METHODS

Resting-state functional MR imaging was acquired in 22 MHE, 29 nonHE patients and 33 healthy controls. Functional connectivity networks were obtained by computing temporal correlations between any pairs of 90 cortical and subcortical regions. Graph analysis measures were quantitatively assessed for each subject. One-way analysis of covariance was applied to identify statistical differences of functional connectivity and network parameters among three groups. Correlations between clinical markers, such as Child-Pugh scores, venous blood ammonia level, and number connection test type A (NCT-A)/digit symbol test (DST) scores, and connectivity/graph metrics were calculated.

RESULTS

Thirty functional connectivities represented by edges were found to be abnormal (P<0.05, FDR corrected) in cirrhotic patients, in which 16 edges (53.3%) were related with sub-cortical regions. MHE patients showed abnormal small-world attributes in the functional connectivity networks. Cirrhotic patients had significantly reduced nodal degree in 8 cortical regions and increased nodal centrality in 3 cortical regions. Twenty edges were correlated with either NCT-A or DST scores, in which 13 edges were related with sub-cortical regions. No correlation was found between Child-Pugh scores and graph theoretical measures in cirrhotic patients.

CONCLUSION

Disturbances of brain functional connectivity and small world property loss are associated with neurocognitive impairment of cirrhotic patients. Reorganization of brain network occurred during disease progression from nonHE to MHE.

Changes in MEG resting-state networks are related to cognitive decline in type 1 diabetes mellitus patients

Objective

Integrity of resting-state functional brain networks (RSNs) is important for proper cognitive functioning. In type 1 diabetes mellitus (T1DM) cognitive decrements are commonly observed, possibly due to alterations in RSNs, which may vary according to microvascular complication status. Thus, we tested the hypothesis that functional connectivity in RSNs differs according to clinical status and correlates with cognition in T1DM patients, using an unbiased approach with high spatio-temporal resolution functional network.

Methods

Resting-state magnetoencephalographic (MEG) data for T1DM patients with (n = 42) and without (n = 41) microvascular complications and 33 healthy participants were recorded. MEG time-series at source level were reconstructed using a recently developed atlas-based beamformer. Functional connectivity within classical frequency bands, estimated by the phase lag index (PLI), was calculated within eight commonly found RSNs. Neuropsychological tests were used to assess cognitive performance, and the relation with RSNs was evaluated.

Results

Significant differences in terms of RSN functional connectivity between the three groups were observed in the lower alpha band, in the default-mode (DMN), executive control (ECN) and sensorimotor (SMN) RSNs. T1DM patients with microvascular complications showed the weakest functional connectivity in these networks relative to the other groups. For DMN, functional connectivity was higher in patients without microangiopathy relative to controls (all p < 0.05). General cognitive performance for both patient groups was worse compared with healthy controls. Lower DMN alpha band functional connectivity correlated with poorer general cognitive ability in patients with microvascular complications.

Discussion

Altered RSN functional connectivity was found in T1DM patients depending on clinical status. Lower DMN functional connectivity was related to poorer cognitive functioning. These results indicate that functional connectivity may play a key role in T1DM-related cognitive dysfunction.

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MEG RSN functional connectivity was estimated among T1DM⁺ and T1DM⁻ patients and controls.

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Lower alpha band in DMN, ECN and SMN significantly differed among groups.

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Functional connectivity may play a key role in T1DM-related cognitive dysfunction.

Altered modular organization of functional connectivity networks in cirrhotic patients without overt hepatic encephalopathy

Minimal hepatic encephalopathy (MHE) is associated with changes in functional connectivity. To investigate the patterns of modular changes of the functional connectivity in the progression of MHE, resting-state functional magnetic resonance imaging was acquired in 24 MHE patients, 31 cirrhotic patients without minimal hepatic encephalopathy (non-HE), and 38 healthy controls. Newman's metric, the modularity Q value, was maximized and compared in three groups. Topological roles with the progression of MHE were illustrated by intra- and intermodular connectivity changes. Results showed that the Q value of MHE patients was significantly lower than that of controls (P < 0.01) rather than that of non-HE patients (P > 0.05), which was correlated with neuropsychological test scores rather than the ammonia level and Child-Pugh score. Less intrasubcortical connections and more isolated subcortical modules were found with the progression of MHE. The non-HE patients had the same numbers of connect nodes as controls and had more hubs compared with MHE patients and healthy controls. Our findings supported that both intra- and intermodular connectivity, especially those related to subcortical regions, were continuously impaired in cirrhotic patients. The adjustments of hubs and connector nodes in non-HE patients could be a compensation for the decreased modularity in their functional connectivity networks.

Abnormal functional connectivity within the default mode network in patients with HBV-related cirrhosis without hepatic encephalopathy revealed by resting-state functional MRI

By means of "task free" resting-state functional magnetic resonance imaging (rs-fMRI), abnormal functional connectivity (FC) of the default mode network (DMN) in cirrhotic patients with hepatic encephalopathy (HE) has been reported; however, little is known about the changes of DMN in cirrhotic patients without overt or minimal HE. The aim of this study was to investigate whether there was a disruption of the FC within the DMN in patients with hepatitis B virus (HBV)-related cirrhosis without any signs of HE. Fifty one patients with HBV-related cirrhosis without HE and 61 age- and gender-matched healthy controls underwent the rs-fMRI. Seed-based region-to-region FC was used to analyze the connectivity between each pair of regions within the DMN, including posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), hippocampal formation (HF), inferior parietal cortex (IPC), and medial temporal lobe (MTL). Pearson correlation analysis was performed between the abnormal FC strength within the DMN and venous blood ammonia levels in patients. Compared with the controls, patients with HBV-related cirrhosis without HE demonstrated significantly decreased region-to-region FC between the mPFC and bilateral MTL, right HF, and left IPC, as well as between the right MTL and left IPC, right HF, and PCC. A significant negative relationship was observed between blood ammonia levels and connectivity strength between the mPFC and left IPC in patients. These results suggest that patients with HBV-related cirrhosis without HE had disrupted functional connectivty within the DMN, even before the appearance of minimal HE.

Generation of the probabilistic template of default mode network derived from resting-state fMRI

Default-mode network (DMN) has become a prominent network among all large-scale brain networks which can be derived from the resting-state fMRI (rs-fMRI) data. Statistical template labeling the common location of hubs in DMN is favorable in the identification of DMN from tens of components resulted from the independent component analysis (ICA). This paper proposed a novel iterative framework to generate a probabilistic DMN template from a coherent group of 40 healthy subjects. An initial template was visually selected from the independent components derived from group ICA analysis of the concatenated rs-fMRI data of all subjects. An effective similarity measure was designed to choose the best-fit component from all independent components of each subject computed given different component numbers. The selected DMN components for all subjects were averaged to generate an updated DMN template and then used to select the DMN for each subject in the next iteration. This process iterated until the convergence was reached, i.e., the overlapping region between the DMN areas of the current template and the one generated from the previous stage is more than 95%. By validating the constructed DMN template on the rs-fMRI data from another 40 subjects, the generated probabilistic DMN template and the proposed similarity matching mechanism were demonstrated to be effective in automatic selection of independent components from the ICA analysis results.

Classification of cirrhotic patients with or without minimal hepatic encephalopathy and healthy subjects using resting-state attention-related network analysis

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.