An increase in mental disorders has been suggested, but the interpretation of such trends remains unclear. This study examines changes in the 12-month prevalence of anxiety and mood disorders over 12 years and evaluates whether clinical characteristics or sociodemographic, vulnerability and health-lifestyle risk factors contributed to these trends.

To assess trends in the 12-month prevalence of anxiety disorders (i.e. panic disorder, agoraphobia, social anxiety disorder or generalised anxiety disorder) and mood disorders (major depressive disorder, dysthymia or bipolar disorder) and explore whether changes in clinical profiles or risk factors influenced these trends.

Data from 11 615 respondents (mean age 43.5 years, 53.5% female) in the Netherlands Mental Health Survey and Incidence Studies (NEMESIS) were analysed, covering 2007–2009 (NEMESIS-2, n = 6646) and 2019–2022 (NEMESIS-3, n = 4969). Diagnoses were determined using the Composite International Diagnostic Interview 3.0.

The 12-month prevalence of all anxiety and mood disorders was significantly higher in 2019–2022 compared to 2007–2009, with relative increases across disorders ranging from approximately a half to more than double their previous rates. Any anxiety or mood disorder increased from 10.2 to 16.7%. Clinical profiles were equally severe in 2019–2022; rather, there was increased mental health care use, a higher number of comorbid disorders and earlier onset. Examination of 14 risk factors showed no consistent evidence of greater prevalence or increased relative impact over time.

There was a consistent rise in the 12-month prevalence of anxiety and mood disorders over 12 years. This increase was not explained by changes in risk factors or less severe disorder reporting. Instead, these findings suggest a concerning decline in public mental health, highlighting the need for effective prevention strategies, timely interventions and better mental health resource allocation to address growing clinical demands.

Emotion regulation relies on the interplay between prefrontal and limbic brain regions, with prefrontal regions implicated in the top-down modulation of the amygdala. In social anxiety disorder, disruptions in these networks have been reported, but most studies used undirected functional connectivity.

Dynamic causal modelling (DCM) was used to assess effective (i.e. directed) connectivity differences during emotion processing and regulation in individuals with social anxiety disorder compared with healthy controls.

A total of 102 participants (61 with social anxiety disorder, 41 healthy controls) performed a functional magnetic resonance imaging emotion regulation task under two conditions: viewing neutral/negative faces, and downregulating emotions using a self-chosen strategy. DCM was applied to model effective connectivity among the amygdala and key prefrontal regions. Connectivity patterns were characterised in healthy controls, and group comparisons tested how social anxiety disorder differed from this baseline model using parametric empirical Bayes. Leave-one-out cross-validation (LOOCV) evaluated whether connectivity differences predicted diagnostic group, symptom severity and emotion regulation difficulties.

In healthy controls, observation of negative faces was characterised by reciprocal influences between the amygdala and prefrontal cortex (PFC), including increased amygdala-to-ventromedial PFC (vmPFC) connectivity and inhibitory vmPFC-to-amygdala connectivity. During emotion regulation, healthy controls showed negative modulation from the amygdala to all prefrontal regions. Patients with social anxiety disorder did not differ from controls in amygdala–prefrontal connectivity; their alterations were confined to prefrontal circuits, with inhibitory connectivity from the pre-supplementary motor area (preSMA) to dorsolateral PFC during observation and bidirectional excitatory connectivity between the preSMA and vmPFC during regulation. LOOCV indicated that connectivity differences predicted diagnostic group.

The results support the idea that emotion processing and regulation influence connectivity between prefrontal areas and the amygdala in a complex, feedback-driven manner. Our findings suggest that aberrant emotion regulation in social anxiety disorder appears to be more closely linked to differences in intra-prefrontal circuits than deficits in amygdala–prefrontal connectivity.

We need to identify novel, tractable therapeutic targets for anxiety disorders. Converging evidence suggests the endogenous opioid system plays a role in modulating affective processing, but its contribution to regulation of threat processing in humans remains unclear.

We investigated the neural correlates of non-specific opioid antagonism on explicit and implicit regulation of threatening stimuli in healthy volunteers, using functional magnetic resonance imaging (fMRI).

In a randomised, double-blind, placebo-controlled, crossover design, 38 healthy participants received the opioid antagonist naltrexone (50 mg) or placebo before completing two tasks during fMRI: (a) a cognitive emotional reappraisal task probing explicit regulation and (b) a face-viewing task probing implicit processing.

