Differentiating between bipolar and unipolar depression in functional and structural MRI studies

Potential associations between altered brain function, cognitive deficits and gene expressing profiles in bipolar disorder across three clinical stages

AIMS

We aimed to determine the relationship between altered brain imaging characteristics, cognitive function and profiles of gene expression of bipolar disorder (BD).

METHODS

Functional magnetic resonance imaging (fMRI) was presented in three groups of BD participants (depressed, manic and euthymic) and healthy controls. Regional Homogeneity (ReHo) and region of interest based functional analysis combining with neuroimaging-transcription association analysis were utilized to investigate abnormalities and their correlation with clinical symptoms.

RESULTS

Our data showed that all three groups of BD patients exhibited significantly altered ReHo values whilst the bilateral precuneus/posterior cingulate cortex (PCC) and lateral occipital cortex exhibited significant increase in BD. Functional connectivity (FC) revealed distinct characteristics of the precuneus/PCC-based default mode network. ReHo values in the Precuneus/middle cingulate cortex displayed significantly negative correlations with cognition and YMRS scores. Gene enrichment analysis also revealed that ReHo values were spatially correlated with pathways including chromatin organization and innate immune response.

CONCLUSION

Altered ReHo values in specific brain regions may be associated with different clinical stages and increased FC in brain may potentially function as BD imaging biomarkers. The heterogeneity of gene expression was associated with altered brain imaging properties in BD, contributing to distinguishing different stages of BD from healthy individuals.

Distinct White Matter Fiber Density Patterns in Bipolar and Depressive Disorders: Insights from Fixel-Based Analysis

Background

Differentiating Bipolar (BD) and depressive (DD) disorders remains challenging in clinical practice due to overlapping symptoms. Our study employs fixel-based analysis (FBA) to examine fiber-specific white matter differences in BD and DD and gain insights into the ability of FBA metrics to predict future spectrum mood symptoms.

Methods

163 individuals between 18 and 45 years with BD, DD, and healthy controls (HC) underwent Diffusion Magnetic Resonance Imaging. FBA was used to assess fiber density (FD), fiber cross-section (FC), and fiber density cross-section (FDC) in major white matter tracts. A longitudinal follow-up evaluated whether FBA measures predicted future spectrum depressive and hypomanic symptom trajectories over six months.

Results

Direct comparisons between BD and DD indicated lower FD in the right superior longitudinal and uncinate fasciculi and left thalamo-occipital tract in BD versus DD. Individuals with DD exhibited lower FD in the left arcuate fasciculus than those with BD. Compared to HC, both groups showed lower FD in the splenium of the corpus callosum and left striato-occipital and optic radiation tracts. FD in these tracts predicted future spectrum symptom severity. Exploratory analyses revealed associations between FD, medication use, and marijuana exposure.

Conclusions

Our findings highlight distinct and overlapping white matter alterations in BD and DD. Furthermore, FD in key tracts may serve as a predictor of future symptom trajectories, supporting the potential clinical utility of FD as a biomarker for mood disorder prognosis. Future longitudinal studies are needed to explore the impact of treatment and disease progression on white matter microstructure.

Common and disease-specific patterns of functional connectivity and topology alterations across unipolar and bipolar disorder during depressive episodes: a transdiagnostic study

Bipolar disorder (BD) and unipolar depression (UD) are defined as distinct diagnostic categories. However, due to some common clinical and pathophysiological features, it is a clinical challenge to distinguish them, especially in the early stages of BD. This study aimed to explore the common and disease-specific connectivity patterns in BD and UD. This study was constructed over 181 BD, 265 UD and 204 healthy controls. In addition, an independent group of 90 patients initially diagnosed with major depressive disorder at the baseline and then transferred to BD with the episodes of mania/hypomania during follow-up, was identified as initial depressive episode BD (IDE-BD). All participants completed resting-state functional magnetic resonance imaging (R-fMRI) at recruitment. Both network-based analysis and graph theory analysis were applied. Both BD and UD showed decreased functional connectivity (FC) in the whole brain network. The shared aberrant network across groups of patients with depressive episode (BD, IDE-BD and UD) mainly involves the visual network (VN), somatomotor networks (SMN) and default mode network (DMN). Analysis of the topological properties over the three networks showed that decreased clustering coefficient was found in BD, IDE-BD and UD, however, decreased shortest path length and increased global efficiency were only found in BD and IDE-BD but not in UD. The study indicate that VN, SMN, and DMN, which involve stimuli reception and abstraction, emotion processing, and guiding external movements, are common abnormalities in affective disorders. The network separation dysfunction in these networks is shared by BD and UD, however, the network integration dysfunction is specific to BD. The aberrant network integration functions in BD and IDE-BD might be valuable diagnostic biomarkers.

Machine learning-based assessment of morphometric abnormalities distinguishes bipolar disorder and major depressive disorder

INTRODUCTION

Bipolar disorder (BD) and major depressive disorder (MDD) have overlapping clinical presentations which may make it difficult for clinicians to distinguish them potentially resulting in misdiagnosis. This study combined structural MRI and machine learning techniques to determine whether regional morphological differences could distinguish patients with BD and MDD.

METHODS

A total of 123 participants, including BD (n = 31), MDD (n = 48), and healthy controls (HC, n = 44), underwent high-resolution 3D T1-weighted imaging. Cortical thickness, surface area, and subcortical volumes were measured using FreeSurfer software. Common and classic machine learning models were utilized to identify distinct morphometric alterations between BD and MDD.

RESULTS

Significant morphological differences were observed in both common and distinct brain regions between BD, MDD, and HC. Specifically, abnormalities in the amygdala, thalamus, medial orbitofrontal cortex and fusiform were observed in both BD and MDD compared with HC. Relative to HC, unique differences in BD were identified in the lateral occipital and inferior/middle temporal regions, whereas MDD exhibited differences in nucleus accumbens and middle temporal regions. BD exhibited larger surface area in right middle temporal gyrus and greater right nucleus accumbens volume compared to MDD. The integration of two-stage models, including deep neural network (DNN) and support vector machine (SVM), achieved an accuracy rate of 91.2% in discriminating individuals with BD from MDD.

CONCLUSION

These findings demonstrate that structural MRI combined with machine learning techniques can accurately discriminate individuals with BD from MDD, and provide a foundation supporting the potential of this approach to improve diagnostic accuracy.

Cortical folding in distinguishing first-episode bipolar and unipolar depression

BACKGROUNDS

Clinical studies to date have yet to establish distinct boundaries between depression in bipolar disorder (BD) and unipolar depression (UD), leading to misdiagnoses and even exacerbation of the conditions. This study aimed to explore the distinctions in the local gyrification index (LGI) between BD and UD, and to evaluate its potential diagnostic value as a biomarker.

METHODS

LGI values across 68 cortical regions were measured from 42 patients with BD, 45 patients with UD, and 45 healthy controls (HCs) based on the Desikan-Killiany atlas. General linear model was performed to compare LGI values among the three groups. XGBoost classifier was implemented to develop a binary classification model for distinguishing BD from UD. Additionally, the correlation between clinical characteristics and LGI values was investigated separately within the BD and UD groups.

RESULTS

Compared to HCs, individuals with BD and UD exhibited significantly reduced LGI values in various cortical regions. Nine LGI regions in the BD group displayed reduced values compared to the UD group, except for a singular increase in the left frontal pole (ηp2 = 0.173; P = 0.006). No significant association was found between LGI values and clinical characteristics within the patient groups. The XGBoost classifier achieved a distinction accuracy of 73.7 % between BD and UD, with the left frontal pole making the most significant contribution to the model.

CONCLUSIONS

The findings suggest that LGI could be a relatively stable neuroimaging biomarker for distinguishing between BD and UD.

Convergence and divergence in neurostructural signatures of unipolar and bipolar depressions: Insights from surface-based morphometry and prospective follow-up

BACKGROUND

Bipolar disorder (BD) is often misidentified as unipolar depression (UD) during its early stages, typically until the onset of the first manic episode. This study aimed to explore both shared and unique neurostructural changes in patients who transitioned from UD to BD during follow-up, as compared to those with UD.

METHODS

This study utilized high-resolution structural magnetic resonance imaging (MRI) to collect brain data from individuals initially diagnosed with UD. During the average 3-year follow-up, 24 of the UD patients converted to BD (cBD). For comparison, the study included 48 demographically matched UD patients who did not convert and 48 healthy controls. The MRI data underwent preprocessing using FreeSurfer, followed by surface-based morphometry (SBM) analysis to identify cortical thickness (CT), surface area (SA), and cortical volume (CV) among groups.

RESULTS

The SBM analysis identified shared neurostructural characteristics between the cBD and UD groups, specifically thinner CT in the right precentral cortex compared to controls. Unique to the cBD group, there was a greater SA in the right inferior parietal cortex compared to the UD group. Furthermore, no significant correlations were observed between cortical morphological measures and cognitive performance and clinical features in the cBD and UD groups.

LIMITATIONS

The sample size is relatively small.

CONCLUSIONS

Our findings suggest that while cBD and UD exhibit some common alterations in cortical macrostructure, numerous distinct differences are also present. These differences offer valuable insights into the neuropathological underpinnings that distinguish these two conditions.

Machine learning-based discrimination of unipolar depression and bipolar disorder with streamlined shortlist in adolescents of different ages

BACKGROUND

Variations in symptoms and indistinguishable depression episodes of unipolar depression (UD) and bipolar disorder (BD) make the discrimination difficult and time-consuming. For adolescents with high disease prevalence, an efficient diagnostic tool is important for the discrimination and treatment of BU and UD.

