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Abstract Creativity is typically operationalized as divergent thinking (DT) ability, a form of higher-order cognition which relies on memory, attention, and other component processes. Despite recent advances, creativity neuroscience lacks a unified framework to model its complexity across neural, genetic, and cognitive scales. Using task-based fMRI from two independent samples and MVPA, we identified a neural pattern that predicts DT, validated through cognitive decoding, genetic data, and large-scale resting-state fMRI. Our findings reveal that DT neural patterns span brain regions associated with diverse cognitive functions, with positive weights in the default mode and frontoparietal control networks and negative weights in the visual network. The high correlation with the primary gradient of functional connectivity suggests that DT involves extensive integration from concrete sensory information to abstract, higher-level cognition, distinguishing it from other advanced cognitive functions. Moreover, neurobiological analyses show that the DT pattern is positively correlated with dopamine-related neurotransmitters and genes influencing neurotransmitter release, advancing the neurobiological understanding of creativity.

Abstract Background The purpose of the study was to evaluate renal quality and predict posttransplant graft function using ex vivo sound touch elastography (STE). Methods In this prospective study, 106 donor kidneys underwent ex vivo STE examination and biopsy from March 2022 to August 2023. The mean stiffness of the superficial cortex (STEsc), deep cortex (STEdc), and medulla (STEme) was obtained and synthesized into one index (STE) through the factor analysis method. Additionally, 100 recipients were followed up for 6 months. A random forest algorithm was employed to explore significant predictive factors associated with the Remuzzi score and allograft function. The performance of parameters was evaluated by using the area under the receiver operating characteristic curve (AUC). Results STE had AUC values of 0.803 for diagnosing low Remuzzi and 0.943 for diagnosing high Remuzzi. Meanwhile, STE had an AUC of 0.723 for diagnosing moderate to severe ATI. Random forest algorithm identified STE and Remuzzi score as significant predictors for 6-month renal function. The AUC for STE in predicting postoperative allograft function was 0.717, which was comparable with that of the Remuzzi score (AUC = 0.756). Nevertheless, the specificity of STE was significantly higher than that of Remuzzi (0.913 vs 0.652, p

Abstract Previous studies in small samples have identified inconsistent cortical abnormalities in major depressive disorder (MDD). Despite genetic influences on MDD and the brain, it is unclear how genetic risk for MDD is translated into spatially patterned cortical vulnerability. Here, we initially examined voxel-wise differences in cortical function and structure using the largest multi-modal MRI data from 1660 MDD patients and 1341 controls. Combined with the Allen Human Brain Atlas, we then adopted transcription-neuroimaging spatial correlation and the newly developed ensemble-based gene category enrichment analysis to identify gene categories with expression related to cortical changes in MDD. Results showed that patients had relatively circumscribed impairments in local functional properties and broadly distributed disruptions in global functional connectivity, consistently characterized by hyper-function in associative areas and hypo-function in primary regions. Moreover, the local functional alterations were correlated with genes enriched for biological functions related to MDD in general (e.g., endoplasmic reticulum stress, mitogen-activated protein kinase, histone acetylation, and DNA methylation); and the global functional connectivity changes were associated with not only MDD-general, but also brain-relevant genes (e.g., neuron, synapse, axon, glial cell, and neurotransmitters). Our findings may provide important insights into the transcriptomic signatures of regional cortical vulnerability to MDD.

Abstract Novelty and appropriateness are two fundamental components of creativity. However, the way in which novelty and appropriateness are separated at behavioral and neural levels remains poorly understood. In the present study, we aim to distinguish behavioral and neural bases of novelty and appropriateness of creative idea generation. In alignment with two established theories of creative thinking, which respectively, emphasize semantic association and executive control, behavioral results indicate that novelty relies more on associative abilities, while appropriateness relies more on executive functions. Next, employing a connectome predictive modeling (CPM) approach in resting-state fMRI data, we define two functional network-based models—dominated by interactions within the default network and by interactions within the limbic network—that respectively, predict novelty and appropriateness (i.e., cross-brain prediction). Furthermore, the generalizability and specificity of the two functional connectivity patterns are verified in additional resting-state fMRI and task fMRI. Finally, the two functional connectivity patterns, respectively mediate the relationship between semantic association/executive control and novelty/appropriateness. These findings provide global and predictive distinctions between novelty and appropriateness in creative idea generation.

Tooth absorption can be divided into physiological absorption and pathological absorption. Root absorption of mature deciduous teeth is physiological absorption. Pathological absorption includes internal absorption and external absorption. Internal absorption, also known as intramedullary absorption, includes inflammatory absorption and alternative absorption. External tooth absorption originates from the outer surface of the root or the neck of the tooth and can be divided into inflammatory absorption, alternative absorption, pressure resorption and invasive cervical resorption. Invasive cervical resorption (ICR) is pathological damage caused by many factors, which usually begins in the cemento-enamel junction and extends peripherally or horizontally in the dentin. It hardly invades the pulp. Orthodontic devices, trauma, bleaching, systemic diseases, and the use of certain medications can all lead to invasive cervical resorption. The clinical manifestations of ICR are usually asymptomatic or not obvious, and most of which are found in imaging examinations. Because caries and internal absorption are often misdiagnosed through plain apical radiography, cone beam computed tomography (CBCT) can help to better understand the situation of invasive cervical resorption. Because the pathogenesis and etiology of invasive cervical resorption are not fully understood, clinical negligence and inadequate treatment of invasive cervical resorption can even cause unnecessary tooth loss. This article reviews the latest research progress on the histopathologic features, pathogenic mechanism, susceptibility factors, diagnosis and treatment of ICR, with special emphasis on susceptibility factors and their mechanisms.

Abstract Background Despite previous studies highlighting the benefits of social media use during the COVID-19 pandemic, particularly under lockdown, limited research has identified the potential detrimental consequences of social media use during lockdown. Therefore, the purpose of this study is to examine the effects of social media on mental health in particular situations and the mechanisms underlying these effects. Methods A daily diary protocol was adopted. A total of 96 adults (M age = 25.90 ± 8.32 years) were recruited from Xi’an, Shaanxi Province, China. COVID-19-related social media use, psychological distress, COVID-19-related stress and anxiety, and disordered eating were measured each day for a week. Multilevel path analyses for the nested data were conducted. Results Daily COVID-19-related social media use was positively related to daily disordered eating (r = .13 p

Abstract Sleep health is both conceptually and operationally a composite concept containing multiple domains of sleep. In line with this, high dependence and interaction across different domains of sleep health encourage a transition in sleep health research from categorical to dimensional approaches that integrate neuroscience and sleep health. Here, we seek to identify the covariance patterns between multiple sleep health domains and distributed intrinsic functional connectivity by applying a multivariate approach (partial least squares). This multivariate analysis reveals a composite sleep health dimension co-varying with connectivity patterns involving the attentional and thalamic networks and which appear relevant at the neuromolecular level. These findings are further replicated and generalized to several unseen independent datasets. Critically, the identified sleep-health related connectome shows diagnostic potential for insomnia disorder. These results together delineate a potential brain connectome biomarker for sleep health with high potential for clinical translation.

