Context and background. Depression has affected millions of people worldwide and has become one of the most common mental disorders. Early mental disorder detection can reduce costs for public health agencies and prevent other major comorbidities. Additionally, the shortage of specialized personnel is very concerning since depression diagnosis is highly dependent on expert professionals and is time-consuming. Research problems. Recent research has evidenced that machine learning (ML) and natural language processing (NLP) tools and techniques have significantly benefited the diagnosis of depression. However, there are still several challenges in the assessment of depression detection approaches in which other conditions such as post-traumatic stress disorder (PTSD) are present. These challenges include assessing alternatives in terms of data cleaning and pre-processing techniques, feature selection, and appropriate ML classification algorithms. Purpose of the study. This paper tackles such an assessment based on a case study that compares different ML classifiers, specifically in terms of data cleaning and pre-processing, feature selection, parameter setting, and model choices. Methodology. The experimental case study is based on the Distress Analysis Interview Corpus - Wizard-of-Oz (DAIC-WOZ) dataset, which is designed to support the diagnosis of mental disorders such as depression, anxiety, and PTSD. Major findings. Besides the assessment of alternative techniques, we were able to build models with accuracy levels around 84% with Random Forest and XGBoost models, which is significantly higher than the results from the comparable literature which presented the level of accuracy of 72% from the SVM model. Conclusions. More comprehensive assessments of ML classification algorithms and NLP techniques for depression detection can advance the state of the art in terms of improved experimental settings and performance.

The global surge in depression rates, notably severe in China with over 95 million affected, underscores a dire public health issue. This is exacerbated by a critical shortfall in mental health professionals, highlighting an urgent call for innovative approaches. The advancement of Artificial Intelligence (AI), particularly Large Language Models, offers a promising solution by improving mental health diagnostics. However, there is a lack of real data for reliable training and accurate evaluation of AI models. To this end, this paper presents a high-quality multimodal depression consultation dataset, namely Parallel Data of Depression Consultation and Hamilton Depression Rating Scale (PDCH). The dataset is constructed based on clinical consultations from Beijing Anding Hospital, which provides audio recording and transcribed text, as well as corresponding HAMD-17 scales annotated by professionals. The dataset contains 100 consultations and the audio exceeds 2,937 minutes.Each of them is about 30-min long with more than 150 dialogue turns. It enables to fill the gap in mental health services and benefit the creation of more accurate AI models.

According to the World Health Organisation, the number of mental disorder patients, especially depression patients, has grown rapidly and become a leading contributor to the global burden of disease. However, the present common practice of depression diagnosis is based on interviews and clinical scales carried out by doctors, which is not only labour-consuming but also time-consuming. One important reason is due to the lack of physiological indicators for mental disorders. With the rising of tools such as data mining and artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. However, good quality physiological data for mental disorder patients are hard to acquire. We present a multi-modal open dataset for mental-disorder analysis. The dataset includes EEG and audio data from clinically depressed patients and matching normal controls. All our patients were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications. The 128-electrodes EEG signals of 53 subjects were recorded as both in resting state and under stimulation; the 3-electrode EEG signals of 55 subjects were recorded in resting state; the audio data of 52 subjects were recorded during interviewing, reading, and picture description. We encourage other researchers in the field to use it for testing their methods of mental-disorder analysis. Participants: (1) full brain 128-electrodes EEG experiment: 53 participants include a total of 24 outpatients (13 males and 11 females; 18–55-year-old) diagnosed with depression, as well as 29 healthy controls (20 males and 9 females; 18–55-year-old) were recruited; (2) pervasive 3-electrodes EEG experiment: 55 participants include a total of 26 outpatients (15 males and 11 females; 18–55-year-old) diagnosed with depression, as well as 29 healthy controls (19 males and 10 females; 18–55-year-old) were recruited; (3) Audio experiment: 52 participants include a total of 23 outpatients (16 males and 7 females; 18–55-year-old) diagnosed with depression, as well as 29 healthy controls (20 males and 9 females; 18–55-year-old) were recruited. For more information please see the Methodology file.

This dataset presents the prevalence of depression and anxiety among adults, serving as a key indicator of mental health within the population. It is intended to support monitoring and evaluation efforts aimed at improving mental health outcomes and reducing the burden of common mental disorders. The data is expressed as a percentage, reflecting the proportion of adults experiencing depression and/or anxiety.

Rationale

Mental health is a critical component of overall well-being. Monitoring the prevalence of depression and anxiety in adults helps inform public health strategies, allocate resources effectively, and evaluate the impact of mental health interventions. Reducing the prevalence of these conditions is a priority for improving quality of life and reducing associated social and economic costs.

Numerator

The numerator for this indicator is currently unspecified. It would typically represent the number of adults identified as experiencing depression and/or anxiety within a defined population and time period.

Denominator

The denominator is also unspecified in the current metadata. It would generally be the total number of adults in the population under study during the same time period.

Caveats

At present, the dataset lacks detailed definitions for both the numerator and denominator, as well as the data sources. This limits the interpretability and comparability of the indicator. Users should exercise caution when drawing conclusions or making comparisons based on this data.