METHODS

This multi-center cross-sectional study involved 1587 UD and 246 BD adolescents aged 12-18. A combination of standard questionnaires and demographic information was collected for the construction of a full-item list. The unequal patient number was balanced with three data balancing algorithms, and 4 machine learning algorithms were compared for the discrimination ability of UD and BD in three age groups: all ages, 12-15 and 16-18. Random forest (RF) with the highest accuracy were used to rank the importance of features/items and construct the 25-item shortlist. A separate dataset was used for the final performance evaluation with the shortlist, and the discrimination ability for UD and BD was investigated.

RESULTS

RF performed the best for UD and BD discrimination in all 3 age groups (AUC 0.88-0.90). The most important features that differentiate UD from BD belong to Parental Bonding Instrument (PBI) and Loneliness Scale of the University of California at Los Angeles (UCLA). With RF and the 25-item shortlist, the diagnostic accuracy can still reach around 80 %, achieving 95 % of the accuracy levels obtained with all features.

CONCLUSIONS

Through machine learning algorithms, the most influencing factors for UD and BD classification were recombined and applied for rapid diagnosis. This highly feasible method holds the potential for convenient and accurate diagnosis of young patients in research and clinical practice.

Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis

The global prevalence of Major Depressive Disorder (MDD) is increasing at an alarming rate, underscoring the urgent need for timely and accurate diagnoses to facilitate effective interventions and treatments. Electroencephalography remains a widely used neuroimaging technique in psychiatry, due to its non-invasive nature and cost-effectiveness. With the rise of computational psychiatry, the integration of EEG with artificial intelligence has yielded remarkable results in diagnosing depression. This review offers a comparative analysis of two predominant methodologies in research: traditional machine learning and deep learning methods. Furthermore, this review addresses key challenges in current research and suggests potential solutions. These insights aim to enhance diagnostic accuracy for depression and also foster further development in the area of computational psychiatry.

Can retinal nerve fiber layer (RNFL) thickness be a marker for distinguishing bipolar depression from unipolar depression?

OBJECTIVE

We aimed to compare retinal nerve fiber layer (RNFL) thickness and ganglion cell complex (GCC) thickness in bipolar disorder (BD) and major depressive disorder (MDD).

METHOD

The study included thirty MDD, thirty-two BD participants in depressive episode, and thirty-seven controls matched according to age, gender, body mass index (BMI), and smoking status. Optic coherence tomography (OCT) measurements were performed for both participants and controls. The RNFL and GCC thickness were measured and recorded automatically by a spectral OCT device. Participants were also subjected to Hamilton Depression Rating Scale (HAM-D).

RESULTS

RNFL superior thickness was significantly lower in BD participants, compared to the MDD participants and controls (p = 0.001). GCC inferior (p = 0.022) and inferonasal (p = 0.005) thickness were detected lower in BD group, compared to the control groups. In the BD group, HAM-D scores were negatively correlated with RNFL-temporal (p = 0.049, r= -0.357), GCC inferotemporal (p = 0.02, r= -0.416) and superotemporal thickness (p = 0.002, r= -0.546).

CONCLUSIONS

RNFL thickness were lower in BD participants compared to the MDD and controls and, GCC thickness were lower in BD participants compared to the controls. Our findings support the hypothesis that neurodegeneration is part of the pathogenesis of BD. Future research are needed to confirm the lack of RNFL thickness in MDD, which could have immediate therapeutic consequences as well as implications for distinguishing BD from MDD.

Neural correlates of treatment response to ketamine for treatment-resistant depression: A systematic review of MRI-based studies

Treatment-resistant depression (TRD) is defined as patients diagnosed with depression having a history of failure with different antidepressants with an adequate dosage and treatment duration. The NMDA receptor antagonist ketamine rapidly reduces depressive symptoms in TRD. We examined neural correlates of treatment response to ketamine in TRD through a systematic review of brain magnetic resonance imaging (MRI) studies. A comprehensive search in PubMed was performed using "ketamine AND depression AND magnetic resonance." The time span for the database queries was "Start date: 2018/01/01; End date: 2024/05/31." Total 41 original articles comprising 1,396 TRD and 587 healthy controls (HC) were included. Diagnosis of depression was made using the Structured Clinical Interview for DSM Disorders (SCID), the Mini-International Neuropsychiatric Interview (MINI), and/or the clinical assessment by psychiatrists. Patients with affective psychotic disorders were excluded. Most studies applied ketamine [0.5mg/kg racemic ketamine and/or 0.25mg/kg S-ketamine] diluted in 60cc of normal saline via intravenous infusion over 40 min one time, four times, or six times spaced 2-3 days apart over 2 weeks. Clinical outcome was defined as either remission, response, and/or percentage changes of depressive symptoms. Brain MRI of the T2*-weighted imaging (resting-state or task performance), arterial spin labeling, diffusion weighted imaging, and T1-weighted imaging were acquired at baseline and mainly 1-3days after the ketamine administration. Only the study results replicated by ≥ 2 studies and were included in the default-mode, salience, fronto-parietal, subcortical, and limbic networks were regarded as meaningful. Putative brain-based markers of treatment response to ketamine in TRD were found in the structural/functional features of limbic (subgenual ACC, hippocampus, cingulum bundle-hippocampal portion; anhedonia/suicidal ideation), salience (dorsal ACC, insula, cingulum bundle-cingulate gyrus portion; thought rumination/suicidal ideation), fronto-parietal (dorsolateral prefrontal cortex, superior longitudinal fasciculus; anhedonia/suicidal ideation), default-mode (posterior cingulate cortex; thought rumination), and subcortical (striatum; anhedonia/thought rumination) networks. Brain features of limbic, salience, and fronto-parietal networks could be useful in predicting the TRD with better response to ketamine in relief of anhedonia, thought rumination, and suicidal ideation.

Distinct connectivity patterns in bipolar and unipolar depression: a functional connectivity multivariate pattern analysis study

BACKGROUND

Patients with bipolar disorder (BD) and major depressive disorder (MDD) exhibit depressive episodes with similar symptoms despite having different and poorly understood underlying neurobiology, often leading to misdiagnosis and improper treatment. This exploratory study examined whole-brain functional connectivity (FC) using FC multivariate pattern analysis (fc-MVPA) to identify the FC patterns with the greatest ability to distinguish between currently depressed patients with BD type I (BD I) and those with MDD.

METHODOLOGY

In a cross-sectional design, 41 BD I, 40 MDD patients and 63 control participants completed resting state functional magnetic resonance imaging scans. Data-driven fc-MVPA, as implemented in the CONN toolbox, was used to identify clusters with differential FC patterns between BD patients and MDD patients. The identified cluster was used as a seed in a post hoc seed-based analysis (SBA) to reveal associated connectivity patterns, followed by a secondary ROI-to-ROI analysis to characterize differences in connectivity between these patterns among BD I patients, MDD patients and controls.

RESULTS

FC-MVPA identified one cluster located in the right frontal pole (RFP). The subsequent SBA revealed greater FC between the RFP and posterior cingulate cortex (PCC) and between the RFP and the left inferior/middle temporal gyrus (LI/MTG) and lower FC between the RFP and the left precentral gyrus (LPCG), left lingual gyrus/occipital cortex (LLG/OCC) and right occipital cortex (ROCC) in MDD patients than in BD patients. Compared with the controls, ROI-to-ROI analysis revealed lower FC between the RFP and the PCC and greater FC between the RFP and the LPCG, LLG/OCC and ROCC in BD patients; in MDD patients, the analysis revealed lower FC between the RFP and the LLG/OCC and ROCC and greater FC between the RFP and the LI/MTG.

CONCLUSIONS

Differences in the RFP FC patterns between currently depressed patients with BD and those with MDD suggest potential neuroimaging markers that should be further examined. Specifically, BD patients exhibit increased FC between the RFP and the motor and visual networks, which is associated with psychomotor symptoms and heightened compensatory frontoparietal FC to counter distractibility. In contrast, MDD patients exhibit increased FC between the RFP and the default mode network, corresponding to sustained self-focus and rumination.

Deficits in prefrontal metabotropic glutamate receptor 5 are associated with functional alterations during emotional processing in bipolar disorder

BACKGROUND

Elucidating biological mechanisms contributing to bipolar disorder (BD) is key to improved diagnosis and treatment development. With converging evidence implicating the metabotropic glutamate receptor 5 (mGlu5) in the pathology of BD, here, we therefore test the hypothesis that recently identified deficits in mGlu5 are associated with functional brain differences during emotion processing in BD.

METHODS

Positron emission tomography (PET) with [18F]FPEB was used to measure mGlu5 receptor availability and functional imaging (fMRI) was performed while participants completed an emotion processing task. Data were analyzed from 62 individuals (33 ± 12 years, 45 % female) who completed both PET and fMRI, including individuals with BD (n = 18), major depressive disorder (MDD: n = 20), and psychiatrically healthy comparisons (HC: n = 25).

RESULTS

Consistent with some prior reports, the BD group displayed greater activation during fear processing relative to MDD and HC, notably in right lateralized frontal and parietal brain regions. In BD, (but not MDD or HC) lower prefrontal mGlu5 availability was associated with greater activation in bilateral pre/postcentral gyri and cuneus during fear processing. Furthermore, greater prefrontal mGlu5-related brain activity in BD was associated with difficulties in psychomotor function (r≥0.904, p≤0.005) and attention (r≥0.809, p≤0.028).

LIMITATIONS

The modest sample size is the primary limitation.

