Despite extensive research into the neural basis of autism spectrum disorder (ASD), the presence of substantial biological and clinical heterogeneity among diagnosed individuals remains a major barrier. Commonly used case‒control designs assume homogeneity among subjects, which limits their ability to identify biological heterogeneity, while normative modeling pinpoints deviations from typical functional network development at individual level.

Using a world-wide multi-site database known as Autism Brain Imaging Data Exchange, we analyzed individuals with ASD and typically developed (TD) controls (total n = 1218) aged 5–40 years, generating individualized whole-brain network functional connectivity (FC) maps of age-related atypicality in ASD. We then used local polynomial regression to estimate a networkwise normative model of development and explored correlations between ASD symptoms and brain networks.

We identified a subset exhibiting highly atypical individual-level FC, exceeding 2 standard deviation from the normative value. We also identified clinically relevant networks (mainly default mode network) at cohort level, since the outlier rates decreased with age in TD participants, but increased in those with autism. Moreover, deviations were linked to severity of repetitive behaviors and social communication symptoms.

Individuals with ASD exhibit distinct, highly individualized trajectories of brain functional network development. In addition, distinct developmental trajectories were observed among ASD and TD individuals, suggesting that it may be challenging to identify true differences in network characteristics by comparing young children with ASD to their TD peers. This study enhances understanding of the biological heterogeneity of the disorder and can inform precision medicine.

Two established staging models outline the longitudinal progression in bipolar disorder (BD) based on episode recurrence or inter-episodic functioning. However, underlying neurobiological mechanisms and corresponding biomarkers remain unexplored. This study aimed to investigate if global and (sub)cortical brain structures, along with brain-predicted age difference (brain-PAD) reflect illness progression as conceptualized in these staging models, potentially identifying brain-PAD as a biomarker for BD staging.

In total, 199 subjects with bipolar-I-disorder and 226 control subjects from the Dutch Bipolar Cohort with a high-quality T1-weighted magnetic resonance imaging scan were analyzed. Global and (sub)cortical brain measures and brain-PAD (the difference between biological and chronological age) were estimated. Associations between individual brain measures and the stages of both staging models were explored.

A higher brain-PAD (higher biological age than chronological age) correlated with an increased likelihood of being in a higher stage of the inter-episodic functioning model, but not in the model based on number of mood episodes. However, after correcting for the confounding factors lithium-use and comorbid anxiety, the association lost significance. Global and (sub)cortical brain measures showed no significant association with the stages.

These results suggest that brain-PAD may be associated with illness progression as defined by impaired inter-episodic functioning. Nevertheless, the significance of this association changed after considering lithium-use and comorbid anxiety disorders. Further research is required to disentangle the intricate relationship between brain-PAD, illness stages, and lithium intake or anxiety disorders. This study provides a foundation for potentially using brain-PAD as a biomarker for illness progression.

Online treatments are increasing in number and are currently available for a wide range of clinical problems. To date little is known about the role of treatment expectations and other placebo-like mechanisms in online settings compared to traditional face-to-face treatment. To address this knowledge gap, we analyzed individual participant data from randomized clinical trials that compared online and face-to-face psychological interventions.

MEDLINE (Ovid) and PsycINFO (Ovid) were last searched on 2 February 2021. Randomized clinical trials of therapist guided online v. face-to-face psychological interventions for psychiatric or somatic conditions using a randomized controlled design were included. Titles, abstracts, and full texts of studies were independently screened by multiple observers. The Preferred Reporting Items for Systematic Reviews and Meta-analyses guideline was followed. Authors of the matching trials were contacted for individual participant data. Ratings from the Credibility and Expectancy Questionnaire and the primary outcome measure from each trial were used to estimate the association between expectation ratings and treatment outcomes in online v. face-to-face interventions, using a mixed-effects model.

Of 7045 screened studies, 62 full-text articles were retrieved whereof six studies fulfilled the criteria and provided individual participant data (n = 491). Overall, CEQ ratings predicted clinical outcomes (β = 0.27) at end of treatment with no moderating effect of treatment modality (online v. face-to-face).

Online treatment appears to be equally susceptible to expectancy effects as face-to-face therapy. This furthers our understanding of the importance of placebo-like factors in online treatment and may aid the improvement of healthcare in online settings.

