Aberrant dynamic functional network connectivity in cirrhotic patients without overt hepatic encephalopathy.Eur J Radiol. 2020;132:109324. [PMID: 33038576 DOI: 10.1016/j.ejrad.2020.109324]

Altered static and dynamic functional network connectivity in primary angle-closure glaucoma patients

To explore altered patterns of static and dynamic functional brain network connectivity (sFNC and dFNC) in Primary angle-closure glaucoma (PACG) patients. Clinically confirmed 34 PACG patients and 33 age- and gender-matched healthy controls (HCs) underwent evaluation using T1 anatomical and functional MRI on a 3 T scanner. Independent component analysis, sliding window, and the K-means clustering method were employed to investigate the functional network connectivity (FNC) and temporal metrics based on eight resting-state networks. Differences in FNC and temporal metrics were identified and subsequently correlated with clinical variables. For sFNC, compared with HCs, PACG patients showed three decreased interactions, including SMN-AN, SMN-VN and VN-AN pairs. For dFNC, we derived four highly structured states of FC that occurred repeatedly between individual scans and subjects, and the results are highly congruent with sFNC. In addition, PACG patients had a decreased fraction of time in state 3 and negatively correlated with IOP (p < 0.05). PACG patients exhibit abnormalities in both sFNC and dFNC. The high degree of overlap between static and dynamic results suggests the stability of functional connectivity networks in PACG patients, which provide a new perspective to understand the neuropathological mechanisms of optic nerve damage in PACG patients.

Metabolic alterations in the right anterior insula among patients with cirrhosis without overt hepatic encephalopathy: a magnetic resonance spectroscopy study

Purpose

We investigated metabolic alterations in the right anterior insula (rAI) in cirrhotic patients and determined its association with patients' cognitive dysfunction.

Methods

In this study, 31 healthy controls (HCs) and 32 cirrhotic patients without overt hepatic encephalopathy participated. Both blood ammonia level and Child-Pugh score were measured. The psychometric hepatic encephalopathy score (PHES) was used to evaluate cognitive function. 1H-magnetic resonance spectroscopy (MRS) data located in the rAI were recorded on a commercially available 3T magnetic resonance imaging scanner. The ratios of metabolites were measured, including N-acetylaspartate (NAA)/total creatine (tCr), glutamate plus glutamine (Glx)/tCr, myo-inositol (mI)/tCr, and total choline (tCho)/tCr. We adopted the non-parametric Mann-Whitney U-test for intergroup comparison of metabolic ratios. To determine the association between metabolite concentration and clinical parameters, we performed Spearman correlation analyses.

Results

Patients with cirrhosis performed worse on PHES in comparison with HCs (P < 0.001). Patients with cirrhosis had significantly decreased mI/tCr (0.87 ± 0.07 vs. 0.74 ± 0.19, P = 0.025) and increased Glx/tCr (1.79 ± 0.17 vs. 2.07 ± 0.29, P < 0.001) in the rAI. We did not observe any significant between-group differences in tCho/tCr and NAA/tCr. The blood ammonia level was correlated with Glx/tCr (r = 0.405, P = 0.022) and mI/tCr (r = -0.398, P = 0.024) of the rAI. In addition, PHES was negatively correlated with Glx/tCr of the rAI (r = -0.379, P = 0.033).

Conclusion

Metabolic disturbance of the rAI, which is associated with ammonia intoxication, might account for the neural substrate of cirrhosis-related cognitive dysfunction to some extent.

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.

Aberrant dynamic functional network connectivity in type 2 diabetes mellitus individuals

An increasing number of recent brain imaging studies are dedicated to understanding the neuro mechanism of cognitive impairment in type 2 diabetes mellitus (T2DM) individuals. In contrast to efforts to date that are limited to static functional connectivity, here we investigate abnormal connectivity in T2DM individuals by characterizing the time-varying properties of brain functional networks. Using group independent component analysis (GICA), sliding-window analysis, and k-means clustering, we extracted thirty-one intrinsic connectivity networks (ICNs) and estimated four recurring brain states. We observed significant group differences in fraction time (FT) and mean dwell time (MDT), and significant negative correlation between the Montreal Cognitive Assessment (MoCA) scores and FT/MDT. We found that in the T2DM group the inter- and intra-network connectivity decreases and increases respectively for the default mode network (DMN) and task-positive network (TPN). We also found alteration in the precuneus network (PCUN) and enhanced connectivity between the salience network (SN) and the TPN. Our study provides evidence of alterations of large-scale resting networks in T2DM individuals and shed light on the fundamental mechanisms of neurocognitive deficits in T2DM.

