Employing a developmental psychopathology framework, we tested the utility of the hormesis model in examining the strengthening of children and youth through limited levels of adversity in relation to internalizing and externalizing outcomes within a brain-by-development context.

Analyzing data from the Adolescent Brain and Cognitive Development study (N = 11,878), we formed latent factors of threat, deprivation, and unpredictability. We examined linear and nonlinear associations between adversity dimensions and youth psychopathology symptoms and how change of resting-state functional connectivity (rsFC) in the default mode network (DMN) from Time 1 to Time 5 moderates these associations.

A cubic association was found between threat and youth internalizing problems; low-to-moderate family conflict levels reduced these problems. Deprivation also displayed a cubic relation with youth externalizing problems, with moderate deprivation levels associated with fewer problems. Unpredictability linearly increased both problem types. Change in DMN rsFC significantly moderated the cubic link between threat levels and internalizing problems, with declining DMN rsFC levels from Time 1 to Time 5 facilitating hormesis. Hormetic effects peaked earlier, emphasizing the importance of sensitive periods and developmental timing of outcomes related to earlier experiences.

Strengthening through limited environmental adversity is crucial for developing human resilience. Understanding this process requires considering both linear and nonlinear adversity-psychopathology associations. Testing individual differences by brain and developmental context will inform preventive intervention programming.

Cross-sectional studies report high levels of depressive symptoms during the COVID-19 pandemic, especially in youth and females. However, longitudinal research comparing depressive symptoms before and during the pandemic is lacking. Little is known about how the pandemic affected individuals with familial history of mental illness. The present study examines the impact of the pandemic on youth depressive symptoms, including offspring of parents with major mood and psychotic disorders.

Between March 2018 and February 2020, we measured depressive symptoms in 412 youth aged 5–25 years. We measured depressive symptoms again in 371 (90%) of these youth between April 2020 and May 2022. Two thirds (249) participants had a biological parent with a major mood or psychotic disorder. We tested the effect of the pandemic by comparing depression symptoms before and after March 2020. We examined age, sex, and family history as potential moderators.

We found an overall small increase in youth depressive symptoms (b = 0.07, 95% CI −0.01 to 0.15, p = 0.062). This was driven by an increase in female youth without familial history of mental illness (b = 0.35, 95% CI 0.14 to 0.56, p = 0.001). There was no change in depressive symptoms among offspring of parents with mental illness or males.

Our results provide reassurance about the wellbeing of children of parents with mental illness during a period of restricted access to resources outside the family. Rather than increasing symptoms in established risk groups, the pandemic led to a redistribution of depression burden towards segments of the youth population that were previously considered to be low-risk.

Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).

A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.

In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.

Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.

This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.

32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.

Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Self-harm in pregnancy or the year after birth (‘perinatal self-harm’) is clinically important, yet prevalence rates, temporal trends and risk factors are unclear.

A cohort study of 679 881 mothers (1 172 191 pregnancies) was conducted using Danish population register data-linkage. Hospital treatment for self-harm during pregnancy and the postnatal period (12 months after live delivery) were primary outcomes. Prevalence rates 1997–2015, in women with and without psychiatric history, were calculated. Cox regression was used to identify risk factors.

Prevalence rates of self-harm were, in pregnancy, 32.2 (95% CI 28.9–35.4)/100 000 deliveries and, postnatally, 63.3 (95% CI 58.8–67.9)/100 000 deliveries. Prevalence rates of perinatal self-harm in women without a psychiatric history remained stable but declined among women with a psychiatric history. Risk factors for perinatal self-harm: younger age, non-Danish birth, prior self-harm, psychiatric history and parental psychiatric history. Additional risk factors for postnatal self-harm: multiparity and preterm birth. Of psychiatric conditions, personality disorder was most strongly associated with pregnancy self-harm (aHR 3.15, 95% CI 1.68–5.89); psychosis was most strongly associated with postnatal self-harm (aHR 6.36, 95% CI 4.30–9.41). For psychiatric disorders, aHRs were higher postnatally, particularly for psychotic and mood disorders.

Perinatal self-harm is more common in women with pre-existing psychiatric history and declined between 1997 and 2015, although not among women without pre-existing history. Our results suggest it may be a consequence of adversity and psychopathology, so preventative intervention research should consider both social and psychological determinants among women with and without psychiatric history.

In the treatment of psychosis, agitation and aggression in Alzheimer's disease, guidelines emphasise the need to ‘use the lowest possible dose’ of antipsychotic drugs, but provide no information on optimal dosing.

This analysis investigated the pharmacokinetic profiles of risperidone and 9-hydroxy (OH)-risperidone, and how these related to treatment-emergent extrapyramidal side-effects (EPS), using data from The Clinical Antipsychotic Trials of Intervention Effectiveness in Alzheimer's Disease study (Clinicaltrials.gov identifier: NCT00015548).

A statistical model, which described the concentration–time course of risperidone and 9-OH-risperidone, was used to predict peak, trough and average concentrations of risperidone, 9-OH-risperidone and ‘active moiety’ (combined concentrations) (n = 108 participants). Logistic regression was used to investigate the associations of pharmacokinetic biomarkers with EPS. Model-based predictions were used to simulate the dose adjustments needed to avoid EPS.

The model showed an age-related reduction in risperidone clearance (P < 0.0001), reduced renal elimination of 9-OH-risperidone (elimination half-life 27 h), and slower active moiety clearance in 22% of patients, (concentration-to-dose ratio: 20.2 (s.d. = 7.2) v. 7.6 (s.d. = 4.9) ng/mL per mg/day, Mann–Whitney U-test, P < 0.0001). Higher trough 9-OH-risperidone and active moiety concentrations (P < 0.0001) and lower Mini-Mental State Examination (MMSE) scores (P < 0.0001), were associated with EPS. Model-based predictions suggest the optimum dose ranged from 0.25 mg/day (85 years, MMSE of 5), to 1 mg/day (75 years, MMSE of 15), with alternate day dosing required for those with slower drug clearance.

Our findings argue for age- and MMSE-related dose adjustments and suggest that a single measure of the concentration-to-dose ratio could be used to identify those with slower drug clearance.

None.

Although childhood adversities are known to predict increased risk of post-traumatic stress disorder (PTSD) after traumatic experiences, it is unclear whether this association varies by childhood adversity or traumatic experience types or by age.

To examine variation in associations of childhood adversities with PTSD according to childhood adversity types, traumatic experience types and life-course stage.

Epidemiological data were analysed from the World Mental Health Surveys (n = 27017).

Four childhood adversities (physical and sexual abuse, neglect, parent psychopathology) were associated with similarly increased odds of PTSD following traumatic experiences (odds ratio (OR)=1.8), whereas the other eight childhood adversities assessed did not predict PTSD. Childhood adversity–PTSD associations did not vary across traumatic experience types, but were stronger in childhood-adolescence and early-middle adulthood than later adulthood.

Childhood adversities are differentially associated with PTSD, with the strongest associations in childhood-adolescence and early-middle adulthood. Consistency of associations across traumatic experience types suggests that childhood adversities are associated with generalised vulnerability to PTSD following traumatic experiences.