IntroductionDeep learning approaches have become central to brain MRI analysis; however, their reliability under dataset shift remains a critical barrier to safe and scalable deployment in neuroscience and clinical research. While convolutional neural networks (CNNs) provide strong locality-driven inductive biases for robust feature extraction, they lack global contextual awareness. Conversely, transformer-based architectures capture long-range dependencies but often exhibit reduced robustness and miscalibrated confidence when applied to heterogeneous medical imaging data, particularly in Cross-Dataset settings.MethodsIn this work, we propose a calibration-aware hierarchical CNN-Transformer fusion framework designed for robust brain MRI analysis under dataset shift. The architecture integrates a pretrained multi-scale CNN backbone with a hierarchical transformer branch and performs scale-aligned fusion through cross-attention mechanisms. By allowing local convolutional features to selectively query global contextual representations, the proposed design maintains stable feature contributions during fusion and mitigates overconfident reliance on transformer features when generalization degrades across datasets. The framework is evaluated using a strict Cross-Dataset protocol, where models are trained on one dataset and tested on a distinct dataset.ResultsExperimental results demonstrate that the proposed fusion model achieves competitive classification performance while substantially improving probabilistic calibration relative to both CNN-only and transformer-only baselines. Specifically, the model attains an average accuracy of 99.20% and achieves lower Expected Calibration Error (ECE = 0.0041), Brier score (0.0028), and Negative Log-Likelihood (NLL = 0.0277) compared to a standalone Swin Transformer and a strong ResNet50 baseline.DiscussionThese findings demonstrate that calibration-aware hierarchical CNN-Transformer fusion enhances both predictive reliability and robustness under Cross-Dataset evaluation. By improving the alignment between predictive confidence and empirical correctness, the proposed method supports safer large-scale analysis of heterogeneous brain MRI data, with important implications for multi-center neuroscience studies and trustworthy clinical decision support.
Immersive virtual reality as a novel approach to improve social cognition in multiple sclerosis: an EEG-based pilot study
IntroductionMultiple sclerosis (MS) affects different cognitive domains, including social cognition. Immersive Virtual Reality (VR) may provide a novel rehabilitative approach to treat motor and cognitive symptoms of MS. This exploratory pilot study evaluated the effects of immersive VR rehabilitation on social cognition in MS patients and explored related cortical neurophysiological signatures.MethodsSeven MS patients underwent immersive VR rehabilitation with the CAREN system (3 sessions/week, approximately 45 min of active training per session, about 1 h including preparation, 8 weeks), while seven healthy controls (HC) did not undergo any intervention. Patients were evaluated at baseline (T0) and post-treatment (T1) with standardized measures of cognitive, emotional, and motor functioning. EEG data were acquired from all participants, and, after artifact removal, spectral parameterization decomposed signals into aperiodic (exponent, offset) and periodic oscillatory components (alpha and beta power). Power spectral density was analyzed using group comparisons and Pearson correlations with neuropsychological measures.ResultsCompared with HC, MS patients showed reduced alpha-band power, mainly over frontal and parieto-occipital regions, whereas aperiodic parameters did not differ between groups. In patients, alpha and beta power correlated with the Positive Emotions Self-Efficacy Scale (alpha: r = 0.92, p = 0.003; beta: r = 0.83, p = 0.020). Alpha power is also correlated with RAO SRT–LTS (r = 0.85, p = 0.016), and beta with EQ-CE (r = 0.82, p = 0.023). Overall, alpha and beta power were correlated with emotional self-efficacy, balance, memory, and empathy, suggesting that oscillatory markers are potential indicators of clinical outcomes.DiscussionRehabilitation via immersive VR has shown promising clinically significant effects in the cognitive, emotional, and motor domains, supported by convergent EEG spectral signatures. Future studies employing predictive modeling approaches will be required to assess their prognostic value.
