Baseline Depressive Symptom Severity Research Articles

Impact of Depressive Symptom Severity on Buprenorphine/Naloxone and Methadone Outcomes in People With Prescription-Type Opioid Use Disorder: Results From a Pragmatic Randomized Controlled Trial

Objective To evaluate the impact of depressive symptom severity on opioid use and treatment retention in individuals with prescription-type opioid use disorder (POUD). Method We analyzed data from a multi-centric, pragmatic, open-label, randomized controlled trial comparing buprenorphine/naloxone to methadone models of care in 272 individuals with POUD. Opioid use was self-reported every two weeks for 24 weeks using the Timeline Followback. Depressive symptom severity was self-reported with the Beck Depression Inventory at baseline, week 12 and week 24. Results Baseline depressive symptom severity was not associated with opioid use nor treatment retention. At week 12, moderate depressive symptoms were associated with greater opioid use while mild to severe depressive symptoms were associated with lowered treatment retention. At week 24, moderate depressive symptoms were associated with greater opioid use. Conclusions Ongoing depressive symptoms lead to poorer outcomes in POUD. Clinicians are encouraged to use integrative approaches to optimize treatment outcomes. This study was registered in ClinicalTrials.gov (NCT03033732) on January 27th, 2017, prior to participants enrollment.

Evaluating Effect Moderators in Cognitive Versus Behavioral Based CBT-Modules and Sequences Towards Preventing Adolescent Depression

ABSTRACT Objective The aim of this study was to investigate age group, gender, and baseline depressive symptom severity as possible effect moderators in (1) cognitive versus behavioral based CBT-modules and (2) sequences of modules that started either with cognitive or behavioral modules in indicated depression prevention in adolescents. Method We conducted a pragmatic cluster-randomized trial under four parallel conditions. Each condition consisted of four CBT-modules of three sessions (cognitive restructuring, problem solving, behavioral activation, relaxation), but the sequencing of modules differed. The CBT-modules and sequences were clustered into more cognitive versus more behavioral based approaches. The sample involved 282 Dutch adolescents with elevated depressive symptoms (Mage = 13.8; 55.7% girls, 92.9% Dutch). Assessments were conducted at baseline, after three sessions, at post-intervention and 6-month follow-up with self-reported depressive symptoms as the primary outcome. Results We found no evidence for substantial moderation effects. Age group, gender, and depressive symptom severity level at baseline did not moderate the effects of cognitive versus behavioral modules after three sessions. No evidence was also found that these characteristics moderated the effectiveness of sequences of modules that started either with cognitive or behavioral modules at post-intervention and 6-month follow-up. Conclusion Cognitive and behavioral based modules and sequences in the prevention of depression in adolescents might apply to a relatively wide range of adolescents in terms of age group, gender, and severity levels of depressive symptoms. Abbreviation: CDI-2:F: Children’s Depression Inventory-2 Full-length version; CDI-2:S: Children’s Depression Inventory-2 Short version; STARr: Solve, Think, Act, Relax, and repeat

Using Machine Learning to Predict Antidepressant Treatment Outcome From Electronic Health Records.

To evaluate if a machine learning approach can accurately predict antidepressant treatment outcome using electronic health records (EHRs) from patients with depression. This study examined 808 patients with depression at a New York City-based outpatient mental health clinic between June 13, 2016 and June 22, 2020. Antidepressant treatment outcome was defined based on trend in depression symptom severity over time and was categorized as either "Recovering" or "Worsening" (i.e., non-Recovering), measured by the slope of individual-level Patient Health Questionnaire-9 (PHQ-9) score trajectory spanning 6 months following treatment initiation. A patient was designated as "Recovering" if the slope is less than 0 and as "Worsening" if the slope was no less than 0. Multiple machine learning (ML) models including L2 norm regularized Logistic Regression, Naive Bayes, Random Forest, and Gradient Boosting Decision Tree (GBDT) were used to predict treatment outcome based on additional data from EHRs, including demographics and diagnoses. Shapley Additive Explanations were applied to identify the most important predictors. The GBDT achieved the best results of predicting "Recovering" (AUC: 0.7654±0.0227; precision: 0.6002± 0.0215; recall: 0.5131±0.0336). When excluding patients with low PHQ-9 scores (<10) at baseline, the results of predicting "Recovering" (AUC: 0.7254±0.0218; precision: 0.5392±0.0437; recall: 0.4431±0.0513) were obtained. Prior diagnosis of anxiety, psychotherapy, recurrent depression, and baseline depression symptom severity were strong predictors. The results demonstrate the potential utility of using ML in longitudinal EHRs to predict antidepressant treatment outcome. Our predictive tool holds the promise to accelerate personalized medical management in patients with psychiatric illnesses.

