Individuals with high trait anxiety (HA) exhibit maladaptive goal-directed behaviors, which are associated with dysfunctional counterfactual-thinking during decision-making. While lesion studies suggest the causal role of the ventromedial prefrontal cortex (vmPFC) in counterfactual-thinking, its modulatory role in anxiety-related counterfactual decision-making remains uncharacterized. Here, we bridge this gap by examining the characteristics of decision-making (forward counterfactual) and emotion responses (backward counterfactual) in trait anxiety, as well as its underlying modulatory mechanisms by targeting at the vmPFC.

A counterfactual-thinking paradigm was employed to identify the patterns of goal-directed choice and emotional responses in trait anxiety in experiment 1. In all, 107 participants with varied levels of trait anxiety were recruited and counterfactual indices were modeled. In experiment 2, the high-definition transcranial direct current stimulation (HD-tDCS) was applied to modulate forward and backward counterfactual responses targeting at the vmPFC in HA. Based on the exploratory results of experiment 1, 61 participants with HA were randomly assigned to cathodal or sham stimulation.

High level of anxiety was associated with stronger emotional responses to backward counterfactuals, more anticipations of regret to forward counterfactuals, higher value-expectations to potential rewards, and more risk-taking behaviors. Related to sham, cathodal HD-tDCS over the vmPFC in HA showed normalized sensitivity to anticipated regret, which leads to less risk-taking behaviors during goal-directed decision-making.

The findings provide evidences of disrupted forward and backward counterfactual processing in anxious individuals, wherein the vmPFC plays a modulatory role. Targeting vmPFC with noninvasive stimulation may normalize maladaptive decision patterns in anxiety and anxiety disorders.

Early prediction of depressive and anxiety disorders is challenging due to substantial heterogeneity in risk pathways. Conventional machine-learning models trained on aggregated populations may obscure subgroup-specific mechanisms and limit interpretability for prevention. We evaluated whether a hybrid unsupervised–supervised framework can identify meaningful subgroups and yield more interpretable risk prediction.

We analyzed cohort data of 15,897 Japanese adults who completed baseline (August–September 2020) and 6-month follow-up (February–March 2021) surveys and did not screen positive for depressive and anxiety disorders at baseline (K6 score < 13). Using 169 baseline demographic, psychosocial, lifestyle, and behavioral variables, we performed hierarchical clustering to derive data-driven subgroups. Within each cluster, we trained Random Forest models to predict incident screened depressive and anxiety disorders at follow-up (K6 ≥ 13) and interpreted predictors using SHapley Additive exPlanations (SHAP).

The overall 6-month incidence was 6.23%. A five-cluster solution revealed two high-risk subgroups: an older-adult profile with poor quality of life (12.9%) and a working-parent profile characterized by work–family overload (29.8%). Compared with a global model trained on the full sample, the cluster-then-predict framework showed broadly similar overall performance but performed better in the highest-risk subgroup and revealed more differentiated predictor profiles. Loneliness, health-related quality of life, happiness, and personality traits predominated in clusters with moderate adversity, whereas lifestyle disruption (sleep, diet, and irregular routines) characterized the high-risk late-life subgroup and alcohol dependence and work–family burden characterized the high-risk working-parent subgroup.

Addressing risk-factor heterogeneity before prediction may enable more interpretable, context-tailored prevention strategies.

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.

Patients hospitalized with a life-limiting illness, along with their loved ones, frequently experience anxiety, stress, and pain. Legacy building through storytelling and music may alleviate emotional strain and provide comfort. Musical Rounds is a novel music medicine program designed to reduce distress and support legacy building for adult patients receiving palliative care and their loved ones.

This multisite, mixed-methods, pre–post feasibility study was conducted across 3 hospitals in California, USA. Participants engaged in live bedside recording sessions in which personal stories were shared with real-time musical improvisation provided by a clinician-musician. Afterward, participants received a personalized edited recording combining voice and improvised music. Pain, stress, anxiety, and comfort were assessed before and after each session using a 0–10 numeric rating scale. Perceived mood changes were assessed through directed qualitative content analysis.

