Detecting depression severity using weighted random forest and oxidative stress biomarkers.

This study employs machine learning to detect the severity of major depressive disorder (MDD) through binary and multiclass classifications. We compared models that used only biomarkers of oxidative stress with those that incorporate sociodemographic and health-related factors. Data collected from 830 participants, based on the Patient Health Questionnaire (PHQ-9) score, inform our analysis. In binary classification, the Random Forest (RF) classifier achieved the highest Area Under the Curve (AUC) of 0.84 when all features were included. In multiclass classification, the AUC improved from 0.84 with only oxidative stress biomarkers to 0.88 when all characteristics were included. To address data imbalance, weighted classifiers, and Synthetic Minority Over-sampling Technique (SMOTE) approaches were applied. Weighted random forest (WRF) improved multiclass classification, achieving an AUC of 0.91. Statistical tests, including the Friedman test and the Conover post-hoc test, confirmed significant differences between model performances, with WRF using all features outperforming others. Feature importance analysis shows that oxidative stress biomarkers, particularly GSH, are top ranked among all features. Clinicians can leverage the results of this study to improve their decision-making processes by incorporating oxidative stress biomarkers in addition to the standard criteria for depression diagnosis.

Underdiagnosis, Undertreatment, and Noncompliance With Treatment in People Who Died by Suicide.

Background: This study explored the characteristics of people who die by suicide, comparing those who had depression with those who did not.Methods: Clinical data were collected through a postmortem proxy-based semistructured interview (psychological autopsy). Postmortem toxicological analysis provides data on the presence of substances or drugs in the blood of suicides. Participants were adults who died by suicide in the province of Seville, Spain, during 2006-2016. The main independent variables were previous diagnosis, postmortem diagnosis, prescribed treatment, and treatment found in blood. The primary outcome was the postmortem diagnosis of depression, after which the sample was divided into 2 groups according to DSM IV criteria to the presence or absence of major depressive episode (MDE).Results: Our sample is composed of 313 people, of which 200 (63.9%) had a diagnosis of MDE according to the psychological autopsy. Predeath diagnosis of depression was more frequent in MDE suicides than in non-MDE suicides (18.6% vs 3.5%, respectively; Χ2 = 23.420; df = 9; P = .005) and had more access to mental health treatment previous to death (67.7% vs 35.6%, respectively; Χ2 = 27.572; df = 1; P < .001). Antidepressants were prescribed in 21.5% of the MDE suicides, but only 8.5% of them were taking them at the time of death according to the toxicology exam.Conclusions: The underdiagnosis of depression in people who die by suicide is striking, as is the undertreatment. Further efforts must be made to train primary care physicians in the proper identification of persons at risk of suicide, as they are one of the main gatekeepers in the fight for suicide prevention.

Campbell's 1953 Book on "Manic-Depressive Disease": A Counter-Factual History of the DSM Symptomatic "A Criteria" for Major Depression.

ABSTRACT: The DSM-III symptomatic criteria for major depression (MD) were derived from those proposed by Feighner and colleagues in 1972, which closely resembled those published by Cassidy in 1957. I here present a counter-factual history in which Feighner carefully read a key reference in Cassidy, a large 1953 follow-up study by Campbell of depressed patients with detailed tables of depressive signs and symptoms. In this alternative timeline, the Feighner criteria for MD were modified by Campbell's results, which then changed DSM-III and subsequent MD criteria sets. The historical pathway to the current DSM MD criteria was contingent on a range of historical events and could easily have been different. This story is not meant to criticize DSM MD criteria that perform well. Rather, it suggests that these criteria represent a useful but fallible set of symptoms/signs that index but do not constitute MD and therefore are not to be reified.

MDDBranchNet: A Deep Learning Model for Detecting Major Depressive Disorder Using ECG Signal.