Clinical Practice Guidelines for the Use of Electroconvulsive Therapy.

Clinical Practice Guidelines for the Use of Electroconvulsive Therapy.

Perspectives on the success rate of current antidepressant pharmacotherapy

ABSTRACT Introduction There has been growing debate about the effectiveness of traditional antidepressants for the treatment of depression, and whether the clinical trials literature overstates the value of existing agents. Antidepressant efficacy is limited by suboptimal remission rates, lack of robust efficacy across diverse depressed subgroups, slow onset, and challenges managing tolerability. Clinicians can better navigate uncertainties in this area by recognizing patient-specific clinical and prognostic factors that influence the likelihood of antidepressant drug response. Areas covered The author summarizes pertinent literature regarding drug-placebo differences in antidepressant outcome as well as patient-specific factors that influence antidepressant drug responsivity across subtypes of depressive disorders. Expert opinion Standardized effect sizes for most monoaminergic antidepressants are relatively modest. At least one-third of treatment response derives from nonspecific (yet substantial) placebo effects, limiting the ability to compare antidepressant medication effects to that of ‘no treatment.’ Patients with high baseline depressive symptom severity are less likely to respond to placebo but may be more responsive to antidepressant pharmacotherapy than is the case in mild forms of depression. Patient satisfaction with antidepressant response must take into consideration not only efficacy for reducing symptoms but also drug tolerability/acceptability and tangible improvement in functional outcome and quality of life.

Repetitive transcranial magnetic stimulation (r-TMS) and selective serotonin reuptake inhibitor-resistance in obsessive-compulsive disorder: A meta-analysis and clinical implications

IntroductionDespite promising results from several randomized controlled trials (RCTs) and meta-analyses, the efficacy of r-TMS as a treatment for OCD remains controversial, at least in part owing to inconsistency in the trial methodologies and heterogeneity in the trial outcomes. This meta-analysis attempts to explain some of this heterogeneity by comparing the efficacy of r-TMS in patients with or without resistance to treatment with selective serotonin reuptake inhibitors (SSRI), defined using standardized criteria. MethodsWe conducted a pre-registered (PROSPERO ID: 241381) systematic review and meta-analysis. English language articles reporting blinded RCTs were retrieved from searches using MEDLINE, PsycINFO, and Cochrane Library databases. Studies were subjected to subgroup analysis based on four stages of treatment resistance, defined using an adaptation of published criteria (1 = not treatment resistant, 2 = one SSRI trial failed, 3 = two SSRI trials failed, 4 = two SSRI trials failed plus one or more CBT trial failed). Meta-regression analyses investigated patient and methodological factors (age, duration of OCD, illness severity, stage of treatment-resistance, or researcher allegiance) as possible moderators of effect size. ResultsTwenty-five independent comparisons (23 studies) were included. Overall, r-TMS showed a medium-sized reduction of Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) scores (Hedge's g: -0.47; 95%CI: - 0.67 to −0.27) with moderate heterogeneity (I2 = 39.8%). Assessment of publication bias using Trim and Fill analysis suggested a reduced effect size that remained significant (g: -0.29; 95%CI: −0.51 to −0.07). Subgroup analysis found that those studies including patients non-resistant to SSRI (stage 1) (g: -0.65; 95%CI: −1.05 to −0.25, k = 7) or with low SSRI-resistance (stage 2) (g:-0.47; 95%CI: −0.86 to −0.09, k = 6) produced statistically significant results with low heterogeneity, while studies including more highly resistant patients at stage 3 (g: −0.39; 95%CI: −0.90 to 0.11, k = 4) and stage 4 (g: -0.36; 95%CI: −0.75 to 0.03, k = 8) did not. Intriguingly, the only significant moderator of the effect size found by meta-regression was the severity of baseline depressive symptoms. All trials showed evidence of researcher allegiance in favour of the intervention and therefore caution is required in interpreting the reported effect sizes. ConclusionThis meta-analysis shows that r-TMS is an effective treatment for OCD, but largely for those not resistant to SSRI or failing to respond to only one SSRI trial. As a consequence, r-TMS may be best implemented earlier in the care pathway. These findings would have major implications for clinical service development, but further well-powered RCTs, which eliminate bias from researcher allegiance, are needed before definitive conclusions can be drawn.

