A Hybrid Routing Pattern in Human Brain Structural Network Revealed By Evolutionary Computation.

The human brain functional connectivity network (FCN) is constrained and shaped by the communication processes in the structural connectivity network (SCN). The underlying communication mechanism thus becomes a critical issue for understanding the formation and organization of the FCN. A number of communication models supported by different routing strategies have been proposed, with shortest path (SP), random diffusion (DIF), and spatial navigation (NAV) as the most typical, respectively requiring network global knowledge, local knowledge, and both for path seeking. Yet these models all assumed every brain region to use one routing strategy uniformly, ignoring convergent evidence that supports the regional heterogeneity in both terms of biological substrates and functional roles. In this regard, the current study developed a hybrid communication model that allowed each brain region to choose a routing strategy from SP, DIF, and NAV independently. A genetic algorithm was designed to uncover the underlying region-wise hybrid routing strategy (namely HYB). The HYB was found to outperform the three typical routing strategies in predicting FCN and facilitating robust communication. Analyses on HYB further revealed that brain regions in lower-order functional modules inclined to route signals using global knowledge, while those in higher-order functional modules preferred DIF that requires only local knowledge. Compared to regions that used global knowledge for routing, regions using DIF had denser structural connections, participated in more functional modules, but played a less dominant role within modules. Together, our findings further evidenced that hybrid routing underpins efficient SCN communication and locally heterogeneous structure-function coupling.

Trade-offs among cost, integration, and segregation in the human connectome.

The human brain structural network is thought to be shaped by the optimal trade-off between cost and efficiency. However, most studies on this problem have focused on only the trade-off between cost and global efficiency (i.e., integration) and have overlooked the efficiency of segregated processing (i.e., segregation), which is essential for specialized information processing. Direct evidence on how trade-offs among cost, integration, and segregation shape the human brain network remains lacking. Here, adopting local efficiency and modularity as segregation factors, we used a multiobjective evolutionary algorithm to investigate this problem. We defined three trade-off models, which represented trade-offs between cost and integration (Dual-factor model), and trade-offs among cost, integration, and segregation (local efficiency or modularity; Tri-factor model), respectively. Among these, synthetic networks with optimal trade-off among cost, integration, and modularity (Tri-factor model [Q]) showed the best performance. They had a high recovery rate of structural connections and optimal performance in most network features, especially in segregated processing capacity and network robustness. Morphospace of this trade-off model could further capture the variation of individual behavioral/demographic characteristics in a domain-specific manner. Overall, our results highlight the importance of modularity in the formation of the human brain structural network and provide new insights into the original cost-efficiency trade-off hypothesis.

Common and distinct neural substrates of the compassionate and uncompassionate self-responding dimensions of self-compassion.

Self-compassion is beneficial for individuals' emotional health, but debates regarding its conceptualization are increasing. The present study aimed to explore the neural basis of self-compassion and its compassionate and uncompassionate dimensions and the indirect path from neural basis to emotional health. Structural MRI and Resting-state fMRI data were used to measure the gray matter volume (GMV) and the amplitude of low-frequency fluctuation (ALFF) in 88 healthy college students. We found that individuals with higher self-compassion had decreased GMV in the prefrontal cortex, cerebellum as well as lower ALFF in the occipital lobe. The compassionate and uncompassionate dimensions of self-compassion shared some similarities (e.g., common correlation with GMV in the medial prefrontal cortex, ALFF in the occipital lobe) but also had some differences (e.g., only uncompassionate dimensions correlated with GMV in the lateral prefrontal cortex, ALFF in medial temporal lobe/striatum). The indirect path analyses revealed that corresponding brain characteristics could have associations with emotional health through self-compassion, as well as its uncompassionate dimension, but not compassionate dimension. This exploratory whole-brain study showed some preliminary findings that compassionate and uncompassionate dimensions of self-compassion were related to distinct brain regions, which are both important to the current conceptualization of self-compassion and intervention study.

The overlapping modular organization of human brain functional networks across the adult lifespan.