Major depressive disorder (MDD) is a chronic mental illness which affects people's well-being and is often detected at a later stage of depression with a likelihood of suicidal ideation. Early detection of MDD is thus necessary to reduce the impact, however, it requires monitoring vitals in daily living conditions. EEG is generally multi-channel and due to difficulty in signal acquisition, it is unsuitable for home-based monitoring, whereas, wearable sensors can collect single-channel ECG. Classical machine-learning based MDD detection studies commonly use various heart rate variability features. Feature generation, which requires domain knowledge, is often challenging, and requires computation power, often unsuitable for real time processing, MDDBranchNet is a proposed parallel-branch deep learning model for MDD binary classification from a single channel ECG which uses additional ECG-derived signals such as R-R signal and degree distribution time series of horizontal visibility graph. The use of derived branches was able to increase the model's accuracy by around 7%. An optimal 20-second overlapped segmentation of ECG recording was found to be beneficial with a 70% prediction threshold for maximum MDD detection with a minimum false positive rate. The proposed model evaluated MDD prediction from signal excerpts, irrespective of location (first, middle or last one-third of the recording), instead of considering the entire ECG signal with minimal performance variation stressing the idea that MDD phenomena are likely to manifest uniformly throughout the recording.

Endocannabinoid levels in female-sexed individuals with diagnosed depression: a systematic review.

BACKGROUND: Major depressive disorder (MDD) is a highly prevalent mental health disorder with females experiencing higher rates of depression (11.6%), anxiety (15.7%) and physiological distress (14.5%) than males. Recently, the Endocannabinoid system (ECS) has been proposed to be a key contributing factor in the pathogenesis and symptom severity of MDD due to its role in neurotransmitter production, inflammatory response and even regulation of the female reproductive cycle. This review critically evaluates evidence regarding ECS levels in female-sexed individuals with depressive disorders to further understand ECS role. MATERIALS AND METHODS: A systematic literature review of available research published prior to April 2022 was identified using PubMed (U.S. National Library of Medicine), CINAHL (EBSCO), Web of Science, AMED and Scopus (Elsevier). Studies were included if they reported ECS analysis of female-sexed individuals with depression and were excluded if they did not differentiate results between sexes, assessed mental health conditions other than depression, tested efficacy of endocannabinoid/n-acylethanolamine/cannabis or marijuana administration and that were unable to be translated. Critical appraisal of each included study was undertaken using the Joanna Briggs Institute Critical Appraisal Tool for Systematic Reviews. RESULTS: The 894 located citations were screened for duplicates (n = 357) and eligibility by title and abstract (n = 501). The full text of 33 studies were reviewed, and 7 studies were determined eligible for inclusion. These studies indicated that depressed female-sexed individuals have altered levels of ECS however no significant pattern was identified due to variability of study outcomes and measures, limiting overall interpretation. DISCUSSION: This review suggests potential involvement of ECS in underlying mechanisms of MDD in female sexed-individuals, however no pattern was able to be determined. A major contributor to the inability to attain reliable and valid understanding of the ECS levels in female-sexed individuals with depression was the inconsistency of depression screening tools, inclusion criteria's and analysis methods used to measure eCBs. Future studies need to implement more standardised methodology to gain a deeper understanding of ECS in female-sexed individuals with depressive disorders. TRIAL REGISTRATION : This review was submitted to PROSPERO for approval in April 2022 (Registration #CRD42022324212).

Evaluation of serum MIP-1ß and MCP-2 levels in major depressive disorder: A case-control study.

BACKGROUND: Major depressive disorder (MDD) is a common and debilitating mental illness characterized by persistent feelings of sadness, hopelessness, and a lack of interest in daily activities. The objective of this study was to investigate whether levels of macrophage inflammatory protein-1ß (MIP-1ß) and macrophage chemoattractant protein-2 (MCP-2) in the blood were associated with the pathophysiology and development of MDD compared to healthy controls (HCs). METHODS: This case-control study was conducted involving 50 MDD patients and 38 HCs. We performed a comprehensive assessment to match age, sex, BMI, and socio-demographic profile between the groups. The study excluded participants with chronic infection, inflammatory diseases, coexisting psychiatric disorder, history of liver and kidney diseases, and individuals who are under antipsychotic medications. A professional psychiatrist diagnosed MDD patients and evaluated HCs based on the Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5) criteria. The severity of depression was assessed using the Hamilton Depression (Ham-D) rating scale. Commercially available enzyme-linked immunosorbent assay (ELISA) kits were used to quantify the serum MIP-1ß and MCP-2 levels. RESULTS: The results indicated elevated serum MIP-1ß levels (207.73±24.24 pg/ml) in MDD patients compared to HCs (58.77±9.14 pg/ml). This difference in concentration is positively correlated with severity of disease symptoms (r = 0.451; p<0.001). Similarly, the levels of MCP-2 were found to be elevated in patients compared to controls (143.61±19.92 vs. 56.84±4.02 pg/ml; p = 0.003), with a positive correlation with the Ham-D scores (r = 0.373; p = 0.004). CONCLUSION: According to this study, elevated levels of MIP-1ß and MCP-2 may be associated with the pathophysiology and development of MDD. These increased serum MIP-1ß and MCP-2 levels could be used as risk assessment tools for MDD. The present findings urge further research and the development of therapeutic and diagnostic approaches for depression.

