Nature mental health最新文献

{"title":"Serotonin reduces belief stickiness","authors":"Vasco A. Conceição, Frederike H. Petzschner, David M. Cole, Katharina V. Wellstein, Daniel Müller, Sudhir Raman, Tiago V. Maia","doi":"10.1038/s44220-026-00621-9","DOIUrl":"10.1038/s44220-026-00621-9","url":null,"abstract":"Serotonin fosters cognitive flexibility, but how, exactly, remains unclear. We developed a computational theory that proposes that serotonin reduces belief stickiness: the tendency to get ‘stuck’ in a belief about the state of the world despite incoming contradicting evidence. We tested this theory in a randomized, double-blind, placebo-controlled study using a single dose of the selective serotonin reuptake inhibitor escitalopram. In the escitalopram group, higher escitalopram plasma levels reduced belief stickiness more, resulting in better inference about the state of the world. Moreover, participants with sufficiently high escitalopram plasma levels had less belief stickiness, and therefore better state inference, than participants on placebo. We also propose that obsessions may result from excessive belief stickiness. Indeed, participants with more obsessions had greater belief stickiness, and therefore worse state inference. The opposite relations of escitalopram and obsessions with belief stickiness may explain the therapeutic effect of selective serotonin reuptake inhibitors in obsessive–compulsive disorder. This research presents a computational theory linking serotonin to reduced belief stickiness, tested through a randomized, double-blind study. Findings indicate that higher escitalopram levels correlate with improved inference, suggesting implications for obsessive–compulsive disorder treatment.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 5","pages":"775-791"},"PeriodicalIF":8.7,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147924338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"While we argue, schizophrenia advances","authors":"Constanza Morén","doi":"10.1038/s44220-026-00644-2","DOIUrl":"10.1038/s44220-026-00644-2","url":null,"abstract":"In this World View, Constanza Morén, a biomedical researcher and daughter of a man with schizophrenia, calls for humility and collaboration to bridge the divide between biological psychiatry and anti-psychiatry, improving support for patients and families.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 5","pages":"691-691"},"PeriodicalIF":8.7,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147924302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Therapist-rated predictors of response to psychedelic-assisted therapy","authors":"Grace Viljoen, Antonia Bendau, Henrik Walter, Lea J. Mertens, Ricarda Evens, Max Wolff, Uwe Herwig, Matthew Johnson, Mathieu Seynaeve, Michael Koslowski, Felix Betzler","doi":"10.1038/s44220-026-00642-4","DOIUrl":"10.1038/s44220-026-00642-4","url":null,"abstract":"Psychedelic-assisted therapy has shown promise in the treatment of a range of psychiatric disorders, yet therapeutic responses remain highly variable. Although prior research has focused predominantly on features of the acute psychedelic experience, less attention has been paid to baseline characteristics and preparatory factors. Moreover, therapist-derived insights from real-world practice have not yet been explored. Here we conducted a cross-sectional survey distributed to therapists involved in psychedelic-assisted therapy to assess the perceived impact of baseline, preparation and session parameters on therapeutic outcomes. A total of 158 therapists completed the survey and rated predictors of favorable and unfavorable long-term outcomes. Therapists identified several factors as particularly conducive to positive outcomes, with the highest ratings given to a strong therapeutic alliance, robust social support, personality traits such as openness and capacity to surrender, secure attachment and a belief in an active mode of therapeutic action. By contrast, prior use of nonpsychedelic substances was perceived as the most unfavorable predictor of therapeutic response. Differences also emerged according to therapists’ setting of practice and primary substance of experience. Therapists working in unregulated settings rated certain challenging features more favorably. Meanwhile, therapists working with psilocybin placed greater emphasis on preparation and therapeutic presence than therapists working with ketamine. The thematic analysis of open-ended responses further highlighted the importance of preparation, integration, patient mindset and environmental context. These findings provide clinically grounded insights into the key predictors of psychedelic-assisted therapy outcomes and may inform future screening protocols and the optimization of treatment protocols. Psychedelic-assisted therapy shows promise for psychiatric disorders, but therapeutic responses vary widely. Here a survey of therapists’ experience aimed to identify key predictors of successful outcomes, highlighting the importance of therapeutic alliance, social support, openness and active engagement, which could inform future screening and preparation practices to enhance therapeutic efficacy.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 6","pages":"951-961"},"PeriodicalIF":8.7,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148215263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health justice, resource allocation and age in suicide risk assessment","authors":"James Hart, Sapfo Lignou, Seena Fazel","doi":"10.1038/s44220-026-00635-3","DOIUrl":"10.1038/s44220-026-00635-3","url":null,"abstract":"Suicide risk assessment tools and prediction models aim to assess individual suicide risk to assist clinical decision-making and improve health outcomes. While these tools have proliferated in recent years, concerns and criticisms of them have also increased and NICE recommended against their use. In this Perspective, we discuss these tools from the perspective of health justice. First, we argue that, in the context of limited resources, having tools to assist in identifying the highest-risk individuals may help us to prioritize the worst-off and distribute resources more fairly. Second, we argue that, where there are known inequalities in suicide risk and related outcomes between groups, not prioritizing according to risk threatens to indirectly discriminate against higher-risk groups. Particularly salient is the case of older populations who have much higher risks of suicide after self-harm than younger populations, and who already experience inequalities in access to mental healthcare. We conclude by making some recommendations for future research. In this Perspective, Hart et al. challenge recent NICE guidelines on suicide assessment, arguing that risk tools can promote health justice by prioritizing the worst-off and preventing indirect discrimination—especially for older adults.