CONCLUSIONS

Deficits in prefrontal mGlu5 in BD were linked to increased cortical activation during fear processing, which in turn was associated with impulsivity and attentional difficulties. These data further implicate an mGlu5-related mechanism unique to BD. More generally these data suggest integrating PET and fMRI can provide novel mechanistic insights.

Gray matter volume and corresponding covariance connectivity are biomarkers for major depressive disorder

The major depressive disorder (MDD) is a common and severe mental disorder. To identify a reliable biomarker for MDD is important for early diagnosis and prevention. Given easy access and high reproducibility, the structural magnetic resonance imaging (sMRI) is an ideal method to identify the biomarker for depression. In this study, sMRI data of first episode, treatment-naïve 66 MDD patients and 54 sex-, age-, and education-matched healthy controls (HC) were used to identify the differences in gray matter volume (GMV), group-level, individual-level covariance connections. Finally, the abnormal GMV and individual covariance connections were applied to classify MDD from HC. MDD patients showed higher GMV in middle occipital gyrus (MOG) and precuneus (PCun), and higher structural covariance connections between MOG and PCun. In addition, the Allen Human Brain Atlas (AHBA) was applied and revealed the genetic basis for the changes of gray matter volume. Importantly, we reported that GMV in MOG, PCun and structural covariance connectivity between MOG and PCun are able to discriminate MDD from HC. Our results revealed structural underpinnings for MDD, which may contribute towards early discriminating for depression.

Gene expression and brain imaging association study reveals gene signatures in major depressive disorder

Major depressive disorder is often characterized by changes in the structure and function of the brain, which are influenced by modifications in gene expression profiles. How the depression-related genes work together within the scope of time and space to cause pathological changes remains unclear. By integrating the brain-wide gene expression data and imaging data in major depressive disorder, we identified gene signatures of major depressive disorder and explored their temporal-spatial expression specificity, network properties, function annotations and sex differences systematically. Based on correlation analysis with permutation testing, we found 345 depression-related genes significantly correlated with functional and structural alteration of brain images in major depressive disorder and separated them by directional effects. The genes with negative effect for grey matter density and positive effect for functional indices are enriched in downregulated genes in the post-mortem brain samples of patients with depression and risk genes identified by genome-wide association studies than genes with positive effect for grey matter density and negative effect for functional indices and control genes, confirming their potential association with major depressive disorder. By introducing a parameter of dispersion measure on the gene expression data of developing human brains, we revealed higher spatial specificity and lower temporal specificity of depression-related genes than control genes. Meanwhile, we found depression-related genes tend to be more highly expressed in females than males, which may contribute to the difference in incidence rate between male and female patients. In general, we found the genes with negative effect have lower network degree, more specialized function, higher spatial specificity, lower temporal specificity and more sex differences than genes with positive effect, indicating they may play different roles in the occurrence and development of major depressive disorder. These findings can enhance the understanding of molecular mechanisms underlying major depressive disorder and help develop tailored diagnostic and treatment strategies for patients of depression of different sex.

Interleukin-1β moderates the relationships between middle frontal-mACC/insular connectivity and depressive symptoms in bipolar II depression

The functional alterations of the brain in bipolar II depression (BDII-D) and their clinical and inflammatory associations are understudied. We aim to investigate the functional brain alterations in BDII-D and their relationships with inflammation, childhood adversity, and psychiatric symptoms, and to examine the moderating effects among these factors. Using z-normalized amplitude of low-frequency fluctuation (zALFF), we assessed the whole-brain resting-state functional activity between 147 BDII-D individuals and 150 healthy controls (HCs). Differential ALFF regions were selected as seeds for functional connectivity analysis to observe brain connectivity alterations resulting from abnormal regional activity. Four inflammatory cytokines including interleukin (IL)-6, IL-1β, tumor necrosis factor (TNF)-α, and C-reactive protein (CRP) and five clinical scales including Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA), Positive and Negative Syndrome Scale (PANSS), Columbia-Suicide Severity Rating Scale (C-SSRS), and Childhood Trauma Questionnaire (CTQ) were tested and assessed in BDII-D. Partial correlations with multiple comparison corrections identified relationships between brain function and inflammation, childhood adversity, and psychiatric symptoms. Moderation analysis was conducted based on correlation results and previous findings. Compared to HCs, BDII-D individuals displayed significantly lower zALFF in the superior and middle frontal gyri (SFG and MFG) and insula, but higher zALFF in the occipital-temporal area. Only the MFG and insula-related connectivity exhibited significant differences between groups. Within BDII-D, lower right insula zALFF value correlated with higher IL-6, CRP, and emotional adversity scores, while lower right MFG zALFF was related to higher CRP and physical abuse scores. Higher right MFG-mid-anterior cingulate cortex (mACC) connectivity was associated with higher IL-1β. Moreover, IL-1β moderated associations between higher right MFG-mACC/insula connectivity and greater depressive symptoms. This study reveals that abnormal functional alterations in the right MFG and right insula were associated with elevated inflammation, childhood adversity, and depressive symptoms in BDII-D. IL-1β may moderate the relationship between MFG-related connectivity and depressive symptoms.

Simultaneous Utilization of Mood Disorder Questionnaire and Bipolar Spectrum Diagnostic Scale for Machine Learning-Based Classification of Patients With Bipolar Disorders and Depressive Disorders

OBJECTIVE

Bipolar and depressive disorders are distinct disorders with clearly different clinical courses, however, distinguishing between them often presents clinical challenges. This study investigates the utility of self-report questionnaires, the Mood Disorder Questionnaire (MDQ) and Bipolar Spectrum Diagnostic Scale (BSDS), with machine learning-based multivariate analysis, to classify patients with bipolar and depressive disorders.

METHODS

A total of 189 patients with bipolar disorders and depressive disorders were included in the study, and all participants completed both the MDQ and BSDS questionnaires. Machine-learning classifiers, including support vector machine (SVM) and linear discriminant analysis (LDA), were exploited for multivariate analysis. Classification performance was assessed through cross-validation.

RESULTS

Both MDQ and BSDS demonstrated significant differences in each item and total scores between the two groups. Machine learning-based multivariate analysis, including SVM, achieved excellent discrimination levels with area under the ROC curve (AUC) values exceeding 0.8 for each questionnaire individually. In particular, the combination of MDQ and BSDS further improved classification performance, yielding an AUC of 0.8762.

CONCLUSION

This study suggests the application of machine learning to MDQ and BSDS can assist in distinguishing between bipolar and depressive disorders. The potential of combining high-dimensional psychiatric data with machine learning-based multivariate analysis as an effective approach to psychiatric disorders.

Feasibility study on using house-tree-person drawings for automatic analysis of depression

Major depression is a severe psychological disorder typically diagnosed using scale tests and through the subjective assessment of medical professionals. Along with the continuous development of machine learning techniques, computer technology has been increasingly employed to identify depression in recent years. Traditional methods of automatic depression recognition rely on using the patient's physiological data, such as facial expressions, voice, electroencephalography (EEG), and magnetic resonance imaging (MRI) as input. However, the acquisition cost of these data is relatively high, making it unsuitable for large-scale depression screening. Thus, we explore the possibility of utilizing a house-tree-person (HTP) drawing to automatically detect major depression without requiring the patient's physiological data. The dataset we used for this study consisted of 309 drawings depicting individuals at risk of major depression and 290 drawings depicting individuals without depression risk. We classified the eight features extracted from HTP sketches using four machine-learning models and used multiple cross-validations to calculate recognition rates. The best classification accuracy rate among these models reached 97.2%. Additionally, we conducted ablation experiments to analyze the association between features and information on depression pathology. The results of Wilcoxon rank-sum tests showed that seven of the eight features significantly differed between the major depression group and the regular group. We demonstrated significant differences in HTP drawings between patients with severe depression and everyday individuals, and using HTP sketches to identify depression automatically is feasible, providing a new approach for automatic identification and large-scale screening of depression.

Neuroimaging Genomics a Predictor of Major Depressive Disorder (MDD)

Depression is a complex psychiatric disorder influenced by various genetic and environmental factors. Strong evidence has established the contribution of genetic factors in depression through twin studies and the heritability rate for depression has been reported to be 37%. Genetic studies have identified genetic variations associated with an increased risk of developing depression. Imaging genetics is an integrated approach where imaging measures are combined with genetic information to explore how specific genetic variants contribute to brain abnormalities. Neuroimaging studies allow us to examine both structural and functional abnormalities in individuals with depression. This review has been designed to study the correlation of the significant genetic variants with different regions of neural activity, connectivity, and structural alteration in the brain as detected by imaging techniques to understand the scope of biomarkers in depression. This might help in developing novel therapeutic interventions targeting specific genetic pathways or brain circuits and the underlying pathophysiology of depression based on this integrated approach can be established at length.

Engeletin alleviates depressive-like behaviours by modulating microglial polarization via the LCN2/CXCL10 signalling pathway

Microglial polarization and associated inflammatory activity are the key mediators of depression pathogenesis. The natural Smilax glabra rhizomilax derivative engeletin has been reported to exhibit robust anti-inflammatory activity, but no studies to date have examined the mechanisms through which it can treat depressive symptoms. We showed that treatment for 21 days with engeletin significantly alleviated depressive-like behaviours in chronic stress social defeat stress (CSDS) model mice. T1-weighted imaging (T1WI), T2-weighted imaging (T2WI) imaging revealed no significant differences between groups, but the bilateral prefrontal cortex of CSDS mice exhibited significant increases in apparent diffusion coefficient and T2 values relative to normal control mice, with a corresponding reduction in fractional anisotropy, while engeletin reversed all of these changes. CSDS resulted in higher levels of IL-1β, IL-6, and TNF-a production, enhanced microglial activation, and greater M1 polarization with a concomitant decrease in M2 polarization in the mPFC, whereas engeletin treatment effectively abrogated these CSDS-related pathological changes. Engeletin was further found to suppress the LCN2/C-X-C motif chemokine ligand 10 (CXCL10) signalling axis such that adeno-associated virus-induced LCN2 overexpression ablated the antidepressant effects of engeletin and reversed its beneficial effects on the M1/M2 polarization of microglia. In conclusion, engeletin can alleviate CSDS-induced depressive-like behaviours by regulating the LCN2/CXCL10 pathway and thereby altering the polarization of microglia. These data suggest that the antidepressant effects of engeletin are correlated with the polarization of microglia, highlighting a potential avenue for future design of antidepressant strategies that specifically target the microglia.