Changes in dynamic and static brain fluctuation distinguish minimal hepatic encephalopathy and cirrhosis patients and predict the severity of liver damage

Purpose

Minimal hepatic encephalopathy (MHE) is characterized by mild neuropsychological and neurophysiological alterations that are not detectable by routine clinical examination. Abnormal brain activity (in terms of the amplitude of low-frequency fluctuation (ALFF) has been observed in MHE patients. However, little is known concerning temporal dynamics of intrinsic brain activity. The present study aimed to investigate the abnormal dynamics of brain activity (dynamic ALFF; dALFF) and static measures [static ALFF; (sALFF)] in MHE patients and to strive for a reliable imaging neuromarkers for distinguishing MHE patients from cirrhosis patients. In addition, the present study also investigated whether intrinsic brain activity predicted the severity of liver damage.

Methods

Thirty-four cirrhosis patients with MHE, 28 cirrhosis patients without MHE, and 33 age-, sex-, and education-matched healthy controls (HCs) underwent resting-state magnetic resonance imaging (rs-fMRI). dALFF was estimated by combining the ALFF method with the sliding-window method, in which temporal variability was quantized over the whole-scan timepoints and then compared among the three groups. Additionally, dALFF, sALFF and both two features were utilized as classification features in a support vector machine (SVM) to distinguish MHE patients from cirrhosis patients. The severity of liver damage was reflected by the Child–Pugh score. dALFF, sALFF and both two features were used to predict Child–Pugh scores in MHE patients using a general linear model.

Results

Compared with HCs, MHE patients showed significantly increased dALFF in the left inferior occipital gyrus, right middle occipital gyrus, and right insula; increased dALFF was also observed in the right posterior lobe of the cerebellum (CPL) and right thalamus. Compared with HCs, noMHE patients exhibited decreased dALFF in the right precuneus. In contrast, compared with noMHE patients, MHE patients showed increased dALFF in the right precuneus, right superior frontal gyrus, and right superior occipital gyrus. Furthermore, the increased dALFF values in the left precuneus were positively associated with poor digit-symbol test (DST) scores (r = 0.356, p = 0.038); however, dALFF in the right inferior temporal gyrus (ITG) was negatively associated with the number connection test–A (NCT-A) scores (r = -0.784, p = 0.000). A significant positive correlation was found between dALFF in the left inferior occipital gyrus (IOG) and high blood ammonia levels (r = 0.424, p = 0.012). Notably, dALFF values yielded a higher classification accuracy than sALFF values in distinguishing MHE patients from cirrhosis patients. Importantly, the dALFF values predicted the Child–Pugh score (r = 0.140, p = 0.030), whereas sALFF values did not in the current dataset. Combining two features had high accuracy in classification in distinguishing MHE patients from cirrhotic patients and yielded prediction in the severity of liver damage.

Conclusion

These findings suggest that combining dALFF and sALFF features is a useful neuromarkers for distinguishing MHE patients from cirrhosis patients and highlights the important role of dALFF feature in predicting the severity of liver damage in MHE.

Altered static and dynamic functional network connectivity in post-stroke cognitive impairment