Previous studies have demonstrated that the brain functional modular organization, which is a fundamental feature of the human brain, would change along the adult lifespan. However, these studies assumed that each brain region belonged to a single functional module, although there has been convergent evidence supporting the existence of overlap among functional modules in the human brain. To reveal how age affects the overlapping functional modular organization, this study applied an overlapping module detection algorithm that requires no prior knowledge to the resting-state fMRI data of a healthy cohort (N = 570) aged from 18 to 88 years old. A series of measures were derived to delineate the characteristics of the overlapping modular structure and the set of overlapping nodes (brain regions participating in two or more modules) identified from each participant. Age-related regression analyses on these measures found linearly decreasing trends in the overlapping modularity and the modular similarity. The number of overlapping nodes was found increasing with age, but the increment was not even over the brain. In addition, across the adult lifespan and within each age group, the nodal overlapping probability consistently had positive correlations with both functional gradient and flexibility. Further, by correlation and mediation analyses, we showed that the influence of age on memory-related cognitive performance might be explained by the change in the overlapping functional modular organization. Together, our results revealed age-related decreased segregation from the brain functional overlapping modular organization perspective, which could provide new insight into the adult lifespan changes in brain function and the influence of such changes on cognitive performance.

Functional connectivity dynamics as a function of the fluctuation of tension during film watching.

To advance the understanding of the dynamic relationship between brain activities and emotional experiences, we examined the neural patterns of tension, a unique emotion that highly depends on how an event unfolds. Specifically, the present study explored the temporal relationship between functional connectivity patterns within and between different brain functional modules and the fluctuation in tension during film watching. Due to the highly contextualized and time-varying nature of tension, we expected that multiple neural networks would be involved in the dynamic tension experience. Using the neuroimaging data of 546 participants, we conducted a dynamic brain analysis to identify the intra- and inter-module functional connectivity patterns that are significantly correlated with the fluctuation of tension over time. The results showed that the inter-module connectivity of cingulo-opercular network, fronto-parietal network, and default mode network is involved in the dynamic experience of tension. These findings demonstrate a close relationship between brain functional connectivity patterns and emotional dynamics, which supports the importance of functional connectivity dynamics in understanding our cognitive and emotional processes.

Aberrant large-scale brain modules in deficit and non-deficit schizophrenia.

OBJECTIVE: Schizophrenia is a heterogenous psychiatric disease, and deficit schizophrenia (DS) is a clinical subgroup with primary and enduring negative symptoms. Although previous neuroimaging studies have identified functional connectome alterations in schizophrenia, the modular organizations in DS and nondeficit schizophrenia (NDS) remain poorly understood. Therefore, this study aimed to investigate the modular-level alterations in DS patients compared with the NDS and healthy control (HC) groups. METHODS: A previously collected dataset was re-analyzed, in which 74 chronic male schizophrenia patients (33 DS and 41 NDS) and 40 HC underwent resting-state functional magnetic resonance imaging with eyes closed in a Siemens 3 T scanner (scanning duration = 8 min). Modular- (intramodule and intermodule connectivity) and nodal- [normalized within-module degree (Zi) and participation coefficient (PCi)] level graph theory properties were computed and compared among the three groups. Receiver operating characteristic curve (ROC) analyses were performed to examine the classification ability of these measures, and partial correlations were conducted between network measures and symptom severity. Validation analyses on head motion, network sparsity, and parcellation scheme were also performed. RESULTS: Both schizophrenia subgroups showed decreased intramodule connectivity in salience network (SN), somatosensory-motor network (SMN), and visual network (VN), and increased intermodule connectivity in SMN-default mode network (DMN) and SMN-frontoparietal network (FPN). Compared with NDS patients, DS patients showed weaker intramodule connectivity in SN and stronger intermodule connectivity in SMN-FPN and SMN-VN. At the nodal level, the schizophrenia-related alterations were distributed in SN, SMN, VN, and DMN, and 7 DS-specific nodal alterations were identified. Intramodule connectivity of SN, intermodule connectivity of SMN-VN, and Zi of left precuneus successfully distinguished the three groups. Partial correlational analyses revealed that these measures were related to negative symptoms, general psychiatric symptoms, and neurocognitive function. CONCLUSION: Our findings suggest that functional connectomes, especially SN, SMN, and VN, may capture the distinct and common disruptions of DS and NDS. These findings may help to understand the neuropathology of negative symptoms of schizophrenia and inform targets for treating different schizophrenia subtypes.

