Impaired topology and connectivity of grey matter structural networks in major depressive disorder: evidence from a multi-site neuroimaging data-set.

BACKGROUND: Major depressive disorder (MDD) has been increasingly understood as a disruption of brain connectome. Investigating grey matter structural networks with a large sample size can provide valuable insights into the structural basis of network-level neuropathological underpinnings of MDD. AIMS: Using a multisite MRI data-set including nearly 2000 individuals, this study aimed to identify robust topology and connectivity abnormalities of grey matter structural network linked to MDD and relevant clinical phenotypes. METHOD: A total of 955 MDD patients and 1009 healthy controls were included from 23 sites. Individualised structural covariance networks (SCN) were established based on grey matter volume maps. Following data harmonisation, network topological metrics and focal connectivity were examined for group-level comparisons, individual-level classification performance and association with clinical ratings. Various validation strategies were applied to confirm the reliability of findings. RESULTS: Compared with healthy controls, MDD individuals exhibited increased global efficiency, abnormal regional centralities (i.e. thalamus, precentral gyrus, middle cingulate cortex and default mode network) and altered circuit connectivity (i.e. ventral attention network and frontoparietal network). First-episode drug-naive and recurrent patients exhibited different patterns of deficits in network topology and connectivity. In addition, the individual-level classification of topological metrics outperforms that of structural connectivity. The thalamus-insula connectivity was positively associated with the severity of depressive symptoms. CONCLUSIONS: Based on this high-powered data-set, we identified reliable patterns of impaired topology and connectivity of individualised SCN in MDD and relevant subtypes, which adds to the current understanding of neuropathology of MDD and might guide future development of diagnostic and therapeutic markers.

Transcriptional Patterns of Brain Structural Covariance Network Abnormalities Associated With Suicidal Thoughts and Behaviors in Major Depressive Disorder.

BACKGROUND: Although brain structural covariance network (SCN) abnormalities have been associated with suicidal thoughts and behaviors (STBs) in individuals with major depressive disorder (MDD), previous studies have reported inconsistent findings based on small sample sizes, and underlying transcriptional patterns remain poorly understood. METHODS: Using a multicenter magnetic resonance imaging dataset including 218 MDD patients with STBs, 230 MDD patients without STBs, and 263 healthy control participants, we established individualized SCNs based on regional morphometric measures and assessed network topological metrics using graph theoretical analysis. Machine learning methods were applied to explore and compare the diagnostic value of morphometric and topological features in identifying MDD and STBs at the individual level. Brainwide relationships between STBs-related connectomic alterations and gene expression were examined using partial least squares regression. RESULTS: Group comparisons revealed that SCN topological deficits associated with STBs were identified in the prefrontal, anterior cingulate, and lateral temporal cortices. Combining morphometric and topological features allowed for individual-level characterization of MDD and STBs. Topological features made a greater contribution to distinguishing between patients with and without STBs. STBs-related connectomic alterations were spatially correlated with the expression of genes enriched for cellular metabolism and synaptic signaling. CONCLUSIONS: These findings revealed robust brain structural deficits at the network level, highlighting the importance of SCN topological measures in characterizing individual suicidality and demonstrating its linkage to molecular function and cell types, providing novel insights into the neurobiological underpinnings and potential markers for prediction and prevention of suicide.

GLI family zinc finger protein 2 promotes skin fibroblast proliferation and DNA damage repair by targeting the miR-200/ataxia telangiectasia mutated axis in diabetic wound healing.

