Distinctions between bipolar and unipolar depression

Cariprazine in the acute treatment of unipolar and bipolar depression: A systematic review and meta-analysis

BACKGROUND

Cariprazine has emerged as a promising augmenting treatment agent for unipolar depression and as a monotherapy option for bipolar depression. We evaluated cariprazine's efficacy in treating acute major depressive episodes in individuals with major depressive disorder (MDD) or bipolar disorder.

METHODS

A systematic review was conducted on MEDLINE, Embase, PsycINFO, Scopus and Web of Science, ClinicalTrials.gov and ScanMedicine. Study quality was assessed using the RoB 2 tool. Pairwise and dose-response meta-analyses were conducted with RStudio. Evidence quality was assessed with GRADE.

RESULTS

Nine RCTs meeting inclusion criteria encompassed 4889 participants. Cariprazine, compared to placebo, significantly reduced the MADRS score (MD = -1.49, 95 % CI: -2.22 to -0.76) and demonstrated significantly higher response (RR = 1.21, 95 % CI: 1.12 to 1.30) and remission (RR = 1.19, 95 % CI: 1.06 to 1.34) rates. Subgroup analysis unveiled statistically significant reductions in MADRS score in MDD (MD = -1.15, 95 % CI: -2.04 to -0.26) and bipolar I disorder (BDI) (MD = -2.53, 95 % CI: -3.61 to -1.45), higher response rates for both MDD (RR = 1.19, 95 % CI: 1.08 to 1.31) and BDI (RR = 1.27, 95 % CI: 1.10 to 1.46), and higher remission rates only for BDI (RR = 1.41, 95 % CI: 1.24 to 1.60). A higher rate of treatment discontinuation due to adverse events was observed.

LIMITATIONS

Reliance solely on RCTs limits generalisability; strict criteria might not reflect real-world diversity.

CONCLUSIONS

Cariprazine demonstrates efficacy in treating major depressive episodes, although variations exist between MDD and BDI and tolerability may be an issue.

How to Solve Clinical Challenges in Mood Disorders; Machine Learning Approaches Using Electrophysiological Markers

Differentiating between the diagnoses of mood disorders and other psychiatric disorders, and predicting treatment response in depression has long been a concern for clinicians. Machine learning (ML) is one part of artificial intelligence that focuses on instructing computers to mimic the cognitive abilities of the human brain through training. This study will review the research on the use of ML techniques to differentiate diagnoses and predict treatment responses in mood disorders based on electroencephalography (EEG) data. There have been several attempts to differentiate between the diagnoses of bipolar disorder and major depressive disorder , mood disorders, and other psychiatric disorders using ML techniques found on EEG markers. Previous studies have shown that accuracy varies depending on which EEG markers are used, the sample size, and the ML technique. Also, precise and improved ML approaches can be developed by adapting the various feature selection and validation methods that reflect each disease's characteristics. Although ML faces some limitations and challenges in solving for consistent and improved accuracy in the diagnosis and treatment of mood disorders, it has a great potential to understand mood disorders better and provide valuable tools to personalize both identification and treatment.

Neurophysiological and neuroimaging markers of repetitive transcranial magnetic stimulation treatment response in major depressive disorder: A systematic review and meta-analysis of predictive modeling studies

Predicting repetitive transcranial magnetic stimulation (rTMS) treatment outcomes in major depressive disorder (MDD) could reduce the financial and psychological risks of treatment failure. We systematically reviewed and meta-analyzed studies that leveraged neurophysiological and neuroimaging markers to predict rTMS response in MDD. Five databases were searched from inception to May 25, 2023. The primary meta-analytic outcome was predictive accuracy pooled from classification models. Regression models were summarized qualitatively. A promising marker was identified if it showed a sensitivity and specificity of 80% or higher in at least two independent studies. Searching yielded 36 studies. Twenty-two classification modeling studies produced an estimated area under the summary receiver operating characteristic curve of 0.87 (95% CI = 0.83-0.92), with 86.8% sensitivity (95% CI = 80.6-91.2%) and 81.9% specificity (95% CI = 76.1-86.4%). Frontal theta cordance measured by electroencephalography is closest to proof of concept. Predicting rTMS response using neurophysiological and neuroimaging markers is promising for clinical decision-making. However, replications by different research groups are needed to establish rigorous markers.

Beyond the Window of Risk? The Dutch Bipolar Offspring Study: 22-Year Follow-up

OBJECTIVE

Adolescent offspring of parents with bipolar disorder (BD) are at high risk to develop BD and other psychopathology, yet how this risk continues into middle adulthood remains unknown. This study aimed to determine the window of risk for BD and other psychopathology in offspring of parents with BD followed from adolescence into adulthood.

METHOD

This study reported on the 22-year follow-up assessment of the Dutch Bipolar Offspring Study, a fixed cohort study of 140 participants established in 1997. Offspring (n = 100; mean [SD] age = 38.28 [2.74] years) of parents with bipolar I disorder or bipolar II disorder were assessed at baseline and 1-, 5-, 12-, and 22-year follow-up.

RESULTS

No new BD onsets occurred since the 12-year follow-up (lifetime prevalence = 11%-13%; bipolar I disorder = 4%; bipolar II disorder = 7%). Lifetime prevalence of any mood disorder was 65%; for major depressive disorder, prevalence was 36%; and for recurrent mood episodes, prevalence was 37%. Prevalence of major depressive disorder more than doubled in the past decade. Point prevalence of any psychopathology peaked between 20 and 25 years (38%-46%), subsiding to 29% to 35% per year after age 30. Overall, 71% of offspring contacted mental health services since the last assessment.

CONCLUSION

The risk for homotypic transmission of BD in offspring of parents with BD is highest during adolescence. The heterotypic risk for mood disorder onset and recurrences continues over the life course. Severe mood disorders are often preceded by milder psychopathology, emphasizing the need for early identification and interventions. This study allows for better understanding of the onset and course of mood disorders and specific windows of risk in a familial high-risk population.

Serum Levels of Zinc, Albumin, Interleukin-6 and CRP in Patients with Unipolar and Bipolar Depression: Cross Sectional Study

Unipolar (UD) and bipolar depression (BDD) show a high degree of similarity in clinical presentations, which complicates the differential diagnosis of these disorders. The aim of this study was to investigate the serum levels of interleukin 6 (IL-6), C-reactive protein (CRP), albumin (Alb), and zinc (Zn) in patients with UD, BDD, and healthy controls (HC). A total of 211 samples were collected: 131 patient samples (65 UD and 68 BDD) and 80 HC. The Montgomery-Asberg Depression Rating Scale (MADRS), along with the Hamilton Depression Rating Scale (HAMD-17), were administered to patient groups to evaluate symptoms. A cross-sectional study was performed to analyse the serum levels of IL-6, CRP, albumin, and zinc. The concentration of CRP was determined using the immunoturbidimetry method, zinc using the colorimetric method, and albumin using the colorimetric method with bromocresol green on the Alinity c device. IL-6 cytokine concentration in serum samples was ascertained using a commercial enzyme immunoassay, ELISA. We found no significant differences in serum concentrations of zinc, albumin, CRP, and IL-6 between the groups of patients with unipolar and bipolar depression. There was a significant statistical difference (p < 0.001) between serum levels of all investigated parameters in both groups of depressed patients in comparison with HC. Furthermore, correlations with specific items on HAMD-17; (namely, hypochondrias, work and activities, somatic symptoms-general, and weight loss) and on MADRS (concentration difficulties, lassitude) were observed in both patient groups. These findings confirm the presence of low-grade inflammation in depression, thus adding better insight into the inflammation hypothesis directed to explain the aetiology of depressive disorders. Our results do not indicate potential biomarkers for distinguishing between unipolar and bipolar depression.

Digital Phenotyping for Mood Disorders: Methodology-Oriented Pilot Feasibility Study

BACKGROUND

In the burgeoning area of clinical digital phenotyping research, there is a dearth of literature that details methodology, including the key challenges and dilemmas in developing and implementing a successful architecture for technological infrastructure, patient engagement, longitudinal study participation, and successful reporting and analysis of diverse passive and active digital data streams.

OBJECTIVE

This article provides a narrative rationale for our study design in the context of the current evidence base and best practices, with an emphasis on our initial lessons learned from the implementation challenges and successes of this digital phenotyping study.