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 5","pages":"707-714"},"PeriodicalIF":8.7,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147924306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early attention deficit hyperactivity disorder prediction from longitudinal electronic health records","authors":"Elliot D. Hill, De Rong Loh, Naomi O. Davis, Benjamin A. Goldstein, Geraldine Dawson, Matthew Engelhard","doi":"10.1038/s44220-026-00628-2","DOIUrl":"10.1038/s44220-026-00628-2","url":null,"abstract":"Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition that can negatively impact long-term outcomes for individuals. Early diagnosis is critical, yet demographic and clinical disparities can delay detection. Using electronic health records (EHRs) from a cohort of over 720,000 patients, we pretrained an EHR foundation model. We then fine tuned it to predict the likelihood of ADHD diagnosis and timing from birth until age 9 years in a pediatric cohort of over 140,000 patients. By age 5 years, the model achieved a time-dependent area under the receiver operating characteristic curve of 0.92 at a 4-year time horizon. Overall, the model maintained its performance across patients with differing demographics, including sex, race, ethnicity and insurance status. Our feature importance analysis found that ADHD was strongly associated with developmental, behavioral and psychiatric conditions. Our results suggest that EHR-based predictive models could help providers reliably identify children with ADHD in a timely manner. Using electronic health records from over 140,000 children, the authors developed a neural network model that predicts attention deficit hyperactivity disorder diagnosis years before a typical clinical identification.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 5","pages":"806-815"},"PeriodicalIF":8.7,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147924315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Racism-related developmental origins of mental health: a conceptual model and scoping review","authors":"Sabrina R. Liu, Natasha G. Lindert, Megan Y. Maxwell, Laura M. Glynn","doi":"10.1038/s44220-026-00611-x","DOIUrl":"10.1038/s44220-026-00611-x","url":null,"abstract":"Mental health disparities faced by racially and ethnically minoritized populations in the USA are persistent and widespread. This analysis presents a conceptual model and scoping review of an overlooked pathway to racial/ethnic mental health disparities: racism-related developmental origins of mental health. The model links historical and structural racism to an increased likelihood of exposure to disproportionate and unique stressors among racially and ethnically minoritized populations, which in turn confers heightened vulnerability to mental health disorders in the next generation. Here, through a scoping review, we describe the state of the evidence supporting this model. Our results show how preconception, prenatal and early-life experiences—including exposure to trauma and stressful life events, socioeconomic disadvantage, environmental toxins, sociocultural factors (such as discrimination and acculturative stress), and aspects of maternal mental, behavioral and physical health—are linked to an increased risk of poor mental health. Areas where more research is needed are discussed, including further investigation of sociocultural stressors and potential protective factors. Liu et al. present a scoping review and propose a model linking historical and structural racism to early-life stress exposures that heighten intergenerational mental health risk, outlining supporting evidence and gaps in research on sociocultural stressors and resilience.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 5","pages":"837-850"},"PeriodicalIF":8.7,"publicationDate":"2026-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147924340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative effectiveness of treatment strategies for bipolar disorder during and after lithium treatment","authors":"Johannes Lieslehto, Jari Tiihonen, Bergný Ármannsdóttir, Cagatay Ermis, Markku Lähteenvuo, Ellenor Mittendorfer-Rutz, Antti Tanskanen, Heidi Taipale","doi":"10.1038/s44220-026-00645-1","DOIUrl":"10.1038/s44220-026-00645-1","url":null,"abstract":"Lithium is the gold-standard maintenance treatment for bipolar disorder, yet many patients experience inadequate response or intolerance. Using harmonized nationwide cohorts from Sweden (n = 105,495) and Finland (n = 60,045) with an average of 9 years of follow-up, we assessed the comparative effectiveness of mood stabilizers, antipsychotics and their combinations in preventing psychiatric hospitalization. Within-individual analyses revealed that, compared with lithium monotherapy, clozapine plus aripiprazole (adjusted hazard ratio = 0.42; 95% confidence interval, 0.22–0.80), clozapine alone (0.61; 0.49–0.75) and long-acting injectable antipsychotics + lithium (0.70; 0.59–0.84) were associated with lower relapse risk. Among 20,645 lithium discontinuers, long-acting injectable antipsychotics + valproate (0.41; 0.19–0.92), quetiapine + lamotrigine (0.64; 0.51–0.79), olanzapine + valproate (0.65; 0.50–0.84) and risperidone + valproate (0.63; 0.42–0.93) were linked to reduced hospitalization risk. These findings highlight specific regimens that provide potential options for patients in whom lithium is ineffective or discontinued. This longitudinal study using data from two large nationwide cohorts in Sweden and Finland examined the comparative effectiveness of mood stabilizers, antipsychotics and their combinations in reducing psychiatric hospitalization risk for people with bipolar disorder. The authors found that certain drug combinations, particularly clozapine-based regimens and long-acting injectable antipsychotics with lithium, were linked to lower relapse risk than lithium alone.