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

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.

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.

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.

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.

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.

Disrupted Balance of Gray Matter Volume and Directed Functional Connectivity in Mild Cognitive Impairment and Alzheimer's Disease

BACKGROUND

Alterations in functional connectivity have been demonstrated in Alzheimer's disease (AD), an age-progressive neurodegenerative disorder that affects cognitive function; however, directional information flow has never been analyzed.

OBJECTIVE

This study aimed to determine changes in resting-state directional functional connectivity measured using a novel approach, granger causality density (GCD), in patients with AD, and mild cognitive impairment (MCI) and explore novel neuroimaging biomarkers for cognitive decline detection.

METHODS

In this study, structural MRI, resting-state functional magnetic resonance imaging, and neuropsychological data of 48 Alzheimer's Disease Neuroimaging Initiative participants were analyzed, comprising 16 patients with AD, 16 with MCI, and 16 normal controls. Volume-based morphometry (VBM) and GCD were used to calculate the voxel-based gray matter (GM) volumes and directed functional connectivity of the brain. We made full use of voxel-based between-group comparisons of VBM and GCD values to identify specific regions with significant alterations. In addition, Pearson's correlation analysis was conducted between directed functional connectivity and several clinical variables. Furthermore, receiver operating characteristic (ROC) analysis related to classification was performed in combination with VBM and GCD.

RESULTS

In patients with cognitive decline, abnormal VBM and GCD (involving inflow and outflow of GCD) were noted in default mode network (DMN)-related areas and the cerebellum. GCD in the DMN midline core system, hippocampus, and cerebellum was closely correlated with the Mini- Mental State Examination and Functional Activities Questionnaire scores. In the ROC analysis combining VBM with GCD, the neuroimaging biomarker in the cerebellum was optimal for the early detection of MCI, whereas the precuneus was the best in predicting cognitive decline progression and AD diagnosis.

CONCLUSION

Changes in GM volume and directed functional connectivity may reflect the mechanism of cognitive decline. This discovery could improve our understanding of the pathology of AD and MCI and provide available neuroimaging markers for the early detection, progression, and diagnosis of AD and MCI.

A graph theory neuroimaging approach to distinguish the depression of bipolar disorder from major depressive disorder in adolescents and young adults

BACKGROUND

Markers to differentiate depressions of bipolar disorder (BD-Dep) from depressions of major depressive disorder (MDD-Dep), and for more targeted treatments, are critically needed to decrease current high rates of misdiagnosis that can lead to ineffective or potentially deleterious treatments. Distinguishing, and specifically treating the depressions, during the adolescent/young adult epoch is especially important to decrease illness progression and improve prognosis, and suicide, as it is the epoch when suicide thoughts and behaviors often emerge. With differences in functional connectivity patterns reported when BD-Dep and MDD-Dep have been studied separately, this study used a graph theory approach aimed to identify functional connectivity differences in their direct comparison.

METHODS

Functional magnetic resonance imaging whole-brain functional connectivity (Intrinsic Connectivity Distribution, ICD) measures were compared across adolescents/young adults with BD-Dep (n = 28), MDD-Dep (n = 20) and HC (n = 111). Follow-up seed-based connectivity was conducted on regions of significant ICD differences. Relationships with demographic and clinical measures were assessed.

RESULTS

Compared to the HC group, both the BD-Dep and MDD-Dep groups exhibited left-sided frontal, insular, and medial temporal ICD increases. The BD-Dep group had additional right-sided ICD increases in frontal, basal ganglia, and fusiform areas. In seed-based analyses, the BD-Dep group exhibited increased interhemispheric functional connectivity between frontal areas not seen in the MDD-Dep group.

LIMITATIONS

Modest sample size; medications not studied systematically.

CONCLUSIONS

This study supports bilateral and interhemispheric functional dysconnectivity as features of BD-Dep that may differentiate it from MDD-Dep in adolescents/young adults and serve as a target for early diagnosis and treatment strategies.

Distinguishing major depressive disorder from bipolar disorder in remission: A brain structural network analysis

BACKGROUND

It is challenging to differentiate major depressive disorder (MDD) from bipolar disorder (BD) in depression and remission. To exclude the potential influence of depressive episodes, we compared the white matter (WM) network between MDD and BD patients in remission to find disease-specific alterations in MDD and BD, and then distinguish these two affective disorders.

METHODS

We recruited 33 patients with remitted MDD (rMDD), 54 patients with remitted BD (rBD), and 60 healthy controls (HCs). Diffusion tensor imaging and high-resolution 3D T1-weighted image were acquired. Global and nodal topological parameters were used to depict the alterations of the whole-brain WM network.