METHODS

We describe the design and implementation approach for a digital phenotyping pilot feasibility study with attention to synthesizing key literature and the reasoning for pragmatic adaptations in implementing a multisite study encompassing distinct geographic and population settings. This methodology was used to recruit patients as study participants with a clinician-validated diagnostic history of unipolar depression, bipolar I disorder, or bipolar II disorder, or healthy controls in 2 geographically distinct health care systems for a longitudinal digital phenotyping study of mood disorders.

RESULTS

We describe the feasibility of a multisite digital phenotyping pilot study for patients with mood disorders in terms of passively and actively collected phenotyping data quality and enrollment of patients. Overall data quality (assessed as the amount of sensor data obtained vs expected) was high compared to that in related studies. Results were reported on the relevant demographic features of study participants, revealing recruitment properties of age (mean subgroup age ranged from 31 years in the healthy control subgroup to 38 years in the bipolar I disorder subgroup), sex (predominance of female participants, with 7/11, 64% females in the bipolar II disorder subgroup), and smartphone operating system (iOS vs Android; iOS ranged from 7/11, 64% in the bipolar II disorder subgroup to 29/32, 91% in the healthy control subgroup). We also described implementation considerations around digital phenotyping research for mood disorders and other psychiatric conditions.

CONCLUSIONS

Digital phenotyping in affective disorders is feasible on both Android and iOS smartphones, and the resulting data quality using an open-source platform is higher than that in comparable studies. While the digital phenotyping data quality was independent of gender and race, the reported demographic features of study participants revealed important information on possible selection biases that may result from naturalistic research in this domain. We believe that the methodology described will be readily reproducible and generalizable to other study settings and patient populations given our data on deployment at 2 unique sites.

Understanding of Depressive Symptomatology across Major Depressive Disorder and Bipolar Disorder: A Network Analysis

Background and Objectives: Depressive symptoms are prominent in both major depressive disorder (MDD) and bipolar disorder (BD). However, comparative research on the network structure of depressive symptoms in these two diagnostic groups has been limited. This study aims to compare the network structure of depressive symptoms in MDD and BD, providing a deeper understanding of the depressive symptomatology of each disorder. Materials and Methods: The Zung Self-Rating Depressive Scale, a 20-item questionnaire, was administered to assess the depressive symptoms in individuals with MDD (n = 322) and BD (n = 516). A network analysis was conducted using exploratory graph analysis (EGA), and the network structure was analyzed using regularized partial correlation models. To validate the dimensionality of the Zung SDS, principal component analysis (PCA) was adopted. Centrality measures of the depressive symptoms within each group were assessed, followed by a network comparison test between the two groups. Results: In both diagnostic groups, the network analysis revealed four distinct categories, aligning closely with the PCA results. "Depressed affect" emerged as the most central symptom in both MDD and BD. Furthermore, non-core symptoms, "Personal devaluation" in MDD and "Confusion" in BD, displayed strong centrality. The network comparison test did not reveal significant differences in the network structure between MDD and BD. Conclusions: The absence of significant differences in the network structures between MDD and BD suggests that the underlying mechanisms of depressive symptoms may be similar across these disorders. The identified central symptoms, including "Depressed affect", in both disorders and the distinct non-core symptoms in each highlight the complexity of the depressive symptomatology. Future research should focus on validating these symptoms as therapeutic targets and incorporate various methodologies, including non-metric dimension reduction techniques or canonical analysis.

Classifying and clustering mood disorder patients using smartphone data from a feasibility study

Differentiating between bipolar disorder and major depressive disorder can be challenging for clinicians. The diagnostic process might benefit from new ways of monitoring the phenotypes of these disorders. Smartphone data might offer insight in this regard. Today, smartphones collect dense, multimodal data from which behavioral metrics can be derived. Distinct patterns in these metrics have the potential to differentiate the two conditions. To examine the feasibility of smartphone-based phenotyping, two study sites (Mayo Clinic, Johns Hopkins University) recruited patients with bipolar I disorder (BPI), bipolar II disorder (BPII), major depressive disorder (MDD), and undiagnosed controls for a 12-week observational study. On their smartphones, study participants used a digital phenotyping app (mindLAMP) for data collection. While in use, mindLAMP gathered real-time geolocation, accelerometer, and screen-state (on/off) data. mindLAMP was also used for EMA delivery. MindLAMP data was then used as input variables in binary classification, three-group k-nearest neighbors (KNN) classification, and k-means clustering. The best-performing binary classification model was able to classify patients as control or non-control with an AUC of 0.91 (random forest). The model that performed best at classifying patients as having MDD or bipolar I/II had an AUC of 0.62 (logistic regression). The k-means clustering model had a silhouette score of 0.46 and an ARI of 0.27. Results support the potential for digital phenotyping methods to cluster depression, bipolar disorder, and healthy controls. However, due to inconsistencies in accuracy, more data streams are required before these methods can be applied to clinical practice.

Sexual dysfunction in bipolar depression: Gender differences

Objective

To find sexual dysfunction in acute-phase bipolar depression patients and subsequently characterize the gender-wise differences in sexual functioning.

Materials and Methods

A cross-sectional, descriptive, observational, purposeful, and hospital-based study was done with 45 patients (age range: 18-59 years) with moderate to severe acute phase bipolar depression (HAM-D scores >18). The domain-wise (Pleasure, Desire/Frequency Desire/Interest, Arousal/Excitement, and Orgasm/Completion) sexual functioning was assessed by the Change in Sexual Functioning Questionnaire (CSFQ-14) (≤41 for females, ≤47 for males as a cut-off for dysfunction). This study is registered in the CTRI (Clinical Trials Registry India, Number: CTRI-2021-07-035182).

Results

The prevalence of sexual dysfunction was 91% of bipolar disorder patients with more male participants (53.3%) compared to females (46.7%). The mean HAM-D score for the study sample was 27.93 ± 8.035. The female gender had more dysfunctional scores in desire/frequency (t = 2.229, P = 0.031), desire/interest (t = 2.448, P = 0.019), orgasm/completion (t = 2.974, P = 0.005), and overall total CSFQ (t = 2.946, P = 0.005). The odds of sexual dysfunction were significant given a one-unit increase in suicidal ideation in the index episode (adjusted OR = 1.222, 95% CI: 1.004-1.488, P = .049).

Conclusion

Acute-phase bipolar patients have very high sexual dysfunction rates. Females have both global and specific sexual response cycle deficits in comparison to acute phase bipolar depressed males. Future trials shall amuse neurobiology grounded, more individualistic sexual rehabilitation-based interventional paradigms, and longitudinal research models in acute phase bipolar depression.

Explainable machine-learning algorithms to differentiate bipolar disorder from major depressive disorder using self-reported symptoms, vital signs, and blood-based markers

BACKGROUND AND OBJECTIVE

Caused by shared genetic risk factors and similar neuropsychological symptoms, bipolar disorder (BD) and major depressive disorder (MDD) are at high risk of misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. We aimed to develop a machine learning (ML)-based diagnostic system, based on electronic medical records (EMR) data, to mimic the clinical reasoning of human physicians to differentiate MDD and BD (especially BD depressive episodes) patients about to be admitted to a hospital and, hence, reduce the misdiagnosis of BD as MDD on admission. In addition, we examined to what extent our ML model could be made interpretable by quantifying and visualizing the features that drive the predictions.

METHODS

By identifying 16,311 patients admitted to a hospital located in western China between 2009 and 2018 with a recorded main diagnosis of MDD or BD, we established three sub-cohorts with different combinations of features for both the MDD-BD cohort and the MDD-BD depressive episodes cohort, respectively. Four different ML algorithms (logistic regression, extreme gradient boosting (XGBoost), random forest, and support vector machine) and four train-test splits were used to train and validate diagnostic models, and explainable methods (SHAP and Break Down) were utilized to analyze the contribution of each of the features at both population-level and individual-level, including feature importance, feature interaction, and feature effect on prediction decision for a specific subject.

RESULTS

The XGBoost algorithm provided the best test performance (AUC: 0.838 (0.810-0.867), PPV: 0.810 and NPV: 0.834) for separating patients with BD from those with MDD. Core predictors included symptoms (mood-up, exciting, bad sleep, loss of interest, talking, mood-down, provoke), along with age, job, myocardial enzyme markers (creatine kinase, hydroxybutyrate dehydrogenase), diabetes-associated marker (glucose), bone function marker (alkaline phosphatase), non-enzymatic antioxidant (uric acid), markers of immune/inflammation (white blood cell count, lymphocyte count, basophil percentage, monocyte count), cardiovascular function marker (low density lipoprotein), renal marker (total protein), liver biochemistry marker (indirect bilirubin), and vital signs like pulse. For separating patients with BD depressive episodes from those with MDD, the test AUC was 0.777 (0.732-0.822), with PPV 0.576 and NPV 0.899. Additional validation in models built with self-reported symptoms removed from the feature set, showed test AUC of 0.701 (0.666-0.736) for differentiating BD and MDD, and AUC of 0.564 (0.515-0.614) for detecting patients in BD depressive episodes from MDD patients. Validation in the datasets without removing the patients with comorbidity showed an AUC of 0.826 (0.806-0.846).

