Bipolar disorder diagnosis: challenges and future directions

Mitochondria at the crossroad of dysregulated inflammatory and metabolic processes in bipolar disorders

In last few decades, considerable evidence has emphasized the significant involvement of mitochondria, often referred to as the "powerhouse of the cell," in the pathophysiology of bipolar disorder (BD). Given crucial mitochondrial functions in cellular metabolism and inflammation, both of which are compromised in BD, this perspective review examines the central role of mitochondria in inflammation and metabolism within the context of this disorder. We first describe the significance of mitochondria in metabolism before presenting the dysregulated inflammatory and metabolic processes. Then, we present a synthetic and hypothetical model of the importance of mitochondria in those dysfunctional pathways. The article also reviews different techniques for assessing mitochondrial function and discuss diagnostic and therapeutic implications. This review aims to improve the understanding of the inflammatory and metabolic comorbidities associated with bipolar disorders along with mitochondrial alterations within this context.

Functional network characteristics in anxiety- and mania-based subgroups of bipolar I disorder

BACKGROUND

Bipolar disorder I (BD-I) is a heterogeneous disorder with a high prevalence of comorbid anxiety. The aim of this study was to investigate whether anxiety and mania symptoms define distinct subgroups within BD-I and to explore potential differences in functional network characteristics between these subgroups.

METHODS

Subgroups were identified using scores from clinical anxiety and mania scales. After dimension reduction of these scores, data-driven clustering analysis with cross-validation was employed to reveal the existence of subgroups. Resting-state functional magnetic resonance imaging (rs-fMRI) scans were pre-processed using fMRIPrep. After parcellation and network construction, global and regional graph theoretical measures were calculated per subgroup.

RESULTS

Clustering results revealed that, based on anxiety symptomatology, subjects fell into two distinct subgroups, whereas mania symptoms divided subjects into four unique subgroups. These subgroups varied notably on several symptom scales. Network assortativity was significantly associated with anxiety subgroups. Post-hoc pairwise comparisons did not reveal significant global functional network differences between the anxiety subgroups or between mania subgroups. Regional network differences between clinical subgroups were especially apparent for strength and degree in the temporal and frontal lobes.

LIMITATIONS

Small sample size of some subgroups is a limitation of this study as is the categorical rather than continuous representation of anxiety and mania symptoms.

CONCLUSIONS

BD-I populations may be stratified into robust subgroups based on anxiety and mania symptoms, showing differences in functional network connectivity. Our findings highlight new avenues of research for investigating heterogeneity in psychiatric populations.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Comparing suicide completion rates in bipolar I versus bipolar II disorder: A systematic review and meta-analysis

BACKGROUND

Bipolar disorder (BD) has a high disease burden and the highest mortality risk in BD comes from suicide. Bipolar disorder type II (BD-II) has been described as a milder form of bipolar disorder; however, extant literature is inconsistent with this description and instead describe illness burden and notably suicidality comparable to persons with bipolar I disorder (BD-I). Towards quantifying the hazard of BD-II, herein we aim via systematic review and meta-analysis to evaluate the rates of completed suicide in BD-I and BD-II.

METHOD

We conducted a literature search on PubMed, OVID (Embase, Medline) and PsychINFO databases from inception to June 30th, 2023, according to PRISMA guidelines. Articles were selected based on the predetermined eligibility criteria. A meta-analysis was performed, comparing the risk of completed suicide between individuals diagnosed with BD-I to BD-II.

RESULTS

Four out of eight studies reported higher suicide completion rates in persons living with BD-II when compared to persons living with BD-I; however, two of the studies reported non-significance. Two studies reported significantly higher suicide completion rates for BD-I than BD-II. The pooled odds ratio of BD-II suicide rates to BD-I was 1.00 [95 % CI = 0.75, 1.34].

LIMITATIONS

The overarching limitation is the small number of studies and heterogeneity of studies that report on suicide completion in BD-I and BD-II.

CONCLUSION

Our study underscores the severity of BD-II, with a risk for suicide not dissimilar from BD-I. The greater propensity to depression, comorbidity and rapid-cycling course reported in BD-II are contributing factors to the significant mortality hazard in BD-II.

Co-occurring psychiatric disorders in young people with eating disorders: An multi-state and real-time analysis of real-world administrative data

OBJECTIVE

We aimed to use real-world data to characterize the burden of psychiatric comorbidities in young people with eating disorders (EDs) relative to peers without EDs.

METHOD

This retrospective cohort study used a large federated multi-national network of real-time electronic health records. Our cohort consisted of 124,575 people (14,524 people receiving their index, first-ever, ED diagnosis, compared to 110,051 peers without EDs initiating antidepressants). After 1:1 propensity score matching of the two cohorts by pre-existing demographic and clinical characteristics, we used multivariable logistic regression to compute the adjusted odds ratio (aOR) of psychiatric diagnoses arising in the year following the index event (either first ED diagnosis or first antidepressant script).

RESULTS

Over 50% of people with EDs had prior psychiatric diagnoses in the year preceding the index EDs diagnosis, with mood disorders, generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), specific phobia (SP), attention-deficit hyperactivity disorder (ADHD), and autism spectrum disorder (ASD) being the most common. Adjusted analyses showed higher odds for mood disorders (aOR = 1.20 [95% CI = 1.14-1.26]), GAD (aOR = 1.28 [1.21-1.35]), PTSD (aOR = 1.29 [1.18-1.40]), and SP (aOR = 1.45 [1.31-1.60]) in the EDs cohort compared to antidepressant-initiating peers without EDs, although rates of ADHD and ASD were similar in both cohorts.

CONCLUSION

This large-scale real-time analysis of administrative data illustrates a high burden of co-occurring psychiatric disorders in people with EDs.

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.

Female genital prolapse and risk of psychiatric disorders: A two-sample Mendelian randomization analysis

BACKGROUND

There is a growing body of evidence suggests a strong link between female genital prolapse (FGP) and mental health. However, the causal relationship between FGP and psychological disorders remains unclear.

OBJECTIVES

Bidirectional Mendelian Randomization (MR) analysis has been applied to investigate the potential impact of FGP on the risk of seven common psychiatric disorders.

METHODS

The two-sample MR analysis was conducted using genetic instruments such as Inverse variance weighted (IVW), MR Egger, weighted median, simple mode, and weighted mode from the genome-wide association study (GWAS) summary data in European populations. In addition, the Cochrane's Q test, MR-Egger intercept test, MR pleiotropy residual sum and outliers (MR-PRESSO) test and leave-one-out analysis were employed to assess the sensitivity and heterogeneity.

RESULTS

The MR results revealed that FGP exhibited a potential marginal protective effect on bipolar disorder (BD) (odds ratio(OR) = 0.92, 95%confidence interval (95%CI: 0.85-0.99, P = 0.03) as well as schizophrenia(OR = 0.91, 95%CI:0.85-0.98, P = 0.01). Nevertheless, there was no causal correlation between genetically predicted FGP and obsessive compulsive disorder (OCD) (OR = 0.98, 95%CI:0.80-1.20, P = 0.84),depression (broad) (OR = 1.00, 95%CI:0.99-1.01, P = 0.76), major depression(OR = 0.98, 95%CI:0.94-1.03, P = 0.43), anxiety disorders (OR = 1.00, 95%CI:0.94-1.07,P = 0.97) and post-traumatic stress disorder(PTSD) (OR = 1.18, 95%CI:0.88-1.57,P = 0.27),respectively. In addition, BD was found to have a potential significant influence on FGP in the inverse MR analysis (OR = 0.83, 95%CI:0.72-0.97, P = 0.02). No significant heterogeneity or horizontal pleiotropy detected, and the results were deemed stable based on sensitivity analysis and leave-one-out test .

LIMITATIONS

There are shortcomings such as data limitations, population bias, potential pleiotropy, and stratified analysis.

CONCLUSIONS

While there is potential causal relationship between FGP and BD or schizophrenia, it does not exhibit any correction with OCD, depression (broad), major depression, anxiety disorders and PTSD among European populations.

Sociodemographic and clinical characteristics of suspected difficult-to-treat depression

Introduction

Difficult-to-treat depression (DTD) represents a broad spectrum of patients with persistent depression where standard treatment modalities are insufficient, yet specific characteristics of this group remain insufficiently understood. This investigation aims to delineate the sociodemographic and clinical profiles of suspected DTD patients in real-world clinical settings.