RESULTS

We found that rMDD displayed increased global network efficiency (E_glob) and local network efficiency (E_loc) compared with HCs, whereas we found no significance between rBD and HCs. Compared with rBD and HCs, patients in the rMDD group showed increased nodal degree and nodal efficiency, and decreased nodal shortest path length in the four cerebral regions, including the right calcarine fissure (CAL.R), right cuneus (CUN.R), left lingual gyrus (LING.L), and left middle occipital gyrus (MOG.L). We did not find any rBD specific changes of nodal topological metrics.

LIMITATIONS

The main limitation is the possible effects of medication and BD subtypes on the results.

CONCLUSIONS

Our findings indicate that rMDD exhibited elevated global properties compared with HCs group, and increased nodal properties in the CAL.R, CUN.R, LING.L, and MOG.L specifically compared with rBD and HCs, which may underlie the distinction of the two affective disorders in remission.

Global hippocampus functional connectivity as a predictive neural marker for conversion to future mood disorder in unaffected offspring of bipolar disorder parents

OBJECTIVES

Hippocampus-related functional alteration in genetically at-risk individuals may reflect an endophenotype of a mood disorder. Herein, we performed a prospective study to investigate whether baseline hippocampus functional connectivity (FC) in offspring of patients with bipolar disorder (BD) would predict subsequent conversion to mood disorder.

METHODS

Eighty bipolar offspring and 40 matched normal controls (NC) underwent resting state functional MRI (rsfMRI) scanning on a 3.0 Tesla MR scanner. The offspring were subdivided into asymptomatic offspring (AO) (n = 41) and symptomatic offspring (SO) (n = 39) according to whether they manifested subthreshold mood symptoms. After identifying the different hippocampus FCs between the AO and SO, a logistic regression analysis was conducted to investigate whether the baseline hippocampus FCs predicted a future mood disorder during a 6-year follow-up.

RESULTS

We identified seven baseline para/hippocampus FCs that showed differences between AO and SO, which were entered as predictive features in the logistic regressive model. Of the 80 bipolar offspring entering the analysis, the FCs between left hippocampus and left precuneus, and between right hippocampus and left posterior cingulate, showed a discriminative capacity for predicting future mood disorder (area-under-curve, or AUC=75.76 % and 75.00 % respectively), and for predicting BD onset (AUC=77.46 % and 81.63 %, respectively).

CONCLUSIONS

The present findings revealed high predictive utility of the hippocampus resting state FCs for future mood disorder and BD onset in individuals at familial risk. These neural markers can potentially improve early detection of individuals carrying particularly high risk for future mood disorder.

Neurovascular alterations in bipolar disorder: A review of perfusion weighted magnetic resonance imaging studies

BACKGROUND

Bipolar Disorder (BD) is a severe chronic psychiatric disorder whose aetiology is still largely unknown. However, increasing literature reported the involvement of neurovascular factors in the pathophysiology of BD, suggesting that a measure of Cerebral Blood Flow (CBF) could be an important biomarker of the illness. Therefore, since, to date, Magnetic Resonance Perfusion Weighted Imaging (PWI) techniques, such as Dynamic Susceptibility Contrast (DSC) and Arterial Spin Labelling (ASL), are the most common approaches that allow non-invasive in-vivo perfusion measurements,this review aims to summarize the results from all PWI studies that evaluated the CBF in BD.

METHODS

A bibliographic search in PubMed up until May 2021 was performed. 16 PWI studies that used DSC or ASL sequences met our inclusion criteria.

RESULTS

Overall, the results supported the presence of hyper-perfusion in the cingulate cortex and fronto-temporal regions, as well as the presence of hypo-perfusion in the cerebellum in BD, compared with both healthy controls and patients with unipolar depression. CBF changes after cognitive and aerobic training, as well as in relation with other physiological, clinical, and neurocognitive variables were also reported.

LIMITATIONS

The heterogeneity across the studies, in terms of experimental designs, sample selection, and methodological approach employed, limited the studies' comparison.

CONCLUSIONS

These findings showed CBF alterations in the cingulate cortex, fronto-temporal regions, and cerebellum in BD, suggesting that CBF may be an important pathophysiological marker of BD that merits further investigation to clarify the extent of neurovascular alterations.