CONCLUSION

The diagnostic system accurately identified patients with BD in various clinical scenarios, and differences in patterns of peripheral markers between BD and MDD could enrich our understanding of potential underlying pathophysiological mechanisms of them.

Metabolic regulation to treat bipolar depression: mechanisms and targeting by trimetazidine

Bipolar disorder's core feature is the pathological disturbances in mood, often accompanied by disrupted thinking and behavior. Its complex and heterogeneous etiology implies that a range of inherited and environmental factors are involved. This heterogeneity and poorly understood neurobiology pose significant challenges to existing drug development paradigms, resulting in scarce treatment options, especially for bipolar depression. Therefore, novel approaches are needed to discover new treatment options. In this review, we first highlight the main molecular mechanisms known to be associated with bipolar depression-mitochondrial dysfunction, inflammation and oxidative stress. We then examine the available literature for the effects of trimetazidine in said alterations. Trimetazidine was identified without a priori hypothesis using a gene-expression signature for the effects of a combination of drugs used to treat bipolar disorder and screening a library of off-patent drugs in cultured human neuronal-like cells. Trimetazidine is used to treat angina pectoris for its cytoprotective and metabolic effects (improved glucose utilization for energy production). The preclinical and clinical literature strongly support trimetazidine's potential to treat bipolar depression, having anti-inflammatory and antioxidant properties while normalizing mitochondrial function only when it is compromised. Further, trimetazidine's demonstrated safety and tolerability provide a strong rationale for clinical trials to test its efficacy to treat bipolar depression that could fast-track its repurposing to address such an unmet need as bipolar depression.

The Role of Ketamine in the Treatment of Bipolar Depression: A Scoping Review

Bipolar depression remains a clinical challenge with a quarter of patients failing to respond to initial conventional treatments. Although ketamine has been extensively studied in unipolar depression, its role in bipolar disorder remains inconclusive. The aim of our scoping review was to comprehensively synthesize the current clinical literature around ketamine use in bipolar depression. A total of 10 clinical studies (5 randomized controlled trials and 5 open label studies) were selected. The preliminary evidence, albeit weak, suggests that ketamine is a promising treatment and calls for further interest from the research community. Overall, ketamine treatment appeared to be tolerable with minimal risk for manic/hypomanic switching and showed some effectiveness across parameters of depression and suicidality. Moreover, ketamine is a potential treatment agent in patients with treatment-resistant bipolar depression with promising data extracted from extant controlled trials and real-world effectiveness studies. Future studies are needed to identify ketamine's role in acute and maintenance treatment phases of bipolar depression. Moreover, future researchers should study the recurrence prevention and anti-suicidal effects of ketamine in the treatment of bipolar depression.

Neuroimaging Correlates of Treatment Response to Transcranial Magnetic Stimulation in Bipolar Depression: A Systematic Review

Transcranial magnetic stimulation (TMS) has become a promising strategy for bipolar disorder (BD). This study reviews neuroimaging findings, indicating functional, structural, and metabolic brain changes associated with TMS in BD. Web of Science, Embase, Medline, and Google Scholar were searched without any restrictions for studies investigating neuroimaging biomarkers, through structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI), magnetic resonance spectroscopy (MRS), positron emission tomography (PET), and single photon emission computed tomography (SPECT), in association with response to TMS in patients with BD. Eleven studies were included (fMRI = 4, MRI = 1, PET = 3, SPECT = 2, and MRS = 1). Important fMRI predictors of response to repetitive TMS (rTMS) included higher connectivity of emotion regulation and executive control regions. Prominent MRI predictors included lower ventromedial prefrontal cortex connectivity and lower superior frontal and caudal middle frontal volumes. SPECT studies found hypoconnectivity of the uncus/parahippocampal cortex and right thalamus in non-responders. The post-rTMS changes using fMRI mostly showed increased connectivity among the areas neighboring the coil. Increased blood perfusion was reported post-rTMS in PET and SPECT studies. Treatment response comparison between unipolar depression and BD revealed almost equal responses. Neuroimaging evidence suggests various correlates of response to rTMS in BD, which needs to be further replicated in future studies.

Association between childhood maltreatment and social functioning in individuals with affective disorders: A systematic review and meta-analysis

OBJECTIVE

Childhood maltreatment has been linked to impairments in social functioning and social cognition in adults with affective disorders. However, conclusions have been limited by inconsistent findings across different maltreatment subtypes and social domains. We conducted a systematic review and meta-analysis to quantify associations between childhood maltreatment (overall and subtypes - physical, emotional and/or sexual abuse, and/or physical and/or emotional neglect) and different domains of social functioning and social cognition in adults with affective disorders (bipolar disorder or major depressive disorder). We also examined effect moderators and mediators of these associations.

METHODS

A systematic search was performed on 12.12.2022 which identified 29 studies included in qualitative synthesis (n = 3022 individuals with affective disorders), of which 27 (n = 2957) were pooled in meta-analyses. Across studies, five social functioning and five social cognition domains were examined, of which four domains of social functioning and two domains of social cognition had sufficient data for meta-analysis (PROSPERO CRD42022288976).

RESULTS

Social functioning: childhood maltreatment was associated with lower global social functioning (r = -0.11 to -0.20), poorer interpersonal relations (r = -0.18 to -0.33), and with aggressive behaviour (r = 0.20-0.29) but was unrelated to vocational functioning. Emotional abuse and emotional neglect showed the largest magnitudes of effect. Social cognition: there was no meta-analytic evidence of associations between maltreatment and social cognition domains. Exploratory moderation analyses did not identify any consistent moderators. Narrative synthesis identified attachment style as possible moderator, and sensory patterns, anxiety, and depressive symptoms as possible mediators between childhood maltreatment and social outcomes. Overall, the available evidence was limited, particularly in relation to social cognition.

CONCLUSIONS

Adults with affective disorders are at risk of social functioning difficulties after childhood maltreatment exposure, an effect observed across multiple maltreatment subtypes, social functioning domains, and diagnoses. Addressing social functioning problems may benefit maltreated adults with both bipolar disorder and major depressive disorder.

Hope and resilience among patients affected by unipolar and bipolar depression

BACKGROUND

Mood disorders, including unipolar and bipolar depression, are disabling mental conditions and patients' full-functional recovery may be challenging. Hope and Resilience are relevant factors in the framework of personal recovery and it is of interest to explore their association with the severity of depressive illness and other variables, including suicidality.

METHODS

in this cross-sectional study, 69 patients affected by unipolar (n = 31) and bipolar depression (n = 38) have been recruited and information about their sociodemographics, clinical characteristics were collected as well as the following assessment has been performed: BPRS (Brief Psychiatric Rating Scale); HAMD ( Hamilton Rating Scale for Depression); MADRS (Montgomery Asberg Depression rating scale); HADS (Hospital Anxiety and Depression Scale); SSI (Scale for Suicide Ideation); Synder (The Adult Hope Scale); CD-Risk (The Connor-Davidson Resilience Scale).

RESULTS

patients affected by bipolar depression reported significantly higher level of psychopathological issues (BPRS) as well as higher scores of depressive and anxious symptoms (MADRS and HADS), suicidality (SSI). Also, bipolar depression patients reported lower levels of hope (Synder) and resilience (CD-Risk), with hope and resilience levels positively correlated each other, and inversely correlated to psychopathology and psychopathology, depressive and suicidal symptoms, respectively.

CONCLUSIONS

these findings suggested that bipolar depression is characterized by higher clinical severity and lower levels of hope and resilience. Specific psycho-educational and psychotherapeutic interventions should be promoted to increase levels of hope and resilience in mood disorders, especially in bipolar depression.

