Mapping the mentalizing brain: An ALE meta-analysis to differentiate the representation of social scenes and ages on theory of mind.

Theory of mind (ToM) involves understanding others' mental states and relies on brain regions like the temporoparietal junction (TPJ) and medial prefrontal cortex (mPFC). This meta-analytic review categorizes ToM studies into six sub-components across three pairs: (1) Theory of collective mind (ToCM) and individualized theory of mind (iToM), (2) Social intention ToM and private intention ToM, and (3) ToM in adults and ToM in children. We conducted coordinate-based activation likelihood estimation (ALE) analyses and meta-analytic connectivity modeling (MACM) for each sub-component. We found that the ToM components utilized in social or group situations were associated with both the dorsomedial PFC (dmPFC) and right superior temporal sulcus (STS), whereas the ToM components focused on personal concentration were associated with both the lateral PFC and the left STS. The coactivation patterns for the group and age sub-component pairs showed significant spatial overlap with the language networks. These findings indicate that ToM is a multidimensional construct that is related to distinct functional networks for processing each of the ToM sub-components.

Functional specialization of medial and lateral orbitofrontal cortex in inferential decision-making.

Inferring prospective outcomes and updating behavior are prerequisites for making flexible decisions in the changing world. These abilities are highly associated with the functions of the orbitofrontal cortex (OFC) in humans and animals. The functional specialization of OFC subregions in decision-making has been established in animals. However, the understanding of how human OFC contributes to decision-making remains limited. Therefore, we studied this issue by examining the information representation and functional interactions of human OFC subregions during inference-based decision-making. We found that the medial OFC (mOFC) and lateral OFC (lOFC) collectively represented the inferred outcomes which, however, were context-general coding in the mOFC and context-specific in the lOFC. Furthermore, the mOFC-motor and lOFC-frontoparietal functional connectivity may indicate the motor execution of mOFC and the cognitive control of lOFC during behavioral updating. In conclusion, our findings support the dissociable functional roles of OFC subregions in decision-making.

The long-term intensive gymnastic training influences functional stability and integration: A resting-state fMRI study.

INTRODUCTION: Long-term motor skill training has been shown to induce anatomical and functional neuroplasticity. World class gymnasts (WCGs) provide a unique opportunity to investigate the effect of long-term intensive training on neuroplasticity. Previous resting-state fMRI studies have demonstrated a high efficient information processing related to motor and cognitive functions in gymnasts compared with healthy controls (HCs). However, most research treated brain signals as static, overlooking the fact that the brain is a complex and dynamic system. In this study, we employed functional stability, a new metric based on dynamic functional connectivity (FC), to examine the impact of long-term intensive training on the functional architecture in the WCGs. METHODS: We first conducted a voxel-wise analysis of functional stability between the WCGs and HCs. Then, we applied FC density (FCD) to explore whether regions with modified functional stability were also accompanied by changes in connection patterns in the WCGs. We identified overlapping regions showing significant differences in both functional stability and FCD. Finally, we applied seed-based correlation analysis (SCA) to determine the detailed changes in connection patterns between the WCGs and HCs within these overlapping regions. RESULTS: Compared with the HCs, the WCGs exhibited higher functional stability in the bilateral angular gyrus (AG), bilateral inferior temporal gyrus (ITG), bilateral precentral gyrus, and right superior frontal gyrus and lower functional stability in the bilateral hippocampus, bilateral caudate, right rolandic operculum, left superior temporal gyrus, right middle frontal gyrus, right middle cingular cortex, and right precuneus than the HCs. We found that the bilateral AG and ITG not only showed higher functional stability but also increased global and long-range FCD in the WCGs relative to the HCs. The right precuneus displayed lower functional stability as well as decreased local, long-range, and global FCD in the WCGs. Both AG and ITG showed higher FC with regions in the default mode network (DMN) in the WCGs than in the HCs. CONCLUSIONS: The increased functional stability in the AG and ITG might be associated with enhanced functional integration within the DMN in the WCGs. These findings may offer new spatiotemporal evidence for the impact of long-term intensive training on neuroplasticity.