Method

We conducted a retrospective analysis of data from patients comprehensively evaluated for suspected DTD at Kyorin University Hospital, Tokyo, Japan, between October 2014 and September 2018. The study participants consisted of individuals with persistent depression unresponsive to conventional antidepressant treatments during the current episode. Diagnoses adhered to the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision. Additional evaluations included the Montgomery-Åsberg Depression Rating Scale (MADRS) and other pertinent measures. The analysis focused on comparing demographic and clinical characteristics across diagnosed groups.

Results

The analysis encompassed 122 patients, with diagnoses of major depressive disorder (MDD) in 41.8%, bipolar disorder (BD) in 28.7%, and subthreshold depression in 29.5%. Notably, high incidences of psychiatric comorbidities were present across all groups, with anxiety disorders exceeding 30% and personality disorders surpassing 50%. The only significant distinction among the three groups was observed in the MADRS scores, with the MDD group exhibiting the highest values (20.9 ± 9.7 vs. 18.6 ± 9.3 vs. 11.3 ± 7.4, p<0.01).

Conclusions

This study sheds light on the intricate nature of suspected DTD, emphasizing the coexistence of MDD, BD, and subthreshold depression within this category. Our findings underscore the necessity for thorough evaluations and tailored treatment approaches for managing suspected DTD.

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.

Helpful or harmful? The effect of a diagnostic label and its later retraction on person impressions

Diagnostic labels for mental health conditions can inadvertently reinforce harmful stereotypes and exacerbate stigma. If a diagnosis is incorrect and a label is wrongly applied, this may negatively impact person impressions even if the inaccurate label is later corrected. This registered report examined this issue. Participants (N = 560) read a vignette about a hospital patient who was either diagnosed with schizophrenia, diagnosed with major depressive disorder, or not diagnosed with a mental health condition. The diagnostic labels were later retracted strongly, retracted weakly, or not retracted. Participants completed several stigma measures (desire for social distance, perceived dangerousness, and unpredictability), plus several inferential-reasoning measures that tested their reliance on the diagnostic label. As predicted, each mental health diagnosis elicited stigma, and influenced inferential reasoning. This effect was stronger for the schizophrenia diagnosis compared to the major depressive disorder diagnosis. Importantly, the diagnostic label continued to influence person judgments after a clear retraction (strong or weak), highlighting the limitations of corrections in reducing reliance on person-related misinformation and mental health stigma.

Depressive and mania mood state detection through voice as a biomarker using machine learning

Introduction

Depressive and manic states contribute significantly to the global social burden, but objective detection tools are still lacking. This study investigates the feasibility of utilizing voice as a biomarker to detect these mood states. Methods:From real-world emotional journal voice recordings, 22 features were retrieved in this study, 21 of which showed significant differences among mood states. Additionally, we applied leave-one-subject-out strategy to train and validate four classification models: Chinese-speech-pretrain-GRU, Gate Recurrent Unit (GRU), Bi-directional Long Short-Term Memory (BiLSTM), and Linear Discriminant Analysis (LDA).

Results

Our results indicated that the Chinese-speech-pretrain-GRU model performed the best, achieving sensitivities of 77.5% and 54.8% and specificities of 86.1% and 90.3% for detecting depressive and manic states, respectively, with an overall accuracy of 80.2%.

Discussion

These findings show that machine learning can reliably differentiate between depressive and manic mood states via voice analysis, allowing for a more objective and precise approach to mood disorder assessment.

The Effect of Neuropsychiatric Drugs on the Oxidation-Reduction Balance in Therapy

The effectiveness of available neuropsychiatric drugs in the era of an increasing number of patients is not sufficient, and the complexity of neuropsychiatric disease entities that are difficult to diagnose and therapeutically is increasing. Also, discoveries about the pathophysiology of neuropsychiatric diseases are promising, including those initiating a new round of innovations in the role of oxidative stress in the etiology of neuropsychiatric diseases. Oxidative stress is highly related to mental disorders, in the treatment of which the most frequently used are first- and second-generation antipsychotics, mood stabilizers, and antidepressants. Literature reports on the effect of neuropsychiatric drugs on oxidative stress are divergent. They are starting with those proving their protective effect and ending with those confirming disturbances in the oxidation-reduction balance. The presented publication reviews the state of knowledge on the role of oxidative stress in the most frequently used therapies for neuropsychiatric diseases using first- and second-generation antipsychotic drugs, i.e., haloperidol, clozapine, risperidone, olanzapine, quetiapine, or aripiprazole, mood stabilizers: lithium, carbamazepine, valproic acid, oxcarbazepine, and antidepressants: citalopram, sertraline, and venlafaxine, along with a brief pharmacological characteristic, preclinical and clinical studies effects.

Improving Quality of Life in Bipolar Disorders with an Immersive Virtual Reality Remediation Training Randomized Controlled Trial (RCT)

Background: Health-related quality of life (H-QoL) is a critical measure in bipolar disorder (BD). Recent trials using virtual reality (VR) have shown potential in improving H-QoL. However, VR's effect on the H-QoL of people with BD needs to be further explored. Methods: This study involved a secondary analysis of a feasibility randomized controlled trial, focusing on "quality of life". Participants (aged 18-75) diagnosed with bipolar disorder were randomized into two groups. The experimental group used the CEREBRUM VR app, while the control group received the usual care. Quality of life was assessed using the Short-Form Health Survey (SF-12). Results: A total of 39 individuals in the experimental group and 25 in the control group represent the final samples. The results showed a greater improvement in the SF-12 total score in the experimental group (8.7%) compared to the control group (F = 66.851 p < 0.0001), specifically in the dimension of physical activity limitation, emotional impact, concentration, pain, calmness, energy levels, discouragement, and social activities. Conclusions: This study demonstrated an improvement in QoL for individuals with BD following a VR intervention. As a feasibility study, this secondary outcome needs to be confirmed by further phase III studies. If confirmed, VR could offer valuable rehabilitation tools and insights into the pathogenesis and treatment of BD.

Abnormal functional connectivity of the intrinsic networks in adolescent bipolar I versus bipolar II disorder

BACKGROUND

The symptoms of pediatric bipolar disorder (PBD)-I and PBD-II differ, but accurate identification at an early stage is difficult and may prevent effective treatment of this disorder. Therefore, it is urgent to elucidate a biological marker based on objective imaging indicators to help distinguish the two. Therefore, this research aims to compare the functional connectivity between PBD-I patient and PBD-II patient in different brain networks.

METHODS

Our study enrolled 31 PBD-I and 23 PBD-II patients from 12 to 17 years of age. They were analyzed by resting state-functional connectivity through Independent component analysis (ICA).

RESULTS

We found differences between PBD-I and PBD-II in functional connectivity of the default network, frontoparietal network, salience network and limbic system. In addition, the clinical features, cognitive functions are associated with the functional connectivity of the intrinsic networks in PBD-I and PBD-II separately.

CONCLUSION

This research is the first to find differences in functional connectivity between PBD-I and PBD-II, suggesting that abnormality of the functional connectivity within large networks may be biomarkers that help differentiate PBD-I from PBD-II in the future.

Resting-state fMRI is associated with trauma experiences, mood and psychosis in Afro-descendants with bipolar disorder and schizophrenia

BACKGROUND

Bipolar disorder (BD) and schizophrenia (SCZ) may exhibit functional abnormalities in several brain areas, including the medial temporal and prefrontal cortex and hippocampus; however, a less explored topic is how brain connectivity is linked to premorbid trauma experiences and clinical features in non-Caucasian samples of SCZ and BD.

METHODS

Sixty-two individuals with SCZ (n = 20), BD (n = 21), and healthy controls (HC, n = 21) from indigenous and African ethnicity were submitted to clinical screening (Di-PAD), traumata experiences (ETISR-SF), cognitive and functional MRI assessment. The item psychosis/hallucinations in SCZ patients showed a negative correlation with the global efficiency (GE) in the right dorsal attention network. The items mania, irritable mood, and racing thoughts in the Di-PAD scale had a significant negative correlation with the GE in the parietal right default mode network.

