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

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.

Metabolic disorder and functional disturbance in the central executive network in minimal hepatic encephalopathy

The aim of this paper is to investigate dynamical functional disturbance in central executive network in minimal hepatic encephalopathy and determine its association with metabolic disorder and cognitive impairment. Data of magnetic resonance spectroscopy and resting-state functional magnetic resonance imaging were obtained from 27 cirrhotic patients without minimal hepatic encephalopathy, 20 minimal hepatic encephalopathy patients, and 24 healthy controls. Central executive network was identified utilizing seed-based correlation approach. Dynamic functional connectivity across central executive network was calculated using sliding-window approach. Functional states were estimated by K-means clustering. Right dorsolateral prefrontal cortex metabolite ratios (i.e. glutamate and glutamine complex/total creatine, myo-inositol / total creatine, and choline / total creatine) were determined. Neurocognitive performance was determined by psychometric hepatic encephalopathy scores. Minimal hepatic encephalopathy patients had decreased myo-inositol / total creatine and choline / total creatine and increased glutamate and glutamine complex / total creatine in right dorsolateral prefrontal cortex (all P ≤ 0.020); decreased static functional connectivity between bilateral dorsolateral prefrontal cortex and between right dorsolateral prefrontal cortex and lateral-inferior temporal cortex (P ≤ 0.001); increased frequency and mean dwell time in state-1 (P ≤ 0.001), which exhibited weakest functional connectivity. Central executive network dynamic functional indices were significantly correlated with right dorsolateral prefrontal cortex metabolic indices and psychometric hepatic encephalopathy scores. Right dorsolateral prefrontal cortex myo-inositol / total creatine and mean dwell time in state-1 yielded best potential for diagnosing minimal hepatic encephalopathy. Dynamic functional disturbance in central executive network may contribute to neurocognitive impairment and could be correlated with metabolic disorder.

Altered dynamic spontaneous neural activity in minimal hepatic encephalopathy

Background and aims:

Abnormal regional neural activity has been identified by the analysis of the static amplitude of low-frequency fluctuation (ALFF) in the setting of minimal hepatic encephalopathy (MHE). Brain activity is highly dynamic. This work sought to evaluate the temporal variability of ALFF to reveal MHE-related alterations in the dynamics of spontaneous neural activity.

Methods

A total of 29 healthy controls and 49 patients with cirrhosis [including 20 patients with MHE and 29 patients without MHE (NHE)] who underwent resting-state functional magnetic resonance imaging and Psychometric Hepatic Encephalopathy Score (PHES) examination were enrolled in this investigation. Utilizing a sliding-window approach, we calculated the dynamic ALFF (dALFF) variability to reflect the temporal dynamics of regional neural activity. An analysis of the correlation between dALFF variability and PHES was performed, and receiver operating characteristic (ROC) curve analysis to determine the potential of the dALFF variability index in identifying MHE was completed.

Results

The dALFF variability in the bilateral precuneus/posterior cingulate gyrus and left middle frontal gyrus progressively decreased from NHE to MHE group. In cirrhotic patients, the value of dALFF variability in the bilateral precuneus/posterior cingulate gyrus was positively correlated with their neurocognitive performance (r = 0.383 and P = 0.007). The index of dALFF variability in the bilateral precuneus/posterior cingulate gyrus could be used to distinguish NHE and MHE patients, with moderate power (area under the ROC curve = 0.712 and P = 0.012).

Conclusion

Our findings highlight the existence of aberrant dynamic brain function in MHE, which could underlie the neural basis of cognitive impairments and could be associated with the development of the disease. Analyzing dALFF could facilitate new biomarker identification for MHE.

Aberrant stability of brain functional architecture in cirrhotic patients with minimal hepatic encephalopathy