Correction: The effect of musical sensory orientation training in improving consciousness level in patients with disorders of consciousness: a pilot study
How stressful life events are associated with depression: the mediating pathway of security in a clinical adolescent sample
BackgroundStressful life events are well-established risk factors for adolescent depression; however, the psychological mechanisms underlying this association remain insufficiently understood, particularly regarding which types of stress and which dimensions of security are most closely linked to depression. This study aimed to investigate whether security and its two sub-dimensions statistically mediated the association between stressful life events and depression among clinically diagnosed adolescents, while also examining the relative strength of indirect associations across specific stress types.MethodsA cross-sectional study was conducted with 284 adolescents (70.1% female; mean age = 15.82 ± 1.86 years) diagnosed with major depressive disorder according to the DSM-5 criteria at a tertiary psychiatric hospital in Western China. Participants completed the Adolescent Self-Rating Life Events Checklist (ASLEC), Self-Rating Depression Scale (SDS), and Security Questionnaire (SQ) questionnaires. Simple mediation, parallel mediation, and dimension-specific analyses were performed using the PROCESS macro (Model 4) with 5,000 bootstrap resamples, controlling for gender and parental marital status.Resultsstressful life events were significantly positively correlated with depression (r = 0.491, p < 0.001) and negatively correlated with security (r = −0.464, p < 0.001). Simple mediation analysis revealed that security demonstrated a significant indirect association through security (indirect effect = 0.176, 95% CI [0.126, 0.232]), accounting for 53.8% of the total association. Parallel mediation analysis further indicated a dual-pathway model: both Interpersonal Security (indirect effect = 0.083, 95% CI [0.037, 0.133]) and Certainty in Control (indirect effect = 0.093, 95% CI [0.043, 0.152]) functioned as significant statistical mediators of comparable magnitude, with no significant difference between them (Contrast = −0.010, 95% CI [−0.065, 0.042]). Furthermore, dimension-specific analyses revealed that Interpersonal Stress (standardized indirect effect = 0.266) and Academic Stress (standardized indirect effect = 0.231) showed the strongest indirect associations with depression through the security pathway. Exploratory subgroup analyses revealed a gender-crossed pattern: for male adolescents (n = 85), the indirect association was significant only through Interpersonal Security (effect = 0.116, 95% CI [0.048, 0.199]); for female adolescents (n = 199), it was significant only through Certainty in Control (effect = 0.136, 95% CI [0.067, 0.212]).ConclusionSecurity functions as a significant statistical mediator in the association between stressful life events and adolescent depression. The findings are consistent with a “dual-pathway” model wherein stress is concurrently associated with lower levels of both relational security (Interpersonal Security) and personal agency (Certainty in Control). Exploratory analyses suggest that the relative importance of these two pathways may differ by gender. If confirmed by future longitudinal research, clinical interventions may benefit from an integrated approach that addresses both dimensions, with particular attention to interpersonal conflicts and academic pressure as the stressors most strongly associated with depression through security pathways.
A longitudinal analysis of the prevalence of restrictive interventions involving women with mental health conditions, learning disabilities or autism in mental health services in England
IntroductionRestrictive interventions, including physical restraint, seclusion, chemical restraint, and segregation, continue to be used within mental health services, despite sustained policy efforts to promote least-restrictive and trauma-informed care. However, little is known about national trends affecting women, for whom restrictive interventions often carry heightened risks of re-traumatisation and stigma.MethodsWe conducted a longitudinal secondary analysis of publicly available administrative data from the Mental Health Bulletin covering NHS-funded mental health services in England between 2017 and 2025. Annual counts of restrictive interventions involving women were examined relative to the number of women detained under the Mental Health Act to estimate annual rates per 1,000 detained. Regression modelling was used to assess temporal trends overall, by age group and type of restrictive intervention, and interrupted time-series analyses to examine changes following implementation of the Mental Health Units (Use of Force) Act 2018 (“Seni’s Law”). Trends were also examined alongside available national data on restrictive interventions involving men.ResultsRates of restrictive interventions involving women increased by approximately 12 percent per year over the study period, with no evidence of a reduction following the introduction of Seni’s Law. Increases were most pronounced for chemical restraint, seclusion, and segregation, while physical and mechanical restraint remained stable. Restrictive interventions declined among women under 18 but increased consistently across all adult age groups, indicating a widening age-related divergence. Although overall trends broadly mirrored those observed among men, the types of restrictive interventions used and their potential impact may differ, highlighting gendered dimensions in how restrictive practices are experienced and applied.DiscussionDespite extensive national initiatives, restrictive interventions involving women have continued to rise in England, highlighting a persistent gap between policy intent and practice. The findings suggest that legislative frameworks alone are insufficient to achieve meaningful reductions without operational changes in clinical practice, organisational culture, and monitoring systems. Internationally, the study contributes rare gender-disaggregated longitudinal evidence and highlights the need for comparable monitoring systems and coordinated research to inform rights-based, trauma-informed strategies to reduce restrictive interventions in mental health services.
