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.

Pronounced variations in suicide mortality persist across Europe. Understanding long-term temporal patterns through age, period and cohort (APC) effects, alongside suicide means, is essential for tailored prevention. This study aims to determine how suicide mortality rates in Europe have changed across APC dimensions at national and subregional levels.

Our analysis was restricted to European countries with complete age- and sex-specific suicide mortality data from 1990 to 2019 within the World Health Organization mortality database. The analysis comprised two components. The first component disentangled long-term suicide mortality trends (1990–2019) into APC dimensions using an age-period-cohort model via the National Cancer Institute’s APC Web Tool. The second component involved an assessment of suicide means, restricted to 2010–2019 and to countries with detailed International Classification of Diseases, 10th Revision (ICD-10) cause-of-death data.

In 2019, Europe recorded 47,793 male and 13,111 female suicide deaths. Overall suicide mortality rates declined in most subregions from 1990 to 2019, with the largest reductions among Eastern European men, from 77.81 (95% CI: 77.17–78.45) per 100,000 in the mid-1990s to 22.93 (95% CI: 22.58–23.28) per 100,000 by 2019, although this region retained the highest male suicide burden. Age-specific risk patterns differed markedly: among men, risk peaked in early adulthood and then declined in Eastern Europe, while in Western and Southern Europe, it was lower and more stable but rose after age 60; for women, risk was generally lower, with peaks in early adulthood in Eastern Europe and in midlife elsewhere. Period reflected continued improvement, especially in Eastern Europe where the period risk in 2015–2019 was approximately 60% lower than 2000–2004. Cohort effects similarly showed progressive declines. However, upward trends emerged among younger generations. In Northern Europe, the cohort relative risk for females increased from 0.73 (95% CI: 0.68–0.78) in the 1980 cohort to 0.90 (95% CI: 0.70–1.04) in the 2000 cohort. While the completeness of suicide means analysis varied by subregion, the primary data indicated that hanging was the predominant means for both sexes during 2010–2019.

Despite an overall decline, suicide mortality in Europe exhibits persistent regional and demographic differences. This study reveals emerging risks among younger cohorts, specifically Northern European women and Southern European men, signalling shifting patterns that are not apparent from overall temporal trends alone. This evolving risk profile calls for sustained surveillance and research to investigate the drivers of these population-specific vulnerabilities.

Prior neuroimaging studies and meta-analyses investigating brain correlates of placebo analgesia (PA) have yielded neuroanatomically heterogeneous findings, which may be reconciled from a connectomics perspective. The objective of this study was to examine network localization of brain functional alterations related to PA.

We initially identified PA-induced brain activation alterations (hyper-activation and hypo-activation separately) during experimental pain from 29 published studies with 674 individuals. By combining these implicated dysfunctional brain regions with large-scale discovery (N = 1113) and validation (N = 1093) resting-state functional magnetic resonance imaging datasets, we then employed a novel functional connectivity network mapping approach to construct PA hyper-activation and hypo-activation networks, respectively.

The PA hyper-activation network manifested as a pattern of circumscribed brain regions mainly involving the limbic, default, and frontoparietal networks. By contrast, the PA hypo-activation network comprised a broadly distributed set of brain regions primarily implicating the ventral attention, somatomotor, and subcortical networks.

Our findings regarding the brain network representations of PA may contribute to a deeper understanding of its action mechanisms and provide a neural framework that may inform future clinical translation.

The term ‘schizo-obsessive comorbidity (SOC)’ is used to describe the presence of obsessive-compulsive symptoms or obsessive-compulsive disorder (OCD) in patients with schizophrenia (SOC). Recent studies have found overlapped executive dysfunctions in SCZ and OCD implicating shared pathophysiology. However, specific deficits in the components of executive function (EF) in patients with SOC remains unclear.