Visit-To-Visit Blood Pressure Variability and Subthreshold Depressive Symptoms in Older Adults

ObjectivesDepression is related to increased risk for dementia, possibly through links with cerebrovascular disease. Blood pressure variability is an emerging risk factor for cerebrovascular disease and dementia, but relationships with affective symptoms remain understudied. DesignRetrospective analysis of prospective cohort study. SettingAlzheimer's Disease Neuroimaging Initiative. Participants505 older adults without history of dementia or recent depression underwent three to four blood pressure measurements over 12 months and completed a self-report measure of depressive symptoms (Geriatric Depression Scale – 15 Item) at study baseline and 24-months follow-up. MeasurementsBlood pressure variability was calculated as variability independent of mean and maximum minus minimum. Regression models investigated relationships between blood pressure variability and severity of self-reported depressive symptoms at 24-months follow-up after controlling for several variables, including average blood pressure, antihypertensive use, antidepressant use, and baseline depressive symptom severity. ResultsElevated diastolic blood pressure variability was related to greater total depressive symptom score at follow-up (ß = .16 [95% CI 0.02, .30]; p = 0.03), with specific contribution from increased severity of symptoms of dysphoria (odds ratio = 1.35 [95% CI 1.07, 1.75]; p = 0.02). Blood pressure variability was not significantly related to other symptom subscales, including those reflecting life satisfaction or withdrawal. ConclusionsFindings suggest that elevated diastolic blood pressure variability is related to subthreshold depressive symptomatology in older adults without history of dementia or recent depression, independent of average blood pressure. Blood pressure variability may be an understudied vascular risk factor linked with depression and cognitive impairment, with potential therapeutic implications.

Effect of Sleep Disturbance Symptoms on Treatment Outcome in Blended Cognitive Behavioral Therapy for Depression (E-COMPARED Study): Secondary Analysis.

