Nature mental health最新文献

{"title":"Functional brain network dynamics mediate the relationship between female reproductive aging and interpersonal adversity","authors":"Raluca Petrican, Sidhant Chopra, Ashlea Segal, Nick Fallon, Alex Fornito","doi":"10.1038/s44220-024-00352-9","DOIUrl":"10.1038/s44220-024-00352-9","url":null,"abstract":"Premature reproductive aging is linked to heightened stress sensitivity and psychological maladjustment across the life course. However, the brain dynamics underlying this relationship are poorly understood. Here, to address this issue, we analyzed multimodal data from female participants in the Adolescent Brain and Cognitive Development (longitudinal, N = 441; aged 9–12 years) and Human Connectome-Aging (cross-sectional, N = 130; aged 36–60 years) studies. Age-specific intrinsic functional brain network dynamics mediated the link between reproductive aging and perceptions of greater interpersonal adversity. The adolescent profile overlapped areas of greater glutamatergic and dopaminergic receptor density, and the middle-aged profile was concentrated in visual, attentional and default mode networks. The two profiles showed opposite relationships with patterns of functional neural network variability and cortical atrophy observed in psychosis versus major depressive disorder. Our findings underscore the divergent patterns of brain aging linked to reproductive maturation versus senescence, which may explain developmentally specific vulnerabilities to distinct disorders. Age-specific intrinsic functional brain network dynamics mediates the link between female reproductive aging and perceptions of interpersonal adversity in adolescence and middle adulthood.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 1","pages":"104-123"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44220-024-00352-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941321","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":"Predicting suicide attempts among US Army soldiers using information available at the time of periodic health assessments","authors":"James A. Naifeh, Emily R. Edwards, Kate H. Bentley, Sarah M. Gildea, Chris J. Kennedy, Andrew J. King, Evan M. Kleiman, Alex Luedtke, Thomas H. Nassif, Matthew K. Nock, Nancy A. Sampson, Nur Hani Zainal, Murray B. Stein, Vincent F. Capaldi, Robert J. Ursano, Ronald C. Kessler","doi":"10.1038/s44220-024-00360-9","DOIUrl":"10.1038/s44220-024-00360-9","url":null,"abstract":"The value of population screening for suicide risk remains unclear. The US Army’s annual medical examination, the Periodic Health Assessment (PHA), screens for suicidality and other mental and physical health problems. Here in our 2014–2019 cohort study we used PHA and Army administrative data (n = 1,042,796 PHAs from 452,473 soldiers) to develop a model to predict 6-month nonfatal and fatal suicide attempts (SAs). The model was designed to establish eligibility for a planned high-risk SA prevention intervention. The PHA suicide risk screening questions had limited value, as 95% of SAs occurred among soldiers who denied suicidality. However, a simple least absolute shrinkage and selection operator (LASSO) penalized regression model that included a wide range of administrative predictors had good test sample discrimination (0.794 (standard error 0.009) area under the receiver operating characteristic curve) and calibration (integrated calibration index 0.0001). The 25% of soldiers at highest predicted risk accounted for 69.5% of 6-month SAs, supporting use of the model to target preventive interventions. A machine learning model incorporating a wide range of administrative medical and demographic data from the US Army outperformed suicide risk screening questions in predicting suicide attempts over the 6 month period following soldiers’ annual medical examinations.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 2","pages":"242-252"},"PeriodicalIF":0.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143381081","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":"Compulsivity is linked to suboptimal choice variability but unaltered reinforcement learning under uncertainty","authors":"Junseok K. Lee, Marion Rouault, Valentin Wyart","doi":"10.1038/s44220-024-00364-5","DOIUrl":"10.1038/s44220-024-00364-5","url":null,"abstract":"Compulsivity has been associated with variable behavior under uncertainty. However, previous work has not distinguished between two main sources of behavioral variability: the stochastic selection of choice options that do not maximize expected reward (choice variability) and random noise in the reinforcement learning process that updates option values from choice outcomes (learning variability). Here we study the relation between dimensional compulsivity and behavioral variability using a computational model that dissociates its two sources. Across two independent datasets (137 and 123 participants), we found that compulsivity is associated with more frequent switches between options, triggered by increased choice variability, but no change in learning variability. This effect of compulsivity on the ‘trait’ component of choice variability is observed even in conditions where this source of behavioral variability yields no cognitive benefits. These findings indicate that compulsive individuals make variable and suboptimal choices under uncertainty, but do not hold degraded representations of option values. Lee et al. find that compulsivity is associated with choice variability under uncertainty, resulting in frequent switching between choice options but no alteration in the ability to learn from the positive or negative outcomes of these choices.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 2","pages":"229-241"},"PeriodicalIF":0.