Diabetic foot ulcer (DFU) is a serious complication of diabetic patients which negatively affects their foot health. This study aimed to estimate the role and mechanism of the miR-200 family in DNA damage of diabetic wound healing. Human foreskin fibroblasts (HFF-1 cells) were stimulated with high glucose (HG). Db/db mice were utilized to conduct the DFU in vivo model. Cell viability was evaluated using 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide assays. Superoxide dismutase activity was determined using detection kits. Reactive oxygen species determination was conducted via dichlorodihydrofluorescein-diacetate assays. Enzyme-linked immunosorbent assay was used to evaluate 8-oxo-7,8-dihydro-2'deoxyguanosine levels. Genes and protein expression were analyzed by quantitative real-time polymerase chain reaction, western blotting, or immunohistochemical analyses. Luciferase reporter gene and RNA immunoprecipitation assays determined the interaction with miR-200a/b/c-3p and GLI family zinc finger protein 2 (GLI2) or ataxia telangiectasia mutated (ATM) kinase. HG repressed cell proliferation and DNA damage repair, promoted miR-200a/b/c-3p expression, and suppressed ATM and GLI2. MiR-200a/b/c-3p inhibition ameliorated HG-induced cell proliferation and DNA damage repair repression. MiR-200a/b/c-3p targeted ATM. Then, the silenced ATM reversed the miR-200a/b/c-3p inhibition-mediated alleviative effects under HG. Next, GLI2 overexpression alleviated the HG-induced cell proliferation and DNA damage repair inhibition via miR-200a/b/c-3p. MiR-200a/b/c-3p inhibition significantly promoted DNA damage repair and wound healing in DFU mice. GLI2 promoted cell proliferation and DNA damage repair by regulating the miR-200/ATM axis to enhance diabetic wound healing in DFU.

Multivariate patterns of brain functional connectome associated with COVID-19-related negative affect symptoms.

Severe mental health problems with the representation of negative affect symptoms (NAS) have been increasingly reported during the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to explore the multivariate patterns of brain functional connectome predicting COVID-19-related NAS. This cohort study encompassed a group of university students to undergo neuroimaging scans before the pandemic, and we re-contacted participants for 1-year follow-up COVID-related NAS evaluations during the pandemic. Regularized canonical correlation analysis was used to identify connectome-based dimensions of NAS to compute pairs of canonical variates. The predictive ability of identified functional connectome to NAS dimensional scores was examined with a nested cross-validation. Two dimensions (i.e. mode stress and mode anxiety) were related to distinct patterns of brain functional connectome (r2 = 0.911, PFDR = 0.048; r2 = 0.901, PFDR = 0.037, respectively). Mode anxiety was characterized by high loadings in connectivity between affective network (AFN) and visual network (VN), while connectivity of the default mode network with dorsal attention network (DAN) were remarkably prominent in mode stress. Connectivity patterns within the DAN and between DAN and VN, ventral attention network, and AFN was common for both dimensions. The identified functional connectome can reliably predict mode stress (r = 0.37, MAE = 5.1, p < 0.001) and mode anxiety (r = 0.28, MAE = 5.4, p = 0.005) in the cross-validation. Our findings provide new insight into multivariate dimensions of COVID-related NAS, which may have implications for developing network-based biomarkers in psychological interventions for vulnerable individuals in the pandemic.

Aberrant brain network topology in youth with a familial risk for bipolar disorder: a task-based fMRI connectome study.

BACKGROUND: Youth with a family history of bipolar disorder (BD) may be at increased risk for mood disorders and for developing side effects after antidepressant exposure. The neurobiological basis of these risks remains poorly understood. We aimed to identify biomarkers underlying risk by characterizing abnormalities in the brain connectome of symptomatic youth at familial risk for BD. METHODS: Depressed and/or anxious youth (n = 119, age = 14.9 ± 1.6 years) with a family history of BD but no prior antidepressant exposure and typically developing controls (n = 57, age = 14.8 ± 1.7 years) received functional magnetic resonance imaging (fMRI) during an emotional continuous performance task. A generalized psychophysiological interaction (gPPI) analysis was performed to compare their brain connectome patterns, followed by machine learning of topological metrics. RESULTS: High-risk youth showed weaker connectivity patterns that were mainly located in the default mode network (DMN) (network weight = 50.1%) relative to controls, and connectivity patterns derived from the visual network (VN) constituted the largest proportion of aberrant stronger pairs (network weight = 54.9%). Global local efficiency (Elocal , p = .022) and clustering coefficient (Cp , p = .029) and nodal metrics of the right superior frontal gyrus (SFG) (Elocal : p < .001; Cp : p = .001) in the high-risk group were significantly higher than those in healthy subjects, and similar patterns were also found in the left insula (degree: p = .004; betweenness: p = .005; age-by-group interaction, p = .038) and right hippocampus (degree: p = .003; betweenness: p = .003). The case-control classifier achieved a cross-validation accuracy of 78.4%. CONCLUSIONS: Our findings of abnormal connectome organization in the DMN and VN may advance mechanistic understanding of risk for BD. Neuroimaging biomarkers of increased network segregation in the SFG and altered topological centrality in the insula and hippocampus in broader limbic systems may be used to target interventions tailored to mitigate the underlying risk of brain abnormalities in these at-risk youth.

