Depression is associated with pathological dysregulations affecting both the brain and the body, with the latter being reflected in plasma proteins. While plasma protein signatures of depression have been increasingly recognized, a holistic examination of interactions with brain features is lacking.

Leveraging data from 3,966 UK Biobank participants, we identified a multimodal neuroimaging-plasma protein component of depression (NeuroPro-Dep) by integrating plasma proteins and five brain modalities via an ICD-10 diagnosis-constrained multimodal fusion approach.

Notably, NeuroPro-Dep demonstrates detectable associations with depression symptoms across datasets from diverse populations, underscoring its clinical potential. This capability is anchored in its five brain modalities alterations, including hippocampal atrophy, reduced cortical sensorimotor network functional connectivity, and impaired internetwork structural connectivity of the frontoparietal network. The multimodal neuroimaging-derived plasma protein modality of NeuroPro-Dep is enriched in metabolic pathways, as further supported by association analysis linking this modality to body mass index (BMI), type 2 diabetes, and other metabolic indicators. Crucially, two-step Mendelian randomization analysis revealed that the NeuroPro-Dep plasma protein modality exerts a causal effect on depression through BMI (plasma protein to BMI: or=0.28, p=0.035; BMI to depression: or=1.14, p=4.37×10−11).

Overall, this study underscores metabolic dysfunction as a bridge between brain changes, depression, and physical diseases, while providing a novel multimodal biological signature and valuable insights that may inform future treatment strategies.

Individuals with severe mental illness (SMI) have increased risk of physical comorbidities, linked to worse outcomes such as greater psychopathology, frailty, and neurocognitive impairment. Mechanisms underlying this burden remain unclear. This study examined whether frailty and psychopathology predict evening chronotype, especially in SMI with comorbidities.

A longitudinal study assessed 165 participants at two time points over one year: schizophrenia (n = 30), bipolar disorder (n = 42), major depressive disorder (n = 35), and healthy controls (n = 58). The SMI group (n = 107) was divided into SMI with comorbidities (SMI-C; n = 47) and without (SMI; n = 60). Measures included psychopathology, frailty, chronotype, neurocognitive and functional performance, and hematological biomarkers.

Neurocognitive and functional impairments were greater in SMI groups than controls (F = 10.3–31.4; p < 0.0001; η²p = 0.12–0.34). The SMI-C group showed worse frailty than controls at T1 (F = 4.3; p < 0.01; η²p = 0.05) and than SMI at T2 (F = 8.5; p < 0.0001; η²p = 0.12), and elevated MCV/MCH (F = 3.8–9.4; p < 0.05–0.0001; η²p = 0.04–0.11). Chronotype distribution did not differ. Frailty and psychopathology predicted chronotype in SMI (p < 0.05–0.01); in controls, frailty and performance did so (p < 0.05).

Psychopathological and hematological profiles are associated with chronotype and may help identify subgroups for chronobiology-informed interventions. These findings support more personalized treatment approaches.

Smartphone-based cognitive behavioral therapy (CBT) programs offer accessible interventions for subthreshold depression, yet engagement needed for meaningful benefit remains unclear. We examined how lesson and worksheet engagement relate to depressive symptom improvements in a behavioral activation (BA) intervention, accounting for time-varying confounders.

This secondary analysis included 298 adults assigned to the BA arm of the RESiLIENT trial, a randomized controlled trial in Japan. Lesson and worksheet completion were treated as time-varying exposures, each yielding four engagement patterns: minimal (Few-Few), early (Many-Few), late (Few-Many), and consistently high (Many-Many). Outcomes were depressive symptom changes measured by the Patient Health Questionnaire-9 (PHQ-9) at weeks 6 and 26. We applied the parametric g-formula to estimate counterfactual PHQ-9 changes under each pattern, adjusting for baseline and time-varying confounders.