Abstract During the past decade, cognitive neuroscience has been calling for population diversity to address the challenge of validity and generalizability, ushering in a new era of population neuroscience. The developing Chinese Color Nest Project (devCCNP, 2013–2022), the first ten-year stage of the lifespan CCNP (2013–2032), is a two-stages project focusing on brain-mind development. The project aims to create and share a large-scale, longitudinal and multimodal dataset of typically developing children and adolescents (ages 6.0–17.9 at enrolment) in the Chinese population. The devCCNP houses not only phenotypes measured by demographic, biophysical, psychological and behavioural, cognitive, affective, and ocular-tracking assessments but also neurotypes measured with magnetic resonance imaging (MRI) of brain morphometry, resting-state function, naturalistic viewing function and diffusion structure. This Data Descriptor introduces the first data release of devCCNP including a total of 864 visits from 479 participants. Herein, we provided details of the experimental design, sampling strategies, and technical validation of the devCCNP resource. We demonstrate and discuss the potential of a multicohort longitudinal design to depict normative brain growth curves from the perspective of developmental population neuroscience. The devCCNP resource is shared as part of the “Chinese Data-sharing Warehouse for In-vivo Imaging Brain” in the Chinese Color Nest Project (CCNP) – Lifespan Brain-Mind Development Data Community ( https://ccnp.scidb.cn ) at the Science Data Bank.

Major depressive disorder (MDD) is a serious and heterogeneous psychiatric disorder that needs accurate diagnosis. Resting-state functional MRI (rsfMRI), which captures multiple perspectives on brain structure, function, and connectivity, is increasingly applied in the diagnosis and pathological research of MDD. Different machine learning algorithms are then developed to exploit the rich information in rsfMRI and discriminate MDD patients from normal controls. Despite recent advances reported, the MDD discrimination accuracy has room for further improvement. The generalizability and interpretability of the discrimination method are not sufficiently addressed either. Here, we propose a machine learning method (MFMC) for MDD discrimination by concatenating multiple features and stacking multiple classifiers. MFMC is tested on the REST-meta-MDD data set that contains 2428 subjects collected from 25 different sites. MFMC yields 96.9% MDD discrimination accuracy, demonstrating a significant improvement over existing methods. In addition, the generalizability of MFMC is validated by the good performance when the training and testing subjects are from independent sites. The use of XGBoost as the meta classifier allows us to probe the decision process of MFMC. We identify 13 feature values related to 9 brain regions including the posterior cingulate gyrus, superior frontal gyrus orbital part, and angular gyrus, which contribute most to the classification and also demonstrate significant differences at the group level. The use of these 13 feature values alone can reach 87% of MFMC’s full performance when taking all feature values. These features may serve as clinically useful diagnostic and prognostic biomarkers for MDD in the future.

Shen Qi Wan (SQW) has been proven to exert anti-inflammatory effects in the kidneys of CKD models accompanied by unclear therapeutic mechanisms. This study aims to evaluate the kidney-protective and anti-inflammatory effects of SQW and to elucidate its fundamental mechanisms for CKD treatment. Firstly, the main active components of SQW were identified by UPLC-Q-TOF/MS technique. Subsequently, we evaluated inflammatory factors, renal function and renal pathology changes following SQW treatment utilizing adenine-induced CKD mice and aquaporin 1 knockout (AQP1-/-) mice. Additionally, we conducted RNA-seq analysis and bioinformatics analysis to predict the SQW potential therapeutic targets and anti-nephritis pathways. Simultaneously, WGCNA analysis method and machine learning algorithms were used to perform a clinical prognostic analysis of potential biomarkers in CKD patients from the GEO database and validated through clinical samples. Lipopolysaccharide-induced HK-2 cells were further used to explore the mechanism. We found that renal collagen deposition was reduced, serum inflammatory cytokine levels decreased, and renal function was improved after SQW intervention. It can be inferred that β-defensin 1 (DEFB1) may be a pivotal target, as confirmed by serum and renal tissue samples from CKD patients. Furthermore, SQW assuages inflammatory responses by fostering AQP1-mediated DEFB1 expression was confirmed in in vitro and in vivo studies. Significantly, the renal-protective effect of SQW is to some extent attenuated after AQP1 gene knockout. SQW could reduce inflammatory responses by modulating AQP1 and DEFB1. These findings underscore the potential of SQW as a promising contender for novel prevention and treatment strategies within the ambit of CKD management.

IntroductionValue hierarchies, as motivational goals anchored in the self-schema, may be correlated with spontaneous activity in the resting brain, especially those involving self-relevance. This study aims to investigate the neural correlates of value hierarchies from the perspective of typology.MethodsA total of 610 Chinese college students (30.31% women), aged 18 to 23, completed the personal values questionnaire and underwent resting-state functional magnetic resonance imaging.ResultsThe latent profile analysis revealed three personal value profiles: traditional social orientation, modernized orientation, and undifferentiated orientation. Neuroimaging results revealed that individuals with modernized orientation prioritized openness to change value, and this personal-focus is related to the higher low-frequency amplitude of the posterior insula; individuals with traditional social orientation prioritized self-transcendence and conservation values, and this social-focus is related to the stronger functional connectivity of the middle insula with the inferior temporal gyrus, temporal gyrus, posterior occipital cortex, and basal ganglia, as well as weaker functional connections within the right middle insula.DiscussionTaken together, these findings potentially indicate the intra-generational differentiation of contemporary Chinese emerging adults’ value hierarchies. At the neural level, these are correlated with brain activities involved in processing self- and other-relevance.

Ankylosis of primary molars is a kind of eruption abnormality of the teeth, where the periodontal membrane disappears, owing to a bony union between bone and root. Studies have shown that the common proportion of ankylosed primary molars is 1.3%~8.9% with an equal occurrence. In the primary dentition, the mandibular first primary molar is the most commonly affected tooth, while in the middle mixed dentition stage of development, the second primary molar is more affected. Its etiology may be related to genetics, signaling pathways of mineralization metabolism of local alveolar bone or cementum, cytokines secreted by epithelial rest cells of Malassez, and enhanced inflammatory reactions during physiological absorption of roots. Ankylosis of primary molars can be diagnosed by clinical symptoms and imaging and is classified as mild, moderate and severe according to the degree of infraocclusion. As it may cause a series of complications, such as occlusal disturbances, delayed exfoliation and incomplete alveolar process development, multidisciplinary treatment, including in the departments of pediatric dentistry, orthodontics, periodontics and prosthodontics, should be adopted, and long-term treatment is determined based on the patient's age, severity of infraocclusion, and presence of permanent teeth. This review summarizes the etiology, diagnosis, complications and treatment of ankylosed primary molars to provide a reference for the clinical diagnosis and treatment of decidual molar fixation.

Abstract Lupus nephritis (LN) is a type of immune-complex nephritis caused by systemic lupus erythematosus and is a major contributor to mortality and morbidity. Honokiol (HNK) has been found to have a therapeutic effect on LN, but its action mechanism remains unclear. In this study, we first demonstrated that HNK attenuates kidney injury in MRL/lpr mice. Results from RNA sequencing combined with ingenuity pathway analysis suggested that HNK plays an anti-LN role through inhibition of the NLRP3 inflammasome and IL33. GEO chip data, single-cell data, and clinical samples from LN patients demonstrated that the pyroptosis and IL-33/ST2 pathways are abnormally activated during the stage of LN. In vivo, similar to the results of the AAV-mediated NLRP3 shRNA MRL/lpr model, HNK downregulated serum and renal IL-33 levels, and suppressed NLRP3 inflammasome and the IL-33/ST2 axis in the kidney. In vitro, co-culturing NLRP3-overexpressing or IL-33 knocked-down rat renal macrophages with NRK-52E cells confirmed that NLRP3 activation in resident macrophages directly upregulates IL-33, which in turn mediates the IL-33/ST2/NF-κB pathway to promote the inflammatory response of renal tubular epithelial cells. Furthermore, a molecular docking model and surface plasmon resonance analysis were utilized to demonstrate a direct interaction between HNK and NLRP3. In conclusion, this study provides a novel anti-LN treatment strategy in which HNK plays a preventive and therapeutic role against LN by suppressing the abnormal crosstalk between renal resident macrophages and renal tubular epithelial cells by inhibiting the activation of the NLRP3/IL-33/ST2 axis.

