Posttraumatic stress disorder (PTSD) has been associated with advanced epigenetic age cross-sectionally, but the association between these variables over time is unclear. This study conducted meta-analyses to test whether new-onset PTSD diagnosis and changes in PTSD symptom severity over time were associated with changes in two metrics of epigenetic aging over two time points.

We conducted meta-analyses of the association between change in PTSD diagnosis and symptom severity and change in epigenetic age acceleration/deceleration (age-adjusted DNA methylation age residuals as per the Horvath and GrimAge metrics) using data from 7 military and civilian cohorts participating in the Psychiatric Genomics Consortium PTSD Epigenetics Workgroup (total N = 1,367).

Meta-analysis revealed that the interaction between Time 1 (T1) Horvath age residuals and new-onset PTSD over time was significantly associated with Horvath age residuals at T2 (meta β = 0.16, meta p = 0.02, p-adj = 0.03). The interaction between T1 Horvath age residuals and changes in PTSD symptom severity over time was significantly related to Horvath age residuals at T2 (meta β = 0.24, meta p = 0.05). No associations were observed for GrimAge residuals.

Results indicated that individuals who developed new-onset PTSD or showed increased PTSD symptom severity over time evidenced greater epigenetic age acceleration at follow-up than would be expected based on baseline age acceleration. This suggests that PTSD may accelerate biological aging over time and highlights the need for intervention studies to determine if PTSD treatment has a beneficial effect on the aging methylome.

Recent research highlights the dynamics of suicide risk, resulting in a shift toward real-time methodologies, such as ecological momentary assessment (EMA), to improve suicide risk identification. However, EMA’s reliance on active self-reporting introduces challenges, including participant burden and reduced response rates during crises. This study explores the potential of Screenomics—a passive digital phenotyping method that captures intensive, real-time smartphone screenshots—to detect suicide risk through text-based analysis.

Seventy-nine participants with past-month suicidal ideation or behavior completed daily EMA prompts and provided smartphone data over 28 days, resulting in approximately 7.5 million screenshots. Text from screenshots was analyzed using a validated dictionary encompassing suicide-related and general risk language.

Results indicated significant associations between passive and active suicidal ideation and suicide planning with specific language patterns. Detection of words related to suicidal thoughts and general risk-related words strongly correlated with self-reported suicide risk, with distinct between- and within-person effects highlighting the dynamic nature of suicide risk factors.

This study demonstrates the feasibility of leveraging smartphone text data for real-time suicide risk detection, offering a scalable, low-burden alternative to traditional methods. Findings suggest that dynamic, individualized monitoring via passive data collection could enhance suicide prevention efforts by enabling timely, tailored interventions. Future research should refine language models and explore diverse populations to extend the generalizability of this innovative approach.

To understand the pathogenetic mechanisms shared among schizophrenia (SCZ), bipolar disorder (BP), and major depression (MDD), we investigated the pleiotropic mechanisms using large-scale genome-wide and brain transcriptomic data.

We analyzed SCZ, BP, and MDD genome-wide association datasets available from the Psychiatric Genomics Consortium using the PLEIO framework and characterized the pleiotropic loci identified using pathway and tissue enrichment analyses. Pleiotropic and disorder-specific loci were also assessed.

Our pleiotropy-informed genome-wide analysis identified 553 variants that included 192 loci not reaching genome-wide significance in input datasets. These were enriched for five molecular pathways: cadherin signaling (p = 2.18 × 10−8), Alzheimer’s disease-amyloid secretase (p = 4 × 10−4), oxytocin receptor-mediated signaling (p = 1.47 × 10−3), metabotropic glutamate receptor group III (p = 5.82 × 10−4) and Wnt signaling (p = 1.61 × 10−11). Pleiotropic loci demonstrated the strongest enrichment in the brain cortex (p = 5.8 × 10−28), frontal cortex (p = 3 × 10−31), and cerebellar hemisphere (p = 9.8 × 10−28). SCZ-BP-MDD pleiotropic variants were also enriched for neurodevelopmental brain transcriptomic profiles related to the second-trimester post-conception (week 21, p = 7.35 × 10−5; week 17, p = 6.36 × 10−4) and first year of life (p = 3.25 × 10−5).

Genetic mechanisms shared among SCZ, BP, and MDD appear to be related to early neuronal development. Because the genetic architecture of psychopathology transcends diagnostic boundaries, pleiotropy-focused analyses can lead to increased gene discovery and novel insights into relevant pathogenic mechanisms.

Although cognitive behavioral therapy for people diagnosed with schizophrenia (CBTp) is recommended in clinical guidelines internationally, rates of implementation are low. One consequence of this has been the development of brief individual psychological interventions, which are shorter than the recommended minimum of 16 sessions for CBTp. This article is the first to systematically identify the brief interventions that exist for people diagnosed with schizophrenia and to determine their effectiveness using meta-analysis.

