Cortical signature of patients with HBV-related cirrhosis without overt hepatic encephalopathy: a morphometric analysis

Wu, Xiaoqin; Lv, Xiaofei; Zhang, Yuling; Wu, Huawang; Cai, Peiqiang; Qiu, Yingwei; Zhang, Xuelin; Jiang, Guihua

doi:10.3389/fnana.2015.00082

Cited by 12 publications

(12 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular in the occipital lobe, Region 12R (right lingual) and 20R (right peri calcarine) in Figure 12 seem to be the most affected regions related to visuospatial processing since they have more connections with differences between the two groups. This agrees with the findings that damage to the lingual gyrus leads to a form of topographic disorientation (Kravitz et al, 2011) and abnormalities in calcarine sulcus, which is a key node of the ventral visual pathway, are related to impaired visual information processing (Wu et al, 2015).…”

Section: 5supporting

confidence: 92%

Common and individual structure of brain networks

Wang¹,

Zhang²,

Dunson³

2019

Ann. Appl. Stat.

View full text Add to dashboard Cite

This article focuses on the problem of studying shared-and individualspecific structure in replicated networks or graph-valued data. In particular, the observed data consist of n graphs, Gi, i = 1, . . . , n, with each graph consisting of a collection of edges between V nodes. In brain connectomics, the graph for an individual corresponds to a set of interconnections among brain regions. Such data can be organized as a V ×V binary adjacency matrix Ai for each i, with ones indicating an edge between a pair of nodes and zeros indicating no edge. When nodes have a shared meaning across replicates i = 1, . . . , n, it becomes of substantial interest to study similarities and differences in the adjacency matrices. To address this problem, we propose a method to estimate a common structure and low-dimensional individual-specific deviations from replicated networks. The proposed Multiple GRAph Factorization (M-GRAF) model relies on a logistic regression mapping combined with a hierarchical eigenvalue decomposition. We develop an efficient algorithm for estimation and study basic properties of our approach. Simulation studies show excellent operating characteristics and we apply the method to human brain connectomics data.

show abstract

Section: 5supporting

confidence: 92%

Common and individual structure of brain networks

Wang¹,

Zhang²,

Dunson³

2019

Ann. Appl. Stat.

View full text Add to dashboard Cite

show abstract

“…The lateral occipital cortex plays a key role in visual object processing 32 , and this region was significantly activated by attention 33 and during object-based selection in previous functional MRI (fMRI) studies 34 . Previous studies have also shown that significantly increased local gyrification index in the lateral occipital cortex and the mean cortical thickness of the regions with structural abnormalities were negatively correlated with the PHES of patients with hepatitis B virus-related cirrhosis without overt HE 35 . In addition, judgement-related blood oxygenation level-dependent (BOLD) activation was decreased in CHE compared to controls in the right inferior parietal cortex (IPL).…”

Section: Discussionmentioning

confidence: 79%

“…Similarly, the corticocortical projections from the precuneus to the lateral parietal areas and premotor cortex 42 play a pivotal role in the motor coordination with visual guidance of hand movements 43 and reaching 44 . Prior structural fMRI studies have identified significantly increased cortical thickness in the precuneus in HBV-related cirrhotics, suggesting low-grade brain edema in this region that potentially contributes to the impaired connectivity in the precuneus 35 . These findings are consistent with several studies showing decreased regional homogeneity in the precuneus and changes of amplitude of low frequency fluctuation and fraction in CHE patients 45,46 .…”

Section: Discussionmentioning

confidence: 96%

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

Tsai¹,

Wang³

et al. 2019

Sci Rep

View full text Add to dashboard Cite

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.

show abstract

“…In agreement with this result, previous studies also revealed similar enlargements in these specific GM regions in cirrhotic patients. For example, cirrhotic patients with cognitive impairment have demonstrated a significant increase in cortical thickness in the bilateral lingual and parahippocampal gyrus, right posterior cingulate cortex, precuneus, peri-calcarine sulcus, and the fusiform gyrus 47 . In addition, cirrhosis is often accompanied by an increase in thalamic volume 19,42,48 , so much so that increased GMV in the thalamus has been regarded as an additional characteristic of MHE.…”

Section: Discussionmentioning

confidence: 99%

“…(2020) 10:2490 | https://doi.org/10.1038/s41598-020-59433-1 www.nature.com/scientificreports www.nature.com/scientificreports/ It is important to note that the mechanisms underlying increased GMV in MHE are not well understood. One possible reason may be the diffuse, low-grade, cerebral edema related to Alzheimer's type II astrocytes during chronic liver disease 47,49 . The existence of both decreases and increases in GMV in MHE may reflect brain structural reorganization due to chronic liver failure.…”

Section: Scientific Reports |mentioning

confidence: 99%

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

Chen

Zou

Yang

et al. 2020

Sci Rep

View full text Add to dashboard Cite

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.In summary, we successfully differentiated between cirrhotic patients with and without MHE using gray matter volumetry and an SVM classification system. The brain regions with the highest discriminant power included both cortical and subcortical structures. Therefore, our findings suggest that regional changes in GMV can be employed as a biomarker to detect MHE in cirrhotic patients.

show abstract

Cortical signature of patients with HBV-related cirrhosis without overt hepatic encephalopathy: a morphometric analysis

Cited by 12 publications

References 48 publications

Common and individual structure of brain networks

Common and individual structure of brain networks

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

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

Contact Info

Product

Resources

About