Features Of Depression Research Articles

The moderating role of depressive symptoms in the association between heart rate variability and cognitive performance in cardiac patients

IntroductionCoronary heart disease (CHD) is strongly associated with cognitive impairment, which is a core feature of depression, highly prevalent in patients with CHD. Interestingly, patients with CHD and individuals with depression display reduced heart rate variability (HRV), which proxies a complex network integrating autonomic and attentional systems. This study investigated the moderating role of depressive symptoms in the relation between reduced HRV and cognitive performance in patients with CHD. MethodThe sample included 274 patients with CHD (mean [standard deviation] age = 62 [9.5] years; 13 % women) admitted to cardiac rehabilitation units. Visual attention and task switching were assessed through the Trail Making Test (TMT). Depressive symptoms were assessed with the Beck Depression Inventory-II (BDI-II). Resting electrocardiographic recordings were collected to compute HRV indices. ResultsPatients with more severe depressive symptoms displayed an inverse association between HRV and cognitive performance (TMT-A: b = −0.08, p = .022; TMTB: b = −0.07, p = .042), whereas patients with milder depressive symptoms showed no significant association (TMT-A: b = −0.00, p = .90; TMTB: b = −0.02, p = .44). ConclusionsDepressive symptoms may strengthen the relation between reduced HRV and poorer cognitive performance in cardiac patients. The presence of depressive symptoms may signal the dysfunction of a network subserving autonomic and cognitive function.

Resilience and self-compassion among persons with depressive disorders: Prerequisite for a positive mental health approach.

Treatment gap for common mental health problems, especially of the depressive disorders is consequential in developing countries like India. Positive mental health domains like resilience and self-compassion have been long hailed as protective factors against depression and viable for use in therapeutic aspects. The objectives were to find an association between resilience, self-compassion, and depression. The study was conducted using a cross-sectional design among 75 respondents who were seeking treatment for major depressive disorders from a tertiary care center during the second wave of COVID-19. Three scales were administered online, namely Beck's Depression Inventory-II (BDI-II), Connor Davidson Resilience Scale (CD RISC-25), and Self-Compassion Scale-Short Form (SCS-SF). Spearman's rank correlation test, Chi-square with Fisher's exact test, and Kruskal Wallis H test were used to study the relationships and differences in average scores with respect to the severity of depression. Most of the respondents had moderate depressive features along with moderately high levels of resilience (CD RISC 25) and self-compassion (SCS-SF) scores. Resilience and self-compassion were found to have no significant relationship with respect to the severity of depression. There was a weakly positive correlation between resilience and self-compassion among those with moderate and severe depression. Since the individuals with depression had higher scores on resilience and self-compassion, the levels of depression remained at moderate levels of severity despite the devastating impact of the second wave of COVID-19 in the Indian sub-continent. Results are to be interpreted with respect to psychosocial contexts arising during the COVID-19 pandemic. Mental health programs can incorporate the variables of resilience and self-compassion in intervention among individuals with depression which have likely been beneficial in their process of recovery.

Detection of spreading depression features from the scalp of epileptic patients.

Spreading depression (SD), a pathological cortical negative DC potential, is caused by an elevation of potassium ions in the extracellular space. This leads to a transient relocation of ions within neurons and a slow spread through brain tissue. Our previous research established a correlation between scalp SD and seizures in patients with intractable epilepsy using our novel electroencephalography (EEG). In this study, we enhanced our EEG system by incorporating a Near-infrared spectroscopy (NIRS) module for multi-modal EEG-NIRS measurements. The aim is to provide an investigation into the defining characteristics and methods for detecting SD.Clinical Relevance-: The detection of SD serves as a novel biomarker for epilepsy, capable of forewarning seizures within a time range from 10 secs to 30 min. This detection plays a crucial role in predicting and preventing seizures and providing diagnostic information for drug-resistant epilepsy patients.

Adolescent mentalizing and childhood emotional abuse: implications for depression, anxiety, and borderline personality disorder features.

