Misdiagnosis, detection rate, and associated factors of severe psychiatric disorders in specialized psychiatry centers in Ethiopia

Extracellular vesicle biomarkers for complement dysfunction in schizophrenia

Schizophrenia, a complex neuropsychiatric disorder, frequently experiences a high rate of misdiagnosis due to subjective symptom assessment. Consequently, there is an urgent need for innovative and objective diagnostic tools. In this study, we used cutting-edge extracellular vesicles' (EVs) proteome profiling and XGBoost-based machine learning to develop new markers and personalized discrimination scores for schizophrenia diagnosis and prediction of treatment response. We analysed plasma and plasma-derived EVs from 343 participants, including 100 individuals with chronic schizophrenia, 34 first-episode and drug-naïve patients, 35 individuals with bipolar disorder, 25 individuals with major depressive disorder and 149 age- and sex-matched healthy controls. Our innovative approach uncovered EVs-based complement changes in patients, specific to their disease-type and status. The EV-based biomarkers outperformed their plasma counterparts, accurately distinguishing schizophrenia individuals from healthy controls with an area under curve (AUC) of 0.895, 83.5% accuracy, 85.3% sensitivity and 82.0% specificity. Moreover, they effectively differentiated schizophrenia from bipolar disorder and major depressive disorder, with AUCs of 0.966 and 0.893, respectively. The personalized discrimination scores provided a personalized diagnostic index for schizophrenia and exhibited a significant association with patients' antipsychotic treatment response in the follow-up cohort. Overall, our study represents a significant advancement in the field of neuropsychiatric disorders, demonstrating the potential of EV-based biomarkers in guiding personalized diagnosis and treatment of schizophrenia.

Identification of Predictors of Mood Disorder Misdiagnosis and Subsequent Help-Seeking Behavior in Individuals With Depressive Symptoms: Gradient-Boosted Tree Machine Learning Approach

BACKGROUND

Misdiagnosis and delayed help-seeking cause significant burden for individuals with mood disorders such as major depressive disorder and bipolar disorder. Misdiagnosis can lead to inappropriate treatment, while delayed help-seeking can result in more severe symptoms, functional impairment, and poor treatment response. Such challenges are common in individuals with major depressive disorder and bipolar disorder due to the overlap of symptoms with other mental and physical health conditions, as well as, stigma and insufficient understanding of these disorders.

OBJECTIVE

In this study, we aimed to identify factors that may contribute to mood disorder misdiagnosis and delayed help-seeking.

METHODS

Participants with current depressive symptoms were recruited online and data were collected using an extensive digital mental health questionnaire, with the World Health Organization World Mental Health Composite International Diagnostic Interview delivered via telephone. A series of predictive gradient-boosted tree algorithms were trained and validated to identify the most important predictors of misdiagnosis and subsequent help-seeking in misdiagnosed individuals.

RESULTS

The analysis included data from 924 symptomatic individuals for predicting misdiagnosis and from a subset of 379 misdiagnosed participants who provided follow-up information when predicting help-seeking. Models achieved good predictive power, with area under the receiver operating characteristic curve of 0.75 and 0.71 for misdiagnosis and help-seeking, respectively. The most predictive features with respect to misdiagnosis were high severity of depressed mood, instability of self-image, the involvement of a psychiatrist in diagnosing depression, higher age at depression diagnosis, and reckless spending. Regarding help-seeking behavior, the strongest predictors included shorter time elapsed since last speaking to a general practitioner about mental health, sleep problems disrupting daily tasks, taking antidepressant medication, and being diagnosed with depression at younger ages.

CONCLUSIONS

This study provides a novel, machine learning-based approach to understand the interplay of factors that may contribute to the misdiagnosis and subsequent help-seeking in patients experiencing low mood. The present findings can inform the development of targeted interventions to improve early detection and appropriate treatment of individuals with mood disorders.