Pharmacogenetic testing in primary care could bolster depression treatment: A value proposition.

Pharmacogenetic testing could reduce the time to identify a safe and effective medication for depression; however, it is underutilized in practice. Major depression constitutes the most common mental disorder in the US, and while antidepressant therapy can help, the current trial -and error approach can require patients to endure multiple medication trials before finding one that is effective. Tailoring the fit of pharmacogenetic testing with prescribers' needs across a variety of settings could help to establish a generalizable value proposition to improve likelihood of adoption. We conducted a study to explore the value proposition for health systems using pharmacogenetic testing for mental health medications through prescribers' real-world experiences using implementation science concepts and systematic interviews with prescribers and administrators from four health care systems. To identify a value proposition, we organized the themes according to the Triple Aim framework, a leading framework for health care policy which asserts that high-value care should focus on three key metrics: (1) better health care quality and (2) population-level outcomes with (3) reduced per capita costs. Primary care providers whom we interviewed said that they value pharmacogenetic testing because it would provide more information about medications that they can prescribe, expanding their ability to identify medications that best-fit patients and reducing their reliance on referrals to specialists; they said that this capacity would help meet patients' needs for access to mental health care through primary care. At the same time, prescribers expressed differing views about how pharmacogenetic testing can help with quality of care and whether their views about out-of-pocket cost would prevent them from offering it. Thus, implementation should focus on integrating pharmacogenetic testing into primary care and using strategies to support prescribers' interactions with patients.

Developing prediction algorithms for late-life depression using wearable devices: a cohort study protocol.

INTRODUCTION: Despite the high prevalence of major depressive disorder (MDD) among the elderly population, the rate of treatment is low due to stigmas and barriers to medical access. Wearable devices such as smartphones and smartwatches can help to screen MDD symptoms earlier in a natural setting while forgoing these concerns. However, previous research using wearable devices has mostly targeted the younger population. By collecting longitudinal data using wearable devices from the elderly population, this research aims to produce prediction algorithms for late-life depression and to develop strategies that strengthen medical access in community care systems. METHODS AND ANALYSIS: The current cohort study recruited a subsample of 685 elderly people from the Korean Genome and Epidemiology Study-Cardiovascular Disease Association Study, a national large cohort established in 2004. The current study has been conducted over a 3-year period to explore the development patterns of late-life depression. Participants have completed three annual face-to-face interviews (baseline, the first follow-up and the second follow-up) and 2 years of app-based surveys and passive sensing data collection. All the data collection will end at the second follow-up interview. The collected self-report, observational and passive sensing data will be primarily analysed by machine learning. ETHICS AND DISSEMINATION: This study protocol has been reviewed and approved by the Yonsei University Mirae Campus Institutional Review Board (1041849-2 02 111 SB-180-06) in South Korea. All participants provided written informed consent. The findings of this research will be disseminated by academic publications and conference presentations.

EEG based functional connectivity in resting and emotional states may identify major depressive disorder using machine learning.