Elevated Matrix Metalloproteinase 9 in Treatment Resistant Bipolar Depression

Objective: Matrix metalloproteinase is a family of proteases with different pathophysiological roles. Matrix metalloproteinase 9 (MMP9) plays an enzymatic role in the restructuring of the extracellular matrix and adhesion molecules. MMP9 is upregulated in pro-inflammatory states and leads to breakdown of tight junctions thereby increasing blood-brain barrier (BBB) permeability. MMP9 may contribute to the pathophysiology of bipolar disorder (BD) via proteolysis of the BBB thus allowing entry of cytokines and neurotoxic agents into CNS. Polymorphisms of the MMP9 gene may pose increased risk for BD and schizophrenia. In this study we sought to determine MMP9 levels in treatment resistant bipolar depressed patients before and after treatment. Methods: Treatment resistant bipolar depressed patients were treated with escitalopram, in combination with the COX-2 inhibitor, celecoxib. It was hypothesized that combination treatment would reverse resistance and augmented treatment responses. This was a 10-week, randomized, double-blind, two-arm, placebo-controlled study. Results: MMP9 levels were higher in bipolar depressed patients compared to healthy controls at baseline, however, the difference did not reach significance. Levels decreased after treatment reaching significance in the escitalopram plus placebo group. Female patients had significantly lower MMP9 levels at end of treatment. MMP9 was higher in carriers the MMP9 SNP, rs3918242, than in noncarriers, but the difference did not reach statistical significance. Conclusion: MMP9 decreased in bipolar depressed patients with treatment. Age, sex and the rs3918242 polymorphism play a role in MMP9 levels. Future studies should confirm the role of MMP9 in the pathogenesis and pathophysiology of bipolar disorder, as a potential diagnostic biomarker.

Working memory updating in individuals with bipolar and unipolar depression: fMRI study

Understanding neurobiological characteristics of cognitive dysfunction in distinct psychiatric disorders remains challenging. In this secondary data analysis, we examined neurobiological differences in brain response during working memory updating among individuals with bipolar disorder (BD), those with unipolar depression (UD), and healthy controls (HC). Individuals between 18-45 years of age with BD (n = 100), UD (n = 109), and HC (n = 172) were scanned using fMRI while performing 0-back (easy) and 2-back (difficult) tasks with letters as the stimuli and happy, fearful, or neutral faces as distractors. The 2(n-back) × 3(groups) × 3(distractors) ANCOVA examined reaction time (RT), accuracy, and brain activation during the task. HC showed more accurate and faster responses than individuals with BD and UD. Difficulty-related activation in the prefrontal, posterior parietal, paracingulate cortices, striatal, lateral occipital, precuneus, and thalamic regions differed among groups. Individuals with BD showed significantly lower difficulty-related activation differences in the left lateral occipital and the right paracingulate cortices than those with UD. In individuals with BD, greater difficulty-related worsening in accuracy was associated with smaller activity changes in the right precuneus, while greater difficulty-related slowing in RT was associated with smaller activity changes in the prefrontal, frontal opercular, paracingulate, posterior parietal, and lateral occipital cortices. Measures of current depression and mania did not correlate with the difficulty-related brain activation differences in either group. Our findings suggest that the alterations in the working memory circuitry may be a trait characteristic of reduced working memory capacity in mood disorders. Aberrant patterns of activation in the left lateral occipital and paracingulate cortices may be specific to BD.

Shared and disease-sensitive dysfunction across bipolar and unipolar disorder during depressive episodes: a transdiagnostic study

Patients with depressive episodes (PDE), such as unipolar disorder (UD) and bipolar disorder (BD), are often defined as distinct diagnostic categories, but increasing converging evidence indicated shared etiologies and pathophysiological characteristics across different clinical diagnoses. We explored whether these transdiagnostic deficits are caused by the common neural substrates across diseases or disease-sensitive mechanisms, or a combination of both. In this study, we utilized a Bayesian model to decompose the resting-state brain activity into multiple hyper- and hypo-activity patterns (refer to as "factors"), so as to explore the shared and disease-sensitive alteration patterns in PDE. The model was constructed over a total of 259 patients (131 UD and 128 BD) with 100 healthy controls as the reference. The other 32 initial depressive episode BD (IDE-BD) patients who had symptoms of mania or hypomania during follow-up were taken as an independent set to estimate the factor composition using the established model for further analysis. We revealed three transdiagnostic alteration factors in PDE. Based on the distribution of factors and the tendency of factor composition at the group level, these factors were defined as BD sensitive factor, UD sensitive factor and shared basic alteration factor. We further found that the factor composition and the ROIs-based alteration degree (mainly involving in orbitofrontal gyrus and part of parietal lobe) were associated with the bipolar index in IDE-BD patients. Our findings contributed to understanding the core transdiagnostic shared and disease-sensitive alterations in PDE and to predicting the risk of emotional state transition in IDE-BD patients.