Post-stroke cognitive impairment (PSCI) is a common symptom following brain stroke, yet the mechanisms remain unknown. This study aimed to investigate alterations of static and dynamic functional network connectivity (sFNC and dFNC) in PSCI patients. We prospectively recruited 17 PSCI patients and 24 Healthy controls (HC). Restingstate fMRI (rs-fMRI) and Mini-Mental State Examination (MMSE) were performed. Independent component analysis combined with sliding-window and K-means clustering approach were applied to examine the FNC among 11 resting-state networks: auditory network (AUDN), left executive control network (lECN), language network (LN), precuneus network (PCUN), right executive control network (rECN), salience network (SN), visuospatial network (VN), dorsal default mode network (dDMN), higher visual network (hVIS), primary visual network (pVIS), and ventral mode network (vDMN). The FNC and dynamic indices (fraction time, mean dwell time, transition number) were calculated. Static and dynamic measures were compared between two groups and the correlation between clinical and imaging indicators was analyzed. For sFNC, PSCI group showed decreased interactions in dDMN-vDMN, vDMN-SN, dDMN-hVIS, AUDN-rECN, and AUDN-VN. For dFNC, we derived 3 states of FNC that occurred repeatedly. Significant group differences were found, including decreased interactions in the AUDN-VN, AUDN-pVIS in state 2 and dDMN-VN in state 3. The mean dwell time in PSCI group was longer in state 1, and negatively correlated with MMSE score. These results demonstrated that PSCI patients are characterized with altered sFNC and dFNC, which could help us explore the neural mechanisms of the PSCI from a new perspective.

Abnormal Dynamic Functional Network Connectivity in Adults with Autism Spectrum Disorder

PURPOSE

This study sought to explore changes of brain dynamic functional network connectivity (dFNC) in adults with autism spectrum disorder (ASD) and investigate their relationship with clinical manifestations.

METHODS

Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 78 adult ASD patients from autism brain imaging data exchange datasets, and 65 age-matched healthy controls subjects from the local community. Independent component analysis was conducted to evaluate dFNC patterns on the basis of 13 independent components (ICs) within 11 resting-state networks (RSN), namely, auditory network (AUDN), basal ganglia network (BGN), language network (LN), sensorimotor network (SMN), precuneus network (PUCN), salience network (SN), visuospatial network (VSN), dorsal default mode network (dDMN), high visual network (hVIS), primary visual network (pVIS), ventral default mode network (vDMN). Fraction time, mean dwell time, number of transitions, and RSN connectivity were calculated for group comparisons. Correlation analyses were performed between abnormal metrics and autism diagnostic observation schedule (ADOS) scores.

RESULTS

Compared with controls, ASD patients had higher fraction time and mean dwell time in state 2 (P = 0.017, P = 0.014). Reduced dFNC was found in the SMN with PUCN, SMN with hVIS, and increased dFNC was observed in the dDMN with SN in state 2 in the ASD group. Fraction time and mean dwell time was positively correlated with stereotyped behavior scores of ADOS.

CONCLUSION

The findings demonstrated the importance of evaluating transient alterations in specific neural networks of adult ASD patients. The abnormal metrics and connectivity may be related to symptoms such as stereotyped behavior.

Abnormal dynamic functional network connectivity in male obstructive sleep apnea with mild cognitive impairment: A data-driven functional magnetic resonance imaging study

Objective

The purpose of this study was to investigate the dynamic functional network connectivity (FNC) and its relationship with cognitive function in obstructive sleep apnea (OSA) patients from normal cognition (OSA-NC) to mild cognitive impairment (OSA-MCI).

Materials and methods

Eighty-two male OSA patients and 48 male healthy controls (HC) were included in this study. OSA patients were classified to OSA-MCI (n = 41) and OSA-NC (n = 41) based on cognitive assessments. The independent component analysis was used to determine resting-state functional networks. Then, a sliding-window approach was used to construct the dynamic FNC, and differences in temporal properties of dynamic FNC and functional connectivity strength were compared between OSA patients and the HC. Furthermore, the relationship between temporal properties and clinical assessments were analyzed in OSA patients.

Results

Two different connectivity states were identified, namely, State I with stronger connectivity and lower frequency, and State II with lower connectivity and relatively higher frequency. Compared to HC, OSA patients had a longer mean dwell time and higher fractional window in stronger connectivity State I, and opposite result were found in State II, which was mainly reflected in OSA-MCI patients. The number of transitions was an increasing trend and positively correlated with cognitive assessment in OSA-MCI patients. Compared with HC, OSA patients showed extensive abnormal functional connectivity in stronger connected State I and less reduced functional connectivity in lower connected State II, which were mainly located in the salience network, default mode network, and executive control network.

Conclusion

Our study found that OSA patients showed abnormal dynamic FNC properties, which was a continuous trend from HC, and OSA-NC to OSA-MCI, and OSA patients showed abnormal dynamic functional connectivity strength. The number of transformations was associated with cognitive impairment in OSA-MCI patients, which may provide new insights into the neural mechanisms in OSA patients.