We invited 100 adult patients hospitalized with a life-limiting illness and their loved ones to participate. If patients were unable to respond, loved ones participated on their behalf. Patients (n = 79) demonstrated statistically significant within-group differences between pre- and post-session assessments, including lower pain (−1.58, p < .001), stress (−2.89, p < .001), and anxiety (−2.73, p < .001), and higher comfort (+1.61, p < .001). Loved ones (n = 42) reported lower stress (−3.14, p < .001) and anxiety (−2.86, p < .001), and higher comfort (+1.83, p = .004). Directed content analysis indicated perceived mood improvement in 59% (47/80) of patients and 68% (30/44) of loved ones.

Musical Rounds, a personalized music and storytelling session for hospitalized patients with life-limiting illness and their loved ones, was associated with lower self-reported stress, pain (patients only), and anxiety, and higher comfort and perceived mood across 3 hospitals. Findings demonstrate the feasibility and suggest potential benefits of music medicine–supported legacy building in palliative care. Controlled studies with independent assessors are needed to further evaluate efficacy.

The present study aimed to investigate the association between adherence to the Dietary Approaches to Stop Hypertension (DASH) diet and the risk of depression and anxiety severity in a large group of Iranian adults.

In this cross-sectional study, dietary intakes were assessed using a validated sixty-five-item FFQ. The Beck Anxiety Inventory and the Beck Depression Inventory II were used to assess anxiety and depression, respectively. Ordinal logistic regression was applied to evaluate the association between DASH diet and depression and anxiety severity.

Mashhad Stroke and Heart Atherosclerotic disorder (MASHAD) study.

6537 and 6539 adults aged 35–65 years for depression and anxiety, respectively.

We found no significant association between adherence to the DASH diet and depression severity, in the total participants as well as both gender in either crude or fully adjusted models. Regarding anxiety, we found that men in the third tertile of DASH diet score had lower risk of experiencing more severe anxiety compared to those in the first tertile (OR: 0·80; 95 % CI: 0·67, 0·96). However, after controlling for potential confounders, this relationship became non-significant (OR: 0·89; 95 % CI: 0·74, 1·07). In the total participants as well as women, we failed to find any significant association between adherence to the DASH diet and anxiety severity either before or after controlling for possible confounders.

We found no significant association between adherence to DASH diet and depression and anxiety severity among adults.

Clinically relevant anxiety can be detected in patients with amyotrophic lateral sclerosis (ALS), but its prevalence and determinants have not yet been fully assessed.

This study aimed at assessing the prevalence and clinical underpinnings of anxiety in ALS.

Non-demented ALS patients (N = 433) and healthy controls (N = 313) were administered the State- and Trait-Anxiety Inventory – Form Y (STAI-Y1 for state-anxiety and STAI-Y2 for trait-anxiety) and the Beck Depression Inventory (BDI). Patients were further assessed for cognition (Edinburgh Cognitive and Behavioural ALS Screen), behaviour (Frontal Behavioural Inventory) and motor status (disease duration, ALS Functional Rating Scale-Revised and progression rate). The prevalence of clinically significant state- and trait-anxiety were estimated by applying age-stratified cut-offs to STAI-Y1/-Y2 t-scores. Linear and logistic regressions were run to test the determinants of STAI-Y1/-Y2 scores.

STAI-Y1 and -Y2 scores above cut-off were detected in 18.2 and 13.9% of patients, respectively – with proportions being higher in cases versus controls (ps < 0.001). BDI, but neither cognitive/behavioural nor motor variables, was identified as a significant predictor of STAI-Y1/-Y2 scores (ps < 0.003). The cognitive–affective subscale of BDI was the sole predictor of scores above cut-off on both STAI-Y1 and STAI-Y2 (ps < 0.001).

Clinically significant levels of state- and trait-anxiety occur in ∼18 and ∼14% of non-demented ALS patients, respectively, mostly driven by cognitive and affective facets of depression, and are independent of motor and cognitive/behavioural features.