Gene expression associated with individual variability in intrinsic functional connectivity.

It has been revealed that intersubject variability (ISV) in intrinsic functional connectivity (FC) is associated with a wide variety of cognitive and behavioral performances. However, the underlying organizational principle of ISV in FC and its related gene transcriptional profiles remain unclear. Using resting-state fMRI data from the Human Connectome Project (299 adult participants) and microarray gene expression data from the Allen Human Brain Atlas, we conducted a transcription-neuroimaging association study to investigate the spatial configurations of ISV in intrinsic FC and their associations with spatial gene transcriptional profiles. We found that the multimodal association cortices showed the greatest ISV in FC, while the unimodal cortices and subcortical areas showed the least ISV. Importantly, partial least squares regression analysis revealed that the transcriptional profiles of genes associated with human accelerated regions (HARs) could explain 31.29% of the variation in the spatial distribution of ISV in FC. The top-related genes in the transcriptional profiles were enriched for the development of the central nervous system, neurogenesis and the cellular components of synapse. Moreover, we observed that the effect of gene expression profile on the heterogeneous distribution of ISV in FC was significantly mediated by the cerebral blood flow configuration. These findings highlighted the spatial arrangement of ISV in FC and their coupling with variations in transcriptional profiles and cerebral blood flow supply.

Cost-efficiency trade-offs of the human brain network revealed by a multiobjective evolutionary algorithm.

It is widely believed that the formation of brain network architecture is under the pressure of optimal trade-off between reducing wiring cost and promoting communication efficiency. However, the questions of whether this trade-off exists in empirical human brain structural networks and, if so, how it takes effect are still not well understood. Here, we employed a multiobjective evolutionary algorithm to directly and quantitatively explore the cost-efficiency trade-off in human brain structural networks. Using this algorithm, we generated a population of synthetic networks with optimal but diverse cost-efficiency trade-offs. It was found that these synthetic networks could not only reproduce a large portion of connections in the empirical brain structural networks but also embed a resembling small-world organization. Moreover, the synthetic and empirical brain networks were found similar in terms of the spatial arrangement of hub regions and the modular structure, which are two important topological features widely assumed to be outcomes of cost-efficiency trade-offs. The synthetic networks had high robustness against random attacks as the empirical brain networks did. Additionally, we also revealed some differences between the synthetic networks and the empirical brain networks, including lower segregated processing capacity and weaker robustness against targeted attacks in the synthetic networks. These findings provide direct and quantitative evidence that the structure of human brain networks is indeed largely influenced by optimal cost-efficiency trade-offs. We also suggest that some additional factors (e.g., segregated processing capacity) might jointly determine the network organization with cost and efficiency.

Disruption of functional and structural networks in first-episode, drug-naïve adolescents with generalized anxiety disorder.

BACKGROUND: Individuals with generalized anxiety disorder (GAD) tend to worry exaggeratedly and uncontrollably about various daily routines. Previous studies have demonstrated that the GAD patients exhibited widespread alternations in both functional networks (FN) and structural networks (SN). However, the simultaneous alternations of the topological organization of FN, SN, as well as their couplings in GAD still remain unknown. METHODS: Using multimodal approach, we constructed FN from resting-state functional magnetic imaging (R-fMRI) data and SN from diffusion magnetic resonance imaging (dMRI) data of 32 adolescent GAD patients and 25 healthy controls (HC). Graph theory analysis was employed to investigate the topological properties of FN, SN, and FN-SN coupling. RESULTS: Compared to HC, the GAD patients showed disruptions in global (i.e., decreased clustering coefficient, global, and local efficiency) and subnetwork (i.e., reduced intermodular connections, rich club, and feeder connections) levels in FN. Abnormal global level properties (i.e., increased characteristic path length and reduced global efficiency) were also observed in SN. Altered FN-SN couplings in normalized characteristic path length and feeder connections were identified in the GAD patients. The identified network measures were correlated with anxiety severity in the GAD patients. LIMITATIONS: The sample size of the current study is small and the cross-sectional nature can not infer causal relationship. CONCLUSIONS: Our findings identified GAD-related topological alternations in both FN and SN, together with the couplings between FN and SN, providing us with a novel perspective for understanding the pathophysiological mechanisms of GAD.

