Hydrogen Peroxide Inhibits Hepatitis B Virus Replication by Downregulating HBx Levels via Siah-1-Mediated Proteasomal Degradation in Human Hepatoma Cells.

The hepatitis B virus (HBV) is constantly exposed to significant oxidative stress characterized by elevated levels of reactive oxygen species (ROS), such as H2O2, during infection in hepatocytes of patients. In this study, we demonstrated that H2O2 inhibits HBV replication in a p53-dependent fashion in human hepatoma cell lines expressing sodium taurocholate cotransporting polypeptide. Interestingly, H2O2 failed to inhibit the replication of an HBV X protein (HBx)-null HBV mutant, but this defect was successfully complemented by ectopic expression of HBx. Additionally, H2O2 upregulated p53 levels, leading to increased expression of seven in absentia homolog 1 (Siah-1) levels. Siah-1, an E3 ligase, induced the ubiquitination-dependent proteasomal degradation of HBx. The inhibitory effect of H2O2 was nearly abolished not only by treatment with a representative antioxidant, N-acetyl-L-cysteine but also by knockdown of either p53 or Siah-1 using specific short hairpin RNA, confirming the role of p53 and Siah-1 in the inhibition of HBV replication by H2O2. The present study provides insights into the mechanism that regulates HBV replication under conditions of oxidative stress in patients.

Characteristic functional cores revealed by hyperbolic disc embedding and k-core percolation on resting-state fMRI.

Hyperbolic disc embedding and k-core percolation reveal the hierarchical structure of functional connectivity on resting-state fMRI (rsfMRI). Using 180 normal adults' rsfMRI data from the human connectome project database, we visualized inter-voxel relations by embedding voxels on the hyperbolic space using the [Formula: see text] model. We also conducted k-core percolation on 30 participants to investigate core voxels for each individual. It recursively peels the layer off, and this procedure leaves voxels embedded in the center of the hyperbolic disc. We used independent components to classify core voxels, and it revealed stereotypes of individuals such as visual network dominant, default mode network dominant, and distributed patterns. Characteristic core structures of resting-state brain connectivity of normal subjects disclosed the distributed or asymmetric contribution of voxels to the kmax-core, which suggests the hierarchical dominance of certain IC subnetworks characteristic of subgroups of individuals at rest.

All-trans retinoic acid inhibits HCV replication by downregulating core levels via E6AP-mediated proteasomal degradation.

Here, we found that all-trans retinoic acid (ATRA), the most biologically active metabolite of vitamin A, strengthens the anti-viral defense mechanism of E6-associated protein (E6AP) that downregulates hepatitis C virus (HCV) Core levels via ubiquitin-dependent proteasomal degradation. For this effect, ATRA downregulated both protein and enzyme activity levels of DNA methyltransferase 1 and 3b and activated E6AP expression via promoter hypomethylation in HepG2 cells but not in Hep3B cells, in which p53 was absent. Ectopic p53 expression but not E6AP overexpression restored the ability of ATRA to downregulate HCV Core levels in Hep3B cells, suggesting a direct role of p53 in the E6AP-mediated ubiquitination of HCV Core. ATRA also downregulated HCV Core levels during HCV infection in Huh7D cells to inhibit virus replication, providing theoretical basis for the clinical application of ATRA against HCV infection.

Alterations in Social Brain Network Topology at Rest in Children With Autism Spectrum Disorder.

OBJECTIVE: Underconnectivity in the resting brain is not consistent in autism spectrum disorder (ASD). However, it is known that the functional connectivity of the default mode network is mainly decreased in childhood ASD. This study investigated the brain network topology as the changes in the connection strength and network efficiency in childhood ASD, including the early developmental stages. METHODS: In this study, 31 ASD children aged 2-11 years were compared with 31 age and sex-matched children showing typical development. We explored the functional connectivity based on graph filtration by assessing the single linkage distance and global and nodal efficiencies using resting-state functional magnetic resonance imaging. The relationship between functional connectivity and clinical scores was also analyzed. RESULTS: Underconnectivities within the posterior default mode network subregions and between the inferior parietal lobule and inferior frontal/superior temporal regions were observed in the ASD group. These areas significantly correlated with the clinical phenotypes. The global, local, and nodal network efficiencies were lower in children with ASD than in those with typical development. In the preschool-age children (2-6 years) with ASD, the anterior-posterior connectivity of the default mode network and cerebellar connectivity were reduced. CONCLUSION: The observed topological reorganization, underconnectivity, and disrupted efficiency in the default mode network subregions and social function-related regions could be significant biomarkers of childhood ASD.