Effects of inflammation, childhood adversity, and psychiatric symptoms on brain morphometrical phenotypes in bipolar II depression

BACKGROUND

The neuroanatomical alteration in bipolar II depression (BDII-D) and its associations with inflammation, childhood adversity, and psychiatric symptoms are currently unclear. We hypothesize that neuroanatomical deficits will be related to higher inflammation, greater childhood adversity, and worse psychiatric symptoms in BDII-D.

METHODS

Voxel- and surface-based morphometry was performed using the CAT toolbox in 150 BDII-D patients and 155 healthy controls (HCs). Partial Pearson correlations followed by multiple comparison correction was used to indicate significant relationships between neuroanatomy and inflammation, childhood adversity, and psychiatric symptoms.

RESULTS

Compared with HCs, the BDII-D group demonstrated significantly smaller gray matter volumes (GMVs) in frontostriatal and fronto-cerebellar area, insula, rectus, and temporal gyrus, while significantly thinner cortices were found in frontal and temporal areas. In BDII-D, smaller GMV in the right middle frontal gyrus (MFG) was correlated with greater sexual abuse (r = -0.348, q < 0.001) while larger GMV in the right orbital MFG was correlated with greater physical neglect (r = 0.254, q = 0.03). Higher WBC count (r = -0.227, q = 0.015) and IL-6 levels (r = -0.266, q = 0.015) was associated with smaller GMVs in fronto-cerebellar area in BDII-D. Greater positive symptoms was correlated with larger GMVs of the left middle temporal pole (r = 0.245, q = 0.03).

CONCLUSIONS

Neuroanatomical alterations in frontostriatal and fronto-cerebellar area, insula, rectus, temporal gyrus volumes, and frontal-temporal thickness may reflect a core pathophysiological mechanism of BDII-D, which are related to inflammation, trauma, and psychiatric symptoms in BDII-D.

Abnormal stability of spontaneous neuronal activity as a predictor of diagnosis conversion from major depressive disorder to bipolar disorder

OBJECTIVE

Bipolar disorder (BD) is often misdiagnosed as major depressive disorder (MDD) in the early stage, which may lead to inappropriate treatment. This study aimed to characterize the alterations of spontaneous neuronal activity in patients with depressive episodes whose diagnosis transferred from MDD to BD.

METHODS

532 patients with MDD and 132 healthy controls (HCs) were recruited over 10 years. During the follow-up period, 75 participants with MDD transferred to BD (tBD), and 157 participants remained with the diagnosis of unipolar depression (UD). After excluding participants with poor image quality and excessive head movement, 68 participants with the diagnosis of tBD, 150 participants with the diagnosis of UD, and 130 HCs were finally included in the analysis. The dynamic amplitude of low-frequency fluctuations (dALFF) of spontaneous neuronal activity was evaluated in tBD, UD and HC using functional magnetic resonance imaging at study inclusion. Receiver operating characteristic (ROC) analysis was performed to evaluate sensitivity and specificity of the conversion prediction from MDD to BD based on dALFF.

RESULTS

Compared to HC, tBD exhibited elevated dALFF at left premotor cortex (PMC_L), right lateral temporal cortex (LTC_R) and right early auditory cortex (EAC_R), and UD showed reduced dALFF at PMC_L, left paracentral lobule (PCL_L), bilateral medial prefrontal cortex (mPFC), right orbital frontal cortex (OFC_R), right dorsolateral prefrontal cortex (DLPFC_R), right posterior cingulate cortex (PCC_R) and elevated dALFF at LTC_R. Furthermore, tBD exhibited elevated dALFF at PMC_L, PCL_L, bilateral mPFC, bilateral OFC, DLPFC_R, PCC_R and LTC_R than UD. In addition, ROC analysis based on dALFF in differential areas obtained an area under the curve (AUC) of 72.7%.

CONCLUSIONS

The study demonstrated the temporal dynamic abnormalities of tBD and UD in the critical regions of the somatomotor network (SMN), default mode network (DMN), and central executive network (CEN). The differential abnormal patterns of temporal dynamics between the two diseases have the potential to predict the diagnosis transition from MDD to BD.

deepPGSegNet: MRI-based pituitary gland segmentation using deep learning

Introduction

In clinical research on pituitary disorders, pituitary gland (PG) segmentation plays a pivotal role, which impacts the diagnosis and treatment of conditions such as endocrine dysfunctions and visual impairments. Manual segmentation, which is the traditional method, is tedious and susceptible to inter-observer differences. Thus, this study introduces an automated solution, utilizing deep learning, for PG segmentation from magnetic resonance imaging (MRI).

Methods

A total of 153 university students were enrolled, and their MRI images were used to build a training dataset and ground truth data through manual segmentation of the PGs. A model was trained employing data augmentation and a three-dimensional U-Net architecture with a five-fold cross-validation. A predefined field of view was applied to highlight the PG region to optimize memory usage. The model's performance was tested on an independent dataset. The model's performance was tested on an independent dataset for evaluating accuracy, precision, recall, and an F1 score.

Results and discussion

The model achieved a training accuracy, precision, recall, and an F1 score of 92.7%, 0.87, 0.91, and 0.89, respectively. Moreover, the study explored the relationship between PG morphology and age using the model. The results indicated a significant association between PG volume and midsagittal area with age. These findings suggest that a precise volumetric PG analysis through an automated segmentation can greatly enhance diagnostic accuracy and surveillance of pituitary disorders.

Diagnostic deep learning algorithms that use resting EEG to distinguish major depressive disorder, bipolar disorder, and schizophrenia from each other and from healthy volunteers

BACKGROUND

Mood disorders and schizophrenia affect millions worldwide. Currently, diagnosis is primarily determined by reported symptomatology. As symptoms may overlap, misdiagnosis is common, potentially leading to ineffective or destabilizing treatment. Diagnostic biomarkers could significantly improve clinical care by reducing dependence on symptomatic presentation.

METHODS

We used deep learning analysis (DLA) of resting electroencephalograph (EEG) to differentiate healthy control (HC) subjects (N = 239), from those with major depressive disorder (MDD) (N = 105), MDD-atypical (MDD-A) (N = 27), MDD-psychotic (MDD-P) (N = 35), bipolar disorder-depressed episode (BD-DE) (N = 71), BD-manic episode (BD-ME) (N = 49), and schizophrenia (SCZ) (N = 122) and also differentiate subjects with mental disorders on a pair-wise basis. DSM-III-R diagnoses were determined and supplemented by computerized Quick Diagnostic Interview Schedule. After EEG preprocessing, robust exact low-resolution electromagnetic tomography (ReLORETA) computed EEG sources for 82 brain regions. 20 % of all subjects were then set aside for independent testing. Feature selection methods were then used for the remaining subjects to identify brain source regions that are discriminating between diagnostic categories.

RESULTS

Pair-wise classification accuracies between 90 % and 100 % were obtained using independent test subjects whose data were not used for training purposes. The most frequently selected features across various pairs are in the postcentral, supramarginal, and fusiform gyri, the hypothalamus, and the left cuneus. Brain sites discriminating SCZ from HC were mainly in the left hemisphere while those separating BD-ME from HC were on the right.

LIMITATIONS

The use of superseded DSM-III-R diagnostic system and relatively small sample size in some disorder categories that may increase the risk of overestimation.

CONCLUSIONS

DLA of EEG could be trained to autonomously classify psychiatric disorders with over 90 % accuracy compared to an expert clinical team using standardized operational methods.

Systemic inflammation and oxidative stress markers in patients with unipolar and bipolar depression: A large-scale study

OBJECTIVE

Numerous studies have demonstrated that neutrophil/HDL ratio (NHR), lymphocyte/HDL ratio (LHR), monocyte/HDL (MHR) ratio, platelet/HDL ratio (PHR), neutrophil/ALB ratio (NAR) and platelet/ALB ratio (PAR) can serve as systemic inflammation and oxidative stress markers in a variety of diseases. However, few studies have estimated the associations of these markers with unipolar depression (UD) and bipolar depression (BD), as well as psychotic symptoms in UD and BD.

METHODS

6297 UD patients, 1828 BD patients and 7630 healthy subjects were recruited. The differences in these indicators among different groups were compared, and the influencing factors for the occurrence of UD or BD and psychotic symptoms were analyzed.

RESULTS

These ratios displayed unique variation patterns across different diagnostic groups. BD group exhibited higher NHR, LHR, MHR, NAR and lower PAR than UD and HC groups, UD group showed higher MHR than HC group. The psychotic UD group had higher NHR, LHR, MHR and NAR than non-psychotic UD group. Higher LHR, MHR, NAR and lower PAR were risk factors in BD when compared to UD group.

CONCLUSIONS

Our study demonstrated differences in inflammation and oxidative stress profile between UD and BD patients, as well as between subjects with or without psychotic symptom exist, highlighting the role of inflammation and oxidative stress in the pathophysiology of UD and BD.