OBJECTIVE: Disrupted brain network connectivity underlies major depressive disorder (MDD). Altered EEG based Functional connectivity (FC) with Emotional stimuli in major depressive disorder (MDD) in addition to resting state FC may help in improving the diagnostic accuracy of machine learning classification models. We explored the potential of EEG-based FC during resting state and emotional processing, for diagnosing MDD using machine learning approach. METHODS: EEG was recorded during resting state and while watching emotionally contagious happy and sad videos in 24 drug-naïve MDD patients and 25 healthy controls. FC was quantified using the Phase Lag Index. Three Random Forest classifier models were constructed to classify MDD patients and healthy controls, Model-I incorporating FC features from the resting state and Model-II and Model-III incorporating FC features while watching happy and sad videos respectively. RESULTS: Important features distinguishing MDD and healthy controls were from all frequency bands and represent functional connectivity between fronto-temporal, fronto-parietal and fronto occipital regions. The cross-validation accuracies for Model-I, Model-II and Model-III were 92.3%, 94.9% and 89.7% and test accuracies were 60%, 80% and 70% respectively. Incorporating emotionally contagious videos improved the classification accuracies. CONCLUSION: Findings support EEG FC patterns during resting state and emotional processing along with machine learning can be used to diagnose MDD. Future research should focus on replicating and validating these results. SIGNIFICANCE: EEG FC pattern combined with machine learning may be used for assisting in diagnosing MDD.

Discriminative analysis of schizophrenia and major depressive disorder using fNIRS.

BACKGROUND: Research into the shared and distinct brain dysfunctions in patients with schizophrenia (SCZ) and major depressive disorder (MDD) has been increasing. However, few studies have explored the application of functional near-infrared spectroscopy (fNIRS) in investigating brain dysfunction and enhancing diagnostic methodologies in these two conditions. METHODS: A general linear model was used for analysis of brain activation following task-state fNIRS from 131 patients with SCZ, 132 patients with MDD and 130 healthy controls (HCs). Subsequently, seventy-seven time-frequency analysis methods were used to construct new features of fNIRS, followed by the implementation of five machine learning algorithms to develop a differential diagnosis model for the three groups. This model was evaluated by comparing it to both a diagnostic model relying on traditional fNIRS features and assessments made by two psychiatrists. RESULTS: Brain activation analysis revealed significantly lower activation in Broca's area, the dorsolateral prefrontal cortex, and the middle temporal gyrus for both the SCZ and MDD groups compared to HCs. Additionally, the SCZ group exhibited notably lower activation in the superior temporal gyrus and the subcentral gyrus compared to the MDD group. When distinguishing among the three groups using independent validation datasets, the models utilizing new fNIRS features achieved an accuracy of 85.90 % (AUC = 0.95). In contrast, models based on traditional fNIRS features reached an accuracy of 52.56 % (AUC = 0.66). The accuracies of the two psychiatrists were 42.00 % (AUC = 0.60) and 38.00 % (AUC = 0.50), respectively. CONCLUSION: This investigation brings to light the shared and distinct neurobiological abnormalities present in SCZ and MDD, offering potential enhancements for extant diagnostic systems.

Relationship between Patient Health Questionnaire (PHQ-9) and Montgomery-Asberg Depression Rating Scale (MADRS) total scores in older adults with major depressive disorder: An analysis of the OPTIMUM clinical trial.

BACKGROUND: The Patient Health Questionnaire (PHQ-9) and Montgomery-Asberg Depression Rating Scale (MADRS) are commonly used scales to measure depression severity in older adults. METHODS: We utilized data from the Optimizing Outcomes of Treatment-Resistant Depression in Older Adults (OPTIMUM) clinical trial to produce conversion tables relating PHQ-9 and MADRS total scores. We split the sample into training (N = 555) and validation samples (N = 187). Equipercentile linking was performed on the training sample to produce conversion tables for PHQ-9 and MADRS. We compared the original and estimated scores in the validation sample with Bland-Altman analysis. We compared the depression severity level using the original and estimated scores with Chi-square tests. RESULTS: The Bland-Altman analysis confirmed that differences between the original and estimated scores for at least 95 % of the sample fit within 1.96 standard deviations of the mean difference. Chi-square tests showed a significant difference in the proportion of participants at each depression severity category determined using the original and estimated scores. LIMITATIONS: The conversion tables should be used with caution when comparing depression severity at the individual level. CONCLUSIONS: Our conversion tables relating PHQ-9 and MADRS scores can be used to compare treatment outcomes using aggregate data in studies that only used one of these scales.

Usability of the Japanese Late-Stage Elderly Questionnaire for screening major depression.