Decision support system for the differentiation of schizophrenia and mood disorders using multiple deep learning models on wearable devices data

In the modern world, with so much inherent stress, mental health disorders (MHDs) are becoming more common in every country around the globe, causing a significant burden on society and patients' families. MHDs come in many forms with various severities of symptoms and differing periods of suffering, and as a result it is difficult to differentiate between them and simple to confuse them with each other. Therefore, we propose a support system that employs deep learning (DL) with wearable device data to provide physicians with an objective reference resource by which to make differential diagnoses and plan treatment. We conducted experiments on open datasets containing activity motion signal data from wearable devices to identify schizophrenia and mood disorders (bipolar and unipolar), the datasets being named Psykose and Depresjon. The results showed that, in both workflow approaches, the proposed framework performed well in comparison with the traditional machine learning (ML) and DL methods. We concluded that applying DL models using activity motion signal data from wearable devices represents a prospective objective support system for MHD differentiation with a good performance.

Neuromolecular Etiology of Bipolar Disorder: Possible Therapeutic Targets of Mood Stabilizers

Bipolar disorder is a mental illness that causes extreme mood swings and has a chronic course. However, the mechanism by which mood episodes with completely opposite characteristics appear repeatedly, or a mixture of symptoms appears, in patients with bipolar disorder remains unknown. Therefore, mood stabilizers are indicated only for single mood episodes, such as manic episodes and depressive episodes, and no true mood-stabilizing drugs effective for treating both manic and depressive episodes currently exist. Therefore, in this review, therapeutic targets that facilitate the development of mood stabilizers were examined by reviewing the current understanding of the neuromolecular etiology of bipolar disorder.

False dogmas in mood disorders research: Towards a nomothetic network approach

The current understanding of major depressive disorder (MDD) and bipolar disorder (BD) is plagued by a cacophony of controversies as evidenced by competing schools to understand MDD/BD. The DSM/ICD taxonomies have cemented their status as the gold standard for diagnosing MDD/BD. The aim of this review is to discuss the false dogmas that reign in current MDD/BD research with respect to the new, data-driven, machine learning method to model psychiatric illness, namely nomothetic network psychiatry (NNP). This review discusses many false dogmas including: MDD/BD are mind-brain disorders that are best conceptualized using a bio-psycho-social model or mind-brain interactions; mood disorders due to medical disease are attributable to psychosocial stress or chemical imbalances; DSM/ICD are the gold standards to make the MDD/BD diagnosis; severity of illness should be measured using rating scales; clinical remission should be defined using threshold values on rating scale scores; existing diagnostic BD boundaries are too restrictive; and mood disorder spectra are the rule. In contrast, our NNP models show that MDD/BD are not mind-brain or psycho-social but systemic medical disorders; the DSM/ICD taxonomies are counterproductive; a shared core, namely the reoccurrence of illness (ROI), underpins the intertwined recurrence of depressive and manic episodes and suicidal behaviors; mood disorders should be ROI-defined; ROI mediates the effects of nitro-oxidative stress pathways and early lifetime trauma on the phenome of mood disorders; severity of illness and treatment response should be delineated using the NNP-derived causome, pathway, ROI and integrated phenome scores; and MDD and BD are the same illness.

Smartphone Sensor Data for Identifying and Monitoring Symptoms of Mood Disorders: A Longitudinal Observational Study

BACKGROUND

Mood disorders are burdensome illnesses that often go undetected and untreated. Sensor technologies within smartphones may provide an opportunity for identifying the early changes in circadian rhythm and social support/connectedness that signify the onset of a depressive or manic episode.

OBJECTIVE

Using smartphone sensor data, this study investigated the relationship between circadian rhythm, which was determined by GPS data, and symptoms of mental health among a clinical sample of adults diagnosed with major depressive disorder or bipolar disorder.

METHODS

A total of 121 participants were recruited from a clinical setting to take part in a 10-week observational study. Self-report questionnaires for mental health outcomes, social support, social connectedness, and quality of life were assessed at 6 time points throughout the study period. Participants consented to passively sharing their smartphone GPS data for the duration of the study. Circadian rhythm (ie, regularity of location changes in a 24-hour rhythm) was extracted from GPS mobility patterns at baseline.

RESULTS

Although we found no association between circadian rhythm and mental health functioning at baseline, there was a positive association between circadian rhythm and the size of participants' social support networks at baseline (r=0.22; P=.03; R²=0.049). In participants with bipolar disorder, circadian rhythm was associated with a change in anxiety from baseline; a higher circadian rhythm was associated with an increase in anxiety and a lower circadian rhythm was associated with a decrease in anxiety at time point 5.

CONCLUSIONS

Circadian rhythm, which was extracted from smartphone GPS data, was associated with social support and predicted changes in anxiety in a clinical sample of adults with mood disorders. Larger studies are required for further validations. However, smartphone sensing may have the potential to monitor early symptoms of mood disorders.

Postpartum anhedonia: Emergent patterns in bipolar and unipolar depression

The objective of this study was to identify differences in the longitudinal course anhedonia symptoms during postpartum in women diagnosed with unipolar or bipolar disorder. Female participants diagnosed with either bipolar (n = 104) or unipolar (n = 136) depression at week 20 during pregnancy were evaluated prospectively at weeks 2, 12, 26, and 52 postpartum using clinical interviews. A semi-parametric, group-based mixture model was applied to separate distinct longitudinal patterns of symptoms of anhedonia. Across time, among those who reported anhedonia, twice as many women had the diagnoses of bipolar depression relative to unipolar depression (65.03% versus 39.47%, respectively). Moreover, the rate and stability of anhedonia was higher in women with bipolar relative to unipolar depression. Across groups, anhedonia was associated with significantly higher depressive symptom severity. Anhedonia is a more stable and frequent symptom in women with postpartum bipolar relative to unipolar depressive disorder.

The burden of caring for adults with depression and suicidal ideation in five large European countries: analysis from the 2020 National Health and Wellness Survey

BACKGROUND

Suicidal ideation (SI) affects approximately 30-40% of those with major depressive disorder (MDD). To date, studies have examined the substantial humanistic and economic burden to caregivers of persons with MDD, however little is known of the impact of caring for persons with MDD when SI is present. This study examined the additional burden imposed on caregivers for persons with unipolar depression and SI in five major European countries.

METHODS

A retrospective, cross-sectional analysis was conducted in five European countries using 2020 Europe National Health Wellness Survey (France, Germany, Italy, Spain and the United Kingdom) to compare differences in health and economic outcomes between caregivers of adults with unipolar depression and SI (CAUD-SI) and caregivers of adults with unipolar depression without SI (CAUD-nSI). The outcomes assessed included health-related quality of life (HRQoL; Medical Outcomes Study Short Form [SF-12v2]), health status (Short-Form 6 Dimension [SF-6D], EuroQol 5-Dimension 5-Level [EQ-5D-5L]), Work productivity and activity impairment (WPAI) and healthcare resource utilization (HRU). Linear mixed models and generalized linear mixed models adjusted for covariates were used to compare the two groups on outcomes of interest.

RESULTS

Of 62,319 respondents, 0.89% (n = 554) were CAUD-SI and 1.34% (n = 837) were CAUD-nSI. In adjusted models, CAUD-SI reported greater humanistic burden than CAUD-nSI, with lower HRQoL (PCS: 42.7 vs. 45.0, p < 0.001 and MCS: 37.5 vs. 38.9, p = 0.007) and health status (SF-6D: 0.57 vs 0.60, p < 0.001 and EQ-5D-5L: 0.58 vs 0.66, p < 001). CAUD-SI respondents reported significantly higher economic burden than CAUD-nSI respondents for WPAI (percent activity impairment: 64.9% vs. 52.5%, p = 0.026) and HRU (provider visits: 10.0 vs. 7.9, p < 0.001, emergency room visits: 1.49 vs. 0.73, p < 0.001 and hospitalizations: 1.03 vs. 0.52, p < 0.001).

CONCLUSION

In five European countries, caregivers of adults with depression and SI experience additional humanistic and economic burden than caregivers of adults with depression and no SI. Distinguishing caregiver groupings and their unique burden provide important insights for providing targeted support and interventions for both the patient and caregiver.

Characterising the severity of treatment resistance in unipolar and bipolar depression

Background

Treatment-resistant depression (TRD) is classically defined according to the number of suboptimal antidepressant responses experienced, but multidimensional assessments of TRD are emerging and may confer some advantages. Patient characteristics have been identified as risk factors for TRD but may also be associated with TRD severity. The identification of individuals at risk of severe TRD would support appropriate prioritisation of intensive and specialist treatments.

Aims

To determine whether TRD risk factors are associated with TRD severity when assessed multidimensionally using the Maudsley Staging Method (MSM), and univariately as the number of antidepressant non-responses, across three cohorts of individuals with depression.