Forming cognitive maps for abstract spaces: the roles of the human hippocampus and orbitofrontal cortex.

How does the human brain construct cognitive maps for decision-making and inference? Here, we conduct an fMRI study on a navigation task in multidimensional abstract spaces. Using a deep neural network model, we assess learning levels and categorized paths into exploration and exploitation stages. Univariate analyses show higher activation in the bilateral hippocampus and lateral prefrontal cortex during exploration, positively associated with learning level and response accuracy. Conversely, the bilateral orbitofrontal cortex (OFC) and retrosplenial cortex show higher activation during exploitation, negatively associated with learning level and response accuracy. Representational similarity analysis show that the hippocampus, entorhinal cortex, and OFC more accurately represent destinations in exploitation than exploration stages. These findings highlight the collaboration between the medial temporal lobe and prefrontal cortex in learning abstract space structures. The hippocampus may be involved in spatial memory formation and representation, while the OFC integrates sensory information for decision-making in multidimensional abstract spaces.

Initiating PeriCBD to probe perinatal influences on neurodevelopment during 3-10 years in China.

Adverse perinatal factors can interfere with the normal development of the brain, potentially resulting in long-term effects on the comprehensive development of children. Presently, the understanding of cognitive and neurodevelopmental processes under conditions of adverse perinatal factors is substantially limited. There is a critical need for an open resource that integrates various perinatal factors with the development of the brain and mental health to facilitate a deeper understanding of these developmental trajectories. In this Data Descriptor, we introduce a multicenter database containing information on perinatal factors that can potentially influence children's brain-mind development, namely, periCBD, that combines neuroimaging and behavioural phenotypes with perinatal factors at county/region/central district hospitals. PeriCBD was designed to establish a platform for the investigation of individual differences in brain-mind development associated with perinatal factors among children aged 3-10 years. Ultimately, our goal is to help understand how different adverse perinatal factors specifically impact cognitive development and neurodevelopment. Herein, we provide a systematic overview of the data acquisition/cleaning/quality control/sharing, processes of periCBD.

Neural substrates of affective temperaments: An intersubject representational similarity analysis to resting-state functional magnetic resonance imaging in nonclinical subjects.

Previous research has suggested that certain types of the affective temperament, including depressive, cyclothymic, hyperthymic, irritable, and anxious, are subclinical manifestations and precursors of mental disorders. However, the neural mechanisms that underlie these temperaments are not fully understood. The aim of this study was to identify the brain regions associated with different affective temperaments. We collected the resting-state functional magnetic resonance imaging (fMRI) data from 211 healthy adults and evaluated their affective temperaments using the Temperament Evaluation of Memphis, Pisa, Paris and San Diego Autoquestionnaire. We used intersubject representational similarity analysis to identify brain regions associated with each affective temperament. Brain regions associated with each affective temperament were detected. These regions included the prefrontal cortex, anterior cingulate cortex (ACC), precuneus, amygdala, thalami, hippocampus, and visual areas. The ACC, lingual gyri, and precuneus showed similar activity across several affective temperaments. The similarity in related brain regions was high among the cyclothymic, irritable, and anxious temperaments, and low between hyperthymic and the other affective temperaments. These findings may advance our understanding of the neural mechanisms underlying affective temperaments and their potential relationship to mental disorders and may have potential implications for personalized treatment strategies for mood disorders.

Dissociable contributions of the hippocampus and orbitofrontal cortex to representing task space in a social context.

The hippocampus (HC) and the orbitofrontal cortex (OFC) jointly encode a map-like representation of a task space to guide behavior. It remains unclear how the OFC and HC interact in encoding this map-like representation, though previous studies indicated that both regions have different functions. We acquired the functional magnetic resonance imaging data under a social navigation task in which participants interacted with characters in a two-dimensional "social space." We calculate the social relationships between the participants and characters and used a drift-diffusion model to capture the inner process of social interaction. Then we used multivoxel pattern analysis to explore the brain-behavior relationship. We found that (i) both the HC and the OFC showed higher activations during the selective trial than the narrative trial; (ii) the neural pattern of the right HC was associated with evidence accumulation during social interaction, and the pattern of the right lateral OFC was associated with the social relationship; (iii) the neural pattern of the HC can decode the participants choices, while the neural pattern of the OFC can decode the task information about trials. The study provided evidence for distinct roles of the HC and the OFC in encoding different information when representing social space.