Maturational delay and asymmetric information flow of brain connectivity in SHR model of ADHD revealed by topological analysis of metabolic networks.

Attention-deficit hyperactivity disorder (ADHD) is a complex brain development disorder characterized by hyperactivity/impulsivity and inattention. A major hypothesis of ADHD is a lag of maturation, which is supported mainly by anatomical studies evaluating cortical thickness. Here, we analyzed changes of topological characteristics of whole-brain metabolic connectivity in twelve SHR rats selected as ADHD-model rats by confirming behavior abnormalities using the marble burying test, open field test, and delay discounting task and 12 Wistar Kyoto rats as the control group, across development from 4 weeks old (childhood) and 6 weeks old (entry of puberty). A topological approach based on graph filtrations revealed a lag in the strengthening of limbic-cortical/subcortical connections in ADHD-model rats. This in turn related to impaired modularization of memory and reward-motivation associated regions. Using mathematical network analysis techniques such as single linkage hierarchical clustering and volume entropy, we observed left-lateralized connectivity in the ADHD-model rats at 6 weeks old. Our findings supported the maturational delay of metabolic connectivity in the SHR model of ADHD, and also suggested the possibility of impaired and compensative reconfiguration of information flow over the brain network.

STATISTICAL INFERENCE ON THE NUMBER OF CYCLES IN BRAIN NETWORKS.

A cycle in a graph is a subset of a connected component with redundant additional connections. If there are many cycles in a connected component, the connected component is more densely connected. While the number of connected components represents the integration of the brain network, the number of cycles represents how strong the integration is. However, enumerating cycles in the network is not easy and often requires brute force enumerations. In this study, we present a new scalable algorithm for enumerating the number of cycles in the network. We show that the number of cycles is monotonically decreasing with respect to the filtration values during graph filtration. We further develop a new statistical inference framework for determining the significance of the number of cycles. The methods are applied in determining if the number of cycles is a statistically significant heritable network feature in the functional human brain network.

Exact topological inference of the resting-state brain networks in twins.

A cycle in a brain network is a subset of a connected component with redundant additional connections. If there are many cycles in a connected component, the connected component is more densely connected. Whereas the number of connected components represents the integration of the brain network, the number of cycles represents how strong the integration is. However, it is unclear how to perform statistical inference on the number of cycles in the brain network. In this study, we present a new statistical inference framework for determining the significance of the number of cycles through the Kolmogorov-Smirnov (KS) distance, which was recently introduced to measure the similarity between networks across different filtration values by using the zeroth Betti number. In this paper, we show how to extend the method to the first Betti number, which measures the number of cycles. The performance analysis was conducted using the random network simulations with ground truths. By using a twin imaging study, which provides biological ground truth, the methods are applied in determining if the number of cycles is a statistically significant heritable network feature in the resting-state functional connectivity in 217 twins obtained from the Human Connectome Project. The MATLAB codes as well as the connectivity matrices used in generating results are provided at http://www.stat.wisc.edu/∼mchung/TDA.

Deep learning only by normal brain PET identify unheralded brain anomalies.

BACKGROUND: Recent deep learning models have shown remarkable accuracy for the diagnostic classification. However, they have limitations in clinical application due to the gap between the training cohorts and real-world data. We aimed to develop a model trained only by normal brain PET data with an unsupervised manner to identify an abnormality in various disorders as imaging data of the clinical routine. METHODS: Using variational autoencoder, a type of unsupervised learning, Abnormality Score was defined as how far a given brain image is from the normal data. The model was applied to FDG PET data of Alzheimer's disease (AD) and mild cognitive impairment (MCI) and clinical routine FDG PET data for assessing behavioral abnormality and seizures. Accuracy was measured by the area under curve (AUC) of receiver-operating-characteristic (ROC) curve. We investigated whether deep learning has additional benefits with experts' visual interpretation to identify abnormal patterns. FINDINGS: The AUC of the ROC curve for differentiating AD was 0.90. The changes in cognitive scores from baseline to 2-year follow-up were significantly correlated with Abnormality Score at baseline. The AUC of the ROC curve for discriminating patients with various disorders from controls was 0.74. Experts' visual interpretation was helped by the deep learning model to identify abnormal patterns in 60% of cases initially not identified without the model. INTERPRETATION: We suggest that deep learning model trained only by normal data was applicable for identifying wide-range of abnormalities in brain diseases, even uncommon ones, proposing its possible use for interpreting real-world clinical data.