Efficacy of novel attention-based gated recurrent units transformer for depression detection using electroencephalogram signals

Purpose

Depression is a global challenge causing psychological and intellectual problems that require efficient diagnosis. Electroencephalogram (EEG) signals represent the functional state of the human brain and can help build an accurate and viable technique for the early prediction and treatment of depression.

Methods

An attention-based gated recurrent units transformer (AttGRUT) time-series model is proposed to efficiently identify EEG perturbations in depressive patients. Statistical, spectral and wavelet features were first extracted from the 60-channel EEG signal data. Then, two feature selection techniques, recursive feature elimination and the Boruta algorithm, both with Shapley additive explanations, were utilised for selecting essential features.

Results

The proposed model outperformed the two baseline and two hybrid time-series models-long short-term memory (LSTM), gated recurrent units (GRU), convolutional neural network-LSTM (CNN-LSTM), and CNN-GRU-achieving an accuracy of up to 98.67%. Feature selection considerably increased the performance across all time-series models.

Conclusion

Based on the obtained results, novel feature selection greatly affected the results of the baseline and hybrid time-series models. The proposed AttGRUT can be implemented and tested in other domains by using different modalities for prediction.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13755-022-00205-8.

Incidence, prevalence, and global burden of schizophrenia - data, with critical appraisal, from the Global Burden of Disease (GBD) 2019

Schizophrenia substantially contributes to the burden of mental disorders. Schizophrenia's burden and epidemiological estimates in some countries have been published, but updated estimates of prevalence, incidence, and schizophrenia-related disability at the global level are lacking. Here, we present the data from and critically discuss the Global Burden of Diseases, Injuries, and Risk Factors Study data, focusing on temporal changes in schizophrenia's prevalence, incidence, and disability-adjusted life years (DALYs) globally. From 1990 to 2019, schizophrenia raw prevalence (14.2 to 23.6 million), incidence (941,000 to 1.3 million), and DALYs (9.1 to 15.1 million) increased by over 65%, 37%, and 65% respectively, while age-standardized estimates remained stable globally. In countries with high socio-demographic index (SDI), both prevalence and DALYs increased, while in those with low SDI, the age-standardized incidence decreased and DALYs remained stable. The male/female ratio of burden of schizophrenia has remained stable in the overall population over the past 30 years (i.e., M/F = 1.1), yet decreasing from younger to older age groups (raw prevalence in females higher than males after age 65, with males having earlier age of onset, and females longer life expectancy). Results of this work suggest that schizophrenia's raw prevalence, incidence, and burden have been increasing since 1990. Age-adjusted estimates did not reduce. Schizophrenia detection in low SDI countries is suboptimal, and its prevention/treatment in high SDI countries should be improved, considering its increasing prevalence. Schizophrenia sex ratio inverts throughout the lifespan, suggesting different age of onset and survival by sex. However, prevalence and burden estimates for schizophrenia are probably underestimated. GBD does not account for mortality from schizophrenia (and other mental disorders, apart from anorexia nervosa).

Treatment of schizotypal disorder: a protocol for a systematic review of the evidence and recommendations for clinical practice

INTRODUCTION

Schizotypal disorder is associated with a high level of disability at an individual level and high societal costs. However, clinical recommendations for the treatment of schizotypal disorder are scarce and based on limited evidence. This review aims to synthesise the current evidence on treatment for schizotypal disorder making recommendations for clinical practice.

METHODS AND ANALYSIS

This systematic review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A systematic literature search will be performed in PsychArticles, Embase, Medline and Cochrane Central Register of Controlled Trials. Additionally, we will search for relevant articles manually. Inclusion criteria are published studies including individuals diagnosed with schizotypal personality disorder according to Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria, or schizotypal disorder according to International Classification of Diseases (ICD) criteria. We will include interventional studies comprising any pharmacological and non-pharmacological treatment trials for patients with schizotypal disorder, and all relevant outcome measures will be reported. Risk of bias will be assessed by Cochrane risk-of-bias tools. Data will be synthesised using narrative or thematic analysis and, if suitable, through meta-analysis.