Brain structure and synaptic protein expression alterations after antidepressant treatment in a Wistar-Kyoto rat model of depression

BACKGROUND

Structural MRI has demonstrated brain alterations in depression pathology and antidepressants treatment. While synaptic plasticity has been previously proposed as the potential underlying mechanism of MRI findings at a cellular and molecular scale, there is still insufficient evidence to link the MRI findings and synaptic plasticity mechanisms in depression pathology.

METHODS

In this study, a Wistar-Kyoto (WKY) depression rat model was treated with antidepressants (citalopram or Jie-Yu Pills) and tested in a series of behavioral tests and a 7.0 MRI scanner. We then measured dendritic spine density within altered brain regions. We also examined expression of synaptic marker proteins (PSD-95 and SYP).

RESULTS

WKY rats exhibited depression-like behaviors in the sucrose preference test (SPT) and forced swim test (FST), and anxiety-like behaviors in the open field test (OFT). Both antidepressants reversed behavioral changes in SPT and OFT but not in FST. We found a correlation between SPT performance and brain volumes as detected by MRI. All structural changes were consistent with alterations of the corpus callosum (white matter), dendritic spine density, as well as PSD95 and SYP expression at different levels. Two antidepressants similarly reversed these macro- and micro-changes.

LIMITATIONS

The single dose of antidepressants was the major limitation of this study. Further studies should focus on the white matter microstructure changes and myelin-related protein alterations, in addition to comparing the effects of ketamine.

CONCLUSION

Translational evidence links structural MRI changes and synaptic plasticity alterations, which promote our understanding of SPT mechanisms and antidepressant response in WKY rats.

A brainnetome atlas-based methamphetamine dependence identification using neighborhood component analysis and machine learning on functional MRI data

Addiction to methamphetamine (MA) is a major public health concern. Developing a predictive model that can classify and characterize the brain-based biomarkers predicting MA addicts may directly lead to improved treatment outcomes. In the current study, we applied the support vector machine (SVM)-based classification method to resting-state functional magnetic resonance imaging (rs-fMRI) data obtained from individuals with methamphetamine use disorder (MUD) and healthy controls (HCs) to identify brain-based features predictive of MUD. Brain connectivity analyses were conducted for 36 individuals with MUD as well as 37 HCs based on the brainnetome atlas, and the neighborhood component analysis was applied for feature selection. Eighteen most relevant features were screened out and fed into the SVM to classify the data. The classifier was able to differentiate individuals with MUD from HCs with a high prediction accuracy, sensitivity, specificity, and AUC of 88.00, 86.84, 89.19, and 0.94, respectively. The top six discriminative features associated with changes in the functional activity of key nodes in the default mode network (DMN), all the remaining discriminative features are related to the thalamic connections within the cortico-striato-thalamo-cortical (CSTC) loop. In addition, the functional connectivity (FC) between the bilateral inferior parietal lobule (IPL) and right cingulate gyrus (CG) was significantly correlated with the duration of methamphetamine use. The results of this study not only indicated that MUD-related FC alterations were predictive of group membership, but also suggested that machine learning techniques could be used for the identification of MUD-related imaging biomarkers.

Relationship between psychosocial stress-induced prefrontal cortex activity and gut microbiota in healthy Participants-A functional near-infrared spectroscopy study

Brain and gut microbes communicate in a bidirectional manner with each affecting a person's response to psychosocial stress. Although human studies demonstrated that the intake of probiotics can alter stress-related behavior in both patients and healthy participants, the association between stress-related brain functions and the gut microbiota has mostly been investigated in patients with depression. However, the response to psychosocial stress differs, even among healthy individuals, and elucidating the natural state of the gut microbiota would broaden the understanding of responses to psychosocial stress. We investigated the relationship between psychosocial stress response in the prefrontal cortex and the abundance of gut microbes in healthy male participants. The participants were exposed to psychosocial stress during a task while brain activation data were recorded using functional near-infrared spectroscopy. The heart rate and subjective stress were recorded, and fecal samples were collected. The stressful condition was accompanied by high subjective stress, high heart rate, and higher prefrontal activation in the right pre-motor cortex/supplementary motor area, right dorsolateral prefrontal cortex, right frontal pole, and right inferior prefrontal gyrus. The psychosocial stress response in the prefrontal cortex was also associated with changes in the gut microbiota abundance. The abundance of Alistipes, Clostridium IV, Clostridium XI, Faecalibacterium, and Blautia in healthy participants who had high psychosocial stress resembled that noted in patients with depression. These results suggest that the gut microbiota differs, among healthy participants, depending on the psychosocial stress response. We believe that this study is the first to report a direct relationship between brain function and the gut microbiota in healthy participants, and our findings would shed a new light on this field in the near future.