Method

Three cohorts of individuals without significant TRD, with established TRD and with severe TRD, were assessed (n = 528). Preselected characteristics were included in linear regressions to determine their association with each outcome.

Results

Participants with more severe TRD according to the MSM had a lower age at onset, fewer depressive episodes and more physical comorbidities. These associations were not consistent across cohorts. The number of episodes was associated with the number of antidepressant treatment failures, but the direction of association varied across the cohorts studied.

Conclusions

Several risk factors for TRD were associated with the severity of resistance according to the MSM. Fewer were associated with the raw number of inadequate antidepressant responses. Multidimensional definitions may be more useful for identifying patients at risk of severe TRD. The inconsistency of associations across cohorts has potential implications for the characterisation of TRD.

Irritability Is Associated with Illness Severity and Anhedonia Is Associated with Functional Impairment Among Depressed Children and Adolescents

Objectives: Irritability and anhedonia are cardinal symptoms of depression for children and adolescents. However, anhedonia may be more strongly associated with illness severity compared with irritability. The present study evaluated the impact of irritability and anhedonia on symptom severity and functional impairment among depressed children and adolescents. Methods: Participants were 383 children and adolescents presenting for outpatient treatment at a community mental health center or academic medical center. Children and adolescents were diagnosed with unipolar depression or bipolar disorder. Regression models predicted depression severity and functional impairment from irritability and anhedonia after covarying age, gender, depressive and hypomanic symptoms, and diagnosis. Results: Greater irritability and anhedonia were associated with more severe depression symptoms. Greater irritability, but not anhedonia, was associated with lower global functioning and family quality of life (QoL), and more externalizing problems. Greater anhedonia was associated with lower overall, emotional, self-esteem, and social QoL. Neither irritability nor anhedonia was associated with school or physical QoL, nonsuicidal self-injury, suicidal ideation, number of comorbid diagnoses, or internalizing problems. Conclusions: Irritability was associated with more markers of depression severity, whereas anhedonia was associated with indicators of functional impairment. This study used a cross-sectional observational design and therefore cannot provide information about cause and effect relationships between variables. Irritability and anhedonia were derived from their respective subscales of the General Behavior Inventory and included only caregiver-reported symptoms but not child- or adolescent-reported symptoms. Identifying the impact of specific symptoms of depression may assist clinicians in delivering more individualized interventions to target symptoms that result in greater impairment.

Bipolar Depression: A Historical Perspective of the Current Concept, with a Focus on Future Research

The aim of this narrative review is to trace the origin of the concept of bipolar depression and to expose some of its limitations. Bipolar depression is a broad clinical construct including experiences ranging from traditional melancholic and psychotic episodes ascribed to "manic-depressive insanity," to another heterogeneous group of depressive episodes originally described in the context of binary models of unipolar depression (e.g., psychogenic depression, neurotic depression). None of the available empirical evidence suggests, however, that these subsets of "bipolar" depression are equivalent in terms of clinical course, disability, family aggregation, and response to treatment, among other relevant diagnostic validators. Therefore, the validity of the current concept of bipolar depression should be a matter of concern. Here, we discuss some of the potential limitations that this broad construct might entail in terms of pathophysiological, clinical, and therapeutic aspects. Finally, we propose a clinical research program for bipolar depression in order to delimit diagnostic entities based on empirical data, with subsequent validation by laboratory or neuroimaging biomarkers. This process will then aid in the development of more specific treatments.

Insula activity in resting-state differentiates bipolar from unipolar depression: a systematic review and meta-analysis

Symptomatic overlap of depressive episodes in bipolar disorder (BD) and major depressive disorder (MDD) is a major diagnostic and therapeutic problem. Mania in medical history remains the only reliable distinguishing marker which is problematic given that episodes of depression compared to episodes of mania are more frequent and predominantly present at the beginning of BD. Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive, task-free, and well-tolerated method that may provide diagnostic markers acquired from spontaneous neural activity. Previous rs-fMRI studies focused on differentiating BD from MDD depression were inconsistent in their findings due to low sample power, heterogeneity of compared samples, and diversity of analytical methods. This meta-analysis investigated resting-state activity differences in BD and MDD depression using activation likelihood estimation. PubMed, Web of Science, Scopus and Google Scholar databases were searched for whole-brain rs-fMRI studies which compared MDD and BD currently depressed patients between Jan 2000 and August 2020. Ten studies were included, representing 234 BD and 296 MDD patients. The meta-analysis found increased activity in the left insula and adjacent area in MDD compared to BD. The finding suggests that the insula is involved in neural activity patterns during resting-state that can be potentially used as a biomarker differentiating both disorders.

Lithium as a Neuroprotective Agent for Bipolar Disorder: An Overview

Lithium (Li⁺) is a first option treatment for adult acute episodes of Bipolar Disorder (BD) and for the prophylaxis of new depressed or manic episodes. It is also the preferred choice as maintenance treatment. Numerous studies have shown morphological abnormalities in the brains of BD patients, suggesting that this highly heritable disorder may exhibit progressive and deleterious changes in brain structure. Since treatment with Li⁺ ameliorates these abnormalities, it has been postulated that Li⁺ is a neuroprotective agent in the same way atypical antipsychotics are neuroprotective in patients diagnosed with schizophrenia spectrum disorders. Li⁺'s neuroprotective properties are related to its modulation of nerve growth factors, inflammation, mitochondrial function, oxidative stress, and programmed cell death mechanisms such as autophagy and apoptosis. Notwithstanding, it is not known whether Li⁺-induced neuroprotection is related to the inhibition of its putative molecular targets in a BD episode: the enzymes inositol-monophosphatase, (IMPase), glycogen-synthase-kinase 3β (GSK3), and Protein kinase C (PKC). Furthermore, it is uncertain whether these neuroprotective mechanisms are correlated with Li⁺'s clinical efficacy in maintaining mood stability. It is expected that in a nearby future, precision medicine approaches will improve diagnosis and expand treatment options. This will certainly contribute to ameliorating the medical and economic burden created by this devastating mood disorder.

Magnetic seizure therapy for treatment-resistant depression

BACKGROUND

Magnetic seizure therapy (MST) is a potential alternative to electroconvulsive therapy (ECT). Reports to date on use of MST for patients with treatment-resistant depression (TRD) are limited.

OBJECTIVES

To evaluate the effects of MST in comparison with sham-MST, antidepressant, and other forms of electric or magnetic treatment for adults with TRD.

SEARCH METHODS

In March 2020, we searched a wide range of international electronic sources for published, unpublished, and ongoing studies. We handsearched the reference lists of all included studies and relevant systematic reviews and conference proceedings of the Annual Meeting of the American College of Neuropsychopharmacology (ACNP), the Annual Scientific Convention and Meeting, and the Annual Meeting of the European College of Neuropsychopharmacology (ECNP) to identify additional studies.

SELECTION CRITERIA

All randomised clinical trials (RCTs) focused on MST for adults with TRD.

DATA COLLECTION AND ANALYSIS

Two review authors extracted data independently. For binary outcomes, we calculated risk ratios (RRs) and 95% confidence intervals (CIs). For continuous data, we estimated mean differences (MDs) between groups and 95% CIs. We employed a random-effects model for analyses. We assessed risk of bias for included studies and created a 'Summary of findings' table using the GRADE approach. Our main outcomes of interest were symptom severity, cognitive function, suicide, quality of life, social functioning, dropout for any reason, serious adverse events, and adverse events that led to discontinuation of treatment.

MAIN RESULTS

We included three studies (65 participants) comparing MST with ECT. Two studies reported depressive symptoms with the Hamilton Rating Scale for Depression (HAMD). However, in one study, the data were skewed and there was an imbalance in baseline characteristics. Analysis of these two studies showed no clear differences in depressive symptoms between treatment groups (MD 0.71, 95% CI -2.23 to 3.65; 2 studies, 40 participants; very low-certainty evidence). Two studies investigated multiple domains of cognitive function. However most of the outcomes were not measured by validated neuropsychological tests, and many of the data suffered from unbalanced baseline and skewed distribution. Analysis of immediate memory performance measured by the Wechsler Memory Scale showed no clear differences between treatment groups (MD 0.40, 95% CI -4.16 to 4.96; 1 study, 20 participants; very low-certainty evidence). Analysis of delayed memory performance measured by the Wechsler Memory Scale also showed no clear differences between treatment groups (MD 2.57, 95% CI -2.39 to 7.53; 1 study, 20 participants; very low-certainty evidence). Only one study reported quality of life, but the data were skewed and baseline data were unbalanced across groups. Analysis of quality of life showed no clear differences between treatment groups (MD 14.86, 95% CI -42.26 to 71.98; 1 study, 20 participants; very low-certainty evidence). Only one study reported dropout and adverse events that led to discontinuation of treatment. Analysis of reported data showed no clear differences between treatment groups for this outcome (RR 1.38, 95% CI 0.28 to 6.91; 1 study, 25 participants; very low-certainty evidence). Adverse events occurred in only two participants who received ECT (worsening of preexisting coronary heart disease and a cognitive adverse effect). None of the included studies reported outcomes on suicide and social functioning. No RCTs comparing MST with other treatments were identified.