Five electronic databases (PsycINFO, MEDLINE, CINAHL, EMBASE, and Web of Science) were searched for peer-reviewed randomized controlled trials or experimental studies of brief individual psychological interventions delivered in community settings. Random effects meta-analysis was used to integrate effect sizes, due to the heterogeneity of included studies.

Fourteen studies were identified (n = 1,382) that measured thirty clinical outcomes and included six intervention types - brief CBT, memory training, digital motivation support, reasoning training, psychoeducation, and virtual reality. Collectively, brief psychological interventions were found to be effective for psychotic symptoms (SMD −0.285, p < 0.01), paranoia (SMD −0.277, p < 0.05), data gathering (SMD 0.38, p < 0.01), depression (SMD −0.906, p < 0.05) and wellbeing (SMD 0.405, p < 0.01). For intervention types, brief CBT was effective for psychotic symptoms (SMD −0.32, p < .001), and reasoning training was effective for data gathering (SMD 0.38, p < 0.01).

Overall, the evidence suggests that brief psychological interventions are effective for several key difficulties associated with schizophrenia, providing an opportunity to improve both access to, and choice of, treatment for individuals diagnosed with schizophrenia.

Internet addiction (IA) refers to excessive internet use that causes cognitive impairment or distress. Understanding the neurophysiological mechanisms underpinning IA is crucial for enabling an accurate diagnosis and informing treatment and prevention strategies. Despite the recent increase in studies examining the neurophysiological traits of IA, their findings often vary. To enhance the accuracy of identifying key neurophysiological characteristics of IA, this study used the phase lag index (PLI) and weighted PLI (WPLI) methods, which minimize volume conduction effects, to analyze the resting-state electroencephalography (EEG) functional connectivity. We further evaluated the reliability of the identified features for IA classification using various machine learning methods.

Ninety-two participants (42 with IA and 50 healthy controls (HCs)) were included. PLI and WPLI values for each participant were computed, and values exhibiting significant differences between the two groups were selected as features for the subsequent classification task.

Support vector machine (SVM) achieved an 83% accuracy rate using PLI features and an improved 86% accuracy rate using WPLI features. t-test results showed analogous topographical patterns for both the WPLI and PLI. Numerous connections were identified within the delta and gamma frequency bands that exhibited significant differences between the two groups, with the IA group manifesting an elevated level of phase synchronization.

Functional connectivity analysis and machine learning algorithms can jointly distinguish participants with IA from HCs based on EEG data. PLI and WPLI have substantial potential as biomarkers for identifying the neurophysiological traits of IA.

Binge-eating disorder (BED) is characterized by highly distressing episodes of loss-of-control over-eating. We have examined the use of repetitive transcranial magnetic stimulation (rTMS) for the treatment of people with BED and associated obesity. Such non-invasive brain stimulation (NIBS) techniques are used therapeutically in several psychiatric conditions and there is an associated scientific rationale.

Sixty participants were randomly allocated to receive 20 sessions of neuronavigated 10 Hz rTMS administered to the left dorsolateral prefrontal cortex (dlPFC) or sham treatment. Primary outcomes were the frequency of binge eating episodes (BEE) and the ‘urge to eat’ (craving) evaluated at baseline and end-of-treatment (8 weeks post-randomization). Secondary outcomes included body mass index (BMI), hunger, general and specific eating disorder psychopathology. Follow-up analyses were conducted for most outcomes at 16 weeks post-randomization. Multilevel models were used to evaluate group, time, and group-by-time interactions for the association between rTMS exposure and outcomes.

The real rTMS group (compared with sham treatment), showed a significantly greater decrease in the number of BEE at the end of treatment (Estimated Mean [EM]: 2.41 95% CI: 1.84–3.15 versus EM: 1.45 95% CI: 1.05–1.99, p = 0.02), and at follow-up (EM: 3.79 95% CI: 3–4.78 versus EM: 2.45 95% CI: 1.88–3.17, p = 0.02; group × time interaction analysis p = 0.02). No group differences were found for other comparisons.

rTMS was associated with reduced BEE during and after treatment: it suggests rTMS is a promising intervention for BED.

Posttraumatic stress disorder (PTSD) is often chronic and impairing. Mechanisms that maintain symptoms remain poorly understood because of heterogenous presentation. We parsed this heterogeneity by examining how individual differences in stress-symptom dynamics relate to the long-term maintenance of PTSD.