BackgroundRumination impedes problem solving and is one of the most important factors in the onset and maintenance of multiple psychiatric disorders. The current study aims to investigate the impact of social rejection on rumination and explore the underlying neural mechanisms involved in this process.MethodsWe utilized psychological questionnaire and resting-state brain imaging data from a sample of 560 individuals. The predictive model for rumination scores was constructed using resting-state functional connectivity data through connectome-based predictive modeling. Additionally, a mediation analysis was conducted to investigate the mediating role of the prediction network in the relationship between social rejection and rumination.ResultsA positive correlation between social rejection and rumination was found. We obtained the prediction model of rumination and found that the strongest contributions came from the intra- and internetwork connectivity within the default mode network (DMN), dorsal attention network (DAN), frontoparietal control network (FPCN), and sensorimotor networks (SMN). Analysis of node strength revealed the significance of the supramarginal gyrus (SMG) and angular gyrus (AG) as key nodes in the prediction model. In addition, mediation analysis showed that the strength of the prediction network mediated the relationship between social rejection and rumination.ConclusionThe findings highlight the crucial role of functional connections among the DMN, DAN, FPCN, and SMN in linking social rejection and rumination, particular in brain regions implicated in social cognition and emotion, namely the SMG and AG regions. These results enhance our understanding of the consequences of social rejection and provide insights for novel intervention strategies targeting rumination.

Numerous studies on reducing the urban heat island effect have concentrated on isolated cold islands, analyzing their cooling impact in terms of size and shape. From an international perspective, studies have shown that enhancing the connectivity of cold islands can enhance the cooling effect of cold islands, but they do not suggest specific processes and ideas for cold island connectivity. This study aims to investigate how to connect cold islands to optimize the spatial pattern of the cold island. Therefore, a cold island connectivity framework is constructed in this study: cooling source area - cooling network. Firstly, the core area of the cold island was identified by the morphological spatial pattern analysis. Then, connectivity analysis was applied to identify the cooling sources. Afterward, a minimum cumulative resistance model is used to construct the cooling network. In Nanjing, for case in point, the results reveal that a total of 27 cooling source areas and 52 cooling corridors have been identified. 6 first-level CSAs situated in the northern suburbs of Nanjing prevent the spread of the urban heat island effect. 2 second-level CSAs and 18 third-level CSAs are scattered throughout Nanjing to improve the urban climate. The 29 primary corridors help to mitigate heat transfer from the city center. The 23 secondary corridors are mainly located in urban centers contributing to preventing heat island from aggregating. The framework can be used as a strategic measure to prevent the fragmentation and isolation of the cold island, which provides implications for preventing the further expansion of urban heat island.

During the acquisition of electroencephalographic (EEG) signals, various factors can influence the data and lead to the presence of one or multiple bad channels. Bad channel interpolation is the use of good channels data to reconstruct bad channel, thereby maintaining the original dimensions of the data for subsequent analysis tasks. The mainstream interpolation algorithm assigns weights to channels based on the physical distance of the electrodes and does not take into account the effect of physiological factors on the EEG signal. The algorithm proposed in this study utilizes an attention mechanism to allocate channel weights (AMACW). The model gets the correlation among channels by learning from good channel data. Interpolation assigns weights based on learned correlations without the need for electrode location information, solving the difficulty that traditional methods cannot interpolate bad channels at unknown locations. To avoid an overly concentrated weight distribution of the model when generating data, we designed the channel masking (CM). This method spreads attention and allows the model to utilize data from multiple channels. We evaluate the reconstruction performance of the model using EEG data with 1 to 5 bad channels. With EEGLAB’s interpolation method as a performance reference, tests have shown that the AMACW models can effectively reconstruct bad channels.

Abstract Background Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. Methods We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. Results We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. Conclusion We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

Abstract Major depressive disorder (MDD) is one of the most common mental health conditions that has been intensively investigated for its association with brain atrophy and mortality. Recent studies suggest that the deviation between the predicted and the chronological age can be a marker of accelerated brain aging to characterize MDD. However, current conclusions are usually drawn based on structural MRI information collected from Caucasian participants. The universality of this biomarker needs to be further validated by subjects with different ethnic/racial backgrounds and by different types of data. Here we make use of the REST-meta-MDD, a large scale resting-state fMRI dataset collected from multiple cohort participants in China. We develop a stacking machine learning model based on 1101 healthy controls, which estimates a subject’s chronological age from fMRI with promising accuracy. The trained model is then applied to 1276 MDD patients from 24 sites. We observe that MDD patients exhibit a +4.43 years (p

This paper proposes a self-learning Monte Carlo tree search algorithm (SL-MCTS), which has the ability to continuously improve its problem-solving ability in single-player scenarios. SL-MCTS combines the MCTS algorithm with a two-branch neural network (PV-Network). The MCTS architecture can balance the search for exploration and exploitation. PV-Network replaces the rollout process of MCTS and predicts the promising search direction and the value of nodes, which increases the MCTS convergence speed and search efficiency. The paper proposes an effective method to assess the trajectory of the current model during the self-learning process by comparing the performance of the current model with that of its best-performing historical model. Additionally, this method can encourage SL-MCTS to generate optimal solutions during the self-learning process. We evaluate the performance of SL-MCTS on the robot path planning scenario. The experimental results show that the performance of SL-MCTS is far superior to the traditional MCTS and single-player MCTS algorithms in terms of path quality and time consumption, especially its time consumption is half less than that of the traditional MCTS algorithms. SL-MCTS also performs comparably to other iterative-based search algorithms designed specifically for path planning tasks.

Abstract The nucleus accumbens (NAc) is considered a hub of reward processing and a growing body of evidence has suggested its crucial role in the pathophysiology of major depressive disorder (MDD). However, inconsistent results have been reported by studies on reward network-focused resting-state functional MRI (rs-fMRI). In this study, we examined functional alterations of the NAc-based reward circuits in patients with MDD via meta- and mega-analysis. First, we performed a coordinated-based meta-analysis with a new SDM-PSI method for all up-to-date rs-fMRI studies that focused on the reward circuits of patients with MDD. Then, we tested the meta-analysis results in the REST-meta-MDD database which provided anonymous rs-fMRI data from 186 recurrent MDDs and 465 healthy controls. Decreased functional connectivity (FC) within the reward system in patients with recurrent MDD was the most robust finding in this study. We also found disrupted NAc FCs in the DMN in patients with recurrent MDD compared with healthy controls. Specifically, the combination of disrupted NAc FCs within the reward network could discriminate patients with recurrent MDD from healthy controls with an optimal accuracy of 74.7%. This study confirmed the critical role of decreased FC in the reward network in the neuropathology of MDD. Disrupted inter-network connectivity between the reward network and DMN may also have contributed to the neural mechanisms of MDD. These abnormalities have potential to serve as brain-based biomarkers for individual diagnosis to differentiate patients with recurrent MDD from healthy controls.

