Which Are the Most Burdensome Functioning Areas in Depression? A Cross-National Study

Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology

Depression has become the prevailing global mental health concern. The accuracy of traditional depression diagnosis methods faces challenges due to diverse factors, making primary identification a complex task. Thus, the imperative lies in developing a method that fulfills objectivity and effectiveness criteria for depression identification. Current research underscores notable disparities in brain activity between individuals with depression and those without. The Electroencephalogram (EEG), as a biologically reflective and easily accessible signal, is widely used to diagnose depression. This article introduces an innovative depression prediction strategy that merges time-frequency complexity and electrode spatial topology to aid in depression diagnosis. Initially, time-frequency complexity and temporal features of the EEG signal are extracted to generate node features for a graph convolutional network. Subsequently, leveraging channel correlation, the brain network adjacency matrix is employed and calculated. The final depression classification is achieved by training and validating a graph convolutional network with graph node features and a brain network adjacency matrix based on channel correlation. The proposed strategy has been validated using two publicly available EEG datasets, MODMA and PRED+CT, achieving notable accuracy rates of 98.30 and 96.51%, respectively. These outcomes affirm the reliability and utility of our proposed strategy in predicting depression using EEG signals. Additionally, the findings substantiate the effectiveness of EEG time-frequency complexity characteristics as valuable biomarkers for depression prediction.

Crime Risk and Depression Differentially Relate to Aspects of Sleep in Patients with Major Depression or Social Anxiety

Individuals with internalizing conditions such as depression or anxiety are at risk of sleep difficulties. Social-ecological models of sleep health propose factors at the individual (e.g., mental health) and neighborhood (e.g., crime risk) levels that contribute to sleep difficulties. However, these relationships have been under-researched in terms of internalizing conditions. Therefore, the current study comprised participants diagnosed with major depression (n = 24) or social anxiety (n = 35). Sleep measures included actigraphic variables (i.e., total sleep time, waking after sleep onset, sleep onset latency) and subjective sleep quality. Geocoding was used to assess nationally-normed crime risk exposure at the person level (e.g., murder, assault) and property level (e.g., robbery, burglary). Analyses consisted of independent t-tests to evaluate potential differences between diagnostic groups. To examine relationships, multiple regressions were used with internalizing symptoms, crime risk, and age as independent variables and sleep measures as the dependent variable. The t-test results revealed that groups differed in symptoms and age but not sleep or neighborhood crime. Regression results revealed crime risk positively corresponded with sleep onset latency but no other sleep measures. Also, only depression positively corresponded with total sleep time. Preliminary findings suggest exposure to crime and depression relate differentially to facets of sleep in individuals with internalizing conditions.

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.

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.

Cognitive Control, Cognitive Biases and Emotion Regulation in Depression: A New Proposal for an Integrative Interplay Model

Research traditions on cognition and depression focus on relatively unconnected aspects of cognitive functioning. On one hand, the neuropsychological perspective has concentrated on cognitive control difficulties as a prominent feature of this condition. On the other hand, the clinical psychology perspective has focused on cognitive biases and repetitive negative patterns of thinking (i.e., rumination) for emotional information. A review of the literature from both fields reveals that difficulties are more evident for mood-congruent materials, suggesting that cognitive control difficulties interact with cognitive biases to hinder cognitive switching, working memory updating, and inhibition of irrelevant information. Connecting research from these two traditions, we propose a novel integrative cognitive model of depression in which the interplay between mood-congruent cognitive control difficulties, cognitive biases, and rumination may ultimately lead to ineffective emotion-regulation strategies to downregulate negative mood and upregulate positive mood.

Relationship between subjective quality of life and perceptions of recovery orientation of treatment service in patients with schizophrenia and major depressive disorder

OBJECTIVE

This study aimed to investigate the relationship between subjective quality of life (QOL) and the specific domains of perceptions of recovery orientation of treatment services in patients with schizophrenia and major depressive disorder (MDD).

METHODS

One hundred and seventy-nine patients with schizophrenia spectrum disorders and fifty-seven patients with MDD were recruited. Patients were assessed on subjective QOL, self-reported depressive symptoms, illness severity, functioning, and perception of recovery orientation of the service environment (RSA). A multiple linear regression model was used to assess the relationship between QOL and RSA score, controlling for all other factors. Spearman correlation analysis was used to examine the relationship between RSA domains and total QOL in each diagnostic group separately.

