Intercellular communication in the central nervous system (CNS) is essential for brain growth, development, and homeostasis maintenance and, when dysfunctional, is involved in the occurrence and development of neurodegenerative diseases. Increasing evidence indicates that extracellular vesicles, especially exosomes, are critical mediators of intercellular signal transduction. Under physiological and pathological conditions, neural cells secret exosomes with the influence of many factors. These exosomes can carry specific proteins, lipids, nucleic acids, and other bioactive substances to the recipient cells to regulate their function. Depending on the CNS environment, as well as the origin and physiological or pathological status of parental cells, exosomes can mediate a variety of different effects, including synaptic plasticity, nutritional metabolic support, nerve regeneration, inflammatory response, anti-stress effect, cellular waste disposal, and the propagation of toxic components, playing an important role in health and neurodegenerative diseases. This review will discuss the possible roles of exosomes in CNS intercellular communication in both physiologic and neurodegenerative conditions.

Background: The prevalence of depressive and anxiety symptoms in patients with COVID-19 is higher than usual. Previous studies have shown that there are drug-to-drug interactions between antiretroviral drugs and antidepressants. Therefore, an effective and safe treatment method was needed. Cognitive behavioral therapy (CBT) is the first-line psychological therapy in clinical treatment. Computerized CBT (cCBT) was proven to be an effective alternative to CBT and does not require face-to-face therapy between a therapist and the patient, which suited the COVID-19 pandemic response. Objective: This study aims to evaluate the efficacy of the cCBT program we developed in improving depressive and anxiety symptoms among patients with COVID-19. Methods: We customized a cCBT program focused on improving depressive and anxiety symptoms among patients with COVID-19, and then, we assessed its effectiveness. Screening was based on symptoms of depression or anxiety for patients who scored ≥7 on the Hamilton Depression Rating Scale (HAMD17) or the Hamilton Anxiety Scale (HAMA). A total of 252 patients with COVID-19 at five sites were randomized into two groups: cCBT + treatment as usual (TAU; n=126) and TAU without cCBT (n=126). The cCBT + TAU group received the cCBT intervention program for 1 week. The primary efficacy measures were the HAMD17 and HAMA scores. The secondary outcome measures were the Self-Rating Depression Scale (SDS), Self-Rating Anxiety Scale (SAS), and Athens Insomnia Scale (AIS). Assessments were carried out pre- and postintervention. The patients’ symptoms of anxiety and depression in one of the centers were assessed again within 1 month after the postintervention assessment. Results: The cCBT + TAU group displayed a significantly decreased score on the HAMD17, HAMA, SDS, SAS, and AIS after the intervention compared to the TAU group (all P<.001). A mixed-effects repeated measures model revealed significant improvement in symptoms of depression (HAMD17 and SDS scores, both P<.001), anxiety (HAMA and SAS scores, both P<.001), and insomnia (AIS score, P=.002) during the postintervention and follow-up periods in the cCBT + TAU group. Additionally, the improvement of insomnia among females (P=.14) and those with middle school education (P=.48) in the cCBT + TAU group showed no significant differences when compared to the TAU group. Conclusions: The findings of this study suggest that the cCBT program we developed was an effective nonpharmacological treatment for symptoms of anxiety, depression, and insomnia among patients with COVID-19. Further research is warranted to investigate the long-term effects of cCBT for symptoms of anxiety, depression, and insomnia in patients with COVID-19. Trial Registration: Chinese Clinical Trial Registry ChiCTR2000030084; http://www.chictr.org.cn/showprojen.aspx?proj=49952

Structural and functional brain alterations are common in patients with major depressive disorder (MDD). In this review, we assessed the recent literature (1995–2018) on the structural and functional magnetic resonance imaging (MRI) studies of MDD. Despite the growing number of MRI studies on MDD, reverse inference is not possible as MRI scans cannot be used to aid in the diagnosis or treatment planning of patients with MDD. Hence, researchers must develop “bridges” to overcome the reverse inference fallacy in order to build effective tools for MDD diagnostics. From our findings, we proposed that the “bridges” may be built using multidisciplinary technologies, such as artificial intelligence, multimodality imaging, and nanotheranostics, allowing for the further study of MDD at the biological level. In return, the “bridges” will aid in the development of future diagnostics for MDD and other mental disorders.