To investigate the stability changes of brain functional architecture and the relationship between stability change and cognitive impairment in cirrhotic patients. Fifty-one cirrhotic patients (21 with minimal hepatic encephalopathy (MHE) and 30 without MHE (NHE)) and 29 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging and neurocognitive assessment using the Psychometric Hepatic Encephalopathy Score (PHES). Voxel-wise functional connectivity density (FCD) was calculated as the sum of connectivity strength between one voxel and others within the entire brain. The sliding window correlation approach was subsequently utilized to calculate the FCD dynamics over time. Functional stability (FS) is measured as the concordance of dynamic FCD. From HCs to the NHE and MHE groups, a stepwise reduction of FS was found in the right supramarginal gyrus (RSMG), right middle cingulate cortex, left superior frontal gyrus, and bilateral posterior cingulate cortex (BPCC), whereas a progressive increment of FS was observed in the left middle occipital gyrus (LMOG) and right temporal pole (RTP). The mean FS values in RSMG/LMOG/RTP (r = 0.470 and P = 0.001; r = -0.458 and P = 0.001; and r = -0.384 and P = 0.005, respectively) showed a correlation with PHES in cirrhotic patients. The FS index in RSMG/LMOG/BPCC/RTP showed moderate discrimination potential between the NHE and MHE groups. Changes in FS may be linked to neuropathological bias of cognitive impairment in cirrhotic patients and could serve as potential biomarkers for MHE diagnosis and monitoring the progression of hepatic encephalopathy.

Coupling analysis between functional and structural brain networks in Alzheimer's disease

The coupling between functional and structural brain networks is difficult to clarify due to the complicated alterations in gray matter and white matter for the development of Alzheimer's disease (AD). A cohort of 112 participants [normal control group (NC, 62 cases), mild cognitive impairment group (MCI, 31 cases) and AD group (19 cases)], was recruited in our study. The brain networks of rsfMRI functional connectivity (rsfMRI-FC) and diffusion tensor imaging structural connectivity (DTI-SC) across the three groups were constructed, and their correlations were evaluated by Pearson's correlation analyses and multiple comparison with Bonferroni correction. Furthermore, the correlations between rsfMRI-SC/DTI-FC coupling and four neuropsychological scores of mini-mental state examination (MMSE), clinical dementia rating-sum of boxes (CDR-SB), functional activities questionnaire (FAQ) and montreal cognitive assessment (MoCA) were inferred by partial correlation analyses, respectively. The results demonstrated that there existed significant correlation between rsfMRI-FC and DTI-SC (p < 0.05), and the coupling of rsfMRI-FC/DTI-SC showed negative correlation with MMSE score (p < 0.05), positive correlations with CDR-SB and FAQ scores (p < 0.05), and no correlation with MoCA score (p > 0.05). It was concluded that there existed FC/SC coupling and varied network characteristics for rsfMRI and DTI, and this would provide the clues to understand the underlying mechanisms of cognitive deficits of AD.

Classification of type 2 diabetes mellitus with or without cognitive impairment from healthy controls using high-order functional connectivity

Type 2 diabetes mellitus (T2DM) is associated with cognitive impairment and may progress to dementia. However, the brain functional mechanism of T2DM-related dementia is still less understood. Recent resting-state functional magnetic resonance imaging functional connectivity (FC) studies have proved its potential value in the study of T2DM with cognitive impairment (T2DM-CI). However, they mainly used a mass-univariate statistical analysis that was not suitable to reveal the altered FC "pattern" in T2DM-CI, due to lower sensitivity. In this study, we proposed to use high-order FC to reveal the abnormal connectomics pattern in T2DM-CI with a multivariate, machine learning-based strategy. We also investigated whether such patterns were different between T2DM-CI and T2DM without cognitive impairment (T2DM-noCI) to better understand T2DM-induced cognitive impairment, on 23 T2DM-CI and 27 T2DM-noCI patients, as well as 50 healthy controls (HCs). We first built the large-scale high-order brain networks based on temporal synchronization of the dynamic FC time series among multiple brain region pairs and then used this information to classify the T2DM-CI (as well as T2DM-noCI) from the matched HC based on support vector machine. Our model achieved an accuracy of 79.17% in T2DM-CI versus HC differentiation, but only 59.62% in T2DM-noCI versus HC classification. We found abnormal high-order FC patterns in T2DM-CI compared to HC, which was different from that in T2DM-noCI. Our study indicates that there could be widespread connectivity alterations underlying the T2DM-induced cognitive impairment. The results help to better understand the changes in the central neural system due to T2DM.

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.

Aberrant brain structural network and altered topological organization in minimal hepatic encephalopathy

PURPOSE

We aimed to investigate the multilevel impairments of brain structural network in patients with minimal hepatic encephalopathy (MHE).

METHODS

Twenty-two patients with MHE and 22 well-matched healthy controls (HC) underwent structural magnetic resonance imaging (MRI) brain scans and neuropsychological evaluations. Individual brain structural networks were constructed using diffusion tensor imaging. Comparing with HC, we investigated the possible impairments of brain structural network in MHE, by applying graph-theory approaches to analyze the topological organization at global, modular, and local levels. The correlations between altered brain structural network and neuropsychological tests scores and venous ammonia levels were also examined in MHE patients.

