Summary: Background: Disturbances in emotion regulation are the hallmarks of major depressive disorder (MDD). The incapacity to control negative emotion in patients has been associated with abnormal hyperactivation of the limbic system and hypoactivation of the frontal cortex. The amygdala and orbital frontal cortex (OFC) are two critical regions of the emotion regulation neural systems.Methods: This study investigated the anatomical basis of abnormal emotion regulation by tracking the fiber tracts connecting the amygdala and OFC. In addition, using dynamic casual modeling on resting‐state fMRI data of 20 MDD patients and equivalent controls, we investigated the exact neural mechanism through which abnormal communications between these two nodes were mediated in MDD.Key Results: The results revealed disrupted white matter integrity of fiber tracts in MDD, suggesting that functional abnormalities were accompanied by underlying anatomical basis. We also detected a failure of inhibition of the OFC on the activity of the amygdala in MDD, suggesting dysconnectivity was mediated through “top‐down” influences from the frontal cortex to the amygdala. Following 8 weeks of antidepressant treatment, the patients showed significant clinical improvement and normalization of the abnormal OFC‐amygdala structural and effective connectivity in the left hemisphere.Conclusions & Inferences: Our findings suggest that pathways connecting these two nodes may be core targets of the antidepressant treatment. In particular, it raised the intriguing question: Does the reversal of structural markers of connectivity reflect a response to antidepressant medication or activity‐dependent myelination following a therapeutic restoration of effective connectivity?

Introduction: Gut microbial disturbance has been established as potential pathogenesis of mental disorders. However, the signatures and differences regarding patients with schizophrenia (SCH) or bipolar disorder (BD) in emerging adulthood as well as their subtypes have been poorly addressed.Methods: In the present study, stool samples obtained from 63 emerging adult patients with schizophrenia (SCH), 50 with bipolar disorder (BD), and 40 healthy controls (HC) were analyzed by 16 S rRNA gene sequencing; psychiatric symptoms and psychological, social, and professional functioning were also assessed.Results: We found that gut microbiota composition was remarkably changed in the patients with SCH and BD. Moreover, the distinct gut microbiome signatures and their potential function in bipolar depression (BP‐D) and SCH with predominantly negative symptoms (SCH‐N) as well as bipolar mania (BP‐M) and SCH with predominantly positive symptoms (SCH‐P) were also observed. Furthermore, we identified diagnostic potential biomarkers that can distinguish BD from HC (38 genera, AUC = 0.961), SCH from HC (32 genera, AUC = 0.962), and BD from Scheme (13 genera, AUC = 0.823). Potential diagnostic biomarkers that can distinguish BD‐D from SCH‐N (16 genera, AUC = 0.969) and BD‐M from SCH‐P (31 genera, AUC = 0.938) were also identified.Conclusion: This study provides further understanding of abnormal gut microbiome in emerging adulthood patients with SCH and BD and lay the potential foundation for the development of microbe‐based clinical diagnosis for BD and SCH.

Introduction: Understanding the neurological basis of autism spectrum disorder (ASD) is important for the diagnosis and treatment of this mental disorder. Emerging evidence has suggested aberrant functional connectivity of large-scale brain networks in individuals with ASD. However, whether the effective connectivity which measures the causal interactions of these networks is also impaired in these patients remains unclear.Objects: The main purpose of this study was to investigate the effective connectivity of large-scale brain networks in patients with ASD during resting state.Materials and methods: The subjects were 42 autistic children and 127 age-matched normal children from the ABIDE II dataset. We investigated effective connectivity of 7 large-scale brain networks including visual network (VN), default mode network (DMN), cerebellum, sensorimotor network (SMN), auditory network (AN), salience network (SN), frontoparietal network (FPN), with spectral dynamic causality model (spDCM). Parametric empirical Bayesian (PEB) was used to perform second-level group analysis and furnished group commonalities and differences in effective connectivity. Furthermore, we analyzed the correlation between the strength of effective connectivity and patients’ clinical characteristics.Results: For both groups, SMN acted like a hub network which demonstrated dense effective connectivity with other large-scale brain network. We also observed significant causal interactions within the “triple networks” system, including DMN, SN and FPN. Compared with healthy controls, children with ASD showed decreased effective connectivity among some large-scale brain networks. These brain networks included VN, DMN, cerebellum, SMN, and FPN. In addition, we also found significant negative correlation between the strength of the effective connectivity from right angular gyrus (ANG_R) of DMN to left precentral gyrus (PreCG_L) of SMN and ADOS-G or ADOS-2 module 4 stereotyped behaviors and restricted interest total (ADOS_G_STEREO_BEHAV) scores.Conclusion: Our research provides new evidence for the pathogenesis of children with ASD from the perspective of effective connections within and between large-scale brain networks. The attenuated effective connectivity of brain networks may be a clinical neurobiological feature of ASD. Changes in effective connectivity of brain network in children with ASD may provide useful information for the diagnosis and treatment of the disease.

Background and Objectives: Lipidomics is a pivotal tool for investigating the pathogenesis of mental disorders. However, studies qualitatively and quantitatively analyzing peripheral lipids in adult patients with schizophrenia (SCZ) and major depressive disorder (MDD) are limited. Moreover, there are no studies comparing the lipid profiles in these patient populations. Materials and Method: Lipidomic data for plasma samples from sex- and age-matched patients with SCZ or MDD and healthy controls (HC) were obtained and analyzed by liquid chromatography-mass spectrometry (LC-MS). Results: We observed changes in lipid composition in patients with MDD and SCZ, with more significant alterations in those with SCZ. In addition, a potential diagnostic panel comprising 103 lipid species and another diagnostic panel comprising 111 lipid species could distinguish SCZ from HC (AUC = 0.953) or SCZ from MDD (AUC = 0.920) were identified, respectively. Conclusions: This study provides an increased understanding of dysfunctional lipid composition in the plasma of adult patients with SCZ or MDD, which may lay the foundation for identifying novel clinical diagnostic methods for these disorders.