Functional brain network alterations in the co-occurrence of autism spectrum disorder and attention deficit hyperactivity disorder.

Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are two highly prevalent and commonly co-occurring neurodevelopmental disorders. The neural mechanisms underpinning the comorbidity of ASD and ADHD (ASD + ADHD) remain unclear. We focused on the topological organization and functional connectivity of brain networks in ASD + ADHD patients versus ASD patients without ADHD (ASD-only). Resting-state functional magnetic resonance imaging (rs-fMRI) data from 114 ASD and 161 typically developing (TD) individuals were obtained from the Autism Brain Imaging Data Exchange II. The ASD patients comprised 40 ASD + ADHD and 74 ASD-only individuals. We constructed functional brain networks for each group and performed graph-theory and network-based statistic (NBS) analyses. Group differences between ASD + ADHD and ASD-only were analyzed at three levels: nodal, global, and connectivity. At the nodal level, ASD + ADHD exhibited topological disorganization in the temporal and occipital regions, compared with ASD-only. At the global level, ASD + ADHD and ASD-only displayed no significant differences. At the connectivity level, the NBS analysis revealed that ASD + ADHD showed enhanced functional connectivity between the prefrontal and frontoparietal regions, as well as between the orbitofrontal and occipital regions, compared with ASD-only. The hippocampus was the shared region in aberrant functional connectivity patterns in ASD + ADHD and ASD-only compared with TD. These findings suggests that ASD + ADHD displays altered topology and functional connectivity in the brain regions that undertake social cognition, language processing, and sensory processing.

Global-brain functional connectivity related with trait anxiety and its association with neurotransmitters and gene expression profiles.

BACKGROUND: Numerous studies have explored the neural correlates of trait anxiety, a predisposing factor for several stress-related disorders. However, the findings from previous studies are inconsistent, which might be due to the limited regions of interest (ROI). A recent approach, named global-brain functional connectivity (GBC), has been demonstrated to address the shortcomings of ROI-based analysis. Furthermore, research on the transcriptome-connectome association has provided an approach to link the microlevel transcriptome profile with the macroscale brain network. In this paper, we aim to explore the neurobiology of trait anxiety with an imaging transcriptomic approach using GBC, biological neurotransmitters, and transcriptome profiles. METHODS: Using a sample of resting-state fMRI data, we investigated trait anxiety-related alteration in GBC. We further used behavioral analysis, spatial correlation analysis, and postmortem gene expression to separately assess the cognitive functions, neurotransmitters, and transcriptional profiles related to alteration in GBC in individuals with trait anxiety. RESULTS: GBC values in the ventromedial prefrontal cortex and the precuneus were negatively correlated with levels of trait anxiety. This alteration was correlated with behavioral terms including social cognition, emotion, and memory. A strong association was revealed between trait anxiety-related alteration in GBC and neurotransmitters, including dopaminergic, serotonergic, GABAergic, and glutamatergic systems in the ventromedial prefrontal cortex and the precuneus. The transcriptional profiles explained the functional connectivity, with correlated genes enriched in transmembrane signaling. LIMITATIONS: Several limitations should be taken into account in this research. For example, future research should consider using some different approaches based on dynamic or task-based functional connectivity analysis, include more neurotransmitter receptors, additional gene expression data from different samples or more genes related to other stress-related disorders. Meanwhile, it is of great significance to include a larger sample size of individuals with a diagnosis of major depression disorder or other disorders for analysis and comparison and apply stricter multiple-comparison correction and threshold settings in future research. CONCLUSIONS: Our research employed multimodal data to investigate GBC in the context of trait anxiety and to establish its associations with neurotransmitters and transcriptome profiles. This approach may improve understanding of the neural mechanism, together with the biological and molecular genetic foundations of GBC in trait anxiety.

