Clinical distinctions in symptomatology and psychiatric comorbidities between misdiagnosed bipolar I and bipolar II disorder versus major depressive disorder.

BACKGROUND: To explore the demographic and clinical features of current depressive episode that discriminate patients diagnosed with major depressive disorder (MDD) from those with bipolar I (BP-I) and bipolar II (BP-II) disorder who were misdiagnosed as having MDD . METHODS: The Mini-International Neuropsychiatric Interview (MINI) assessment was performed to establish DSM-IV diagnoses of MDD, and BP-I and BP-II, previously being misdiagnosed as MDD. Demographics, depressive symptoms and psychiatric comorbidities were compared between 1463 patients with BP-I, BP-II and MDD from 8 psychiatric settings in mainland China. A multinomial logistic regression model was performed to assess clinical correlates of diagnoses. RESULTS: A total of 14.5% of the enrolled patients initially diagnosed with MDD were eventually diagnosed with BP. Broad illness characteristics including younger age, higher prevalence of recurrence, concurrent dysthymia, suicidal attempts, agitation, psychotic features and psychiatric comorbidities, as well as lower prevalence of insomnia, weight loss and somatic symptoms were featured by patients with BP-I and/or BP-I, compared to those with MDD. Comparisons between BP-I and BP-II versus MDD indicated distinct symptom profiles and comorbidity patterns with more differences being observed between BP-II and MDD, than between BP-I and MDD . CONCLUSION: The results provide evidence of clinically distinguishing characteristics between misdiagnosed BP-I and BP- II versus MDD. The findings have implications for guiding more accurate diagnoses of bipolar disorders.

Relationship between biological rhythm dysregulation and suicidal ideation in patients with major depressive disorder.

BACKGROUND: Although the disturbance of circadian rhythms represents a significant clinical feature of major depressive disorder (MDD), the relationship between biological rhythm disturbances and the severity of suicidal ideation in individuals with MDD remains unclear. We aimed to explore the characteristics of different biological rhythm dimensions in MDD and their association with the severity of depressive symptoms and suicidal ideation. METHODS: A total of 50 MDD patients and 50 healthy controls were recruited and their general information was collected. The severity of depressive symptoms was assessed with the 17-item Hamilton Depression Rating Scale (HDRS17). The intensity of suicidal ideation was evaluated with the Beck Scale for Suicide Ideation (BSS). The Chinese version of the Biological Rhythms Interview of Assessment in Neuropsychiatry (BRIAN) scale was utilized to assess the participants' biological rhythm dysregulation. Multiple logistic regression analysis was conducted to explore the relationship between biological rhythm and the risk of MDD. Multiple linear regression analysis was performed in the MDD group to investigate the relationship between different biological rhythm dimensions and suicide ideation. RESULTS: Significant differences were observed between the MDD group and the control group in total BRIAN score (Z=-5.41, P < 0.001) as well as scores for each dimension. After adjusting for confounding factors, multiple logistic regression analysis revealed a significant association between total BRIAN score and the presence of MDD (OR = 1.20, 95% CI = 1.10-1.29, P < 0.001), as well as between scores in different BRIAN dimensions and the presence of MDD (activity: OR = 1.47, 95% CI = 1.24-1.74, P < 0.001; sleep: OR = 1.52, 95% CI = 1.28-1.79, P < 0.001; social: OR = 1.80, 95% CI = 1.32-2.46, P < 0.001; eating pattern: OR = 1.34, 95% CI = 1.12-1.60, P = 0.001). In patients with MDD, linear regression analysis demonstrated a positive relationship between BSS scores and BRIAN eating pattern scores (ß = 0.34, P = 0.022), even after adjusting for demographic factors and the severity of depression. CONCLUSIONS: Patients with MDD exhibited significantly higher levels of dysregulation in all four biological rhythm dimensions compared to healthy controls and the degree of dysregulation was associated with the severity of depression. More importantly, dysregulation of eating pattern may increase the intensity of suicidal ideation in MDD, thus elevating the risk of suicide.

Altered N-linked glycosylation in depression: A pre-clinical study.

BACKGROUND: Neuroimmune plays an important role in major depressive disorders (MDD). N-linked protein glycosylation (NLG) might contribute to depression by regulating the neuroinflammatory response. As microglia is the main executor of neuroimmune function in the central neural system (CNS), targeting the process of N-linked protein glycosylation of microglia in the mice used for studying depression might potentially offer new avenues for the strategy for MDD. METHODS: The chronic unpredictable mild stress (CUMS) mouse model was established for the whole brain microglia isolating. Then, RNA samples of microglia were extracted for transcriptome sequencing and mRNA analysis. Immunofluorescence (IF) was used to identify the expression level of NLG-related enzyme, B4galt1, in microglia. RESULTS: The data showed that NLG was positively related to depression. Moreover, the NLG-related gene, B4galt1 increased expression in the microglia of CUMS mice. Then, the inhibition of NLG reversed the depressive behavior in CUMS mice. The expression level of B4galt1 in CUMS mice was upregulating following the NLG-inhibitor treatment. Similar results haven't been observed in neurons. Information obtained from these experiments showed increasing expression of B4galt1 in microglia following depressive-like behaviors. CONCLUSIONS: These findings indicate that NLG in microglia is associated with MDD, and suggest that therapeutically targeting NLG might be an effective strategy for depression. LIMITATIONS: How to modulate the B4galt1 or NLG pathways in microglia efficiently and economically request new technologies.

Randomizing Human Brain Function Representation for Brain Disease Diagnosis.