Sex differences in the relationship between brain gray matter volume and psychological resilience in late adolescence.

Psychological resilience reflects an individual's ability to adapt and cope successfully in adverse environments and situations, making it a crucial trait in resisting stress-linked mental disorders and physical diseases. Although prior literature has consistently shown that males are more resilient than females, the sex-linked neuroanatomical correlates of psychological resilience are largely unknown. This study aims to explore the sex-specific relation between psychological resilience and brain gray matter volume (GMV) in adolescents via structural magnetic resonance imaging (s-MRI). A cohort of 231 healthy adolescents (121/110 females/males), aged 16 to 20 completed brain s-MRI scanning and Connor-Davidson Resilience Scale (CD-RISC) and other controlling behavioral tests. With s-MRI data, an optimized voxel-based morphometry method was used to estimate regional GMV, and a whole-brain condition-by-covariate interaction analysis was performed to identify the brain regions showing sex effects on the relation between psychological resilience and GMV. Male adolescents scored significantly higher than females on the CD-RISC. The association of psychological resilience with GMV differed between the two sex groups in the left ventrolateral prefrontal cortex extending to the adjacent anterior insula, with a positive correlation among males and a negative correlation among females. The sex-specific association between psychological resilience and GMV might be linked to sex differences in the hypothalamic-pituitary-adrenal axis and brain maturation during adolescence. This study may be novel in revealing the sex-linked neuroanatomical basis of psychological resilience, highlighting the need for a more thorough investigation of the role of sex in future studies of psychological resilience and stress-related illness.

Common and distinct neural correlates of emotional processing in individuals at familial risk for major depressive disorder and bipolar disorder: A comparative meta-analysis.

Individuals at familial risk for mood disorders exhibit deficits in emotional processing and associated brain dysfunction prior to illness onset. However, such brain-behavior abnormalities related to familial predisposition remain poorly understood. To investigate robust abnormal functional activation patterns during emotional processing in unaffected at-risk relatives of patients with major depressive disorder (UAR-MDD) and bipolar disorder (UAR-BD), we performed a meta-analysis of task-based functional magnetic resonance imaging studies using Seed-based d Mapping (SDM) toolbox. Common and distinct patterns of abnormal functional activation between UAR-MDD and UAR-BD were detected via conjunction and differential analyses. A total of 17 studies comparing 481 UAR and 670 healthy controls (HC) were included. Compared with HC, UAR-MDD exhibited hyperactivation in the parahippocampal gyrus, amygdala and cerebellum, while UAR-BD exhibited parahippocampal hyperactivation and hypoactivation in the striatum and middle occipital gyrus (MOG). Conjunction analysis revealed shared hyperactivated PHG in both groups. Differential analysis indicated that the activation patterns of amygdala and MOG significantly differed between UAR-MDD and UAR-BD. These findings provide novel insights into common and distinct neural phenotypes for familial risk and associated risk mechanisms in MDD and BD, which may have implications in guiding precise prevention strategies tailored to the family context.

Shared and Distinct Neurobiological Bases of Bipolar Disorder and Attention-Deficit/Hyperactivity Disorder in Children and Adolescents: A Comparative Meta-analysis of Structural Abnormalities.

OBJECTIVE: Pediatric bipolar disorder (PBD) and attention-deficit/hyperactivity disorder (ADHD) frequently co-occur and share dysfunctions in affective and cognitive domains. As the neural substrates underlying their overlapping and dissociable symptomatology have not been well delineated, a meta-analysis of whole-brain voxel-based morphometry studies in PBD and ADHD was conducted. METHOD: A systematic literature search was performed in PubMed, Web of Science, and Embase. The seed-based d mapping toolbox was used to identify altered clusters of PBD or ADHD and obtain their conjunctive and comparative abnormalities. Suprathreshold patterns were subjected to large-scale network analysis to identify affected brain networks. RESULTS: The search revealed 10 PBD studies (268 patients) and 32 ADHD studies (1,333 patients). Decreased gray matter volumes in the right insula and anterior cingulate cortex relative to typically developing individuals were conjunctive in PBD and ADHD. Reduced volumes in the right inferior frontal gyrus, left orbitofrontal cortex, and hippocampus were more substantial in PBD, while decreased volumes in the left precentral gyrus, left inferior frontal gyrus, and right superior frontal gyrus were more pronounced in ADHD. Neurodevelopmental effects modulated patterns of the left hippocampus in PBD and those of the left inferior frontal gyrus in ADHD. CONCLUSION: These findings suggest that PBD and ADHD are characterized by both common and distinct patterns of gray matter volume alterations. Their overlapping abnormalities may represent a transdiagnostic problem of attention and emotion regulation shared by PBD and ADHD, whereas the disorder-differentiating substrates may contribute to the relative differences in cognitive and affective features that define the 2 disorders. STUDY PREREGISTRATION INFORMATION: Structural Brain Abnormalities of Attention-Deficit/Hyperactivity Disorder and Bipolar Disorder in Children/Adolescents: An Overlapping Meta-analysis; https://osf.io/trg4m.