Altered brain spontaneous activity in patients with cerebral small vessel disease using the amplitude of low-frequency fluctuation of different frequency bands.

Background: Previous studies showed that cerebral small vessel disease (cSVD) is a leading cause of cognitive decline in elderly people and the development of Alzheimer's disease. Although brain structural changes of cSVD have been documented well, it remains unclear about the properties of brain intrinsic spontaneous activity in patients with cSVD. Methods: We collected resting-state fMRI (rs-fMRI) and T1-weighted 3D high-resolution brain structural images from 41 cSVD patients and 32 healthy controls (HC). By estimating the amplitude of low-frequency fluctuation (ALFF) under three different frequency bands (typical band: 0.01-0.1 Hz; slow-4: 0.027-0.073 Hz; and slow-5: 0.01-0.027 Hz) in the whole-brain, we analyzed band-specific ALFF differences between the cSVD patients and controls. Results: The cSVD patients showed uniformly lower ALFF than the healthy controls in the typical and slow-4 bands (pFWE < 0.05). In the typical band, cSVD patients showed lower ALFF involving voxels of the fusiform, hippocampus, inferior occipital cortex, middle occipital cortex, insula, inferior frontal cortex, rolandic operculum, and cerebellum compared with the controls. In the slow-4 band, cSVD patients showed lower ALFF involving voxels of the cerebellum, hippocampus, occipital, and fusiform compared with the controls. However, there is no significant between-group difference of ALFF in the slow-5 band. Moreover, we found significant "group × frequency" interactions in the left precuneus. Conclusion: Our results suggested that brain intrinsic spontaneous activity of cSVD patients was abnormal and showed a frequency-specific characteristic. The ALFF in the slow-4 band may be more sensitive to detecting a malfunction in cSVD patients.

Functional connectome through the human life span.

The functional connectome of the human brain represents the fundamental network architecture of functional interdependence in brain activity, but its normative growth trajectory across the life course remains unknown. Here, we aggregate the largest, quality-controlled multimodal neuroimaging dataset from 119 global sites, including 33,809 task-free fMRI and structural MRI scans from 32,328 individuals ranging in age from 32 postmenstrual weeks to 80 years. Lifespan growth charts of the connectome are quantified at the whole cortex, system, and regional levels using generalized additive models for location, scale, and shape. We report critical inflection points in the non-linear growth trajectories of the whole-brain functional connectome, particularly peaking in the fourth decade of life. Having established the first fine-grained, lifespan-spanning suite of system-level brain atlases, we generate person-specific parcellation maps and further show distinct maturation timelines for functional segregation within different subsystems. We identify a spatiotemporal gradient axis that governs the life-course growth of regional connectivity, transitioning from primary sensory cortices to higher-order association regions. Using the connectome-based normative model, we demonstrate substantial individual heterogeneities at the network level in patients with autism spectrum disorder and patients with major depressive disorder. Our findings shed light on the life-course evolution of the functional connectome and serve as a normative reference for quantifying individual variation in patients with neurological and psychiatric disorders.

Functional network segregation is associated with higher functional connectivity in endurance runners.

Neuroimaging studies have identified significant differences in brain structure, function, and connectivity between endurance runners and healthy controls. However, the topological organization of large-scale functional brain networks remains unexplored in endurance runners. Using resting-state functional magnetic resonance imaging data, this study examined the differences in the topological organization of functional networks between endurance runners (n = 22) and healthy controls (n = 20). Endurance runners had significantly higher clustering coefficients in the whole-brain functional network than healthy controls, but the two did not differ regarding the shortest path length or small-world index. Using network-based statistics, we identified one subnetwork in endurance runners with higher functional connectivity than healthy controls, and the mean functional connectivity of the subnetwork significantly correlated with the three aforementioned small-world parameters. In this subnetwork, the mean clustering coefficient of nodes associated with short-range connections was higher in endurance runners than in healthy controls, but the mean clustering coefficient of nodes associated with long-range connections did not differ between the two groups. In conclusion, using graph theoretical approaches, we revealed significant differences in the topological organization of the whole-brain functional network and functional connectivity between endurance runners and healthy controls. The relationship between these two features suggests that a more segregated network may arise from the optimization of the identified subnetwork in endurance runners. These findings are possibly the neural basis underlying the good performance of endurance runners in endurance running.