External References

No external references have been provided. For further context or methodological guidance, users may refer to national health surveys or reports from organizations such as the World Health Organization.

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

Background: Exercise interventions are efficacious in reducing disorder-specific symptoms in various mental disorders. However, little is known about long-term transdiagnostic efficacy of exercise across heterogenous mental disorders and the potential mechanisms underlying treatment effects. Methods: Physically inactive outpatients, with depressive disorders, anxiety disorders, insomnia or attention deficit hyperactivity disorder were randomized to a standardized 12-week exercise intervention, combining moderate exercise with behaviour change techniques (BCTs) (n = 38), or a passive control group (n = 36). Primary outcome was global symptom severity (Symptom Checklist-90, SCL-90-R) and secondary outcomes were self-reported exercise (Physical Activity, Exercise, and Sport Questionnaire), exercise-specific affect regulation (Physical Activity-related Health Competence Questionnaire) and depression (SCL-90-R) assessed at baseline (T1), post-treatment (T2) and one year after post-treatment (T3). Intention-to-treat analyses were conducted using linear mixed models and structural equations modeling. Results: From T1 to T3, the intervention group significantly improved on global symptom severity (d = -0.43, p = .031), depression among a depressed subsample (d = -0.62, p = .014), exercise (d = 0.45, p= .011) and exercise-specific affect regulation (d = 0.44, p = .028) relative to the control group. The intervention group was more likely to reveal clinically significant changes from T1 to T3 (p = .033). Increases in exercise-specific affect regulation mediated intervention effects on global symptom severity (ß = -0.28, p = .037) and clinically significant changes (ß = -0.24, p = .042). Conclusions: The exercise intervention showed long-term efficacy among a diagnostically heterogeneous outpatient sample and led to long-lasting exercise behaviour change. The long-term increases in exercise-specific affect regulation within exercise interventions seems to be essential for long-lasting symptom reduction beyond an intervention period.

Background: Depression and anxiety are leading causes of disability worldwide but often remain undetected and untreated. Smartphone and wearable devices may offer a unique source of data to detect moment by moment changes in risk factors associated with mental disorders that overcome many of the limitations of traditional screening methods.Objective: The current study aimed to explore the extent to which data from smartphone and wearable devices could predict symptoms of depression and anxiety.Methods: A total of N = 60 adults (ages 24–68) who owned an Apple iPhone and Oura Ring were recruited online over a 2-week period. At the beginning of the study, participants installed the Delphi data acquisition app on their smartphone. The app continuously monitored participants' location (using GPS) and smartphone usage behavior (total usage time and frequency of use). The Oura Ring provided measures related to activity (step count and metabolic equivalent for task), sleep (total sleep time, sleep onset latency, wake after sleep onset and time in bed) and heart rate variability (HRV). In addition, participants were prompted to report their daily mood (valence and arousal). Participants completed self-reported assessments of depression, anxiety and stress (DASS-21) at baseline, midpoint and the end of the study.Results: Multilevel models demonstrated a significant negative association between the variability of locations visited and symptoms of depression (beta = −0.21, p = 0.037) and significant positive associations between total sleep time and depression (beta = 0.24, p = 0.023), time in bed and depression (beta = 0.26, p = 0.020), wake after sleep onset and anxiety (beta = 0.23, p = 0.035) and HRV and anxiety (beta = 0.26, p = 0.035). A combined model of smartphone and wearable features and self-reported mood provided the strongest prediction of depression.Conclusion: The current findings demonstrate that wearable devices may provide valuable sources of data in predicting symptoms of depression and anxiety, most notably data related to common measures of sleep.

These Census Tract-level datasets described here provide de-identified diagnosis data for customers of three managed care organizations in Allegheny County (Gateway Health Plan, Highmark Health, and UPMC) that have filed a claim for depression medications in 2015 and 2016. The data also includes the number of enrolled members in the three participating managed care organizations in 2015 and 2016.

Disclaimer: Users should be cautious of using administrative claims data as a measure of disease prevalence and interpreting trends over time, as data provided were collected for purposes other than surveillance. Limitations of these data include but are not limited to: misclassification, duplicate individuals, exclusion of individuals who did not seek care in past two years and those who are: uninsured, enrolled in plans not represented in the dataset, or were not enrolled in one of the represented plans for at least 90 days.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

The National Institute of Mental Health (NIMH) Research Volunteer (RV) Data Set

A comprehensive dataset characterizing healthy research volunteers in terms of clinical assessments, mood-related psychometrics, cognitive function neuropsychological tests, structural and functional magnetic resonance imaging (MRI), along with diffusion tensor imaging (DTI), and a comprehensive magnetoencephalography battery (MEG).

In addition, blood samples are currently banked for future genetic analysis. All data collected in this protocol are broadly shared in the OpenNeuro repository, in the Brain Imaging Data Structure (BIDS) format. In addition, task paradigms and basic pre-processing scripts are shared on GitHub. This dataset is unprecedented in its depth of characterization of a healthy population and will allow a wide array of investigations into normal cognition and mood regulation.