There is preliminary evidence that childhood emotional abuse (CEA) is a risk factor for adolescent mentalizing difficulties (Uncertainty/Confusion about mental states) and borderline personality features and that Uncertainty/Confusion about mental states mediate the relationship between CEA and adolescent borderline personality features, but these findings need replication. Furthermore, no previous studies have examined the relationship between adolescent mentalizing deficits, anxiety, and depression in the context of CEA. This study examined the associations between CEA, adolescent borderline personality features, depression and anxiety symptoms and tested a pathway model where Uncertainty/Confusion about mental states mediates the relationships between CEA and adolescent borderline personality features, depression and anxiety symptoms. A clinical sample of 94 adolescents completed the Reflective Function Questionnaire for Youth (RFQY) to assess mentalizing, the Childhood Experiences of Care and Abuse Questionnaire (CECA-Q), the Borderline Personality Disorders Features Scale (BPFS-C), and the Beck Youth Inventories for Depression (BDI-Y) and Anxiety (BAI-Y). Uncertainty/Confusion about mental states partially mediated the relationship between CEA and borderline traits as well as anxiety. In addition, there was an indirect effect where CEA predicted Uncertainty/Confusion about mental states, which then predicted depression. The findings are consistent with the mentalizing model of psychopathology and provide new evidence that Uncertainty/Confusion about mental states might be a critical mentalizing deficit that characterizes the associations between CEA and adolescent BPD features and depression and anxiety symptoms. Uncertainty/Confusion may be a transdiagnostic risk factor for adolescent psychological distress and dysfunction. We discuss the clinical implications.

Machine learning-based identification and related features of depression in patients with diabetes mellitus based on the Korea National Health and Nutrition Examination Survey: A cross-sectional study.

Patients with diabetes mellitus (DM) are twice as likely as nondiabetic individuals to develop depression, which is a prevalent but often undiagnosed psychiatric comorbidity. Patients with DM who are depressed have poor glycemic control, worse quality of life, increased risk of diabetic complications, and higher mortality rate. The present study aimed to develop machine learning (ML) models that identify depression in patients with DM, determine the best performing model by evaluating multiple ML algorithms, and investigate features related to depression. We developed six ML models, including random forest, K-nearest neighbor, support vector machine (SVM), Adaptive Boosting, light gradient-boosting machine, and Extreme Gradient Boosting, based on the Korea National Health and Nutrition Examination Survey. The results showed that the SVM model performed well, with a cross-validated area under the receiver operating characteristic curve of 0.835 (95% confidence interval [CI] = 0.730-0.901). Thirteen features were related to depression in patients with DM. Permutation feature importance showed that the most important feature was subjective health status, followed by level of general stress awareness; stress recognition rate; average monthly income; triglyceride (mg/dL) level; activity restriction status; European quality of life (EuroQoL): usual activity and lying in a sickbed in the past 1 month; EuroQoL: pain / discomfort, self-care, and physical discomfort in the last 2 weeks; and EuroQoL: mobility and chewing problems. The current findings may offer clinicians a better understanding of the relationship between DM and depression using ML approaches and may be an initial step toward developing a more predictive model for the early detection of depressive symptoms in patients with DM.

Prevalence and Clinical Characteristics of Depression among Elderly Patients Attending Primary Health Care Centers in Diyala Governorate

Background: Depression is one of the most common mood conditions among the elderly, which is linked to severe consequences such as difficulty in concentration and mood disturbances, followed by a lack of interest in social activities, apathy, pessimism, and changes in appetite and sleep. Aim of the study: To determine the prevalence and clinical characteristics of depression and the associated factors among 60-year-olds and over. Methodology: A descriptive cross-sectional study conducted on a group of 218 patients attending selected primary health care centers (PHCC) in Diyala governorate from Al-Khales and Baquba first sectors from first of September 2020 till first of April 2021. A direct interview was conducted by the researcher. Depression in the studied group was measured using the Arabic version of the short form of Geriatric Depression Scale (GDS). Socio-demographical factors were also inquired about in this study. Results: The prevalence of depression among the study participants was 63.3%. There was a statistically significant association between depression and gender (higher in females 69.2%), marital status (in widowed 84.8%), current residence (93.3% in those living alone), and source of monthly income (78.1% in those on financial assistance), medical illnesses (70.2%), and past history of depression (71.3%). Conclusions: Depressive symptoms are prevalent among elderly patients attending primary health centers in Diyala Governorate and are associated with female gender, being a widow, living alone, financial insecurity and chronic illnesses.