RESULTS

In the MHE group, small-worldness showed significant decrease and normalized characteristic path length showed increase at the global level. In the modular section, six modules were identified. The inter-modular connective strengths showed significant increase between modules 2 and 4 and between modules 4 and 5. The results of node analysis showed similar hub distributions in the MHE and HC groups except for the right postcentral gyrus, which was only found in the MHE group. No significant differences were found in connective strength of edges between MHE and HC groups using network-based statistics.

CONCLUSION

The altered brain structural networks with reduced network integration and module segregation were demonstrated in patients with MHE. The dysconnectivity of brain structural network could provide an explanation for the brain dysfunctions of MHE.

Thalamic Structural Connectivity Abnormalities in Minimal Hepatic Encephalopathy

Background and Aims: Numerous studies have demonstrated thalamus-related structural, functional, and metabolic abnormalities in minimal hepatic encephalopathy (MHE). We conducted the first study to investigate thalamic structural connectivity alterations in MHE.

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.

Comparison of topological properties of functional brain networks with graph theory in temporal lobe epilepsy with different duration of disease

Background

Our study was performed to measure the alterations in topological properties of the functional brain network of temporal lobe epilepsy (TLE) at different durations, exploring the potential progression and neuropathophysiological mechanisms of TLE.

Methods

Fifty-eight subjects, including 17 TLE patients with a disease duration of ≤5 years (TLE-SD), 20 TLE patients with a disease duration of >5 years (TLE-LD), and 21 healthy controls firstly underwent the Attention Network Test (ANT) to assess the alertness function and received the resting-state functional magnetic resonance imaging (rs-fMRI). Next, a functional brain network was set up, and then the related graph of theoretical network analysis was conducted. Finally, the correlation between network property and the neuropsychological score was analyzed.

Results

The global and local efficiencies of functional brain networks in TLE-SD patients significantly decreased and tended toward random alterations. Also, the degree centrality (DC) and nodal efficiency (Ne) in right medial pre-frontal thalamus (mPFtha) and right rostral temporal thalamus (rTtha) of TLE-SD patients significantly reduced. Further analysis showed that alertness was positively associated with the characteristic path length but negatively related to the global and local efficiencies in TLE-SD patients; alertness was negatively related to the Ne of mPFtha in TLE-LD patients.

Conclusions

Our study showed that the functional brain network of TLE patients might undergo compensatory reorganization as the disease progresses, which provides useful insights into the progression and mechanism of TLE.

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.

Minimal hepatic encephalopathy: current clinical and pathogenetic aspects

The review considers modern ideas about the clinic and pathogenesis of minimal hepatic encephalopathy (MHE). It is discussed the present of cognitive impairment in this category of patients. The data of functional MRI are analyzed, and these results allow taking a fresh look at the origin of clinical disorders in this condition. The importance of cerebral connections disruption is emphasized. It is focused on the fact that in the functioning of the central nervous system the spontaneous activity of the brain has a significant importance. Separately is analyzed "the resting state". It is concluded that MHE, despite its minimal manifestations, is a clinically significant condition requiring attention of a specialists. With that, it is often not diagnosed on time in clinical practice, which could lead to more severe damage of the cerebral functions. As evidenced by the data obtained at the present time, quite extensive changes in the neuronal activity are underlid of the cognitive deficit.

Brain network disintegration as a final common pathway for delirium: a systematic review and qualitative meta-analysis

Delirium is an acute neuropsychiatric syndrome characterized by altered levels of attention and awareness with cognitive deficits. It is most prevalent in elderly hospitalized patients and related to poor outcomes. Predisposing risk factors, such as older age, determine the baseline vulnerability for delirium, while precipitating factors, such as use of sedatives, trigger the syndrome. Risk factors are heterogeneous and the underlying biological mechanisms leading to vulnerability for delirium are poorly understood. We tested the hypothesis that delirium and its risk factors are associated with consistent brain network changes. We performed a systematic review and qualitative meta-analysis and included 126 brain network publications on delirium and its risk factors. Findings were evaluated after an assessment of methodological quality, providing N=99 studies of good or excellent quality on predisposing risk factors, N=10 on precipitation risk factors and N=7 on delirium. Delirium was consistently associated with functional network disruptions, including lower EEG connectivity strength and decreased fMRI network integration. Risk factors for delirium were associated with lower structural connectivity strength and less efficient structural network organization. Decreased connectivity strength and efficiency appear to characterize structural brain networks of patients at risk for delirium, possibly impairing the functional network, while functional network disintegration seems to be a final common pathway for the syndrome.