Altered cortical myelin in the salience and default mode networks in major depressive disorder patients: A surface-based analysis.

INTRODUCTION: Evidence from previous genetic and post-mortem studies suggested that the myelination abnormality contributed to the pathogenesis of major depressive disorder (MDD). However, image-level alterations in cortical myelin content associated with MDD are still unclear. METHODS: The high-resolution T1-weighted (T1w) and T2-weighted (T2w) brain 3D structural images were obtained from 52 MDD patients and 52 healthy controls (HC). We calculated the vertex-based T1w/T2w ratio using the HCP structural pipelines to characterize individual cortical myelin maps at the fs_LR 32 k surface. We attempted to detect the clusters with significant differences in cortical myelin content between MDD and HC groups. We correlated the cluster-wise averaged myelin value and the clinical performances in MDD patients. RESULTS: The MDD patients showed significantly lower cortical myelin content in the cluster involving the left insula, orbitofrontal cortex, superior temporal cortex, transverse temporal gyrus, inferior frontal cortex, superior frontal gyrus, anterior cingulate cortex, precentral cortex, and postcentral cortex. The correlation analysis showed a significantly positive correlation between the cluster-wise cortical myelin content and the onset age of MDD patients. CONCLUSION: The MDD patients showed lower cortical myelin content in regions of the default mode network regions and salience network than healthy controls.

Multi-modal MRI measures reveal sensory abnormalities in major depressive disorder patients: A surface-based study.

BACKGROUND: Multi-modal magnetic resonance imaging (MRI) measures are supposed to be able to capture different brain neurobiological aspects of major depressive disorder (MDD). A fusion analysis of structural and functional modalities may better reveal the disease biomarker specific to the MDD disease. METHODS: We recruited 30 MDD patients and 30 matched healthy controls (HC). For each subject, we acquired high-resolution brain structural images and resting-state fMRI (rs-fMRI) data using a 3 T MRI scanner. We first extracted the brain morphometric measures, including the cortical volume (CV), cortical thickness (CT), and surface area (SA), for each subject from the structural images, and then detected the structural clusters showing significant between-group differences in each measure using the surface-based morphology (SBM) analysis. By taking the identified structural clusters as seeds, we performed seed-based functional connectivity (FC) analyses to determine the regions with abnormal FC in the patients. Based on a logistic regression model, we performed a classification analysis by selecting these structural and functional cluster-wise measures as features to distinguish the MDD patients from the HC. RESULTS: The MDD patients showed significantly lower CV in a cluster involving the right superior temporal gyrus (STG) and middle temporal gyrus (MTG), and lower SA in three clusters involving the bilateral STG, temporal pole gyrus, and entorhinal cortex, and the left inferior temporal gyrus, and fusiform gyrus, than the controls. No significant difference in CT was detected between the two groups. By taking the above-detected clusters as seeds to perform the seed-based FC analysis, we found that the MDD patients showed significantly lower FC between STG/MTG (CV's cluster) and two clusters located in the bilateral visual cortices than the controls. The logistic regression model based on the structural and functional features reached a classification accuracy of 86.7% (p < 0.001) between MDD and controls. CONCLUSION: The present study showed sensory abnormalities in MDD patients using the multi-modal MRI analysis. This finding may act as a disease biomarker distinguishing MDD patients from healthy individuals.

The Effect of Moral Behavior on Facial Attractiveness.