AUTHORS' CONCLUSIONS

Evidence regarding effects of MST on patients with TRD is currently insufficient. Our analyses of available data did not reveal clearly different effects between MST and ECT. We are uncertain about these findings because of risk of bias and imprecision of estimates. Large, long, well-designed, and well-reported trials are needed to further examine the effects of MST.

The dual-system theory of bipolar spectrum disorders: A meta-analysis

Bipolar spectrum disorders are characterized by alternating intervals of extreme positive and negative affect. We performed a meta-analysis to test the hypothesis that such disorders would be related to dysregulated reinforcement sensitivity. First, we reviewed 23 studies that reported the correlation between self-report measures of (hypo)manic personality and measures of reinforcement sensitivity. A large relationship was found between (hypo)manic personality and BAS sensitivity (g = .74), but not with BIS sensitivity (g = -.08). This stands in contrast to self-reported depression which has a small, negative relationship with BAS sensitivity and a large positive one with BIS sensitivity (Katz et al., 2020). Next, we reviewed 33 studies that compared reinforcement sensitivity between euthymic, bipolar participants and healthy controls. There, bipolar disorder had a small, positive relationship with BAS sensitivity (g = .20) and a medium, positive relationship with BIS sensitivity (g = .64). These findings support a dualsystem theory of bipolar disorders, wherein BAS sensitivity is more closely related to mania and BIS sensitivity more closely to bipolar depression. Bipolar disorders show diatheses for both states with euthymic participants being BAS- and BIS- hypersensitive. Implications for further theory and research practice are expounded upon in the discussion.

EEG Frontal Asymmetry and Theta Power in Unipolar and Bipolar Depression

BACKGROUND

Distinguishing between unipolar and bipolar depression is of high clinical relevance. However, there is sparse research directly comparing these groups in terms of EEG activity.

METHOD

We investigated 87 participants' left and right EEG frontal alpha-1, alpha-2, and theta activity related to happy and sad face stimuli in unipolar (UD, n=33) and bipolar (BD, n=22) depressed participants, and controls without depression (HC, n=32).

RESULTS

Post-hoc analysis of an observed hemisphere x group interaction (p< .037) showed significant differences in alpha-1 asymmetry only for the comparison of UD and HC (p< .006). Further analysis of a significant emotion x group interaction (p= .001) revealed a differential impact of stimulus valence on theta power between the groups (p< .001). The valence dependent theta power of the BD differed from that of the UD (p< .0002) and the HC (p< .004). Alpha-1 asymmetry classified HC and both depressed groups with an accuracy of .69. Valence-related theta classified BD from UD with an accuracy of .83. Leave-one-out cross validation resulted in slightly reduced accuracy.

LIMITATIONS

Important limitations were the small sample size and that subjects were not medication-free.

CONCLUSIONS

Our results demonstrate the value of simple, task related EEG activity for differentiating not only healthy individuals from those with depression, but also individuals with unipolar depression from those with bipolar depression.

Disrupted dynamic functional connectivity in right amygdalar subregions differentiates bipolar disorder from major depressive disorder

Notwithstanding being the object of a growing field of clinical research, the investigation of the dynamic resting-state functional connectivity alterations in psychiatric illnesses is still in its early days. Current research on major depressive disorder (MDD) and bipolar disorder (BD) has evidenced abnormal resting-state functional connectivity (rsFC), especially in regions subserving emotional processing and regulation such as the amygdala. However, dynamic changes in functional connectivity within the amygdalar subregions in distinguishing BD and MDD has not yet been fully understood. In this paper, we aim at analyzing the patterns characterizing dynamic FC (dFC) in the right amygdala to investigate the differences between similarly depressed BD and MDD. A number of 40 BD patients, 61 MDD patients and 63 healthy controls (HCs) underwent functional magnetic resonance imaging (fMRI) at rest. Using the right-amygdala as seed region, we compared the dFC within three subdivisions, namely, laterobasal (LB), centromedial (CM) and superficial (SF) between all the groups. To do so, one-way ANOVA followed by post-hoc t-tests were employed. Compared to HCs, patients with BD had a decreased dFC between right LB and the left postcentral gyrus as well as an increased dFC between right CM and the right cerebellum.Compared to BD patients, patients with MDD showed a decreased dFC between right CM and the cerebellum and an increased dFC between right LB and the left postcentral gyrus. These findings present initial evidence that abnormal patterns of the right-amygdalar subregions shared by BD and MDD supports the differential pathophysiology of these disorders.

Mismatch Negativity and Loudness Dependence of Auditory Evoked Potentials among Patients with Major Depressive Disorder, Bipolar II Disorder, and Bipolar I Disorder

Mismatch negativity (MMN) and loudness dependence of auditory evoked potentials (LDAEP), which are event-related potentials, have been investigated as biomarkers. MMN indicates the pre-attentive function, while LDAEP may be an index of central serotonergic activity. This study aimed to test whether MMN and LDAEP are useful biological markers for distinguishing patients with bipolar disorder (BD) and major depressive disorder (MDD), as well as the relationship between MMN and LDAEP. Fifty-five patients with major depressive episodes, aged 20 to 65 years, who had MDD (n = 17), BD type II (BIID) (n = 27), and BD type I (BID) (n = 11), were included based on medical records. Patients with MDD had a higher MMN amplitude than those with BID. In addition, the MMN amplitude in F4 positively correlated with the Korean version of mood disorder questionnaire scores (r = 0.37, p = 0.014), while the MMN amplitude in F3 correlated negatively with LDAEP (r = −0.30, p = 0.024). The odds ratios for the BID group and some variables were compared with those for the MDD group using multinomial logistic regression analysis. As a result, a significant reduction of MMN amplitude was found under BID diagnosis compared to MDD diagnosis (p = 0.015). This study supported the hypothesis that MMN amplitude differed according to MDD, BIID, and BID, and there was a relationship between MMN amplitude and LDAEP. These findings also suggested that BID patients had a reduced automatic and pre-attentive processing associated with serotonergic activity or N-methyl-D-aspartate receptor.

The novel seizure quality index for the antidepressant outcome prediction in electroconvulsive therapy: association with biomarkers in the cerebrospinal fluid

For patients with depression treated with electroconvulsive therapy (ECT), the novel seizure quality index (SQI) can predict the risk of non-response (and non-remission)-as early as after the second ECT session-based the extent of several ictal parameters of the seizure. We aim to test several CSF markers on their ability to predict the degree of seizure quality, measured by the SQI to identify possible factors, that could explain some variability of the seizure quality. Baseline CSF levels of metabolites from the kynurenine pathway, markers of neurodegeneration (tau proteins, β-amyloids and neurogranin), elements of the innate immune system, endocannabinoids, sphingolipids, neurotrophic factors (VEGF) and Klotho were measured before ECT in patients with depression (n = 12) to identify possible correlations with the SQI by Pearson's partial correlation. Negative, linear relationships with the SQI for response were observed for CSF levels of T-tau (r_partial = - 0.69, p = 0.019), phosphatidylcholines (r_partial = - 0.52, p = 0.038) and IL-8 (r_partial = - 0.67, p = 0.047). Regarding the SQI for remission, a negative, linear relationship was noted with CSF levels of the endocannabinoid AEA (r_partial = - 0.70, p = 0.024) and CD163 (r_partial = - 0.68, p = 0.029). In sum, CSF Markers for the innate immune system, for neurodegeneration and from lipids were found to be associated with the SQI for response and remission after adjusting for age. Consistently, higher CSF levels of the markers were always associated with lower seizure quality. Based on these results, further research regarding the mechanism of seizure quality in ECT is suggested.