BackgroundSleep disturbance symptoms are common in major depressive disorder (MDD) and have been found to hamper the treatment effect of conventional face-to-face psychological treatments such as cognitive behavioral therapy. To increase the dissemination of evidence-based treatment, blended cognitive behavioral therapy (bCBT) consisting of web-based and face-to-face treatment is on the rise for patients with MDD. To date, no study has examined whether sleep disturbance symptoms have an impact on bCBT treatment outcomes and whether it affects bCBT and treatment-as-usual (TAU) equally.ObjectiveThe objectives of this study are to investigate whether baseline sleep disturbance symptoms have an impact on treatment outcomes independent of treatment modality and whether sleep disturbance symptoms impact bCBT and TAU in routine care equally.MethodsThe study was based on data from the E-COMPARED (European Comparative Effectiveness Research on Blended Depression Treatment Versus Treatment-as-Usual) study, a 2-arm, multisite, parallel randomized controlled, noninferiority trial. A total of 943 outpatients with MDD were randomized to either bCBT (476/943, 50.5%) or TAU consisting of routine clinical MDD treatment (467/943, 49.5%). The primary outcome of this study was the change in depression symptom severity at the 12-month follow-up. The secondary outcomes were the change in depression symptom severity at the 3- and 6-month follow-up and MDD diagnoses at the 12-month follow-up, assessed using the Patient Health Questionnaire-9 and Mini-International Neuropsychiatric Interview, respectively. Mixed effects models were used to examine the association of sleep disturbance symptoms with treatment outcome and treatment modality over time.ResultsOf the 943 patients recruited for the study, 558 (59.2%) completed the 12-month follow-up assessment. In the total sample, baseline sleep disturbance symptoms did not significantly affect change in depressive symptom severity at the 12-month follow-up (β=.16, 95% CI –0.04 to 0.36). However, baseline sleep disturbance symptoms were negatively associated with treatment outcome for bCBT (β=.49, 95% CI 0.22-0.76) but not for TAU (β=–.23, 95% CI −0.50 to 0.05) at the 12-month follow-up, even when adjusting for baseline depression symptom severity. The same result was seen for the effect of sleep disturbance symptoms on the presence of depression measured with Mini-International Neuropsychiatric Interview at the 12-month follow-up. However, for both treatment formats, baseline sleep disturbance symptoms were not associated with depression symptom severity at either the 3- (β=.06, 95% CI −0.11 to 0.23) or 6-month (β=.09, 95% CI −0.10 to 0.28) follow-up.ConclusionsBaseline sleep disturbance symptoms may have a negative impact on long-term treatment outcomes in bCBT for MDD. This effect was not observed for TAU. These findings suggest that special attention to sleep disturbance symptoms might be warranted when MDD is treated with bCBT. Future studies should investigate the effect of implementing modules specifically targeting sleep disturbance symptoms in bCBT for MDD to improve long-term prognosis.

Supervised machine learning to predict reduced depression severity in people with epilepsy through epilepsy self-management intervention

ObjectiveTo develop a classifier that predicts reductions in depression severity in people with epilepsy after participation in an epilepsy self-management intervention. MethodsNinety-three people with epilepsy from three epilepsy self-management randomized controlled trials from the Managing Epilepsy Well (MWE) Network integrated research database met the inclusion criteria. Supervised machine learning algorithms were utilized to develop prediction models for changes in self-reported depression symptom severity. Features considered by the machine learning classifiers include age, gender, race, ethnicity, education, study type, baseline quality of life, and baseline depression symptom severity. The models were trained and evaluated on their ability to predict clinically meaningful improvement (i.e., a reduction of greater than three points on the nine-item Patient Health Questionnaire (PHQ-9)) between baseline and follow-up (<=12 weeks) depression scores. Models tested were a Multilayer Perceptron (ML), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression with Stochastic Gradient Descent (SGD), K-nearest Neighbors (KNN), and Gradient Boosting (GB). A separate, outside dataset of 41 people with epilepsy was used in a validation exercise to examine the top-performing model’s generalizability and performance with external data. ResultsAll six classifiers performed better than our baseline mode classifier. Support Vector Machine had the best overall performance (average area under the curve [AUC] = 0.754, highest subpopulation AUC = 0.963). Our analysis of the SVM features revealed that higher baseline depression symptom severity, study type (i.e., intervention program goals), higher baseline quality of life, and race had the strongest influence on increasing the likelihood that a subject would experience a clinically meaningful improvement in depression scores. From the validation exercise, our top-performing SVM model performed similarly or better than the average SVM model with the outside dataset (average AUC = 0.887). SignificanceWe trained an SVM classifier that offers novel insight into subject-specific features that are important for predicting a clinically meaningful improvement in subjective depression scores after enrollment in a self-management program. We provide evidence for machine learning to select subjects that may benefit most from a self-management program and indicate important factors that self-management programs should collect to develop improved digital tools.