While previous studies have reported high rates of documented suicide attempts (SAs) in the U.S. Army, the extent to which soldiers make SAs that are not identified in the healthcare system is unknown. Understanding undetected suicidal behavior is important in broadening prevention and intervention efforts.

Representative survey of U.S. Regular Army enlisted soldiers (n = 24 475). Reported SAs during service were compared with SAs documented in administrative medical records. Logistic regression analyses examined sociodemographic characteristics differentiating soldiers with an undetected SA v. documented SA. Among those with an undetected SA, chi-square tests examined characteristics associated with receiving a mental health diagnosis (MH-Dx) prior to SA. Discrete-time survival analysis estimated risk of undetected SA by time in service.

Prevalence of undetected SA (unweighted n = 259) was 1.3%. Annual incidence was 255.6 per 100 000 soldiers, suggesting one in three SAs are undetected. In multivariable analysis, rank ⩾E5 (OR = 3.1[95%CI 1.6–5.7]) was associated with increased odds of undetected v. documented SA. Females were more likely to have a MH-Dx prior to their undetected SA (Rao-Scott χ 21 = 6.1, p = .01). Over one-fifth of undetected SAs resulted in at least moderate injury. Risk of undetected SA was greater during the first four years of service.

Findings suggest that substantially more soldiers make SAs than indicated by estimates based on documented attempts. A sizable minority of undetected SAs result in significant injury. Soldiers reporting an undetected SA tend to be higher ranking than those with documented SAs. Undetected SAs require additional approaches to identifying individuals at risk.

We aimed to identify the common types of outcome trajectories for patients with psychosis who take up specialist psychological therapy for persecutory delusions. Knowing the different potential responses to therapy can inform expectations. Further, determining predictors of different outcomes may help in understanding who may benefit.

We analyzed delusion conviction data from 767 therapy sessions with 64 patients with persistent persecutory delusions (held with at least 60% conviction) who received a six-month psychological intervention (Feeling Safe) during a clinical trial. Latent class trajectory analysis was conducted to identify groups with distinct outcome profiles. The trajectories were validated against independent assessments, including a longer-term follow-up six months after the end of therapy. We also tested potential predictors of the trajectories.

There were four outcome trajectories: (1) Very high delusion conviction/Little improvement (n = 14, 25%), (2) Very high delusion conviction/Large improvement (n = 9, 16%), (3) High delusion conviction/Moderate improvement (n = 17, 31%) and (4) High delusion conviction/Large improvement (n = 15, 27%). The groups did not differ in initial overall delusion severity. The trajectories were consistent with the independent assessments and sustained over time. Three factors predicted trajectories: persecutory delusion conviction, therapy expectations, and positive beliefs about other people.

There are variable responses to psychological therapy for persecutory delusions. Patients with very high delusion conviction can have excellent responses to therapy, though this may take a little longer to observe and such high conviction reduces the likelihood of positive responses. A trajectory approach requires testing in larger datasets but may prove highly informative.

The nature of the pathway from conduct disorder (CD) in adolescence to antisocial behavior in adulthood has been debated and the role of certain mediators remains unclear. One perspective is that CD forms part of a general psychopathology dimension, playing a central role in the developmental trajectory. Impairment in reflective functioning (RF), i.e., the capacity to understand one's own and others' mental states, may relate to CD, psychopathology, and aggression. Here, we characterized the structure of psychopathology in adult male-offenders and its role, along with RF, in mediating the relationship between CD in their adolescence and current aggression.

A secondary analysis of pre-treatment data from 313 probation-supervised offenders was conducted, and measures of CD symptoms, general and specific psychopathology factors, RF, and aggression were evaluated through clinical interviews and questionnaires.

Confirmatory factor analyses indicated that a bifactor model best fitted the sample's psychopathology structure, including a general psychopathology factor (p factor) and five specific factors: internalizing, disinhibition, detachment, antagonism, and psychoticism. The structure of RF was fitted to the data using a one-factor model. According to our mediation model, CD significantly predicted the p factor, which was positively linked to RF impairments, resulting in increased aggression.