Integrated and segregated frequency architecture of the human brain network.

The frequency of brain activity modulates the relationship between the brain and human behavior. Insufficient understanding of frequency-specific features may thus lead to inconsistent explanations of human behavior. However, to date, the frequency-specific features of the human brain functional network at the whole-brain level remain poorly understood. Here, we used resting-state fMRI data and graph-theory analyses to investigate the frequency-specific characteristics of fMRI signals in 12 frequency bands (frequency range 0.01-0.7 Hz) in 75 healthy participants. We found that brain regions with higher level and more complex functions had a more variable functional connectivity pattern but engaged less in higher frequency ranges. Moreover, brain regions that engaged in fewer frequency bands played more integrated roles (i.e., higher network participation coefficient and lower within-module degree) in the functional network, whereas regions that engaged in broader frequency ranges exhibited more segregated functions (i.e., lower network participation coefficient and higher within-module degree). Finally, behavioral analyses revealed that regional frequency variability was associated with a spectrum of behavioral functions from sensorimotor functions to complex cognitive and social functions. Taken together, our results showed that segregated functions are executed in wide frequency ranges, whereas integrated functions are executed mainly in lower frequency ranges. These frequency-specific features of brain networks provided crucial insights into the frequency mechanism of fMRI signals, suggesting that signals in higher frequency ranges should be considered for their relation to cognitive functions.

Predicting visual working memory with multimodal magnetic resonance imaging.

The indispensability of visual working memory (VWM) in human daily life suggests its importance in higher cognitive functions and neurological diseases. However, despite the extensive research efforts, most findings on the neural basis of VWM are limited to a unimodal context (either structure or function) and have low generalization. To address the above issues, this study proposed the usage of multimodal neuroimaging in combination with machine learning to reveal the neural mechanism of VWM across a large cohort (N = 547). Specifically, multimodal magnetic resonance imaging features extracted from voxel-wise amplitude of low-frequency fluctuations, gray matter volume, and fractional anisotropy were used to build an individual VWM capacity prediction model through a machine learning pipeline, including the steps of feature selection, relevance vector regression, cross-validation, and model fusion. The resulting model exhibited promising predictive performance on VWM (r = .402, p < .001), and identified features within the subcortical-cerebellum network, default mode network, motor network, corpus callosum, anterior corona radiata, and external capsule as significant predictors. The main results were then compared with those obtained on emotional regulation and fluid intelligence using the same pipeline, confirming the specificity of our findings. Moreover, the main results maintained well under different cross-validation regimes and preprocess strategies. These findings, while providing richer evidence for the importance of multimodality in understanding cognitive functions, offer a solid and general foundation for comprehensively understanding the VWM process from the top down.

Abnormal dynamic functional connectivity in Alzheimer's disease.

AIMS: Alzheimer's disease (AD) is a progressive neurodegenerative disorder. Previous studies have demonstrated abnormalities in functional connectivity (FC) of AD under the assumption that FC is stationary during scanning. However, studies on the FC dynamics of AD, which may provide more insightful perspectives in understanding the neural mechanisms of AD, remain largely unknown. METHODS: Combining the sliding-window approach and the k-means algorithm, we identified three reoccurring dynamic FC states from resting-state fMRI data of 26 AD and 26 healthy controls. The between-group differences both in FC states and in regional temporal variability were calculated, followed by a correlation analysis of these differences with cognitive performances of AD patients. RESULTS: We identified three reoccurring FC states and found abnormal FC mainly in the frontal and temporal cortices. The temporal properties of FC states were changed in AD as characterized by decreased dwell time in State I and increased dwell time in State II. Besides, we found decreased regional temporal variability mainly in the somatomotor, temporal and parietal regions. Disrupted dynamic FC was significantly correlated with cognitive performances of AD patients. CONCLUSION: Our findings suggest abnormal dynamic FC in AD patients, which provides novel insights for understanding the pathophysiological mechanisms of AD.