Adolescence is the peak period for the onset of generalized anxiety disorder (GAD). Brain networks of cognitive and affective control in adolescents are not well developed when their exposure to external stimuli suddenly increases. Reasonable parental monitoring is especially important during this period. To examine the role of parental monitoring in the development of functional brain networks of GAD, we conducted a cross-validation-based predictive study based on the functional brain networks of 192 participants. We found that a set of functional brain networks, especially the default mode network and its connectivity with the frontoparietal network, could predict the ages of adolescents, which was replicated in three independent samples. Importantly, the difference between predicted age and chronological age significantly mediated the relationship between parental monitoring and anxiety levels. These findings suggest that inadequate parental monitoring plays a crucial role in the delayed development of specific brain networks associated with GAD in adolescents. Our work highlights the important role of parental monitoring in adolescent development.

Accumulating evidence showed that major depressive disorder (MDD) is characterized by a dysfunction of serotonin neurotransmission. Raphe nuclei are the sources of most serotonergic neurons that project throughout the brain. Incorporating measurements of activity within the raphe nuclei into the analysis of connectivity characteristics may contribute to understanding how neurotransmitter synthesized centers are involved in the pathogenesis of MDD. Here, we analyzed the resting-state functional magnetic resonance imaging (RS-fMRI) dataset from 1,148 MDD patients and 1,079 healthy individuals recruited across nine centers. A seed-based analysis with the dorsal raphe and median raphe nuclei was performed to explore the functional connectivity (FC) alterations. Compared to controls, for dorsal raphe, the significantly decreased FC linking with the right precuneus and median cingulate cortex were found; for median raphe, the increased FC linking with right superior cerebellum (lobules V/VI) was found in MDD patients. In further exploratory analyzes, MDD-related connectivity alterations in dorsal and median raphe nuclei in different clinical factors remained highly similar to the main findings, indicating these abnormal connectivities are a disease-related alteration. Our study highlights a functional dysconnection pattern of raphe nuclei in MDD with multi-site big data. These findings help improve our understanding of the pathophysiology of depression and provide evidence of the theoretical foundation for the development of novel pharmacotherapies.

Abstract Suppression-induced forgetting (SIF) refers to a memory impairment resulting from repeated attempts to stop the retrieval of unwanted memory associates. SIF has become established in the literature through a growing number of reports built upon the Think/No-Think (TNT) paradigm. Not all individuals and not all reported experiments yield reliable forgetting, however. Given the reliance on task instructions to motivate participants to suppress target memories, such inconsistencies in SIF may reasonably owe to differences in compliance or expectations as to whether they will again need to retrieve those items (on, say, a final test). We tested these possibilities on a large (N = 497) sample of TNT participants. In addition to successfully replicating SIF, we found that the magnitude of the effect was significantly and negatively correlated with participants’ reported compliance during the No-Think trials. This pattern held true on both same- and independent-probe measures of forgetting, as well as when the analysis was conditionalized on initial learning. In contrast, test expectancy was not associated with SIF. Supporting previous intuition and more limited post-hoc examinations, this study provides robust evidence that a lack of compliance with No-Think instructions significantly compromises SIF. As such, it suggests that diminished effects in some studies may owe, at least in part, to non-compliance—a factor that should be carefully tracked and/or controlled. Motivated forgetting is possible, provided that one is sufficiently motivated and capable of following the task instructions.

Abstract Background Early diagnosis of adolescent psychiatric disorder is crucial for early intervention. However, there is extensive comorbidity between affective and psychotic disorders, which increases the difficulty of precise diagnoses among adolescents. Methods We obtained structural magnetic resonance imaging scans from 150 adolescents, including 67 and 47 patients with major depressive disorder (MDD) and schizophrenia (SCZ), as well as 34 healthy controls (HC) to explore whether psychiatric disorders could be identified using a machine learning technique. Specifically, we used the support vector machine and the leave-one-out cross-validation method to distinguish among adolescents with MDD and SCZ and healthy controls. Results We found that cortical thickness was a classification feature of a) MDD and HC with 79.21% accuracy where the temporal pole had the highest weight; b) SCZ and HC with 69.88% accuracy where the left superior temporal sulcus had the highest weight. Notably, adolescents with MDD and SCZ could be classified with 62.93% accuracy where the right pars triangularis had the highest weight. Conclusions Our findings suggest that cortical thickness may be a critical biological feature in the diagnosis of adolescent psychiatric disorders. These findings might be helpful to establish an early prediction model for adolescents to better diagnose psychiatric disorders.

Abstract NLRP3, a decisive role in inflammation regulation, is obviously upregulated by oxidative stress in kidney injury. The NLRP3 upregulation leads to unsolved inflammation and other pathological effects, contributing to aggravation of kidney injury and even transition to chronic kidney disease (CKD). However, the mechanism for NLRP3 upregulation and further aggravation of kidney injury remains largely elusive. In this study, we found NLRP3 3′UTR was shortened in response to kidney injury in vivo and oxidative stress in vitro. Functionally, such NLRP3 3′UTR shortening upregulated NLRP3 expression and amplified inflammation, fibrogenesis, ROS production and apoptosis, depending on stabilizing NLRP3 mRNA. Mechanistically, FIP1 was found to bind to pPAS of NLRP3 mRNA via its arginine-rich domain and to induce NLRP3 3′UTR shortening. In addition, FIP1 was upregulated in CKD specimens and negatively associated with renal function of CKD patients. More importantly, we found FIP1 was upregulated by oxidative stress and required for oxidative stress-induced NLRP3 upregulation, inflammation activation, cell damage and apoptosis. Finally, we proved that FIP1 silencing attenuated the inflammation activation, fibrogenesis, ROS production and apoptosis induced by UUO or IRI. Taken together, our results demonstrated that oxidative stress-upregulated FIP1 amplified inflammation, fibrogenesis, ROS production and apoptosis via inducing 3′UTR shortening of NLRP3, highlighting the importance of crosstalk between oxidative stress and alternative polyadenylation in AKI-CKD transition, as well as the therapeutic potential of FIP1 in kidney injury treatment.

The correlation between brain structural changes in major depressive disorder (MDD) and gene expression is unclear. Here, the authors explore the correlation between cell type-specific gene expression changes and cortical structural difference in individuals with major depressive disorder.

Objective This study investigated whether participants could process the same/different pointing relation between two sequentially displayed subliminal arrows in a prime across a window of time. Method The motor‐response priming was distinguished from the visual feature priming. Of the two types of priming, only the motor‐response priming could reflect an unconscious integrated processing of the pointing directions of two arrows. In two experiments, the two masked arrows pointing in the same or different directions (e.g., > > or >