These findings highlight the critical role of a transdiagnostic approach provided by RF and general psychopathology in explaining the link between CD and aggression. Furthermore, they underscore the potential utility of treatments focusing on RF, such as mentalization-based treatment, in mitigating aggression in offenders with diverse psychopathologies.

Convergent evidence has suggested atypical relationships between brain structure and function in major psychiatric disorders, yet how the abnormal patterns coincide and/or differ across different disorders remains largely unknown. Here, we aim to investigate the common and/or unique dynamic structure–function coupling patterns across major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ).

We quantified the dynamic structure–function coupling in 452 patients with psychiatric disorders (MDD/BD/SZ = 166/168/118) and 205 unaffected controls at three distinct brain network levels, such as global, meso-, and local levels. We also correlated dynamic structure–function coupling with the topological features of functional networks to examine how the structure–function relationship facilitates brain information communication over time.

The dynamic structure–function coupling is preserved for the three disorders at the global network level. Similar abnormalities in the rich-club organization are found in two distinct functional configuration states at the meso-level and are associated with the disease severity of MDD, BD, and SZ. At the local level, shared and unique alterations are observed in the brain regions involving the visual, cognitive control, and default mode networks. In addition, the relationships between structure–function coupling and the topological features of functional networks are altered in a manner indicative of state specificity.

These findings suggest both transdiagnostic and illness-specific alterations in the dynamic structure–function relationship of large-scale brain networks across MDD, BD, and SZ, providing new insights and potential biomarkers into the neurodevelopmental basis underlying the behavioral and cognitive deficits observed in these disorders.

Suicidal ideation arises from a complex interplay of multiple interacting risk factors over time. Recently, ecological momentary assessment (EMA) has increased our understanding of factors associated with real-time suicidal ideation, as well as those predicting ideation at the level of hours and days. Here we used statistical network methods to investigate which cognitive-affective risk and protective factors are associated with the temporal dynamics of suicidal ideation.

The SAFE study is a longitudinal cohort study of 82 participants with current suicidal ideation who completed 4×/day EMA over 21 days. We modeled contemporaneous (t) and temporal (t + 1) associations of three suicidal ideation components (passive ideation, active ideation, and acquired capability) and their predictors (positive and negative affect, anxiety, hopelessness, loneliness, burdensomeness, and optimism) using multilevel vector auto-regression models.

Contemporaneously, passive suicidal ideation was positively associated with sadness, hopelessness, loneliness, and burdensomeness, and negatively with happiness, calmness, and optimism; active suicidal ideation was positively associated with passive suicidal ideation, sadness, and shame; and acquired capability only with passive and active suicidal ideation. Acquired capability and hopelessness positively predicted passive ideation at t + 1, which in turn predicted active ideation; acquired capability was positively predicted at t + 1 by shame, and negatively by burdensomeness.

Our findings show that systematic real-time associations exist between suicidal ideation and its predictors, and that different factors may uniquely influence distinct components of ideation. These factors may represent important targets for safety planning and risk detection.

Common adolescent psychiatric symptoms cluster into two dominant domains: internalizing and externalizing. Both domains are linked to self-esteem, which serves as a protective factor against a wide range of internalizing and externalizing problems. This study examined trends in US adolescents' self-esteem and externalizing symptoms, and their correlation, by sex and patterns of time use.

Using Monitoring the Future data (N = 338 896 adolescents, grades:8/10/12, years:1991–2020), we generated six patterns of time use using latent profile analysis with 17 behavior items (e.g. sports participation, parties, paid work). Groups were differentiated by high/low engagement in sports and either paid work or high/low peer socialization. Within each group, we mapped annual, sex-stratified means of (and correlation between) self-esteem and externalizing factors. We also examined past-decade rates of change for factor means using linear regression and mapped proportions with top-quartile levels of poor self-esteem, externalizing symptoms, or both.

We found consistent increases in poor self-esteem, decreases in externalizing symptoms, and a positive correlation between the two across nearly all activity groups. We also identified a relatively constant proportion of those with high levels of both in every group. Increases in poor self-esteem were most pronounced for female adolescents with low levels of socializing, among whom externalizing symptoms also increased.

Rising trends in poor self-esteem are consistent across time use groups, as is the existence of a group facing poor self-esteem and externalizing symptoms. Effective interventions for adolescents' poor self-esteem/co-occurring symptoms are needed broadly, but especially among female adolescents with low peer socialization.