Quantifying seasonal, depression focused recharge in the context of public supply well vulnerability

AbstractDepression focused recharge (DFR) may be a hydrologically important process that impacts the vulnerability of public supply wells, specifically related to pathogenic contaminants. The nature of DFR in glacial moraine environments, such as those located in northern latitudes within North America and Europe, is less well established than in other regions such as the Prairie Pothole Region (Northern United States, Western Canada) and the High Plains Aquifer (Central United States). The objectives of this study were to quantify seasonal infiltration flux beneath a topographically‐closed depression within 50 m of a public supply well and to interpret the impact of this DFR process on well vulnerability. Field instruments including groundwater monitoring wells, pressure transducers, soil moisture sensors and temperature sensors were installed in vertical clusters to capture the dynamics of infiltration, drainage and recharge within the depression feature. Continuous weather data were recorded by a meteorological station at the site. Transient infiltration was quantified during two contrasting hydrological events. The first event (~2 days) was an intense rainfall (>50 mm) on a melting snowpack during the fall season when the soils were unfrozen. The second was a longer (35 day) period during the spring freshet when the surficial soils were initially frozen and subject to diurnal freezing and thawing and occasional precipitation events. The water table fluctuation method augmented by Darcy flux contributions, in addition to numerical modelling using the HYDRUS‐1D model, were used to quantify recharge rates beneath the depression. Numerical DFR estimates and analytical results differed by ±8%. Results indicate that recharge rates on the order of the annual regional average can occur beneath localized features in response to extreme events associated with snowmelt and intense rainfall. Such events may represent a microbial threat to groundwater quality if public supply wells are located nearby.

Present-day features of maladjustment states in English medium students

Background. During studying, international students require integration into a new linguistic and socio-cultural environment, an unusual climate, large volumes of rather complex new information, new forms and methods of educational process organization, emerging problems outside the educational process, separation from relatives and friends, all this inevitably causes students have new emotional experiences and mental states. The aim of the research was to study the modern features of the states of psychological maladjustment of international medical English medium (EM) students. Materials & Methods. 372 international year 1-6 EM students of Kharkiv National Medical University (223 men and 149 women) with an average age of (19±3) years were examined. All the examinees were divided into three groups: Group 1 – 194 students from India; Group 2 – 96 students from other Asian countries (Pakistan, Lebanon, and Syria); Group 3 – 82 students from African countries (Sudan, Tunisia, Morocco, Egypt, Namibia, and Israel). Results & Conclusions. The conducted psychodiagnostic study showed that students with a high and moderate level of maladjustment have clinical manifestations of anxiety according to Hamilton Anxiety and Depression Rating Scales (29.8% of students of Group 1, 30.1% of Group 2, 29.9% of Group 3) and subclinical manifestations of anxiety (36.2%; 39.5% and 41.1% of students, respectively). Clinical manifestations of depression were typical for 12.4% of students of Group 1, 14.2% of students of Group 2, and 13.4% of students of Group 3; subclinical manifestations of depression – for 21.1%, 19.6% and 19.9%, respectively. The analysis of the expressiveness of the manifestations of hypotensive states allowed distinguishing asthenodepressive (23.1% of the examined Group 1, 15.8% of Group 2 and 25.6% of Group 3), hypersthenic (29.9%; 29.8% and 22.2% of the examinees, respectively), anxious (25.5% of the students of Group 1, 18.9% – of Group 2, 31.1% – of Group 3) and dysphoric (21.5%; 35.5% and 21.1%, respectively) syndromes. Keywords: maladaptation, international students of higher education, anxiety, depression.