BACKGROUND: Older adults with major depression are at risk of frailty and long-term care needs. Consequently, screening for major depression is imperative to prevent such risks. In Japan, the Late-Stage Elderly Questionnaire was developed to evaluate older adults' holistic health, including mental well-being. It comprises one specific question to gauge life satisfaction, but the effectiveness of this question to screen for major depression remains unclear. Therefore, we aimed to assess the usability of this question to screen for major depression. METHODS: This retrospective cohort study used a large, commercially available claims database in Japan. Participants were older adults aged ≥75 years who completed the Late-Stage Elderly Questionnaire and were classified with and without new major depression within 1 year. We evaluated the questionnaire's ability to screen for major depression using C-statistics, developing three models to assess the cut-off value based on responses to the life satisfaction question ('Satisfied', 'Somewhat satisfied', 'Somewhat unsatisfied', or 'Unsatisfied'), estimating the sensitivity and specificity of each model. RESULTS: Among 11 117 older adults, 77 newly experienced major depression within 1 year. The C-statistic for screening major depression was 0.587. The model setting the cut-off between 'Somewhat unsatisfied' and 'Unsatisfied' the demonstrated lowest sensitivity and highest specificity, while the model setting the cut-off between 'Satisfied' and 'Somewhat satisfied' demonstrated highest sensitivity and lowest specificity. CONCLUSIONS: Our results suggest that due to its poor screening ability and high rate of false negatives, the question assessing life satisfaction in the Late-Stage Elderly Questionnaire may not be useful for screening major depression in older adults and may require modification.

Recognizing depression as an inflammatory disease: the search for endotypes.

Major depressive disorder (MDD) affects millions of individuals worldwide, leading to considerable social and economic costs. Despite advancements in pharmacological treatments, achieving remission remains a key challenge, with a substantial number of patients showing resistance to existing therapies. This resistance is often associated with elevated levels of proinflammatory cytokines, suggesting a connection between inflammation, MDD pathophysiology, and treatment efficacy. The observation of increased immune activation in about a quarter of patients with MDD resulted in the distinction between inflammatory and noninflammatory endotypes. Although anti-inflammatory treatments show promise in alleviating depression-like symptoms, responses are heterogeneous, thus highlighting the importance of identifying distinct inflammatory endotypes to tailor effective therapeutic strategies. The intestinal microbiome emerges as a crucial modulator of mental health, mediating its effects partially through different immune pathways. Microbiota-derived short-chain fatty acids (SCFAs) significantly impact innate and adaptive immune cells, regulating their differentiation, function, and cellular response. Furthermore, gut-educated immune cells reach the border regions of the central nervous system (CNS), regulating glial cell functions. Although the CNS modulates immune responses via efferent parts of the vagus nerve, afferent tracts concurrently transport information on peripheral inflammation back to the brain. This bidirectional communication is particularly relevant in depression, allowing for therapeutic stimulation of the vagus nerve in the context of inflammatory depression endotypes. In this review, we explore the intricate relationship between inflammation and depression, discuss how inflammatory signals are translated into depressive-like symptoms, and highlight immune-modulating therapeutic avenues.

Developing a Five-Minute Normative Database of Heart Rate Variability for Diagnosing Cardiac Autonomic Dysregulation for Patients with Major Depressive Disorder.

Heart rate variability (HRV) is related to cardiac vagal control and emotional regulation and an index for cardiac vagal control and cardiac autonomic activity. This study aimed to develop the Taiwan HRV normative database covering individuals aged 20 to 70 years and to assess its diagnosing validity in patients with major depressive disorder (MDD). A total of 311 healthy participants were in the HRV normative database and divided into five groups in 10-year age groups, and then the means and standard deviations of the HRV indices were calculated. We recruited 272 patients with MDD for cross-validation, compared their HRV indices with the normative database, and then converted them to Z-scores to explore the deviation of HRV in MDD patients from healthy groups. The results found a gradual decline in HRV indices with advancing age in the HC group, and females in the HC group exhibit higher cardiac vagal control and parasympathetic activity than males. Conversely, patients in the MDD group demonstrate lower HRV indices than those in the HC group, with their symptoms of depression and anxiety showing a negative correlation with HRV indices. The Taiwan HRV normative database has good psychometric characteristics of cross-validation.

Can the DEX/CRH test or markers of oxidative stress distinguish work-related stress from major depressive disorder and normal controls?