Distinct functional patterns in child and adolescent bipolar and unipolar depression during emotional processing

Accumulating evidence from functional magnetic resonance imaging studies supported brain dysfunction during emotional processing in bipolar disorder (BD) and major depressive disorder (MDD). However, child and adolescent BD and MDD could display different activation patterns, which have not been fully understood. This study aimed to investigate common and distinct activation patterns of pediatric BD (PBD) and MDD (p-MDD) during emotion processing using meta-analytic approaches. Literature search identified 25 studies, contrasting 252 PBD patients, and 253 healthy controls (HCs) as well as 311 p-MDD patients and 263 HCs. A total of nine meta-analyses were conducted pulling PBD and p-MDD experiments together and separately. The results revealed that PBD and p-MDD showed distinct patterns during negative processing. PBD patients exhibited activity changes in bilateral precuneus, left inferior parietal gyrus, left angular gyrus, and right posterior cingulate cortex while p-MDD patients showed functional disruptions in the left rectus, left triangular part of the inferior frontal gyrus, left orbital frontal cortex, left insula, and left putamen. In conclusion, the activity changes in PBD patients were mainly in regions correlated with emotion perception while the dysfunction among p-MDD patients was in the fronto-limbic circuit and reward-related regions in charge of emotion appraisal and regulation.

Regional Homogeneity of the Left Posterior Cingulate Gyrus May Be a Potential Imaging Biomarker of Manic Episodes in First-Episode, Drug-Naive Bipolar Disorder

Introduction

Abnormal brain networks with emotional response in bipolar disorder (BD). However, there have been few studies on the local consistency between manic episodes in drug-naive first-episode BD patients and healthy controls (HCs). The purpose of this study is to evaluate the utility of neural activity values analyzed by Regional Homogeneity (ReHo).

Methods

Thirty-seven manic episodes in first-episode, drug-naive BD patients and 37 HCs participated in resting-state functional magnetic resonance rescanning and scale estimation. Reho and receiver operating characteristic (ROC) curve methods were used to analyze the imaging data. Support vector machine (SVM) method was used to analyze ReHo in different brain regions.

Results

Compared to HCs, ReHo increased in the left middle temporal gyrus (MTG.L), posterior cingulate gyrus (PCG), inferior parietal gyrus, and bilateral angular gyrus, and decreased in the left dorsolateral superior frontal gyrus in target group. The ROC results showed that the ReHo value of the left PCG could discriminate the target group from the HCs, and the AUC was 0.8766. In addition, the results of the support vector machine show that the increase in ReHo value in the left PCG can effectively discriminate the patients from the controls, with accuracy, sensitivity, and specificity of 86.02%, 86.49%, and 81.08%, respectively.

Conclusion

The increased activity of the left PCG may contribute new evidence of participation in the pathophysiology of manic episodes in first-episode, drug-naive BD patients. The Reho value of the left posterior cingulate gyrus may be a potential neuroimaging biomarker to discriminate target group from HCs.

Diagnosis of Major Depressive Disorder Using Machine Learning Based on Multisequence MRI Neuroimaging Features

BACKGROUND

Previous studies have found qualitative structural and functional brain changes in major depressive disorder (MDD) patients. However, most studies ignored the complementarity of multisequence MRI neuroimaging features and cannot determine accurate biomarkers.

PURPOSE

To evaluate machine-learning models combined with multisequence MRI neuroimaging features to diagnose patients with MDD.

STUDY TYPE

Prospective.

SUBJECTS

A training cohort including 111 patients and 90 healthy controls (HCs) and a test cohort including 28 patients and 22 HCs.

FIELD STRENGTH/SEQUENCE

A 3.0 T/T1-weighted imaging, resting-state functional MRI with echo-planar sequence, and single-shot echo-planar diffusion tensor imaging.

ASSESSMENT

Recruitment and integration were used to reflect the dynamic changes of functional networks, while gray matter volume and fractional anisotropy were used to reflect the changes in the morphological and anatomical network. We then fused features with significant differences in functional, morphological, and anatomical networks to evaluate a random forest (RF) classifier to diagnose patients with MDD. Furthermore, a support vector machine (SVM) classifier was used to verify the stability of neuroimaging features. Linear regression analyses were conducted to investigate the relationships among multisequence neuroimaging features and the suicide risk of patients.

STATISTICAL TESTS

The comparison of functional network attributes between patients and controls by two-sample t-test. Network-based statistical analysis was used to identify structural and anatomical connectivity changes between MDD and HCs. The performance of the model was evaluated by receiver operating characteristic (ROC) curves.

RESULTS

The performance of the RF model integrating multisequence neuroimaging features in the diagnosis of depression was significantly improved, with an AUC of 93.6%. In addition, we found that multisequence neuroimaging features could accurately predict suicide risk in patients with MDD (r = 0.691).

DATA CONCLUSION

The RF model fusing functional, morphological, and anatomical network features performed well in diagnosing patients with MDD and provided important insights into the pathological mechanisms of MDD.

EVIDENCE LEVEL

1.

TECHNICAL EFFICACY

Stage 2.

Characterizing the clinical profile of mania without major depressive episodes: a systematic review and meta-analysis of factors associated with unipolar mania

BACKGROUND

The diagnostic concept of unipolar mania (UM), i.e. the lifetime occurrence of mania without major depressive episodes, remains a topic of debate despite the evidence accumulated in the last few years. We carried out a systematic review and meta-analysis of observational studies testing factors associated with UM as compared to bipolar disorder with a manic-depressive course (md-BD).

METHODS

Studies indexed up to July 2022 in main electronic databases were searched. Random-effects meta-analyses of the association between UM and relevant correlates yielded odds ratio (OR) or standardized mean difference (SMD), with 95% confidence intervals (CIs).

RESULTS

Based on data from 21 studies, factors positively or negatively associated with UM, as compared to md-BD, were: male gender (OR 1.47; 95% CI 1.11-1.94); age at onset (SMD -0.25; 95% CI -0.46 to -0.04); number of hospitalizations (SMD 0.53; 95% CI 0.21-0.84); family history of depression (OR 0.55; 95% CI 0.36-0.85); suicide attempts (OR 0.25; 95% CI 0.19-0.34); comorbid anxiety disorders (OR 0.35; 95% CI 0.26-0.49); psychotic features (OR 2.16; 95% CI 1.55-3.00); hyperthymic temperament (OR 1.99; 95% CI 1.17-3.40). The quality of evidence for the association with previous suicide attempts was high, moderate for anxiety disorders and psychotic features, and low or very low for other correlates.

CONCLUSIONS

Despite the heterogeneous quality of evidence, this work supports the hypothesis that UM might represent a distinctive diagnostic construct, with peculiar clinical correlates. Additional research is needed to better differentiate UM in the context of affective disorders, favouring personalized care approaches.

Differences in Non-suicidal Self-injury Behaviors between Unipolar Depression and Bipolar Depression in Adolescent Outpatients

OBJECTIVE

Non-suicidal self-injury (NSSI) has a higher prevalence in adolescents with depressive disorders than in community adolescents. This study examined the differences in NSSI behaviors between adolescents with unipolar depression (UD) and those with bipolar depression (BD).

METHODS

Adolescents with UD or BD were recruited from 20 general or psychiatric hospitals across China. The methods, frequency, and function of NSSI were assessed by Functional Assessment of Self-Mutilation. The Beck Suicide Ideation Scale was used to evaluate adolescents' suicidal ideation, and the 10-item Kessler Psychological Distress Scale to estimate the anxiety and depression symptoms.

RESULTS

The UD group had higher levels of depression (19.16 vs.17.37, F=15.23, P<0.001) and anxiety symptoms (17.73 vs.16.70, F=5.00, P=0.026) than the BD group. Adolescents with BD had a longer course of NSSI than those with UD (2.00 vs.1.00 year, Z=-3.39, P=0.001). There were no statistical differences in the frequency and the number of methods of NSSI between the UD and BD groups. Depression (r=0.408, P<0.01) and anxiety (r=0.391, P<0.01) were significantly and positively related to NSSI frequency.

CONCLUSION

Adolescents with BD had a longer course of NSSI than those with UD. More importantly, NSSI frequency were positively and strongly correlated with depression and anxiety symptoms, indicating the importance of adequate treatment of depression and anxiety in preventing and intervening adolescents' NSSI behaviors.

Association of the plasma complement system with brain volume deficits in bipolar and major depressive disorders

BACKGROUND

Inflammation plays a crucial role in the pathogenesis of major depressive disorder (MDD) and bipolar disorder (BD). This study aimed to examine whether the dysregulation of complement components contributes to brain structural defects in patients with mood disorders.

METHODS

A total of 52 BD patients, 35 MDD patients, and 53 controls were recruited. The human complement immunology assay was used to measure the levels of complement factors. Whole brain-based analysis was performed to investigate differences in gray matter volume (GMV) and cortical thickness (CT) among the BD, MDD, and control groups, and relationships were explored between neuroanatomical differences and levels of complement components.

RESULTS

GMV in the medial orbital frontal cortex (mOFC) and middle cingulum was lower in both patient groups than in controls, while the CT of the left precentral gyrus and left superior frontal gyrus were affected differently in the two disorders. Concentrations of C1q, C4, factor B, factor H, and properdin were higher in both patient groups than in controls, while concentrations of C3, C4 and factor H were significantly higher in BD than in MDD. Concentrations of C1q, factor H, and properdin showed a significant negative correlation with GMV in the mOFC at the voxel-wise level.