BackgroundBK polyomavirus (BKPyV)-associated nephropathy (BKPyVAN) causes renal allograft dysfunction and graft loss. However, the mechanism of BKPyV replication after kidney transplantation is unclear. Clinical studies have demonstrated that immunosuppressants and renal ischemia–reperfusion injury (IRI) are risk factors for BKPyV infection. Studying the pathogenic mechanism of BKPyV is limited by the inability of BKPyV to infect the animal. Mouse polyomavirus (MPyV) is a close homolog of BKPyV. We used a model of MPyV infection to investigate the core genes and underlying mechanism of IRI and immunosuppressants to promote polyomavirus replication.Materials and MethodsOne-day-old male C57BL/6 mice were intraperitoneally injected with MPyV. At week 9 post-infection, all mice were randomly divided into IRI, immunosuppressant, and control groups and treated accordingly. IRI was established by clamping the left renal pedicle. Subsequently, kidney specimens were collected for detecting MPyV DNA, histopathological observation, and high-throughput RNA sequencing. Weighted gene correlation network analysis (WGCNA), protein–protein interaction network analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to screen for core genes and common signaling pathways involved in promoting MPyV replication by IRI and immunosuppressants.ResultsAfter primary infection, MPyV established persistent infection in kidneys and subsequently was significantly increased by IRI or immunosuppressant treatment individually. In the IRI group, viral loads peaked on day 3 in the left kidney, which were significantly higher than those in the right kidney and the control group. In the immunosuppressant group, viral loads in the left kidney were significantly increased on day 3, which were significantly higher than those in the control group. Protein–protein interaction network analysis and WGCNA screened complement C3, epidermal growth factor receptor (EGFR), and FN1 as core genes. Pathway enrichment analysis based on the IRI- or immunosuppressant-related genes selected by WGCNA indicated that the NF-κB signaling pathway was the main pathway involved in promoting MPyV replication. The core genes were further confirmed using published datasets GSE47199 and GSE75693 in human polyomavirus-associated nephropathy.ConclusionsOur study demonstrated that IRI and immunosuppressants promote polyomavirus replication through common molecular mechanisms. In future studies, knockdown or specific inhibition of C3, EGFR, FN1, and NF-κB signaling pathway will further validate their critical roles in promoting polyomavirus replication.

BK polyomavirus (BKPyV) infection is the main factor affecting the prognosis of kidney transplant recipients, as no antiviral agent is yet available. A better understanding of the renal-cell-type tropism of BKPyV can serve to develop new treatment strategies. In this study, the single-cell transcriptomic analysis demonstrated that the ranking of BKPyV tropism for the kidney was proximal tubule cells (PT), collecting duct cells (CD), and glomerular endothelial cells (GEC) according to the signature of renal cell type and immune microenvironment. In normal kidneys, we found that BKPyV infection-related transcription factors P65 and CEBPB were PT-specific transcription factors, and PT showed higher glycolysis/gluconeogenesis activities than CD and GEC. Furthermore, in the BKPyV-infected kidneys, the percentage of late viral transcripts in PT was significantly higher than in CD and GEC. In addition, PT had the smallest cell–cell interactions with immune cells compared to CD and GEC in both normal and BKPyV-infected kidneys. Subsequently, we indirectly demonstrated the ranking of BKPyV tropism via the clinical observation of sequential biopsies. Together, our results provided in-depth insights into the renal cell-type tropism of BKPyV in vivo at single-cell resolution and proposed a novel antiviral target.

Abstract Background To investigate predictive factors related to graft failure of IgA nephropathy(IgAN) in renal allografts following living donor transplantation. Methods We identified a series of 102 biopsies diagnosed as IgAN in renal allografts following living donor transplantation from July 2004 to January 2017 at our center, and assess the predict value of the Lee’s classification and the 2009 Oxford classification in IgAN in renal allografts, clinical, ultrasonic and pathological characteristics at biopsy and the outcomes were retrospectively analyzed. Results The 5-year graft cumulative survival rate after transplantation was 91.4%. The 4-year graft cumulative survival rate after biopsy diagnosis of IgAN in renal allografts was 59.6%. The mean time ± SD to disease was 4.7 ± 3.5 years. The color doppler ultrasound and blood flow imagine showed the echo enhancement, the reduced blood flow distribution, the reduced peak systolic velocity of main renal artery, and the increased resistance index of arcuate renal artery were valuable in evaluating the graft dysfunction. The Cox multivariate analysis revealed that the 24-h urinary protein level (HR 1.6 for 1-g increase, 95%CI 1.2–2.0), estimated glomerular filtration rate (eGFR) (HR 1.0 for 1-mL/min/1.73 m^2 decline, 95%CI 1.0–1.1), and mesangial C1q deposition (HR 3.0, 95%CI 1.2–7.4) at biopsy were independent predictive factors of graft failure of IgAN in renal allografts. Conclusions IgAN in renal allografts occurred frequently within 5 years after transplantation. The risk of graft failure should be taken seriously in patients who exhibit heavy proteinuria and/or a declined eGFR as the initial symptoms; a high lesion grade (grade IV-V of Lee’s classification) and/or mesangial C1q deposition may also indicated a poor outcome.

Background: The novel coronavirus (COVID-19) pandemic has affected humans worldwide and led to unprecedented stress and mortality. Detrimental effects of the pandemic on mental health, including risk of post-traumatic stress disorder (PTSD), have become an increasing concern. The identification of prospective neurobiological vulnerability markers for developing PTSD symptom during the pandemic is thus of high importance. Methods: Before the COVID-19 outbreak (September 20, 2019–January 11, 2020), some healthy participants underwent resting-state functional connectivity MRI (rs-fcMRI) acquisition. We assessed the PTSD symptomology of these individuals during the peak of COVID-19 pandemic (February 21, 2020–February 28, 2020) in China. This pseudo-prospective cohort design allowed us to test whether the pre-pandemic neural connectome status could predict the risk of developing PTSD symptom during the pandemic. Results: A total of 5.60% of participants (n = 42) were identified as being high-risk to develop PTSD symptom and 12.00% (n = 90) exhibited critical levels of PTSD symptoms during the COVID-19 pandemic. Pre-pandemic measures of functional connectivity (the neural connectome) prospectively classified those with heightened risk to develop PTSD symptom from matched controls (Accuracy = 76.19%, Sensitivity = 80.95%, Specificity = 71.43%). The trained classifier generalized to an independent sample. Continuous prediction models revealed that the same connectome could accurately predict the severity of PTSD symptoms within individuals (r2 = 0.31p

Health and financial uncertainties, as well as enforced social distancing, during the COVID-19 pandemic have adversely affected the mental health of people. These impacts are expected to continue even after the pandemic, particularly for those who lack support from family and friends. The salience network (SN), default mode network (DMN), and frontoparietal network (FPN) function in an interconnected manner to support information processing and emotional regulation processes in stressful contexts. In this study, we examined whether functional connectivity of the SN, DMN, and FPN, measured using resting-state functional magnetic resonance imaging before the pandemic, is a neurobiological marker of negative affect (NA) during the COVID-19 pandemic and after its peak in a large sample (N = 496, 360 females); the moderating role of social support in the brain-NA association was also investigated. We found that participants reported an increase in NA during the pandemic compared to before the pandemic, and the NA did not decrease, even after the peak period. People with higher connectivity within the SN and between the SN and the other two networks reported less NA during and after the COVID-19 outbreak peak, and the buffer effect was stronger if their social support was greater. These findings suggest that the functional networks that are responsible for affective processing and executive functioning, as well as the social support from family and friends, play an important role in protecting against NA under stressful and uncontrollable situations.

Abstract Background Recipients of living donor renal transplantation are typically considered to have a relatively lower immunological risk. This retrospective study aimed to compare the therapeutic efficacy and safety between rabbit antithymocyte globulin (rATG) or interleukin-2 receptor antagonist (IL2-RA) induction therapies in Chinese population. Methods A total of 188 patients receiving living donor renal transplantation between February 2004 and December 2013 were included and divided into the rATG group and based on their induction therapy. The primary outcome was clinically-suspected rejection. The incidences of de novo donor-specific antigen (dn-DSA), graft survival, and infection were also compared between groups. A multivariate Cox regression analysis was performed to investigate the influential factors associated with clinically-suspected acute rejection and graft survival. Results The rATG group had a higher panel reactive antibody (PRA) score and more complete HLA mismatches than the IL2-RA group (both P 0.05). The incidences of complications (infections, pneumonia, liver injury and myelosuppression) were all comparable between groups (all P > 0.05). Conclusions These results suggested that rATG could be a safe and efficient immunosuppressant when used in a Chinese recipient population with a higher immunological risk in living donor renal transplantation.