Multi-target repetitive transcranial magnetic stimulation (rTMS) protocol for the treatment of comorbid depression and chronic pain

Abstract Background: Major Depressive Disorder (MDD) and chronic pain are frequently co-occurring, and the onset of either condition can either precede or follow the onset of the other. Notably, clinical outcome for pharmacological and rTMS treatment for depression is significantly worse in the presence of chronic pain symptoms. To improve clinical care for depression it is thus critical to address the comorbidity of depression and chronic pain in a unified approach rather than treating them as separate conditions. Objective: We examined whether a multi-target rTMS protocol including stimulation of the left dorsolateral prefrontal cortex (DLPFC) and primary motor cortex (M1) would improve depressive and chronic pain symptoms more than a standard single-target rTMS protocol. Methods: We treated 20 comorbid depressive and chronic pain patients with a multi-target rTMS protocol and another 20 age- and sex-matched comorbid patients that had comparable baseline depression and pain symptom severity were treated only at the left DLPFC, for a minimum of 10 sessions. We have also recorded electroencephalography (EEG) at baseline session and examined a previously reported metric of pain sensitivity, peak alpha frequency (PAF), as a possible marker of treatment outcome. Preliminary Results: The multi-target rTMS achieved on average 15% greater improvement than stimulation at left DLPFC alone. Cohen’s D effect size was D = 0.8, which represents a moderate effect. We will also present EEG data examining the associations between PAF and pain and depression treatment outcomes. Conclusions: Multi-target rTMS to DLPFC and M1 may be more effective in treating the comorbidity of depression and chronic pain than rTMS to DLPFC only. Multi-site protocols could represent a treatment augmentation strategy with significant benefit for comorbid subjects. Other potential rTMS target site should be evaluated in future studies. Keywords: Chronic Pain, Depression, EEG, TMS

Depression and inflammation: Correlation between changes in inflammatory markers with antidepressant response and long-term prognosis

Inflammation may correlate with a specific subgroup of depression and differential antidepressant response, but no trial has studied changes of many inflammatory markers over several time points and evaluated symptom-specific antidepressant response and long-term prognosis.We performed secondary analyses among 90 outpatients with moderate-severe depression (71% female, mean age 38 years) treated for 26 weeks with escitalopram or nortriptyline. We measured 27 pro- and anti-inflammatory markers at week 0, 8, 12, and 26 and calculated composite inflammation scores. Three depression rating scales were applied and symptom dimensions of depression calculated. Via Danish nationwide registers, 10 years follow-up were included on psychiatric hospital contacts, indicating relapse. Pearson correlation analyses were performed between baseline inflammatory markers and depressive symptom severity, mixed effects models during the 26-week trial, and Cox regression analyses for the register-based outcomes, adjusted for age, sex, BMI, and smoking.Baseline inflammatory markers correlated with differential severity on specific symptom dimensions but not with overall depression severity. A total of 17 of 27 inflammatory markers decreased significantly during treatment. We found no correlation between baseline nor change in inflammatory markers nor composite inflammation scores with differential treatment response on the MADRS, but small correlations between changes in inflammatory markers and differential response on neurovegetative symptoms. Findings were similar among 59 treatment-naïve patients. Inflammatory markers were not associated with differential risks for 10-year relapse.These findings support the importance of studying specific depressive symptoms to further explore the correlation between inflammation with differential antidepressant response in a subgroup of depression.Clinical Trial Registration number: GENDEP is registered at EudraCT2004–001723–38(http://eudract.emea.europa.eu) and ISRCTN03693000 (www.controlled-trials.com).

Traumatic events and mental health: The amplifying effects of pre-trauma systemic inflammation

BackgroundTraumatic experiences are strongly predictive of adverse mental health outcomes. Experimental studies have demonstrated that systemic inflammation can increase reactivity to threatening stimuli. It is not known whether naturally occurring inflammation amplifies the impact of traumatic experiences on mental health. Here we test whether incident traumatic events are more predictive of adverse mental health outcomes for individuals with greater pre-trauma systemic inflammation in a racially and ethnically diverse cohort study of youth assigned male at birth who identify as sexual or gender minorities (ages 16–29, n = 518), a group at high risk for trauma exposure. MethodsMeasures of inflammation, depression symptom severity, and perceived stress were measured at baseline. One year later, depression symptom severity and perceived stress were measured again, and participants reported the traumatic events they had experienced in the intervening year. ResultsIn a model adjusted for baseline depression symptom severity and other key covariates, we found that higher baseline levels of interleukin-1β amplified the effect of incident trauma exposure on depression symptom severity at follow-up (β = 0.234, SE = 0.080, P = 0.004). In a model adjusted for baseline perceived stress and other key covariates, we found that higher baseline scores on a multi-marker inflammatory index amplified the effect of incident trauma exposure on perceived stress at follow-up (β = 0.243, SE = 0.083, P = 0.003). ConclusionsThese findings suggest that greater pre-trauma inflammation may predict poorer mental health following trauma exposure. Understanding how inflammation interacts with trauma to shape mental health may generate novel insights for preventing and treating the debilitating psychological consequences of trauma.