RESULTS

The regression model explained 47.4 % of the variance observed in total QOL. Depressive symptoms, functioning and RSA were significantly associated with total QOL in the model. Domains one (life goals) and five (individually tailored services) of the RSA were associated with QOL in both groups. Domains two (patient involvement) and three (diversity of treatment options) were associated with total QOL only in patients with schizophrenia.

CONCLUSION

Our findings highlight that perceptions of recovery orientation of service, depressive symptoms and functioning significantly affected the subjective QOL of patients with serious mental illness. The differential relationship observed between QOL and domains of RSA in patients with MDD and schizophrenia suggests that targeted interventions meeting the needs of different patient groups may be crucial to improve QOL of patients.

Long-term disability in major depressive disorder: a 6-year follow-up study

BACKGROUND

Major depressive disorder (MDD) represents a leading cause of disability. This study examines the course of disability in patients with chronic, recurrent and remitting MDD compared to healthy controls and identifies predictors of disability in remitting MDD.

METHODS

We included 914 participants from the Netherlands Study of Depression and Anxiety (NESDA). DSM-IV MDD and WHO DAS II disability were assessed at baseline and at 2, 4 and 6 years. Six-year total and domain-specific disability were analysed and compared in participants with chronic (n = 57), recurrent (n = 120), remitting (n = 127) MDD and in healthy controls (n = 430). Predictors of residual disability were identified using linear regression analysis.

RESULTS

At baseline, most disability was found in chronic MDD, followed by recurrent MDD, remitting MDD and healthy controls. Across diagnostic groups, most disability was found in household activities, interpersonal functioning, participation in society and cognition. A chronic course was associated with chronic disability. Symptom remission was associated with a decrease in disability, but some disability remained. In remitting MDD, higher residual disability was predicted by older age, more severe avoidance symptoms, higher disability at baseline and late symptom remission. Severity of residual disability correlated with the severity of residual depressive symptoms.

CONCLUSIONS

Symptomatic remission is a prerequisite for improvements in disability. However, disability persists despite symptom remission. Therefore, treatment of MDD should include an explicit focus on disability, especially on the more complex domains. To this end, treatments should promote behavioural activation and address subthreshold depressive symptoms in patients with remitted MDD.

Inflammation and central adiposity as mediators of depression and uncontrolled diabetes in the study on global AGEing and adult health (SAGE)

OBJECTIVES

Diabetes and depression are commonly present in the same individuals, suggesting the possibility of underlying shared physiological processes. Inflammation, as assessed with the biomarker C-reactive protein (CRP), has not consistently explained the observed relationship between diabetes and depression, although both are associated with inflammation and share proposed inflammatory mechanisms. Central adiposity has also been associated with both conditions, potentially by causing increased inflammation. This study uses the World Health Organization's Study on global AGEing and adult health (SAGE) Mexico Wave 1 biomarker data (n = 1831) to evaluate if inflammation and central adiposity mediate the relationship between depression and diabetes.

METHODS

Depression was estimated using a behavior-based diagnostic algorithm, inflammation using venous dried blood spot (DBS) CRP, central adiposity using waist-to-height ratio (WHtR), and uncontrolled diabetes using venous DBS-glycated hemoglobin (HbA1c).

RESULTS

The association between depression and uncontrolled diabetes was partially mediated by CRP before but not after WHtR was considered. When WHtR was added to the model, it partially mediated the relationship between diabetes and depression while fully mediating the relationship between depression and CRP.

CONCLUSIONS

These findings suggest that central adiposity may be a more significant mediator between diabetes and depression than inflammation and account for the relationship between these disorders and inflammation. Depression may cause an increase in central adiposity, which then may lead to diabetes, but the increase in known systemic inflammatory pathways caused by central adiposity may not be the key pathological mechanism.

The association of cognitive deficits with mental and physical Quality of Life in Major Depressive Disorder

BACKGROUND

Patients with Major Depressive Disorder experience significantly reduced subjective Quality of Life (QOL), including impaired social and emotional functioning and greater fatigue and physical pain. Mounting evidence suggests that cognitive dysfunction (e.g., deficits in memory, executive function) contributes independently to the onset of reduced QOL, however the domain-specific nature of this relationship has not been investigated. The present study examined the relationship between specific cognitive domains (e.g., attention, spatial cognition) and specific deficits in mental and physical QOL in subjects with lifetime MDD, as well as acutely depressed, remitted and healthy participants.