TGR5 promotes cholangiocarcinoma by interacting with mortalin.

Takeda-G-protein-receptor-5 (TGR5) is a G-protein-coupled receptor (GPCR) activated by bile acids, and mortalin is a multipotent chaperone of the HSP70 family. In the present study, TGR5 was detected by immunohistochemistry (IHC) in extrahepatic cholangiocarcinoma (ECC) specimens, and TGR5 expression in ECC tissues and adjacent tissues was compared. In vitro TGR5 was overexpressed and knocked down in human intrahepatic cholangiocarcinoma (ICC) cell line RBE and human extrahepatic cholangiocarcinoma (ECC) cell line QBC-939 to observe its effects on the biological behavior of cholangiocarcinoma (CC) cells, including proliferation, apoptosis and migration. In vivo xenograft model was constructed to explore the role of TGR5 in CC growth. Proteins that interacted with TGR5 were screened using an immunoprecipitation spectrometry approach, and the identified protein was down-regulated to investigate its contribution to CC growth. The present study demonstrated that TGR5 is highly expressed in CC tissues, and strong TGR5 expression may indicate high malignancy in CC. Furthermore, TGR5 promotes CC cell proliferation, migration, and apoptosis resistance. TGR5 boosts CC growth in vivo. In addition, TGR5 combines with mortalin and regulates mortalin expression in the CC cell line. Mortalin participates in the TGR5-induced increase in CC cell proliferation. In conclusion, TGR5 is of clinical significance based on its implications for the degree of malignancy in patients with CC. Mortalin may be a downstream component regulated by TGR5, and TGR5 promotes cholangiocarcinoma at least partially by interacting with mortalin and upregulating its expression. Both TGR5 and mortalin are positive regulators, and may serve as potential therapeutic targets for CC.

Locostatin Alleviates Liver Fibrosis Induced by Carbon Tetrachloride in Mice.

BACKGROUND AND AIMS: Liver fibrosis is featured with excessive deposition of extracellular matrix and fibrous connective tissue hyperplasia. The specific inhibitor of Raf-1 kinase inhibitor protein, locostatin, inhibits the migration of hepatic stellate cells. In this study, we investigated the effect of locostatin on liver fibrosis and its underlying mechanism. METHODS: Carbon tetrachloride (CCl4) was used to induce liver fibrosis in mice, and locostatin was injected intraperitoneally. Liver fibrosis was assessed by Masson and Sirius red staining, hydroxyproline (HYP) assay, and collagen percentage area. Collagen I, collagen III, and α-SMA were detected by RT-PCR and western blot. The levels of MMP-13, MMP-2, TIMP-1, and TIMP-2 were estimated by ELISA. Liver inflammation was evaluated by HE staining and immunohistochemistry; liver myeloperoxidase (MPO), superoxide dismutase, and malondialdehyde were measured by ELISA; and cytokines were by Mouse Cytokine Array Q4000. RESULTS: Compared to the CCl4 group, HYP (208.56 ± 6.12) µg/g, percentage of total collagen at overall region (1.91 ± 0.13), MMP-13/TIMP-1 (0.19 ± 0.01), MPO (1.45 ± 0.04) U/g, TGF-ß (2652 ± 91.20), PDGF-AA (3897 ± 290.69), and E-selectin (1569 ± 66.48) in the liver tissues were decreased significantly in the locostatin-treated group. CONCLUSIONS: Locostatin mitigated liver fibrosis and inflammation induced by CCl4. The mechanism is via inhibition inflammatory cytokines, TGF-ß, PDGF-AA, and E-selectin.

Brain activity mediates the relation between emotional but not instrumental support and trait loneliness.