Self images: an empirical enquiry into Rembrandt’s self-portraits
Many have speculated that events of personal and financial loss in the life of Rembrandt van Rijn (Rembrandt) caused depression and that this is revealed by examination of his work particularly self-portraits painted in old age. Some report detecting various physiological diseases associated with aging, including vision impairment, which may have affected his mood and work. Aging and neurodegenerative disease which often accompanies it, are both associated with depression. Depression is characterised by visual deficits including perception of reduced contrast and colour. Age-related neurological disorders are associated in artists with reduced complexity. Recent advances in imaging and computer technology make it possible to empirically examine changes in artistic style which can contribute to understanding artists’ physical and mental health. Previous studies have identified associations between adverse events in artists’ lives and altered contrast and colour in their self-portraits. In the current study changes in contrast, colour and fractal dimension were measured in the entire corpus of Rembrandt’s painted self-portraits and portraits to determine whether changes in style indicate depression, cognitive decline, or neurological disease and whether differences in style can be detected between self-portraits and portraits of related and unrelated others. Productivity was also examined as an indirect indicator. The results suggest that it is unlikely that Rembrandt suffered from unipolar or bipolar depression, age-related cognitive decline, or neurodegenerative disease. The data are consistent with someone experiencing episodes of low mood associated with normal grieving and adversity followed by resilient recovery. There is evidence of a gradient in saliency and complexity between self-portraits and related and unrelated portraits and of a ‘late’ style identified by leading art historians consistent with macular degeneration.
Unmasking deep-rooted trauma: long-term effects of childhood adversities on posttraumatic stress disorder in healthcare workers facing acute multi-trauma
PurposeIn recent years, healthcare workers (HCWs) in Lebanon have encountered compounded traumatic exposures, including the Beirut Port blast, COVID-19, and an ongoing economic crisis, often preceded by early-life adversities such as adverse childhood experiences (ACEs). Understanding how these acute stressors interact with early adversities is crucial for assessing their long-term psychological impact. Accordingly, this study examines the extent to which these combined factors predict the development of full and subthreshold posttraumatic stress disorder (PTSD) over time.MethodsA cohort study was conducted following 296 HCWs from Saint George Hospital University Medical Center, with assessments at two timepoints: 6–7 months and 2–2.5 years after the Beirut Blast. PTSD symptoms were measured using the PCL-5, applying both full-threshold criteria and six definitions of subthreshold PTSD. Bivariable and multivariable analysis were conducted.ResultsAt 6–7 months, acute stressors (financial hardship, Beirut Blast, and COVID-19) were significantly associated with PTSD across most definitions. However, by 2–2.5 years, ACEs became the strongest and most consistent predictor of both full-threshold and subthreshold PTSD, while the impact of acute stressors diminished.ConclusionThe impact of acute trauma on the risk of PTSD fades over time, while early-life adversity has an enduring impact. The findings highlight the importance of including developmental trauma histories in PTSD assessments. In concordance with stress sensitization and neurobiological models, the results indicate a marked temporal shift, where the diminishing effects of acute stressors give way to the enduring role of early life adversity in shaping PTSD symptom trajectories.