CONCLUSIONS

Differences in the activation of specific networks were associated with earlier disease onset, history of physical abuse, and more severe psychotic and mood symptoms in SCZ and BD subjects of indigenous and black ethnicity. Findings provide further evidence on SZ and BD's brain connectivity disturbances, and their clinical significance, in non-Caucasian samples.

A Magnetic Resonance Spectroscopy Study on Polarity Subphenotypes in Bipolar Disorder

Although magnetic resonance spectroscopy (MRS) has provided in vivo measurements of brain chemical profiles in bipolar disorder (BD), there are no data on clinically and therapeutically important onset polarity (OP) and predominant polarity (PP). We conducted a proton MRS study in BD polarity subphenotypes, focusing on emotion regulation brain regions. Forty-one euthymic BD patients stratified according to OP and PP and sixteen healthy controls (HC) were compared. 1H-MRS spectra of the anterior and posterior cingulate cortex (ACC, PCC), left and right hippocampus (LHIPPO, RHIPPO) were acquired at 3.0T to determine metabolite concentrations. We found significant main effects of OP in ACC mI, mI/tNAA, mI/tCr, mI/tCho, PCC tCho, and RHIPPO tNAA/tCho and tCho/tCr. Although PP had no significant main effects, several medium and large effect sizes emerged. Compared to HC, manic subphenotypes (i.e., manic-OP, manic-PP) showed greater differences in RHIPPO and PCC, whereas depressive suphenotypes (i.e., depressive-OP, depressive-PP) in ACC. Effect sizes were consistent between OP and PP as high intraclass correlation coefficients (ICC) were confirmed. Our findings support the utility of MRS in the study of the neurobiological underpinnings of OP and PP, highlighting that the regional specificity of metabolite changes within the emotion regulation network consistently marks both polarity subphenotypes.

Treatment adherence rates across different psychiatric disorders and settings: findings from a large patient cohort

Approximately 50% of patients with psychiatric disorders do not fully adhere to the prescribed psychopharmacological therapy, significantly impacting the progression of the disorder and the patient's quality of life. The present study aimed to assess potential differences in terms of rates and clinical features of treatment adherence in a large cohort of psychiatric patients with different diagnoses attending various psychiatric services. The study included 307 psychiatric patients diagnosed with a primary major depressive disorder, bipolar disorder, anxiety disorder, schizophrenic spectrum disorder, or personality disorder. Patient's adherence to treatment was evaluated using the Clinician Rating Scale, with a cutoff of at least five defining adherence subgroups. One-third of the sample reported poor medication adherence. A lower rate of adherence emerged among patients with schizophrenic spectrum disorder and bipolar disorder. Subjects with poor adherence were more frequently inpatients and showed higher current substance use, a greater number of previous hospitalizations, and more severe scores at psychopathological assessment compared with patients with positive adherence. Poor adherence was associated with symptom severity and increased rates of relapses and rehospitalizations. In addition, substance use appears to be an unfavorable transdiagnostic factor for treatment adherence.

The Potential Role of the Ketogenic Diet in Serious Mental Illness: Current Evidence, Safety, and Practical Advice

The ketogenic diet (KD) is a high-fat, low-carbohydrate diet that mimics the physiological state of fasting. The potential therapeutic effects in many chronic conditions have led to the gaining popularity of the KD. The KD has been demonstrated to alleviate inflammation and oxidative stress, modulate the gut microbiota community, and improve metabolic health markers. The modification of these factors has been a potential therapeutic target in serious mental illness (SMI): bipolar disorder, major depressive disorder, and schizophrenia. The number of clinical trials assessing the effect of the KD on SMI is still limited. Preliminary research, predominantly case studies, suggests potential therapeutic effects, including weight gain reduction, improved carbohydrate and lipid metabolism, decrease in disease-related symptoms, increased energy and quality of life, and, in some cases, changes in pharmacotherapy (reduction in number or dosage of medication). However, these findings necessitate further investigation through larger-scale clinical trials. Initiation of the KD should occur in a hospital setting and with strict care of a physician and dietitian due to potential side effects of the diet and the possibility of exacerbating adverse effects of pharmacotherapy. An increasing number of ongoing studies examining the KD's effect on mental disorders highlights its potential role in the adjunctive treatment of SMI.

Combining predominant polarity and affective spectrum concepts in bipolar disorder: towards a novel theoretical and clinical perspective

This is an overview of recent advances on predominant polarity conceptualization in bipolar disorder (BD). Current evidence on its operationalized definitions, possible contextualization within the affective spectrum, along with its epidemiological impact, and treatment implications, are summarized. Predominant polarity identifies three subgroups of patients with BD according to their mood recurrencies: (i) those with depressive or (ii) manic predominance as well as (iii) patients without any preponderance ('nuclear' type). A predominant polarity can be identified in approximately half of patients, with similar rates for depressive and manic predominance. Different factors may influence the predominant polarity, including affective temperaments. More generally, affective disorders should be considered as existing on a spectrum ranging from depressive to manic features, also accounting for disorders with 'ultrapredominant' polarity, i.e., unipolar depression and mania. While mixed findings emerge on its utility in clinical practice, it is likely that the construct of predominant polarity, in place of conventional differentiation between BD-I and BD-II, may be useful to clarify the natural history of the disorder and select the most appropriate interventions. The conceptualization of predominant polarity seems to reconcile previous theoretical views of both BD and affective spectrum into a novel perspective. It may provide useful information to clinicians for the early identification of possible trajectories of BD and thus guide them when selecting interventions for maintenance treatment. However, further research is needed to clarify the specific role of predominant polarity as a key determinant of BD course, outcome, and treatment response.

A Systematic Review of Misdiagnosis of Pediatric Bipolar Disorder: Assessments and Recommendations

Bipolar disorders (BP) are a class of psychiatric disorders with a complex symptom presentation. This systematic review aims to summarize literature pertaining to the misdiagnosis of pediatric BP using the DSM-IV and DSM-5 criteria, while emphasizing the negative impact that untreated BP has on life outcomes. This paper also attempts to outline and summarize available recommendations which may aid in improving diagnostic accuracy of pediatric BP. Scholars Portal Journals, PsychINFO, and MEDLINE databases were used to search articles until March 21, 2023. Inclusion criteria limited this review to articles published between 1995 and 2022 using a pediatric (age < 18) sample. Exclusion criteria omitted articles containing samples with self-reported diagnoses. A total of 15 articles are included in this review; study results were synthesized using a narrative summary. Youth with BP are most frequently misdiagnosed with attention-deficit hyperactive disorder (ADHD), schizophrenia, and major depressive disorder (MDD). Misdiagnosis can lead to inappropriate intervention plans and a delay in proper treatment, negatively impacting a child's quality of life by contributing to social, occupational, and economic adversity. Finally, this review addresses the need for future quantitative research on the implications of false negative diagnoses of pediatric BP.

The Pathophysiological Underpinnings of Gamma-Band Alterations in Psychiatric Disorders

Investigating the biophysiological substrates of psychiatric illnesses is of great interest to our understanding of disorders' etiology, the identification of reliable biomarkers, and potential new therapeutic avenues. Schizophrenia represents a consolidated model of γ alterations arising from the aberrant activity of parvalbumin-positive GABAergic interneurons, whose dysfunction is associated with perineuronal net impairment and neuroinflammation. This model of pathogenesis is supported by molecular, cellular, and functional evidence. Proof for alterations of γ oscillations and their underlying mechanisms has also been reported in bipolar disorder and represents an emerging topic for major depressive disorder. Although evidence from animal models needs to be further elucidated in humans, the pathophysiology of γ-band alteration represents a common denominator for different neuropsychiatric disorders. The purpose of this narrative review is to outline a framework of converging results in psychiatric conditions characterized by γ abnormality, from neurochemical dysfunction to alterations in brain rhythms.

