Based on facial expression experiments, childhood adversity may be associated with threat-related information processing bias. Yet, it is unclear whether this generalizes to other threat-related stimuli, such as social and non-social visual scenes.

We combined fast periodic visual stimulation with frequency-tagging electroencephalography (EEG) and eye-tracking to assess automatic and implicit neural discrimination, neural salience and preferential looking towards negative versus neutral social and non-social visual scenes in young adults aged 16–24 years (51 with childhood adversity and psychiatric symptoms and 43 controls).

Controls showed enhanced negative-neutral neural discrimination within a social versus non-social context. However, this facilitating effect of social content was absent in those with adversity, suggesting a selective alteration in social threat processing. Moreover, individual differences in adversity severity, and more specifically threat experiences (but not neglect experiences), were associated with decreased neural discrimination of negative versus neutral social scenes, corresponding to similar findings in facial expression processing, indicating the robustness of adversity-related deficits in threat-safety discrimination across social visual stimuli.

The adversity-related decreased threat-safety discrimination might impact individuals’ perception of social cues in daily life and relate to poor social functioning and future development of psychopathology.

Post-traumatic stress disorder (PTSD) is a mental health condition caused by the dysregulation or overgeneralization of memories related to traumatic events. Investigating the interplay between explicit narrative and implicit emotional memory contributes to a better understanding of the mechanisms underlying PTSD.

This case–control study focused on two groups: unmedicated patients with PTSD and a trauma-exposed control (TEC) group who did not develop PTSD. Experiments included real-time measurements of blood oxygenation changes using functional near-infrared spectroscopy during trauma narration and processing of emotional and linguistic data through natural language processing (NLP).

Real-time fNIRS monitoring showed that PTSD patients (mean [SD] Oxy-Hb activation, 0.153 [0.084], 95% CI 0.124 to 0.182) had significantly higher brain activity in the left anterior medial prefrontal cortex (L-amPFC) within 10 s after expressing negative emotional words compared with the control group (0.047 [0.026], 95% CI 0.038 to 0.056; p < 0.001). In the control group, there was a significant time-series correlation between the use of negative emotional memory words and activation of the L-amPFC (latency 3.82 s, slope = 0.0067, peak value = 0.184, difference = 0.273; Spearman’s r = 0.727, p < 0.001). In contrast, the left anterior cingulate prefrontal cortex of PTSD patients remained in a state of high activation (peak value = 0.153, difference = 0.084) with no apparent latency period.

PTSD patients display overactivity in pathways associated with rapid emotional responses and diminished regulation in cognitive processing areas. Interventions targeting these pathways may alleviate symptoms of PTSD.

Adverse childhood experiences (ACEs) have been associated with increased risks of autoimmune diseases. However, data are scarce on the role of specific ACEs as well as the potential mediating role of adverse mental health symptoms in this association.

A cohort study using the nationwide Icelandic Stress-And-Gene-Analysis (SAGA, 22,423 women) cohort and the UK Biobank (UKB, 86,492 women) was conducted. Participants self-reported on five ACEs. Twelve autoimmune diseases were self-reported in SAGA and identified via hospital records in UKB. Poisson regression was used to assess the cross-sectional association between ACEs and autoimmune diseases in both cohorts. Using longitudinal data on self-reported mental health symptoms in the UKB, we used causal mediation analyses to study potential mediation by depressive, anxiety, and PTSD symptoms in the association between ACEs and autoimmune diseases.

The prevalence of ACEs was 50% in SAGA and 35% in UKB, while the prevalence of autoimmune diseases was 29% (self-reported) and 14% (clinically confirmed), respectively. In both cohorts, ACEs were associated with an increased prevalence ratio (PR) of any studied autoimmune disease in a dose–response manner (PR = 1.10 (95%CI = 1.08–1.12) per ACE), particularly for Sjögrens (PR = 1.34), polymyalgia rheumatica (PR = 1.20), rheumatoid arthritis (PR = 1.14), systemic lupus erythematosus (PR = 1.13), and thyroid disease (PR = 1.11). Sexual abuse and physical and emotional neglect were consistently associated with an elevated prevalence of autoimmune diseases when including all ACEs in the model. Approximately one fourth of the association was mediated through depression, anxiety, and PTSD.

These findings based on two large cohorts indicate a role of ACEs and corresponding mental health distress in autoimmune diseases among adult women.

Previous findings in psychosis have revealed mixed findings on glutamate (Glu) levels in the dorsal anterior cingulate cortex (dACC). Factors such as illness chronicity, methodology, and medication status have impeded a more nuanced evaluation of Glu in psychosis. The goal of this longitudinal neuroimaging study was to investigate the role of antipsychotics on Glu in the dACC in antipsychotic-naïve first-episode psychosis (FEP) patients.