Should COVID-19 have a direct impact on the risk of depression, it would suggest specific pathways for prevention and treatment. In this retrospective population-based study, we aimed to examine the association of prior SARS-CoV-2 infection with depressive symptoms, distinguishing self-reported v. biologically confirmed COVID-19.

32 007 participants from the SAPRIS survey nested in the French CONSTANCES cohort were included. COVID-19 was measured as followed: ad hoc serologic testing, self-reported PCR or serology positive test results, and self-reported COVID-19. Depressive symptoms were measured with the Center of Epidemiologic Studies-Depression Scale (CES-D). Outcomes were depressive symptoms (total CES-D score, its four dimensions, and clinically significant depressive symptoms) and exposure was prior COVID-19 (no COVID-19/self-reported unconfirmed COVID-19/biologically confirmed COVID-19).

In comparison to participants without COVID-19, participants with self-reported unconfirmed COVID-19 and biologically confirmed COVID-19 had higher CES-D scores (β for one interquartile range increase [95% CI]: 0.15 [0.08–0.22] and 0.09 [0.05–0.13], respectively) and somatic complaints dimension scores (0.15 [0.09–0.21] and 0.10 [0.07–0.13]). Only those with self-reported but unconfirmed COVID-19 had higher depressed affect dimension scores (0.08 [0.01–0.14]). Accounting for ad hoc serologic testing only, the CES-D score and the somatic complaints dimension were only associated with the combination of self-reported COVID-19 and negative serology test results.

The association between COVID-19 and depressive symptoms was merely driven by somatic symptoms of depression and did not follow a gradient consistent with the hypothesis of a direct impact of SARS-CoV-2 infection on the risk of depression.

Research implicates inflammation in the vicious cycle between depression and obesity, yet few longitudinal studies exist. The rapid weight loss induced by bariatric surgery is known to improve depressive symptoms dramatically, but preoperative depression diagnosis may also increase the risk for poor weight loss. Therefore, we investigated longitudinal associations between depression and inflammatory markers and their effect on weight loss and clinical outcomes in bariatric patients.

This longitudinal observational study of 85 patients with obesity undergoing bariatric surgery included 41 cases with depression and 44 controls. Before and 6 months after surgery, we assessed depression by clinical interview and measured serum high-sensitivity C-reactive protein (hsCRP) and inflammatory cytokines, including interleukin (IL)-6 and IL-10.

Before surgery, depression diagnosis was associated with significantly higher serum hsCRP, IL-6, and IL-6/10 ratio levels after controlling for confounders. Six months after surgery, patients with pre-existing depression still had significantly higher inflammation despite demonstrating similar weight loss to controls. Hierarchical regression showed higher baseline hsCRP levels predicted poorer weight loss (β = −0.28, p = 0.01) but had no effect on depression severity at follow-up (β = −0.02, p = 0.9). Instead, more severe baseline depressive symptoms and childhood emotional abuse predicted greater depression severity after surgery (β = 0.81, p < 0.001; and β = 0.31, p = 0.001, respectively).

Depression was significantly associated with higher inflammation beyond the effect of obesity and other confounders. Higher inflammation at baseline predicted poorer weight loss 6 months after surgery, regardless of depression diagnosis. Increased inflammation, rather than depression, may drive poor weight loss outcomes among bariatric patients.

Theory and research indicated that executive functioning (EF) correlated with, preceded, and stemmed from worry in generalized anxiety disorder (GAD). The present secondary analysis (Zainal & Newman, 2023b) thus determined whether EF domains mediated the effect of a 14-day (5 prompts/day) mindfulness ecological momentary intervention (MEMI) against a self-monitoring control (SM) for GAD.

Participants (N = 110) diagnosed with GAD completed self-reported (Attentional Control Scale, GAD Questionnaire, Perseverative Cognitions Questionnaire) and performance-based tests (Letter-Number Sequencing, Stroop, Trail Making Test-B, Verbal Fluency) at baseline, post-treatment, and one-month follow-up (1MFU). Causal mediation analyses determined if pre-post changes in EF domains preceded and mediated the effect of MEMI against SM on pre-1MFU changes in GAD severity and trait repetitive negative thinking (RNT).

