Symptom clusters in adolescent depression and differential response to treatment: a secondary analysis of the Treatment for Adolescents with Depression Study randomised trial

Gray matter atrophy is constrained by normal structural brain network architecture in depression

BACKGROUND

There is growing evidence that gray matter atrophy is constrained by normal brain network (or connectome) architecture in neuropsychiatric disorders. However, whether this finding holds true in individuals with depression remains unknown. In this study, we aimed to investigate the association between gray matter atrophy and normal connectome architecture at individual level in depression.

METHODS

In this study, 297 patients with depression and 256 healthy controls (HCs) from two independent Chinese dataset were included: a discovery dataset (105 never-treated first-episode patients and matched 130 HCs) and a replication dataset (106 patients and matched 126 HCs). For each patient, individualized regional atrophy was assessed using normative model and brain regions whose structural connectome profiles in HCs most resembled the atrophy patterns were identified as putative epicenters using a backfoward stepwise regression analysis.

RESULTS

In general, the structural connectome architecture of the identified disease epicenters significantly explained 44% (±16%) variance of gray matter atrophy. While patients with depression demonstrated tremendous interindividual variations in the number and distribution of disease epicenters, several disease epicenters with higher participation coefficient than randomly selected regions, including the hippocampus, thalamus, and medial frontal gyrus were significantly shared by depression. Other brain regions with strong structural connections to the disease epicenters exhibited greater vulnerability. In addition, the association between connectome and gray matter atrophy uncovered two distinct subgroups with different ages of onset.

CONCLUSIONS

These results suggest that gray matter atrophy is constrained by structural brain connectome and elucidate the possible pathological progression in depression.

Symptom clusters in adolescent depression and differential responses of clusters to pharmacologic treatment

OBJECTIVE

Symptoms of depression in adolescents are widely variable, but they are often interactive and clustered. The analysis of interactions and clusters among individual symptoms may help predict treatment outcomes. We aimed to determine clusters of individual symptoms in adolescent depression and their changes in the response to pharmacological treatment.

METHOD

A total of 95 adolescents, aged 12-17 years, with major depressive disorder were included. Participants were treated with escitalopram, and depressive symptoms were assessed at baseline (V1) and 1, 2, 4, 6, and 8 weeks (V6). The severity of depression was assessed using the Children's Depression Rating Scale-Revised. To construct network and clustering structures among symptoms, the Gaussian graphical model and Exploratory Graph Analysis with the tuning parameter to minimize the extended Bayesian information criterion were adopted.

RESULTS

Exploratory Graph Analysis revealed that symptoms of depression comprised four clusters: impaired activity, somatic concerns, subjective mood, and observed affect. The main effect of visit with decreased symptom severity was significant in all four clusters; however, the degree of symptom improvement differed among the four clusters. The effect size of score differences from V1 to V6 was the highest in the subjective mood (Cohen's d = 1.075), and lowest in impaired activity (d = 0.501) clusters.

CONCLUSION

The present study identified four symptom clusters associated with adolescent depression and their differential changes related to antidepressant treatment. This finding suggests that escitalopram was the most effective at improving subjective mood among different clusters. However, other therapeutic modalities may be needed to improve other clusters of symptoms, consequently leading to increased overall improvement of depression in adolescents.

Resolving heterogeneity in schizophrenia, bipolar I disorder, and attention-deficit/hyperactivity disorder through individualized structural covariance network analysis

Disruptions in large-scale brain connectivity are hypothesized to contribute to psychiatric disorders, including schizophrenia, bipolar I disorder, and attention-deficit/hyperactivity disorder. However, high inter-individual variation among patients with psychiatric disorders hinders achievement of unified findings. To this end, we adopted a newly proposed method to resolve heterogeneity of differential structural covariance network in schizophrenia, bipolar I disorder, and attention-deficit/hyperactivity disorder. This method could infer individualized structural covariance aberrance by assessing the deviation from healthy controls. T1-weighted anatomical images of 114 patients with psychiatric disorders (schizophrenia: n = 37; bipolar I disorder: n = 37; attention-deficit/hyperactivity disorder: n = 37) and 110 healthy controls were analyzed to obtain individualized differential structural covariance network. Patients exhibited tremendous heterogeneity in profiles of individualized differential structural covariance network. Despite notable heterogeneity, patients with the same disorder shared altered edges at network level. Moreover, individualized differential structural covariance network uncovered two distinct psychiatric subtypes with opposite differences in structural covariance edges, that were otherwise obscured when patients were merged, compared with healthy controls. These results provide new insights into heterogeneity and have implications for the nosology in psychiatric disorders.