CONCLUSIONS

BD and MDD are associated with shared and different alterations in levels of complement factors and structural impairment in the brain. Structural defects in mOFC may be associated with elevated levels of certain complement factors, providing insight into the shared neuro-inflammatory pathogenesis of mood disorders.

Reduced fractional anisotropy in bipolar disorder v. major depressive disorder independent of current symptoms

BACKGROUND

Patients with bipolar disorder (BD) show reduced fractional anisotropy (FA) compared to patients with major depressive disorder (MDD). Little is known about whether these differences are mood state-independent or influenced by acute symptom severity. Therefore, the aim of this study was (1) to replicate abnormalities in white matter microstructure in BD v. MDD and (2) to investigate whether these vary across depressed, euthymic, and manic mood.

METHODS

In this cross-sectional diffusion tensor imaging study, n = 136 patients with BD were compared to age- and sex-matched MDD patients and healthy controls (HC) (n = 136 each). Differences in FA were investigated using tract-based spatial statistics. Using interaction models, the influence of acute symptom severity and mood state on the differences between patient groups were tested.

RESULTS

Analyses revealed a main effect of diagnosis on FA across all three groups (ptfce-FWE = 0.003). BD patients showed reduced FA compared to both MDD (ptfce-FWE = 0.005) and HC (ptfce-FWE < 0.001) in large bilateral clusters. These consisted of several white matter tracts previously described in the literature, including commissural, association, and projection tracts. There were no significant interaction effects between diagnosis and symptom severity or mood state (all ptfce-FWE > 0.704).

CONCLUSIONS

Results indicated that the difference between BD and MDD was independent of depressive and manic symptom severity and mood state. Disruptions in white matter microstructure in BD might be a trait effect of the disorder. The potential of FA values to be used as a biomarker to differentiate BD from MDD should be further addressed in future studies using longitudinal designs.

Arketamine for bipolar depression: Open-label, dose-escalation, pilot study

There are significantly fewer options for the treatment of bipolar depression than major depressive disorder, with an urgent need for alternative therapies. In this pilot study, we treated six subjects with bipolar disorder types I and II (according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria) who had been in a current depressive episode for at least four weeks. Four subjects were female (66.66%), and the mean age was 45.33 (±12.32). Subjects received adjunct treatment with two arketamine intravenous infusions one week apart-0.5 mg/kg first and then 1 mg/kg. The mean baseline Montgomery-Åsberg Depression Rating Scale (MADRS) total score was 36.66, which decreased to 27.83 24h after the first infusion of 0.5 mg/kg of arketamine (p = 0.036). In respect of the 1 mg/kg dose, the mean MADRS total score before the second infusion was 32.0, which dropped to 17.66 after 24h (p < 0.001). Arketamine appears to have rapid-acting antidepressant properties, consistent with previous animal studies on major depression. All individuals tolerated both doses, exhibiting nearly absent dissociation, and no manic symptoms. To the best of our knowledge, this pilot trial is the first to test the feasibility and safety of the (R)-enantiomer of ketamine (arketamine) for bipolar depression.

The efficacy and cerebral mechanism of intradermal acupuncture for major depressive disorder: a study protocol for a randomized controlled trial

Background

Major depressive disorder (MDD) has emerged as the fifth leading cause of years lived with disability, with a high prevalent, affecting nearly 4% of the global population. While available evidence suggests that intradermal acupuncture may enhance the effectiveness of antidepressants, whether its efficacy is a specific therapeutic effect or a placebo effect has not been reported. Moreover, the cerebral mechanism of intradermal acupuncture as a superficial acupuncture (usually subcutaneous needling to a depth of 1-2 mm) for MDD remains unclear.

Methods

A total of 120 participants with MDD will be enrolled and randomized to the waiting list group, sham intradermal acupuncture group and active intradermal acupuncture group. All 3 groups will receive a 6-week intervention and a 4-week follow-up. The primary outcome will be measured by the Hamilton Depression Rating Scale-17 and the secondary outcome measures will be the Self-Rating depression scale and Pittsburgh sleep quality index. Assessments will be conducted at baseline, 3 weeks, 6 weeks, and during the follow-up period. In addition, 20 eligible participants in each group will be randomly selected to undergo head magnetic resonance imaging before and after the intervention to explore the effects of intradermal acupuncture on brain activity in MDD patients.

Discussion

If the intradermal acupuncture is beneficial, it is promising to be included in the routine treatment of MDD.

Clinical Trial Registration

Clinicaltrials.gov, NCT05720637.

Differences in intracellular protein levels in monocytes and CD4+ lymphocytes between bipolar depressed patients and healthy controls: A pilot study with tyramine-based signal-amplified flow cytometry

BACKGROUND

Molecular biomarkers for bipolar disorder (BD) that distinguish it from other manifestations of depressive symptoms remain unknown. The aim of this study was to determine if a very sensitive tyramine-based signal-amplification technology for flow cytometry (CellPrint™) could facilitate the identification of cell-specific analyte expression profiles of peripheral blood cells for bipolar depression (BPD) versus healthy controls (HCs).

METHODS

The diagnosis of psychiatric disorders was ascertained with Mini International Neuropsychiatric Interview for DSM-5. Expression levels for eighteen protein analytes previously shown to be related to bipolar disorder were assessed with CellPrint™ in CD4⁺ T cells and monocytes of bipolar patients and HCs. Implementation of protein-protein interaction (PPI) network and pathway analysis was subsequently used to identify new analytes and pathways for subsequent interrogations.

RESULTS

Fourteen drug-naïve or -free patients with bipolar I or II depression and 17 healthy controls (HCs) were enrolled. The most distinguishable changes in analyte expression based on t-tests included GSK3β, HMGB1, IRS2, phospho-GSK3αβ, phospho-RELA, and TSPO in CD4⁺ T cells and calmodulin, GSK3β, IRS2, and phospho-HS1 in monocytes. Subsequent PPI and pathway analysis indicated that prolactin, leptin, BDNF, and interleukin-3 signal pathways were significantly different between bipolar patients and HCs.

LIMITATION

The sample size of the study was small and 2 patients were on medications.

CONCLUSION

In this pilot study, CellPrint™ was able to detect differences in cell-specific protein levels between BPD patients and HCs. A subsequent study including samples from patients with BPD, major depressive disorder, and HCs is warranted.

Optimizing multi-domain hematologic biomarkers and clinical features for the differential diagnosis of unipolar depression and bipolar depression

There is a lack of objective features for the differential diagnosis of unipolar and bipolar depression, especially those that are readily available in practical settings. We investigated whether clinical features of disease course, biomarkers from complete blood count, and blood biochemical markers could accurately classify unipolar and bipolar depression using machine learning methods. This retrospective study included 1160 eligible patients (918 with unipolar depression and 242 with bipolar depression). Patient data were randomly split into training (85%) and open test (15%) sets 1000 times, and the average performance was reported. XGBoost achieved the optimal open-test performance using selected biomarkers and clinical features-AUC 0.889, sensitivity 0.831, specificity 0.839, and accuracy 0.863. The importance of features for differential diagnosis was measured using SHapley Additive exPlanations (SHAP) values. The most informative features include (1) clinical features of disease duration and age of onset, (2) biochemical markers of albumin, low density lipoprotein (LDL), and potassium, and (3) complete blood count-derived biomarkers of white blood cell count (WBC), platelet-to-lymphocyte ratio (PLR), and monocytes (MONO). Overall, onset features and hematologic biomarkers appear to be reliable information that can be readily obtained in clinical settings to facilitate the differential diagnosis of unipolar and bipolar depression.

Abnormal degree centrality can be a potential imaging biomarker in first-episode, drug-naive bipolar mania

Brain network abnormalities in emotional response exist in bipolar mania. However, few studies have been published on network degree centrality of first-episode, drug-naive bipolar mania, and healthy controls. This study aimed to assess the utility of neural activity values analyzed via degree centrality methods. Sixty-six first-episode, drug-naive patients with bipolar mania and 60 healthy controls participated in resting-state functional magnetic resonance rescanning and scale estimating. The degree centrality and receiver operating characteristic (ROC) curve methods were used for an analysis of the imaging data. Relative to healthy controls, first-episode bipolar mania patients displayed increased degree centrality values in the left middle occipital gyrus, precentral gyrus, supplementary motor area, Precuneus, and decreased degree centrality values in the left parahippocampal gyrus, right insula and superior frontal gyrus, medial. ROC results exhibited degree centrality values in the left parahippocampal gyrus that could distinguish first-episode bipolar mania patients from healthy controls with 0.8404 for AUC. Support vector machine results showed that reductions in degree centrality values in the left parahippocampal gyrus can be used to effectively differentiate between bipolar disorder patients and healthy controls with respective accuracy, sensitivity, and specificity values of 83.33%, 85.51%, and 88.41%. Increased activity in the left parahippocampal gyrus may be a distinctive neurobiological feature of first-episode, drug-naive bipolar mania. Degree centrality values in the left parahippocampal gyrus might be served as a potential neuroimaging biomarker to discriminate first-episode, drug-naive bipolar mania patients from healthy controls.

Cross-sectional analysis of whole-brain microstructural changes in adult patients with bipolar and unipolar depression by diffusion kurtosis imaging

RATIONALE AND OBJECTIVES

More than half of the bipolar depression (BD) subjects are misdiagnosed as unipolar depression (UD) due to lack of objective diagnostic criteria. We aimed to identify microstructural neuronal changes differentiating BD from UD groups using diffusion kurtosis imaging (DKI). The objective of the study is to identify an objective neuro-imaging marker to differentiate BD from UD.