METHODS

Data were obtained (N = 387) from the Cognitive Function and Mood Study (COFAMS), a cross-sectional study of emotional, functional and cognitive status in individuals with mood disorders. Participants' (acutely depressed n = 93, remitted n = 170, and healthy control n = 124) QOL was assessed with the 36-Item Short Form Health Survey (SF-36) and cognitive functioning was evaluated with the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), the Colorado Assessment Tests (CATs) and the Psychology Experiment Building Language (PEBL).

RESULTS

Analyses revealed that poor immediate and delayed memory were associated with reduced mental QOL in individuals with lifetime MDD, acutely depressed, and healthy controls. In contrast, cognitive functioning was not associated with mental QOL in remitted patients. No cognitive domains were significantly related to physical QOL in any participant group.

CONCLUSIONS

The result suggests that deficits in immediate and delayed memory may contribute to reduced mental QOL in acute MDD, whereas cognition does not appear to play a role in physical QOL. Memory should be considered important cognitive treatment targets for MDD patients suffering specifically from reduced mental QOL.

Long-term outcomes in patients with severe depression after in-hospital treatment - study protocol of the depression long-term Augsburg (DELTA) study

INTRODUCTION

Depressive disorders are very common diseases entailing a great burden on affected people. However, comprehensive information on long-term disease course in patients with severe depression is lacking so far. The objectives of the DELTA study are to examine long-term outcomes and their predicting factors, to assess clinical response of antidepressant pharmacotherapy by applying therapeutic drug monitoring, to identify predictors of therapeutic non-response, to describe the long-term healthcare utilisation and to investigate the role of biomarkers in disease course.

METHODS AND ANALYSIS

A cohort study including all adult hospitalised cases (age range 18 to 75 years) of severe major depression who are admitted to the Bezirkskrankenhaus Augsburg is established. It is planned to include 300 patients. During the hospital stay, information is gathered through personal interview, self-administered questionnaires, cognitive tests and chart review. Furthermore, biomaterials are collected. After hospital discharge, patients are repeatedly re-examined over time (3, 6, 12, 24 and 36 months) to collect information about mortality, relapse, depression severity, health-related quality of life (HRQOL), perceived stigma, cognitive functions, diet, physical activity, treatment and healthcare utilisation. Follow-up blood samples are collected to determine therapeutic drug levels. The primary study aim is to investigate long-term therapeutic response, survival, relapse, HRQOL and cognitive functions. Survival time and time to relapse or re-hospitalisation will be analysed using Cox regression models. Changes of HRQOL, depressive symptoms and cognitive functions over time will be examined using generalised linear regression models for repeated measures or mixed models. Correlates of the disease course will be modelled using suitable generalised linear, mixed, estimating equation and growth curve models.

ETHICS AND DISSEMINATION

The study protocol was approved by the Ethics Committee of the Ludwig-Maximilians-Universität München (date of approval: 23 October 2017, reference number: 17-625). Study results will be presented at scientific conferences and published in peer-reviewed scientific journals.

Older People, Mobility and Transport in Low- and Middle-Income Countries: A Review of the Research

Older populations are rising globally, which in high-income countries has helped to generate a growing literature on the impact of ageing on travel requirements and transport policy. This article aims to provide an initial assessment of the state of knowledge on the impact on transportation policy and usage of the increasing numbers of older people in low- and middle-income countries (LAMICs), through a review of the literature relating to older people and transportation. As both the academic and policy/practice-related literature specifically addressing ageing and transport in LAMICs is limited, the study looks beyond transportation to assess the state of knowledge regarding the ways in which older people’s mobility is affected by issues, such as health, well-being, social (dis)engagement and gender. We find significant knowledge gaps, resulting in an evidence base to support the implementation of policy is lacking. Most research in low-income countries (LICs) is either broad quantitative analysis based on national survey data or small-scale qualitative studies. We conclude that, although study of the differing contexts of ageing in LAMICs as they relate to older people’s mobilities and transport use has barely begun, institutions which both make and influence policymaking recognise the existence of significant knowledge gaps. This should provide the context in which research agendas can be established.