Hypothalamic-pituitary-adrenal (HPA)-axis hyperactivity measured by the combined dexamethasone-CRH test (DEX-CRH test) has been found in patients with major depressive disorder (MDD), whereas hypoactivity has been found in patients with work-related stress. We aimed to investigate the DEX-CRH test as a biomarker to distinguish between MDD and work-related stress (exhaustion disorder - ED). We hypothesized that there would be lower cortisol and ACTH response in participants with ED compared to MDD and healthy controls (HC). Also, we explored if the cortisol response of those patients interacted with robust markers of oxidative stress. Thirty inpatients with MDD and 23 outpatients with ED were recruited. Plasma cortisol and ACTH were sampled during a DEX-CRH test. The main outcome measure, area under the curve (AUC) for cortisol and ACTH, was compa-red between MDD vs. ED participants and a historical HC group. Secondary markers of oxidative stress urinary 8-oxodG and 8-oxoGuo; quality of sleep and psychometrics were obtained. Cortisol concentrations were higher in MDD and ED participants compared to HC, and no differences in AUC cortisol and ACTH were found between ED vs. MDD. Compared to ED, MDD participants had higher stress symptom severity and a lower sense of well-being. No differences in oxidative stress markers or quality of sleep between the groups were found. The result indicates that the patients with ED, like patients with MDD, are non-suppressors in DEX-CRH test and not hypocortisolemic as suggested.

High-definition transcranial direct current stimulation (HD-tDCS) in major depressive disorder with anxious distress-a study protocol for a double-blinded randomized sham-controlled trial.

BACKGROUND: Comorbid anxiety disorders and anxious distress are highly prevalent among individuals with major depressive disorder (MDD). The presence of the DSM-5 anxious distress specifier (ADS) has been associated with worse treatment outcomes and chronic disease course. Few studies have evaluated the therapeutic effects of High-definition transcranial direct current stimulation (HD-tDCS) on depressive and anxiety symptoms among MDD patients with ADS. The current randomized controlled trial aims to assess the efficacy of HD-tDCS as an augmentation therapy with antidepressants compared to sham-control in subjects of MDD with ADS. METHODS: MDD patients with ADS will be recruited and randomly assigned to the active HD-tDCS or sham HD-tDCS group. In both groups, patients will receive the active or sham intervention in addition to their pre-existing antidepressant therapy, for 2 weeks with 5 sessions per week, each lasting 30 min. The primary outcome measures will be the change of depressive symptoms, clinical response, and the remission rate as measured with the 17-item Hamilton Depression Rating Scale (HDRS-17) before and after the intervention and at the 2nd and 6th week after the completed intervention. Secondary outcome measures include anxiety symptoms, cognitive symptoms, disability assessment, and adverse effects. DISCUSSION: The HD-tDCS applied in this trial may have treatment effects on MDD with ADS and have minimal side effects. TRIAL REGISTRATION: The trial protocol is registered with www.chictr.org.cn under protocol registration number ChiCTR2300071726. Registered 23 May 2023.

GCTNet: a graph convolutional transformer network for major depressive disorder detection based on EEG signals.

Objective.Identifying major depressive disorder (MDD) using objective physiological signals has become a pressing challenge.Approach.Hence, this paper proposes a graph convolutional transformer network (GCTNet) for accurate and reliable MDD detection using electroencephalogram (EEG) signals. The developed framework integrates a residual graph convolutional network block to capture spatial information and a Transformer block to extract global temporal dynamics. Additionally, we introduce the contrastive cross-entropy (CCE) loss that combines contrastive learning to enhance the stability and discriminability of the extracted features, thereby improving classification performance.Main results. The effectiveness of the GCTNet model and CCE loss was assessed using EEG data from 41 MDD patients and 44 normal controls, in addition to a publicly available dataset. Utilizing a subject-independent data partitioning method and 10-fold cross-validation, the proposed method demonstrated significant performance, achieving an average Area Under the Curve of 0.7693 and 0.9755 across both datasets, respectively. Comparative analyses demonstrated the superiority of the GCTNet framework with CCE loss over state-of-the-art algorithms in MDD detection tasks.Significance. The proposed method offers an objective and effective approach to MDD detection, providing valuable support for clinical-assisted diagnosis.