Are Advanced Oxidation Protein Products (AOPPs) Levels Altered in Neuropsychiatric Disorders? An Integrative Review

Neuropsychiatric disorders such as major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ) are considered a public health problem since it interferes in personal relationships and at work. The pathophysiological mechanisms of these mental disorders are still not completely understood. The variety and heterogeneity of symptoms, as well as the absence of biomarkers, make the diagnosis, prognosis, and treatment of these disorders difficult. However, oxidative stress appears to play a role in the pathophysiology of these diseases. In this context, advanced oxidation protein products (AOPPs) are considered a biomarker of protein oxidative damage and have been associated with neuroinflammatory diseases. In patients with neuropsychiatric disorders, increased levels of AOPPs were associated with the severity of symptoms and decreased quality of life. Thus, the objective of this integrative review is to investigate and discuss the relationship between AOPPs levels and MDD, BD, and SZ. Different databases were consulted and approximately 112 scientific articles were found relating AOPPs and psychiatric disorders. In the majority of studies, the blood levels of AOPPs were increased in MDD, BD, and SZ and associated with the severity of the disorders. Although the association of this marker with the risk of developing one of these mental disorders is more uncertain, some studies have suggested this relationship. Of the twenty-four studies highlighted, only four did not find significant differences in AOPPs levels in patients with the disorders mentioned. In summary, it may be suggested that the assessment of AOPPs levels can be a useful tool in the evaluation of neuropsychiatric disorders, at least for prognostic evaluation. However, the role of this biomarker in the pathophysiology of mental disorders is still unclear, as well as whether reducing its levels represents a potential therapeutic strategy.

Lower risk of hospitalisation for depression following hospitalisation for COVID-19 versus for another reason

INTRODUCTION

Although hospitalisation for COVID-19 is associated with a higher post-discharge risk of mood disorders, including major depressive disorder (MDD) and bipolar disorder (BD), this risk has not been compared to that following hospitalisation for a reason other than COVID-19.

METHODS

Using data from France's National Health Data System (SNDS) database, we compared patients hospitalised for mood disorders in the 12 months following COVID-19/another reason hospitalisation.

RESULTS

96,313 adult individuals were hospitalised for COVID-19, and 2,979,775 were hospitalised for another reason. In the 12 months post-discharge, 110,976 (3.83 %) patients were hospitalised for mood disorders. In unadjusted analyses, patients initially hospitalised for COVID-19 (versus another reason) were more likely to be subsequently hospitalised for a mood disorder (4.27 % versus 3.82 % versus, respectively, p < 0.0001). These patients were also more likely to have a history of mood disorders, especially depressive disorders (6.45 % versus 5.77 %, respectively, p < 0.0001). Women, older age, lower social deprivation, a history of mood disorders, longer initial hospitalisation (COVID-19 or other), and a higher level of clinical care during initial hospitalisation were all significantly associated with the risk of subsequent hospitalisation for MDD and BD. In contrast, after adjusting for all these factors, persons initially hospitalised for COVID-19 were less likely to be subsequently hospitalised for MDD (OR = 0.902 [0.870-0.935]; p < 0.0001). No difference between both groups was observed for BD.

LIMITATIONS

Other reasons were not separately studied.

CONCLUSIONS

After adjusting for confounding factors, initial hospitalisation for COVID-19 versus for another reason was associated with a lower risk of hospitalisation for a mood disorder.

White and gray matter alterations in bipolar I and bipolar II disorder subtypes compared with healthy controls - exploring associations with disease course and polygenic risk

Patients with bipolar disorder (BD) show alterations in both gray matter volume (GMV) and white matter (WM) integrity compared with healthy controls (HC). However, it remains unclear whether the phenotypically distinct BD subtypes (BD-I and BD-II) also exhibit brain structural differences. This study investigated GMV and WM differences between HC, BD-I, and BD-II, along with clinical and genetic associations. N = 73 BD-I, n = 63 BD-II patients and n = 136 matched HC were included. Using voxel-based morphometry and tract-based spatial statistics, main effects of group in GMV and fractional anisotropy (FA) were analyzed. Associations between clinical and genetic features and GMV or FA were calculated using regression models. For FA but not GMV, we found significant differences between groups. BD-I patients showed lower FA compared with BD-II patients (ptfce-FWE = 0.006), primarily in the anterior corpus callosum. Compared with HC, BD-I patients exhibited lower FA in widespread clusters (ptfce-FWE < 0.001), including almost all major projection, association, and commissural fiber tracts. BD-II patients also demonstrated lower FA compared with HC, although less pronounced (ptfce-FWE = 0.049). The results remained unchanged after controlling for clinical and genetic features, for which no independent associations with FA or GMV emerged. Our findings suggest that, at a neurobiological level, BD subtypes may reflect distinct degrees of disease expression, with increasing WM microstructure disruption from BD-II to BD-I. This differential magnitude of microstructural alterations was not clearly linked to clinical and genetic variables. These findings should be considered when discussing the classification of BD subtypes within the spectrum of affective disorders.

Differences in prefrontal cortex activation in Chinese college students with different severities of depressive symptoms: A large sample of functional near-infrared spectroscopy (fNIRS) findings

BACKGROUND

Previous studies proposed that functional near-infrared spectroscopy (fNIRS) can be used to distinguish between not only different severities of depressive symptoms but also different subgroups of depression, such as anxious and non-anxious depression, bipolar and unipolar depression, and melancholia and non-melancholia depression. However, the differences in brain haemodynamic activation between depression subgroups (such as confirmed depression [CD] and suspected depression [SD]) with different symptom severities and the possible correlation between symptom severity and haemodynamic activation in specific brain regions using fNIRS have yet to be clarified.

METHODS

The severity of depression symptoms was classified using the Hospital Anxiety and Depression scale (HADS) and the Mini International Neuropsychiatric Interview by psychiatrists. We recruited 654 patients with depression who had varying severities of depressive symptoms, including 276 with SD and 378 with CD, and 317 with HCs from among Chinese college students. The 53-channel fNIRS was used to detect the cerebral hemodynamic difference of the three groups during the VFT (verbal fluency task).

RESULTS

Compared with the HC, region-specific fNIRS leads indicate CD patients had significant lower haemodynamic activation in three particular prefrontal regions: 1) right dorsolateral prefrontal cortex (DLPFC), 2) bilateral frontopolar cortex (FPC), and 3) right Broca's area (BA). SD vs. HC comparisons revealed only significant lower haemodynamic activation in the right FPC area. Compared to SD patients, CD patients exhibited decreased hemodynamic activation changes in the right DLPFC and the right BA. Correlation analysis established a significant negative correlation between the hemodynamic changes in the bilateral FPC and the severity of depressive symptoms.

CONCLUSIONS

The right DLPFC and right BA are expected to be physiological mechanisms to distinguish depression subgroups (CD, SD) with different symptom severities. The haemodynamic changes in the bilateral FPC was nagatively associated with the symptom severity of depression.

Making sense of narcolepsy: A qualitative exploration of how persons with narcolepsy perceive symptoms and their illness experience

INTRODUCTION

Understanding how persons with narcolepsy conceptualize symptoms, daily impact and illness experience is key to facilitating dialogue between patients and healthcare professionals. These concepts are usually explored from the perspective of healthcare professionals/researchers and rarely from the perspective of those with narcolepsy.

METHODS

127 self-reported persons with narcolepsy were recruited from an Australian patient support group. A short demographic survey was completed. All agreed to participate in a subsequent 1:1 semi-structured interview. Saturation was reached after 24 interviews (mean age = 33 years (SD 11) with 44% reporting cataplexy). A multidisciplinary team of researchers/clinicians analyzed interview transcripts using thematic analysis.

RESULTS

Participants perceived physical fatigue, sleepiness, and two separate experiences of 'falling asleep/sleep attacks' as distinct symptoms rather than a multidimensional construct (i.e. excessive daytime sleepiness). We also identified two experiences of cataplexy, one triggered by acute emotion and another by a stressor. Participants determined their narcolepsy to be 'well-managed' by the level of functional impairment rather than the frequency of any symptom. Almost all participants described experiencing anticipated stigma and internalized or 'self-' stigma, likely stemming from societal devaluation of sleep and the conflation of sleepiness with laziness.

CONCLUSION

Descriptions of common symptoms often differed between participants and the existing literature. These differences likely impact patient-physician communication, with both parties utilizing the same terminology to communicate different concepts. The characterization of stigma in narcolepsy presents opportunities for future research exploring the impact and possible development of interventions to reduce the substantial psychological comorbidity in persons with narcolepsy.

Based on white matter microstructure to early identify bipolar disorder from patients with depressive episode

OBJECTIVE

Because of similar clinical manifestations, bipolar disorder (BD) patients are often misdiagnosed as major depressive disorder (MDD). This study aimed to compare the difference between depressed patients later converting to BD and unipolar depression (UD) according to diffusion tensor imaging (DTI).