Resting-state fMRI (rs-fMRI) is an effective tool for quantifying functional connectivity (FC), which plays a crucial role in exploring various brain diseases. Due to the high dimensionality of fMRI data, FC is typically computed based on the region of interest (ROI), whose parcellation relies on a pre-defined atlas. However, utilizing the brain atlas poses several challenges including 1) subjective selection bias in choosing from various brain atlases, 2) parcellation of each subject's brain with the same atlas yet disregarding individual specificity; 3) lack of interaction between brain region parcellation and downstream ROI-based FC analysis. To address these limitations, we propose a novel randomizing strategy for generating brain function representation to facilitate neural disease diagnosis. Specifically, we randomly sample brain patches, thus avoiding ROI parcellations of the brain atlas. Then, we introduce a new brain function representation framework for the sampled patches. Each patch has its function description by referring to anchor patches, as well as the position description. Furthermore, we design an adaptive-selection-assisted Transformer network to optimize and integrate the function representations of all sampled patches within each brain for neural disease diagnosis. To validate our framework, we conduct extensive evaluations on three datasets, and the experimental results establish the effectiveness and generality of our proposed method, offering a promising avenue for advancing neural disease diagnosis beyond the confines of traditional atlas-based methods. Our code is available at https://github.com/mjliu2020/RandomFR.

Vacancy-Driven High-Performance Metabolic Assay for Diagnosis and Therapeutic Evaluation of Depression.

Depression is one of the most common mental illnesses and is a well-known risk factor for suicide, characterized by low overall efficacy (<50%) and high relapse rate (40%). A rapid and objective approach for screening and prognosis of depression is highly desirable but still awaits further development. Herein, a high-performance metabolite-based assay to aid the diagnosis and therapeutic evaluation of depression by developing a vacancy-engineered cobalt oxide (Vo-Co3O4) assisted laser desorption/ionization mass spectrometer platform is presented. The easy-prepared nanoparticles with optimal vacancy achieve a considerable signal enhancement, characterized by favorable charge transfer and increased photothermal conversion. The optimized Vo-Co3O4 allows for a direct and robust record of plasma metabolic fingerprints (PMFs). Through machine learning of PMFs, high-performance depression diagnosis is achieved, with the areas under the curve (AUC) of 0.941-0.980 and an accuracy of over 92%. Furthermore, a simplified diagnostic panel for depression is established, with a desirable AUC value of 0.933. Finally, proline levels are quantified in a follow-up cohort of depressive patients, highlighting the potential of metabolite quantification in the therapeutic evaluation of depression. This work promotes the progression of advanced matrixes and brings insights into the management of depression.

The DIRECT consortium and the REST-meta-MDD project: towards neuroimaging biomarkers of major depressive disorder.

Despite a growing neuroimaging literature on the pathophysiology of major depressive disorder (MDD), reproducible findings are lacking, probably reflecting mostly small sample sizes and heterogeneity in analytic approaches. To address these issues, the Depression Imaging REsearch ConsorTium (DIRECT) was launched. The REST-meta-MDD project, pooling 2428 functional brain images processed with a standardized pipeline across all participating sites, has been the first effort from DIRECT. In this review, we present an overview of the motivations, rationale, and principal findings of the studies so far from the REST-meta-MDD project. Findings from the first round of analyses of the pooled repository have included alterations in functional connectivity within the default mode network, in whole-brain topological properties, in dynamic features, and in functional lateralization. These well-powered exploratory observations have also provided the basis for future longitudinal hypothesis-driven research. Following these fruitful explorations, DIRECT has proceeded to its second stage of data sharing that seeks to examine ethnicity in brain alterations in MDD by extending the exclusive Chinese original sample to other ethnic groups through international collaborations. A state-of-the-art, surface-based preprocessing pipeline has also been introduced to improve sensitivity. Functional images from patients with bipolar disorder and schizophrenia will be included to identify shared and unique abnormalities across diagnosis boundaries. In addition, large-scale longitudinal studies targeting brain network alterations following antidepressant treatment, aggregation of diffusion tensor images, and the development of functional magnetic resonance imaging-guided neuromodulation approaches are underway. Through these endeavours, we hope to accelerate the translation of functional neuroimaging findings to clinical use, such as evaluating longitudinal effects of antidepressant medications and developing individualized neuromodulation targets, while building an open repository for the scientific community.

Peripheral mitochondrial DNA as a neuroinflammatory biomarker for major depressive disorder.

In the pathogenesis of major depressive disorder, chronic stress-related neuroinflammation hinders favorable prognosis and antidepressant response. Mitochondrial DNA may be an inflammatory trigger, after its release from stress-induced dysfunctional central nervous system mitochondria into peripheral circulation. This evidence supports the potential use of peripheral mitochondrial DNA as a neuroinflammatory biomarker for the diagnosis and treatment of major depressive disorder. Herein, we critically review the neuroinflammation theory in major depressive disorder, providing compelling evidence that mitochondrial DNA release acts as a critical biological substrate, and that it constitutes the neuroinflammatory disease pathway. After its release, mitochondrial DNA can be carried in the exosomes and transported to extracellular spaces in the central nervous system and peripheral circulation. Detectable exosomes render encaged mitochondrial DNA relatively stable. This mitochondrial DNA in peripheral circulation can thus be directly detected in clinical practice. These characteristics illustrate the potential for mitochondrial DNA to serve as an innovative clinical biomarker and molecular treatment target for major depressive disorder. This review also highlights the future potential value of clinical applications combining mitochondrial DNA with a panel of other biomarkers, to improve diagnostic precision in major depressive disorder.