Purpose: Evaluating face attractiveness is a core aspect of face perception, which plays an important role in impression formation. A more reliable source of information in impression formation is moral behavior, which forms the primary basis for the comprehensive evaluation of others. Previous studies have found that one can easily form an association when faces and moral behaviors are presented together, which in turn affects facial attractiveness evaluation. However, little is known of the extent to which these learned associations affect facial attractiveness and whether the influence of moral behavior on facial attractiveness was related to facial appearance. Methods: We used the associative learning paradigm and manipulated face presentation duration (Experiment 1 and Experiment 2) and response deadline (Experiment 2) to investigate these issues. Under these conditions, the association information was difficult to be retrieved. Participants learned associations between faces and scenes of moral behavior, and then evaluated facial attractiveness. Results: We found that both moral behavior and facial appearance influence facial attractiveness under conditions where associated information was difficult to retrieve, and their effects increased with the increase of face presentation time. With increasing response deadlines, the effect of moral behavior on facial attractiveness increased. The influence of moral behavior on facial attractiveness was associated with facial appearance. Conclusion: These results suggest that moral behavior continuously affects facial attractiveness. Our findings expand previous research by showing a robust influence of moral behavior on facial attractiveness evaluation, and highlight the important role of moral character in impression formation.

Representation of the inferred relationships in a map-like space.

A cognitive map is an internal representation of the external world that guides flexible behavior in a complex environment. Cognitive map theory assumes that relationships between entities can be organized using Euclidean-based coordinates. Previous studies revealed that cognitive map theory can also be generalized to inferences about abstract spaces, such as social spaces. However, it is still unclear whether humans can construct a cognitive map by combining relational knowledge between discrete entities with multiple abstract dimensions in nonsocial spaces. Here we asked subjects to learn to navigate a novel object space defined by two feature dimensions, price and abstraction. The subjects first learned the rank relationships between objects in each feature dimension and then completed a transitive inferences task. We recorded brain activity using functional magnetic resonance imaging (fMRI) while they performed the transitive inference task. By analyzing the behavioral data, we found that the Euclidean distance between objects had a significant effect on response time (RT). The longer the one-dimensional rank distance and two-dimensional (2D) Euclidean distance between objects the shorter the RT. The task-fMRI data were analyzed using both univariate analysis and representational similarity analysis. We found that the hippocampus, entorhinal cortex, and medial orbitofrontal cortex were able to represent the Euclidean distance between objects in 2D space. Our findings suggest that relationship inferences between discrete objects can be made in a 2D nonsocial space and that the neural basis of this inference is related to cognitive maps.

Disrupted multi-scale topological organization of directed functional brain networks in patients with disorders of consciousness.

Disorders of consciousness are impaired states of consciousness caused by severe brain injuries. Previous resting-state functional magnetic resonance imaging studies have reported abnormal brain network properties at different topological scales in patients with disorders of consciousness by using graph theoretical analysis. However, it is still unclear how inter-regional directed propagation activities affect the topological organization of functional brain networks in patients with disorders of consciousness. To reveal the altered topological organization in patients with disorders of consciousness, we constructed whole-brain directed functional networks by combining functional connectivity analysis and time delay estimation. Then we performed graph theoretical analysis based on the directed functional brain networks at three topological scales, from the nodal scale, the resting-state network scale to the global scale. Finally, the canonical correlation analysis was used to determine the correlations between altered topological properties and clinical scores in patients with disorders of consciousness. At the nodal scale, we observed decreased in-degree and increased out-degree in the precuneus in patients with disorders of consciousness. At the resting-state network scale, the patients with disorders of consciousness showed reorganized motif patterns within the default mode network and between the default mode network and other resting-state networks. At the global scale, we found a lower global clustering coefficient in the patients with disorders of consciousness than in the controls. The results of the canonical correlation analysis showed that the abnormal degree and the disrupted motif were significantly correlated with the clinical scores of the patients with disorders of consciousness. Our findings showed that consciousness impairment can be revealed by abnormal directed connection patterns at multiple topological scales in the whole brain, and the disrupted directed connection patterns may serve as clinical biomarkers to assess the dysfunction of patients with disorders of consciousness.

Disrupted resting-state brain functional network properties in non-neuropsychiatric systemic lupus erythematosus patients.