METHOD

Patients with MDD (562 participants) in depressive episode states and healthy controls (HCs) (145 participants) were recruited over 10 years. Demographic and magnetic resonance imaging (MRI) data were collected at the time of recruitment. All patients with MDD were followed up for 5 years and classified into the transfer to BD (tBD) group (83 participants) and UD group (160 participants) according to the follow-up results. DTI and functional magnetic resonance imaging at baseline were compared.

RESULTS

Common abnormalities were found in both tBD and UD groups, including left superior cerebellar peduncle (SCP.L), right anterior limb of the internal capsule (ALIC.R), right superior fronto-occipital fasciculus (SFOF.R), and right inferior fronto-occipital fasciculus (IFOF.R). The tBD showed more extensive abnormalities than the UD in the body of corpus callosum, fornix, left superior corona radiata, left posterior corona radiata, left superior longitudinal fasciculus, and left superior fronto-occipital fasciculus.

CONCLUSION

The study demonstrated the common and distinct abnormalities of tBD and UD when compared to HC. The tBD group showed more extensive disruptions of white matter integrity, which could be a potential biomarker for the early identification of BD.

Distinguishing schizophrenia and bipolar disorder through a Multiclass Classification model based on multimodal neuroimaging data

This study aimed to identify neural biomarkers for schizophrenia (SZ) and bipolar disorder (BP) by analyzing multimodal neuroimaging. Utilizing data from structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and resting-state functional magnetic resonance imaging (rs-fMRI), multiclass classification models were created for SZ, BP, and healthy controls (HC). A total of 113 participants (BP: 31, SZ: 39, and HC: 43) were recruited under strict enrollment control, from which 272, 200, and 1875 features were extracted from sMRI, DTI, and rs-fMRI data, respectively. A support vector machine (SVM) with recursive feature elimination (RFE) was employed to build the models using a one-against-one approach and leave-one-out cross-validation, achieving a classification accuracy of 70.8%. The most discriminative features were primarily from rs-fMRI, along with significant findings in sMRI and DTI. Key biomarkers identified included the increased thickness of the left cuneus cortex and decreased regional functional connectivity strength (rFCS) in the left supramarginal gyrus as shared indicators for BP and SZ. Additionally, decreased fractional anisotropy in the left superior fronto-occipital fasciculus was suggested as specific to BP, while decreased rFCS in the left inferior parietal area might serve as a specific biomarker for SZ. These findings underscore the potential of multimodal neuroimaging in distinguishing between BP and SZ and contribute to the understanding of their neural underpinnings.

Arginine metabolomics in mood disorders

Alterations of nitric oxide (NO) homeostasis have been described in mood disorders. However, the analytical challenges associated with the direct measurement of NO have prompted the search for alternative biomarkers of NO synthesis. We investigated the published evidence of the association between these alternative biomarkers and mood disorders (depressive disorder or bipolar disorder). Electronic databases were searched from inception to the June 30, 2023. In 20 studies, there was a trend towards significantly higher asymmetric dimethylarginine (ADMA) in mood disorders vs. controls (p = 0.072), and non-significant differences in arginine (p = 0.29), citrulline (p = 0.35), symmetric dimethylarginine (SDMA; p = 0.23), and ornithine (p = 0.42). In subgroup analyses, the SMD for ADMA was significant in bipolar disorder (p < 0.001) and European studies (p = 0.02), the SMDs for SDMA (p = 0.001) and citrulline (p = 0.038) in European studies, and the SMD for ornithine in bipolar disorder (p = 0.007), Asian (p = 0.001) and American studies (p = 0.005), and patients treated with antidepressants (p = 0.029). The abnormal concentrations of ADMA, SDMA, citrulline, and ornithine in subgroups of mood disorders, particularly bipolar disorder, warrant further research to unravel their pathophysiological role and identify novel treatments in this group (The protocol was registered in PROSPERO: CRD42023445962).

Brain functional changes across mood states in bipolar disorder: from a large-scale network perspective

BACKGROUND

Exploring the neural basis related to different mood states is a critical issue for understanding the pathophysiology underlying mood switching in bipolar disorder (BD), but research has been scarce and inconsistent.

METHODS

Resting-state functional magnetic resonance imaging data were acquired from 162 patients with BD: 33 (hypo)manic, 64 euthymic, and 65 depressive, and 80 healthy controls (HCs). The differences of large-scale brain network functional connectivity (FC) between the four groups were compared and correlated with clinical characteristics. To validate the generalizability of our findings, we recruited a small longitudinal independent sample of BD patients (n = 11). In addition, we examined topological nodal properties across four groups as exploratory analysis.

RESULTS

A specific strengthened pattern of network FC, predominantly involving the default mode network (DMN), was observed in (hypo)manic patients when compared with HCs and bipolar patients in other mood states. Longitudinal observation revealed an increase in several network FCs in patients during (hypo)manic episode. Both samples evidenced an increase in the FC between the DMN and ventral attention network, and between the DMN and limbic network (LN) related to (hypo)mania. The altered network connections were correlated with mania severity and positive affect. Bipolar depressive patients exhibited decreased FC within the LN compared with HCs. The exploratory analysis also revealed an increase in degree in (hypo)manic patients.

CONCLUSIONS

Our findings identify a distributed pattern of large-scale network disturbances in the unique context of (hypo)mania and thus provide new evidence for our understanding of the neural mechanism of BD.

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.

The Deep Learning Method Differentiates Patients with Bipolar Disorder from Controls with High Accuracy Using EEG Data

Background: Bipolar disorder (BD) is a mental disorder characterized by depressive and manic or hypomanic episodes. The complexity in the diagnosis of Bipolar disorder (BD) due to its overlapping symptoms with other mood disorders prompted researchers and clinicians to seek new and advanced techniques for the precise detection of Bipolar disorder (BD). One of these methods is the use of advanced machine learning algorithms such as deep learning (DL). However, no study of BD has previously adopted DL techniques using EEG signals. Method: EEG signals of 169 BD patients and 45 controls were cleaned from the artifacts and processed using two different DL methods: a one-dimensional convolutional neural network (1D-CNN) combined with the long-short term memory (LSTM) and a two-dimensional convolutional neural network (2D-CNN). Additionally, Class Activation Maps (CAMs) acquired from the bipolar and control groups were used to obtain distinctive regions to specify a particular class in an image. Results: Group identifications were confirmed with 95.91% overall accuracy through the 2D-CNN method, demonstrating very high sensitivity and lower specificity. Also, the overall accuracy obtained from the 1D-CNN + LSTM method was 93%. We also found that F4, C3, F7, and F8 electrode activities produce predominant features to detect the bipolar group. Conclusion: To our knowledge, this study used EEG-based DL analysis for the first time in BD. Our results suggest that the raw EEG-based DL algorithm can successfully differentiate individuals with BD from controls. Class Activation Map (CAM) analysis suggests that prefrontal changes are predominant in EEG data of patients with BD.

French Society for Biological Psychiatry and Neuropsychopharmacology (AFPBN) guidelines for the management of patients with partially responsive depression and treatment-resistant depression: Update 2024

INTRODUCTION

The purpose of this update is to add newly approved nomenclatures and treatments as well as treatments yet to be approved in major depressive disorder, thus expanding the discussions on the integration of resistance factors into the clinical approach.

METHODS

Unlike the first consensus guidelines based on the RAND/UCLA Appropriateness Method, the French Association for Biological Psychiatry and Neuropsychopharmacology (AFPBN) developed an update of these guidelines for the management of partially responsive depression (PRD) and treatment-resistant depression (TRD). The expert guidelines combine scientific evidence and expert clinicians' opinions to produce recommendations for PRD and TRD.

RESULTS

The recommendations addressed three areas judged as essential for updating the previous 2019 AFPBN guidelines for the management of patients with TRD: (1) the identification of risk factors associated with TRD, (2) the therapeutic management of patients with PRD and TRD, and (3) the indications, the modalities of use and the monitoring of recent glutamate receptor modulating agents (esketamine and ketamine).

CONCLUSION

These consensus-based guidelines make it possible to build bridges between the available empirical literature and clinical practice, with a highlight on the 'real world' of the clinical practice, supported by a pragmatic approach centred on the experience of specialised prescribers in TRD.