Early lesson engagement during weeks 0–3 was associated with larger PHQ-9 reductions at both weeks 6 and 26, even when later engagement declined (Many-Few vs. Few-Few: week 6: −1.47 [95% CI −2.52 to −0.53]; week 26: −1.27 [−2.53 to −0.17]). In contrast, higher worksheet engagement was linked to improved PHQ-9 at week 6, with maximal benefit among consistently high engagers (Many-Many vs. Few-Few: −1.25 [−2.17 to −0.44]) and late engagers (Few-Many vs. Few-Few: −1.18 [−2.20 to −0.08]), but not persist to week 26.

Greater engagement with smartphone-delivered BA is associated with larger symptom reductions. Early lesson engagement drives sustained benefit, whereas worksheet engagement did not persist. These findings may guide digital CBT design by emphasizing early lesson completion alongside concurrent skill practice.

The COVID-19 pandemic raised concerns about the mental health of an already burdened healthcare workforce. This study examined mental health trajectories among healthcare workers (HCWs) across the pandemic and identified personal and employment factors associated with different symptom patterns.

Longitudinal data were drawn from the NHS CHECK cohort, including clinical and non-clinical staff from 18 NHS Trusts in England (April 2020–April 2023). Growth curve and growth mixture models identified latent classes of HCWs characterized by distinct trajectories of probable common mental disorders. Secondary outcomes included anxiety, depression, alcohol misuse, and post-traumatic stress symptoms. Logistic regression examined associations between baseline personal and employment characteristics and class membership.

The analytical sample included 22,764 participants. For each outcome, growth mixture models identified two latent classes. Approximately 31% of HCWs experienced persistently high symptoms of probable common mental disorders, while 69% experienced persistently low symptoms. Similar patterns were observed for secondary outcomes, with small subgroups demonstrating worsening symptoms followed by improvement. Logistic regression analyses showed that being female, younger, single, working as a nurse, or having a pre-existing mental health diagnosis increased the odds of belonging to a high symptom class. Perceived support from colleagues and managers was protective.

While many HCWs reported consistently low mental health symptom levels, almost a third belonged to a latent class characterized by persistently high symptoms across all time points. These findings underscore the need for mental health support for vulnerable HCW groups, embedded within routine NHS practice rather than limited to crisis periods.

Schizophrenia (SCZ) and bipolar disorder (BD) share substantial clinical and neuroanatomical features, yet the neurogenetic basis underlying their shared gray matter volume (GMV) deficits remains poorly understood.

We conducted meta-analyses to identify convergent GMV alterations across the two disorders. Genome-wide association studies (GWAS) were performed to uncover genetic variants associated with the shared GMV deficits region in UK Biobank participants. Polygenic risk score (PRS)-GMV associations were analyzed to examine the cumulative influence of genetic risk on GMV in regions with shared deficits. Furthermore, pleiotropic SNPs jointly associated with SCZ, BD, and shared GMV deficits were identified. Spatiotemporal gene expression profiling was utilized to characterize the developmental trajectories, and molecular docking was performed to explore potential drugs.

Meta-analysis revealed consistent overlapping GMV reductions in frontal, temporal, and insular regions across SCZ and BD, based on 6,620 patients and 7,762 controls. GWAS identified 14 SNPs associated with the shared GMV deficits. PRS analyses showed that modestly higher SCZ polygenic risk correlated with decreased GMV of shared regions. Two pleiotropic SNPs – rs11191368 and rs79668541 – were linked to both disorders and the shared GMV deficits. Spatiotemporal expression analyses demonstrated distinct developmental trajectories, and molecular docking highlighted 168 drugs with binding interactions for shared genes.

This study delineates shared neurogenetic mechanisms linking GMV abnormalities to genetic risk across SCZ and BD. Given the cross-sectional design, future longitudinal studies in independent cohorts are warranted to validate these findings and clarify the temporal relationships.