The clinical relationship between cognitive impairment and psychosocial functioning in major depressive disorder: A systematic review

Cognitive deficits are frequently observed in major depressive disorder (MDD), as well as impaired long-term psychosocial functioning. However, the relationship between cognitive deficits and psychosocial functioning in MDD is under-investigated. We aim to systematically review the literature on the relationship between specific cognitive impairments and psychosocial functioning in MDD. We systematically reviewed English-language literature in PubMed, PsychINFO, Scopus and Web of Science using search terms related to psychosocial functioning. Additional studies were identified by searching reference lists. Following our inclusion/exclusion criteria, 28 studies were reviewed. Inclusion criteria included age (> 18), MDD diagnosed by standard tools (e.g., DSM-IV), use of cognitive and psychosocial assessments. Cross-sectional studies indicated that cognitive deficits in domains of executive functioning, attention, memory, and global cognition are associated with psychosocial dysfunction in domains of as quality of life, and social, occupational, and global functioning. The cognition-functioning relationship was also observed in longitudinal studies, showing that only specific cognitive domains affected psychosocial outcomes over the long-term course of illness. Older age and greater MDD symptom severity appear to enhance cognition-psychosocial dysfunction relationship, however little is known regarding the role of a number of other clinical factors (e.g., psychosis, illness duration).

Quality of Life in Patients With a Major Mental Disorder in Singapore

Background: There has been a paradigm shift in mental health service delivery, from a focus on reducing symptoms to a more holistic approach, which considers Quality of Life (QoL). Method: This study aimed to explore prediction of Quality of Life (QoL) in Asian patients with a major mental disorder i.e., depression or schizophrenia in Singapore. In the current study, there were 43 patients (65.1% females) with depression. Their ages ranged from 18 to 65 (M = 44.63, SD = 12.22). The data were combined with the data on patients with schizophrenia, where there were 43 patients (65.1% females) with schizophrenia, their ages ranging from 18 to 65 (M = 44.60, SD = 12.19). Results: The components of QoL were examined i.e., Physical Component Summary (PCS) and Mental Component Summary (MCS). For all patients, social support and age accounted for 17.3% of the variance in PCS, F _{(2, 83)} = 8.66, p < 0.001. For patients with depression, disorder severity, age, and duration of treatment accounted for 48.3% of the variance in PCS, F _{(3, 39)} = 12.15, p < 0.001. For patients with schizophrenia, education (Primary or Lower vs. Post-Secondary or Higher) and emotional coping accounted for 21.3% of the variance in PCS, F _{(2, 40)} = 5.40, p < 0.01. For all patients, self-efficacy and age accounted for 27.0% of the variance in MCS, F _{(2, 83)} = 15.37, p < 0.001. For patients with depression, disorder severity accounted for 45.6% of the variance in MCS, F _{(1, 41)} = 34.33, p < 0.001. For patients with schizophrenia, number of hospitalizations accounted for 18.5% of the variance in MCS, F _{(1, 41)} = 9.29, p < 0.01. Conclusion: The findings were discussed in regards to implications in interventions to enhance QoL of patients with schizophrenia and depression in Singapore.

Association of experienced and evaluative well-being with health in nine countries with different income levels: a cross-sectional study

BACKGROUND

It is important to know whether the relationships between experienced and evaluative well-being and health are consistent across countries with different income levels. This would allow to confirm whether the evidence found in high income countries is the same as in low- and middle-income countries and to suggest policy recommendations that are generalisable across countries. We assessed the association of well-being with health status; analysed the differential relationship that positive affect, negative affect, and evaluative well-being have with health status; and examined whether these relationships are similar across countries.

METHODS

In this cross-sectional study, interviews were conducted amongst 53,269 adults from nine countries in Africa, Asia, Europe, and Latin America. Evaluative well-being was measured with a short version of the World Health Organization (WHO) Quality of Life instrument, and experienced well-being was measured with the Day Reconstruction Method. Decrements in health were assessed with the 12-item version of WHO Disability Assessment Schedule 2.0. Block-wise linear regression and structural equation models were employed.

RESULTS

Considering the overall sample, evaluative well-being was more strongly associated with health (β = -0.35) than experienced well-being (β = -0.14), and negative affect was more strongly associated with health (β = 0.10) than positive affect (β = -0.02). The relationship between health and well-being was similar across countries. Lower scores in evaluative well-being and a higher age were the factors more strongly related with a worse health.

CONCLUSIONS

The different patterns observed across countries may be related to differences in the countries' gross domestic product, social protection system, economic situation, health care provision, lifestyle behaviours, or living conditions. The fact that evaluative well-being is more predictive of health than experienced well-being suggests that our level of satisfaction with our lives might be more important for our health than the actual emotions than we experience in our day-to-day lives and points out the need of interventions that improve the way people evaluate their lives.