Volume entropy for modeling information flow in a brain graph.

Brain regions send and receive information through neuronal connections in an efficient way. In this paper, we modelled the information propagation in brain networks by a generalized Markov system associated with a new edge-transition matrix, based on the assumption that information flows through brain networks forever. From this model, we derived new global and local network measures, called a volume entropy and the capacity of nodes and edges on FDG PET and resting-state functional MRI. Volume entropy of a metric graph, a global measure of information, measures the exponential growth rate of the number of network paths. Capacity of nodes and edges, a local measure of information, represents the stationary distribution of information propagation in brain networks. On the resting-state functional MRI of healthy normal subjects, these measures revealed that volume entropy was significantly negatively correlated to the aging and capacities of specific brain nodes and edges underpinned which brain nodes or edges contributed these aging-related changes.

Altered dynamic electroencephalography connectome phase-space features of emotion regulation in social anxiety.

Emotion regulation deficits are commonly observed in social anxiety disorder (SAD). We used manifold-learning to learn the phase-space connectome manifold of EEG brain dynamics in twenty SAD participants and twenty healthy controls. The purpose of the present study was to utilize manifold-learning to understand EEG brain dynamics associated with emotion regulation processes. Our emotion regulation task (ERT) contains three conditions: Neutral, Maintain and Reappraise. For all conditions and subjects, EEG connectivity data was converted into series of temporally-consecutive connectomes and aggregated to yield this phase-space manifold. As manifold geodesic distances encode intrinsic geometry, we visualized this space using its geodesic-informed minimum spanning tree and compared neurophysiological dynamics across conditions and groups using the corresponding trajectory length. Results showed that SAD participants had significantly longer trajectory lengths during Neutral and Maintain. Further, trajectory lengths during Reappraise were significantly associated with the habitual use of reappraisal strategies, while Maintain trajectory lengths were significantly associated with the negative affective state during Maintain. In sum, an unsupervised connectome manifold-learning approach can reveal emotion regulation associated phase-space features of brain dynamics.

ABNORMAL HOLE DETECTION IN BRAIN CONNECTIVITY BY KERNEL DENSITY OF PERSISTENCE DIAGRAM AND HODGE LAPLACIAN.

Community and rich-club detection are a well-known method to extract functionally specialized subnetwork in brain connectivity analysis. They find densely connected subregions with large modularity or high degree in brain connectivity studies. However, densely connected nodes are not the only representation of network shape. In this study, we propose a new method to extract abnormal holes, which are another representation of network shape. While densely connected component characterizes network's efficiency, abnormal holes characterize inefficiency. The proposed method differs from the existing hole detection in two respects. One is to use Hodge Laplacian to obtain a harmonic hole in the linear combination of edges, rather than a subset of edges. The other is to use the kernel density estimation of persistence diagram of random networks to determine the significance of a hole, rather than using the persistence of a hole. We applied the proposed method to find the abnormality of metabolic connectivity in the FDG PET data of ADNI. We found that, as AD severely progressed, the brain network had more abnormal holes. The localized holes showed how inefficient the structure of brain network became as the disease progressed.

Exact Topological Inference for Paired Brain Networks via Persistent Homology.

We present a novel framework for characterizing paired brain networks using techniques in hyper-networks, sparse learning and persistent homology. The framework is general enough for dealing with any type of paired images such as twins, multimodal and longitudinal images. The exact nonparametric statistical inference procedure is derived on testing monotonic graph theory features that do not rely on time consuming permutation tests. The proposed method computes the exact probability in quadratic time while the permutation tests require exponential time. As illustrations, we apply the method to simulated networks and a twin fMRI study. In case of the latter, we determine the statistical significance of the heritability index of the large-scale reward network where every voxel is a network node.

Gating of memory encoding of time-delayed cross-frequency MEG networks revealed by graph filtration based on persistent homology.

To explain gating of memory encoding, magnetoencephalography (MEG) was analyzed over multi-regional network of negative correlations between alpha band power during cue (cue-alpha) and gamma band power during item presentation (item-gamma) in Remember (R) and No-remember (NR) condition. Persistent homology with graph filtration on alpha-gamma correlation disclosed topological invariants to explain memory gating. Instruction compliance (R-hits minus NR-hits) was significantly related to negative coupling between the left superior occipital (cue-alpha) and the left dorsolateral superior frontal gyri (item-gamma) on permutation test, where the coupling was stronger in R than NR. In good memory performers (R-hits minus false alarm), the coupling was stronger in R than NR between the right posterior cingulate (cue-alpha) and the left fusiform gyri (item-gamma). Gating of memory encoding was dictated by inter-regional negative alpha-gamma coupling. Our graph filtration over MEG network revealed these inter-regional time-delayed cross-frequency connectivity serve gating of memory encoding.