Resolving heterogeneity in depression using individualized structural covariance network analysis

BACKGROUND

Elucidating individual aberrance is a critical first step toward precision medicine for heterogeneous disorders such as depression. The neuropathology of depression is related to abnormal inter-regional structural covariance indicating a brain maturational disruption. However, most studies focus on group-level structural covariance aberrance and ignore the interindividual heterogeneity. For that reason, we aimed to identify individualized structural covariance aberrance with the help of individualized differential structural covariance network (IDSCN) analysis.

METHODS

T1-weighted anatomical images of 195 first-episode untreated patients with depression and matched healthy controls (n = 78) were acquired. We obtained IDSCN for each patient and identified subtypes of depression based on shared differential edges.

RESULTS

As a result, patients with depression demonstrated tremendous heterogeneity in the distribution of differential structural covariance edges. Despite this heterogeneity, altered edges within subcortical-cerebellum network were often shared by most of the patients. Two robust neuroanatomical subtypes were identified. Specifically, patients in subtype 1 often shared decreased motor network-related edges. Patients in subtype 2 often shared decreased subcortical-cerebellum network-related edges. Functional annotation further revealed that differential edges in subtype 2 were mainly implicated in reward/motivation-related functional terms.

CONCLUSIONS

In conclusion, we investigated individualized differential structural covariance and identified that decreased edges within subcortical-cerebellum network are often shared by patients with depression. The identified two subtypes provide new insights into taxonomy and facilitate potential clues to precision diagnosis and treatment of depression.

Parsing altered gray matter morphology of depression using a framework integrating the normative model and non-negative matrix factorization

The high inter-individual heterogeneity in individuals with depression limits neuroimaging studies with case-control approaches to identify promising biomarkers for individualized clinical decision-making. We put forward a framework integrating the normative model and non-negative matrix factorization (NMF) to quantitatively assess altered gray matter morphology in depression from a dimensional perspective. The proposed framework parses altered gray matter morphology into overlapping latent disease factors, and assigns patients distinct factor compositions, thus preserving inter-individual variability. We identified four robust disease factors with distinct clinical symptoms and cognitive processes in depression. In addition, we showed the quantitative relationship between the group-level gray matter morphological differences and disease factors. Furthermore, this framework significantly predicted factor compositions of patients in an independent dataset. The framework provides an approach to resolve neuroanatomical heterogeneity in depression.

Variations in sociodemographic and health-related factors are linked to distinct clusters of individuals with depression based on the PHQ-9 instrument: NHANES 2007-2018

BACKGROUND

Depression is a heterogeneous disease. Identification of latent depression subgroups and differential associations across these putative groups and sociodemographic and health-related factors might pave the way toward targeted treatment of individuals.

METHODS

We used model-based clustering to identify relevant subgroups of 2900 individuals with moderate to severe depression (defined as scores ≥10 on the PHQ-9 instrument) from the NHANES cross-sectional survey. We used ANOVA and chi-squared tests to assess associations between cluster membership and sociodemographics, health-related variables, and prescription medication use.

RESULTS

We identified six latent clusters of individuals, three based on depression severity and three differentially loaded by somatic and mental components of the PHQ-9. The Severe mental depression cluster had the most individuals with low education and income (P < 0.05). We observed differences in the prevalence of numerous health conditions, with the Severe mental depression cluster showing the worst overall physical health. We observed marked differences between the clusters regarding prescription medication use: the Severe mental depression cluster had the highest use of cardiovascular and metabolic agents, while the Uniform severe depression cluster showed the highest use of central nervous system and psychotherapeutic agents.

LIMITATIONS

Due to the cross-sectional design we cannot make conclusions about causal relationships. We used self-reported data. We did not have access to a replication cohort.

CONCLUSIONS

We show that socioeconomic factors, somatic diseases, and prescription medication use are differentially associated with distinct and clinically relevant clusters of individuals with moderate to severe depression.

Identification of shared and distinct patterns of brain network abnormality across mental disorders through individualized structural covariance network analysis

BACKGROUND

Mental disorders, including depression, obsessive compulsive disorder (OCD), and schizophrenia, share a common neuropathy of disturbed large-scale coordinated brain maturation. However, high-interindividual heterogeneity hinders the identification of shared and distinct patterns of brain network abnormalities across mental disorders. This study aimed to identify shared and distinct patterns of altered structural covariance across mental disorders.

METHODS

Subject-level structural covariance aberrance in patients with mental disorders was investigated using individualized differential structural covariance network. This method inferred structural covariance aberrance at the individual level by measuring the degree of structural covariance in patients deviating from matched healthy controls (HCs). T1-weighted anatomical images of 513 participants (105, 98, 190 participants with depression, OCD and schizophrenia, respectively, and 130 age- and sex-matched HCs) were acquired and analyzed.