MEMI was more efficacious than SM in improving pre–post inhibition (β = −2.075, 95% [−3.388, −0.762], p = .002), working memory (β = 0.512, 95% [0.012, 1.011], p = .045), and set-shifting (β = −2.916, 95% [−5.142, −0.691], p = .010) but not verbal fluency and attentional control. Within groups, MEMI but not SM produced improvements in all examined pre–post EF outcomes except attentional control. Only pre–post improvements in inhibition mediated the effect of MEMI against SM on pre-1MFU reductions in GAD severity (β = −0.605, 95% [−1.357, −0.044], p = .030; proportion mediated = 7.1%) and trait RNT (β = −0.024, 95% [−0.054, −0.001], p = .040; proportion mediated = 7.4%). These patterns remained after conducting sensitivity analyses with non-linear mediator-outcome relations.

Optimizing MEMI for GAD might entail specifically boosting inhibition plausibly by augmenting it with dialectical behavioral therapy, encouraging high-intensity physical exercises, and targeting negative emotional contrast avoidance.

An urgent need exists to identify neural correlates associated with differing levels of suicide risk and develop novel, rapid-acting therapeutics to modulate activity within these neural networks.

Electrophysiological correlates of suicide were evaluated using magnetoencephalography (MEG) in 75 adults with differing levels of suicide risk. During MEG scanning, participants completed a modified Life-Death Implicit Association Task. MEG data were source-localized in the gamma (30–58 Hz) frequency, a proxy measure of excitation-inhibition balance. Dynamic causal modeling was used to evaluate differences in connectivity estimates between risk groups. A proof-of-concept, open-label, pilot study of five high risk participants examined changes in gamma power after administration of ketamine (0.5 mg/kg), an NMDAR antagonist with rapid anti-suicide ideation effects.

Implicit self-associations with death were stronger in the highest suicide risk group relative to all other groups, which did not differ from each other. Higher gamma power for self-death compared to self-life associations was found in the orbitofrontal cortex for the highest risk group and the insula and posterior cingulate cortex for the lowest risk group. Connectivity estimates between these regions differentiated the highest risk group from the full sample. Implicit associations with death were not affected by ketamine, but enhanced gamma power was found for self-death associations in the left insula post-ketamine compared to baseline.

Differential implicit cognitive processing of life and death appears to be linked to suicide risk, highlighting the need for objective measures of suicidal states. Pharmacotherapies that modulate gamma activity, particularly in the insula, may help mitigate risk.

Clinicaltrials.gov identifier: NCT02543983, NCT00397111.

Substantial evidence indicates structural abnormalities in the cerebral cortex of patients with schizophrenia (SCZ), although their clinical implications remain unclear. Previous case-control studies have investigated group-level differences in structural abnormalities, although the study design cannot account for interindividual differences. Recent research has focused on the association between the heterogeneity of the cerebral cortex morphometric features and clinical heterogeneity.

We used neuroimaging data from 420 healthy controls and 695 patients with SCZ from seven studies. Four cerebral cortex measures were obtained: surface area, gray matter volume, thickness, and local gyrification index. We calculated the coefficient of variation (CV) and person-based similarity index (PBSI) scores and performed group comparisons. Associations between the PBSI scores and cognitive functions were evaluated using Spearman's rho test and normative modeling.

Patients with SCZ had a greater CV of surface area and cortical thickness than those of healthy controls. All PBSI scores across cortical measures were lower in patients with SCZ than in HCs. In the patient group, the PBSI scores for gray matter volume and all cortical measures taken together positively correlated with the full-scale IQ scores. Patients with deviant PBSI scores for gray matter volume and all cortical measures taken together had lower full-scale IQ scores than those of other patients.

The cerebral cortex in patients with SCZ showed greater regional and global structural variability than that in healthy controls. Patients with deviant similarity of cortical structural profiles exhibited a lower general intelligence than those exhibited by the other patients.

Among those with common mental health disorders (e.g. mood, anxiety, and stress disorders), comorbidity of substance and other addictive disorders is prevalent. To simplify the seemingly complex relationships underlying such comorbidity, methods that include multiple measures to distill which specific addictions are uniquely associated with specific mental health disorders rather than due to the co-occurrence of other related addictions or mental health disorders can be used.

