Nature mental health最新文献

{"title":"Impaired hippocampal circuitry and memory dysfunction in schizophrenia","authors":"Asieh Zadbood, Yingying Tang, Wenjun Su, Hao Hu, Gillian Capichioni, Shuwen Yang, Junjie Wang, Vishnu P. Murty, Camille Gasser, Oded Bein, Li Hui, Qiufang Jia, Tianhong Zhang, Yawen Hong, Michael F. Green, Jijun Wang, Donald C. Goff, Lila Davachi","doi":"10.1038/s44220-024-00376-1","DOIUrl":"10.1038/s44220-024-00376-1","url":null,"abstract":"Pattern separation and pattern completion are opposing yet complementary components of mnemonic processing that rely heavily on the hippocampus. It has been shown that processing within the dentate gyrus (DG) subfield promotes pattern separation while operations within the CA3 subfield are important for pattern completion. Schizophrenia has been associated with anatomical and functional hippocampal abnormalities, including within the DG and CA3. We hypothesized that an impairment in hippocampal circuitry in individuals with first-episode schizophrenia leads to deficits in pattern separation (mnemonic discrimination) and pattern completion (recognition memory), that these deficits contribute to delusions and that antipsychotic treatment improves circuit functioning. We measured behavioral and neural responses during the identification of new, repeated and similar stimuli using high-resolution fMRI in 45 medication-free or minimally treated individuals with first-episode schizophrenia (FES) and 49 matched healthy controls (HC). We found recognition memory and pattern separation deficits in FES and a negative association between memory performance and the severity of delusions. Neural analyses revealed deficits in BOLD responses in the hippocampus during mnemonic discrimination in FES compared with HC. Importantly, by investigating the association between trial-level neural activity and behavior before and after treatment, we found that antipsychotics normalized DG activity during pattern separation. Last, trial-level cortical responses during mnemonic discrimination predicted performance in FES at baseline, suggesting a compensatory role. This case-control study provides important insight into the impact of schizophrenia and antipsychotic treatment on memory systems and uncovers systems-level contributions to pattern separation and pattern completion. Dysfunction in the hippocampal circuitry in individuals with first-episode schizophrenia and delusions is linked to deficits in behavioral pattern separation and recognition memory.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 3","pages":"332-345"},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143595743","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":"What is MORE? Enhancing recovery with mindfulness","authors":"Rebecca Cooney","doi":"10.1038/s44220-024-00374-3","DOIUrl":"10.1038/s44220-024-00374-3","url":null,"abstract":"We speak to Eric Garland, an endowed professor in Health Sciences at the T. Denny Sanford Institute for Empathy and Compassion, professor in the Department of Psychiatry at the University of California San Diego, director of UCSD ONEMIND (optimized neuroscience-enhanced mindfulness intervention design) and the developer of mindfulness-oriented recovery enhancement (MORE), an evidence-based mind–body therapy for addiction, emotion dysregulation and chronic pain.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 2","pages":"160-161"},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143381111","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":"Transforming women’s mental health services through co-research with survivors of intimate partner violence","authors":"Elnaz Moghimi, N. Zoe Hilton","doi":"10.1038/s44220-024-00379-y","DOIUrl":"10.1038/s44220-024-00379-y","url":null,"abstract":"Engaging survivors of intimate partner violence in research brings valuable perspectives that can drive meaningful improvements in mental health services.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 2","pages":"164-166"},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143381107","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":"Machine learning in mental health — getting better all the time","authors":"","doi":"10.1038/s44220-024-00383-2","DOIUrl":"10.1038/s44220-024-00383-2","url":null,"abstract":"Machine learning for mental health and psychiatry research has emerged as a powerful set of tools for harnessing increased computing power to analyze relationships in massive and complex datasets. These findings are ultimately poised to help inform research directions, the diagnosis and prediction of psychopathology, and clinical recommendations for treating mental health disorders.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44220-024-00383-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941344","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":"Relationships of eating behaviors with psychopathology, brain maturation and genetic risk for obesity in an adolescent cohort study","authors":"Xinyang Yu, Zuo Zhang, Moritz Herle, Tobias Banaschewski, Gareth J. Barker, Arun L. W. Bokde, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Eric Artiges, Frauke Nees, Dimitri Papadopoulos Orfanos, Hervé Lemaître, Tomáš Paus, Luise Poustka, Sarah Hohmann, Nathalie Holz, Christian Bäuchl, Michael N. Smolka, Nilakshi Vaidya, Henrik Walter, Robert Whelan, Ulrike Schmidt, Gunter Schumann, Sylvane Desrivières, on behalf of the IMAGEN consortium","doi":"10.1038/s44220-024-00354-7","DOIUrl":"10.1038/s44220-024-00354-7","url":null,"abstract":"Unhealthy eating, a risk factor for eating disorders (EDs) and obesity, often coexists with emotional and behavioral problems; however, the underlying neurobiological mechanisms are poorly understood. Analyzing data from the longitudinal IMAGEN adolescent cohort, we investigated