This dataset is licensed under the Creative Commons Zero (CC0) v1.0 License.

Release Notes

Release v2.0.0

This release includes data collected between 2020-06-03 (cut-off date for v1.0.0) and 2024-04-01. Notable changes in this release:

1. 769 new participants have been added along with re-evaluation data for 15 participants. Total unique participants count is now 1859.
2. visit and age_at_visit columns added to phenotype files to distinguish between visits and intervals between them.
3. Follow-up online survey data included.
4. Replaced Beck Anxiety Inventory (BAI) and Beck Depression Inventory-II (BDI-II) with General Anxiety Disorder-7 (GAD7) and Patient Health Questionnaire 9 (PHQ9) surveys, respectively.
5. Discontinued the Perceived Health rating survey.
6. Added Brief Trauma Questionnaire (BTQ) and Big Five personality survey to online screening questionnaires.
7. MRI:
   • Replaced ADNI-3 resting state sequence with a multi-echo sequence with higher spatial resolution.
   • Replaced field map scans with a shorter reversed-blipped EPI scan.
8. MEG:
   • Some participants have 6-minute empty room data instead of the shorter duration empty room acquisition.

See the CHANGES file for complete version-wise changelog.

Participant Eligibility

To be eligible for the study, participants need to be medically healthy adults over 18 years of age with the ability to read, speak and understand English. All participants provided electronic informed consent for online pre-screening, and written informed consent for all other procedures. Participants with a history of mental illness or suicidal or self-injury thoughts or behavior are excluded. Additional exclusion criteria include current illicit drug use, abnormal medical exam, and less than an 8th grade education or IQ below 70. Current NIMH employees, or first degree relatives of NIMH employees are prohibited from participating. Study participants are recruited through direct mailings, bulletin boards and listservs, outreach exhibits, print advertisements, and electronic media.

Clinical Measures

All potential volunteers visit the study website, check a box indicating consent, and fill out preliminary screening questionnaires. The questionnaires include basic demographics, the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0), the DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure, the DSM-5 Level 2 Cross-Cutting Symptom Measure - Substance Use, the Alcohol Use Disorders Identification Test (AUDIT), the Edinburgh Handedness Inventory, and a brief clinical history checklist. The WHODAS 2.0 is a 15 item questionnaire that assesses overall general health and disability, with 14 items distributed over 6 domains: cognition, mobility, self-care, “getting along”, life activities, and participation. The DSM-5 Level 1 cross-cutting measure uses 23 items to assess symptoms across diagnoses, although an item regarding self-injurious behavior was removed from the online self-report version. The DSM-5 Level 2 cross-cutting measure is adapted from the NIDA ASSIST measure, and contains 15 items to assess use of both illicit drugs and prescription drugs without a doctor’s prescription. The AUDIT is a 10 item screening assessment used to detect harmful levels of alcohol consumption, and the Edinburgh Handedness Inventory is a systematic assessment of handedness. These online results do not contain any personally identifiable information (PII). At the conclusion of the questionnaires, participants are prompted to send an email to the study team. These results are reviewed by the study team, who determines if the participant is appropriate for an in-person interview.

Participants who meet all inclusion criteria are scheduled for an in-person screening visit to determine if there are any further exclusions to participation. At this visit, participants receive a History and Physical exam, Structured Clinical Interview for DSM-5 Disorders (SCID-5), the Beck Depression Inventory-II (BDI-II), Beck Anxiety Inventory (BAI), and the Kaufman Brief Intelligence Test, Second Edition (KBIT-2). The purpose of these cognitive and psychometric tests is two-fold. First, these measures are designed to provide a sensitive test of psychopathology. Second, they provide a comprehensive picture of cognitive functioning, including mood regulation. The SCID-5 is a structured interview, administered by a clinician, that establishes the absence of any DSM-5 axis I disorder. The KBIT-2 is a brief (20 minute) assessment of intellectual functioning administered by a trained examiner. There are three subtests, including verbal knowledge, riddles, and matrices.

Biological and physiological measures

Biological and physiological measures are acquired, including blood pressure, pulse, weight, height, and BMI. Blood and urine samples are taken and a complete blood count, acute care panel, hepatic panel, thyroid stimulating hormone, viral markers (HCV, HBV, HIV), c-reactive protein, creatine kinase, urine drug screen and urine pregnancy tests are performed. In addition, three additional tubes of blood samples are collected and banked for future analysis, including genetic testing.

Imaging Studies

Participants were given the option to enroll in optional magnetic resonance imaging (MRI) and magnetoencephalography (MEG) studies.