In a general population sample of Jewish adults in Israel (N = 4002), network analysis methods were used to create partial correlation networks of continuous measures of problematic substance (non-medical use of alcohol, tobacco, cannabis, and prescription sedatives, stimulants, and opioid painkillers) and behavioral (gambling, electronic gaming, sexual behavior, pornography, internet, social media, and smartphone) addictions and common mental health problems (depression, anxiety, and post-traumatic stress disorder [PTSD]), adjusted for all variables in the model.

Strongest associations were observed within these clusters: (1) PTSD, anxiety, and depression; (2) problematic substance use and gambling; (3) technology-based addictive behaviors; and (4) problematic sexual behavior and pornography. In terms of comorbidity, the strongest unique associations were observed for PTSD and problematic technology-based behaviors (social media, smartphone), and sedatives and stimulants use; depression and problematic technology-based behaviors (gaming, internet) and sedatives and cannabis use; and anxiety and problematic smartphone use.

Network analysis isolated unique relationships underlying the observed comorbidity between common mental health problems and addictions, such as associations between mental health problems and technology-based behaviors, which is informative for more focused interventions.

Discrepancy between objective and subjective cognitive deficit is common among patients with major depressive disorders (MDDs) and may play a key role in the mechanism linking cognition with recovery of symptom and psychosocial function. This study, therefore, explores the cognitive discrepancy, and its association with the trajectory of symptoms and functioning over a 6-month period.

We used data from the Prospective Research Observation to Assess Cognition in Treated patients with MDD (PROACT) study, from which 598 patients were included. Cognitive discrepancy scores were computed using a novel methodology, with positive values indicating more subjective than objective deficit (i.e. ‘underestimation’) and negative values indicating more objective than subjective difficulties (i.e. ‘overestimation’). Linear growth curve models were employed to examine the association of the cognitive discrepancy with the trajectory of depressive symptoms, psychosocial function, and quality of life.

About 68% of patients displayed disproportionately more objective than subjective cognitive deficit at baseline, and the mean cognitive discrepancy score was −1.4 (2.7). Overestimation was associated with a faster decrease of HDRS-17 (β = −0.46, p = 0.002) and a faster decrease of psychosocial function in social life (β = −0.13, p = 0.013) and family life (β = −0.12, p = 0.026), and a greater improvement of EQ-5D utility score (β = 0.01, p < 0.001).

We found a lower sensitivity of cognitive deficit at baseline and its decrease was associated with better health outcomes. Our findings have clinical implications of the necessity to assess both subjective and objective cognition for identification and categorization and to incorporate cognitive and psychological therapies for optimized treatment outcomes.

Internet-based interventions produce comparable effectiveness rates as face-to-face therapy in treating depression. Still, more than half of patients do not respond to treatment. Machine learning (ML) methods could help to overcome these low response rates by predicting therapy outcomes on an individual level and tailoring treatment accordingly. Few studies implemented ML algorithms in internet-based depression treatment using baseline self-report data, but differing results hinder inferences on clinical practicability. This work compares algorithms using features gathered at baseline or early in treatment in their capability to predict non-response to a 6-week online program targeting depression.

Our training and test sample encompassed 1270 and 318 individuals, respectively. We trained random forest algorithms on self-report and process features gathered at baseline and after 2 weeks of treatment. Non-responders were defined as participants not fulfilling the criteria for reliable and clinically significant change on PHQ-9 post-treatment. Our benchmark models were logistic regressions trained on baseline PHQ-9 sum or PHQ-9 early change, using 100 iterations of randomly sampled 80/20 train-test-splits.

Best performances were reached by our models involving early treatment characteristics (recall: 0.75–0.76; AUC: 0.71–0.77). Therapeutic alliance and early symptom change constituted the most important predictors. Models trained on baseline data were not significantly better than our benchmark.

Fair accuracies were only attainable by involving information from early treatment stages. In-treatment adaptation, instead of a priori selection, might constitute a more feasible approach for improving response when relying on easily accessible self-report features. Implementation trials are needed to determine clinical usefulness.