associations between eating behaviors, genetic predispositions for high body mass index (BMI) using polygenic scores (PGSs), and trajectories (ages 14–23 years) of ED-related psychopathology and brain maturation. Clustering analyses at age 23 years (N = 996) identified 3 eating groups: restrictive, emotional/uncontrolled and healthy eaters. BMI PGS, trajectories of ED symptoms, internalizing and externalizing problems, and brain maturation distinguished these groups. Decreasing volumes and thickness in several brain regions were less pronounced in restrictive and emotional/uncontrolled eaters. Smaller cerebellar volume reductions uniquely mediated the effects of BMI PGS on restrictive eating, whereas smaller volumetric reductions across multiple brain regions mediated the relationship between elevated externalizing problems and emotional/uncontrolled eating, independently of BMI. These findings shed light on distinct contributions of genetic risk, protracted brain maturation and behaviors in ED symptomatology. This study identifies distinct eating behavior profiles and links them to eating disorder symptoms, genetic predispositions for high body mass index and brain maturation during adolescence.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 1","pages":"58-70"},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44220-024-00354-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941331","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":"An initiative for living evidence synthesis in clinical psychedelic research","authors":"S. Parker Singleton, Brooke L. Sevchik, Simon N. Vandekar, Eric C. Strain, Sandeep M. Nayak, Robert H. Dworkin, J. Cobb Scott, Theodore D. Satterthwaite","doi":"10.1038/s44220-024-00373-4","DOIUrl":"10.1038/s44220-024-00373-4","url":null,"abstract":"Renewed interest in psychedelics as treatments for mental disorders has recently emerged, but substantial challenges remain in obtaining evidence from available data to inform clinical decision-making. This Comment explores the current landscape of clinical psychedelic research, highlighting the need for a systematic approach to evidence synthesis.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 1","pages":"3-5"},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941323","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":"Dynamic effects of psychiatric vulnerability, loneliness and isolation on distress during the first year of the COVID-19 pandemic","authors":"Lauren Y. Atlas, Cristan Farmer, Jacob S. Shaw, Alison Gibbons, Emily P. Guinee, Juan Antonio Lossio-Ventura, Elizabeth D. Ballard, Monique Ernst, Shruti Japee, Francisco Pereira, Joyce Y. Chung","doi":"10.1038/s44220-024-00371-6","DOIUrl":"10.1038/s44220-024-00371-6","url":null,"abstract":"The COVID-19 pandemic’s impact on mental health is challenging to quantify because pre-existing risk, disease burden and public policy varied across individuals, time and regions. Longitudinal, within-person analyses can determine whether pandemic-related changes in social isolation impacted mental health. We analyzed time-varying associations between psychiatric vulnerability, loneliness, psychological distress and social distancing in a US-based study during the first year of the pandemic. We surveyed 3,655 participants about psychological health and COVID-19-related circumstances every 2 weeks for 6 months. We combined self-reports with regional social distancing estimates and a classifier that predicted probability of psychiatric diagnosis at enrollment. Loneliness and psychiatric vulnerability both impacted psychological distress. Loneliness and distress were also linked to social isolation and stress associated with distancing, and psychiatric vulnerability shaped how regional distancing affected loneliness across time. Public health policies should address loneliness when encouraging social distancing, particularly in those at risk for psychiatric conditions. In this new study, the authors analyzed data from a longitudinal US-based survey during the first year of the pandemic, focusing on social distancing, psychiatric vulnerability and loneliness in adults.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 2","pages":"199-211"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44220-024-00371-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143381112","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":"Applying analytics to sociodemographic disparities in mental health","authors":"Aaron Baird, Yusen Xia","doi":"10.1038/s44220-024-00359-2","DOIUrl":"10.1038/s44220-024-00359-2","url":null,"abstract":"Mental health services and treatment are unfortunately subject to sociodemographic disparities. To address this issue, recent studies have begun to apply analytics methods—that is, artificial intelligence in general, machine learning and deep learning in particular—toward the identification of such disparities and, where possible, mitigation of bias within models used in mental health research. However, it is difficult to understand the scope and status of such research as it is spread across many journals and contexts of study. Here we conducted an analysis of articles in this area. We identified 40 articles from 2017 to July 2023 related to the use of analytics in the context of sociodemographic disparities in mental health. We find that prediction, clustering/grouping and fairness models were most often applied in the articles analyzed. A number of mental health-related sociodemographic disparities were identified in these articles, for example, associated with race/ethnicity, gender, age and socioeconomic status, but such findings were typically context dependent. Thus, we also provide suggestions in this Analysis on how to both enhance generalizability and embrace context-dependent findings, especially via the identification of heterogeneous treatment effects, model bias mitigation, use of generative artificial intelligence, incorporation of data from devices, and translation of findings into practice. In this study, the authors analyzed articles examining the use of artificial intelligence, machine learning and deep learning analytics for identifying sociodemographic disparities, such as in race/ethnicity and age, to make recommendations for improving models and generalizability.