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Brain and gut microbe communication affects the response to psychosocial stress.

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Gut microbiota related to psychosocial stress in healthy individuals is unknown.

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Stress-induced brain activation was observed in the right prefrontal cortex.

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Brain activity was associated with gut microbiota related to depression.

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Gut microbiota differs depending on the response to psychosocial stress.

Intrinsic Connectivity and Family Dynamics: Striatolimbic Markers of Risk and Resilience in Youth at Familial Risk for Mood Disorders

BACKGROUND

Few studies to date have characterized functional connectivity (FC) within emotion and reward networks in relation to family dynamics in youth at high familial risk for bipolar disorder (HR-BD) and major depressive disorder (HR-MDD) relative to low-risk youth (LR). Such characterization may advance our understanding of the neural underpinnings of mood disorders and lead to more effective interventions.

METHODS

A total of 139 youth (43 HR-BD, 46 HR-MDD, and 50 LR) aged 12.9 ± 2.7 years were longitudinally followed for 4.5 ± 2.4 years. We characterized differences in striatolimbic FC that distinguished between HR-BD, HR-MDD, and LR and between resilience and conversion to psychopathology. We then examined whether risk status moderated FC-family dynamic associations. Finally, we examined whether baseline between-group FC differences predicted resilence versus conversion to psychopathology.

RESULTS

HR-BD had greater amygdala-middle frontal gyrus and dorsal striatum-middle frontal gyrus FC relative to HR-MDD and LR, and HR-MDD had lower amygdala-fusiform gyrus and dorsal striatum-precentral gyrus FC relative to HR-BD and LR (voxel-level p < .001, cluster-level false discovery rate-corrected p < .05). Resilient youth had greater amygdala-orbitofrontal cortex and ventral striatum-dorsal anterior cingulate cortex FC relative to youth with conversion to psychopathology (voxel-level p < .001, cluster-level false discovery rate-corrected p < .05). Greater family rigidity was inversely associated with amygdala-fusiform gyrus FC across all groups (false discovery rate-corrected p = .017), with a moderating effect of bipolar risk status (HR-BD vs. HR-MDD p < .001; HR-BD vs. LR p = .005). Baseline FC differences did not predict resilence versus conversion to psychopathology.

CONCLUSIONS

Findings represent neural signatures of risk and resilience in emotion and reward processing networks in youth at familial risk for mood disorders that may be targets for novel interventions tailored to the family context.

Distinguishing between depression in bipolar disorder and unipolar depression using magnetic resonance imaging: a systematic review

OBJECTIVES

Magnetic resonance imaging (MRI) studies comparing bipolar and unipolar depression characterize pathophysiological differences between these conditions. However, it is difficult to interpret the current literature due to differences in MRI modalities, analysis methods, and study designs.

METHODS

We conducted a systematic review of publications using MRI to compare individuals with bipolar and unipolar depression. We grouped studies according to MRI modality and task design. Within the discussion, we critically evaluated and summarized the functional MRI research and then further complemented these findings by reviewing the structural MRI literature.

RESULTS

We identified 88 MRI publications comparing participants with bipolar depression and unipolar depressive disorder. Compared to individuals with unipolar depression, participants with bipolar disorder exhibited heightened function, increased within network connectivity, and reduced grey matter volume in salience and central executive network brain regions. Group differences in default mode network function were less consistent but more closely associated with depressive symptoms in participants with unipolar depression but distractibility in bipolar depression.

CONCLUSIONS

When comparing mood disorder groups, the neuroimaging evidence suggests that individuals with bipolar disorder are more influenced by emotional and sensory processing when responding to their environment. In contrast, depressive symptoms and neurofunctional response to emotional stimuli were more closely associated with reduced central executive function and less adaptive cognitive control of emotionally oriented brain regions in unipolar depression. Researchers now need to replicate and refine network-level trends in these heterogeneous mood disorders and further characterize MRI markers associated with early disease onset, progression, and recovery.