MRI

On the same visit as the MRI scan, participants are administered a subset of tasks from the NIH Toolbox Cognition Battery. The four tasks asses attention and executive functioning (Flanker Inhibitory Control and Attention Task), executive functioning (Dimensional Change Card Sort Task), episodic memory (Picture Sequence Memory Task), and working memory (List Sorting Working Memory Task). The MRI protocol used was initially based on the ADNI-3 basic protocol, but was later modified to include portions of the ABCD protocol in the following manner:

1. The T1 scan from ADNI3 was replaced by the T1 scan from the ABCD protocol.
2. The Axial T2 2D FLAIR acquisition from ADNI2 was added, and fat saturation turned on.
3. Fat saturation was turned on for the pCASL acquisition.
4. The high-resolution in-plane hippocampal 2D T2 scan was removed, and replaced with the whole brain 3D T2 scan from the ABCD protocol (which is resolution and bandwidth matched to the T1 scan).
5. The slice-select gradient reversal method was turned on for DTI acquisition, and reconstruction interpolation turned off.
6. Scans for distortion correction were added (reversed-blip scans for DTI and resting state scans).
7. The 3D FLAIR sequence was made optional, and replaced by one where the prescription and other acquisition parameters provide resolution and geometric correspondence between the T1 and T2 scans.

MEG

The optional MEG studies were added to the protocol approximately one year after the study was initiated, thus there are relatively fewer MEG recordings in comparison to the MRI dataset. MEG studies are performed on a 275 channel CTF MEG system. The position of the head was localized at the beginning and end of the recording using three fiducial coils. These coils were placed 1.5 cm above the nasion, and at each ear, 1.5 cm from the tragus on a line between the tragus and the outer canthus of the eye. For some participants, photographs were taken of the three coils and used to mark the points on the T1 weighted structural MRI scan for co-registration. For the remainder of the participants, a BrainSight neuro-navigation unit was used to coregister the MRI, anatomical fiducials, and localizer coils directly prior to MEG data acquisition.

Specific Survey and Test Data within Data Set

NOTE: In the release 2.0 of the dataset, two measures Brief Trauma Questionnaire (BTQ) and Big Five personality survey were added to the online screening questionnaires. Also, for the in-person screening visit, the Beck Anxiety Inventory (BAI) and Beck Depression Inventory-II (BDI-II) were replaced with the General Anxiety Disorder-7 (GAD7) and Patient Health Questionnaire 9 (PHQ9) surveys, respectively. The Perceived Health rating survey was discontinued.

1. Preliminary Online Screening Questionnaires

BackgroundDepression may negatively affect stroke outcomes and the progress of recovery. However, there is a lack of updated comprehensive evidence to inform clinical practice and directions of future studies. In this review, we report the multidimensional impact of depression on stroke outcomes.MethodsData sources. PubMed, PsycINFO, EMBASE, and Global Index Medicus were searched from the date of inception.Eligibility criteria. Prospective studies which investigated the impact of depression on stroke outcomes (cognition, returning to work, quality of life, functioning, and survival) were included.Data extraction. Two authors extracted data independently and solved the difference with a third reviewer using an extraction tool developed prior. The extraction tool included sample size, measurement, duration of follow-up, stroke outcomes, statistical analysis, and predictors outcomes.Risk of bias. We used Effective Public Health Practice Project (EPHPP) to assess the quality of the included studies.ResultsEighty prospective studies were included in the review. These studies investigated the impact of depression on the ability to return to work (n = 4), quality of life (n = 12), cognitive impairment (n = 5), functioning (n = 43), and mortality (n = 24) where a study may report on more than one outcome. Though there were inconsistencies, the evidence reported that depression had negative consequences on returning to work, functioning, quality of life, and mortality rate. However, the impact on cognition was not conclusive. In the meta-analysis, depression was associated with premature mortality (HR: 1.61 (95% CI; 1.33, 1.96)), and worse functioning (OR: 1.64 (95% CI; 1.36, 1.99)).ConclusionDepression affects many aspects of stroke outcomes including survival The evidence is not conclusive on cognition and there was a lack of evidence in low-income settings. The results showed the need for early diagnosis and intervention of depression after stroke.The protocol was pre-registered on the International Prospective Register of Systematic Review (PROSPERO) (CRD42021230579).

The World Health Organization estimates that almost 300 million people suffer from depression worldwide. Depression is the most common mental health disorder and shows racial disparities in disease prevalence, age of onset, severity of symptoms, frequency of diagnosis, and treatment utilization across the United States. Since depression has both social and genetic risk factors, we propose a conceptual model wherein social stressors are primary risk factors for depression, but genetic variants increase or decrease individual susceptibility to the effects of the social stressors. Our research strategy incorporates both social and genetic data to investigate variation in symptoms of depression (CES-D scores). We collected data on financial strain (difficulty paying bills) and personal social networks (a model of an individual’s social environment), and we genotyped genetic variants in five genes involved in stress reactivity (HTR1a, BDNF, GNB3, SLC6A4, and FKBP5) in 135 African Americans residing in Tallahassee, Florida. We found that high financial strain and a high percentage of people in one’s social network who are a source of stress or worry were significantly associated with higher CES-D scores and explained more variation in CES-D scores than did genetic factors. Only one genetic variant (rs1360780 in FKBP5) was significantly associated with CES-D scores and only when the social stressors were included in the model. Interestingly, the effect of FKPB5 appeared to be strongest in individuals with high financial strain such that participants with a T allele at rs1360780 in FKBP5 and high financial strain had the highest mean CES-D scores in our study population. These results suggest that material disadvantage and a stressful social environment increases the risk of depression, but that individual-level genetic variation may increase susceptibility to the adverse health consequences of social stressors.