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 1","pages":"124-138"},"PeriodicalIF":0.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941299","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":"Identification of risk variants and cross-disorder pleiotropy through multi-ancestry genome-wide analysis of alcohol use disorder","authors":"Romain Icick, Alexey Shadrin, Børge Holen, Naz Karadag, Nadine Parker, Kevin S. O’Connell, Oleksandr Frei, Shahram Bahrami, Margrethe Collier Høegh, Trine Vik Lagerberg, Weiqiu Cheng, Tyler M. Seibert, Srdjan Djurovic, Anders M. Dale, Hang Zhou, Howard J. Edenberg, Joel Gelernter, Olav B. Smeland, Guy Hindley, Ole A. Andreassen","doi":"10.1038/s44220-024-00353-8","DOIUrl":"10.1038/s44220-024-00353-8","url":null,"abstract":"Alcohol use disorder (AUD) is highly heritable and burdensome worldwide. Genome-wide association studies can provide new evidence regarding the etiology of AUD. We report a multi-ancestry genome-wide association study focusing on a narrow AUD phenotype, using novel statistical tools in a total sample of 1,041,450 individuals (102,079 cases; European, 75,583; African, 20,689 (mostly African American); Hispanic American, 3,449; East Asian, 2,254; South Asian, 104; descent). Cross-ancestry functional analyses were performed with European and African samples. Thirty-seven genome-wide significant loci (105 variants) were identified, of which seven were novel for AUD and six for other alcohol phenotypes. Loci were mapped to genes, which show altered expression in brain regions relevant for AUD (striatum, hypothalamus and prefrontal cortex) and encode potential drug targets (GABAergic, dopaminergic and serotonergic neurons). African-specific analysis yielded a unique pattern of immune-related gene sets. Polygenic overlap and positive genetic correlations showed extensive shared genetic architecture between AUD and both mental and general medical phenotypes, suggesting that they are not only complications of alcohol use but also share genetic liability with AUD. Leveraging a cross-ancestry approach allowed identification of novel genetic loci for AUD and underscores the value of multi-ancestry genetic studies. These findings advance our understanding of AUD risk and clinically relevant comorbidities. This multi-ancestral meta-analysis of alcohol use disorder in over one million individuals identifies genome-wide significant risk variants from independent genomic loci and shared genetic architecture between alcohol use disorder and other mental and general medical conditions.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 2","pages":"253-265"},"PeriodicalIF":0.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143381086","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":"A systematic review of machine learning findings in PTSD and their relationships with theoretical models","authors":"Wivine Blekic, Fabien D’Hondt, Arieh Y. Shalev, Katharina Schultebraucks","doi":"10.1038/s44220-024-00365-4","DOIUrl":"10.1038/s44220-024-00365-4","url":null,"abstract":"In recent years, the application of machine learning (ML) techniques in research on the prediction of post-traumatic stress disorder (PTSD) has increased. However, concerns regarding the clinical relevance and generalizability of ML findings hamper their implementation by clinicians and researchers. Here in this systematic review we examined (1) the extent to which pre-, peri- and post-traumatic risk factors identified using ML approaches coincide with the theoretical understanding of the disorder; (2) whether new insights were gained through ML techniques; and (3) whether ML findings, combined with previous research, enable an integrative model of PTSD risk encompassing both predictor categories and their theoretical relevance. We reviewed ML studies on PTSD risk factors in PubMed, Web of Science and Scopus. Studies were included if they specified when predictors and PTSD symptoms were collected in temporal relation to the traumatic event. A total of 30 studies with 12,908 participants (mean age 36.5 years) were included. After extracting the 15 most important predictors from all studies, we categorized them into pre-, peri- and post-trauma exposure predictors and examined their associations with established theoretical models of PTSD. Many studies exhibited a risk of bias, assessed using the prediction model risk of bias assessment tool (PROBAST). However, we found overlaps in identified predictors across studies, a concordance between data-driven results and theory-driven research, and underexplored predictors identified through ML. We propose an integrative model of PTSD risk that incorporates both data-driven and theory-driven findings and discuss future directions. We emphasize the importance of standards on how to apply and report ML approaches for mental health. This systematic review synthesizes evidence from 30 studies using machine learning approaches to identify predictors for post-traumatic stress disorder risk. The authors detect underexplored predictors and overlaps in predictors across studies and find an alignment between data-driven results and theory-based models.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 1","pages":"139-158"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941353","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}