Mapping the global, regional and national burden of bipolar disorder from 1990 to 2019: trend analysis on the Global Burden of Disease Study 2019

BACKGROUND

Data on trends in the epidemiological burden of bipolar disorder are scarce.

AIMS

To provide an overview of trends in bipolar disorder burden from 1990 to 2019.

METHOD

Revisiting the Global Burden of Disease Study 2019, we analysed the number of cases, calculated the age-standardised rate (per 100 000 population) and estimated annual percentage change (EAPC) of incidence, prevalence and years lived with disability (YLDs) for bipolar disorder from 1990 to 2019. The independent effects of age, period and cohort were estimated by the age-period-cohort modelling.

RESULTS

Globally, the bipolar disorder-related prevalent cases, incident cases and number of YLDs all increased from 1990 to 2019. Regionally, the World Health Organization Region of the Americas accounted for the highest estimated YLD number and rate, with the highest age-standardised prevalence rate in 1990 and 2019 and highest EAPC of prevalence. By sociodemographic index (SDI) quintiles, all five SDI regions saw an increase in estimated incident cases. Nationally, New Zealand reported the highest age-standardised rate of incidence, prevalence and YLDs in 1990 and 2019. The most prominent age effect on incidence rate was in those aged 15-19 years. Decreased effects of period on incidence, prevalence and YLD rates was observed overall and in females, not in males. The incidence, prevalence and YLD rates showed an unfavourable trend in the younger cohorts born after 1990, with males reporting a higher cohort risk than females.

CONCLUSIONS

From 1990 to 2019, the overall trend of bipolar disorder burden presents regional and national variations and differs by age, sex, period and cohort.

Dynamic time warp analysis of individual symptom trajectories in individuals with bipolar disorder

BACKGROUND

Manic and depressive mood states in bipolar disorder (BD) may emerge from the non-linear relations between constantly changing mood symptoms exhibited as a complex dynamic system. Dynamic Time Warp (DTW) is an algorithm that may capture symptom interactions from panel data with sparse observations over time.

METHODS

The Young Mania Rating Scale and Quick Inventory of Depressive Symptomatology were repeatedly assessed in 141 individuals with BD, with on average 5.5 assessments per subject every 3-6 months. Dynamic Time Warp calculated the distance between each of the 27 × 27 pairs of standardized symptom scores. The changing profile of standardized symptom scores of BD participants was analyzed in individual subjects, yielding symptom dimensions in aggregated group-level analyses. Using an asymmetric time-window, symptom changes that preceded other symptom changes (i.e., Granger causality) yielded a directed network.

RESULTS

The mean age of the BD participants was 40.1 (SD 13.5) years old, and 60% were female participants. Idiographic symptom networks were highly variable between subjects. Yet, nomothetic analyses showed five symptom dimensions: core (hypo)mania (6 items), dysphoric mania (5 items), lethargy (7 items), somatic/suicidality (6 items), and sleep (3 items). Symptoms of the "Lethargy" dimension showed the highest out-strength, and its changes preceded those of "somatic/suicidality," while changes in "core (hypo)mania" preceded those of "dysphoric mania."

CONCLUSION

Dynamic Time Warp may help to capture meaningful BD symptom interactions from panel data with sparse observations. It may increase insight into the temporal dynamics of symptoms, as those with high out-strength (rather than high in-strength) could be promising targets for intervention.

Identifying genetic differences between bipolar disorder and major depression through multiple GWAS

Background

Accurate diagnosis of bipolar disorder (BD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A key reason is that the first manic episode is often preceded by a depressive one, making it difficult to distinguish BD from unipolar major depressive disorder (MDD).

Aims

Here, we use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores that may aid early differential diagnosis.

Methods

Based on individual genotypes from case-control cohorts of BD and MDD shared through the Psychiatric Genomics Consortium, we compile case-case-control cohorts, applying a careful merging and quality control procedure. In a resulting cohort of 51,149 individuals (15,532 BD cases, 12,920 MDD cases and 22,697 controls), we perform a variety of GWAS and polygenic risk scores (PRS) analyses.

Results

While our GWAS is not well-powered to identify genome-wide significant loci, we find significant SNP-heritability and demonstrate the ability of the resulting PRS to distinguish BD from MDD, including BD cases with depressive onset. We replicate our PRS findings, but not signals of individual loci in an independent Danish cohort (iPSYCH 2015 case-cohort study, N=25,966). We observe strong genetic correlation between our case-case GWAS and that of case-control BD.

Conclusions

We find that MDD and BD, including BD with a depressive onset, are genetically distinct. Further, our findings support the hypothesis that Controls - MDD - BD primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BD and, importantly, BD with depressive onset from MDD.

Advances in functional MRI research in bipolar disorder: from the perspective of mood states

Bipolar disorder is characterised by recurrent and alternating episodes of mania/hypomania and depression. Current breakthroughs in functional MRI techniques have uncovered the functional neuroanatomy of bipolar disorder. However, the pathophysiology underlying mood instability, mood switching and the development of extreme mood states is less well understood. This review presents a comprehensive overview of current evidence from functional MRI studies from the perspective of mood states. We first summarise the disrupted brain activation patterns and functional connectivity that have been reported in bipolar disorder, irrespective of the mood state. We next focus on research that solely included patients in a single mood state for a better understanding of the pathophysiology of bipolar disorder and research comparing patients with different mood states to dissect mood state-related effects. Finally, we briefly summarise current theoretical models and conclude this review by proposing potential avenues for future research. A comprehensive understanding of the pathophysiology with consideration of mood states could not only deepen our understanding of how acute mood episodes develop at a neurophysiological level but could also facilitate the identification of biological targets for personalised treatment and the development of new interventions for bipolar disorder.

Immune, RNA, and Neurocognitive Genetic Networks in Bipolar Disorder Subtypes: A Transcriptomic Meta-Analysis

Background

Little is known about the pathogenesis of Bipolar Disorder, and even less is known about the genetic differences between its subtypes. Bipolar Disorder is classified into different subtypes, which present different symptoms and lifetime courses. While genetic studies have been conducted in Bipolar Disorder, most examined the gene expression of only Bipolar Disorder Type 1. Studies that include Bipolar Disorder Type 1 and Bipolar Disorder Type 2 often fail to differentiate them into separate conditions. Few large transcriptomic meta-analyses in Bipolar Disorder have been conducted to identify genetic pathways. Thus, using publicly available data sets we aim here to uncover significant differential gene expression that allows distinguishing Type 1 and Type 2 Bipolar Disorders, as well as find patterns in Bipolar Disorder as a whole.

Methods

We analyze 17 different gene expression data sets from different tissue in Bipolar Disorder using GEO2R and manual analysis, of which 15 contained significant differential gene expression results. We use STRING and Cytoscape to examine Gene Ontology to find significantly affected genetic pathways. We identify hub genes using cytoHubba, a plugin in Cytoscape. We find genes common to data sets of the same material or subtype.

Results

12 out of 15 data sets are enriched for immune system and RNA related pathways. 9 out of 15 data sets are enriched for neurocognitive and metal ion related GO terms. Analysis of Bipolar Disorder Type 1 vs Bipolar Disorder Type 2 revealed most differentially expressed genes were related to immune function, especially cytokines. Terms related to synaptic signaling and neurotransmitter secretion were found in down-regulated GO terms while terms related to neuron apoptosis and death were up-regulated. We identify the gene SNCA as a potential biomarker for overall Bipolar Disorder diagnosis due to its prevalence in our data sets.

Conclusions

The immune system and RNA related pathways are significantly enriched across the Bipolar Disorder data sets. The role of these pathways is likely more critically important to the function of Bipolar Disorder than currently understood. Further studies should clearly label the subtype of Bipolar Disorder used in their research and more effort needs to be undertaken to collect samples from Cyclothymic Disorder and Bipolar Disorder Type 2.