RESULTS

Patients with mental disorders exhibited notable heterogeneity in terms of altered edges, which were otherwise obscured by group-level analysis. The three disorders shared high difference variability in edges attached to the frontal network and the subcortical-cerebellum network, and they also exhibited disease-specific variability distributions. Despite notable variability, patients with the same disorder shared disease-specific groups of altered edges. Specifically, depression was characterized by altered edges attached to the subcortical-cerebellum network; OCD, by altered edges linking the subcortical-cerebellum and motor networks; and schizophrenia, by altered edges related to the frontal network.

CONCLUSIONS

These results have potential implications for understanding heterogeneity and facilitating personalized diagnosis and interventions for mental disorders.

Psychological dominant stressor modification to an animal model of depression with chronic unpredictable mild stress

Background and Aim

Chronic unpredictable mild stress (CUMS) is a protocol widely used to create an animal model of depression with food deprivation, water deprivation, and physical-dominant stressors as routine procedures. However, human depression mainly involves psychological stressors and does not always involve a lack of food and water; thus, CUMS procedures should be modified accordingly. Therefore, this study aimed to create an animal model of depression, mainly focusing on a psychologically dominant stressor without food and water deprivation.

Materials and Methods

The CUMS and control groups, respectively, received CUMS modification (psychologically dominant stressors without food and water deprivation) for 21 days. A 24-h sucrose preference test (SPT) was used to assess the successful creation of an animal model of depression. Daily food intake measurements, weekly weight monitoring, and weight gain calculations were performed. Either an independent sample t-test or the Mann-Whitney test was used.

Results

Of the 42 rats included, 39 completed the study. Chronic unpredictable mild stress procedures for 21 days significantly reduced the SPT (p < 0.05), mean body weight (p < 0.05), and weekly weight gain (p < 0.05) in the CUMS group compared to the control group. However, the weekly average food intake did not statistically differ between the two groups.

Conclusion

Psychological dominant CUMS modification to an animal model of depression resulted in lower SPT, body weight, and weekly weight gain in the CUMS group than in the control group.

Trends in calls to a state poisons centre and presentation to emergency departments for intentional antidepressant overdose in Victoria, Australia

OBJECTIVE

To describe the rates and trends of emergency department presentations and calls to a state poisons centre for antidepressant overdose.

METHODS

A retrospective cohort study utilising the Victorian Emergency Minimum Dataset and Victorian Poisons Information Centre call registry between January 2009 and December 2018 was conducted. This captured all presentations to Victorian emergency departments and calls to the Victorian Poisons Information Centre. Any intentional overdose involving an antidepressant was included. Annual rates of emergency department presentations and calls per 100,000 persons and 100,000 prescriptions for antidepressants overall and individual antidepressant classes, in addition to age-group-specific rates, were reported.

RESULTS

A total of 3650 presentations to emergency department and 7096 calls to the poisons centre were included. No changes were seen in overall emergency department presentation rates when controlled for population or prescription numbers, but large and significant increases were seen for younger age groups. The 10-14- and 15-19-year age groups had average annual increases of 13.1% (95% CI = [6.5%, 19.7%], p < 0.001) and 7.2% (95% CI = [2.8%, 11.5%], p < 0.001) per 100,000 persons, respectively. Increases were seen in overall annual call rates of 6.7% (95% CI = [5.2%, 8.1%], p < 0.001) per 100,000 persons and 7.5% (95% CI = [4.9%, 10.1%], p < 0.001) per 100,000 prescriptions.

CONCLUSION

Overall, emergency department presentation rates remained stable during the study period. Overall poisons centre call rates increased moderately. However, when examining younger persons, large increases were seen in both emergency department presentations and poison centre call rates. These findings highlight the need for future interventions to mitigate against intentional overdose in younger populations.

Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review

BACKGROUND

Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges.

OBJECTIVE

This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality.

METHODS

A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided.

RESULTS

A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126).

CONCLUSIONS

These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.

Differential symptom cluster responses to repetitive transcranial magnetic stimulation treatment in depression

BACKGROUND

Repetitive transcranial magnetic stimulation (rTMS) can target specific neural circuits, which may allow for personalized treatment of depression. Treatment outcome is typically determined using sum scores from validated measurement scales; however, this may obscure differential improvements within distinct symptom domains. The objectives for this work were to determine: (1) whether a standard depression measure can be represented using a four symptom cluster model and (2) whether these symptom clusters had a differential response to rTMS treatment.

METHODS

Data were obtained from two multi-centre randomized controlled trials of rTMS delivered to the left dorsolateral prefrontal cortex (DLPFC) for participants with treatment-resistant depression (TRD) conducted in Canada (THREE-D [Conducted between Sept 2013, and Oct 2016] and CARTBIND [Conducted between Apr 2016 and Feb 2018]). The first objective used confirmatory factor analytic techniques, and the second objective used a linear mixed effects model. Trial Registration: NCT01887782, NCT02729792.