MATERIALS AND METHODS

A prospective, cross-sectional study included total of 62 subjects with diagnosis of bipolar depression (n = 21), unipolar depression (n = 21), and healthy controls (n = 20). All subjects underwent diffusion-weighted imaging (b = 0,1000,2000) of the whole brain on 3-Tesla MR scanner. DKI data was analyzed using 189 region whole-brain atlas. Eight diffusion and kurtosis metrics including mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), fractional anisotropy (FA), mean kurtosis (MK), axial kurtosis (AK), radial kurtosis (RK), and kurtosis fractional anisotropy (FKA) were measured against these 189 regions. Principle component analysis (PCA) was utilized to identify the most significant regions of the brain. ANOVA with post hoc tests was used for analyzing these regions.

RESULTS

BD group showed increased MD, RD, decreased AK at the left amygdala and decreased MK and RK at the right hemi-cerebellum. UD group showed increased MK and RK at the right external capsule; and increased AK, MK, and RK at the right amygdala.

CONCLUSION

The right and left amygdala, right external capsule, and right hemi-cerebellum showed microstructural abnormalities capable of differentiating BD and UD groups. Diffusion imaging especially DKI can aid in management of depression patients.

Brain Inflammatory Marker Abnormalities in Major Psychiatric Diseases: a Systematic Review of Postmortem Brain Studies

Schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) are common neuropsychiatric disorders that lead to neuroinflammation in the pathogenesis. It is possible to further explore the connection between inflammation in the brain and SCZ, BD, and MDD. Therefore, we systematically reviewed PubMed and Web of Science on brain inflammatory markers measured in SCZ, BD, and MDD postmortem brains. Out of 2166 studies yielded by the search, 46 studies met the inclusion criteria in SCZ, BD, and MDD postmortem brains. The results were variable across inflammatory markers. For example, 26 studies were included to measure the differential expression between SCZ and control subjects. Similarly, seven of the included studies measured the differential expression of inflammatory markers in patients with BD. The heterogeneity from the included studies is not clear at present, which may be caused by several factors, including the measured brain region, disease stage, brain source, medication, and other factors.

Electroencephalogram-Based Major Depressive Disorder Classification Using Convolutional Neural Network and Transfer Learning

Major Depressive Disorder (MDD) is a worldwide common disease with a high risk of becoming chronic, suicidal, and recurrence, with serious consequences such as loss of workforce. Objective tests such as EEG, EKG, brain MRI, and Doppler USG are used to aid diagnosis in MDD detection. With advances in artificial intelligence and sample data from objective testing for depression, an early depression detection system can be developed as a way to reduce the number of individuals affected by MDD. In this study, MDD was tried to be diagnosed automatically with a deep learning-based approach using EEG signals. In the study, 3-channel modma dataset was used as a dataset. Modma dataset consists of EEG signals of 29 controls and 26 MDD patients. ResNet18 convolutional neural network was used for feature extraction. The ReliefF algorithm is used for feature selection. In the classification phase, kNN was preferred. The accuracy was yielded 95.65% for Channel 1, 87.00% for Channel 2, and 86.94% for Channel 3.

Emotion regulation in bipolar disorder type-I: multivariate analysis of fMRI data

BACKGROUND

Bipolar disorder type-I (BD-I) patients are known to show emotion regulation abnormalities. In a previous fMRI study using an explicit emotion regulation paradigm, we compared responses from 19 BD-I patients and 17 matched healthy controls (HC). A standard general linear model-based univariate analysis revealed that BD patients showed increased activations in inferior frontal gyrus when instructed to decrease their emotional response as elicited by neutral images. We implemented multivariate pattern recognition analyses on the same data to examine if we could classify conditions within-group as well as HC versus BD.

METHODS

We reanalyzed explicit emotion regulation data using a multivariate pattern recognition approach, as implemented in PRONTO software. The original experimental paradigm consisted of a full 2 × 2 factorial design, with valence (Negative/Neutral) and instruction (Look/Decrease) as within subject factors.

RESULTS

The multivariate models were able to accurately classify different task conditions when HC and BD were analyzed separately (63.24%-75.00%, p = 0.001-0.012). In addition, the models were able to correctly classify HC versus BD with significant accuracy in conditions where subjects were instructed to downregulate their felt emotion (59.60%-60.84%, p = 0.014-0.018). The results for HC versus BD classification demonstrated contributions from the salience network, several occipital and frontal regions, inferior parietal lobes, as well as other cortical regions, to achieve above-chance classifications.

CONCLUSIONS

Our multivariate analysis successfully reproduced some of the main results obtained in the previous univariate analysis, confirming that these findings are not dependent on the analysis approach. In particular, both types of analyses suggest that there is a significant difference of neural patterns between conditions within each subject group. The multivariate approach also revealed that reappraisal conditions provide the most informative activity for differentiating HC versus BD, irrespective of emotional valence (negative or neutral). The current results illustrate the importance of investigating the cognitive control of emotion in BD. We also propose a set of candidate regions for further study of emotional control in BD.

Structural and functional MRI correlates of inflammation in bipolar disorder: A systematic review

BACKGROUND

Bipolar disorder (BD) is a common affective disorder characterized by recurrent oscillations between mood states and associated with inflammatory diseases and chronic inflammation. However, data on MRI abnormalities in BD and their relationship with inflammation are heterogeneous and no review has recapitulated them.

METHODS

In this pre-registered (PROSPERO: CRD42022308461) systematic review we searched Web of Science Core Collection and PubMed for articles correlating functional or structural MRI measures with immune-related markers in BD.

RESULTS

We included 23 studies (6 on functional, 16 on structural MRI findings, 1 on both, including 1'233 BD patients). Overall, the quality of the studies included was fair, with a low risk of bias.

LIMITATIONS

Heterogeneity in the methods and results of the studies and small sample sizes limit the generalizability of the conclusions.

CONCLUSIONS

A qualitative synthesis suggests that the links between immune traits and functional or structural MRI alterations point toward brain areas involved in affective and somatomotor processing, with a trend toward a negative correlation between peripheral inflammatory markers and brain regions volume. We discuss how disentangling the complex relationship between the immune system and MRI alterations in BD may unveil mechanisms underlying symptoms pathophysiology, potentially with quickly translatable diagnostic, prognostic, and therapeutic implications.

Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: A comprehensive review

Mental disorders are rapidly increasing each year and have become a major challenge affecting the social and financial well-being of individuals. There is a need for phenotypic characterization of psychiatric disorders with biomarkers to provide a rich signature for Major Depressive Disorder, improving the understanding of the pathophysiological mechanisms underlying these mental disorders. This comprehensive review focuses on depression and relapse detection modalities such as self-questionnaires, audiovisuals, and EEG, highlighting noteworthy publications in the last ten years. The article concentrates on the literature that adopts machine learning by audiovisual and EEG signals. It also outlines preprocessing, feature extraction, and public datasets for depression detection. The review concludes with recommendations that will help improve the reliability of developed models and the determinism of computational intelligence-based systems in psychiatry. To the best of our knowledge, this survey is the first comprehensive review on depression and relapse prediction by self-questionnaires, audiovisual, and EEG-based approaches. The findings of this review will serve as a useful and structured starting point for researchers studying clinical and non-clinical depression recognition and relapse through machine learning-based approaches.

Total healthcare cost savings through improved bipolar I disorder identification using the Rapid Mood Screener in patients diagnosed with major depressive disorder

INTRODUCTION

Misdiagnosis of bipolar I disorder (BP-I) as major depressive disorder (MDD) leads to increased healthcare resource utilization and costs. The cost-effectiveness of the Rapid Mood Screener (RMS), a tool to identify BP-I in patients with depressive symptoms, was assessed in patients diagnosed with MDD presenting with depressive episodes.

METHODS

A decision-tree model of a hypothetical cohort of 1000 patients in a US health plan was used to estimate the number of correct diagnoses and overall total, direct healthcare costs over a 3-year timeframe for RMS-screened versus unscreened patients. Model inputs included the prevalence of BP-I in patients diagnosed with MDD, RMS sensitivity/specificity, and the cost of misdiagnosing BP-I as MDD.

RESULTS

Screening with the RMS resulted in 171, 159, and 143 additional correct BP-I or MDD diagnoses at Years 1, 2, and 3, respectively. Total healthcare plan cost savings were $1279 per patient in Year 1. Cumulative cost savings per patient for RMS screening versus no RMS screening were $2307 over 2 years and $3011 over 3 years. Scenario analyses showed that the RMS would remain cost-saving assuming a lower prevalence of BP-I (20% or 10%) versus the base case (24.3%).

CONCLUSION

The RMS is a cost-effective tool to identify BP-I in patients who would otherwise be misdiagnosed with MDD. Screening with the RMS resulted in cost-savings over 3 years, with model results remaining robust even with lower prevalence of BP-I and reduced RMS sensitivity assumptions.

Effect of high-definition transcranial direct current stimulation on improving depression and modulating functional activity in emotion-related cortical-subcortical regions in bipolar depression

Preliminary studies have suggested that transcranial direct current stimulation (tDCS) is effective for bipolar depression, However, brain correlates of the depression alleviating are unclear. To determine the efficacy and safety of tDCS as an add-on treatment for patients with bipolar depression and further to identify the effect of tDCS on the resting-state brain activities, we recruited fifty patients with bipolar depression to complete the double-blind, sham-controlled and randomized clinical trial. Fourteen sessions of tDCS were performed once a day for 14 days. The anode was placed over F3 with return electrodes placed at FP1, FZ, C3 and F7. Regional homogeneity (ReHo) was examined on 50 patients with bipolar depression before and after 14-day active or sham tDCS. Patients in the active group showed significantly superior alleviating the depression symptoms compared with those receiving sham. The active group after 14-day active tDCS showed increased ReHo values in the orbitofrontal cortex and middle frontal gyrus and decreased ReHo values in subcortical structures including hippocampus, parahippocampa gyrus, amygdala, putamen and lentiform nucleus. The reduction of depression severity showed positive correlation of increased ReHo values in the orbitofrontal cortex and middle frontal gyrus and negative correlation of altered ReHo values in the putamen and lentiform. TDCS was an effective and safe add-on intervention for this small bipolar depression sample. The reduction of depression induced by tDCS is associated with a modulation of neural synchronization in the cortical and subcortical structures (ReHo values) within an emotion-related brain network.