Abstract. Background:. BK virus-associated nephropathy (BKVN) is an important cause of chronic allograft dysfunction. The objective of our study was to evaluate the prognosis of BKVN. Methods:. We retrospectively reviewed the data of 133 renal transplant recipients with BKVN treated at the First Affiliated Hospital of Sun Yat-Sen University between July 2007 and July 2017. BK viral loads, graft function, and pathologic indexes were compared between initial diagnosis and last follow-up. Results:. After a mean follow-up period of 14.4 (range, 0.3–109.6) months after diagnosis of BKVN, BK viruria, and BK viremia become negative in 19.5% and 90.2% of patients, respectively. The mean estimated glomerular filtration rate (eGFR) at last follow-up was lower than at diagnosis of BKVN (18.3 ± 9.2 vs. 32.8 ± 20.6 mL·min−1·1.73 m−2, t = 7.426, P

Although many studies have explored the neural mechanism of the feeling of stress, to date, no effort has been made to establish a model capable of predicting the feeling of stress at the individual level using the resting-state functional connectome. Although individuals may be confronted with multidimensional stressors during the coronavirus disease 2019 (COVID-19) pandemic, their appraisal of the impact and severity of these events might vary. In this study, connectome-based predictive modeling (CPM) with leave-one-out cross-validation was conducted to predict individual perceived stress (PS) from whole-brain functional connectivity data from 817 participants. The results showed that the feeling of stress could be predicted by the interaction between the default model network and salience network, which are involved in emotion regulation and salience attribution, respectively. Key nodes that contributed to the prediction model comprised regions mainly located in the limbic systems and temporal lobe. Critically, the CPM model of PS based on regular days can be generalized to predict individual PS levels during the COVID-19 pandemic, which is a multidimensional, uncontrollable stressful situation. The stability of the results was demonstrated by two independent datasets. The present work not only expands existing knowledge regarding the neural mechanism of PS but also may help identify high-risk individuals in healthy populations.

Creative thinking is a hallmark of human cognition, which enables us to generate novel and useful ideas. Nevertheless, its emergence within the macro-scale neurocognitive circuitry remains largely unknown. Using resting-state fMRI data from two large population samples (SWU: n = 931; HCP: n = 1001) and a novel “travelling pattern prediction analysis”, here we identified the modularized functional connectivity patterns linked to creative thinking ability, which concurrently explained individual variability across ordinary cognitive abilities such as episodic memory, working memory and relational processing. Further interrogation of this neural pattern with graph theoretical tools revealed both hub-like brain structures and globally-efficient information transfer paths that together may facilitate higher creative thinking ability through the convergence of distinct cognitive operations. Collectively, our results provide reliable evidence for the hypothesized emergence of creative thinking from core cognitive components through neural integration, and thus allude to a significant theoretical advancement in the study of creativity.

While a recent upsurge in the application of neuroimaging methods to creative cognition has yielded encouraging progress toward understanding the neural underpinnings of creativity, the neural basis of barriers to creativity are as yet unexplored. Here, we report the first investigation into the neural correlates of one such recently identified barrier to creativity: anxiety specific to creative thinking, or creativity anxiety (Daker et al., 2019). We employed a machine-learning technique for exploring relations between functional connectivity and behavior (connectome-based predictive modeling; CPM) to investigate the functional connections underlying creativity anxiety. Using whole-brain resting-state functional connectivity data, we identified a network of connections or “edges” that predicted individual differences in creativity anxiety, largely comprising connections within and between regions of the executive and default networks and the limbic system. We then found that the edges related to creativity anxiety identified in one sample generalize to predict creativity anxiety in an independent sample. We additionally found evidence that the network of edges related to creativity anxiety were largely distinct from those found in previous work to be related to divergent creative ability (Beaty et al., 2018). In addition to being the first work on the neural correlates of creativity anxiety, this research also included the development of a new Chinese-language version of the Creativity Anxiety Scale, and demonstrated that key behavioral findings from the initial work on creativity anxiety are replicable across cultures and languages.

Openness to experience (OTE) has relatively stable and heritable characteristics. Previous studies have used candidate gene approaches to explore the genetic mechanisms of OTE, but genome-wide polygenic scores have a greater genetic effect than other genetic analysis methods, and previous studies have never examined the potential effect of OTE on this cumulative effect at the level of the brain mechanism. In the present study, we aim to explore the associations between polygenic scores (PGSs) of OTE and brain structure and functions. First, the results of PGSs of OTE at seven different thresholds were calculated in a large Chinese sample (N = 586). Then, we determined the associations between PGSs of OTE and cortical thickness and functional connectivity. The results showed that PGSs of OTE was negatively correlated with the thickness of the fusiform gyrus, and PGSs of OTE were negatively associated with the functional connectivity between the left intraparietal sulcus (IPS) and the right posterior occipital lobe. These findings may suggest that the brain structure of fusiform gyrus and brain functions of IPS and posterior occipital lobe are partly regulated by OTE-related genetic factors.

Previous neuroimaging research of eating disorders such as anorexia nervosa and bulimia nervosa has mainly focused on clinical patients, indicating the crucial role of intrinsic connectivity networks involved in aberrant behavioral control (i.e., executive control network), reward reactivity (i.e., basal ganglia network), and excessive self-focused and body-focused ruminations (i.e., default mode network) in the onset and maintenance of eating disturbances. However, examinations of large-scale resting-state networks that support the role of cognitive control, reward sensitivity, and self-directed thinking in disordered eating have rarely involved non-clinical samples from the general population. This study, involving a total of 693 healthy young adults (68.69% females; mean age, 18.37 years), investigated these issues by using pre-defined functional regions of interest from the executive control network, basal ganglia network, default mode network, and a seed-based region of interest-to-region of interest approach. After statistically controlling for differences in age, sex, body mass index, and head motion, we observed significant associations of higher levels of eating disorder symptoms, especially bulimia-type eating (i.e., binge eating and a combination of binge eating and compensatory behaviors, such as purging via self-induced vomiting or laxative use, and compulsive exercise), with weaker intra-network and inter-network functional synchrony. These results remained significant after excluding underweight, overweight, and obese participants. These findings suggest that these neural circuits may play a key role in the symptoms of disordered eating in healthy adults. They further reveal that the less efficient information exchange within and between intrinsic networks associated with self-referential thinking, inhibitory control, and reward sensitivity are strongly related to eating disorder symptoms.

Stress-related psychosomatic responses are viewed as important risks to our physical health. Growing evidence from structural imaging studies has implicated that stress and trauma exposures have negative effects on brain structural alterations. However, whether stress-related emotional distress and somatic symptoms are related to the structure of brain systems remains unclear. Also, stress-related somatic symptoms have adverse effects on emotional distress. In turn, emotional distress may influence somatic symptom reports via negative cognitive bias. However, whether this relationship is mediated by specific brain morphology remains poorly understood. First, we used voxel-based morphometric approaches to investigate the neuroanatomical basis underlying somatic symptoms and emotional distress in a large sample of healthy subjects (ages 18–27 years). We found that relatively high stress-related somatic symptoms were associated with reduced gray matter volumes (GMVs) in the ventral medial prefrontal cortex (vmPFC), anterior insula, somatosensory cortex, hippocampus, and amygdala. Furthermore, a moderator analysis was performed to investigate the impact of recent stressful life events (moderators) on the association between specific GMVs (independent variables) and emotional distress (dependent variables). Interestingly, high levels of emotional distress were associated with small volumes of the vmPFC, anterior insula, hippocampus, and amygdala in participants with experience with more recent stressful life events. Finally, we performed mediation analyses to investigate the specific brain areas that mediate the association between emotional distress and somatic symptoms. The results showed that the effect of emotional distress on somatic symptoms is mediated by reductions in the volume of the hippocampus, the impact of somatic symptoms on emotional distress is mediated by the volume of the vmPFC. These results provided evidence that higher stress-related somatic symptoms are associated with smaller volume in prefrontal, insula, and limbic regions involved in emotion, interoception, and memory processing. The vmPFC and hippocampus play different roles in the relationship between emotional distress and somatic symptoms.