Distinct pre-COVID brain structural signatures in COVID-19-related post-traumatic stress symptoms and post-traumatic growth.

Post-traumatic stress symptoms and post-traumatic growth are common co-occurring psychological responses following exposure to traumatic events (such as COVID-19 pandemic), their mutual relationship remains unclear. To explore this relationship, structural magnetic resonance imaging data were acquired from 115 general college students before the COVID-19 pandemic, and follow-up post-traumatic stress symptoms and post-traumatic growth measurements were collected during the pandemic. Voxel-based morphometry was conducted and individual structural covariance networks based on gray matter volume were further analyzed using graph theory and partial least squares correlation. Behavioral correlation found no significant relationship between post-traumatic stress symptoms and post-traumatic growth. Voxel-based morphometry analyses showed that post-traumatic stress symptoms were positively correlated with gray matter volume in medial prefrontal cortex/dorsal anterior cingulate cortex, and post-traumatic growth was negatively correlated with gray matter volume in left dorsolateral prefrontal cortex. Structural covariance network analyses found that post-traumatic stress symptoms were negatively correlated with the local efficiency and clustering coefficient of the network. Moreover, partial least squares correlation showed that post-traumatic stress symptoms were correlated with pronounced nodal properties patterns in default mode, sensory and motor regions, and a marginal correlation of post-traumatic growth with a nodal property pattern in emotion regulation-related regions. This study advances our understanding of the neurobiological substrates of post-traumatic stress symptoms and post-traumatic growth, and suggests that they may have different neuroanatomical features.

Pre-COVID brain network topology prospectively predicts social anxiety alterations during the COVID-19 pandemic.

Background: Social anxiety (SA) is a negative emotional response that can lead to mental health issues, which some have experienced during the coronavirus disease 2019 (COVID-19) pandemic. Little attention has been given to the neurobiological mechanisms underlying inter-individual differences in SA alterations related to COVID-19. This study aims to identify neurofunctional markers of COVID-specific SA development. Methods: 110 healthy participants underwent resting-state magnetic resonance imaging and behavioral tests before the pandemic (T1, October 2019 to January 2020) and completed follow-up behavioral measurements during the pandemic (T2, February to May 2020). We constructed individual functional networks and used graph theoretical analysis to estimate their global and nodal topological properties, then used Pearson correlation and partial least squares correlations examine their associations with COVID-specific SA alterations. Results: In terms of global network parameters, SA alterations (T2-T1) were negatively related to pre-pandemic brain small-worldness and normalized clustering coefficient. In terms of nodal network parameters, SA alterations were positively linked to a pronounced degree centrality pattern, encompassing both the high-level cognitive networks (dorsal attention network, cingulo-opercular task control network, default mode network, memory retrieval network, fronto-parietal task control network, and subcortical network) and low-level perceptual networks (sensory/somatomotor network, auditory network, and visual network). These findings were robust after controlling for pre-pandemic general anxiety, other stressful life events, and family socioeconomic status, as well as by treating SA alterations as categorical variables. Conclusions: The individual functional network associated with SA alterations showed a disrupted topological organization with a more random state, which may shed light on the neurobiological basis of COVID-related SA changes at the network level.

Exosomal circHIPK3 derived from umbilical cord-derived mesenchymal stem cells enhances skin fibroblast autophagy by blocking miR-20b-5p/ULK1/Atg13 axis.