FINDINGS

In the total sample of 596 participants with TRD, we found a model consisting of four symptom clusters adequately fit the data. The primary analysis using the THREE-D treatment trial found that symptom clusters demonstrated a differential response to rTMS treatment (F(3,5984) = 31.92, p < 0.001). The anxiety symptom cluster was significantly less responsive to treatment than other symptom clusters (t(6001) = -8.02, p < 0.001). These findings were replicated using data from the CARTBIND trial.

INTERPRETATION

There are distinct symptom clusters experienced by individuals with TRD that have a differential response to rTMS. Future work will determine whether differing rTMS treatment targets have distinct patterns of symptom cluster responses with the eventual goal of personalizing rTMS protocols based on an individual's clinical presentation.

FUNDING

Canadian Institutes of Health Research, Brain Canada.

The Role of Symptom Clusters in Triage of COVID-19 Patients

BACKGROUND AND OBJECTIVE

COVID-19 manifests with a broad range of symptoms. This study investigates whether clusters of respiratory, gastrointestinal, or neurological symptoms can be used to diagnose COVID-19.

METHODS

We surveyed symptoms of 483 subjects who had completed COVID-19 laboratory tests in the last 30 days. The survey collected data on demographic characteristics, self-reported symptoms for different types of infections within 14 days of onset of illness, and self-reported COVID-19 test results. Robust LASSO regression was used to create 3 nested models. In all 3 models, the response variable was the COVID-19 test result. In the first model, referred to as the "main effect model," the independent variables were demographic characteristics, history of chronic symptoms, and current symptoms. The second model, referred to as the "hierarchical clustering model," added clusters of variables to the list of independent variables. These clusters were established through hierarchical clustering. The third model, referred to as the "interaction-terms model," also added clusters of variables to the list of independent variables; this time clusters were established through pairwise and triple-way interaction terms. Models were constructed on a randomly selected 80% of the data and accuracy was cross-validated on the remaining 20% of the data. The process was bootstrapped 30 times. Accuracy of the 3 models was measured using the average of the cross-validated area under the receiver operating characteristic curves (AUROCs).

RESULTS

In 30 bootstrap samples, the main effect model had an AUROC of 0.78. The hierarchical clustering model had an AUROC of 0.80. The interaction-terms model had an AUROC of 0.81. Both the hierarchical cluster model and the interaction model were significantly different from the main effect model (α = .04). Patients with different races/ethnicities, genders, and ages presented with different symptom clusters.

CONCLUSIONS

Using clusters of symptoms, it is possible to more accurately diagnose COVID-19 among symptomatic patients.

Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants

BACKGROUND

The treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo.

METHODS

The study samples included training datasets (N = 271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N = 255) or placebo (N = 265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment.

RESULTS

Variation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self-esteem, and depressed feelings) assessed with the Children's Depression Rating Scale-Revised at 4-6 weeks predicted treatment outcomes with fluoxetine at 10-12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10-12 week outcomes at 4-6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo-treated patients with accuracies of 67%. In placebo-treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants.

CONCLUSIONS

PGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression.

Acceptability, safety and tolerability of antidepressant repetitive transcranial magnetic stimulation for adolescents: A mixed-methods investigation

OBJECTIVE

Major depressive disorder (MDD) is relatively common in adolescence, with far-reaching impacts. Current treatments frequently fail to alleviate depression severity for a substantial portion of adolescents. Repetitive transcranial magnetic stimulation (rTMS) may assist with this unmet clinical need. However, little is known about adverse events (AEs) experienced by adolescents receiving rTMS, subjective treatment experiences of adolescents and their parents, or treatment acceptability.

METHODS

Fourteen adolescents (16.5 years ± 1.2; 71.4% female) with MDD received 20 sessions of either high-frequency (10 Hz; n = 7) left dorsolateral prefrontal cortex (DLPFC) or low-frequency (1 Hz; n = 7) right DLPFC rTMS. AEs were monitored at baseline and at weekly intervals via New York State Psychiatric Institute Side Effects Form for Children and Adolescents. Eight adolescents and nine parents participated in interviews regarding subjective treatment experience, analysed via content analysis.

RESULTS

Drowsiness and lethargy were common AEs, reported by 92.3% of participants in week one. Number of AEs decreased throughout treatment course (after 5 treatments: M = 11.23, SD = 5.00; after 20 treatments: M = 8.92, SD = 5.95). Thirty-five AEs emerged during treatment, most commonly transient dizziness. Frequency, severity, and number of AEs reported were equivalent between treatment groups. Treatment adherence and satisfaction were high. Qualitative findings identified three themes relating to rTMS experience: Preparation and connection; Physical experience of treatment; and Perceived role of treatment.