Neuromelanin-sensitive MRI of the substantia nigra distinguishes bipolar from unipolar depression

Depression in bipolar disorder (BD-II) is frequently misdiagnosed as unipolar depression (UD) leading to inappropriate treatment and downstream complications for many bipolar sufferers. In this study, we evaluated whether neuromelanin-MR signal and volume changes in the substantia nigra (SN) can be used as potential biomarkers to differentiate BD-II from UD. The signal intensities and volumes of the SN regions were measured, and contrast-to-noise ratio (CNR) to the decussation of the superior cerebellar peduncles were calculated and compared between healthy controls (HC), BD-II and UD subjects. Results showed that compare to HC, both BD-II and UD subjects had significantly decreased CNR and increased volume on the right and left sides. Moreover, the volume in BD-II group was significantly increased compared to UD group. The area under the receiver operating characteristic curve (AUC) for discriminating BD from HC was the largest for the Volume-L (AUC, 0.85; 95% confidence interval [CI]: 0.77, 0.93). The AUC for discriminating UD from HC was the largest for the Volume-L (AUC, 0.76; 95% CI: 0.65, 0.86). Furthermore, the AUC for discriminating BD from UD was the largest for the Volume-R (AUC, 0.73; 95% CI: 0.62, 0.84). Our findings suggest that neuromelanin-sensitive magnetic resonance imaging techniques can be used to differentiate BD-II from UD.

Dopamine pathways mediating affective state transitions after sleep loss

The pathophysiology of affective disorders-particularly circuit-level mechanisms underlying bidirectional, periodic affective state transitions-remains poorly understood. In patients, disruptions of sleep and circadian rhythm can trigger transitions to manic episodes, whereas depressive states are reversed. Here, we introduce a hybrid automated sleep deprivation platform to induce transitions of affective states in mice. Acute sleep loss causes mixed behavioral states, featuring hyperactivity, elevated social and sexual behaviors, and diminished depressive-like behaviors, where transitions depend on dopamine (DA). Using DA sensor photometry and projection-targeted chemogenetics, we reveal that elevated DA release in specific brain regions mediates distinct behavioral changes in affective state transitions. Acute sleep loss induces DA-dependent enhancement in dendritic spine density and uncaging-evoked dendritic spinogenesis in the medial prefrontal cortex, whereas optically mediated disassembly of enhanced plasticity reverses the antidepressant effects of sleep deprivation on learned helplessness. These findings demonstrate that brain-wide dopaminergic pathways control sleep-loss-induced polymodal affective state transitions.

The prevalence and associated characteristics of Bipolar Disorder diagnosis among admitted patients at three tertiary psychiatric hospitals in Zimbabwe: A cross sectional study

BACKGROUND

Bipolar Affective Disorder (BD) is a serious condition that affects more than 1% of the world's population. If not treated can cause disability, yet its prevalence in Zimbabwe is not known. This study explores the burden of Bipolar Disorder and its associated factors in Zimbabwe.

METHODS

A cross sectional study with a sample of 272 participants was carried out at three tertiary hospitals in Zimbabwe. Data was collected using an interviewer administered questionnaire and the Mini International Neuropsychiatric Interview (M.I.N.I). The study shows the prevalence and factors associated with Bipolar Disorder at tertiary psychiatric hospitals. Data analysis was done using STATA S/E 13.0 for data management.

RESULTS

The prevalence of BD in the sample was 39.3%. Factors associated with BD were, being formally employed (AOR = 3.69, 95%CI: 1.55-8.79), a history of defaulting medications (AOR = 1.90, 95%CI: 1.02-3.57) and a reported previous diagnosis of BD (AOR = 5.66, 95%CI: 2.72-11.8).

CONCLUSIONS

The prevalence of BD among admitted participants in tertiary psychiatric hospitals in Zimbabwe is high. It is comparable to that from African studies done in clinical settings. There is need for in-service training for clinicians to be more vigilant in diagnosing BD.

Impacts of Omega-3 Fatty Acids, Natural Elixirs for Neuronal Health, on Brain Development and Functions

Omega-3 fatty acids play a seminal role in maintaining the structural and functional integrity of the nervous system. These specialized molecules function as precursors for many lipid-based biological messengers. Also, studies suggest the role of these fatty acids in regulating healthy sleep cycles, cognitive ability, brain development, etc. Dietary intake of essential poly unsaturated fatty acids (PUFA) such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are foundational to the optimal working of the nervous system. Besides regulating health, these biomolecules have great therapeutic value in treating several diseases, particularly nervous system diseases and disorders. Many recent studies conclusively demonstrated the beneficial effects of Omega-3 fatty acids in treating depression, neuropsychiatric disorders, neurodegenerative disorders, neurochemical disorders, and many other illnesses associated with the nervous system. This chapter summates the multifaceted role of poly unsaturated fatty acids, especially Omega-3 fatty acids (EPA and DHA), in the neuronal health and functioning. The importance of dietary intake of these essential fatty acids, their recommended dosages, bioavailability, the mechanism of their action, and therapeutic values are extensively discussed.

Dissecting clinical and biological heterogeneity in clinical states of bipolar disorder: a 10-year retrospective study from China

Objectives

To dissect clinical and biological heterogeneity in clinical states of bipolar disorder (BD), and investigate if neuropsychological symptomatology, comorbidity, vital signs, and blood laboratory indicators are predictors of distinct BD states.

Methods

A retrospective BD cohort was established with data extracted from a Chinese hospital's electronic medical records (EMR) between 2009 and 2018. Subjects were inpatients with a main discharge diagnosis of BD and were assessed for clinical state at hospitalization. We categorized all subjects into manic state, depressive state, and mixed state. Four machine learning classifiers were utilized to classify the subjects. A Shapley additive explanations (SHAP) algorithm was applied to the classifiers to aid in quantifying and visualizing the contributions of each feature that drive patient-specific classifications.

Results

A sample of 3,085 records was included (38.54% as manic, 56.69% as depressive, and 4.77% as mixed state). Mixed state showed more severe suicidal ideation and psychomotor abnormalities, while depressive state showed more common anxiety, sleep, and somatic-related symptoms and more comorbid conditions. Higher levels of body temperature, pulse, and systolic and diastolic blood pressures were present during manic episodes. Xgboost achieved the best AUC of 88.54% in manic/depressive states classification; Logistic regression and Random forest achieved the best AUCs of 75.5 and 75% in manic/mixed states and depressive/mixed states classifications, respectively. Myocardial enzymes and the non-enzymatic antioxidant uric acid and bilirubin contributed significantly to distinguish BD clinical states.

Conclusion

The observed novel biological associations with BD clinical states confirm that biological heterogeneity contributes to clinical heterogeneity of BD.

Distinguishing functional and structural MRI abnormalities between bipolar and unipolar depression

Background

This study aims to investigate the underlying characteristics of spontaneous brain activity by analyzing the volumes of the hippocampus and parahippocampal gyrus, as well as the fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo), in order to differentiate between bipolar disorder (BD) and unipolar depressive disorder.

Methods

A total of 46 healthy controls, 58 patients with major depressive disorder (MDD), and 61 patients with BD participated in the study and underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans. The researchers calculated the differences in volume, fALFF, and ReHo values among the three groups. Additionally, they conducted correlation analyses to examine the relationships between clinical variables and the aforementioned brain measures.

Results

The results showed that the BD group exhibited increased fALFF in the hippocampus compared to the healthy control (HC) and MDD groups. Furthermore, the ReHo values in the hippocampus and parahippocampal gyrus were significantly higher in the BD group compared to the HC group. The findings from the person correlation analysis indicated a positive relationship between ReHo values in the hippocampus and both HAMD and HAMA scores. Moreover, there was no correlation between the volumes, fALFF, and ReHo values in the hippocampus and parahippocampal gyrus, and cognitive function levels (RBANS).

Conclusion

Taken together, these aberrant patterns of intrinsic brain activity in the hippocampus and parahippocampal gyrus may serve as quantitative indicators for distinguishing between BD and unipolar depression.

Co-occurring psychiatric disorders and disparities in buprenorphine utilization in opioid use disorder: An analysis of insurance claims

Background

As the overdose crisis continues in the U.S. and Canada, opioid use disorder (OUD) treatment outcomes for people with co-occurring psychiatric disorders are not well characterized. Our objective was to examine the influence of co-occurring psychiatric disorders on buprenorphine initiation and discontinuation.

Methods

This retrospective cohort study used multi-state administrative claims data in the U.S. to evaluate rates of buprenorphine initiation (relative to psychosocial treatment without medication) in a cohort of 236,198 people with OUD entering treatment, both with and without co-occurring psychiatric disorders, grouping by psychiatric disorder subtype (mood, psychotic, and anxiety-and-related disorders). Among people initiating buprenorphine, we assessed the influence of co-occurring psychiatric disorders on buprenorphine retention. We used multivariable Poisson regression to estimate buprenorphine initiation and Cox regression to estimate time to discontinuation, adjusting for all 3 classes of co-occurring disorders simultaneously and adjusting for baseline demographic and clinical characteristics.