ETHICS AND DISSEMINATION

No original data will be collected as part of this study and ethics approval is, therefore, not applicable. The results will be disseminated through peer-reviewed publication and presented at international scientific meetings. We will aim at submitting the final paper for publication within 4 months of completion of analyses. Furthermore, this systematic review will inform clinicians and researchers on the current state of evidence on treatment for schizotypal disorder. Findings may guide proposals for further research and potentially guide recommendations for clinical practice using the Grading of Recommendations Assessment, Development and Evaluation.

PROSPERO REGISTRATION NUMBER

CRD42022375001.

Psychometric properties of the mini international neuropsychiatric interview (MINI) psychosis module: a Sub-Saharan Africa cross country comparison

BACKGROUND

The Mini International Neuropsychiatric Inventory 7.0.2 (MINI-7) is a widely used tool and known to have sound psychometric properties; but very little is known about its use in low and middle-income countries (LMICs). This study aimed to examine the psychometric properties of the MINI-7 psychosis items in a sample of 8609 participants across four countries in Sub-Saharan Africa.

METHODS

We examined the latent factor structure and the item difficulty of the MINI-7 psychosis items in the full sample and across four countries.

RESULTS

Multiple group confirmatory factor analyses (CFAs) revealed an adequate fitting unidimensional model for the full sample; however, single group CFAs at the country level revealed that the underlying latent structure of psychosis was not invariant. Specifically, although the unidimensional structure was an adequate model fit for Ethiopia, Kenya, and South Africa, it was a poor fit for Uganda. Instead, a 2-factor latent structure of the MINI-7 psychosis items provided the optimal fit for Uganda. Examination of item difficulties revealed that MINI-7 item K7, measuring visual hallucinations, had the lowest difficulty across the four countries. In contrast, the items with the highest difficulty were different across the four countries, suggesting that MINI-7 items that are the most predictive of being high on the latent factor of psychosis are different for each country.

CONCLUSIONS

The present study is the first to provide evidence that the factor structure and item functioning of the MINI-7 psychosis vary across different settings and populations in Africa.

Whole Person Modeling: a transdisciplinary approach to mental health research

The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area.

Association Between Human Blood Metabolome and the Risk of Psychiatric Disorders

BACKGROUND AND HYPOTHESIS

To identify promising drug targets for psychiatric disorders, we applied Mendelian randomization (MR) design to systematically screen blood metabolome for potential mediators of psychiatric disorders and further predict target-mediated side effects.

STUDY DESIGN

We selected 92 unique blood metabolites from 3 metabolome genome-wide association studies (GWASs) with totally 147 827 participants. Summary statistics for bipolar disorder (BIP), attention deficit hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), major depressive disorder (MDD), schizophrenia (SCZ), panic disorder (PD), autistic spectrum disorder (ASD), and anorexia nervosa (AN) originated from the Psychiatric Genomics Consortium, involving 1 143 340 participants. Mendelian randomization (MR) analyses were conducted to estimate associations of blood metabolites with psychiatric disorders. Phenome-wide MR analysis was further performed to predict side effects mediated by metabolite-targeted interventions.

RESULTS

Eight metabolites were identified associated with psychiatric disorders, including five established mediators: N-acetylornithine (BIP: OR, 0.72 [95% CI, 0.66-0.79]; SCZ: OR, 0.74 [0.64-0.84]), glycine (BIP: OR, 0.62 [0.50-0.77]), docosahexaenoic acid (MDD: OR, 0.96 [0.94-0.97]), 3-Hydroxybutyrate (MDD: OR, 1.14 [1.08-1.21]), butyrylcarnitine (SCZ: OR, 1.22 [1.12-1.32]); and three novel mediators: 1-arachidonoylglycerophosphocholine (1-arachidonoyl-GPC)(BIP: OR, 0.31 [0.23-0.41]), glycoproteins (BIP: OR, 0.94 [0.92-0.97]), sphingomyelins (AN: OR, 1.12 [1.06-1.19]). Phenome-wide MR analysis showed that all identified metabolites except for N-acetylornithine and 3-Hydroxybutyrate had additional effects on nonpsychiatric diseases, while glycine, 3-Hydroxybutyrate, N-acetylornithine, and butyrylcarnitine had no adverse side effects.