This is the README file for the scripts of the preprint "Self-Perceived Loneliness and Depression During the COVID-19 Pandemic: a Two-Wave Replication Study" by Carollo et al. (2022) Access the pre-print here: https://ucl.scienceopen.com/document/read?vid=0769d88b-e572-48eb-9a71-23ea1d32cecf Abstract: Background: The global COVID-19 pandemic has forced countries to impose strict lockdown restrictions and mandatory stay-at-home orders with varying impacts on individual’s health. Combining a data-driven machine learning paradigm and a statistical approach, our previous paper documented a U-shaped pattern in levels of self-perceived loneliness in both the UK and Greek populations during the first lockdown (17 April to 17 July 2020). The current paper aimed to test the robustness of these results by focusing on data from the first and second lockdown waves in the UK. Methods: We tested a) the impact of the chosen model on the identification of the most time-sensitive variable in the period spent in lockdown. Two new machine learning models - namely, support vector regressor (SVR) and multiple linear regressor (MLR) were adopted to identify the most time-sensitive variable in the UK dataset from wave 1 (n = 435). In the second part of the study, we tested b) whether the pattern of self-perceived loneliness found in the first UK national lockdown was generalizable to the second wave of UK lockdown (17 October 2020 to 31 January 2021). To do so, data from wave 2 of the UK lockdown (n = 263) was used to conduct a graphical and statistical inspection of the week-by-week distribution of self-perceived loneliness scores. Results: In both SVR and MLR models, depressive symptoms resulted to be the most time-sensitive variable during the lockdown period. Statistical analysis of depressive symptoms by week of lockdown resulted in a U-shaped pattern between week 3 to 7 of wave 1 of the UK national lockdown. Furthermore, despite the sample size by week in wave 2 was too small for having a meaningful statistical insight, a qualitative and descriptive approach was adopted and a graphical U-shaped distribution between week 3 and 9 of lockdown was observed. Conclusions: Consistent with past studies, study findings suggest that self-perceived loneliness and depressive symptoms may be two of the most relevant symptoms to address when imposing lockdown restrictions. In particular, the folder includes the scripts for the pre-processing, training, and post-processing phases of the research. ==== PRE-PROCESSING WAVE 1 DATASET ==== - "01_preprocessingWave1.py": this file include the pre-processing of the variables of interest for wave 1 data; - "02_participantsexcludedWave1.py": this file include the script adopted to implement the exclusion criteria of the study for wave 1 data; - "03_countryselectionWave1.py": this file include the script to select the UK dataset for wave 1. ==== PRE-PROCESSING WAVE 2 DATASET ==== - "04_preprocessingWave1.py": this file include the pre-processing of the variables of interest for wave 2 data; - "05_participantsexcludedWave1.py": this file include the script adopted to implement the exclusion criteria of the study for wave 2 data; - "06_countryselectionWave1.py": this file include the script to select the UK dataset for wave 2. ==== TRAINING ==== - "07_MLR.py": this file includes the script to run the multiple regression model; - "08_SVM.py": this file includes the script to run the support vector regression model. ==== POST-PROCESSING: STATISTICAL ANALYSIS ==== - "09_KruskalWallisTests.py": this file includes the script to run the multipair and the pairwise Kruskal-Wallis tests.

Depression is the leading cause of disability worldwide. Psychotherapy is an integral part of depression treatment, however rates of response and remission remain limited. Research on the neurobiological mechanisms involved in depression may contribute to a better understanding of recovery processes and inform a more targeted use of treatments. To date, critical gaps in the knowledge of the neurobiology of depression remain and the neurobiological mechanisms involved in its psychotherapeutic treatment are poorly understood. The aim of this study is to identify specific neurofunctional and neurochemical brain alterations in depression and the effects of psychotherapy on these alterations. We employed functional magnetic resonance imaging and magnetic resonance spectroscopy in two samples: A) 56 depressed participants compared to 52 healthy controls were assessed once; and B) 32 depressed patients were assessed before and after six months of once a week psychotherapy and compared to 30 healthy controls.