FEATURES OF THE DEMONSTRATION OF DEPRESSION IN NARCISSISTS: A WARTIME STUDY

The tendency of narcissistic individuals to set unrealistic goals and unsatisfied needs for respect and love from others contribute to the disappointment of such people in their environment and in themselves. The purpose of the study is to theoretically investigate and empirically test the relationship between depression and the level of narcissism of an individual. Hypotheses have also been put forward: it is likely that there is a relationship between a person's level of narcissism and their tendency to a depressive state, which is associated with unsatisfied needs for recognition, respect and love; we assume that individuals with an average and high level of narcissism will show aggravation and sensitivity, which will cause depressive states of various levels. The object of the research is depression as a psychological phenomenon of the individual, and the subject is the peculiarities of depression in persons with different levels of narcissism. As a result of such tension and unsatisfied whimsical needs, narcissistic personalities are more prone to the development of depressive states against the background of emotional burnout, frustrations and narcissistic traumatization. In this connection, there is a need for a more detailed study of а narcissism phenomenon. The article examines depression as a psychological phenomenon of the individual and defines the features of depression in people with different levels of narcissism. Based on the theoretical analysis, a theoretical-hypothetical model of the features of depression in persons with different levels of narcissism was built. The results of an empirical study of the relationship between depression and the level of narcissism of an individual are presented. It was found that three main factors influence the occurrence of depressive states in narcissistic individuals: self-admiration, grandeur and egotism. An improved theoretical-hypothetical model of features of depression in persons with different levels of narcissism is proposed.

Anti-depressant Potentials of Some Bioactive Components of Basella alba Leaves in Chronic Unpredictable Stress Rat Model

Bioactive components of Basella alba leaves are responsible for their antidepressant-like activity. However, the component with the greatest anti-depressant activity is unknown. This study investigated the antidepressant-like activities of the bioactive components (phenols, flavonoids, and glycosides) in Basella alba leaves. Forty-two male Wistar rats weighing 50–200 g were randomly divided into six groups (n=7). All the groups, except for the control, were subjected to chronic unpredictable stress (CUS) for five weeks. The rats in the CUS groups were treated with normal saline (1 mL/kg), escitalopram (5 mg/kg), and each of the phenol-, flavonoid-, and glycoside-rich Basella alba extracts (200 mg/kg) orally for twenty-one days. The tail suspension, sucrose preference, light-dark box, and hole-board tests were carried out before and after the induction of depression. In the CUS groups, reduced mobility time on tail suspension, increased percentage sucrose consumption, frequency of head dips on the hole board, and line-cross frequency in the light-dark box were observed. The latency on the hole board was reduced with Basella alba components, while there was a significant (p<0.05) decrease in serum brain-derived neurotrophic factor and an increase in serum IL-6 in the CUS animals not treated with extracts. The phenol-rich Basella alba extract showed the most potent antidepressant-like activity, followed by the flavonoid-rich extract. The bioactive components of Basella alba, particularly phenols, were effective in ameliorating the depressive features of CUS and should be further studied for use as an adjunct or stand-alone antidepressant.

Obesity and depression: the course of depressive disorders depending on the body mass index, the role of physical activity and cognitive-behavioral therapy for their correction

Purpose: to establish the relationship between the severity of the course of depressive disorders and body mass index in patients with non-alcoholic fatty liver disease on the background of abdominal obesity, the role of physical activity and cognitive-behavioral therapy for their correction.
 Materials and methods. 272 people (136 women and 136 men) were examined. The patients were divided into two clinical groups: 1st group – 90 people with non-alcoholic fatty liver disease and overweight (average body mass index =27.5±1.31 kg/m2), who followed only dietary recommendations and a complex physical exercises; 2nd group - 92 people with non-alcoholic fatty liver disease and abdominal obesity of the 1st degree (average body mass index =31.74±1.03 kg/m2), who additional underwent a 6-month course of cognitive-behavioral therapy. The control group consisted of 90 people with normal body weight (average body mass index =22.41±1.43 kg/m2). The Beck scale was used to assess the presence of depression.
 Results. Depression in patients of the 1st and 2nd groups was registered in 2.3 times (x2=28.105, p=4.762e-10<0.05) and 2.6 times more often (x2=12.019, p=0.007<0.05), than in the control group. Mild depressive disorders in 1.9 times (D=0.304, p=0.032<0.005) more often recorded in young women (up to 45 years old). Patients of the 2nd group, who additionally received a course of CBT, recorded a decrease in the number of depressive disorders of moderate severity by 3.33 times (D=0.1837, p=0.0133), compared to the patients of the 1st group.
 Conclusions. Gender-age characteristics and weight affect the severity of depressive disorders in obese patients. To reduce the number of depressive disorders and optimize body mass index, it is mandatory to use combined treatment in the form of aerobic exercises and cognitive-behavioral training.