Abnormal static and dynamic functional network connectivity in stable chronic obstructive pulmonary disease

Objective

Many studies have explored the neural mechanisms of cognitive impairment in chronic obstructive pulmonary disease (COPD) patients using the functional MRI. However, the dynamic properties of brain functional networks are still unclear. The purpose of this study was to explore the changes in dynamic functional network attributes and their relationship with cognitive impairment in stable COPD patients.

Materials and methods

The resting-state functional MRI and cognitive assessments were performed on 19 stable COPD patients and 19 age-, sex-, and education-matched healthy controls (HC). We conducted the independent component analysis (ICA) method on the resting-state fMRI data, and obtained seven resting-state networks (RSNs). After that, the static and dynamic functional network connectivity (sFNC and dFNC) were respectively constructed, and the differences of functional connectivity (FC) were compared between the COPD patients and the HC groups. In addition, the correlation between the dynamic functional network attributes and cognitive assessments was analyzed in COPD patients.

Results

Compared to HC, there were significant differences in sFNC among COPD patients between and within networks. COPD patients showed significantly longer mean dwell time and higher fractional windows in weaker connected State I than that in HC. Besides, in comparison to HC, COPD patients had more extensive abnormal FC in weaker connected State I and State IV, and less abnormal FC in stronger connected State II and State III, which were mainly located in the default mode network, executive control network, and visual network. In addition, the dFNC properties including mean dwell time and fractional windows, were significantly correlated with some essential clinical indicators such as FEV₁, FEV₁/FVC, and c-reactive protein (CRP) in COPD patients.

Conclusion

These findings emphasized the differences in sFNC and dFNC of COPD patients, which provided a new perspective for understanding the cognitive neural mechanisms, and these indexes may serve as neuroimaging biomarkers of cognitive performance in COPD patients.

Alterations of static and dynamic functional network connectivity in acute ischemic brainstem stroke

Background

Prior studies have shown abnormal brain functional network changes in patients with acute ischemic stroke. However, the alterations of dynamic functional network connectivity (FNC) in brainstem strokes have not been elucidated.

Purpose

To assess alterations of static and dynamic FNCs and determine the relationships between these and upper limb movement performance in patients with acute brainstem ischemic stroke.

Material and Methods

In total, 50 patients with acute brainstem ischemic stroke and 50 age- and sex-matched healthy controls were enrolled in the present study and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Independent component analysis was conducted to assess static and dynamic FNC patterns based on seven resting-state networks, namely, the default mode network (DMN), executive control network (ECN), attention network (AN), somatomotor network (SMN), visual network (VN), auditory network (AUN), and cerebellum network (CN).

Results

Compared with controls, patients with acute brainstem ischemic stroke exhibited wide aberrations of static FNC, including increased FNC in DMN–ECN, DMN–VN, ECN–VN, ECN–AN and AN–AUN pairs. Patients with acute brainstem ischemic stroke showed aberrant dynamic FNC in State 1, involving increased FNC aberrance in the DMN with AN, DMN with ECN, and reduced FNC in SMN–VN pairs. In State 5, patients with acute brainstem ischemic stroke showed increased FNC in DMN–VN and AN–AUN, and decreased FNC in AN–SMN pairs.

Conclusion

This study suggests that static and dynamic FNC impairment and aberrant connections exist in acute brainstem ischemic stroke, which expands what is known regarding the relationship between stroke and FNC from static and dynamic perspectives.

Aberrant static and dynamic functional network connectivity in heart failure with preserved ejection fraction

Aims

Heart failure may lead to brain functional alterations related to cognitive impairment. This study aimed to detect alterations of static functional network connectivity (FNC) and dynamic FNC in heart failure with preserved ejection fraction (HFpEF) and to estimate the association between the altered FNC and clinical features related to HFpEF.