Contrary to our hypothesis, we found naltrexone reduced distress ratings during the reappraisal task (p = 0.044) without impairing regulation success. Explicit regulation in the reappraisal task engaged lateral prefrontal regions similarly across drug conditions. However, naltrexone attenuated ventromedial prefrontal cortex, thalamus and caudate activation when viewing negative images. Naltrexone additionally altered ventromedial prefrontal cortex activity and in task-positive regions including right premotor area and frontal pole compared with placebo when viewing emotional faces. In particular, naltrexone increased left middle frontal gyrus activity when viewing fearful faces.

Our results support a role for opioid signalling in automatic emotional regulation, but not in explicit regulation. Furthermore, naltrexone appeared to diminish activity in task-positive regions in response to emotional faces. These findings are consistent with a model where endogenous opioids ‘fine-tune’ affective responses to both negative and positive stimuli. Future research should explore dose–response effects, kappa-opioid contributions and whether similar results are seen in clinical populations.

Explanatory frameworks for mental disorders influence stigmatisation and clinical attitudes. Mechanistic biological explanations often yield negative effects on prognostic optimism and empathy. Evolutionary framings might reduce stigma, but this has rarely been tested empirically.

To experimentally test whether a brief educational intervention presenting an evolutionary explanation of anxiety, compared with a genetic explanation, would influence clinicians’ attitudes in directions consistent with anti-stigma goals.

In this pre-registered, multi-site, cluster-randomised trial, 171 practising mental health clinicians across the UK and Ireland were randomised by session to receive a 30 min educational presentation on either evolutionary or genetic explanations for anxiety. Pre- and post-session questionnaires assessed clinicians’ optimism regarding patient recovery, perceived efficacy of psychosocial interventions, expected patient willingness to share diagnosis and seek help and perceived usefulness of the information. Data were analysed using Bayesian cumulative ordinal regression models.

In line with pre-registered hypotheses, clinicians rated evolutionary explanations as substantially more useful for patients (odds ratio 5.05, 95% credible interval [2.46, 10.28], latent standard deviation shift 1.07) and for clinicians (odds ratio 3.10, 95% credible interval [1.62, 5.81], latent standard deviation shift 0.76) compared with genetic explanations. Evolutionary explanations also resulted in higher anticipated public willingness to seek psychiatric help (odds ratio 1.79, 95% credible interval [0.93, 3.35]) and share a diagnosis (odds ratio 1.62, 95% credible interval [0.88, 2.97]); optimism about patient recovery (odds ratio 1.58, 95% credible interval [0.71, 3.46]); perceived effectiveness of psychosocial interventions (odds ratio 1.62, 95% credible interval [0.84, 3.10]); and belief in the functional usefulness of negative emotions (β = 0.25 s.d., 95% credible interval [0.01, 0.49]). These effects were driven by both positive pre–post effects of evolutionary education and negative pre–post effects of genetic education compared with pre-education baseline. Exploratory analysis showed further anti-stigma effects.

Framing anxiety through an evolutionary lens substantially improved clinicians’ attitudes on various measures of stigmatisation compared with genetic explanations, and was rated as highly useful for both clinicians and patients.

Anxiety disorders are highly prevalent yet lack objective biomarkers. Whereas threat-related attentional biases are well documented, less is known about broader eye movement alterations that may characterise anxiety.

To characterise multi-paradigm eye movement profiles in anxiety disorders and evaluate their potential as behavioural markers for disorder differentiation.

Eye movements were recorded in 91 patients with anxiety disorders, 118 with depressive disorders and 98 healthy controls during free viewing of neutral-stimuli, smooth-pursuit and fixation-stability tasks. Principal component analysis was applied to derive latent eye movement dimensions, which were then tested for group differences, associations with symptom severity and classification performance.

Compared with both patients with depression and healthy controls, patients with anxiety disorders exhibited hyper-scanning during free viewing, characterised by increased saccade frequency and path length, and hyper-pursuit during smooth pursuit, reflected in increased velocity gain, fewer intrusive saccades and more catch-up saccades. Principal component analysis identified six latent components, among which active visual exploration, pupillary arousal and smooth-pursuit control demonstrated robust group differences. Machine learning models trained on 6 components yielded areas under the receiver operating characteristic curve of 0.82 for anxiety versus healthy controls, 0.83 for depression versus healthy controls and 0.61 for anxiety versus depression.

Hyper-scanning and hyper-pursuit emerge as defining eye movement signatures of anxiety, linking core mechanisms of vigilance and prediction with measurable behavioural markers. These insights position eye-tracking as a promising behavioural modality for mechanism-informed differentiation across affective disorders.