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 6","pages":"962-970"},"PeriodicalIF":8.7,"publicationDate":"2026-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s44220-026-00645-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148215290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using computational eye-tracking to understand individual differences in psychiatric disorders","authors":"Qianying Wu, Na Yeon Kim, Ralph Adolphs","doi":"10.1038/s44220-026-00638-0","DOIUrl":"10.1038/s44220-026-00638-0","url":null,"abstract":"The human eyes are constantly moving, and gaze allocation reveals the cognitive processes underlying attention, memory, and decision-making. Although traditional laboratory-based eye-tracking protocols generate findings based on well-controlled stimuli, modern methods now allow data collection at a larger scale with more naturalistic stimuli. Powerful computational tools also enable new analyses of high-dimensional data, incorporating stimulus feature annotation, spatiotemporal analysis, and machine learning. These advances provide new insights into individual differences in both health and disease. Here we discuss four key approaches to modeling eye movement data: saliency-based attention phenotyping, data-driven gaze-pattern identification, supervised machine-learning classification, and unsupervised subgroup clustering. We highlight their advantages in psychiatry research, as they inform a better understanding of visual attention, provide more fine-grained characterization of individual differences, and make more powerful clinical predictions. Finally, we address key methodological considerations in applying these methods and discuss directions for future research. In this Perspective, the authors outline four model-based approaches for analyzing eye-tracking data to investigate individual differences and psychiatric disorders.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 5","pages":"715-726"},"PeriodicalIF":8.7,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147924307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing functional connectivity scanning conditions for predicting autistic traits","authors":"Corey Horien, Francesca Mandino, Abigail S. Greene, Xilin Shen, Kelly Powell, Angelina Vernetti, David O’Connor, Brendan D. Adkinson, Link Tejavibulya, James C. McPartland, Fred R. Volkmar, Marvin Chun, Katarzyna Chawarska, Evelyn M. R. Lake, Monica D. Rosenberg, Theodore Satterthwaite, Dustin Scheinost, Emily S. Finn, R. Todd Constable","doi":"10.1038/s44220-026-00623-7","DOIUrl":"10.1038/s44220-026-00623-7","url":null,"abstract":"Autism is a heterogeneous condition, and functional magnetic resonance imaging-based studies have advanced understanding of neurobiological correlates of autistic features. Little work has focused on the optimal brain states to reveal brain–phenotype relationships. Here, using connectome-based predictive modeling, we interrogated four datasets to determine scanning conditions that boost prediction of clinically relevant phenotypes and assess generalizability. In dataset one, a sample of youth with autism and neurotypical participants (n = 63), we found that a sustained attention task resulted in high prediction performance of autistic traits compared with a free-viewing social attention task and a resting-state condition. In dataset two (n = 25), we observed the predictive network model of autistic traits generated from the sustained attention task generalized to predict measures of attention in neurotypical adults. In datasets three and four, we determined the same predictive network model further generalized to predict measures of social responsiveness in the Autism Brain Imaging Data Exchange (n = 229) and the Healthy Brain Network (n = 643). Our data suggest an in-scanner sustained attention challenge can help delineate robust markers of autistic traits. This research investigates optimal brain states for predicting autistic traits using connectome-based predictive modeling across four datasets. Findings indicate that a sustained attention task enhances prediction accuracy, generalizing effectively to neurotypical adults and various measures of social responsiveness.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 5","pages":"792-805"},"PeriodicalIF":8.7,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s44220-026-00623-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147924300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolving OCD treatment: breaking the loop","authors":"","doi":"10.1038/s44220-026-00643-3","DOIUrl":"10.1038/s44220-026-00643-3","url":null,"abstract":"Despite obsessive–compulsive disorder (OCD) being recognized as a global diagnosis with similar rates across countries, there is still a severe lack of trained specialists and high levels of underdiagnosis. This ‘treatment gap,’ together with high treatment resistance in OCD, poses a major challenge to healthcare systems and patients. To overcome barriers to access, there is an urgent need to expand the workforce via specialized training and to provide personalized treatment via digital tools, home-based scalable interventions and alternative treatment modalities.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 4","pages":"469-470"},"PeriodicalIF":8.7,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s44220-026-00643-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147666377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}