LIMITATIONS

Sample size was small, therefore findings are preliminary.

CONCLUSIONS

rTMS was an acceptable treatment for adolescent MDD, from both adolescents' and parents' perspectives, and was safe and well-tolerated, as AE frequency and type did not differ from rTMS treatment courses in adults.

Getting Real-er

Dilemmas of diagnostic heterogeneity in psychiatric disorders occur at many levels. Symptoms themselves and the experience of distress with symptoms may differ in nature. Put plainly, one child's sadness is not the same as another's, and the degree to which that young person finds their sadness distressing or impairing may differ from that of their peers.

Item Response Analyses of the Children's Depression Rating Scale-Revised Using Data from Clinical Studies

Objective: We aimed to identify which of the 17 items comprising the Children's Depression Rating Scale-Revised (CDRS-R) can sensitively capture changes in depression severity. Methods: We used data from four studies involving two antidepressants. For each of the 17 CDRS-R items, we conducted item response analyses to identify and evaluate those that reflect changes in depression severity. We created plots of the item characteristic curves (ICCs) estimated by the graded response model, and option characteristic curves and ICCs using nonparametric item response theory. The change from baseline in the CDRS-R subscale score with specified reflective items by item response analyses and the effect size between the treatment group and placebo group were calculated and compared with those of the CDRS-R total score. Results: CDRS-R items #2 (difficulty having fun), #3 (social withdrawal), #10 (low self-esteem), #11 (depressed feelings), and #15 (depressed facial expression) have favorable profiles that reflect disease severity. Changes from baseline in the CDRS-R total score (least square mean ± standard error) at week 8 were -22.3 ± 0.7 and -23.9 ± 0.7 in the placebo group and treatment group, respectively (difference, -1.5; estimated effect size, -0.113), changes from baseline in the CDRS-R5 (CDRS-R subscale consisting of the specified reflective items [#2, #3, #10, #11, and #15]) score at week 8 were -8.4 ± 0.3 and -9.6 ± 0.3 in each group, respectively (difference, -1.2; effect size, -0.202). Conclusions: The item response analyses clarified the properties of 17 items of the CDRS-R for major depressive disorder in children and adolescents. The CDRS-R5 might optimize the assessment of changes in overall depression severity and differentiation of treatment responses.

Depression symptom clusters in adolescents: A latent class analysis in a clinical sample

BACKGROUND

Major depression is clinically heterogeneous. We aimed to identify classes of depressed adolescents with different symptom presentations and examine if these were differentially associated with illness severity, functioning, engagement with treatment, and clinical outcomes.

METHOD

Baseline depression symptoms of 454 depressed adolescents (age 11-17) from the IMPACT trial were subjected to latent class analysis. We compared classes on self-reported symptoms and social impairment at baseline and follow-up and their engagement in treatment.

RESULTS

We identified three classes of participants which differed in the number and pattern of depression symptoms; Class 1-Severe- (37.2%)-endorsed almost all symptoms and were most functionally impaired; Class 2-Moderate- (41.9%)-endorsed fewer symptoms with high suicidal ideation, self-harm, and worthlessness; Class 3-Somatic (20.9%)-endorsed fewest symptoms, with high somatic symptoms. Groups did not differ on engagement, therapeutic alliance, or post-treatment symptom reduction. Adolescents in the severe and moderate subgroups reported symptom reductions after treatment ended, whilst those in the somatic subgroup did not.

CONCLUSIONS

At presentation, high somatic features in depressed adolescents, rather than severity, or impairment levels, may indicate lower liability for responding to psychological treatment.

Sex and age moderate the trajectory of guilt among children and adolescents with and without recent suicidal ideation

We aimed to examine whether the trajectories of ecologically derived guilt differ among a transdiagnostic sample of youth with and without recent suicidal ideation and whether sex and age moderated this association. We assessed guilt 3 times a day over a 2-week period via ecological momentary assessment (EMA) technology in 102 children recruited from the community, outpatient, and inpatient settings. The average age of children was 10.95 y.o. (SD = 2.26, range 8-16) and the majority were male (54.9%) and White (76.5%). We found that the real-world guilt during a two-week EMA period was higher among youth with greater suicidal ideation severity in the past six months. Moreover, there was a significant moderating effect of sex and age on this association, such that the association between suicidal ideation severity and guilt was particularly strong among females compared to males and youth who were 10 years old or older. The findings were maintained when we adjusted for the relevant demographic and clinical characteristics, including age, minority status, parental income, EMA response rate, and current internalising symptoms. These preliminary findings highlight the clinical relevance of assessing and targeting feelings of guilt in the day-to-day lives of youth, particularly for females and older youth.