Recent research indicates that neuropathological alterations may propagate via brain networks, as illustrated by network diffusion models (NDMs). The application of NDM to internet gaming disorder (IGD) has yet to be evaluated. This study was set to identify possible epicenters of neuroanatomical alterations using NDM in IGD.

Structural magnetic resonance imaging (MRI) data were obtained from 288 IGD participants and 165 matched recreational game users. Gray matter volume (GMV) was computed through CAT12 and segmented according to the Brainnetome Atlas. NMD was utilized to simulate the propagation of pathology. We initiated diffusion from each location to pinpoint probable epicenters of GMV alterations in IGD and correlated eigenmodes of the Laplacian matrix with observed atrophy and expansion patterns.

Abnormal brain regions with altered GMV were observed in IGD. Specifically, IGD demonstrated a great loss in GMV in the caudal cuneus gyrus, precentral and postcentral gyrus, as well as the cingulate cortex while simultaneously exhibiting an increase in the amygdala. The pallidus and putamen showed positive correlations with gaming craving. Both the right cingulate gyrus and the left amygdala were identified by the model as significant epicenters of disease dissemination.

The results suggest that gray matter morphological abnormalities can predict temporal sequencing of pathology progression in IGD. Subcortical gray matter volume increases in reward-processing-related regions were positively correlated with gaming craving severity in IGD, consistent with altered reward processing and motivational drive in addiction models.

Social cognitive deficits are common across many psychiatric conditions and contribute to broader social dysfunction. One hypothesized mechanism involves altered basic visual processing, which may disrupt the perception of low-level social cues and, in turn, compromise broader social cognitive processes. Here, we examined relations between basic visual processing and different levels of social cognition in a transdiagnostic youth sample.

A sample of 148 youth, ranging from healthy individuals to individuals with neuropsychiatric diagnoses and significant social dysfunction, completed two measures of basic visual processing (contrast sensitivity and visual integration) and a battery of social cognition tasks spanning lower-level (gaze perception) to mid-level (emotion recognition) to higher-level (theory of mind) social cognition. We used a four-level path model to test whether basic visual processing predicts gaze perception, which in turn predicts emotion recognition, which predicts theory of mind.

Poorer contrast sensitivity and visual integration were associated with less precise gaze perception, which was, in turn, associated with worse emotion recognition, which was associated with worse theory of mind. This four-level path model demonstrated good fit and showed superior fit compared to alternative models.

These findings suggest that basic visual processing influences the perception of basic social cues (e.g. gaze direction), which subsequently impairs more complex social perception and inference. Notably, this study extends prior observations from individuals with chronic schizophrenia to a transdiagnostic youth sample, indicating that altered basic visual processing may be a shared mechanism contributing to social cognitive deficits across psychiatric disorders and illness stages.

Recent proposals for revising the Diagnostic and Statistical Manual of Mental Disorders (DSM) aim to improve psychiatric diagnosis. While these efforts reflect substantial ambition, they continue to operate within assumptions embedded in the DSM’s underlying classificatory logic. This editorial examines whether such incremental revision is sufficient.

We provide a critical analysis of the recently published DSM roadmap and accompanying subcommittee commentaries. Drawing on contemporary literature, we identify five structural blind spots in the current reform agenda: public mental health, scientific inference, lived experience, epistemic governance, and the function of diagnosis. Based on this analysis, we propose an alternative dialogical redesign for the DSM.

We argue that current revision considerations risk increasing complexity without resolving fundamental limitations in psychiatric classification. Specifically, our analysis highlights several areas that warrant further consideration, including the relationship between diagnostic expansion and societal conditions, the applicability of group-level scientific findings to individual care, the incorporation of experiential knowledge, participatory governance in revision processes, and the identity-related implications of diagnosis. In response, we propose redesigning the DSM as a hybrid dialogical system that retains coarse-grained classificatory categories for pragmatic purposes while shifting diagnostic practice toward contextual interpretation, collaborative meaning-making, relational understanding, and individualized care formulation.