BACKGROUND: Umbilical cord-derived mesenchymal stem cells (UCMSCs) could alleviate diabetes-induced injury. Hence, this investigation aimed to explore the role and mechanism of UCMSCs-derived exosomal circHIPK3 (exo-circHIPK3) in diabetes mellitus (DM). METHODS: HFF-1 cells were cultured in high glucose (HG) medium or normal medium, and treated with UCMSCs-derived exo-circHIPK3 or miR-20b-5p mimics or Unc-51-like autophagy activating kinase 1 (ULK1) overexpression vector. The surface markers of UCMSCs were analyzed using a flow cytometer. The differentiation potential of UCMSCs was evaluated using oil red O staining, alizarin red staining and alkaline phosphatase (ALP) staining. Cell proliferation was determined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. The miRNA expressions were analyzed by reverse transcription-quantitative polymerase chain reaction (qRT-PCR). Protein levels were quantified by western blot. An immunofluorescence staining was used for observing LC3 expression. The interaction between miR-20b-5p and circHIPK3, and between miR-20b-5b and ULK1 were identified by a RNA immunoprecipitation (RIP) assay and a luciferase reporter assay. RESULTS: Up-regulation of circHIPK3 was found in UCMSCs-derived exosomes. Exo-circHIPK3 decreased the miR-20b-5p level while increasing the contents of ULK1 and autophagy-related gene 13 (Atg13) in HG-induced fibroblasts. In addition, exo-circHIPK3 activated HG-induced fibroblast autophagy and proliferation. Overexpressed miR-20b-5p promoted fibroblast injury by inhibiting cell autophagy via the ULK1/Atg13 axis in HG conditions of high glucose. Moreover, exo-circHIPK3 enhanced autophagy and cell viability in HG-induced fibroblasts through the miR-20b-5p/ULK1/Atg13 axis. CONCLUSION: UCMSCs-derived exosomal circHIPK3 promoted cell autophagy and proliferation and accelerated the fibroblast injury repair by the miR-20b-5p/ULK1/Atg13 axis.

The alpha-2A-adrenergic receptor gene polymorphism modulates gray matter structural networks, visual memory, and inhibitory cognitive control in children with attention deficit/hyperactivity disorder.

The ADRA2A-1291 C > G polymorphism and deficits in visual memory and inhibitory control were associated with attention deficit hyperactivity disorder (ADHD). The present study aimed to examine whether the ADRA2A G/G genotype affected gray matter (GM) networks in ADHD and whether these gene-brain modulations were associated with cognitive function in ADHD. Seventy-five drug-naïve ADHD children and 70 healthy controls were recruited. The GM networks were obtained based on areal similarities of GM, and network topological properties were analyzed using graph theory. Visual memory and inhibitory control were assessed by the visual memory test and the Stroop test, respectively. SNP genotyping of rs1800544 was performed. A significant interaction between ADHD diagnosis and gene polymorphism was observed in the nodal degree of the left inferior parietal lobule and left inferior (opercular) frontal gyrus. In the ADHD group, nodal efficiency in the left inferior (orbital) frontal gyrus in ADHD with G/G was lower than that in ADHD without G/G. Moreover, the ADRA2A-modulated alterations in nodal properties were associated with visual memory and inhibitory control. Our findings provide novel gene-brain behavior association evidence that GM network alterations, especially in the frontoparietal loop, were related to visual memory and inhibitory control in ADHD children with ADRA2A-G/G.

Psychological resilience mediates the protective role of default-mode network functional connectivity against COVID-19 vicarious traumatization.

Vicarious traumatization (VT), a negative reaction to witnessing others' trauma, has been experienced by some people during the COVID-19 pandemic, and can lead to mental health problems. This study aimed to identify functional brain markers of COVID-specific VT and explore the psychological mechanism underlying the brain-VT link. One hundred healthy participants underwent resting-state functional magnetic resonance imaging before the pandemic (October 2019-January 2020) and completed VT measurement during the pandemic (February-April 2020). Whole-brain correlation analysis based on global functional connectivity density (FCD) mapping revealed that VT was negatively correlated with FCD in the right inferior temporal gyrus (ITG) (i.e., the lower FCD in ITG, the worse the VT), identified by mapping onto known large-scale networks as part of the default-mode network (DMN). Resting-state functional connectivity (RSFC) analysis using ITG as seed found that VT was predicted by lower functional connectivity between ITG and other DMN regions including left medial prefrontal cortex, left orbitofrontal cortex, right superior frontal gyrus, right inferior parietal lobule and bilateral precuneus (i.e., the lower the ITG-DMN connectivity, the worse the VT). Mediation analyses suggested that psychological resilience served as a mediator in these associations of ITG FCD and ITG-DMN RSFC with VT. Our results provide novel evidence on the brain basis of VT and emphasize psychological resilience as an important link from DMN functional connectivity to COVID-specific-VT. This may facilitate public health interventions by helping identify individuals at risk of stress- and trauma-related psychopathologies.