Informant Discrepancy in Report of Parent-adolescent Conflict as a Predictor of Hopelessness among Depressed Adolescents: A Replication Study

AbstractMore knowledge is needed about how family factors influence depression features in adolescents. We aimed to replicate a previous small sample study showing that higher discrepancy between adolescents and caregivers in report of conflict predicted more hopelessness in adolescents with major depression. The current sample comprised 417 adolescent and maternal caregiver dyads. The adolescents were aged between 12 and 17 years (55.4% female, 44.6% male). The sample was drawn from the Treatment of Adolescents with Depression Study (TADS). Analyses were conducted using latent difference score modelling, item response theory, and linear regression in a Bayesian framework. Higher adolescent-perceived conflict relative to caregiver-perceived conflict predicted more adolescent hopelessness. This replicated the original findings in a much larger sample. Adolescent gender did not influence the associations. Addressing divergence in adolescent and parent perceptions of conflict may be relevant to ameliorate hopelessness in treatment of adolescents with depression.

A Deep Learning Driven Simulation Analysis of the Emotional Profiles of Depression Based on Facial Expression Dynamics.

: Diagnosis and assessment of depression rely on scoring systems based on questionnaires, either self-reported by patients or administered by clinicians, and observation of patient facial expressions during the interviews plays a crucial role in making impressions in clinical settings. Deep learning driven approaches can assist clinicians in the course of diagnosis of depression by recognizing subtle facial expressions and emotions in depression patients. : Seventeen simulated patients who acted as depressed patients participated in this study. A trained psychiatrist structurally interviewed each participant with moderate depression in accordance with a prepared scenario and without depressive features. Interviews were video-recorded, and a facial emotion recognition algorithm was used to classify emotions of each frame. : Among seven emotions (anger, disgust, fear, happiness, neutral, sadness, and surprise), sadness was expressed in a higher proportion on average in the depression-simulated group compared to the normal group. Neutral and fear were expressed in higher proportions on average in the normal group compared to the normal group. The overall distribution of emotions between the two groups was significantly different (p ＜ 0.001). Variance in emotion was significantly less in the depression-simulated group (p ＜ 0.05). : This study suggests a novel and practical approach to understand the emotional expression of depression patients based on deep learning techniques. Further research would allow us to obtain more perspectives on the emotional profiles of clinical patients, potentially providing helpful insights in making diagnosis of depression patients.

What Do We Know About Depression Among Youth and How Can We Make Progress Toward Improved Understanding and Reducing Distress? A New Hope.

This paper summarizes many findings about depression among children and adolescents. Depression is prevalent, highly distressing, and exerts considerable burden worldwide. Rates surge from childhood through young adulthood and have increased over the last decade. Many risk factors have been identified, and evidence-based interventions exist targeting mostly individual-level changes via psychological or pharmacological means. At the same time, the field appears stuck and has not achieved considerable progress in advancing scientific understanding of depression's features or delivering interventions to meet the challenge of youth depression's high and growing prevalence. This paper adopts several positions to address these challenges and move the field forward. First, we emphasize reinvigoration of construct validation approaches that may better characterize youth depression's phenomenological features and inform more valid and reliable assessments that can enhance scientific understanding and improve interventions for youth depression. To this end, history and philosophical principles affecting depression's conceptualization and measurement are considered. Second, we suggest expanding the range and targets of treatments and prevention efforts beyond current practice guidelines for evidence-based interventions. This broader suite of interventions includes structural- and system-level change focused at community and societal levels (e.g., evidence-based economic anti-poverty interventions) and personalized interventions with sufficient evidence base. We propose that by focusing on the FORCE (Fundamentals, Openness, Relationships, Constructs, Evidence), youth depression research can provide new hope.