Topological Distances Between Brain Networks.

Many existing brain network distances are based on matrix norms. The element-wise differences may fail to capture underlying topological differences. Further, matrix norms are sensitive to outliers. A few extreme edge weights may severely affect the distance. Thus it is necessary to develop network distances that recognize topology. In this paper, we introduce Gromov-Hausdorff (GH) and Kolmogorov-Smirnov (KS) distances. GH-distance is often used in persistent homology based brain network models. The superior performance of KS-distance is contrasted against matrix norms and GH-distance in random network simulations with the ground truths. The KS-distance is then applied in characterizing the multimodal MRI and DTI study of maltreated children.

Integrated multimodal network approach to PET and MRI based on multidimensional persistent homology.

Finding underlying relationships among multiple imaging modalities in a coherent fashion is one of the challenging problems in multimodal analysis. In this study, we propose a novel approach based on multidimensional persistence. In the extension of the previous threshold-free method of persistent homology, we visualize and discriminate the topological change of integrated brain networks by varying not only threshold but also mixing ratio between two different imaging modalities. The multidimensional persistence is implemented by a new bimodal integration method called 1D projection. When the mixing ratio is predefined, it constructs an integrated edge weight matrix by projecting two different connectivity information onto the one dimensional shared space. We applied the proposed methods to PET and MRI data from 23 attention deficit hyperactivity disorder (ADHD) children, 21 autism spectrum disorder (ASD), and 10 pediatric control subjects. From the results, we found that the brain networks of ASD, ADHD children and controls differ, with ASD and ADHD showing asymmetrical changes of connected structures between metabolic and morphological connectivities. The difference of connected structure between ASD and the controls was mainly observed in the metabolic connectivity. However, ADHD showed the maximum difference when two connectivity information were integrated with the ratio 0.6. These results provide a multidimensional homological understanding of disease-related PET and MRI networks that disclose the network association with ASD and ADHD. Hum Brain Mapp 38:1387-1402, 2017. © 2016 Wiley Periodicals, Inc.

Disrupted brain metabolic connectivity in a 6-OHDA-induced mouse model of Parkinson's disease examined using persistent homology-based analysis.

Movement impairments in Parkinson's disease (PD) are caused by the degeneration of dopaminergic neurons and the consequent disruption of connectivity in the cortico-striatal-thalamic loop. This study evaluated brain metabolic connectivity in a 6-Hydroxydopamine (6-OHDA)-induced mouse model of PD using (18)F-fluorodeoxy glucose positron emission tomography (FDG PET). Fourteen PD-model mice and ten control mice were used for the analysis. Voxel-wise t-tests on FDG PET results yielded no significant regional metabolic differences between the PD and control groups. However, the PD group showed lower correlations between the right caudoputamen and the left caudoputamen and right visual cortex. Further network analyses based on the threshold-free persistent homology framework revealed that brain networks were globally disrupted in the PD group, especially between the right auditory cortex and bilateral cortical structures and the left caudoputamen. In conclusion, regional glucose metabolism of PD was preserved, but the metabolic connectivity of the cortico-striatal-thalamic loop was globally impaired in PD.

Optimal likelihood-ratio multiple testing with application to Alzheimer's disease and questionable dementia.

BACKGROUND: Controlling the false discovery rate is important when testing multiple hypotheses. To enhance the detection capability of a false discovery rate control test, we applied the likelihood ratio-based multiple testing method in neuroimage data and compared the performance with the existing methods. METHODS: We analysed the performance of the likelihood ratio-based false discovery rate method using simulation data generated under independent assumption, and positron emission tomography data of Alzheimer's disease and questionable dementia. We investigated how well the method detects extensive hypometabolic regions and compared the results to those of the conventional Benjamini Hochberg-false discovery rate method. RESULTS: Our findings show that the likelihood ratio-based false discovery rate method can control the false discovery rate, giving the smallest false non-discovery rate (for a one-sided test) or the smallest expected number of false assignments (for a two-sided test). Even though we assumed independence among voxels, the likelihood ratio-based false discovery rate method detected more extensive hypometabolic regions in 22 patients with Alzheimer's disease, as compared to the 44 normal controls, than did the Benjamini Hochberg-false discovery rate method. The contingency and distribution patterns were consistent with those of previous studies. In 24 questionable dementia patients, the proposed likelihood ratio-based false discovery rate method was able to detect hypometabolism in the medial temporal region. CONCLUSIONS: This study showed that the proposed likelihood ratio-based false discovery rate method efficiently identifies extensive hypometabolic regions owing to its increased detection capability and ability to control the false discovery rate.