We recruited 37 patients with SOC, 68 patients with SCZ, 70 patients with OCD, and 59 healthy controls (HCs). All participants completed a battery of measures for EF components, namely initiation, sustained attention, online updating, switching, disinhibition, and planning. Apart from traditional group-mean analysis, we applied machine learning approaches to identify the unique patterns of EF among different clinical groups.

The results showed that the three clinical groups could be distinguished from HCs. The feature importance analysis showed that, to classify clinical groups from HC, online updating was the core feature of SCZ patients, whereas disinhibition and online updating jointly determine classification between OCD patients and HC. In differentiating SOC from HC, online updating, planning, and disinhibition collectively served as key features. Machine learning algorithms classified SOC and OCD with acceptable performance but classified SOC and SCZ with lower performance.

Deficits of EF are shared features among patients with SOC, SCZ, and OCD. However, the specific components of executive dysfunction in these clinical groups appeared distinct.

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.

Empathy involves communicating and understanding others’ emotion in multisensory contexts, including visual and auditory modalities. Schizophrenia (SCZ) patients have impaired empathy, but whether the impact of visual/auditory context would be altered in SCZ patients and people with high social anhedonia (HSoA) remained unclear.

We administered the modified Chinese version of the Empathic Accuracy Task (EAT) to clinical (50 SCZ patients and 50 healthy controls) and subclinical samples (59 HSoA and 60 low social anhedonia [LSoA] participants). The EAT employed audio-only, audiovisual, and audioavatar visual conditions to assess the impact of multimodal information on empathy during positive and negative emotional events.

In positive-valenced context, SCZ patients performed worse than controls in cognitive and affective empathy. The Modality-by-Group interaction on empathic accuracy was significant, that is, SCZ patients performed worse than controls in both audiovisual and audioavatar visual conditions, but comparable to controls in audio-only condition. In negative-valenced context, SCZ patients performed worse than controls in cognitive empathy. The Modality-by-Group interaction on empathic accuracy was significant, that is, SCZ patients performed worse than controls in audio-only and audiovisual conditions. Moreover, HSoA participants exhibited lower cognitive empathy than controls in positive-valenced context; and lower cognitive empathy and empathic motivation in negative-valenced context. No significant Modality-by-Group interaction was found in the HSoA–LSoA sample.

SCZ patients have generalized impairments of cognitive and affective empathy across positive and negative contexts, particularly in multimodal conditions. HSoA individuals are primarily impaired in cognitive empathy and empathic motivation.

Effective disaster medical response hinges on timely data management and efficient resource allocation. The Hualien Disaster Medical Assistance Team (DMAT) in Taiwan functions as a mobile emergency department during disasters, integrating resources from health bureaus, emergency centers, hospitals, and private organizations. Previous disaster responses revealed significant challenges in data capture and real-time management; reliance on frequent calls and messages led to information errors, overburdened medical staff, and diminished rescue efficiency.

To address these issues, Hualien DMAT developed the iDMAT system, a digital platform designed to simplify medical record keeping and data management during disaster response. The user operates the application downloaded on the mobile phone. Feedback and operational data from users have been collected during exercises since 2023 and during the Hualien earthquake on April 3, 2024.

Implementation of the iDMAT system significantly improved operational efficiency. The team members reduced the average medical record completion time from 5.89 minutes (paper-based) to 2.68 minutes, achieving a 45% reduction. Approximately 78.57% of records were completed within 2–3 minutes. Data integration time decreased dramatically from 35.91 minutes to 6.80 minutes, with 70% of cases integrated within 3 minutes—a fivefold increase in efficiency.

By providing accurate and timely data to government agencies, the iDMAT system enhanced on-site medical care and resource allocation, facilitating rapid decision-making and resource deployment. These improvements led to better patient outcomes and reduced mortality during disasters. The system is scalable to other out-of-hospital care scenarios, such as major accidents and large-scale events. Future developments include integrating international standards like FHIR to promote cross-border collaboration and establishing a seamless care continuum among hospitals, fire departments, and ambulances.