CONCLUSIONS

This MR study identified five established and three novel mediators for psychiatric disorders. N-acetylornithine, glycine, 3-Hydroxybutyrate, and butyrylcarnitine might be promising targets against psychiatric disorders with no predicted adverse side effects.

Network analysis links adolescent depression with childhood, peer, and family risk environment factors

BACKGROUND

Adolescent mental health is influenced by various adverse environmental conditions. However, it remains unclear how these factors jointly affect adolescent depression. This study aimed to use network analysis to assess the associations between different environmental factors and depressive symptoms in adolescents and to identify key pathways between them.

METHODS

This study included 610 adolescents with depression from inpatient and outpatient units recruited between March 2020 and November 2021. The mean age was 14.86 ± 1.96, with no significant difference between males (n = 155, 15.10 ± 2.19) and females (n = 455, 14.78 ± 1.88). Depressive symptoms were measured using the Children's Depression Inventory, and individual risk environment factors included childhood trauma, social peer and family risk factors. Network features, including network centrality, stability, and bridge centrality, were investigated.

RESULTS

Anhedonia and self-esteem were found to be more central in depressive symptoms. Insult experiences from the social peer and emotional abuse experience from childhood were more central environmental factors. Childhood trauma experiences were more related to adolescent depressive symptoms compared to family and peer factors. Bridge analyses identified emotional abuse, emotional neglect and physical neglect as the main bridges linking environment risk to depressive symptoms.

LIMITATIONS

This was a cross-sectionally designed study, which limited its ability to examine longitudinal dynamic interactions between environmental factors and adolescent depressive symptoms.

CONCLUSIONS

Our findings suggested that childhood trauma experiences might have greater psychological impacts on adolescent depression than family and social peer environments, and should be considered as crucial targets for preventing severe depressive moods.

Schizophrenia or schizoaffective disorder? A 50-year assessment of diagnostic stability based on a national case registry

BACKGROUND

Schizoaffective disorder (SAD) remains a controversial diagnosis in terms of necessity and reliability.

OBJECTIVES

We assessed diagnostic patterns of SAD and schizophrenia (SZ) among hospitalized psychiatric patients over a fifty-year period.

METHOD

Data from the Israeli National Psychiatric Registry on 16,341 adults diagnosed with SZ or SAD, hospitalized at least twice in 1963-2017, were analyzed. Stability between most-frequent, first and last diagnosis, and diagnostic-constancy (the same diagnosis in >75 % of a person's hospitalizations) were calculated. Three groups were compared: People with both SAD and SZ diagnoses over the years (SZ-SAD), and people with only one of these diagnoses (SZ-only; SAD-only). The incidence of SAD and SZ before and after DSM-5 publication was compared.

RESULTS

Reliability between last and first diagnosis was 60 % for SAD and 94 % for SZ. Agreement between first and most-frequent diagnosis was 86 % for SAD and 92 % for SZ. Diagnostic shifts differ between persons with SAD and with SZ. Diagnostic-constancy was observed for 50 % of SAD-only patients. In the SZ-SAD group, 9 % had a constant SAD diagnosis. Compared to the other groups, the SZ-SAD group exhibited a higher substance use prevalence, younger age at first-hospitalization, and more hospitalizations/person (p < 0.0001). The incidence of a first-hospitalization SAD diagnosis increased by 2.2 % in the 4-years after vs. prior to DSM-5.

CONCLUSIONS

A SAD diagnosis is less stable than SZ. The incidence of a SAD diagnosis increased after DSM-5, despite stricter diagnostic criteria. The SZ-SAD group exhibited the poorest outcomes. SAD may evolve over time necessitating periodic re-evaluation.