Reward Responsiveness and Ruminative Styles Interact to Predict Inflammation and Mood Symptomatology

Abnormal reward responsiveness and rumination each are associated with elevated inflammation and mood symptoms. Ruminating on positive and negative affect, or dampening positive affect, may amplify, or buffer, the associations of reward hyper/hyposensitivity with inflammation and mood symptoms. Young adults (N = 109) with high or moderate reward sensitivity completed reward responsiveness and ruminative style measures at the initial visit of a longitudinal study of mood symptoms, a blood draw to assess inflammatory biomarkers, and mood symptom measures at the study visits before and after the day of the blood draw. The interaction between high reward responsiveness and rumination on positive affect was associated with higher levels of an inflammatory composite measure and hypomanic symptoms. The interaction between lower reward responsiveness and high dampening of positive affect was associated with higher levels of the inflammatory composite measure and depressive symptoms. Lower reward responsiveness also interacted with low rumination on positive affect to predict increases in depressive symptoms and higher levels of the inflammatory composite. Thus, levels of reward responsiveness and ruminative response styles may synergistically influence the development of inflammatory phenotypes and both hypomanic and depressive mood symptoms.

Magnetoencephalography resting-state spectral fingerprints distinguish bipolar depression and unipolar depression

OBJECTIVES

In clinical practice, bipolar depression (BD) and unipolar depression (UD) appear to have similar symptoms, causing BD being frequently misdiagnosed as UD, leading to improper treatment decision and outcome. Therefore, it is in urgent need of distinguishing BD from UD based on clinical objective biomarkers as early as possible. Here, we aimed to integrate brain neuroimaging data and an advanced machine learning technique to predict different types of mood disorder patients at the individual level.

METHODS

Eyes closed resting-state magnetoencephalography (MEG) data were collected from 23 BD, 30 UD, and 31 healthy controls (HC). Individual power spectra were estimated by Fourier transform, and statistic spectral differences were assessed via a cluster permutation test. A support vector machine classifier was further applied to predict different mood disorder types based on discriminative oscillatory power.

RESULTS

Both BD and UD showed decreased frontal-central gamma/beta ratios comparing to HC, in which gamma power (30-75 Hz) was decreased in BD while beta power (14-30 Hz) was increased in UD vs HC. The support vector machine model obtained significant high classification accuracies distinguishing three groups based on mean gamma and beta power (BD: 79.9%, UD: 81.1%, HC: 76.3%, P < .01).

CONCLUSIONS

In combination with resting-state MEG data and machine learning technique, it is possible to make an individual and objective prediction for mode disorder types, which in turn has implications for diagnosis precision and treatment decision of mood disorder patients.

Understanding electroconvulsive therapy-related anxiety: a prospective study

AIMS OF STUDY

Although electroconvulsive therapy (ECT)-related anxiety is experienced by a significant proportion of patients, it remains understudied. Our aim was to study the course of ECT-related anxiety during ECT.

METHODS

Seventy-four patients with unipolar or bipolar depression, referred for ECT, were included. ECT-related anxiety was assessed the morning before each ECT session using the ECT-related Anxiety Questionnaire (ERAQ).

RESULTS

Female patients reported more anxiety than men (F(1,64.6) = 3.95, P = 0.05). Patients with a psychotic depression were more anxious before the start of ECT (F(64.8) = 4.57, P = 0.04), but experienced a significant decrease in ECT-related anxiety (t(63.9) = -3.63, P = 0.0006), whereas patients with a non-psychotic depression remained stable on anxiety during their ECT course (t(63,9) = 0.76, P = 0.45). In addition, we found a significant correlation between the decrease of ECT-related anxiety and the decrease of depression-severity (r = 0.35; P = 0.04).

CONCLUSION

There are individual differences in ECT-related anxiety trajectories during ECT. Both female patients and patients with psychotic depression experienced more ECT-related anxiety before the start of ECT. The severity of ECT-related anxiety decreased significantly in patients with a psychotic depression, but remained stable in patients without a psychotic depression during ECT. In addition, patients who showed a stronger decrease in depression-severity also showed a stronger decrease in ECT-related anxiety. A better understanding of ECT-related anxiety trajectories can help in designing anxiety-reducing interventions.

Symptom networks in acute depression across bipolar and major depressive disorders: A network analysis on a large, international, observational study

Major Depressive Episode (MDE) is a transdiagnostic nosographic construct straddling Major Depressive (MDD) and Bipolar Disorder (BD). Prognostic and treatment implications warrant a differentiation between these two disorders. Network analysis is a novel approach that outlines symptoms interactions in psychopathological networks. We investigated the interplay among depressive and mixed symptoms in acutely depressed MDD/BD patients, using a data-driven approach. We analyzed 7 DSM-IV-TR criteria for MDE and 14 researched-based criteria for mixed features (RBDC) in 2758 acutely depressed MDD/BD patients from the BRIDGE-II-Mix study. The global network was described in terms of symptom thresholds and symptom centrality. Symptom endorsement rates were compared across diagnostic subgroups. Subsequently, MDD/BD differences in symptom-network structure were examined using permutation-based network comparison test. Mixed symptoms were the most central and highly interconnected nodes in the network, particularly agitation followed by irritability. Despite mixed symptoms, appetite gain and hypersomnia were significantly more endorsed in BD patients, associations between symptoms were highly correlated across MDD/BD (Spearman's r = 0.96, p<0.001). Network comparison tests showed no significant differences among MDD/BD in network strength, structure, or specific edges, with strong edges correlations (0.66-0.78). Upstream differences in MDD/BD may produce similar symptoms networks downstream during acute depression. Yet, mixed symptoms, appetite gain and hypersomnia are associated to BD rather than MDD. Symptoms during mixed-MDE might aggregate according to 2 different clusters, suggesting a possible stratification within mixed states. Future symptom-based studies should implement clinical, longitudinal, and biological factors, in order to establish tailored therapeutic strategies for acute depression.

Bipolar depression: a major unsolved challenge

Depression in bipolar disorder (BD) patients presents major clinical challenges. As the predominant psychopathology even in treated BD, depression is associated not only with excess morbidity, but also mortality from co-occurring general-medical disorders and high suicide risk. In BD, risks for medical disorders including diabetes or metabolic syndrome, and cardiovascular disorders, and associated mortality rates are several-times above those for the general population or with other psychiatric disorders. The SMR for suicide with BD reaches 20-times above general-population rates, and exceeds rates with other major psychiatric disorders. In BD, suicide is strongly associated with mixed (agitated-dysphoric) and depressive phases, time depressed, and hospitalization. Lithium may reduce suicide risk in BD; clozapine and ketamine require further testing. Treatment of bipolar depression is far less well investigated than unipolar depression, particularly for long-term prophylaxis. Short-term efficacy of antidepressants for bipolar depression remains controversial and they risk clinical worsening, especially in mixed states and with rapid-cycling. Evidence of efficacy of lithium and anticonvulsants for bipolar depression is very limited; lamotrigine has long-term benefit, but valproate and carbamazepine are inadequately tested and carry high teratogenic risks. Evidence is emerging of short-term efficacy of several modern antipsychotics (including cariprazine, lurasidone, olanzapine-fluoxetine, and quetiapine) for bipolar depression, including with mixed features, though they risk adverse metabolic and neurological effects.

Quantitative separation of the depressive phase of bipolar disorder and major depressive disorder using electrovestibulography

Objectives: No electrophysiological, neuroimaging or genetic markers have been established that strongly relate to the diagnostic separation of bipolar disorder (BD) and major depressive disorder (MDD). This paper's objective is to describe the potential of features, extracted from the recording of electrical activity from the outer ear canal, in a process called electrovestibulography (EVestG), for identifying depressed and partly remitted/remitted MDD and BD patients from each other.Methods: From EVestG data four sensory vestibulo-acoustic features were extracted from both background (no movement) and using a single supine-vertical translation stimulus to distinguish 27 controls, 39 MDD and 43 BD patients.Results: Using leave-one-out-cross-validation, unbiased parametric and non-parametric classification routines resulted in 78-83% (2-3 features), 80-81% (1-2 features) and 66-68% (3 features) accuracies for separation of MDD from BD, controls from depressed (BD & MDD) and the 3-way separation of BD from MDD from control groups, respectively. The main limitations of this study were the inability to fully disentangle the impact of prescribed medication from the responses and also the limited sample size.Conclusions: EVestG features can reliably identify depressed and partly remitted/remitted MDD and BD patients from each other.