Restoring the missing person to personalized medicine and precision psychiatry

Precision psychiatry has emerged as part of the shift to personalized medicine and builds on frameworks such as the U.S. National Institute of Mental Health Research Domain Criteria (RDoC), multilevel biological "omics" data and, most recently, computational psychiatry. The shift is prompted by the realization that a one-size-fits all approach is inadequate to guide clinical care because people differ in ways that are not captured by broad diagnostic categories. One of the first steps in developing this personalized approach to treatment was the use of genetic markers to guide pharmacotherapeutics based on predictions of pharmacological response or non-response, and the potential risk of adverse drug reactions. Advances in technology have made a greater degree of specificity or precision potentially more attainable. To date, however, the search for precision has largely focused on biological parameters. Psychiatric disorders involve multi-level dynamics that require measures of phenomenological, psychological, behavioral, social structural, and cultural dimensions. This points to the need to develop more fine-grained analyses of experience, self-construal, illness narratives, interpersonal interactional dynamics, and social contexts and determinants of health. In this paper, we review the limitations of precision psychiatry arguing that it cannot reach its goal if it does not include core elements of the processes that give rise to psychopathological states, which include the agency and experience of the person. Drawing from contemporary systems biology, social epidemiology, developmental psychology, and cognitive science, we propose a cultural-ecosocial approach to integrating precision psychiatry with person-centered care.

Discriminating between bipolar and major depressive disorder using a machine learning approach and resting-state EEG data

OBJECTIVE

Distinguishing major depressive disorder (MDD) from bipolar disorder (BD) is a crucial clinical challenge as effective treatment is quite different for each condition. In this study electroencephalography (EEG) was explored as an objective biomarker for distinguishing MDD from BD using an efficient machine learning algorithm (MLA) trained by a relatively large and balanced dataset.

METHODS

A 3 step MLA was applied: (1) a multi-step preprocessing method was used to improve the quality of the EEG signal, (2) symbolic transfer entropy (STE), an effective connectivity measure, was applied to the resultant EEG and (3) the MLA used the extracted STE features to distinguish MDD (N = 71) from BD (N = 71) subjects.

RESULTS

14 connectivity features were selected by the proposed algorithm. Most of the selected features were related to the frontal, parietal, and temporal lobe electrodes. The major involved regions were the Broca region in the frontal lobe and the somatosensory association cortex in the parietal lobe. These regions are near electrodes FC5 and CPz and are involved in processing language and sensory information, respectively. The resulting classifier delivered an evaluation accuracy of 88.5% and a test accuracy of 89.3%, using 80% of the data for training and evaluation and the remaining 20% for testing, respectively.

CONCLUSIONS

The high evaluation and test accuracies of our algorithm, derived from a large balanced training sample suggests that this method may hold significant promise as a clinical tool.

SIGNIFICANCE

The proposed MLA may provide an inexpensive and readily available tool that clinicians may use to enhance diagnostic accuracy and shorten time to effective treatment.

Gamma band VMPFC-PreCG.L connection variation after the onset of negative emotional stimuli can predict mania in depressive patients

OBJECTIVE

Because of the similar clinical symptoms, it is difficult to distinguish unipolar disorder (UD) from bipolar disorder (BD) in the depressive episode using the available clinical features, especially for those who meet the diagnostic criteria of UD, however, experience the manic episode during the follow-up (tBD).

METHODS

Magnetoencephalography recordings during a sad expression recognition task were obtained from 81 patients (27 BD, 24 tBD, 30 UD) and 26 healthy controls (HCs). Source analysis was applied to localize 64 regions of interest in the low gamma band (30-50 Hz). Regional functional connections (FCs) were constructed respectively within three time periods (early: 0-200 ms, middle: 200-400 ms, and post: 400-600 ms). The network-based statistic method was used to explore the abnormal connection patterns in tBD compared to UD and HC. BD was applied to explore whether such abnormality is still significant between every two groups of BD, tBD, UD, and HC.

RESULTS

The VMPFC-PreCG.L connection was found to be a significantly different connection between tBD and UD in the early time period and between tBD and BD in the middle time period. Furthermore, the middle/early time period ratio of FC value of VMPFC-PreCG.L connection was negatively correlated with the bipolarity index in tBD.

CONCLUSIONS

The VMPFC-PreCG.L connection in different time periods after the onset of sad facial stimuli may be a potential biomarker to distinguish the different states of BD. The FC ratio of VMPFC-PreCG.L connection may predict whether patients with depressive episodes subsequently develop mania.

PRA-Net: Part-and-Relation Attention Network for depression recognition from facial expression

Artificial intelligence methods are widely applied to depression recognition and provide an objective solution. Many effective automated methods for detecting depression use facial expressions, which are strong indicators to reflect psychiatric disorders. However, these methods suffer from insufficient representations of depression. To this end, we propose a novel Part-and-Relation Attention Network (PRA-Net), which can enhance depression representations by accurately focusing on features that are highly correlated with depression. Specifically, we first perform partition on the feature map instead of the original image, in order to obtain part features rich in semantic information. Afterwards, self-attention is used to calculate the weight of each part feature. Following, the relationship between the part feature and the global content representation is explored by relation attention to refine the weight. Finally, all features are aggregated into a more compact and depression-informative representation via both weights for depression score prediction. Extensive experiments demonstrate the superiority of our method. Compared to other end-to-end methods, our method achieves state-of-the-art performance on AVEC2013 and AVEC2014.

Neurochemical and microstructural alterations in bipolar and depressive disorders: A multimodal magnetic resonance imaging study

AIMS

Depression in bipolar disorder (BD) is often misdiagnosed as unipolar depression (UD), leading to mistreatments and poor clinical outcomes in many bipolar patients. Herein, we report direct comparisons between medication-free patients with BD and those with UD in terms of the microstructure and neurometabolites in eight brain regions.

METHODS

A total of 20 patients with BD, 30 with UD patients, and 20 matched healthy controls (HCs) underwent 3.0T magnetic resonance imaging with chemical exchange saturation transfer (CEST) for glutamate (Glu; GluCEST) imaging, multivoxel magnetic resonance spectroscopy, and diffusion kurtosis imaging.

RESULTS

Compared with HCs, patients with UD showed significantly lower levels of multiple metabolites, GluCEST% values, and diffusional kurtosis [mean kurtosis (MK)] values in most brain regions. In contrast, patients with BD presented significantly higher levels of Glu in their bilateral ventral prefrontal white matter (VPFWM), higher choline (Cho)-containing compounds in their left VPFWM and anterior cingulate cortex (ACC), and higher GluCEST% values in their bilateral VPFWM and ACC; moreover, reduced MK in these patients was more prominent in the left VPFWM and left thalamus.

CONCLUSION

The findings demonstrated that both patients with UD and BD have abnormal microstructure and metabolic alterations, and the changes are not completely consistent in the prefrontal lobe region. Elevated Glu, Cho, and GluCEST% in the ACC and VPFWM of patients with UD and BD may help in differentiating between these two disorders. Our findings support the significance for the microstructural integrity and brain metabolic changes of the prefrontal lobe region in BD and UD.

Elevated Serum Macrophage Migration Inhibitory Factor Levels are Associated With Major Depressive Disorder

Background

Previous studies have suggested the involvement of an activated inflammatory process in major depressive disorder (MDD), as altered expression of inflammatory cytokines is observed in depression. This alteration can be the cause or a consequence of MDD. However, acknowledging inflammatory cytokines as prospective biomarkers would aid in diagnosing or guiding better therapeutic options. Therefore, we designed this study to assess the macrophage migration inhibitory factor (MIF) in depression.

Method

We collected blood samples from 115 MDD patients and 113 healthy controls (HCs) matched by age and sex. MDD patients were diagnosed by a qualified psychiatrist based on the symptoms mentioned in the diagnostic and statistical manual of mental disorders (DSM-5). We applied the Hamilton depression (Ham-D) rating scale to assess the severity of depression. We assessed serum levels of MIF using ELISA kit (Boster Bio, USA).

Result

We detected increased serum MIF levels in MDD patients compared to HCs (6.15 ± 0.23 ng/mL vs 3.95 ± 0.21 ng/mL, P < 0.001). Moreover, this increase is more among female patients than female controls. Also, we noticed a positive correlation between altered MIF levels and the Ham-D scores (r = 0.233; P = 0.012), where we found that patients who scored higher on the Ham-D scale had higher MIF levels in serum. Moreover, the area under the curve (AUC) of receiver operating characteristic (ROC) curve represented the good diagnostic performance of altered serum MIF.

Conclusion

Our study findings indicate the association of pro-inflammatory cytokine MIF in the pathophysiology of depression as we identified elevated serum MIF levels in depressive patients compared to HCs. However, more researches are required to confirm whether this alteration of cytokine is the causative factor or a consequence of depression. We recommend conducting further studies to understand the pattern of this alteration of MIF levels in MDD patients.