Normative model detects abnormal functional connectivity in psychiatric disorders

INTRODUCTION

The diagnosis of psychiatric disorders is mostly based on the clinical evaluation of the patient's signs and symptoms. Deep learning binary-based classification models have been developed to improve the diagnosis but have not yet reached clinical practice, in part due to the heterogeneity of such disorders. Here, we propose a normative model based on autoencoders.

METHODS

We trained our autoencoder on resting-state functional magnetic resonance imaging (rs-fMRI) data from healthy controls. The model was then tested on schizophrenia (SCZ), bipolar disorder (BD), and attention-deficit hyperactivity disorder (ADHD) patients to estimate how each patient deviated from the norm and associate it with abnormal functional brain networks' (FBNs) connectivity. Rs-fMRI data processing was conducted within the FMRIB Software Library (FSL), which included independent component analysis and dual regression. Pearson's correlation coefficients between the extracted blood oxygen level-dependent (BOLD) time series of all FBNs were calculated, and a correlation matrix was generated for each subject.

RESULTS AND DISCUSSION

We found that the functional connectivity related to the basal ganglia network seems to play an important role in the neuropathology of BD and SCZ, whereas in ADHD, its role is less evident. Moreover, the abnormal connectivity between the basal ganglia network and the language network is more specific to BD. The connectivity between the higher visual network and the right executive control and the connectivity between the anterior salience network and the precuneus networks are the most relevant in SCZ and ADHD, respectively. The results demonstrate that the proposed model could identify functional connectivity patterns that characterize different psychiatric disorders, in agreement with the literature. The abnormal connectivity patterns from the two independent SCZ groups of patients were similar, demonstrating that the presented normative model was also generalizable. However, the group-level differences did not withstand individual-level analysis implying that psychiatric disorders are highly heterogeneous. These findings suggest that a precision-based medical approach, focusing on each patient's specific functional network changes may be more beneficial than the traditional group-based diagnostic classification.

Challenges for Artificial Intelligence in Recognizing Mental Disorders

Artificial Intelligence (AI) appears to be making important advances in the prediction and diagnosis of mental disorders. Researchers have used visual, acoustic, verbal, and physiological features to train models to predict or aid in the diagnosis, with some success. However, such systems are rarely applied in clinical practice, mainly because of the many challenges that currently exist. First, mental disorders such as depression are highly subjective, with complex symptoms, individual differences, and strong socio-cultural ties, meaning that their diagnosis requires comprehensive consideration. Second, there are many problems with the current samples, such as artificiality, poor ecological validity, small sample size, and mandatory category simplification. In addition, annotations may be too subjective to meet the requirements of professional clinicians. Moreover, multimodal information does not solve the current challenges, and within-group variations are greater than between-group characteristics, also posing significant challenges for recognition. In conclusion, current AI is still far from effectively recognizing mental disorders and cannot replace clinicians' diagnoses in the near future. The real challenge for AI-based mental disorder diagnosis is not a technical one, nor is it wholly about data, but rather our overall understanding of mental disorders in general.

Heterogeneity in major depressive disorder: The need for biomarker-based personalized treatments

Major Depressive Disorder (MDD) or depression is a pathological mental condition affecting millions of people worldwide. Identification of objective biological markers of depression can provide for a better diagnostic and intervention criteria; ultimately aiding to reduce its socioeconomic health burden. This review provides a comprehensive insight into the major biomarker candidates that have been implicated in depression neurobiology. The key biomarker categories are covered across all the "omics" levels. At the epigenomic level, DNA-methylation, non-coding RNA and histone-modifications have been discussed in relation to depression. The proteomics system shows great promise with inflammatory markers as well as growth factors and neurobiological alterations within the endocannabinoid system. Characteristic lipids implicated in depression together with the endocrine system are reviewed under the metabolomics section. The chapter also examines the novel biomarkers for depression that have been proposed by studies in the microbiome. Depression affects individuals differentially and explicit biomarkers identified by robust research criteria may pave the way for better diagnosis, intervention, treatment, and prediction of treatment response.