In order to plan, prepare for, and shape events in line with one’s goals, one needs to be able to anticipate future events and/or states. This study investigated the cognitive representations and emotional anticipation pertaining to goal achievement and whether these differ as a function of severity of depressive symptoms. After listing approach and avoidance goals, participants made predictions about these goals (e.g. likelihood of achievement, controllability) and rated their cognitive representations of goal success (vividness, perspective). They also provided ratings of either anticipated (predicted emotions that would accompany goal success) or anticipatory (in-the-moment emotions when imagining goal success) positive emotions. The findings have implications for both future work regarding both anticipated and anticipatory emotions as well as therapeutic techniques to aid depression and dysphoria.Depression is a debilitating condition that causes immense psychological distress to those who experience it. Depression also has profoundly negative effects on many other aspects of everyday living, including physical health, educational attainment, and employment status. Understanding the causes of depression and developing interventions to treat it will, therefore, have significant benefits both for individuals and for society. Previous research has shown that depression is associated with a negative thinking style, whereby individuals hold negative views about themselves, the world, and the future. Recent research has indicated that holding negative views about the future is one of the main factors in causing and maintaining depressive episodes. The research we propose builds on our previous findings that views about the future can be made less negative by an intervention we have termed "Positive Simulation Training". In our previous research, participants were presented with a range of potential life events, 15 positive (e.g., people will admire you) and 15 negative (e.g., someone close to you will reject you). For each event, participants predicted how likely it was to occur in the future, how much control they thought they had over it, and how important it would be to them. They then took part in the Positive Simulation Training task in which they were instructed to mentally simulate a series of positive future events as vividly as possible in response to cue words/phrases that appeared on a computer screen. A control group took part in a neutral visualisation task in which they were instructed to imagine neutral scenes (e.g. the layout of their local shopping centre) as vividly as possible. Participants were then presented with a second set of potential life events and asked to rate them for likelihood of occurrence, control, and importance. We found that Positive Simulation Training led to improvements in participants' expectations about the future events, compared to the neutral visualisation task. Positive future events were rated as more likely to occur and negative events less likely, and individuals rated themselves as having more control over both positive and negative future events. These effects were observed in both depressed and non-depressed individuals. We now wish to build on these preliminary findings and establish whether Positive Simulation Training can be used to treat other negative future biases that have been observed in depression. The questions we plan to address include the following: 1. Can Positive Simulation Training lead to more positive views about future events that are personally important to the participants? 2. Can Positive Simulation Training lead to more positive views about how future events will make one feel? 3. Can Positive Simulation Training enhance beliefs about the likelihood of achieving personal goals? 4. Can Positive Simulation Training improve implicit (unconscious) beliefs about the likelihood of future events? This is important because it has been shown that implicit beliefs have a powerful effect on behaviour. 5. Can Positive Simulation Training enhance more general feelings of optimism about the future? Our eventual aim is to develop an intervention based on Positive Simulation Training that will support recovery from depression by reducing the effects of negative thoughts about the future. 263 undergraduate students participated in online study delivered via Qualtrics survey software. Severity of depressive symptomatology was measured using the Centre for Epidemiological Studies Depression Inventory-Revised (Eaton et al, 2004). The goal task required participants to list 4 approach and 4 avoidance goals. For each goal they provided a series of ratings about the goal (achievement likelihood, controllability, importance for life story, motivation, effort required) and the extent to which they could envisage goal success (vividness, perspective). They also provided ratings of either anticipated (predicted emotions that would accompany goal success) or anticipatory (in-the-moment emotions when imagining goal success) positive emotions. After completion of data collection, one of the experimenters also coded goals for specificity and life domain.

Background - Exercise efficaciously reduces disorder-specific symptoms of psychiatric disorders. The current study aimed to examine the efficacy of a group exercise intervention on global symptom severity and disorder-specific symptoms among a mixed outpatient sample. Methods - Groups of inactive outpatients, waiting for psychotherapy, with depressive disorders, anxiety disorders, insomnia, and attention-deficit/hyperactivity disorders were randomized to a manualized 12-week exercise intervention, combining moderate to vigorous aerobic exercise with techniques for sustainable exercise behaviour change (n = 38, female = 71.1% (n = 27), Mage = 36.66), or a passive control group (n = 36, female = 75.0% (n = 27), Mage = 34.33). Primary outcomes were global symptom severity and disorder-specific symptoms, measured with the Symptom Checklist-90-Revised and Pittsburgh Sleep Quality Index pre- and post-treatment. Secondary outcome was the self-reported amount of exercise (Physical Activity, Exercise, and Sport Questionnaire), measured pre-treatment, intermediate-, and post-treatment. Intention-to-treat analyses were conducted using linear mixed models. Linear regressions were conducted to examine the effect of the change of exercise behaviour on the change of symptoms. Results - The intervention significantly improved global symptom severity (d = 0.77, p = .007), depression (d = 0.68, p = .015), anxiety (d = 0.87, p = .002), sleep quality (d = 0.88, p = .001), and increased the amount of exercise (d = 0.82, p < .001), compared to the control group. Post-treatment differences between groups were significant for depression (d = 0.63, p = .031), sleep quality (d = 0.61, p = .035) and the amount of exercise (d = 1.45, p < .001). Across both groups, the reduction of global symptom severity was significantly predicted by an increase of exercise (b = .35, p = .012). Conclusions - The exercise intervention showed transdiagnostic efficacy among a heterogeneous clinical sample in a realistic outpatient setting and led to sustained exercise behaviour change. Exercise may serve as an efficacious and feasible transdiagnostic treatment option improving the existing treatment gap within outpatient mental health care settings.