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143381088","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 procedural overview of why, when and how to use machine learning for psychiatry","authors":"Christopher Lucasius, Mai Ali, Tanmay Patel, Deepa Kundur, Peter Szatmari, John Strauss, Marco Battaglia","doi":"10.1038/s44220-024-00367-2","DOIUrl":"10.1038/s44220-024-00367-2","url":null,"abstract":"Machine learning (ML) is becoming a tool of choice to analyze high-dimensional datasets pertaining to mental health. Given the rapid integration of ML into research and clinical settings, this article provides a functional overview of a common ML pipeline used for the assessment and prediction of psychiatric disorders. Developing such a construct entails building a data infrastructure, collecting and preprocessing data, training and testing models and interpreting their results. Practical considerations pertaining to data management and preprocessing are first presented. We then describe considerations and best practices for model selection on the basis of the psychiatric disorder and the data modalities available for analysis. A critical analysis of existing works utilizing ML methods for psychiatric disorder assessment, prediction and causal associations is also provided. Last, future ML trends in psychiatry are highlighted. To reinforce learning, the Supplementary Note links to an interactive Jupyter Notebook that offers practical examples and hands-on interaction with a sample dataset. This Review provides a comprehensive overview of the principles, processes and procedures in the application of machine learning for psychiatry and mental health research.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 1","pages":"8-18"},"PeriodicalIF":0.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941305","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":"Physical and mental health after traumatic brain injury","authors":"","doi":"10.1038/s44220-024-00362-7","DOIUrl":"10.1038/s44220-024-00362-7","url":null,"abstract":"The role and effects of traumatic brain injury (TBI) on the development of chronic long-term health conditions are unclear. This umbrella review of existing systematic reviews and meta-analyses synthesizes the effects of TBI on risk of physical and mental health disorders and discusses implications for research and clinical management.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 1","pages":"6-7"},"PeriodicalIF":0.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941315","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":"An umbrella review of health outcomes following traumatic brain injury","authors":"Maya G. T. Ogonah, Stella Botchway, Rongqin Yu, Peter W. Schofield, Seena Fazel","doi":"10.1038/s44220-024-00356-5","DOIUrl":"10.1038/s44220-024-00356-5","url":null,"abstract":"While numerous reviews have assessed the association between traumatic brain injury (TBI) and various mental and physical health outcomes, a comprehensive evaluation of the scope, validity, and quality of evidence is lacking. Here we present an umbrella review of a wide range of health outcomes following TBI and outline outcome risks across subpopulations. On 17 May 2023, we searched Embase, Medline, Global Health, PsycINFO, and Cochrane Database of Systematic Reviews for systematic reviews and meta-analyses. We compared risk ratios across different outcomes for risks compared with people without TBI and examined study quality, including heterogeneity, publication bias, and prediction intervals. The study was registered with PROSPERO ( CRD42023432255 ). We identified 24 systematic reviews and meta-analyses covering 24 health outcomes in 31,397,958 participants. The current evidence base indicates an increased risk of multiple mental and physical health outcomes, including psychotic disorders, attention-deficit/hyperactivity disorder, suicide, and depression. Three outcomes—dementia, violence perpetration, and amyotrophic lateral sclerosis—had meta-analytical evidence of at least moderate quality, which suggest targets for more personalized assessment. Health-care services should review how to prevent adverse long-term outcomes in TBI. This umbrella review synthesizes a large body of evidence on adverse outcomes in over 31 million people with traumatic brain injury and identifies links with dementia, perpetration of violence, and amyotrophic lateral sclerosis.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 1","pages":"83-91"},"PeriodicalIF":0.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44220-024-00356-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941314","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":"Psychological profiles associated with mental, cognitive and brain health in middle-aged and older adults","authors":"David Bartrés-Faz, Harriet Demnitz-King, María Cabello-Toscano, Lídia Vaqué-Alcázar, Rob Saunders, Edelweiss Touron, Gabriele Cattaneo, Julie Gonneaud, Olga Klimecki, Núria Bargalló, Javier Sánchez-Solana, José M. Tormos, Gäel Chételat, Álvaro Pascual-Leone, Natalie L. Marchant, the Medit-Ageing Research Group","doi":"10.1038/s44220-024-00361-8","DOIUrl":"10.1038/s44220-024-00361-8","url":null,"abstract":"Psychological characteristics are associated with varying dementia risk and protective factors. To determine whether these characteristics aggregate into psychological profiles and whether these profiles differentially relate to aging health, we conducted a cross-sectional investigation in two independent middle-aged (51.4 ± 7.0 years (mean ± s.d.); N = 750) and older adult (71.1 ± 5.9 years; N = 282) cohorts, supplemented by longitudinal analyses in the former. Using a person-centered approach, three profiles emerged in both cohorts: those with low protective characteristics (profile 1), high risk characteristics (profile 2) and well-balanced characteristics (profile 3). Profile 1 showed the worst objective cognition in older age and middle age (at follow-up), and most rapid cortical thinning. Profile 2 exhibited the worst mental health symptomology and lowest sleep quality in both older age and middle age. We identified profile-dependent divergent patterns of associations that may suggest two distinct paths for mental, cognitive and brain health, emphasizing the need for comprehensive psychological assessments in dementia prevention research to identify groups for more personalized behavior-change strategies. This cross-sectional study in two independent middle-aged and aged cohorts investigates whether psychological characteristics associated with varying dementia risk aggregate into psychological profiles and relate to aging brain health.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 1","pages":"92-103"},"PeriodicalIF":0.