A 6-year prospective clinical cohort study on the bidirectional association between frailty and depressive disorder.

IntroductionDepressive disorder has been conceptualised as a condition of accelerated biological ageing. We operationalised a frailty index (FI) as marker for biological ageing aimed to explore the bidirectional, longitudinal association between frailty and either depressive symptoms or depressive disorder.MethodsA cohort study with 6‐year follow‐up including 377 older (≥60 years) outpatients with a DSM‐IV‐defined depressive disorder and 132 never‐depressed controls. Site visits at baseline, 2 and 6‐year follow‐up were conducted and included the CIDI 2.0 to assess depressive disorder and relevant covariates. Depressive symptom severity and mortality were assessed every 6 months by mail and telephone. A 41‐item FI was operationalised and validated against the 6‐year morality rate by Cox regression (HRFI = 1.04 [95% CI: 1.02–1.06]).ResultsCox regression showed that a higher FI was associated with a lower chance of remission among depressed patients (HRFI = 0.98 [95% CI: 0.97–0.99]). Nonetheless, this latter effect disappeared after adjustment for baseline depressive symptom severity. Linear mixed models showed that the FI increased over time in the whole sample (B[SE] = 0.94 (0.12), p < .001) with a differential impact of depressive symptom severity and depressive disorder. Higher baseline depressive symptom severity was associated with an attenuated and depressive disorder with an accelerated increase of the FI over time.ConclusionsThe sum score of depression rating scales is likely confounded by frailty. Depressive disorder, according to DSM‐IV criteria, is associated with accelerated biological ageing. This argues for the development of multidisciplinary geriatric care models incorporating frailty to improve the overall outcome of late‐life depression.

The uptake and effectiveness of online cognitive behaviour therapy for symptoms of anxiety and depression during COVID-19

BackgroundThe mental health impacts of COVID-19 have been considerable with many individuals experiencing significant anxiety and depression. Online cognitive behavioural therapy (iCBT) programs provide scalable access to psychological interventions, however the effectiveness of these programs during the pandemic has not been investigated. This study examined the uptake and effectiveness of iCBT for symptoms of anxiety and depression during the first eight months of the pandemic in Australia (March- October 2020) and compared outcomes to the 12 months prior to COVID-19. Methods6,132 adults commenced iCBT (5,074 during the pandemic and 1,058 in the year before) and completed measures of anxiety and depression symptom severity, and psychological distress pre- and post-treatment. ResultsIn the COVID-19 period, we observed a 504% increase in the number of monthly course registrations compared to the year prior (with a peak increase of 1,138% between April and June 2020). Baseline anxiety and depression symptom severity were similar for the COVID and pre-COVID groups. Prior to and during the pandemic, the iCBT course was associated with large effect size reductions in anxiety (g = 0.94–1.18) and depression (g = 0.92–1.12) symptom severity, as well as psychological distress (g = 1.08–1.35). Limitationslack of control group and long-term follow-up, as well as lack of detailed information about course users (e.g., health status and life context). ConclusionResults indicate the considerable increase in demand for psychological support during the COVID-19 pandemic in Australia and demonstrate the effectiveness and scalability of iCBT for symptoms of anxiety and depression.

Effectiveness of cognitive remediation in depression: a meta-analysis.