Loneliness results from lacking satisfied social connections. However, little is known how trait loneliness, which is a stable personal characteristic, is influenced by different types of social support (i.e. emotional and instrumental support) through the brain activity associated with loneliness. To explore these questions, data of resting-state functional magnetic resonance imaging (R-fMRI) of 92 healthy participants were analyzed. We identified loneliness-related brain regions by correlating participants' loneliness scores with amplitudes of low-frequency fluctuation (ALFF) of R-fMRI data. We then conducted mediation analyses to test whether the negative relation between each type of social support and loneliness was explained via the neural activity in the loneliness-related brain regions. The results showed that loneliness was positively related to the mean ALFF value within right inferior temporal gyrus (ITG). In addition, the negative relation between emotional support and loneliness was explained by a decrease in the spontaneous neural activity within right ITG but this pattern was not observed for instrumental support. These results suggest the importance of social information processing on trait loneliness and highlight the need to differentiate the functions of different types of social support on mental health from a neural perspective.

Intrinsic overlapping modular organization of human brain functional networks revealed by a multiobjective evolutionary algorithm.

A wealth of research on resting-state functional MRI (R-fMRI) data has revealed modularity as a fundamental characteristic of the human brain functional network. The modular structure has recently been suggested to be overlapping, meaning that a brain region may engage in multiple modules. However, not only the overlapping modular structure remains inconclusive, the topological features and functional roles of overlapping regions are also poorly understood. To address these issues, the present work utilized the maximal-clique based multiobjective evolutionary algorithm to explore the overlapping modular structure of the R-fMRI data obtained from 57 young healthy adults. Without prior knowledge, brain regions were optimally grouped into eight modules with wide overlap. Based on the topological features captured by graph theory analyses, overlapping regions were classified into an integrated club and a dominant minority club through clustering. Functional flexibility analysis found that overlapping regions in both clubs were significantly more flexible than non-overlapping ones. Lesion simulations revealed that targeted attack at overlapping regions were more damaging than random failure or even targeted attack at hub regions. In particular, overlapping regions in the dominant minority club were more flexible and more crucial for information communication than the others were. Together, our findings demonstrated the highly organized overlapping modular architecture and revealed the importance as well as complexity of overlapping regions from both topological and functional aspects, which provides important implications for their roles in executing multiple tasks and maintaining information communication.

Functional connectivity pattern underlies individual differences in independent self-construal.

Independent vs interdependent self-construal is a concept that reflects how people perceive the relationship between self and other people, which has been extensively examined across disciplines. However, little evidence on the whole-brain functional connectivity (FC) pattern of independent vs interdependent self-construal has been reported. Here, in a sample of 51 healthy participants, we used resting-state functional magnetic resonance imaging and voxel-based FC analysis (i.e. FC strength and seed-based FC) by measuring the temporal correlation of blood oxygen level-dependent signals between spatially separate brain regions to investigate the neural mechanism of independent vs interdependent self-construal. First, we found that FC strength of bilateral posterior cingulate cortex and precuneus, and left inferior frontal gyrus were positively correlated with the independent vs interdependent score. Seed-based FC analysis with these three regions as seeds revealed that, FC within default mode network and executive control network was positively correlated with the independent vs interdependent score. Negative correlation with independent vs interdependent score was shown in the connections between default mode network and executive control regions. Taking together, our results provide a comprehensive FC architecture of the independent vs interdependent self-construal and advance the understanding of the interplay between culture, mind and brain.

Comprehensive investigating of cytokine and receptor related genes variants in patients with chronic hepatitis B virus infection.