Medical evaluation of first presentation of psychotic symptoms in children and adolescents
IntroductionPsychotic symptoms in children and adolescents may represent either normative developmental phenomena or severe psychiatric and medical conditions, requiring careful differential diagnosis.MethodsThis retrospective study aimed to evaluate the medical workup of children and adolescents admitted for a first presentation of psychotic symptoms at a tertiary pediatric center over a 10-year period. The sample included 68 patients (mean age 13.7 ± 3.7 years) who underwent clinician-directed evaluations including physical exams, laboratory tests, toxicology screens, neuroimaging, and lumbar puncture when indicated.ResultsSixteen patients (23.5%) were diagnosed with substance-/medication-induced or medically-associated psychosis. In this cohort, younger age, very early onset psychosis (<13 years), and catatonia at first presentation were more frequently observed among patients with secondary etiologies, whereas documented prior subthreshold symptoms were more frequently documented among those diagnosed with primary psychiatric disorders. Most investigations did not identify a secondary cause, reflecting clinician-directed evaluation in routine practice; however, selected cases (e.g., autoimmune encephalitis, multiple sclerosis) illustrate the clinical importance of careful assessment when specific suspicion is present.ConclusionThese findings suggest that targeted medical evaluation may be useful in pediatric psychosis, particularly when clinical features raise suspicion for secondary etiologies, and may help inform clinical decision-making in tertiary pediatric settings.
Rapid temporal processing in the olfactory bulb underlies concentration-invariant odor identification and signal decorrelation
Nature Neuroscience, Published online: 14 April 2026; doi:10.1038/s41593-026-02250-y
This study shows that the brain’s smell center uses precise timing and inhibition to read out early odor signals, enabling reliable odor identity across concentrations while rapidly separating (decorrelating) similar smell patterns for better discrimination.
Coming soon: 10 Things That Matter in AI Right Now
Each year we compile our 10 Breakthrough Technologies list, featuring our educated predictions for which technologies will have the biggest impact on how we live and work.
This year, however, we had a dilemma. While our final picks encompass all our core coverage areas (energy, AI, and biotech, plus a few more), our 2026 list was harder to wrangle than normal. Why? We had so many worthy AI candidates we couldn’t fit them all in! (The ones that made it were AI companions, generative coding, and hyperscale data centers.) Many great ideas fell by the wayside to keep the list as wide-ranging as possible.
Well, that got us thinking: What if we made an entirely new list that was all about AI? We got excited about that idea—and before we knew it we had the beginnings of what we’re calling 10 Things That Matter in AI Right Now. It’s an entirely new annual list that we’re proud to be publishing for the first time on April 21, 2026. We’ll unveil it on stage for attendees at our signature AI conference, EmTech AI, held on MIT’s campus (it’s not too late to get tickets), and then publish the list online later that day.
The process for coming up with the list was similar to the way we pick our 10 Breakthrough Technologies. We petitioned our AI team of reporters and editors to propose ideas, put them all in a document, and engaged in some robust discussion. Eventually, we voted for our favorites and whittled the long list down to a final 10.
But there’s a slight difference between this list and our 10 Breakthrough Technologies. AI is already such a big part of our lives that we didn’t want to restrict ourselves to nominating only technologies. Instead, we wanted to put together a definitive annual list that highlights what we believe are the biggest ideas, topics, and research directions in AI right now. So yes, it will include cutting-edge AI technologies, but it will also feature other trends and developments in AI that we want to bring to our subscribers’ attention.
Think of it as a sneak peek inside the collective brain of our crack AI reporting team: These are the things that our reporters will be watching this year. We intend to follow the items on this list really closely, and you will see it reflected in the news and feature stories we publish in 2026.
For us, 10 Things That Matter in AI Right Now is a guide to how we view the current AI landscape. It will be a source of discussion, debate, and maybe some arguments! We are so excited to share it with you on April 21. If you want to be among the first to see it—join us at EmTech AI or become a subscriber to livestream the announcement.