BackgroundThe extent and depth of BK polyomavirus (BKPyV) infection in renal allograft correlate with prognosis. This study was designed to evaluate the value of urinary sediment double-immunostaining for predicting BKPyV infection in proximal tubular epithelium.Materials and methodsA total of 76 urine sediment cell blocks, as well as the corresponding transplanted kidney tissues with BK polyomavirus associated-nephropathy (BKPyVAN), were evaluated by automatic double-immunostaining with anti-58-kDa Golgi protein (58K, a proximal renal tubular marker) + anti-SV40-T and anti-homogentisate 1, 2-dioxygenase (HGD, a renal tubular marker) + anti-SV40-T.ResultsImmunohistochemical staining demonstrated that 58K was expressed in proximal tubular epithelium but not in distal tubular epithelium or transitional epithelium. Of the 76 patients, 28 (36.8%) had urinary 58K(+)/SV40-T(+) cells and HGD(+)/SV40-T(+) cells, 41 (53.9%) had only HGD(+)/SV40-T(+) cells, one (1.3%) had only 58K(+)/SV40-T(+) cells, and six (7.9%) had only 58K(−)/HGD(−)/SV40-T(+) cells. The presence of urinary 58K(+)/SV40-T(+) cells was correlated with BKPyV infection in proximal tubular epithelium (P < 0.001, r = 0.806). The mean extent of SV40-T staining was significantly more extensive in patients with urinary 58K(+)/SV40-T(+) cells than those without urinary 58K(+)/SV40-T(+) cells (21.4 vs. 12.0%, P < 0.001). The positive predictive value, negative predictive value, sensitivity, and specificity of urinary 58K(+)/SV40-T(+) cells for predicting BKPyV infection in proximal tubular epithelium were 89.7% (95% CI: 71.5–97.3%), 91.5% (95% CI: 78.7–97.2%), 86.7% (95% CI: 68.4–95.6%), and 93.5% (95% CI: 81.1–98.3%), respectively.ConclusionUrinary sediment double-immunostaining with anti-58K and anti-SV40-T is valuable for predicting the extent and depth of BKPyV infection in renal allograft.

Social anxiety is a common problem that usually emerges at puberty, during which great developmental changes occur both in the brain and mental state. However, little is known about the influence of social anxiety on adolescents’ brain and behavior. The present study investigated the neural basis of social anxiety using voxel-based morphometry (VBM) and functional connectivity analysis. Then we investigated whether social anxiety is associated with attention bias. Furthermore, we investigated the neural basis of this association. Finally, longitudinal data was used to test if these biomarkers could predict social anxiety. The results indicated that social anxiety is positively associated with the grey matter volume (GMV) of orbital-frontal cortex (OFC), and the functional connectivity (FC) of OFC-amygdala. Mediation analysis revealed that the relationship between social anxiety and attention avoidance is partly mediated by the FC of OFC-amygdala. Finally, the present study demonstrated a close relationship between FC of the OFC-amygdala, the GMV of the OFC and the individual’s social anxiety one year later. The present study suggested the aberrant structure of OFC and its connectivity with amygdala as the neural underpinning of social anxiety, which might serve as a compensatory mechanism to decrease attention avoidance and promote effective emotion regulation.

Background: Studies have shown that plasma donor–derived cell-free DNA (dd-cfDNA) can predict renal allograft antibody-mediated rejection. This study was performed to evaluate the value of urine dd-cfDNA concentration and dd-cfDNA fraction (%) for discriminating BK polyomavirus-associated nephropathy (BKPyVAN) in kidney transplant recipients with urinary BK polyomavirus (BKPyV) infection.Methods: In this retrospective single-center observational study, we enrolled kidney transplant recipients who were diagnosed with urine BKPyV infection between August 2018 and May 2019 at the First Affiliated Hospital of Sun Yat-sen University. Urine dd-cfDNA was measured by using a novel target region capture sequencing methodology. The pathological diagnosis of BKPyVAN was confirmed by anti-SV40-T immunohistochemical staining and classified using the American Society for Transplantation schema. Receiver operating characteristic curve analysis was used to investigate the relations of urine dd-cfDNA and dd-cfDNA% to intrarenal allograft BKPyV infection states.Results: In total, 93 patients were enrolled, including 40 cases of proven BKPyVAN, seven cases of probable BKPyVAN, 23 cases of possible BKPyVAN, and 23 cases of resolving BKPyVAN. Urine dd-cfDNA level in proven BKPyVAN (22.09 ± 21.27 ng/ml) was comparable to that in probable BKPyVAN (15.64 ± 6.73 ng/ml, P = 0.434) but was significantly higher than that in possible BKPyVAN (5.60 ± 3.53 ng/ml) and resolving BKPyVAN (5.30 ± 3.34 ng/ml) (both Ps < 0.05). Urine dd-cfDNA% of proven BKPyVAN (0.71 ± 0.21) was lower than that of probable BKPyVAN (0.91 ± 0.04, P < 0.001), but was significantly higher than that of possible BKPyVAN (0.56 ± 0.30) and resolving BKPyVAN (0.46 ± 0.28) (both Ps < 0.05). For distinguishing biopsy-proven BKPyVAN from biopsy-excluded BKPyVAN, the discrimination capacity of urine dd-cfDNA (AUC: 0.842, 95% CI: 0.735, 0.918) was superior to that of plasma BKPyV DNA load (AUC: 0.660, 95% CI: 0.537, 0.769) with 0.181 (95% CI: 0.043, 0.319) difference between areas under ROC curves (P = 0.010).Conclusion: The elevated urine dd-cfDNA level may help discriminate BKPyVAN in kidney transplant recipients with BKPyV viruria.

Background: Major depressive disorder (MDD) is heterogeneous disorder associated with aberrant functional connectivity within the default mode network (DMN). This study focused on data-driven identification and validation of potential DMN-pattern-based MDD subtypes to parse heterogeneity of the disorder. Methods: The sample comprised 1397 participants including 690 patients with MDD and 707 healthy controls (HC) registered from multiple sites based on the REST-meta-MDD Project in China. Baseline resting-state functional magnetic resonance imaging (rs-fMRI) data was recorded for each participant. Discriminative features were selected from DMN between patients and HC. Patient subgroups were defined by K-means and principle component analysis in the multi-site datasets and validated in an independent single-site dataset. Statistical significance of resultant clustering were confirmed. Demographic and clinical variables were compared between identified patient subgroups. Results: Two MDD subgroups with differing functional connectivity profiles of DMN were identified in the multi-site datasets, and relatively stable in different validation samples. The predominant dysfunctional connectivity profiles were detected among superior frontal cortex, ventral medial prefrontal cortex, posterior cingulate cortex and precuneus, whereas one subgroup exhibited increases of connectivity (hyperDMN MDD) and another subgroup showed decreases of connectivity (hypoDMN MDD). The hyperDMN subgroup in the discovery dataset had age-related severity of depressive symptoms. Patient subgroups had comparable demographic and clinical symptom variables. Conclusions: Findings suggest the existence of two neural subtypes of MDD associated with different dysfunctional DMN connectivity patterns, which may provide useful evidence for parsing heterogeneity of depression and be valuable to inform the search for personalized treatment strategies.