Results

Buprenorphine initiation occurred in 29.3 % of those with co-occurring anxiety-and-related disorders, compared to 25.9 % and 17.5 % in people with mood and psychotic disorders. Mood (adjusted-risk-ratio[aRR] = 0.82[95 % CI = 0.82-0.83]) and psychotic disorders (aRR = 0.95[0.94-0.96]) were associated with decreased initiation (versus psychosocial treatment), in contrast to greater initiation in the anxiety disorders cohort (aRR = 1.06[1.05-1.06]). We observed an increase in buprenorphine discontinuation associated with mood (adjusted-hazard-ratio[aHR] = 1.20[1.17-1.24]) and anxiety disorders (aHR = 1.12[1.09-1.14]), in contrast to no association between psychotic disorders and buprenorphine discontinuation.

Conclusions

We observed underutilization of buprenorphine among people with co-occurring mood and psychotic disorders, as well as high buprenorphine discontinuation across anxiety, mood, and psychotic disorders.

Examining the link between impulse control disorder and antidepressant use in Parkinson's disease

INTRODUCTION

Impulse control disorders (ICD) in Parkinson's disease (PD) and hypomanic episodes of bipolar disorder show overlapping symptoms, suggesting a shared neurobiology. To explore this, the following hypotheses are tested: (1) larger changes in affective symptoms from OFF to ON medication states will be associated with ICD, (2) antidepressant exposure will be associated with larger OFF to ON affective symptom changes, and (3) antidepressant exposure will be associated with ICD.

METHODS

200 participants (mean age 65, 61 % male) were evaluated in "off" and "on" dopamine states. Affective symptoms were captured using the Hamilton Anxiety and Depression Rating Scales. Differences in clinical outcomes were compared using two-sample Wilcoxon rank-sum tests and Pearson χ2 tests. We performed multivariable logistic regression to assess the association of antidepressant exposure on ICD.

RESULTS

Participants with an ICD had higher anxiety and depressive scores in "on" and "off" states and larger changes in depressive symptoms from OFF to ON states compared to those without an ICD. Participants on antidepressants had higher anxiety scores in "on" and "off" states, higher depressive scores in the "off" state, and larger changes in anxiety symptoms from OFF to ON states than those not on an antidepressant. Antidepressant use was associated with a higher odds of an ICD (OR 2.3, CI [1.0-4.5], p-value 0.04).

CONCLUSIONS

Affective symptom severity in "on" and "off" dopamine states is associated with ICD. Antidepressant therapy may be associated with ICD. Future prospective studies clarifying temporal associations between antidepressant initiation and ICD emergence are needed.

Effects of Transcranial Direct Current Stimulation on Cognitive Deficits in Depression: A Systematic Review

Background

Major depressive disorder is the leading cause of mental health-related burden globally and up to one-third of major depressive disorder patients never achieve remission. Transcranial Direct Current Stimulation is a non-invasive intervention used to treat individuals diagnosed with major depressive disorder and bipolar disorder. Since the last transcranial direct current stimulation review specifically focusing on cognitive symptoms in major depressive disorder, twice as many papers have been published.

Methods

A systematic review was conducted with 5 electronic databases from database inception until March 21, 2022. Randomized controlled trials with at least 1 arm evaluating transcranial direct current stimulation in adults (diagnosed with major depressive disorder or bipolar disorder using the Diagnostic and Statistical Manual of Mental Disorders or International Classification of Diseases criteria) aged 18 or older were included. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were adopted.

Results

: A total of 972 participants were included across 14 studies (60.5% female; mean age of 47.0 years [SD = 16.8]). Nine studies focused on participants with major depressive disorder and all studies used the Diagnostic and Statistical Manual of Mental Disorders to diagnose the participants. Seven out of the 14 studies showed significant improvements in at least 1 cognitive outcome measure in the active transcranial direct current stimulation group compared to the sham group. Several cognitive measures were used across studies, and 12 of the 14 studies reported mild-to-moderate side effects from treatment.

Conclusion

: Current transcranial direct current stimulation literature has shown limited evidence for the treatment of cognitive impairments in major depressive disorder and bipolar disorder. Future research that applies machine learning algorithms may enable us to distinguish responders from non-responders, increasing clinical benefits of transcranial direct current stimulation.

Classification of bipolar disorders using the multilayer modularity in dynamic minimum spanning tree from resting state fMRI

The diagnosis of bipolar disorders (BD) mainly depends on the clinical history and behavior observation, while only using clinical tools often limits the diagnosis accuracy. The study aimed to create a novel BD diagnosis framework using multilayer modularity in the dynamic minimum spanning tree (MST). We collected 45 un-medicated BD patients and 47 healthy controls (HC). The sliding window approach was utilized to construct dynamic MST via resting-state functional magnetic resonance imaging (fMRI) data. Firstly, we used three null models to explore the effectiveness of multilayer modularity in dynamic MST. Furthermore, the module allegiance exacted from dynamic MST was applied to train a classifier to discriminate BD patients. Finally, we explored the influence of the FC estimator and MST scale on the performance of the model. The findings indicated that multilayer modularity in the dynamic MST was not a random process in the human brain. And the model achieved an accuracy of 83.70% for identifying BD patients. In addition, we found the default mode network, subcortical network (SubC), and attention network played a key role in the classification. These findings suggested that the multilayer modularity in dynamic MST could highlight the difference between HC and BD patients, which opened up a new diagnostic tool for BD patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09907-x.

Changes in thyroid function levels in female patients with first-episode bipolar disorder

Objectives

The identification of molecular biomarkers for bipolar disorder is anticipated to greatly improve the diagnosis and treatment of this disease. The objective of this case-control study is to determine whether the blood thyroid hormone levels in bipolar disorder patients are associated with different types of first onset.

Methods

From August 1, 2020 to July 31, 2021 a total of 120 female patients diagnosed with bipolar disorder and hospitalized at Qingdao Mental Health Center were recruited as the case group, including 60 patients with depression as their first onset (depression first-episode group, DF) and 60 with mania/hypomania as their first onset (mania/hypomania first-episode group, M/HF). A group of 60 healthy adult females matching general demographic data, such as race and age, were selected as the control group. Blood samples were taken from both groups to measure serum triiodothyronine (T3), thyroxine (T4), free triiodothyronine (FT3), free thyroxine (FT4), and thyroid stimulating hormone (TSH) concentrations.

Results

The duration of current onset in the M/HF group was significantly less than that in the DF group (23.1 ± 20.2 vs. 125.2 ± 41.0 days). About 27% of patients in the M/HF group had thyroid abnormalities, in contrast to 60% in the DF group. The blood T3 and T4 levels in both the M/HF group and the DF group, as well as the TF3 levels in the DF group, were significantly lower as compared to control. The M/HF group had significantly higher T3 and FT3 levels than the DF group. The blood T3 levels were inversely correlated with the Young's Mania Scale score and the Hamilton Depression Scale score in both the M/HF and DF groups.

Conclusion

Thyroid dysfunction resulting in reduced levels of blood thyroid levels may be involved in the disease progression of bipolar disorder, and correlated with the clinical symptoms in patients with depression or mania as the first episode.

Untargeted Lipidomics Study of Bipolar Disorder Patients in Serbia

The Lipidomic profiles of serum samples from patients with bipolar disorder (BD) and healthy controls (C) were explored and compared. The sample cohort included 31 BD patients and 31 control individuals. An untargeted lipidomics study applying liquid chromatography (LC) coupled with high-resolution mass spectrometry (HRMS) was conducted to achieve the lipid profiles. Multivariate statistical analyses (principal component analysis and partial least squares discriminant analysis) were performed, and fifty-six differential lipids were confirmed in BD and controls. Our results pointed to alterations in lipid metabolism, including pathways of glycerophospholipids, sphingolipids, glycerolipids, and sterol lipids, in BD patient sera. This study emphasized the role of lipid pathways in BD, and comprehensive research using the LC-HRMS platform is necessary for future application in the diagnosis and improvement of BD treatments.