Methods and results

The clinical and resting‐state functional magnetic resonance imaging (fMRI) data of HFpEF patients (n = 35) and healthy controls (HCs) (n = 35) were acquired at baseline. Resting‐state networks (RSNs) were established based on independent component analysis (ICA) and FNC analyses were performed. The associations between the FNC abnormalities and clinical features related to HFpEF were analysed. Compared with HCs, HFpEF patients showed decreased functional connectivity within the default mode network, left frontoparietal network, and right frontoparietal network and increased functional connectivity within the right frontoparietal network and visual network. Negative correlations were observed between decreased dynamic FNC and the left ventricular end‐diastolic diameter (LVDd) (r = −0.435, P = 0.015) as well as the left ventricular end‐systolic diameter (LVDs) (r = −0.443, P = 0.013).

Conclusions

The FNC disruption and altered temporal properties of functional dynamics in HFpEF patients may reflect the neural mechanisms of brain injury after HFpEF, which may deepen our understanding of the disease.

Abnormal Static and Dynamic Functional Network Connectivity in Patients With Presbycusis

Aim: This study aimed to investigate abnormal static and dynamic functional network connectivity (FNC) and its association with cognitive function in patients with presbycusis.

Methods: In total, 60 patients with presbycusis and 60 age-, sex-, and education-matched healthy controls (HCs) underwent resting-state functional MRI (rs-fMRI) and cognitive assessments. Group independent component analysis (ICA) was carried out on the rs-fMRI data, and eight resting-state networks (RSNs) were identified. Static and dynamic FNCs (sFNC and dFNC) were then constructed to evaluate differences in RSN connectivity between the patients with presbycusis and the HCs. Furthermore, the correlations between these differences and cognitive scores were analyzed.

Results: Patients with presbycusis had differences in sFNC compared with HCs, mainly reflected in decreased sFNC in the default mode network (DMN)-left frontoparietal network (LFPN) and attention network (AN)-cerebellum network (CN) pairs, but they had increased sFNC in the auditory network (AUN) between DMN domains. The decreased sFNC in the DMN-LFPN pair was negatively correlated with their TMT-B score (r = –0.441, p = 0.002). Patients with presbycusis exhibited aberrant dFNCs in State 2 and decreased dFNCs between the CN and AN and the visual network (VN). Moreover, the presbycusis group had a shorter mean dwell time (MDT) and fraction time (FT) in State 3 (p = 0.0027; p = 0.0031, respectively).

Conclusion: This study highlighted differences in static and dynamic functional connectivity in patients with presbycusis and suggested that FNC may serve as an important biomarker of cognitive performance since abnormal alterations can better track cognitive impairment in presbycusis.

Aberrant Static and Dynamic Functional Network Connectivity in Acute Mild Traumatic Brain Injury with Cognitive Impairment

PURPOSE

This study aimed to investigate differences in static and dynamic functional network connectivity (FNC) and explore their association with neurocognitive performance in acute mild traumatic brain injury (mTBI).

METHODS

A total of 76 patients with acute mTBI and 70 age-matched and sex-matched healthy controls were enrolled (age 43.79 ± 10.22 years vs. 45.63 ± 9.49 years; male/female: 34/42 vs. 38/32; all p > 0.05) and underwent resting-state functional magnetic resonance imaging (fMRI) scan (repetition time/echo time = 2000/30 ms, 230 volumes). Independent component analysis was conducted to evaluate static and dynamic FNC patterns on the basis of nine resting-state networks, namely, auditory network (AUDN), dorsal attention network (dAN), ventral attention network (vAN), default mode network (DMN), left frontoparietal network (LFPN), right frontoparietal network (RFPN), somatomotor network (SMN), visual network (VN), and salience network (SN). Spearman's correlation among aberrances in FNC values, and Montreal cognitive assessment (MoCA) scores was further measured in mTBI.

RESULTS

Compared with controls, patients with mTBI showed wide aberrances of static FNC, such as reduced FNC in DMN-vAN and VN-vAN pairs. The mTBI patients exhibited aberrant dynamic FNC in state 2, involving reduced FNC aberrance in the vAN with AUDN, VN with DMN and dAN, and SN with SMN and vAN. Reduced dFNC in the SN-vAN pair was negatively correlated with the MoCA score.

CONCLUSION

Our findings suggest that aberrant static and dynamic FNC at the acute stage may contribute to cognitive symptoms, which not only may expand knowledge regarding FNC cognition relations from the static perspective but also from the dynamic perspective.

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.