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Delirium is consistently associated with functional network impairments.

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Risk factors are associated with lower structural connectivity strength.

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Risk factors are associated with a less efficient structural network organization.

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Structural impairments make the functional network more vulnerable to deterioration.

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Functional network disintegration seems to be a final common pathway for delirium.

Brain Functional Networks in Type 2 Diabetes Mellitus Patients: A Resting-State Functional MRI Study

Background

Previous diabetes mellitus studies of cognitive impairments in the early stages have focused on changes in brain structure and function, and more recently the focus has shifted to the relationships between encephalic regions and diversification of network topology. However, studies examining network topology in diabetic brain function are still limited.

Methods

The study included 102 subjects; 55 type 2 diabetes mellitus (T2DM) patients plus 47 healthy controls. All subjects were examined by resting-state functional magnetic resonance imaging (rs-fMRI) scan. According to Automated Anatomical Labeling, the brain was divided into 90 anatomical regions, and every region corresponds to a brain network analysis node. The whole brain functional network was constructed by thresholding the correlation matrices of the 90 brain regions, and the topological properties of the network were computed based on graph theory. Then, the topological properties of the network were compared between different groups by using a non-parametric test. Finally, the associations between differences in topological properties and the clinical indicators were analyzed.

Results

The brain functional networks of both T2DM patients and healthy controls were found to possess small-world characteristics, i.e., normalized clustering coefficient (γ) > 1, and normalized characteristic path length (λ) close to 1. No significant differences were found in the small-world characteristics (σ). Second, the T2DM patient group displayed significant differences in node properties in certain brain regions. Correlative analytic results showed that the node degree of the right inferior temporal gyrus (ITG) and the node efficiencies of the right ITG and superior temporal gyrus of T2DM patients were positively correlated with body mass index.

Conclusion

The brain network of T2DM patients has the same small-world characteristics as normal people, but the normalized clustering coefficient is higher and the normalized characteristic path length is lower than that of the normal control group, indicating that the brain function network of the T2DM patients has changed. The changes of node properties were mostly concentrated in frontal lobe, temporal lobe and posterior cingulate gyrus. The abnormal changes in these indices in T2DM patients might be explained as a compensatory behavior to reduce cognitive impairments, which is achieved by mobilizing additional neural resources, such as the excessive activation of the network and the efficient networking of multiple brain regions.

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.

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.

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.

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.

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.

Resting-state functional MR imaging shed insights into the brain of diabetes

Diabetes mellitus is a common metabolic disease which is associated with increasing risk for multiple cognitive declines. Alterations in brain functional connectivity are believed to be the mechanisms underlying the cognitive function impairments. During the past decade, resting-state functional magnetic resonance imaging (rs-fMRI) has been developed as a major tool to study brain functional connectivity in vivo. This paper briefly reviews the diabetes-associated cognitive impairment, analysis algorithms and clinical applications of rs-fMRI. We also provide future perspectives of rs-fMRI in diabetes.

Mapping Individual Brain Networks Using Statistical Similarity in Regional Morphology from MRI

Representing brain morphology as a network has the advantage that the regional morphology of ‘isolated’ structures can be described statistically based on graph theory. However, very few studies have investigated brain morphology from the holistic perspective of complex networks, particularly in individual brains. We proposed a new network framework for individual brain morphology. Technically, in the new network, nodes are defined as regions based on a brain atlas, and edges are estimated using our newly-developed inter-regional relation measure based on regional morphological distributions. This implementation allows nodes in the brain network to be functionally/anatomically homogeneous but different with respect to shape and size. We first demonstrated the new network framework in a healthy sample. Thereafter, we studied the graph-theoretical properties of the networks obtained and compared the results with previous morphological, anatomical, and functional networks. The robustness of the method was assessed via measurement of the reliability of the network metrics using a test-retest dataset. Finally, to illustrate potential applications, the networks were used to measure age-related changes in commonly used network metrics. Results suggest that the proposed method could provide a concise description of brain organization at a network level and be used to investigate interindividual variability in brain morphology from the perspective of complex networks. Furthermore, the method could open a new window into modeling the complexly distributed brain and facilitate the emerging field of human connectomics.

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.