Changed cortical risk gene expression in major depression and shared changes in cortical gene expression between major depression and bipolar disorders

BACKGROUND

Mood disorders likely occur in someone with a genetic predisposition who encounters a deleterious environmental factor leading to dysregulated physiological processes due to genetic mutations and epigenetic mechanisms altering gene expression. To gain data to support this hypothesis, we measured levels of gene expression in three cortical regions known to be affected by the pathophysiologies of major depression and bipolar disorders.

METHODS

Levels of RNA were measured using the Affymetrix™ Human Exon 1.0 ST Array in Brodmann's areas 9, 10 and 33 (left hemisphere) from individuals with major depression, bipolar disorder and age- and sex-matched controls with changed expression taken as a fold change in RNA ⩾1.2 at p < 0.01. Data were analysed using JMP^® genomics 6.0 and the probable biological consequences of changes in gene expression determined using Core and Pathway Designer Analyses in Ingenuity Pathway Analysis.

RESULTS

There were altered levels of RNA in Brodmann's area 9 (major depression = 424; bipolar disorder = 331), Brodmann's area 10 (major depression = 52; bipolar disorder = 24) and Brodmann's area 33 (major depression = 59 genes; bipolar disorder = 38 genes) in mood disorders. No gene was differentially expressed in all three regions in either disorder. There was a high correlation between fold changes in levels of RNA from 112 genes in Brodmann's area 9 from major depression and bipolar disorder (r² = 0.91, p < 0.001). Levels of RNA for four risk genes for major depression were lower in Brodmann's area 9 in that disorder.

CONCLUSION

Our data argue that there are complex regional-specific changes in cortical gene expression in major depression and bipolar disorder that includes the expression of some risk genes for major depression in those with that disorder. It could be hypothesised that the common changes in gene expression in major depression and bipolar disorder are involved in the genesis of symptoms common to both disorders.

Working memory interacts with emotion regulation to predict symptoms of mania

Although neurocognitive deficits and emotion regulation are closely linked within unipolar depression, little research has tested links between these two variables within bipolar disorder. The present study tested whether working memory is related to individual differences in emotion regulation strategies in bipolar disorder and whether working memory and emotion regulation can explain variability in symptoms over time. Fifty-nine euthymic adults with bipolar I disorder completed a working memory span task, symptom interviews assessing depression and mania, and questionnaires assessing brooding rumination, reappraisal, and suppression. At baseline, working memory was unrelated to emotion regulation. Symptom interviews were repeated at six months (n = 41) and 12 months (n = 36) follow-up. At 12 months, baseline working memory significantly interacted with baseline suppression to predict higher mania. Tests of simple slopes showed that at lower working memory levels, low use of suppression was associated with significantly greater mania symptoms. These results help to clarify previous inconsistent findings regarding cognitive functioning and emotion regulation strategies in bipolar disorder, suggesting that deficits in both domains combine to predict outcomes.

Looking on the bright side and seeing it vividly: interpretation bias and involuntary mental imagery are related to risk for bipolar disorder

BACKGROUND

Involuntary mental imagery is elevated among people with bipolar disorder, and has been shown to shape biases in interpretation of ambiguous information. However, it is not clear whether biases in interpretation of ambiguous scenarios can be observed in those at risk for bipolar disorder, or whether involuntary imagery is related to such a bias.

AIMS

In the present study, we extended a prominent model of bipolar cognition to an at-risk sample. We specifically tested whether positive interpretation bias and involuntary mental imagery are linked to a greater risk of bipolar disorder.

METHOD

Young adults (N = 169) completed measures of risk for bipolar disorder (the Hypomanic Personality Scale [HPS]), interpretation bias, and involuntary mental imagery.

RESULTS

Higher scores on the HPS were significantly correlated with more positive interpretations of ambiguous scenarios (β = 0.29, p <. 01) and more frequent involuntary mental imagery (β = 0.22, p < .01). There was no evidence of an interaction between interpretation bias and mental imagery in predicting HPS score, β = .04, p = .62.

CONCLUSIONS

Further research is warranted to determine if intrusive imagery or interpretation bias influence the development of bipolar disorder over time in those at risk.

Cognitive function following a major depressive episode: a systematic review and meta-analysis

BACKGROUND

Substantial evidence suggests that cognitive deficits might persist after remission of a major depressive episode. However, results are inconsistent relative to the importance, pattern, severity, and moderating factors of this impairment. We aimed to determine how cognitive function following a major depressive episode compares with normal function, to specify the pattern and severity of persistent cognitive dysfunctions, and to examine the potential moderator effect of ten prespecified clinical and demographic variables.

METHODS

We did a systematic review and meta-analysis of the published research. We searched systematically MEDLINE, Embase, PsycARTICLES, PsycINFO, the Cochrane Library, and relevant reviews identified by our database search, for research published from Jan 1, 1972, up to Jan 31, 2018, for studies of patients with past depression. We included all independent studies of patients who were assessed while in remission from a major depressive episode with at least one cognitive test, with inclusion of a healthy control group assessed with either the same test(s) as the major depressive episode group or with a standardised test with published age-stratified normative data. The main outcome was the difference in cognitive performance between major depressive episode remitters and healthy controls. Effect sizes were calculated using random-effects models for cognitive outcomes classified into 18 standard domains. Moderators of between-study variability were assessed using mixed-effects subgroup analyses and meta-regressions.

FINDINGS

Of 10 126 citations identified by our search, 75 cognitive variables from 252 eligible studies (11 882 major depressive episode remitters and 8533 healthy controls) were included in our meta-analysis. Significant deficits following major depressive episode remission were observed in 55 (73%) of the 75 cognitive variables. These deficits (in the domains of processing speed, visual selective attention, working memory, verbal learning, and executive functioning), were generally small (30 [40%] of the 75 variables) or medium (22 [29%]) in size, although three long-term memory variables showed large deficits: g=-0·81 [95% CI -1·01 to -0·61] for logical memory immediate recall, g=-0·88 [-1·19 to -0·57] for logical memory delayed recall, and g=-0·84 [-1·18 to -0·50] for Cambridge Neuropsychological Test Automated Battery pattern recognition latency. Auditory attention, general autobiographical memory, inhibition ability unconstrained by speed, and intellectual functioning unconstrained by speed were equivalent between major depressive episode remitters and matched controls. The number of previous depressive episodes explained heterogeneity in the majority of variables (z=-2·06 [p=0·039] to z=-4·26 [p<0·0001]).

INTERPRETATION

Deficits in selective attention, working memory, and long-term memory persist in remission from a major depressive episode and worsen with repeated episodes. Depression treatments, including relapse prevention, need to target these cognitive functions to optimise prognosis.

FUNDING

None.

Explicit emotional memory biases in mood disorders: A systematic review

Research suggests that major depressive disorder (MDD) and bipolar disorder (BD) are both associated with unique emotional memory (EM) biases. To better elucidate the EM phenotypes of these disorders, we systematically reviewed the literature on non-autobiographical explicit EM biases in individuals with MDD and BD compared to healthy controls. The following databases were searched: Cochrane, Embase, HAPI, LILACs, Medline, PsycInfo and Web of Science. Grey literature and hand searches were also performed. Fourteen studies met full eligibility criteria. Eleven studies included data from an MDD sample (10 during acute depression, 1 during euthymia) and 3 studies included data from a BD sample (2 during acute mood episodes, 1 during euthymia). Only 3 of the studies in acute depression revealed a negative explicit EM bias. One study in MDD during euthymia revealed an EM deficit for negative stimuli. One of the two studies in BD (type I; BD-I) during an acute mood episode revealed a positive explicit EM bias, while the other showed no bias. One study in BD during euthymia showed an EM deficit for negative stimuli. Overall, this review concludes that current empirical evidence does not readily support the existence of an explicit EM bias in MDD during acute depression. The identification and implications of potential moderating factors on explicit EM performance in MDD and BD during both illness stages are discussed.

Future Directions for Understanding Adolescent Bipolar Spectrum Disorders: A Reward Hypersensitivity Perspective

The idea that bipolar spectrum disorders (BSDs) are characterized by enhanced sensitivity to rewarding stimuli is at the core of the reward hypersensitivity model, one of the most prominent and well-supported theories of BSDs. In this article, we present the reward hypersensitivity model of BSDs, review evidence supporting it, discuss its relevance to explaining why BSDs typically begin and consolidate during the period of adolescence, and consider three major unresolved issues for this model that provide important directions for future research. Finally, we present integrations of the reward hypersensitivity model with circadian rhythm and immune system models that should provide greater understanding of the mechanisms involved in BSDs, and then suggest additional directions for future research deriving from these integrated models.