We studied 7,308 trauma-exposed World Trade Center responders who self-reported PTSD symptoms and stressful life events at annual monitoring visits for up to 20 years (average = 8.8 visits; [range = 4–16]). We used multilevel structural equation models to separate the stable and time-varying components of symptoms and stressors. At the within-person level, we modeled stress reactivity by cross-lagged associations between stress and future symptoms, stress generation by cross-lagged associations between symptoms and future stress, and autoregressive effects represented symptom persistence and stress persistence. The clinical utility of the stress-symptom dynamics was evaluated by associations with PTSD chronicity and mental health care use.

Stress reactivity, stress generation, and symptom persistence were significant on average (bs = 0.03–0.16). There were significant individual differences in the strength of each dynamic (interquartile ranges = 0.06–0.12). Correlations among within-person processes showed some dynamics are intertwined (e.g. more reactive people also generate stress in a vicious cycle) and others represent distinct phenotypes (e.g. people are reactive or have persistent symptoms). Initial trauma severity amplified some dynamics. People in the top deciles of most dynamics had clinically significant symptom levels across the monitoring period and their health care cost 6–17× more per year than people at median levels.

Individual differences in stress-symptom dynamics contribute to the chronicity and clinical burden of PTSD.

Mental health problems commonly persist from childhood to adulthood. This study tested whether young adult life transitions can improve adult mental health symptoms after adjusting for childhood mental health symptoms.

The analysis uses data from the prospective, representative Great Smoky Mountains Study. Life transitions (e.g., high school completion, partnering, parenthood, and living independently) were assessed up to three times in young adulthood (ages 18 to 26; 3,241 observations). A cumulative variable counted the number of young adult transitions. Emotional, substance use, and antisocial personality symptoms were assessed at age 30 (1,154 participants or 81.2% of the original sample). Propensity models adjusted for early life adversities and psychiatric symptoms.

Multiple young adult transitions were common (m = 4.62; SD = 1.57). After adjusting for childhood mental health problems and adversities, each additional transition was significantly associated with a reduction in subsequent adult emotional symptoms (β = −0.34, 95% CI: −0.59, −0.08, p = 0.01) and adult antisocial personality disorder symptoms (β = −0.08, 95% CI: −0.14, −0.02, p < 0.001. These associations were stronger in males than in females. Young adult transitions were not associated with reductions in subsequent substance use symptoms (β = −0.04; 95% CI: −0.11, 0.03, p = 0.30). Young adult transitions related to educational milestones and consistent employment were associated with the largest reductions in symptoms.

In this cohort study, life transitions during young adulthood were associated with reduced emotional and behavioral symptoms in adulthood. These transitions may constitute a potential mental health turning point and a specific, modifiable target for social policies.

Statins are among the most prescribed medications worldwide. Both beneficial (e.g. antidepressant and pro-cognitive) and adverse (e.g. depressogenic and cognitive-impairing) mental health outcomes have been described in clinical studies. The underlying neuropsychological mechanisms, whether positive or negative, are, however, not established. Clarifying such activities has implications for the safe prescribing and repurposing potential of these drugs, especially in people with depression.

In this double-blind, randomized, placebo-controlled experimental medicine study, we investigated the effects of simvastatin on emotional processing, reward learning, working memory, and waking salivary cortisol (WSC) in 101 people at-risk for depression due to reported high loneliness scores (mean 7.3 ± 1.2 on the UCLA scale). This trial was largely conducted during periods of social distancing due to the COVID-19 pandemic (July 2021–February 2023), and we employed a fully remote design within a UK-wide sample.

High retention rates, minimal outlier data, and typical main effects of task condition (e.g. emotion) were seen in all cognitive tasks, indicating this approach was comparable to in-person testing. After 28 days, we found no statistically significant differences (F’s < 3.0, p’s > 0.20) for any of the measures of emotional processing, reward learning, working memory, and WSC.

Study results do not substantiate concerns regarding adverse neuropsychiatric events due to statins and support the safety of their prescribing in at-risk populations. Although other unmeasured cognitive processes may be involved, our null findings are also in line with more recent clinical evidence suggesting statins do not show antidepressant or pro-cognitive efficacy.

Subjective cognitive concerns (SCCs) refer to individuals’ self-identified cognitive limitations, irrespective of objective neurocognitive performance. Previous literature has overwhelmingly found that psychiatric factors, not neurocognitive dysfunction, are a primary correlate of elevated SCCs across a wide range of clinical populations. However, the relationship between SCCs and objective neurocognitive performance is complex and may further be influenced by underlying mechanisms of various impairments or etiologies. Moreover, much of the extant literature has under-utilized performance validity tests (PVTs) when analyzing objective neuropsychological outcomes.

As such, this study examined the associations between SCCs, performance validity, neurocognitive performance, and psychiatric distress among adult clinical patients with primary medical/neurologic (n = 127) and psychiatric (n = 106) etiologies.