BACKGROUND & AIMS: Chronic hepatitis B virus (HBV) infection is a global health problem and the outcome are associated with both viral factors and host genetic factors. High Throughput Sequencing (HTS) technology were used to identify variants associated with liver disease. METHODS: Fifty-five Chronic hepatitis B (CHB) patients, fifty-three self-healing HBV (SH) patients and 53 healthy controls (HC) were recruited, 404 cytokine and cytokine receptor related genes were captured and sequenced at high depth (>900X), both variant (Fischer's exact test, P value < 0.05) and gene (SKAT-O gene level test, adjust P value < 0.05) level association were used to identify variants and genes associated with CHB. RESULTS: Total 5083 variants have been detected, fifty-four variants were found associated with CHB, most (29/32) variants were located in HLA region, including HLA-B, HLA-C, HLA-DQA1, HLA-DQB1, HLA-DQB2, HLA-DRB1 and HLA-DRB5. Several missense variants were found associated with CHB, including p.E226K in PVR (poliovirus receptor), p.E400A and p.C431R in IL4R (interleukin 4 receptor). Four variants located in 3'UTR (untranslated region) have also been found associated with CHB. CONCLUSION: Our study revealed that high through target region sequencing, combined with association analysis at variant and gene level, would be a good way to found variants and genes associated with CHB even at small sample size. Our data implied that chronic hepatitis B patients who carry these variants need intensive monitoring.

Decreased Intrinsic Functional Connectivity in First-Episode, Drug-Naive Adolescents With Generalized Anxiety Disorder.

Generalized anxiety disorder (GAD) is characterized by excessive and uncontrollable worry about everyday life. Prior neuroimaging studies have demonstrated that GAD is associated with disruptions in specific brain regions; however, little is known about the global functional connectivity maps in adolescents with GAD. Here, first-episode, medication-naive, adolescent GAD patients (N = 36) and healthy controls (N = 28) (HCs) underwent resting-state functional MRI (R-fMRI) and completed a package of questionnaires to assess clinical symptoms. Functional connectivity strength and seed-based functional connectivity were employed to investigate the functional connectivity architecture. GAD patients showed reduced functional connectivity strength in right supramarginal gyrus (SMG) and right superior parietal gyrus (SPG) compared with HCs. Further seed-based functional connectivity analysis revealed that GAD patients displayed decreased functional connectivity between right SMG and left fusiform gyrus, inferior temporal gyrus, parahippocampal gyrus, bilateral precuneus and cuneus, and between right SPG and bilateral supplementary motor area and middle cingulate gyrus, as well as between the SMG-based network and the SPG-based network. Moreover, the disrupted intra-network connectivity (i.e., the SMG-based network and the SPG-based network) and inter-network connectivity between the SMG-based network and the SPG-based network accounted for 25.5% variance of the State and Trait Anxiety Inventory (STAI) and 39.5% variance of the trait subscale of STAI. Our findings highlight the abnormal functional architecture in the SMG-based network and the SPG-based network in GAD, providing novel insights into the pathological mechanisms of this disorder.

Comprehensive investigation of cytokine- and immune-related gene variants in HBV-associated hepatocellular carcinoma patients.

Host genotype may be closely related to the different outcomes of Hepatitis B virus (HBV) infection. To identify the association of variants and HBV infection, we comprehensively investigated the cytokine- and immune-related gene mutations in patients with HBV associated hepatocellular carcinoma (HBV-HCC). Fifty-three HBV-HCC patients, 53 self-healing cases (SH) with HBV infection history and 53 healthy controls (HCs) were recruited, the whole exon region of 404 genes were sequenced at >900× depth. Comprehensive variants and gene levels were compared between HCC and HC, and HCC and SH. Thirty-nine variants (adjusted P<0.0001, Fisher's exact test) and 11 genes (adjusted P<0.0001, optimal unified approach for rare variant association test (SKAT-O) gene level test) were strongly associated with HBV-HCC. Thirty-four variants were from eight human leukocyte antigen (HLA) genes that were previously reported to be associated with HBV-HCC. The novelties of our study are: five variants (rs579876, rs579877, rs368692979, NM_145007:c.*131_*130delTG, NM_139165:exon5:c.623-2->TT) from three genes (REAT1E, NOD-like receptor (NLR) protein 11 (NLRP11), hydroxy-carboxylic acid receptor 2 (HCAR2)) were found strongly associated with HBV-HCC. We found 39 different variants in 11 genes that were significantly related to HBV-HCC. Five of them were new findings. Our data implied that chronic hepatitis B patients who carry these variants are at a high risk of developing HCC.