Clinical implementation of risk calculator models in the clinical high-risk for psychosis (CHR-P) population has been hindered by heterogeneous risk distributions across study cohorts which could be attributed to pre-ascertainment illness progression. To examine this, we tested whether the duration of attenuated psychotic symptom (APS) worsening prior to baseline moderated performance of the North American prodrome longitudinal study 2 (NAPLS2) risk calculator. We also examined whether rates of cortical thinning, another marker of illness progression, bolstered clinical prediction models.

Participants from both the NAPLS2 and NAPLS3 samples were classified as either ‘long’ or ‘short’ symptom duration based on time since APS increase prior to baseline. The NAPLS2 risk calculator model was applied to each of these groups. In a subset of NAPLS3 participants who completed follow-up magnetic resonance imaging scans, change in cortical thickness was combined with the individual risk score to predict conversion to psychosis.

The risk calculator models achieved similar performance across the combined NAPLS2/NAPLS3 sample [area under the curve (AUC) = 0.69], the long duration group (AUC = 0.71), and the short duration group (AUC = 0.71). The shorter duration group was younger and had higher baseline APS than the longer duration group. The addition of cortical thinning improved the prediction of conversion significantly for the short duration group (AUC = 0.84), with a moderate improvement in prediction for the longer duration group (AUC = 0.78).

These results suggest that early illness progression differs among CHR-P patients, is detectable with both clinical and neuroimaging measures, and could play an essential role in the prediction of clinical outcomes.

Schizotypy represents an index of psychosis-proneness in the general population, often associated with childhood trauma exposure. Both schizotypy and childhood trauma are linked to structural brain alterations, and it is possible that trauma exposure moderates the extent of brain morphological differences associated with schizotypy.

We addressed this question using data from a total of 1182 healthy adults (age range: 18–65 years old, 647 females/535 males), pooled from nine sites worldwide, contributing to the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Schizotypy working group. All participants completed both the Schizotypal Personality Questionnaire Brief version (SPQ-B), and the Childhood Trauma Questionnaire (CTQ), and underwent a 3D T1-weighted brain MRI scan from which regional indices of subcortical gray matter volume and cortical thickness were determined.

A series of multiple linear regressions revealed that differences in cortical thickness in four regions-of-interest were significantly associated with interactions between schizotypy and trauma; subsequent moderation analyses indicated that increasing levels of schizotypy were associated with thicker left caudal anterior cingulate gyrus, right middle temporal gyrus and insula, and thinner left caudal middle frontal gyrus, in people exposed to higher (but not low or average) levels of childhood trauma. This was found in the context of morphological changes directly associated with increasing levels of schizotypy or increasing levels of childhood trauma exposure.

These results suggest that alterations in brain regions critical for higher cognitive and integrative processes that are associated with schizotypy may be enhanced in individuals exposed to high levels of trauma.

Whether the recent rise in adolescent self-reported depressive symptoms is influenced by changing reporting behavior is much debated. Most studies use observed sum scores to document trends but fail to assess whether their measures are invariant across time, a prerequisite for meaningful inferences about change. We examined whether measurement noninvariance, indicative of changing perceptions and reporting of symptoms, may influence the assessment of time trends in adolescent depressive symptoms.

Data stem from the nationwide repeated cross-sectional Ungdata-surveys (2010–2019) of 560 712 responses from adolescents aged 13 to 19 years. Depressive symptoms were measured with the Kandel and Davies' six-item Depressive Mood Inventory. Using structural equation modeling, we examined measurement invariance across time, gender and age, and estimated the consequences of noninvariance on cross-cohort time trends.

Across most conditions, the instrument was found measurement invariant across time. The few noninvariant parameters detected had negligible impact on trend estimates. From 2014, latent mean depressive symptom scores increased among girls. For boys, a U shaped pattern was detected, whereby an initial decrease in symptoms was followed by an increase from 2016. Larger issues of noninvariance were found across age in girls and between genders.

From a measurement perspective, the notion that changed reporting of symptoms has been an important driver of secular trends in depressive symptoms was not supported. Thus, other causes of these trends should be considered. However, noninvariance across age (in girls) and gender highlights that depressive symptoms are not necessarily perceived equivalently from early to late adolescence and across gender.