Computational Network Pharmacology-Based Strategy to Capture Key Functional Components and Decode the Mechanism of Chai-Hu-Shu-Gan-San in Treating Depression

Traditional Chinese medicine (TCM) usually plays therapeutic roles on complex diseases in the form of formulas. However, the multicomponent and multitarget characteristics of formulas bring great challenges to the mechanism analysis and secondary development of TCM in treating complex diseases. Modern bioinformatics provides a new opportunity for the optimization of TCM formulas. In this report, a new bioinformatics analysis of a computational network pharmacology model was designed, which takes Chai-Hu-Shu-Gan-San (CHSGS) treatment of depression as the case. In this model, effective intervention space was constructed to depict the core network of the intervention effect transferred from component targets to pathogenic genes based on a novel node importance calculation method. The intervention-response proteins were selected from the effective intervention space, and the core group of functional components (CGFC) was selected based on these intervention-response proteins. Results show that the enriched pathways and GO terms of intervention-response proteins in effective intervention space could cover 95.3 and 95.7% of the common pathways and GO terms that respond to the major functional therapeutic effects. Additionally, 71 components from 1,012 components were predicted as CGFC, the targets of CGFC enriched in 174 pathways which cover the 86.19% enriched pathways of pathogenic genes. Based on the CGFC, two major mechanism chains were inferred and validated. Finally, the core components in CGFC were evaluated by in vitro experiments. These results indicate that the proposed model with good accuracy in screening the CGFC and inferring potential mechanisms in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.

Adolescent depression beyond DSM definition: a network analysis

Calls for refining the understanding of depression beyond diagnostic criteria have been growing in recent years. We examined the prevalence and relevance of DSM and non-DSM depressive symptoms in two Brazilian school-based adolescent samples with two commonly used scales, the Patient Health Questionnaire (PHQ-A) and the Mood and Feelings Questionnaire (MFQ). We analyzed cross-sectional data from two similarly recruited samples of adolescents aged 14-16 years, as part of the Identifying Depression Early in Adolescence (IDEA) study in Brazil. We assessed dimensional depressive symptomatology using the PHQ-A in the first sample (n = 7720) and the MFQ in the second sample (n = 1070). We conducted network analyses to study symptom structure and centrality estimates of the two scales. Additionally, we compared centrality of items included (e.g., low mood, anhedonia) and not included in the DSM (e.g., low self-esteem, loneliness) in the MFQ. Sad mood and worthlessness items were the most central items in the network structure of the PHQ-A. In the MFQ sample, self-hatred and loneliness, two non-DSM features, were the most central items and DSM and non-DSM items in this scale formed a highly interconnected network of symptoms. Furthermore, analysis of the MFQ sample revealed DSM items not to be more frequent, severe or interconnected than non-DSM items, but rather part of a larger network of symptoms. A focus on symptoms might advance research on adolescent depression by enhancing our understanding of the disorder.

Updates in treatment of depression in children and adolescents

PURPOSE OF REVIEW

To examine recent evidence that informs the treatment of depression in children and adolescents.

RECENT FINDINGS

There are no new leads in the prevention and early intervention of depression in children and adolescents. For acute treatment of major depressive disorder, talking therapies are moving increasingly to internet-based platforms. Family therapy may have a slight edge over individual psychotherapy in the short-term. Patients with severe depression with endogenous features have a more robust response to pharmacotherapy than do patients with mild-to-moderate depression. Findings in relation to reward sensitivity and changes in brain-derived neurotrophic factor levels contradict research conducted in adults, suggesting developmental differences in the mechanisms underlying depression. Ketamine infusion could have a role for adolescents with treatment refractory depression. There was no new evidence concerning relapse prevention.

SUMMARY

Most new findings have been concerned with moderators and mediators of treatment.

Assessing the causal associations of insomnia with depressive symptoms and subjective well-being: a bidirectional Mendelian randomization study

BACKGROUND

The interactions and associations between insomnia, depressive symptoms, and subjective well-being are complex, thus it is hard to explore the effect and direction of causalities. This bidirectional Mendelian randomization (MR) study was to assess the causal associations of insomnia with depressive symptoms and subjective well-being.

METHODS

Summary statistics for insomnia, depressive symptoms, and subjective well-being were obtained from three large-scale genome-wide association studies (GWAS) of European ancestry. MR analyses were mainly conducted with the inverse-variance-weighted (IVW) method. The weighted-median method, MR-Egger method, and MR-Pleiotropy RESidual Sum and Outlier (PRESSO) test were adopted to test whether the estimates were robust. The adjusted MR analysis was performed to avoid the effect of potential pleiotropy.