We enrolled 117 healthy controls (HCs) and 113 antipsychotic-naïve FEP patients for this study. 3T proton magnetic resonance spectroscopy (1H-MRS; PRESS; TE = 80 ms) data from a voxel prescribed in the dACC were collected from all participants at baseline, 6, and 16 weeks following antipsychotic treatment. Glutamate levels were quantified using the QUEST algorithm and analyzed longitudinally using linear mixed-effects models.

We found that baseline dACC glutamate levels in FEP were not significantly different than those of HCs. Examining Glu levels in FEP revealed a decrease in Glu levels after 16 weeks of antipsychotic treatment; this effect was weaker in HC. Finally, baseline Glu levels were associated with decreases in positive symptomology.

We report a progressive decrease of Glu levels over a period of 16 weeks after initiation of treatment and a baseline Glu level association with a reduction in positive symptomology, suggestive of a potential mechanism of antipsychotic drug (APD) action. Overall, these findings suggest that APDs can influence Glu within a period of 16 weeks, which has been deemed as an optimal window for symptom alleviation using APDs.

Panic disorder (PD) may increase the likelihood of suicidal ideation and behaviors because of psychiatric comorbidities such as major depressive disorder (MDD). However, research has yet to demonstrate a direct relationship between PD and suicide mortality.

Using data from Taiwan’s National Health Insurance Research Database, we identified 171,737 individuals with PD and 686,948 age- and sex-matched individuals without PD during 2003–2017. We assessed the risk of suicide within the same period. Psychiatric comorbidities such as schizophrenia, bipolar disorder, MDD, obsessive-compulsive disorder (OCD), autism, alcohol use disorder (AUD), and substance use disorder (SUD) were also evaluated. Time-dependent Cox regression models were used to compare the risk of suicide in different groups after adjustment for demographic data and psychiatric comorbidities.

Our Cox regression model revealed that PD was an independent risk factor for suicide (hazard ratio [HR] = 1.85, 95% confidence interval [CI] = 1.59–2.14), regardless of psychiatric comorbidities. Among all comorbidities, MDD with PD was associated with the highest risk of suicide (HR = 6.08, 95% CI = 5.48–6.74), followed by autism (HR = 4.52, 95% CI = 1.66–12.29), schizophrenia (HR = 3.34, 95% CI = 2.7–4.13), bipolar disorder (HR = 3.20, 95% CI = 2.71–3.79), AUD (HR = 2.99, 95% CI = 2.41–3.72), SUD (HR = 2.82, 95% CI = 2.28–3.47), and OCD (HR = 2.10, 95% CI = 1.64–2.67).

PD is an independent risk factor for suicide. Psychiatric comorbidities (i.e. schizophrenia, bipolar disorder, MDD, OCD, AUD, SUD, and autism) with PD increase the risk of suicide.

There is an unprecedented societal focus on young people’s mental health, including efforts to expand access to child and adolescent mental health services (CAMHS). There has, however, been a lack of research to date to investigate adult mental health outcomes of young people who attend CAMHS.

We linked Finland’s healthcare registries for all individuals born between 1987 and 1992. We investigated mental disorder diagnoses recorded in specialist adult mental health services (AMHS) and both inpatient and outpatient service use by age 29 (December 31, 2016) for former CAMHS patients.

Before the end of their 20s, more than half (52.4%, n = 21,183) of all CAMHS patients had gone on to attend AMHS. The most prevalent recorded adult psychiatric diagnoses received by former CAMHS patients were depressive disorders (30%, n = 11,768), non-phobic anxiety disorders (21%, n = 7,910), alcohol use disorders (9.5%, n = 3,427), personality disorders (9.3%, n = 3,366), and schizophrenia-spectrum disorders (7.6%, n = 2,945). In the total population, more than half of all AMHS appointments (53.1%, k = 714,239/1,345,060) were for former CAMHS patients. More than half of all inpatient psychiatry bed days were for former CAMHS patients (53.1%, k = 1,192,991/2,245,247).

While there is a strong focus on intervening in childhood and adolescence to reduce the burden of mental illness, these findings suggest that young people who receive childhood intervention very frequently continue to require specialist psychiatric interventions in adulthood, including taking up a majority of both outpatient and inpatient service use. These findings highlight the need for a greater focus on research to alter the long-term trajectories of CAMHS patients.