Disrupted brain gray matter connectome in social anxiety disorder: a novel individualized structural covariance network analysis.

Phenotyping approaches grounded in structural network science can offer insights into the neurobiological substrates of psychiatric diseases, but this remains to be clarified at the individual level in social anxiety disorder (SAD). Using a recently developed approach combining probability density estimation and Kullback-Leibler divergence, we constructed single-subject structural covariance networks (SCNs) based on multivariate morphometry (cortical thickness, surface area, curvature, and volume) and quantified their global/nodal network properties using graph-theoretical analysis. We compared network metrics between SAD patients and healthy controls (HC) and analyzed the relationship to clinical characteristics. We also used support vector machine analysis to explore the ability of graph-theoretical metrics to discriminate SAD patients from HC. Globally, SAD patients showed higher global efficiency, shorter characteristic path length, and stronger small-worldness. Locally, SAD patients showed abnormal nodal centrality mainly involving left superior frontal gyrus, right superior parietal lobe, left amygdala, right paracentral gyrus, right lingual, and right pericalcarine cortex. Altered topological metrics were associated with the symptom severity and duration. Graph-based metrics allowed single-subject classification of SAD versus HC with total accuracy of 78.7%. This finding, that the topological organization of SCNs in SAD patients is altered toward more randomized configurations, adds to our understanding of network-level neuropathology in SAD.

Neural correlates of neuroticism: A coordinate-based meta-analysis of resting-state functional brain imaging studies.

Neuroticism is one of the most robust higher-order personality traits associated with negative emotionality and risk of mental disorders. Many studies have investigated relationships between neuroticism and the brain, but the results have been inconsistent. We conducted a meta-analysis of whole-brain resting-state functional neuroimaging studies to identify the most stable neurofunctional substrates of neuroticism. We found stable significant positive correlations between neuroticism and resting-state brain activity in the left middle temporal gyrus (MTG), left striatum, and right hippocampus. In contrast, resting-state brain activity in the left superior temporal gyrus (STG) and right supramarginal gyrus (SMG) was negatively associated with neuroticism. Additionally, meta-regression analysis revealed brain regions in which sex and age moderated the link of spontaneous activity with neuroticism. This is the first study to provide a comprehensive understanding of resting-state brain activity correlates of neuroticism, and the findings may be useful for the targeting of specific brain regions for interventions to decrease the risks of mental health problems.

Large-scale brain functional network abnormalities in social anxiety disorder.

BACKGROUND: Although aberrant brain regional responses are reported in social anxiety disorder (SAD), little is known about resting-state functional connectivity at the macroscale network level. This study aims to identify functional network abnormalities using a multivariate data-driven method in a relatively large and homogenous sample of SAD patients, and assess their potential diagnostic value. METHODS: Forty-six SAD patients and 52 demographically-matched healthy controls (HC) were recruited to undergo clinical evaluation and resting-state functional MRI scanning. We used group independent component analysis to characterize the functional architecture of brain resting-state networks (RSNs) and investigate between-group differences in intra-/inter-network functional network connectivity (FNC). Furtherly, we explored the associations of FNC abnormalities with clinical characteristics, and assessed their ability to discriminate SAD from HC using support vector machine analyses. RESULTS: SAD patients showed widespread intra-network FNC abnormalities in the default mode network, the subcortical network and the perceptual system (i.e. sensorimotor, auditory and visual networks), and large-scale inter-network FNC abnormalities among those high-order and primary RSNs. Some aberrant FNC signatures were correlated to disease severity and duration, suggesting pathophysiological relevance. Furthermore, intrinsic FNC anomalies allowed individual classification of SAD v. HC with significant accuracy, indicating potential diagnostic efficacy. CONCLUSIONS: SAD patients show distinct patterns of functional synchronization abnormalities both within and across large-scale RSNs, reflecting or causing a network imbalance of bottom-up response and top-down regulation in cognitive, emotional and sensory domains. Therefore, this could offer insights into the neurofunctional substrates of SAD.