Association of categorical diagnoses and psychopathologies with quality of life in patients with depression, anxiety, and somatic symptoms: A cross-sectional study.

OBJECTIVE: Major depressive disorder (MDD), anxiety disorders, and somatic symptom disorder (SSD) are associated with quality of life (QoL) reduction. This cross-sectional study investigated the relationship between these conditions as categorical diagnoses and related psychopathologies with QoL, recognizing their frequent overlap. METHODS: We recruited a total of 403 clinical patients and healthy individuals, administering diagnostic interviews based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. QoL and psychopathologies were assessed using the WHO Quality of Life-BREF (WHOQOL-BREF) and several self-administered questionnaires, respectively. Multiple linear regression analyses examined the associations between psychiatric diagnoses, psychopathologies, and QoL. RESULTS: SSD and MDD were independently associated with impaired global (ß = -0.318 and - 0.287) and all QoL domains (ß = -0.307, -0.150, -0.125, and - 0.133, in physical, psychological, social, and environmental domains respectively for SSD; ß = -0.278, -0.344, -0.275, and - 0.268 for MDD). The Beck Depression Inventory-II score showed pervasive associations with QoL (ß = -0.390, -0.408, -0.685, -0.463, and - 0.420, in global, physical, psychological, social, and environmental domains). The Patient Health Questionnaire-15 and Health Anxiety Questionnaire scores were associated with global (ß = -0.168 and - 0.181) and physical (ß = -0.293 and - 0.121) QoL domain, while the Cognitions About Body and Health Questionnaire score was only associated with environmental QoL domain (ß = -0.157). CONCLUSION: SSD and MDD were independently associated with QoL impairment. Depressive symptoms were associated with all QoL domains, whereas somatic symptom burden and health anxiety primarily affected the physical QoL domain. Clinicians should consider concomitant psychopathologies when managing patients with depression, anxiety, or somatic symptoms.

Diagnosis support of major depressive disorder using event-related potentials during affective priming tasks.

Major Depressive Disorder (MDD) is a global problem. Currently, the most common diagnosis is based on criteria susceptible to the subjectivity of the patient and the clinician. A possible solution to this problem is to look for diagnostic biomarkers that can accurately and early detect this mental condition. Some researchers have focused on electroencephalogram (EEG) analysis to identify biomarkers. In this study we used a dataset composed of EEG recordings from 24 subjects with MDD and 29 healthy controls (HC), during the execution of affective priming tasks with three different emotional stimuli (images): fear, sadness, and happiness. We investigated abnormalities in depressed patients using a novel technique, by directly comparing Event-Related Potential (ERP) waveforms to find statistically significant differences between the MMD and HC groups. Compared to the control group (healthy subjects), we found out that for the emotions fear and happiness there is a decrease in cortical activity at temporal regions in MDD patients. Just the opposite, for the emotion sadness, an increase in MDD brain activity occurs in frontal and occipital regions. Our findings suggest that emotions regulate the attentional control of cognitive processing and are promising for clinical application in diagnosing patients with MDD more objectively.

Abnormal resting-state EEG phase dynamics distinguishes major depressive disorder and bipolar disorder.

Changes in EEG have been reported in both major depressive disorder (MDD) and bipolar disorder (BD). Specifically, power changes in EEG alpha and theta frequency bands during rest and task are known in both disorders. This leaves open whether there are changes in yet another component of the electrophysiological EEG signal, namely phase-related processes that may allow for distinguishing MDD and BD. For that purpose, we investigate EEG-based spontaneous phase in the resting state of MDD, BD and healthy controls. Our main findings show: (i) decreased spontaneous phase variability in frontal theta of both MDD and BD compared to HC; (ii) decreased spontaneous phase variability in central-parietal alpha in MDD compared to both BD and HC; (iii) increased delays or lags of alpha phase cycles in MDD (but not in BD), which (iv) correlate with the decreased phase variability in MDD. Together, we show similar (decreased frontal theta variability) and distinct (decreased central-parietal alpha variability with increased lags or delays) findings in the spontaneous phase dynamics of MDD and BD. This suggests potential relevance of theta and alpha phase dynamics in distinguishing MDD and BD in clinical differential-diagnosis.