Deficits in facial emotion recognition are one of the most common cognitive impairments, and they have been extensively studied in various psychiatric disorders, especially in schizophrenia. However, there is still a lack of conclusive evidence about the factors associated with schizophrenia and impairment at each stage of the disease, which poses a challenge to the clinical management of patients. Based on this, we summarize facial emotion cognition among patients with schizophrenia, introduce the internationally recognized Bruce–Young face recognition model, and review the behavioral and event-related potential studies on the recognition of emotions at each stage of the face recognition process, including suggestions for the future direction of clinical research to explore the underlying mechanisms of schizophrenia.

BACKGROUND. Major depressive disorder (MDD) and posttraumatic stress disorder (PTSD) are highly comorbid and exhibit strong correlations with one another. We aimed to investigate mechanisms of underlying relationships between PTSD and 3 kinds of depressive phenotypes, namely, MDD, depressed affect (DAF), and depression (DEP, including both MDD and the broad definition of depression). METHODS. Genetic correlations between PTSD and the depressive phenotypes were tested using linkage disequilibrium score regression. Polygenic overlap analysis was used to estimate shared and trait-specific causal variants across a pair of traits. Causal relationships between PTSD and the depressive phenotypes were investigated using Mendelian randomization. Shared genomic loci between PTSD and MDD were identified using cross-trait meta-analysis. RESULTS. Genetic correlations of PTSD with the depressive phenotypes were in the range of 0.71–0.80. The estimated numbers of causal variants were 14,565, 12,965, 10,565, and 4,986 for MDD, DEP, DAF, and PTSD, respectively. In each case, causal variants contributing to PTSD were completely or largely covered by causal variants defining each of the depressive phenotypes. Mendelian randomization analysis indicated that the genetically determined depressive phenotypes confer a causal effect on PTSD (b = 0.21–0.31). Notably, genetically determined PTSD confers a causal effect on DEP (b = 0.14) and DAF (b = 0.15), but not MDD. Cross-trait meta-analysis of MDD and PTSD identified 47 genomic loci, including 29 loci shared between PTSD and MDD. CONCLUSION. Evidence from shared genetics suggests that PTSD is a subtype of MDD. This study provides support to the efforts in reducing diagnostic heterogeneity in psychiatric nosology. FUNDING. The National Key Research and Development Program of China and the National Natural Science Foundation of China.

Exploring brain changes across the human lifespan is becoming an important topic in neuroscience. Though there are multiple studies which investigated the relationship between age and brain imaging, the results are heterogeneous due to small sample sizes and relatively narrow age ranges. Here, based on year‐wise estimation of 5,967 subjects from 13 to 72 years old, we aimed to provide a more precise description of adult lifespan variation trajectories of gray matter volume (GMV), structural network correlation (SNC), and functional network connectivity (FNC) using independent component analysis and multivariate linear regression model. Our results revealed the following relationships: (a) GMV linearly declined with age in most regions, while parahippocampus showed an inverted U‐shape quadratic relationship with age; SNC presented a U‐shape quadratic relationship with age within cerebellum, and inverted U‐shape relationship primarily in the default mode network (DMN) and frontoparietal (FP) related correlation. (b) FNC tended to linearly decrease within resting‐state networks (RSNs), especially in the visual network and DMN. Early increase was revealed between RSNs, primarily in FP and DMN, which experienced a decrease at older ages. U‐shape relationship was also revealed to compensate for the cognition deficit in attention and subcortical related connectivity at late years. (c) The link between middle occipital gyrus and insula, as well as precuneus and cerebellum, exhibited similar changing trends between SNC and FNC across the adult lifespan. Collectively, these results highlight the benefit of lifespan study and provide a precise description of age‐related regional variation and SNC/FNC changes based on a large dataset.