Preliminary evidence suggests beneficial effects of cognitive remediation in depression. An update of the current evidence is needed. The aim was to systematically assess the effectiveness of cognitive remediation in depression on three outcomes. The meta-analysis was pre-registered on PROSPERO (CRD42019124316). PubMed, PsycINFO, Embase and Cochrane Library were searched on 2 February 2019 and 8 November 2020 for peer-reviewed published articles. We included randomized and non-randomized clinical trials comparing cognitive remediation to control conditions in adults with primary depression. Random-effects models were used to calculate Hedges' g, and moderators were assessed using mixed-effects subgroup analyses and meta-regression. Main outcome categories were post-treatment depressive symptomatology (DS), cognitive functioning (CF) and daily functioning (DF). We identified 5221 records and included 21 studies reporting on 24 comparisons, with 438 depressed patients receiving cognitive remediation and 540 patients in a control condition. We found a small effect on DS (g = 0.28, 95% CI 0.09-0.46, I2 40%), a medium effect on CF (g = 0.60, 95% CI 0.37-0.83, I2 44%) and a small effect on DF (g = 0.22, 95% CI 0.06-0.39, I2 3%). There were no significant effects at follow-up. Confounding bias analyses indicated possible overestimation of the DS and DF effects in the original studies. Cognitive remediation in depression improves CF in the short term. The effects on DS and DF may have been overestimated. Baseline depressive symptom severity should be considered when administering cognitive remediation.

Predicting Depression Onset in Young People Based on Clinical, Cognitive, Environmental, and Neurobiological Data

BackgroundAdolescent onset of depression is associated with long-lasting negative consequences. Identifying adolescents at risk for developing depression would enable the monitoring of risk factors and the development of early intervention strategies. Using machine learning to combine several risk factors from multiple modalities might allow prediction of depression onset at the individual level. MethodsA subsample of a multisite longitudinal study in adolescents, the IMAGEN study, was used to predict future (subthreshold) major depressive disorder onset in healthy adolescents. Based on 2-year and 5-year follow-up data, participants were grouped into the following: 1) those developing a diagnosis of major depressive disorder or subthreshold major depressive disorder and 2) healthy control subjects. Baseline measurements of 145 variables from different modalities (clinical, cognitive, environmental, and structural magnetic resonance imaging) at age 14 years were used as input to penalized logistic regression (with different levels of penalization) to predict depression onset in a training dataset (n = 407). The features contributing the highest to the prediction were validated in an independent hold-out sample (three independent IMAGEN sites; n = 137). ResultsThe area under the receiver operating characteristic curve for predicting depression onset ranged between 0.70 and 0.72 in the training dataset. Baseline severity of depressive symptoms, female sex, neuroticism, stressful life events, and surface area of the supramarginal gyrus contributed most to the predictive model and predicted onset of depression, with an area under the receiver operating characteristic curve between 0.68 and 0.72 in the independent validation sample. ConclusionsThis study showed that depression onset in adolescents can be predicted based on a combination multimodal data of clinical characteristics, life events, personality traits, and brain structure variables.

Structural Connectivity Between Rostral Anterior Cingulate Cortex and Amygdala Predicts First Onset of Depressive Disorders in Adolescence

BackgroundAdolescent-onset depressive disorders (DDs) are associated with deficits in the regulation of negative affect across modalities (self-report, behavioral paradigms, and neuroimaging), which may manifest prior to first-onset DDs. Whether the neurocircuitry governing emotional regulation predates DDs is unclear. This study tested whether a critical pathway for emotion regulation (rostral anterior cingulate cortex–amygdala structural connectivity) predicts first-onset DDs in adolescent females. MethodsDiffusion tensor imaging data were acquired on adolescent females (n = 212) without a history of DDs and the cohort was reassessed for first-onset DDs over the next 27 months. ResultsA total of 26 girls developed first onsets of DDs in the 27 months after imaging. Multivariate logistic regression showed that lower weighted average fractional anisotropy of uncinate fasciculus tracts between the rostral anterior cingulate cortex and amygdala prospectively predicted first onset of DDs (adjusted odds ratio = 0.44, p = .005), above and beyond established risk factors including baseline depression symptom severity, history of anxiety disorders, parental history of depression, parental education, and age. ConclusionsThis study provides evidence for the first time showing that aberrant structural connectivity between the rostral anterior cingulate cortex and amygdala prospectively predates first onset of DDs in adolescent females. These results highlight the importance of a well-established neural circuit implicated in the regulation of negative affect as a likely etiological factor and a promising target for intervention and prevention of DDs.