RESULTS

There was evidence to support a causal association between genetically predicted insomnia and depressive symptoms (beta (β) = 0.086, 95% confidence interval (CI) = 0.068 to 0.104, P = 8.6E-21). Meanwhile, genetically predicted depressive symptoms was associated with a higher risk of insomnia (β = 0.543, 95% CI = 0.331 to 0.754, P = 4.8E-07). Genetically predicted insomnia was negatively associated with subjective well-being (β = -0.043, 95% CI = -0.063 to -0.024, P = 1.2E-05). There was evidence of reverse causality between insomnia and subjective well-being (β = -0.821, 95% CI = -1.012 to -0.630, P = 4.0E-17).

CONCLUSIONS

MR analysis indicates bidirectional causal associations of insomnia with depressive symptoms and subjective well-being. People should give serious attention to and attempt to resolve the problems of insomnia, depressive symptoms, and subjective well-being, whichever comes first.

The analysis of network structure among the depressive symptoms in a clinical sample of children and adolescents

The symptoms of depressive disorder in children and adolescents vary widely and have complex interconnections with each other. This study aimed to identify the network structures among individual depressive symptoms in clinically referred children and adolescents. A total of 464 children and adolescents who visited the outpatient psychiatry clinic in South Korea were enrolled. The Children's Depression Inventory (CDI) was used to assess depressive symptoms. To construct the network structure and estimate the centrality indices among individual symptoms, the Gaussian graphical model was utilized with the tuning parameter to minimize the extended Bayesian information criterion. Among all symptoms, self-hatred had the highest strength centrality, followed by crying and self-deprecation. Among 191 valid edges constituting the CDI symptom network, sadness-crying, school work difficulty-school performance decrement, disobedience-fights, misbehavior-low self-esteem, self-deprecation-self-blame, school dislike-lack of friendship, self-hatred-negative body image, anhedonia-social withdrawal, self-hatred-suicidal ideation, crying-irritability, and sadness-loneliness showed significantly higher weights than the other edges. The present study identified the network structure among depressive symptoms in children and adolescents. Future studies including more symptoms of depression are warranted to provide insights into the underlying mechanisms of child and adolescent depression.

Screening for regenerative therapy responders in heart failure

Risk of outcome variability challenges therapeutic innovation. Selection of the most suitable candidates is predicated on reliable response indicators. Especially for emergent regenerative biotherapies, determinants separating success from failure in achieving disease rescue remain largely unknown. Accordingly, (pre)clinical development programs have placed increased emphasis on the multi-dimensional decoding of repair capacity and disease resolution, attributes defining responsiveness. To attain regenerative goals for each individual, phenotype-based patient selection is poised for an upgrade guided by new insights into disease biology, translated into refined surveillance of response regulators and deep learning-amplified clinical decision support.

The promise of machine learning in predicting treatment outcomes in psychiatry

For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real-world clinical practice. Relatively few retrospective studies to-date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.

Parsing the antidepressant effects of non-invasive brain stimulation and pharmacotherapy: A symptom clustering approach on ELECT-TDCS

BACKGROUND

Transcranial direct current stimulation (tDCS) presents small antidepressant efficacy at group level and considerable inter-individual variability of response. Its heterogeneous effects bring the need to investigate whether specific groups of patients submitted to tDCS could present comparable or larger improvement compared to pharmacotherapy. Aggregate measurements might be insufficient to address its effects.

OBJECTIVE

/Hypothesis: To determine the efficacy of tDCS, compared to pharmacotherapy and placebo, in depressive symptom clusters.

METHODS

Data from ELECT-TDCS (Escitalopram versus Electrical Direct-Current Therapy for Treating Depression Clinical Study, ClinicalTrials.gov, NCT01894815), in which antidepressant-free, depressed patients were randomized to receive 22 bifrontal tDCS (2 mA, 30 min) sessions (n = 94), escitalopram 20 mg/day (n = 91), or placebo (n = 60) over 10 weeks. Agglomerative hierarchical clustering identified "sleep/insomnia", "core depressive", "guilt/anxiety", and "atypical" clusters that were the dependent measure. Trajectories were estimated using linear mixed regression models. Effect sizes are expressed in raw HAM-D units. P-values were adjusted for multiple comparisons.

RESULTS

For core depressive symptoms, escitalopram was superior to tDCS (ES = -0.56; CI_95% = -0.94 to -0.17, p = .009), which was superior to placebo (ES = 0.49; CI_95% = 0.06 to 0.92, p = .042). TDCS but not escitalopram was superior to placebo in sleep/insomnia symptoms (ES = 0.87; CI_95% = 0.22 to 1.52, p = .015). Escitalopram but not tDCS was superior to placebo in guilt/anxiety symptoms (ES = 1.66; CI_95% = 0.58 to 2.75, p = .006). No active intervention was superior to placebo for atypical symptoms.