The Role of Alpha Oscillations among the Main Neuropsychiatric Disorders in the Adult and Developing Human Brain: Evidence from the Last 10 Years of Research

Alpha oscillations (7-13 Hz) are the dominant rhythm in both the resting and active brain. Accordingly, translational research has provided evidence for the involvement of aberrant alpha activity in the onset of symptomatological features underlying syndromes such as autism, schizophrenia, major depression, and Attention Deficit and Hyperactivity Disorder (ADHD). However, findings on the matter are difficult to reconcile due to the variety of paradigms, analyses, and clinical phenotypes at play, not to mention recent technical and methodological advances in this domain. Herein, we seek to address this issue by reviewing the literature gathered on this topic over the last ten years. For each neuropsychiatric disorder, a dedicated section will be provided, containing a concise account of the current models proposing characteristic alterations of alpha rhythms as a core mechanism to trigger the associated symptomatology, as well as a summary of the most relevant studies and scientific contributions issued throughout the last decade. We conclude with some advice and recommendations that might improve future inquiries within this field.

Diagnostic model development for schizophrenia based on peripheral blood mononuclear cell subtype-specific expression of metabolic markers

A significant proportion of the personal and economic burden of schizophrenia can be attributed to the late diagnosis or misdiagnosis of the disorder. A novel, objective diagnostic approaches could facilitate the early detection and treatment of schizophrenia and improve patient outcomes. In the present study, we aimed to identify robust schizophrenia-specific blood biomarkers, with the goal of developing an accurate diagnostic model. The levels of selected serum and peripheral blood mononuclear cell (PBMC) markers relevant to metabolic and immune function were measured in healthy controls (n = 26) and recent-onset schizophrenia patients (n = 36) using multiplexed immunoassays and flow cytometry. Analysis of covariance revealed significant upregulation of insulin receptor (IR) and fatty acid translocase (CD36) levels in T helper cells (F = 10.75, P = 0.002, Q = 0.024 and F = 21.58, P = 2.8 × 10^-5, Q = 0.0004, respectively), as well as downregulation of glucose transporter 1 (GLUT1) expression in monocytes (F = 21.46, P = 2.9 × 10^-5, Q = 0.0004). The most robust predictors, monocyte GLUT1 and T helper cell CD36, were used to develop a diagnostic model, which showed a leave-one-out cross-validated area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI: 0.66-0.92). The diagnostic model was validated in two independent datasets. The model was able to distinguish first-onset, drug-naïve schizophrenia patients (n = 34) from healthy controls (n = 39) with an AUC of 0.75 (95% CI: 0.64-0.86), and also differentiated schizophrenia patients (n = 22) from patients with other neuropsychiatric conditions, including bipolar disorder, major depressive disorder and autism spectrum disorder (n = 68), with an AUC of 0.83 (95% CI: 0.75-0.92). These findings indicate that PBMC-derived biomarkers have the potential to support an accurate and objective differential diagnosis of schizophrenia.

A Machine Learning Approach Detecting Digital Behavioural Patterns of Depression Using Non-intrusive Smartphone Data - A Complementary Path to PHQ-9 Assessment: A Prospective Observational Study

BACKGROUND

Depression is a major global cause of morbidity, an economic burden and the greatest health challenge leading to chronic disability. Mobile monitoring of mental conditions has long been a sought-after metric to overcome the problems associated with the screening, diagnosis and monitoring of depression and its heterogeneous presentation. The widespread availability of smartphones has made it possible to use its data to generate digital behavioral models which can be used for both clinical and remote screening and monitoring purposes, providing a tentative and scalable solution to the pressing global need for early and effective solutions. This study is novel because it adds to the field by conducting a trial using private and non-intrusive sensors that can help detect and monitor depression in a continuous passive manner.

OBJECTIVE

This study demonstrates a novel mental behavioral profiling metric (Mental Health Similarity Score) derived from analyzing passively monitored, private and non-intrusive smartphone usage data, to identify and track depressive behavior and its progression. The analysis is performed using machine learning models trained on different levels of depression severity measured through the PHQ-9 (Patient Health Questionnaire-9) questionnaire.