Post-traumatic stress disorder (PTSD) is characterized by severe distress and associated with cardiometabolic diseases. Studies in military and clinical populations suggest that dysregulated metabolomic processes may be a key mechanism. Prior work identified and validated a metabolite-based distress score (MDS) linked with depression and anxiety and subsequent cardiometabolic diseases. Here, we assessed whether PTSD shares metabolic alterations with depression and anxiety and if additional metabolites are related to PTSD.

We leveraged plasma metabolomics data from three subsamples nested within the Nurses’ Health Study II, including 2835 women with 2950 blood samples collected across three time points (1996–2014) and 339 known metabolites assayed by mass spectrometry-based techniques. Trauma and PTSD exposures were assessed in 2008 and characterized as follows: lifetime trauma without PTSD, lifetime PTSD in remission, and persistent PTSD symptoms. Associations between the exposures and the MDS or individual metabolites were estimated within each subsample adjusting for potential confounders and combined in random-effects meta-analyses.

Persistent PTSD symptoms were associated with higher levels of the previously developed MDS. Out of 339 metabolites, we identified 29 metabolites (primarily elevated glycerophospholipids and glycerolipids) associated with persistent symptoms (false discovery rate < 0.05; adjusting for technical covariates). No metabolite associations were found with the other PTSD-related exposures.

As the first large-scale, population-based metabolomics analysis of PTSD, our study highlighted shared and distinct metabolic differences linked to PTSD versus depression or anxiety. We identified novel metabolite markers associated with PTSD symptom persistence, suggesting further connections with metabolic dysregulation that may have downstream consequences for health.

Dissecting the exposome linked to mental health outcomes can help identify potentially modifiable targets to improve mental well-being. However, the multiplicity of exposures and the complexity of mental health phenotypes pose a challenge that requires data-driven approaches.

Guided by our previous systematic approach, we conducted hypothesis-free exposome-wide analyses to identify factors associated with 7 psychiatric diagnostic domains and 19 symptom dimensions in 157,298 participants from the UK Biobank Mental Health Survey. After quality control, 294 environmental, lifestyle, behavioral, and economic variables were included. An Exposome-Wide Association Study was conducted per outcome in two equally split datasets. Variables associated with each outcome were then tested in a multivariable model.

Across all diagnostic domains and symptom dimensions, the top three exposures were childhood adversities and traumatic events. Cannabis use was associated with common psychiatric disorders (depressive, anxiety, psychotic, and bipolar manic disorders), with ORs ranging from 1.10 to 1.79 in the multivariable models. Additionally, differential associations were identified between specific outcomes—such as neurodevelopmental disorders, eating disorders, and self-harm behaviors—and exposures, including early life experiences (being adopted), lifestyle (time spent using computers), and dietary habits (vegetarian diet).

This comprehensive mapping of the exposome revealed that several factors, particularly in the domains of those previously well-studied were shared across mental health phenotypes, providing further support for transdiagnostic pathoetiology. Our findings also showed that distinct relations might exist. Continued exposome research through multimodal mechanistic studies guided by the transdiagnostic mental health framework is required to better inform public health policies.

We aimed to study how hormonal status (oral contraceptive [OC] users vs naturally cycling [NC]) affects different dimensions and variability of psychological well-being, and how they relate to sex hormone levels (estradiol, progesterone, and testosterone).

Twenty-two NC participants and 18 OC users reported daily affective and physical symptoms and collected daily salivary samples across 28 days. Groups were compared using psychological well-being averages (linear mixed models), day-to-day variability (Levene’s test), and network models. Within NC participants, cycle phase effects and time-varying associations between hormones and psychological well-being were assessed using both person-centered mean and change (subtracting mean from daily score) scores.

Lowered variability was found for OC users’ agitation, risk-taking, attractiveness, and energy levels. They showed lower overall ratings of happiness, attractiveness, risk-taking, and energy levels (range R2m = .004: .019) but also reported more relaxation, sexual desire, and better sleep quality (range R2m = .005; .01) compared to the NC group. The impact of sex hormones on psychological well-being varied significantly across cycle phases, with the largest effects for progesterone levels.

Our results confirm that hormonal status is associated with a range of psychological well-being domains beyond mood and sexual desire, including energy levels, feelings of attractiveness, risk taking, and agitation. Lowered variability in OC users versus NC participants fit with ‘emotional blunting’ as a possible mechanism behind OC’s side effects. Our findings that show the menstrual cycle and sex hormones differentially influenced markers of psychological well-being emphasize the need to adequately account for the menstrual cycle.

Suicidal ideation (SI) is very common in patients with major depressive disorder (MDD). However, its neural mechanisms remain unclear. The anterior cingulate cortex (ACC) region may be associated with SI in MDD patients. This study aimed to elucidate the neural mechanisms of SI in MDD patients by analyzing changes in gray matter volume (GMV) in brain structures in the ACC region, which has not been adequately studied to date.