Unprecedented increases in risks of depressive disorders and resultant suicide have become the overarching menace for children/adolescent health. Despite global consensus to instigate psychological healthcare policy for them, their effects remain largely unclear neither from a small amount of official data nor from small-scale scientific studies. More importantly, in those underprivileged children/adolescents at lower-middle-economic-status countries/areas, the data collection may be not equally accessible as same as developed ones, thus resulting in underrepresented observations. To address these challenges, we released a living large-scale cohort dataset showing effects of primary psychological healthcare on decreasing depression and suicide ideation in underprivileged conditions, including unattended children/adolescents, orphan, children/adolescents in especially difficult circumstance, “left-behind” and “single-parenting” children/adolescents.

Seismic observations with the space-borne Kepler mission have shown that a number of evolved stars exhibit low-amplitude dipole modes, which is referred to as depressed modes. Recently, these low amplitudes have been attributed to the presence of a strong magnetic field in the stellar core of those stars. Subsequently, and based on this scenario, the prevalence of high magnetic fields in evolved stars has been inferred. It should be noted, however, that this conclusion remains indirect. We intend to study the properties of mode depression in evolved stars, which is a necessary condition before reaching conclusions about the physical nature of the mechanism responsible for the reduction of the dipole mode amplitudes. We perform a thorough characterization of the global seismic parameters of depressed dipole modes and show that these modes have a mixed character. The observation of stars showing dipole mixed modes that are depressed is especially useful for deriving model-independent conclusions on the dipole mode damping. We use a simple model to explain how mode visibilities are connected to the extra damping seen in depressed modes. Results. Observations prove that depressed dipole modes in red giants are not pure pressure modes but mixed modes. This result, observed in more than 90% of the bright stars (m_V_<=11), invalidates the hypothesis that depressed dipole modes result from the suppression of the oscillation in the radiative core of the stars. Observations also show that, except for visibility, seismic properties of the stars with depressed modes are equivalent to those of normal stars. The measurement of the extra damping that is responsible for the reduction of mode amplitudes, without any prior on its physical nature, potentially provides an efficient tool for elucidating the mechanism responsible for the mode depression. The mixed nature of the depressed modes in red giants and their unperturbed global seismic parameters carry strong constraints on the physical mechanism responsible for the damping of the oscillation in the core. This mechanism is able to damp the oscillation in the core but cannot fully suppress it. Moreover, it cannot modify the radiative cavity probed by the gravity component of the mixed modes. The recent mechanism involving high magnetic fields proposed for explaining depressed modes is not compliant with the observations and cannot be used to infer the strength and prevalence of high magnetic fields in red giants.

The burden of depression and anxiety in the world is rising. Identification of individuals at increased risk of developing these conditions would help to target them for prevention and ultimately reduce the healthcare burden. We developed a 10-year predictive algorithm for depression and anxiety using the full cohort of over 400,000 UK Biobank (UKB) participants without pre-existing depression or anxiety using digitally obtainable information. From the initial 167 variables selected from UKB, processed into 429 features, iterative backward elimination using Cox proportional hazards model was performed to select predictors which account for the majority of its predictive capability. Baseline and reduced models were then trained for depression and anxiety using both Cox and DeepSurv, a deep neural network approach to survival analysis. The baseline Cox model achieved concordance of 0.7772 and 0.7720 on the validation dataset for depression and anxiety, respectively. For the DeepSurv model, respective concordance indices were 0.7810 and 0.7728. After feature selection, the depression model contained 39 predictors and the concordance index was 0.7769 for Cox and 0.7772 for DeepSurv. The reduced anxiety model, with 53 predictors, achieved concordance of 0.7699 for Cox and 0.7710 for DeepSurv. The final models showed good discrimination and calibration in the test datasets. We developed predictive risk scores with high discrimination for depression and anxiety using the UKB cohort, incorporating predictors which are easily obtainable via smartphone. If deployed in a digital solution, it would allow individuals to track their risk, as well as provide some pointers to how to decrease it through lifestyle changes.

Background: Depression is one of the most prevalent psychological disorders worldwide. Although psychotherapy for depression is effective, there are barriers to its implementation in primary care in Spain. The use of the Internet has been shown to be a feasible solution. However, the acceptability of Internet-based interventions has not been studied sufficiently.Objective: To assess the acceptability of an Internet-based intervention (IBI) for depression in primary care, and explore the relationship between expectations and satisfaction and the improvement in the clinical variables in primary care patients receiving this intervention. Furthermore, it offers data about the effects of some sociodemographic characteristics on these acceptability variables and analyzes whether the expectations are related to finalizing the intervention.Methods: Data were based on depressive patients who were participants in a randomized controlled trial. In the present study, we present the data from all the participants in the Internet intervention groups (N = 198). All the participants filled out the expectation and satisfaction scales (six-item scales regarding treatment logic, satisfaction, recommending, usefulness for other disorders, usefulness for the patient, and unpleasantness), the Beck Depression Inventory-II, and the secondary outcome measures: depression and anxiety impairment, and positive and negative affect.Results: Results showed that participants’ expectations and satisfaction with the program were both high and differences in expectations and satisfaction depended on some sociodemographic variables (age: older people have higher expectations; sex: women have greater satisfaction). A positive relationship between these variables and intervention efficacy was found: expectations related to “usefulness for the patient” were a statistically related predictor to the results on the BDI-II (Beta = 0.364), and the perception of how logical the treatment is (Beta = 0.528) was associated with change in the clinical variable. Furthermore, the higher the expectations, the higher the improvements exhibited by the patients in all measures evaluated during the ten intervention modules. High expectations were also directly related to finalizing the intervention.Conclusions: This is the first study in Spain to address this issue in the field of IBIs for depression in primary care. The IBI showed high acceptance related to the intervention’s efficacy and completion. Research on IBI acceptability could help to implement the treatment offered.Clinical Trial Registration: www.ClinicalTrials.gov, identifier NCT01611818.