Brain networks engaged in audiovisual integration during speech perception revealed by persistent homology-based network filtration.

The human brain naturally integrates audiovisual information to improve speech perception. However, in noisy environments, understanding speech is difficult and may require much effort. Although the brain network is supposed to be engaged in speech perception, it is unclear how speech-related brain regions are connected during natural bimodal audiovisual or unimodal speech perception with counterpart irrelevant noise. To investigate the topological changes of speech-related brain networks at all possible thresholds, we used a persistent homological framework through hierarchical clustering, such as single linkage distance, to analyze the connected component of the functional network during speech perception using functional magnetic resonance imaging. For speech perception, bimodal (audio-visual speech cue) or unimodal speech cues with counterpart irrelevant noise (auditory white-noise or visual gum-chewing) were delivered to 15 subjects. In terms of positive relationship, similar connected components were observed in bimodal and unimodal speech conditions during filtration. However, during speech perception by congruent audiovisual stimuli, the tighter couplings of left anterior temporal gyrus-anterior insula component and right premotor-visual components were observed than auditory or visual speech cue conditions, respectively. Interestingly, visual speech is perceived under white noise by tight negative coupling in the left inferior frontal region-right anterior cingulate, left anterior insula, and bilateral visual regions, including right middle temporal gyrus, right fusiform components. In conclusion, the speech brain network is tightly positively or negatively connected, and can reflect efficient or effortful processes during natural audiovisual integration or lip-reading, respectively, in speech perception.

Hole detection in metabolic connectivity of Alzheimer's disease using kappa-Laplacian.

Recent studies have found that the modular structure of functional brain network is disrupted during the progress of Alzheimer's is the most basic topological disease. The modular structure of network invariant in determining the shape of network in the view of algebraic topology. In this study, we propose a new method to find another higher order topological invariant, hole, based on persistent homology. If a hole exists in the network, the information can be inefficiently delivered between regions. If we can localize the hole in the network, we can infer the reason of network inefficiency. We propose to detect the persistent hole using the spectrum of kappa-Laplacian, which is the generalized version of graph Laplacian. The method is applied to the metabolic network based on FDG-PET data of Alzheimer disease (AD), mild cognitive impairment (MCI) and normal control (NC) groups. The experiments show that the persistence of hole can be used as a biological marker of disease progression to AD. The localized hole may help understand the brain network abnormality in AD, revealing that the limbic-temporo-parietal association regions disturb direct connections between other regions.

Morphological brain network assessed using graph theory and network filtration in deaf adults.

Prolonged deprivation of auditory input can change brain networks in pre- and postlingual deaf adults by brain-wide reorganization. To investigate morphological changes in these brains voxel-based morphometry, voxel-wise correlation with the primary auditory cortex, and whole brain network analyses using morphological covariance were performed in eight prelingual deaf, eleven postlingual deaf, and eleven hearing adults. Network characteristics based on graph theory and network filtration based on persistent homology were examined. Gray matter density in the primary auditor cortex was preserved in prelingual deafness, while it tended to decrease in postlingual deafness. Unlike postlingual, prelingual deafness showed increased bilateral temporal connectivity of the primary auditory cortex compared to the hearing adults. Of the graph theory-based characteristics, clustering coefficient, betweenness centrality, and nodal efficiency all increased in prelingual deafness, while all the parameters of postlingual deafness were similar to the hearing adults. Patterns of connected components changing during network filtration were different between prelingual deafness and hearing adults according to the barcode, dendrogram, and single linkage matrix representations, while these were the same in postlingual deafness. Nodes in fronto-limbic and left temporal components were closely coupled, and nodes in the temporo-parietal component were loosely coupled, in prelingual deafness. Patterns of connected components changing in postlingual deafness were the same as hearing adults. We propose that the preserved density of auditory cortex associated with increased connectivity in prelingual deafness, and closer coupling between certain brain areas, represent distinctive reorganization of auditory and related cortices compared with hearing or postlingual deaf adults. The differential network reorganization in the prelingual deaf adults could be related to the absence of auditory speech experience.