According to the REST-meta-MDD project, this study subjects consisted of 235 healthy controls and 246 MDD patients, including 123 MDD patients with and 123 without SI, and their structural magnetic resonance imaging data were analyzed. The 17-item Hamilton Depression Rating Scale (HAMD) was used to assess depressive symptoms. Correlation analysis and logistic regression analysis were used to determine whether there was a correlation between GMV of ACC and SI in MDD patients.

MDD patients with SI had higher HAMD scores and greater GMV in bilateral ACC compared to MDD patients without SI (all p < 0.001). GMV of bilateral ACC was positively correlated with SI in MDD patients and entered the regression equation in the subsequent logistic regression analysis.

Our findings suggest that GMV of ACC may be associated with SI in patients with MDD and is a sensitive biomarker of SI.

Improving patient outcomes will be enhanced by understanding “what works, for whom?” enabling better matching of patients to available treatments. However, answering this “what works, for whom?” question requires sample sizes that exceed those of most individual trials. Conventional methods for combining data across trials, including aggregate-data meta-analysis, suffer from key limitations including difficulty accounting for differences across trials (e.g., comparing “apples to oranges”). Causally interpretable meta-analysis (CI-MA) addresses these limitations by pairing individual-participant-data (IPD) across trials using advancements in transportability methods to extend causal inferences to clinical “target” populations of interest. Combining IPD across trials also requires careful acquisition and harmonization of data, a challenging process for which practical guidance is not well-described in the literature.

We describe methods and work to date for a large harmonization project in pediatric obsessive-compulsive disorder (OCD) that employs CI-MA.

We review the data acquisition, harmonization, meta-data coding, and IPD analysis processes for Project Harmony, a study that (1) harmonizes 28 randomized controlled trials, along with target data from a clinical sample of treatment-seeking youth ages 4–20 with OCD, and (2) applies CI-MA to examine “what works, for whom?” We also detail dissemination strategies and partner involvement planned throughout the project to enhance the future clinical utility of CI-MA findings. Data harmonization took approximately 125 hours per trial (3,000 hours total), which was considerably higher than preliminary projections.

Applying CI-MA to harmonize data has the potential to answer “what works for whom?” in pediatric OCD.

Genetically informative twin studies have consistently found that individual differences in anxiety and depression symptoms are stable and primarily attributable to time-invariant genetic influences, with non-shared environmental influences accounting for transient effects.

We explored the etiology of psychological and somatic distress in 2279 Australian twins assessed up to six times between ages 12–35. We evaluated autoregressive, latent growth, dual-change, common, and independent pathway models to identify which, if any, best describes the observed longitudinal covariance and accounts for genetic and environmental influences over time.

An autoregression model best explained both psychological and somatic distress. Familial aggregation was entirely explained by additive genetic influences, which were largely stable from ages 12 to 35. However, small but significant age-dependent genetic influences were observed at ages 20–27 and 32–35 for psychological distress and at ages 16–19 and 24–27 for somatic distress. In contrast, environmental influences were predominantly transient and age-specific.

The longitudinal trajectory of psychological distress from ages 12 to 35 can thus be largely explained by forward transmission of a stable additive genetic influence, alongside smaller age-specific genetic innovations. This study addresses the limitation of previous research by exhaustively exploring alternative theoretical explanations for the observed patterns in distress symptoms over time, providing a more comprehensive understanding of the genetic and environmental factors influencing psychological and somatic distress across this age range.

Few studies have examined the long-term outcomes of first-episode psychosis (FEP) among patients beyond symptomatic and functional remission. This study aimed to broaden the scope of outcome indicators by examining the relationships between 12 outcomes of FEP patients at 20.9 years after their initial diagnosis.

At follow-up, 220 out of 550 original patients underwent a new assessment. Twelve outcomes were assessed via semistructured interviews and complementary scales: symptom severity, functional impairment, personal recovery, social disadvantage, physical health, number of suicide attempts, number of episodes, current drug use, dose-years of antipsychotics (DYAps), cognitive impairment, motor abnormalities, and DSM-5 final diagnosis. The relationships between these outcome measures were investigated using Spearman’s correlation analysis and exploratory factor analysis, while the specific connections between outcomes were ascertained using network analysis.

The outcomes were significantly correlated; specifically, symptom severity, functioning, and personal recovery showed the strongest correlations. Exploratory factor analysis of the 12 outcomes revealed two factors, with 11 of the 12 outcomes loading on the first factor. Network analysis revealed that symptom severity, functioning, social disadvantage, diagnosis, cognitive impairment, DYAps, and number of episodes were the most interconnected outcomes.