Depression and routinization following stroke

Post-stroke depression is a frequent complication of stroke that has detrimental consequences for quality of life and functional outcomes. Daily life routines may increase feelings of security for some individuals confronted with age-related health concerns, but little information is available concerning their role following stroke. The aim of this investigation was to examine the association of depression and other psychological variables at hospitalization for stroke and behavioral routines and mood symptoms three months later using Ecological Momentary Assessment (EMA). Forty-four patients with minor ischemic stroke were consecutively enrolled in the study. Stroke severity, handicap, depression and anxiety symptoms were assessed at baseline. EMA data acquired three months later were coded for the repetition of specific activities or behaviors occurring within the same time period across days. Higher baseline depression and anxiety symptom severity were significantly associated with increased behavioral routines three months after stroke. The occurrence of routines was associated with a higher level of depressive symptomatology over subsequent hours of the day. The findings demonstrate a general correlation between baseline psychological vulnerability and routines three months later, but within-day analyses suggest that routines may increase the risk of negative affect in this vulnerable population.

Psychometric properties of the Marital Adjustment Scale during cognitive therapy for depression: New research opportunities.

Poor dyadic adjustment in marital or similar relationships is common among patients seeking individual cognitive therapy (CT) for major depressive disorder (MDD). Here we examined the psychometric properties of the marital adjustment subscale (MAS) of the Social Adjustment Scale-Self-report (SAS-SR; Weissman & Bothwell, 1976). Among married or cohabiting patients receiving individual CT for recurrent MDD (N = 306) in the context of two randomized controlled trials, the MAS demonstrated moderate internal consistency and test-retest reliability, strong convergence with the Dyadic Adjustment Scale (Spanier, 1976), and moderate relations with interpersonal problems and depressive symptoms. Controlling baseline depressive symptom severity, greater pre-CT relationship discord on the MAS predicted less reduction in depressive symptom severity and lower odds of depression remission during CT. These results support the reliability, validity, and potential utility of the MAS. Using the MAS may help investigators "mine" existing data sets including the SAS-SR to further understanding of dyadic functioning and its potential impact on depression treatment and other health outcomes. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

A randomized trial of aerobic exercise for major depression: examining neural indicators of reward and cognitive control as predictors and treatment targets.

Aerobic exercise has demonstrated antidepressant efficacy among adults with major depression. There is a poor understanding of the neural mechanisms associated with these effects. Deficits in reward processing and cognitive control may be two candidate targets and predictors of treatment outcome to exercise in depression. Sixty-six young adults aged 20.23 years (s.d. = 2.39) with major depression were randomized to 8 weeks of moderate-intensity aerobic exercise (n = 35) or light stretching (n = 31). Depressive symptoms were assessed across the intervention to track symptom reduction. Reward processing [reward positivity (RewP)] and cognitive control [error-related negativity (ERN)] were assessed before and after the intervention using event-related brain potentials. Compared to stretching, aerobic exercise resulted in greater symptom reduction (gs = 0.66). Aerobic exercise had no impact on the RewP (gav = 0.08) or ERN (gav = 0.21). In the aerobic exercise group, individuals with a larger pre-treatment RewP [odds ratio (OR) = 1.45] and increased baseline depressive symptom severity (OR = 1.18) were more likely to respond to an aerobic exercise program. Pre-treatment ERN did not predict response (OR = 0.74). Aerobic exercise is effective in alleviating depressive symptoms in adults with major depression, particularly for those with increased depressive symptom severity and a larger RewP at baseline. Although aerobic exercise did not modify the RewP or ERN, there is preliminary support for the utility of the RewP in predicting who is most likely to respond to exercise as a treatment for depression.