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Insofar as the autistic-like phenotype presents in the general population, it consists of partially dissociable traits, such as social and sensory issues. Here, we investigate individual differences in cortical organisation related to autistic-like traits. Connectome gradient decomposition based on resting state fMRI data reliably reveals a principal gradient spanning from unimodal to transmodal regions, reflecting the transition from perception to abstract cognition. In our non-clinical sample, this gradient's expansion, indicating less integration between visual and default mode networks, correlates with subjective sensory sensitivity (measured using the Glasgow Sensory Questionnaire, GSQ), but not other autistic-like traits (measured using the Autism Spectrum Quotient, AQ). This novel brain-based correlate of the GSQ demonstrates sensory issues can be disentangled from the wider autistic-like phenotype.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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

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.