Network analysis provided new insights into the heterogeneity between outcomes among patients with FEP. By considering outcomes beyond symptom severity, the rich net of interconnections elucidated herein can facilitate the development of interventions that target potentially modifiable outcomes and generalize their impact on the most interconnected outcomes.

Elucidation of transphasic mechanisms (i.e., mechanisms that occur across illness phases) underlying negative symptoms could inform early intervention and prevention efforts and additionally identify treatment targets that could be effective regardless of illness stage. This study examined whether a key reinforcement learning behavioral pattern characterized by reduced difficulty learning from rewards that have been found to underlie negative symptoms in those with a schizophrenia diagnosis also contributes to negative symptoms in those at clinical high-risk (CHR) for psychosis.

CHR youth (n = 46) and 51 healthy controls (CN) completed an explicit reinforcement learning task with two phases. During the acquisition phase, participants learned to select between pairs of stimuli probabilistically reinforced with feedback indicating receipt of monetary gains or avoidance of losses. Following training, the transfer phase required participants to select between pairs of previously presented stimuli during the acquisition phase and novel stimuli without receiving feedback. These test phase pairings allowed for inferences about the contributions of prediction error and value representation mechanisms to reinforcement learning deficits.

In acquisition, CHR participants displayed impaired learning from gains specifically that were associated with greater negative symptom severity. Transfer performance indicated these acquisition deficits were largely driven by value representation deficits. In addition to negative symptoms, this profile of deficits was associated with a greater risk of conversion to psychosis and lower functioning.

Impairments in positive reinforcement learning, specifically effectively representing reward value, may be an important transphasic mechanism of negative symptoms and a marker of psychosis liability.

Diagnosis in psychiatry faces familiar challenges. Validity and utility remain elusive, and confusion regarding the fluid and arbitrary border between mental health and illness is increasing. The mainstream strategy has been conservative and iterative, retaining current nosology until something better emerges. However, this has led to stagnation. New conceptual frameworks are urgently required to catalyze a genuine paradigm shift.

We outline candidate strategies that could pave the way for such a paradigm shift. These include the Research Domain Criteria (RDoC), the Hierarchical Taxonomy of Psychopathology (HiTOP), and Clinical Staging, which all promote a blend of dimensional and categorical approaches.

These alternative still heuristic transdiagnostic models provide varying levels of clinical and research utility. RDoC was intended to provide a framework to reorient research beyond the constraints of DSM. HiTOP began as a nosology derived from statistical methods and is now pursuing clinical utility. Clinical Staging aims to both expand the scope and refine the utility of diagnosis by the inclusion of the dimension of timing. None is yet fit for purpose. Yet they are relatively complementary, and it may be possible for them to operate as an ecosystem. Time will tell whether they have the capacity singly or jointly to deliver a paradigm shift.

Several heuristic models have been developed that separately or synergistically build infrastructure to enable new transdiagnostic research to define the structure, development, and mechanisms of mental disorders, to guide treatment and better meet the needs of patients, policymakers, and society.

Recent neuroimaging studies have demonstrated that the heterogeneous antidepressant responsiveness in patients with major depressive disorder (MDD) is associated with diverse resting-state functional brain network (rsFBN) topology; however, only limited studies have explored the rsFBN using electroencephalography (EEG). In this study, we aimed to identify EEG-derived rsFBN-based biomarkers to predict pharmacotherapeutic responsiveness.

The resting-state EEG signals were acquired for demography-matched three groups: 98 patients with treatment-refractory MDD (trMDD), 269 those with good-responding MDD (grMDD), and 131 healthy controls (HCs). The source-level rsFBN was constructed using 31 sources as nodes and beta-band power envelope correlation (PEC) as edges. The degree centrality (DC) and clustering coefficients (CCs) were calculated for various sparsity levels. Network-based statistic and one-way analysis of variance models were employed for comparing PECs and network indices, respectively. The multiple comparisons were controlled by the false discovery rate.

Patients with trMDD were characterized by the altered dorsal attention network and salience network. Specifically, they exhibited hypoconnection between eye fields and right parietal regions (p = 0.0088), decreased DC in the right supramarginal gyrus (q = 0.0057), and decreased CC in the reward circuit (qs < 0.05). On the other hand, both MDD groups shared increased DC but decreased CC in the posterior cingulate cortex.

We confirmed that network topology was more severely deteriorated in patients with trMDD, particularly for the attention-regulatory networks. Our findings suggested that the altered rsFBN topologies could serve as potential pathologically interpretable biomarkers for predicting antidepressant responsiveness.