Results showed that elevated SCCs are associated with greater degrees of performance invalidity and psychiatric distress, but not neurocognitive performance, among both groups.

Findings support the utility of PVTs in clinical research and further highlight the impact of psychiatric factors on SCCs, regardless of medical/neurologic or psychiatric etiology.

The brain’s default mode network (DMN) plays a role in social cognition, with altered DMN function being associated with social impairments across various neuropsychiatric disorders. However, the genetic basis linking sociability with DMN function remains underexplored. This study aimed to elucidate the shared genetics and causal relationship between sociability and DMN-related resting-state functional MRI (rs-fMRI) traits.

We conducted a comprehensive genomic analysis using large-scale genome-wide association study (GWAS) summary statistics for sociability and 31 activity and 64 connectivity DMN-related rs-fMRI traits (N = 34,691–342,461). We performed global and local genetic correlations analyses and bi-directional Mendelian randomization (MR) to assess shared and causal effects. We prioritized genes influencing both sociability and rs-fMRI traits by combining expression quantitative trait loci MR analyses, the CELLECT framework – integrating single-nucleus RNA sequencing (snRNA-seq) data with GWAS – and network propagation within a protein–protein interaction network.

Significant local genetic correlations were identified between sociability and two rs-fMRI traits, one representing spontaneous activity within the temporal cortex, the other representing connectivity between the cingulate and angular/temporal cortices. MR analyses suggested potential causal effects of sociability on 12 rs-fMRI traits. Seventeen genes were highly prioritized, with LINGO1, ELAVL2, and CTNND1 emerging as top candidates. Among these, DRD2 was also identified, serving as a robust internal validation of our approach.

By combining genomic and transcriptomic data, our gene prioritization strategy may serve as a blueprint for future studies. Our findings can guide further research into the biological mechanisms underlying sociability and its role in the development, prognosis, and treatment of neuropsychiatric disorders.

Precise stratification of patients into homogeneous disease subgroups could address the heterogeneity of phenotypes and enhance understanding of the pathophysiology underlying specific subtypes. Existing literature on subtyping patients with major depressive disorder (MDD) mainly utilized clinical features only. Genomic and imaging data may improve subtyping, but advanced methods are required due to the high dimensionality of features.

We propose a novel disease subtyping framework for MDD by integrating brain structural features, genotype-predicted expression levels in brain tissues, and clinical features. Using a multi-view biclustering approach, we classify patients into clinically and biologically homogeneous subgroups. Additionally, we propose approaches to identify causally relevant genes for clustering.

We verified the reliability of the subtyping model by internal and external validation. High prediction strengths (PS) (average PS: 0.896, minimum: 0.854), a measure of generalizability of the derived clusters in independent datasets, support the validity of our approach. External validation using patient outcome variables (treatment response and hospitalization risks) confirmed the clinical relevance of the identified subgroups. Furthermore, subtype-defining genes overlapped with known susceptibility genes for MDD and were involved in relevant biological pathways. In addition, drug repositioning analysis based on these genes prioritized promising candidates for subtype-specific treatments.

Our approach successfully stratified MDD patients into subgroups with distinct clinical prognoses. The identification of biologically and clinically meaningful subtypes may enable more personalized treatment strategies. This study also provides a framework for disease subtyping that can be extended to other complex disorders.

Eating disorders and psychotic disorders represent two of the most serious psychiatric conditions. Emerging lines of evidence from genetic and epidemiological studies suggest that these disorders may commonly co-occur. This systematic review investigated the association between these disorders across community and clinical populations.

A systematic review was preregistered (CRD42021231771) and conducted according to PRISMA guidelines. Web of Science, PsycINFO and Medline were searched for articles on the association and comorbidity between psychosis and eating disorders up to the 26th February 2024. A random effects meta-analysis was conducted for studies reporting comorbidity of eating disorders and psychotic disorders based on clinical diagnosis or interview measures, to estimate prevalence of the comorbidity between these disorders. A narrative synthesis was conducted for all other studies and grouped by sample (general population, eating disorders or psychotic disorders).

In total 43 studies met inclusion criteria for the systematic review and 16 were included in the meta-analysis. Findings suggest substantial comorbidity between eating disorders and psychotic disorders, with a pooled comorbidity prevalence of 8% (CI: 3, 14) based on clinical diagnosis or interview measures. Studies using self-report questionnaires also highlight the association between eating disorders and psychosis across clinical and community populations.

Eating disorders and psychotic disorders frequently co-occur. Further research should investigate the temporal order of symptom development and consider the need for novel interventions targeted at overlapping psychotic and eating disorder symptoms and associated phenomena.