Nano-crystalline ZnO memristor for neuromorphic computing: Resistive switching and conductance modulation

In this work, a nano-crystalline (NC) ZnO-based memristor was fabricated to investigate the short-term memory characteristics for reservoir computing systems. The crystalline structure of the ZnO film was confirmed through transmission electron microscopy (TEM) and X-ray diffraction pattern (XRD), while X-ray photoelectron spectroscopy (XPS) confirmed the chemical and bonding states of each element. The NC-ZnO-based memristor exhibited remarkable endurance, enduring more than 200 DC cycles, and had a high to low resistance (RH/RL) ratio of 102. Furthermore, it displayed long data retention of 104 s and consistent resistive switching (RS) with restricted variation in the set and reset voltage, showing its excellent performance characteristics. By controlling the pulse amplitude and the time interval between pulses, it was possible to effectively replicate the key features of short-term synaptic plasticity, including potentiation, depression, and paired-pulse depression, through conductance modulation. An artificial neural network (ANN) simulation achieved a pattern recognition accuracy of approximately 90.1% for a 28 × 28-pixel image after 100 training epochs. Based on this extensive study, NC-ZnO-based memristor exhibits immense potential as a crucial element in constructing high-performance neuromorphic computing systems.

Psychoemotional state and bioelectrical brain activity in patients of different ages with metabolic syndrome after COVID-19

The aim of this study was to find out the impact of COVID-19 infection on the psycho-emotional state and bioelectrical brain activity in persons of different ages with metabolic syndrome. Material and methods. We examined 53 people who were divided into groups depending on age (40–59 years and 60 years and older) and status with regard to COVID-19 infection (contracted, not contracted). Patients had metabolic syndrome (ATP III), underwent comprehensive clinical and laboratory examinations, electroencephalography (an 18-channel electroencephalograph Nihon Kohden, Japan) and answered the Hospital Anxiety and Depression Scale (HADS) questionnaire. Results. Symptoms of anxiety or depression after COVID-19 were found in both age groups of MS. Anxiety symptoms were more often found in the group of 60 years and older. At the same time, subclinical anxiety was noted in persons of 60 years and older, while clinical anxiety – in middle-aged individuals. The latter also showed a trend towards an increase in clinically evident depression after COVID-19. Persons after COVID-19 with symptoms of anxiety or depression (according to the HADS scale) demonstrated changes in the frequency-amplitude indicators of the electroencephalogram (EEG) characterized by a power increase in the range of theta rhythm and accompanied by subclinical and clinical manifestations of depression. About 80 % of people who did not suffer from COVID-19 had normal EEG since 9 Hz alpha rhythm power was registered. In patients after COVID-19, there was a redistribution of alpha-rhythm power range: an increase in the ranges of alpha-1 rhythm and decrease in alpha-2 rhythm power as well as an increase in the delta and theta rhythm power range. Conclusions. 1–3 months following the acute period of COVID-19, the frequency of depression is 3 times higher in middle-aged patients with metabolic syndrome. In elderly patients with metabolic syndrome after COVID-19, anxiety is predominantly diagnosed – almost in every second. These manifestations of anxiety and depression are accompanied by disorganization of the bioelectrical brain activity.

Emotion-dependent language featuring depression

Background and objectivesUnderstanding language features of depression contributes to the detection of the disorder. Considering that depression is characterized by dysfunctions in emotion and individuals with depression often show emotion-dependent cognition, the present study investigated the speech features and word use of emotion-dependent narrations in patients with depression. MethodsForty depression patients and forty controls were required to narrate self-relevant memories under five basic human emotions (i.e., sad, angry, fearful, neutral, and happy). Recorded speech and transcribed texts were analyzed. ResultsPatients with depression, as compared to non-depressed individuals, talked slower and less. They also performed differently in using negative emotion, work, family, sex, biology, health, and assent words regardless of emotion manipulation. Moreover, the use of words such as first person singular pronoun, past tense, causation, achievement, family, death, psychology, impersonal pronoun, quantifier and preposition words displayed emotion-dependent differences between groups. With the involvement of emotion, linguistic indicators associated with depressive symptoms were identified and explained 71.6% variances of depression severity. LimitationsWord use was analyzed based on the dictionary which does not cover all the words spoken in the memory task, resulting in text data loss. Besides, a relatively small number of depression patients were included in the present study and therefore the results need confirmation in future research using big emotion-dependent data of speech and texts. ConclusionsOur findings suggest that consideration of different emotional contexts is an effective means to improve the accuracy of depression detection via the analysis of word use and speech features.