Background: Major depressive disorder (MDD) is known to be characterized by altered brain functional connectivity (FC) patterns. However, whether and how the features of dynamic FC would change in patients with MDD are unclear. In this study, we aimed to characterize dynamic FC in MDD using a large multi-site sample and a novel dynamic network-based approach. Methods: Resting-state functional magnetic resonance imaging (fMRI) data were acquired from a total of 460 MDD patients and 473 healthy controls, as a part of the REST-meta-MDD consortium. Resting-state dynamic functional brain networks were constructed for each subject by a sliding-window approach. Multiple spatio-temporal features of dynamic brain networks, including temporal variability, temporal clustering and temporal efficiency, were then compared between patients and healthy subjects at both global and local levels. Results: The group of MDD patients showed significantly higher temporal variability, lower temporal correlation coefficient (indicating decreased temporal clustering) and shorter characteristic temporal path length (indicating increased temporal efficiency) compared with healthy controls (corrected p < 3.14×10−3). Corresponding local changes in MDD were mainly found in the default-mode, sensorimotor and subcortical areas. Measures of temporal variability and characteristic temporal path length were significantly correlated with depression severity in patients (corrected p < 0.05). Moreover, the observed between-group differences were robustly present in both first-episode, drug-naïve (FEDN) and non-FEDN patients. Conclusions: Our findings suggest that excessive temporal variations of brain FC, reflecting abnormal communications between large-scale bran networks over time, may underlie the neuropathology of MDD.

Aim: The aim of this study is to investigate the clinical features of graft dysfunction following living kidney transplantation and to assess its causes. Methods: We retrospectively analyzed a series of 366 living kidney transplantation indication biopsies with a clear etiology and diagnosis from July 2003 to June 2016 at our center. The classifications and diagnoses were performed based on clinical and pathological characteristics. All biopsies were evaluated according to the Banff 2007 schema. Results: Acute rejection (AR) occurred in 85 cases (22.0%), chronic rejection (CR) in 62 cases (16.1%), borderline rejection (BR) in 12 cases (3.1%), calcineurin inhibitor (CNI) toxicity damage in 41 cases (10.6%), BK virus-associated nephropathy (BKVAN) in 43 cases (11.1%), de novo or recurrent renal diseases in 134 cases (34.7%), and other causes in nine cases (2.3%); additionally, 20 cases had two simultaneous causes. The 80 cases with IgA nephropathy (IgAN) had the highest incidence (59.7%) of de novo or recurrent renal diseases. After a mean ± SD follow up of 3.7 ± 2.3 years, the 5-year graft cumulative survival rates of AR, CR, CNI toxicity, BKVAN, and de novo or recurrent renal diseases were 60.1%, 31.2%, 66.6%, 66.9%, and 67.1%, respectively. Conclusions: A biopsy is helpful for the diagnosis of graft dysfunction. De novo or recurrent renal disease, represented by IgAN, is a major cause of graft dysfunction following living kidney transplantation.

Abstract A self-enhancing humor style (SEHS) plays an important role in the regulation of negative emotion through humorous perspective-taking. Following the mind-reading theories of humor, we investigated the relationship between gray-matter volume (GMV) of brain areas related to theory of mind and SEHS in young college students, using voxel-based morphometry analysis. We then performed a voxel-wise analysis of covariance to assess any moderation effect of cognitive empathy on the relationship. Results demonstrated that higher SEHS scores were associated with larger GMV of the precuneus in the group with high cognitive empathy, but there was no association in the group with low cognitive empathy. These results suggest that high cognitive empathy and increased precuneus volume can predict greater use of self-enhancing humor in young healthy individuals.

Background and objectives: Alzheimer’s disease is a progressive brain degeneration and is associated with a high prevalence of sleep disorders. Amyloid β peptide-42/40 (Aβ42/40) and Tau-pT181 are the core biomarkers in cerebrospinal fluid and blood. Accumulated data from studies in mouse models and humans demonstrated an aberrant elevation of these biomarkers due to sleep disturbance, especially sleep-disordered breathing (SDB). However, it is not clear if sleep quality improvement reduces the blood levels of Ab42/40 ratio and Tau-pT181 in Alzheimer’s disease patients. Materials and Methods: In this prospective study, a longitudinal analysis was conducted on 64 patients with mild–moderate cognition impairment (MCI) due to Alzheimer’s disease accompanied by SDB. Another 33 MCI cases without sleep-disordered breathing were included as the control group. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) score system. Neuropsychological assessments were conducted using the Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale (GDS), Clinical Dementia Rating (CDR), 24-h Hamilton Rating Scale for Depression (HRSD-24), and Hamilton Anxiety Rating Scale (HAMA) scoring systems. Aβ42, Aβ40, and Tau-pT181 protein levels in blood specimens were measured using ELISA assays. All patients received donepezil treatment for Alzheimer’s disease. SDB was managed with continuous pressure ventilation. Results: A significant correlation was found among PSQI, HRSD-24, HAMA, Aβ42/40 ratio, and Tau-pT181 level in all cases. In addition, a very strong and negative correlation was discovered between education level and dementia onset age. Compared to patients without SDB (33 non-SD cases), patients with SDB (64 SD cases) showed a significantly lower HRSD-24 score and a higher Aβ42/40 ratio Tau-pT181 level. Sleep treatment for patients with SDB significantly improved all neuropsychological scores, Aβ42/40 ratio, and Tau-pT181 levels. However, 11 patients did not completely recover from a sleep disorder (PSQI > 5 post-treatment). In this subgroup of patients, although HAMA score and Tau-pT181 levels were significantly reduced, MoCA and HRSD-24 scores, as well as Aβ42/40 ratio, were not significantly improved. ROC analysis found that the blood Aβ42/40 ratio held the highest significance in predicting sleep disorder occurrence. Conclusions: This is the first clinical study on sleep quality improvement in Alzheimer’s disease patients. Sleep quality score was associated with patient depression and anxiety scores, as well as Aβ42/40 ratio and Tau-pT181 levels. A complete recovery is critical for fully improving all neuropsychological assessments, Aβ42/40 ratio, and Tau-pT181 levels. Blood Aβ42/40 ratio is a feasible prognostic factor for predicting sleep quality.

Abstract Tendency to forgive refers to one’s global dispositional level of forgiveness across situations and relationships. Brain imaging studies examined activation patterns underlying forgiving response, yet focal differences in brain structures related to tendency to forgive have never been investigated. In this study, voxel-based morphometry was used to investigate relations between gray matter/white matter volume (GMV/WMV) and individual differences in tendency to forgive in a large young sample. Participants were 199 young students (60 men) who completed the tendency to forgive scale (TTF) and underwent an anatomical magnetic resonance imaging scan. Results showed that higher TTF scores were associated with larger GMV in the regions of dorsolateral prefrontal cortex, and smaller GMV in the regions of the right insular cortex and inferior frontal gyrus (IFG). Moreover, higher TTF scores were also related to smaller WMV in the regions of the left IFG. Together, these findings suggest structural variations for individual differences in the tendency to forgive, distributed across different brain regions associated with empathic response and cognitive control.