Supplementary files for article "Stress, depression, and anxiety: psychological complaints across menopausal stages"Introduction: With the number of menopausal women projected estimated to reach 1.2 billion by 2030 worldwide, it is critically important to understand how menopause may affect women’s emotional well-being and how many women are affected by this. This study aimed to explore (i) the relationship between psychological complaints (depression, anxiety, poor memory) across different menopausal stages and (ii) investigate the correlation between resilience, self-efficacy, and perceived stress levels, with psychological complaints and whether this was associated with menopausal stage and/or age.Methods: 287 respondents completed the Menopausal Quality of Life (MenQoL), Perceived Stress Scale (PSS-10), Brief Resilience Scale (BRS), and General Self-efficacy (GSE) scales. Parametric and non-parametric analysis were used to analyse how bothered women were by self-reported poor memory and feelings of depression and anxiety, alongside perceived stress, resilience, and self-efficacy between women in different menopausal stages using STRAW criteria. The association between protective factors (self-efficacy and resilience) and psychological complaints was analysed with partial correlation analysis controlling for menopausal stages and/or age.Results: A significant difference was found between the levels of perceived stress, and how bothered women were by feelings of depression and anxiety between early-perimenopausal and post-menopausal women. However, with the inclusion of age as a covariate, menopausal stage no longer predicted the level of self-reported stress and anxiety in menopausal women. There was also no difference between poor self-reported memory, or of self-efficacy or resilience between women in different menopausal stages. However, self-efficacy and resilience were associated with how bothered women were by feelings of depression and anxiety, and the experience of stress. Stress was the only variable to be associated with poor self-reported memory independent of age and/or menopausal status.Discussion: Early perimenopausal women experienced the highest level of stress and were more severely bothered by feelings of depression and anxiety, with the poorest overall self-reported psychosocial quality of life. Post-menopausal women, however, reported to have similar experiences as premenopausal women. Age explained the associations between menopausal stage, stress and anxiety, but not between depression and different menopausal stages. Resilience and self-efficacy were associated with psychological complaints independent of menopausal stage and age, suggesting that therapies focusing on increasing resilience and self-efficacy may be beneficial to help target these psychological complaints at any time.© The Author(s), CC BY 4.0

IntroductionWith the number of menopausal women projected estimated to reach 1.2 billion by 2030 worldwide, it is critically important to understand how menopause may affect women’s emotional well-being and how many women are affected by this. This study aimed to explore (i) the relationship between psychological complaints (depression, anxiety, poor memory) across different menopausal stages and (ii) investigate the correlation between resilience, self-efficacy, and perceived stress levels, with psychological complaints and whether this was associated with menopausal stage and/or age.Methods287 respondents completed the Menopausal Quality of Life (MenQoL), Perceived Stress Scale (PSS-10), Brief Resilience Scale (BRS), and General Self-efficacy (GSE) scales. Parametric and non-parametric analysis were used to analyse how bothered women were by self-reported poor memory and feelings of depression and anxiety, alongside perceived stress, resilience, and self-efficacy between women in different menopausal stages using STRAW criteria. The association between protective factors (self-efficacy and resilience) and psychological complaints was analysed with partial correlation analysis controlling for menopausal stages and/or age.ResultsA significant difference was found between the levels of perceived stress, and how bothered women were by feelings of depression and anxiety between early-perimenopausal and post-menopausal women. However, with the inclusion of age as a covariate, menopausal stage no longer predicted the level of self-reported stress and anxiety in menopausal women. There was also no difference between poor self-reported memory, or of self-efficacy or resilience between women in different menopausal stages. However, self-efficacy and resilience were associated with how bothered women were by feelings of depression and anxiety, and the experience of stress. Stress was the only variable to be associated with poor self-reported memory independent of age and/or menopausal status.DiscussionEarly perimenopausal women experienced the highest level of stress and were more severely bothered by feelings of depression and anxiety, with the poorest overall self-reported psychosocial quality of life. Post-menopausal women, however, reported to have similar experiences as premenopausal women. Age explained the associations between menopausal stage, stress and anxiety, but not between depression and different menopausal stages. Resilience and self-efficacy were associated with psychological complaints independent of menopausal stage and age, suggesting that therapies focusing on increasing resilience and self-efficacy may be beneficial to help target these psychological complaints at any time.