METHODS

Smartphone data sets and self-reported 9-item PHQ depression assessments were collected from 558 smartphone users on the Android operating system in an observational study over an average of 10.7 days (SD=23.7). We quantified 37 digital behavioral markers from the passive smartphone data set and explored the relationship between the digital behavioral markers and depression using correlation coefficients and random forest models. We leveraged 4 supervised machine learning (ML) classification algorithms with hyperparameter optimization, fifteen-fold cross-validation, bootstrapping and imbalanced data handling to predict depression and its severity using PHQ-9 scores as the ground truth. We also quantified an additional 3 digital markers from gyroscope sensors and explored its feasibility in improving the model's accuracy in detecting depression.

RESULTS

Of the 558 participants, 254 (46%) were males and 286 (51%) were females and 18 (3%) preferred not to say. Participants age distribution is as follows: 474 (85%) users between the ages of 18-25, 29 (5%) aged between 26-35, 42 (7%) aged between 36-55, 10 (2%) were aged between 56-64 and 3 (<1%) above 64 years of age. Of the 558 reported PHQ-9 assessments, 63 responses were non-depressed (scored <5), 124 responses indicated mild depression (scored 5-9), 162 indicated moderate depression (scored 10-14), 131 indicated moderately severe (scored 15-19) and 78 indicated severe depression (scored 20-27), as identified by the PHQ-9 cut off points. Gender imbalance was present within each of the 5 severity groups, with a male majority in the non-depressed and mild groups and female majority in the moderate, moderately severe, and severe groups. Of the 469 individuals that reported having 'No Diagnosis' as their current diagnostic status in their demographic's questionnaire, 307 (65%) scored moderate to severe depression (PHQ-9 scores >=10). The PHQ-9 two class (none vs. severe) model achieved the following metrics: precision 85-89%; recall 85-89%; F1 87%, and overall accuracy is 87%. The PHQ-9 three class (none vs. mild vs. severe) model achieved the following metrics: precision 74-86%; recall 76-83%; F1 75-84%, and overall accuracy is 78%. A significant positive Pearson correlation was found between PHQ-9 questions 2, 6 and 9 within the severely depressed users and the mental behavioral profiling metric (r=0.73). The PHQ-9 question specific (questions 2,6, and 9) model achieved the following metrics: precision 76-80%; recall 75-81%; F1 78-89%, and overall accuracy is 78%. When adding a gyroscope sensor as a feature, the Pearson correlation between 2,6 and 9 dropped from r= 0.73 to r=0.46. Mean activity (P=3.08e-4) and average gap activity (P=1.69e-4) features from the gyroscope sensors had statistically significant differences between none and severe individuals. The PHQ-9 two class model + gyro features achieved the following metrics: precision 74-78%; recall 67-83%; F1 72-78%, and overall accuracy is 76%.

CONCLUSIONS

Our results demonstrate that the Mental Health Similarity Score can be used to identify and track depressive behavior and its progression with high accuracy. Therefore, the current and traditional methods of assessing depression can be coupled with digital behavioral markers to have a significant impact in mitigating depression and its far-reaching consequences.

CLINICALTRIAL

Continuous Scoring of Depression from EEG Signals via a Hybrid of Convolutional Neural Networks

Depression score is traditionally determined by taking the Beck depression inventory (BDI) test, which is a qualitative questionnaire. Quantitative scoring of depression has also been achieved by analyzing and classifying pre-recorded electroencephalography (EEG) signals. Here, we go one step further and apply raw EEG signals to a proposed hybrid convolutional and temporal-convolutional neural network (CNN-TCN) to continuously estimate the BDI score. In this research, the EEG signals of 119 individuals are captured by 64 scalp electrodes through successive eyes-closed and eyes-open intervals. Moreover, all the subjects take the BDI test and their scores are determined. The proposed CNN-TCN provides mean squared error (MSE) of 5.64±1.6 and mean absolute error (MAE) of 1.73±0.27 for eyes-open state and also provides MSE of 9.53±2.94 and MAE of 2.32±0.35 for the eyes-closed state, which significantly surpasses state-of-the-art deep network methods. In another approach, conventional EEG features are elicited from the EEG signals in successive frames and apply them to the proposed CNN-TCN in conjunction with known statistical regression methods. Our method provides MSE of 10.81±5.14 and MAE of 2.41±0.59 that statistically outperform the statistical regression methods. Moreover, the results with raw EEG are significantly better than those with EEG features.