The challenges facing psychiatric diagnosis require more than incremental refinement. We therefore argue for a dialogical redesign of the DSM that better reflects the context-dependent, experiential, and relational nature of mental health conditions, positioning diagnosis as a starting point for collaborative inquiry.

Emotion dysregulation is a transdiagnostic construct associated with multiple mental health conditions and shown to be an amenable target for treatment. The original Emotion Dysregulation Inventory (EDI) was created as a proxy-report measure validated in autistic and nonautistic youth. The goal of the current study was to develop a self-report version, the EDI-Self-Report (EDI-SR), that captures a first-person perspective and creates the option of multi-reporter measurement from adolescence through adulthood.

Using methods developed by the Patient-Reported Outcomes Measurement Information System (PROMIS), potential items and response options were written and tested in cognitive interviews. Two samples (996 participants who are autistic or have other intellectual and developmental disabilities and 1,000 participants selected to be representative of the US census as a nonclinical comparison group) completed the initial item pool. Items were assessed using exploratory and confirmatory factor analyses, item response theory analyses, concurrent calibrations, convergent correlations with comparable legacy measures, internal consistency reliability, and test–retest reliability.

Exploratory factor analyses suggested splitting Reactivity and Dysphoria items for confirmatory factor analyses and subsequent analyses. Following analyses, a 25-item Reactivity scale, a 7-item Dysphoria scale, and a 6-item Reactivity short-form scale were finalized. EDI-SR subscales showed convergent validity and superior total information when compared with similar measures, strong internal consistency reliability, and good test–retest reliability.

The EDI-SR provides an efficient, precise measure of ED in autistic individuals, individuals with other intellectual and developmental disabilities, and a US census-matched representative sample, and allows for multi-reporter assessment in clinical and research contexts.

Cognitive reappraisal deficits are a transdiagnostic risk factor for major depressive disorder (MDD) and social anxiety disorder (SAD) and are observed in patients with these disorders at the neural level. Preliminary research suggests less activation of prefrontal regions during reappraisal (vs. viewing) of negative stimuli associates with overall symptom severity in patients with MDD or SAD, however, this is not reliably observed across studies. Consistent with research showing that reappraisal may only be adaptive when employed to cope with uncontrollable adversity, this study sought to examine whether neighborhood-level adversity (i.e. socioeconomic disadvantage, crime) moderated the relation between internalizing symptom severity and neural correlates of reappraisal.

This study included patients with a current diagnosis of MDD (n = 51) or SAD (n = 39). Patients completed measures of symptom severity as well as an emotion regulation task while in the scanner to assess neural activation during reappraisal. Patients’ addresses were geocoded to assess neighborhood socioeconomic disadvantage and crime.

Results indicated that greater symptom severity was associated with decreased activation of key prefrontal regions underlying reappraisal, but only for patients living in neighborhoods characterized by high levels of personal (i.e. violent) crime. Unexpectedly, the opposite was found for patients living in low-crime neighborhoods, such that greater symptom severity was associated with increased neural activation during reappraisal (vs. viewing) of negative stimuli.

Findings highlight the critical importance of considering patients’ neighborhood contexts when evaluating associations between symptom severity and neural correlates of reappraisal in patients with internalizing disorders.

Social anxiety is characterized by fear and avoidance of social situations, yet many everyday decisions are made in the presence of others and are shaped by social influence. However, the influences of social anxiety on social decision-making and the underlying neural processes are not well understood.

Fifty-five adults with varying levels of social anxiety completed a social risk decision-making task during functional magnetic resonance imaging (fMRI). In each trial, participants chose between a safe option and a risky gamble against either a human or a computer opponent, with or without information about others’ choices. Social influence on choice was quantified using repeated-measures analyses and drift–diffusion modeling, while brain activity and functional connectivity were examined using whole-brain analyses.