Emotional intelligence mediates the protective role of the orbitofrontal cortex spontaneous activity measured by fALFF against depressive and anxious symptoms in late adolescence.

As a stable personality construct, trait emotional intelligence (TEI) refers to a battery of perceived emotion-related skills that make individuals behave effectively to adapt to the environment and maintain well-being. Abundant evidence has consistently shown that TEI is important for the outcomes of many mental health issues, particularly depression and anxiety. However, the neural substrates involved in TEI and the underlying neurobehavioral mechanism of how TEI reduces depression and anxiety symptoms remain largely unknown. Herein, resting-state functional magnetic resonance imaging and a group of behavioral measures were applied to examine these questions among a large sample comprising 231 general adolescent students aged 16-20 years (52% female). Whole-brain correlation analysis and prediction analysis demonstrated that TEI was negatively linked with spontaneous activity (measured with the fractional amplitude of low-frequency fluctuations) in the bilateral medial orbitofrontal cortex (OFC), a critical site implicated in emotion-related processes. Furthermore, structural equation modeling analysis found that TEI mediated the link of OFC spontaneous activity to depressive and anxious symptoms. Collectively, the current findings present new evidence for the neurofunctional bases of TEI and suggest a potential "brain-personality-symptom" pathway for alleviating depressive and anxious symptoms among students in late adolescence.

Common and Distinct Neural Patterns of Attention-Deficit/Hyperactivity Disorder and Borderline Personality Disorder: A Multimodal Functional and Structural Meta-analysis.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) and borderline personality disorder (BPD) have partially overlapping symptom profiles and are highly comorbid in adults. However, whether the behavioral similarities correspond to shared neurobiological substrates is not known. METHODS: An overlapping meta-analysis of 58 ADHD and 66 BPD whole-brain articles incorporating observations from 3401 adult patients and 3238 healthy participants was performed by seed-based d mapping. Brain maps were subjected to meta-analytic connectivity modeling and data-driven functional decoding analyses to identify associated neural circuit alterations and relations to behavioral dimensions. RESULTS: Both groups exhibited hypoactivated abnormalities in the left inferior parietal lobule, and altered clusters of the bilateral superior temporal gyrus were disjunctive in ADHD and BPD. No overlapping structural abnormalities were found. Multimodal alterations of ADHD were located in the right putamen and of BPD in the left orbitofrontal cortex. CONCLUSIONS: The transdiagnostic neural bases of ADHD and BPD in temporoparietal circuitry may underlie overlapping problems of behavioral control, while disorder-specific substrates in frontostriatal circuitry may account for their distinguishing features in motor and emotion domains, respectively.

Pre-COVID brain functional connectome features prospectively predict emergence of distress symptoms after onset of the COVID-19 pandemic.

BACKGROUND: Persistent psychological distress associated with the coronavirus disease 2019 (COVID-19) pandemic has been well documented. This study aimed to identify pre-COVID brain functional connectome that predicts pandemic-related distress symptoms among young adults. METHODS: Baseline neuroimaging studies and assessment of general distress using the Depression, Anxiety and Stress Scale were performed with 100 healthy individuals prior to wide recognition of the health risks associated with the emergence of COVID-19. They were recontacted for the Impact of Event Scale-Revised and the Posttraumatic Stress Disorder Checklist in the period of community-level outbreaks, and for follow-up distress evaluation again 1 year later. We employed the network-based statistic approach to identify connectome that predicted the increase of distress based on 136-region-parcellation with assigned network membership. Predictive performance of connectome features and causal relations were examined by cross-validation and mediation analyses. RESULTS: The connectome features that predicted emergence of distress after COVID contained 70 neural connections. Most within-network connections were located in the default mode network (DMN), and affective network-DMN and dorsal attention network-DMN links largely constituted between-network pairs. The hippocampus emerged as the most critical hub region. Predictive models of the connectome remained robust in cross-validation. Mediation analyses demonstrated that COVID-related posttraumatic stress partially explained the correlation of connectome to the development of general distress. CONCLUSIONS: Brain functional connectome may fingerprint individuals with vulnerability to psychological distress associated with the COVID pandemic. Individuals with brain neuromarkers may benefit from the corresponding interventions to reduce the risk or severity of distress related to fear of COVID-related challenges.