INTRODUCTION: Previous fMRI studies revealed that the abnormal functional connectivity (FC) was related to cognitive impairment in patients with SLE. However, it remains unclear how the disease severity affects the functional topological organization of the whole-brain network in SLE patients without neuropsychiatric symptoms (non-NPSLE). OBJECTIVE: We aim to examine the impairment of the whole-brain functional network in SLE patients without neuropsychiatric symptoms (non-NPSLE), which may improve the understanding of neural mechanism in SLE. METHODS: We acquired resting-state fMRI data from 32 non-NPSLE patients and 32 healthy controls (HC), constructed their whole-brain functional network, and then estimated the topological properties including global and nodal parameters by using graph theory. Meanwhile, we also investigated the differences in intra- and inter-network FC between the non-NPSLE patients and the HC. RESULTS: The non-NPSLE patients showed significantly lower clustering coefficient, global and local efficiency, but higher characteristic path length than the HC. The non-NPSLE patients had significantly lower nodal strength in two regions, ventromedial prefrontal cortex (vmPFC) and anterior PFC (aPFC) than the HC. We found the non-NPSLE patients had significantly lower intra-network FC within frontal-parietal network (FPN) and within default mode network (DMN), and significantly lower inter-network FC between DMN and FPN than the HC. The intra-network FC within DMN was negatively correlated with systemic lupus erythematosus disease activity index (SLEDAI). CONCLUSION: Abnormal whole-brain functional network properties and abnormal intra- and inter-network FC may be related to cognitive impairment and disease degree in the non-NPSLE patients. Our findings provide a network perspective to understand the neural mechanisms of SLE.

Social navigation modulates the anterior and posterior hippocampal circuits in the resting brain.

Social navigation is a dynamic and complex process that requires the collaboration of multiple brain regions. However, the neural networks for navigation in a social space remain largely unknown. This study aimed to investigate the role of hippocampal circuit in social navigation from a resting-state fMRI data. Here, resting-state fMRI data were acquired before and after participants performed a social navigation task. By taking the anterior and posterior hippocampus (HPC) as the seeds, we calculated their connectivity with the whole brain using the seed-based static functional connectivity (sFC) and dynamic FC (dFC) approaches. We found that the sFC and dFC between the anterior HPC and supramarginal gyrus, sFC or dFC between posterior HPC and middle cingulate cortex, inferior parietal gyrus, angular gyrus, posterior cerebellum, medial superior frontal gyrus were increased after the social navigation task. These alterations were related to social cognition of tracking location in the social navigation. Moreover, participants who had more social support or less neuroticism showed a greater increase in hippocampal connectivity. These findings may highlight a more important role of the posterior hippocampal circuit in the social navigation, which is crucial for social cognition.

The prevalence and risk of developing major depression among individuals with subthreshold depression in the general population.

BACKGROUND: Subthreshold depression could be a significant precursor to and a risk factor for major depression. However, reliable estimates of the prevalence and its contribution to developing major depression under different terminologies depicting subthreshold depression have to be established. METHODS: By searching PubMed and Web of Science using predefined inclusion criteria, we included 1 129 969 individuals from 113 studies conducted. The prevalence estimates were calculated using the random effect model. The incidence risk ratio (IRR) was estimated by measuring the ratio of individuals with subthreshold depression who developed major depression compared to that of non-depressed individuals from 19 studies (88, 882 individuals). RESULTS: No significant difference in the prevalence among the different terminologies depicting subthreshold depression (Q = 1.96, p = 0.5801) was found. By pooling the prevalence estimates of subthreshold depression in 113 studies, we obtained a summary prevalence of 11.02% [95% confidence interval (CI) 9.78-12.33%]. The youth group had the highest prevalence (14.17%, 95% CI 8.82-20.55%), followed by the elderly group (12.95%, 95% CI 11.41-14.58%) and the adult group (8.92%, 95% CI 7.51-10.45%). Further analysis of 19 studies' incidence rates showed individuals with subthreshold depression had an increased risk of developing major depression (IRR = 2.95, 95% CI 2.33-3.73), and the term minor depression showed the highest IRR compared with other terms (IRR = 3.97, 95% CI 3.17-4.96). CONCLUSIONS: Depression could be a spectrum disorder, with subthreshold depression being a significant precursor to and a risk factor for major depression. Proactive management of subthreshold depression could be effective for managing the increasing prevalence of major depression.