War Psychiatry: Identifying and Managing the Neuropsychiatric Consequences of Armed Conflicts

War refugees and veterans have been known to frequently develop neuropsychiatric conditions including depression, post-traumatic stress disorder (PTSD), and anxiety disorders that tend to leave a long-lasting scar and impact their emotional response system. The shear stress, trauma, and mental breakdown from overnight displacement, family separation, and killing of friends and families cannot be described enough. Victims often require years of mental health support as they struggle with sleep difficulties, recurring memories, anxiety, grief, and anger. Everyone develops their coping mechanism which can involve dependence and long-term addiction to alcohol, drugs, violence, or gambling. The high prevalence of mental health disorders during and after the war indicates an undeniable necessity for screening those in need of treatment. For medical health professionals, it is crucial to identify such vulnerable groups who are prone to developing neuropsychiatric morbidities and associated risk factors. It is pivotal to develop and deploy effective and affordable multi-sectoral collaborative care models and therapy, which primarily depends upon family and primary care physicians in the conflict zones. Herein, we provide a brief overview regarding the identification and management of vulnerable populations, alongside discussing the challenges and possible solutions to the same.

Mapping the serum proteome to neurological diseases using whole genome sequencing

Despite the increasing global burden of neurological disorders, there is a lack of effective diagnostic and therapeutic biomarkers. Proteins are often dysregulated in disease and have a strong genetic component. Here, we carry out a protein quantitative trait locus analysis of 184 neurologically-relevant proteins, using whole genome sequencing data from two isolated population-based cohorts (N = 2893). In doing so, we elucidate the genetic landscape of the circulating proteome and its connection to neurological disorders. We detect 214 independently-associated variants for 107 proteins, the majority of which (76%) are cis-acting, including 114 variants that have not been previously identified. Using two-sample Mendelian randomisation, we identify causal associations between serum CD33 and Alzheimer's disease, GPNMB and Parkinson's disease, and MSR1 and schizophrenia, describing their clinical potential and highlighting drug repurposing opportunities.

Understanding complex functional wiring patterns in major depressive disorder through brain functional connectome

Brain function relies on efficient communications between distinct brain systems. The pathology of major depressive disorder (MDD) damages functional brain networks, resulting in cognitive impairment. Here, we reviewed the associations between brain functional connectome changes and MDD pathogenesis. We also highlighted the utility of brain functional connectome for differentiating MDD from other similar psychiatric disorders, predicting recurrence and suicide attempts in MDD, and evaluating treatment responses. Converging evidence has now linked aberrant brain functional network organization in MDD to the dysregulation of neurotransmitter signaling and neuroplasticity, providing insights into the neurobiological mechanisms of the disease and antidepressant efficacy. Widespread connectome dysfunctions in MDD patients include multiple, large-scale brain networks as well as local disturbances in brain circuits associated with negative and positive valence systems and cognitive functions. Although the clinical utility of the brain functional connectome remains to be realized, recent findings provide further promise that research in this area may lead to improved diagnosis, treatments, and clinical outcomes of MDD.

A Misdiagnosed Case of Schizoaffective Disorder With Bipolar Manifestations

Bipolar and schizoaffective disorders are both psychiatric illnesses that share common traits, but also significant differences. Due to the close overlap in symptoms, obtaining the correct diagnosis can be difficult. The management of these patients often poses a challenge to clinicians.

Five years ago, our patient was misdiagnosed with bipolar disorder with psychotic features. It was later discovered that she was suffering from schizoaffective disorder, bipolar type. The schizoaffective disorder involves symptoms of both schizophrenia and mood disorder.