CONCLUSIONS

Pharmacotherapy and non-invasive brain stimulation produce distinct effects in depressive symptoms. TDCS or escitalopram could be chosen according to specific clusters of symptoms for a bigger response.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT01894815.

Decision Models and Technology Can Help Psychiatry Develop Biomarkers

Why is psychiatry unable to define clinically useful biomarkers? We explore this question from the vantage of data and decision science and consider biomarkers as a form of phenotypic data that resolves a well-defined clinical decision. We introduce a framework that systematizes different forms of phenotypic data and further introduce the concept of decision model to describe the strategies a clinician uses to seek out, combine, and act on clinical data. Though many medical specialties rely on quantitative clinical data and operationalized decision models, we observe that, in psychiatry, clinical data are gathered and used in idiosyncratic decision models that exist solely in the clinician's mind and therefore are outside empirical evaluation. This, we argue, is a fundamental reason why psychiatry is unable to define clinically useful biomarkers: because psychiatry does not currently quantify clinical data, decision models cannot be operationalized and, in the absence of an operationalized decision model, it is impossible to define how a biomarker might be of use. Here, psychiatry might benefit from digital technologies that have recently emerged specifically to quantify clinically relevant facets of human behavior. We propose that digital tools might help psychiatry in two ways: first, by quantifying data already present in the standard clinical interaction and by allowing decision models to be operationalized and evaluated; second, by testing whether new forms of data might have value within an operationalized decision model. We reference successes from other medical specialties to illustrate how quantitative data and operationalized decision models improve patient care.

The Different Faces of Insomnia

Objectives: The identification of clinically relevant subtypes of insomnia is important. Including a comprehensive literature review, this study also introduces new phenotypical relevant parameters by describing a specific insomnia cohort. Methods: Patients visiting the sleep center and indicating self-reported signs of insomnia were examined by a sleep specialist who confirmed an insomnia diagnosis. A 14-item insomnia questionnaire on symptoms, progression, sleep history and treatment, was part of the clinical routine. Results: A cohort of 456 insomnia patients was described (56% women, mean age 52 ± 16 years). They had suffered from symptoms for about 12 ± 11 years before seeing a sleep specialist. About 40-50% mentioned a trigger (most frequently psychological triggers), a history of being bad sleepers to begin with, a family history of sleep problems, and a negative progression of insomnia. Over one third were not able to fall asleep during the day. SMI (sleep maintenance insomnia) symptoms were most frequent, but only prevalence of EMA (early morning awakening) symptoms significantly increased from 40 to 45% over time. Alternative non-medical treatments were effective in fewer than 10% of cases. Conclusion: Our specific cohort displayed a long history of suffering and the sleep specialist is usually not the first point of contact. We aimed to describe specific characteristics of insomnia with a simple questionnaire, containing questions (e.g., ability to fall asleep during the day, effects of non-medical therapy methods, symptom stability) not yet commonly asked and of unknown clinical relevance as yet. We suggest adding them to anamnesis to help differentiate the severity of insomnia and initiate further research, leading to a better understanding of the severity of insomnia and individualized therapy. This study is part of a specific Research Topic introduced by Frontiers on the heterogeneity of insomnia and its comorbidity and will hopefully inspire more research in this area.

What next? A Bayesian hierarchical modeling re-examination of treatments for adolescents with selective serotonin reuptake inhibitor-resistant depression

BACKGROUND

Psychiatrists frequently struggle with how to sequence treatment for depressed adolescents who do not respond to an adequate trial of a selective serotonin reuptake inhibitor (SSRI). This study leveraged recent statistical and computational advances to create Bayesian hierarchal models (BHMs) of response in the treatment of SSRI-resistant depression in adolescents study to inform treatment planning.

METHODS

BHMs of individual treatment trajectories were developed and estimated using Hamiltonian Monte Carlo no u-turn sampling. From the Monte Carlo pseudorandom sample, 95% credible intervals, means, posterior tail probabilities, and so forth, were determined. Then, for the random effects model, posterior tail probabilities were used to create Bayesian two-tailed p values to evaluate the null hypotheses: no difference in efficacy between SSRIs and venlafaxine. The robustness of the results was examined using the fixed effects model of treatment comparisons.

RESULTS

In patients not receiving cognitive behavioral therapy (CBT; n = 168), SSRIs produced greater and faster improvement in depressive symptoms compared to venlafaxine (p = .015). No differences in response or trajectory of response for symptoms of anxiety were detected between SSRIs and venlafaxine (p = .168). For patients receiving CBT (n = 162), SSRIs and venlafaxine produced similar improvements in symptoms of anxiety and depression.

CONCLUSIONS

Findings from this novel computational approach suggest that a second trial of an SSRI is warranted for depressed adolescents who fail to respond to initial SSRI treatment.