Cluster Analysis on the Mental Health States in a Community Sample of Young Women During Pre-Pregnancy, Pregnancy, or the Postpartum Period.

It is necessary to identify the mental health types of young women considering the importance of the mental health during the peripartum period. This study aimed to classify the mental health types in a community sample of young women with pre-pregnancy, pregnancy, or the postpartum period. A total of 293 young women during pre-pregnancy, pregnancy, or the postpartum period were included in this study. The clinical characteristics of depression, anxiety, perceived stress, and quality of life were assessed. The clinical characteristics of the subject were classified by cluster analysis and compared by analysis of variance. From the cluster analysis, the subjects were classified into three groups. Cluster 1 showed significantly lower depression and anxiety and higher quality of life than those of cluster 2 and 3. Cluster 2 demonstrated significantly higher depression and anxiety and lower quality of life than those of cluster 3 and 1. Cluster 3 represented the intermediate levels between cluster 2 and 1. This study suggested that young women during pre-pregnancy, pregnancy, or the postpartum period might be in a good mental health group, a high-risk group requiring active monitoring, or a group in need of treatment. By monitoring mental health, the groups with high risk or requiring treatment could be discovered and proper management for prevention or improvement of mental health and quality of life can be provided.

A facial depression recognition method based on hybrid multi-head cross attention network.

Deep-learn methods based on convolutional neural networks (CNNs) have demonstrated impressive performance in depression analysis. Nevertheless, some critical challenges need to be resolved in these methods: (1) It is still difficult for CNNs to learn long-range inductive biases in the low-level feature extraction of different facial regions because of the spatial locality. (2) It is difficult for a model with only a single attention head to concentrate on various parts of the face simultaneously, leading to less sensitivity to other important facial regions associated with depression. In the case of facial depression recognition, many of the clues come from a few areas of the face simultaneously, e.g., the mouth and eyes. To address these issues, we present an end-to-end integrated framework called Hybrid Multi-head Cross Attention Network (HMHN), which includes two stages. The first stage consists of the Grid-Wise Attention block (GWA) and Deep Feature Fusion block (DFF) for the low-level visual depression feature learning. In the second stage, we obtain the global representation by encoding high-order interactions among local features with Multi-head Cross Attention block (MAB) and Attention Fusion block (AFB). We experimented on AVEC2013 and AVEC2014 depression datasets. The results of AVEC 2013 (RMSE = 7.38, MAE = 6.05) and AVEC 2014 (RMSE = 7.60, MAE = 6.01) demonstrated the efficacy of our method and outperformed most of the state-of-the-art video-based depression recognition approaches. We proposed a deep learning hybrid model for depression recognition by capturing the higher-order interactions between the depression features of multiple facial regions, which can effectively reduce the error in depression recognition and gives great potential for clinical experiments.

Characterising depression trajectories in young people at high familial risk of depression

BackgroundParental depression is a common and potent risk factor for depression in offspring. However, the developmental course of depression from childhood to early-adulthood has not been characterized in this high-risk group. MethodsUsing longitudinal data from 337 young people who had a parent with a history of recurrent major depressive disorder (MDD), we characterized trajectories of broadly defined depressive disorder using latent class growth analysis. We used clinical descriptions to further characterise trajectory classes. ResultsTwo trajectory classes were identified: childhood-emerging (25 %) and adulthood-emerging (75 %). The childhood-emerging class showed high rates of depressive disorder from age 12.5, which persisted through the study period. The adulthood-emerging class showed low rates of depressive disorder until age 26. Individual factors (IQ and ADHD symptoms) and parent depression severity (comorbidity, persistence and impairment) differentiated the classes but there were no differences in family history score or polygenic scores associated with psychiatric disorder. Clinical descriptions indicated functional impairment in both classes, but more severe symptomatology and impairment in the childhood-emerging class. LimitationsAttrition particularly affected participation in young adulthood. Factors associated with attrition were low family income, single parent household status and low parental education. ConclusionsThe developmental course of depressive disorder in children of depressed parents is variable. When followed up to adult life, most individuals exhibited some functional impairment. An earlier age-of-onset was associated with a more persistent and impairing course of depression. Access to effective prevention strategies is particularly warranted for at-risk young people showing early-onsetting and persistent depressive symptoms.