Compared to individuals with lower social anxiety, those with higher social anxiety showed reduced conformity to others’ risky choices, specifically when interacting with human, but not computer, opponents, together with a stronger starting-point bias toward safe options. These behavioral differences were accompanied by lower dorsolateral prefrontal cortex (dlPFC) activation and stronger dlPFC–temporoparietal junction (TPJ) functional connectivity.

Social anxiety is associated with decreased social approach and reduced social influence from others in social decision contexts. Decreased activation of the prefrontal control system and its increased interactions with the social brain network point toward a conflict between heightened social monitoring and inefficient executive control. By distinguishing social-context effects from general risk aversion, this study provides a refined mechanistic framework for understanding how impaired regulatory control shapes maladaptive social decision-making in social anxiety.

This study evaluated the effectiveness of a one-year smoking cessation intervention for people with severe mental illness compared with treatment as usual (TAU) in outpatient mental healthcare.

The KISMET study is a pragmatic cluster-randomized controlled trial conducted in 21 outpatient mental healthcare teams in the Netherlands. Eleven teams delivered the KISMET intervention comprising cognitive-behavioral and peer support, combined with optional pharmacological reatment. Ten teams participated in the TAU condition. We collected data between October 2022 and July 2024. The primary outcome was self-reported smoking cessation at 12 months, verified through exhaled carbon monoxide levels below 10 parts per million. Secondary outcomes included depression and anxiety (HADS), severity of psychotic symptoms (PANSS-6), quality of life (SF-12), disease self-management (PAM-13), lipid profile, blood pressure, body mass index, glucose level, and physical fitness. Crude and adjusted linear and multivariable logistic regression and mixed model analyses were performed.

Eighty-nine participants were included in the KISMET intervention and 44 in TAU. Smoking cessation rates were significantly higher in the KISMET group at 3 months (OR 12.1, 95% CI 1.4 to 103.7) and at 12 months (OR 4.2, 95% CI 1.0 to 17.2) but not at 6 months (OR 1.9, 95% CI 0.5 to 6.9). No significant differences between groups were found for secondary outcomes. Dropout rates were 58% in the intervention and 32% in the TAU group.

The KISMET intervention shows potential without signs of physical or psychopathological complications. However, results must be interpreted with the high dropout rates in mind.

Anxiety disorders are associated with disrupted amygdala connectivity; however, resting-state functional MRI studies have reported heterogeneous findings. To clarify these inconsistencies, we conducted a meta-analysis of amygdala-based connectivity studies.

A systematic search of Embase, PubMed, and Web of Science was performed through December 26, 2025. Studies comparing amygdala-based whole-brain resting-state functional connectivity in patients with anxiety disorders versus healthy controls were included. Meta-analysis was conducted with the latest software – Seed-based d Mapping with Permutation of Subject Images (SDM-PSI), which employs voxel-wise tests and multiple corrections to minimize false positives. Subgroup analyses were performed to examine differences by age and hemisphere.

Fifteen datasets (378 patients, 405 controls) were included. Compared to healthy controls, patients with anxiety disorders had decreased amygdala-anterior cingulate cortex (ACC, g = −0.54, 95% confidence interval [CI]: −0.73 to −0.35) connectivity and increased connectivity with the left superior temporal gyrus (g = 0.46, 95% CI: 0.27–0.65), middle temporal gyrus (g = 0.38, 95% CI: 0.19–0.57), and cuneus (g = 0.35, 95% CI: 0.17–0.53). After threshold-free cluster enhancement correction, only reduced amygdala-ACC connectivity remained significant (g = −0.54, 95% CI: −0.73 to −0.35). Subgroup analyses confirmed this effect was driven mainly by adult patients and the left amygdala.

Reduced connectivity between the left amygdala and the ipsilateral ACC was the most robust neuroimaging marker of anxiety disorders, which suggests a lateralized vulnerability. By applying updated analytic methods, this study refines our understanding of the neuropathology of anxiety disorders and provides a potential primary target for biomarker development and novel interventions.