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44220-024-00361-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941258","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":"Machine learning for identifying caregiving adversities associated with greatest risk for mental health problems in children","authors":"Anna Vannucci, Andrea Fields, Charlotte Heleniak, Paul A. Bloom, Chelsea Harmon, Aki Nikolaidis, Ian J. Douglas, Lisa Gibson, Nicolas L. Camacho, Tricia Choy, Syntia S. Hadis, Michelle VanTieghem, Mary Dozier, Michael P. Milham, Nim Tottenham","doi":"10.1038/s44220-024-00355-6","DOIUrl":"10.1038/s44220-024-00355-6","url":null,"abstract":"Developmental and experiential heterogeneity associated with caregiving-related early adversities (crEAs) poses a major challenge to identifying replicable, generalizable findings. Here conditional random forests evaluated the importance of unique crEA experiences for estimating risks to mental health in 306 children, 6–12 years of age, with heterogeneous crEA experiences (different forms of caregiver-involved abuse and/or neglect or permanent/substantial parent–child separation). The better that crEAs improved the accuracy of symptom estimates in held-out, never-before-seen children, the more important and generalizable they were considered. Here we show that earlier timing and longer duration of crEAs was especially important for elevated general psychopathology (p-factor scores). The mere presence (versus absence) of crEAs was more valuable for estimating symptom risk than were specific adversities in a broad sample. Specific adversities became more important when only looking within the crEA-exposed subsample, with adversities of an interpersonal-affective nature being the most likely to increase transdiagnostic symptom risk. Concurrent consistent caregiving also had high importance, motivating consideration of later-occurring environmental experiences in future studies of early adversity. Using a machine learning approach to improve risk estimates across heterogeneous samples, the authors demonstrate patterns of increased transdiagnostic symptom risk in children who have experienced caregiving-related early adversities.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 1","pages":"71-82"},"PeriodicalIF":0.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941329","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 multimodal neural signature of face processing in autism within the fusiform gyrus","authors":"Dorothea L. Floris, Alberto Llera, Mariam Zabihi, Carolin Moessnang, Emily J. H. Jones, Luke Mason, Rianne Haartsen, Nathalie E. Holz, Ting Mei, Camille Elleaume, Bruno Hebling Vieira, Charlotte M. Pretzsch, Natalie J. Forde, Sarah Baumeister, Flavio Dell’Acqua, Sarah Durston, Tobias Banaschewski, Christine Ecker, Rosemary J. Holt, Simon Baron-Cohen, Thomas Bourgeron, Tony Charman, Eva Loth, Declan G. M. Murphy, Jan K. Buitelaar, Christian F. Beckmann, the EU–AIMS LEAP group, Nicolas Langer","doi":"10.1038/s44220-024-00349-4","DOIUrl":"10.1038/s44220-024-00349-4","url":null,"abstract":"Atypical face processing is commonly reported in autism. Its neural correlates have been explored extensively across single neuroimaging modalities within key regions of the face processing network, such as the fusiform gyrus (FFG). Nonetheless, it is poorly understood how variation in brain anatomy and function jointly impacts face processing and social functioning. Here we leveraged a large multimodal sample to study the cross-modal signature of face processing within the FFG across four imaging modalities (structural magnetic resonance imaging (MRI), resting-state functional magnetic resonance imaging, task-functional magnetic resonance imaging and electroencephalography) in 204 autistic and nonautistic individuals aged 7–30 years (case–control design). We combined two methodological innovations—normative modeling and linked independent component analysis—to integrate individual-level deviations across modalities and assessed how multimodal components differentiated groups and informed social functioning in autism. Groups differed significantly in a multimodal component driven by bilateral resting-state functional MRI, bilateral structure, right task-functional MRI and left electroencephalography loadings in face-selective and retinotopic FFG. Multimodal components outperformed unimodal ones in differentiating groups. In autistic individuals, multimodal components were associated with cognitive and clinical features linked to social, but not nonsocial, functioning. These findings underscore the importance of elucidating multimodal neural associations of social functioning in autism, offering potential for the identification of mechanistic and prognostic biomarkers. The authors leveraged a large multimodal sample and combined normative modeling and linked independent component analysis to study a cross-modal signature of face processing within the fusiform gyrus in autism.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 1","pages":"31-45"},"PeriodicalIF":0.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44220-024-00349-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941325","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":"Network temperature as a metric of stability in depression symptoms across adolescence.","authors":"Poppy Z Grimes, Aja L Murray, Keith Smith, Andrea G Allegrini, Giulia G Piazza, Henrik Larsson, Sacha Epskamp, Heather C Whalley, Alex S F Kwong","doi":"10.1038/s44220-025-00415-5","DOIUrl":"10.1038/s44220-025-00415-5","url":null,"abstract":"<p><p>Depression is characterized by diverse symptom combinations that can be represented as dynamic networks. While previous research has focused on central symptoms for targeted interventions, less attention has been given to whole-network properties. Here we show that 'network temperature', a novel measure of psychological network stability, captures symptom alignment across adolescence-a critical period for depression onset. Network temperature reflects system stability, with higher values indicating less symptom alignment and greater variability. In three large longitudinal adolescent cohorts (total <i>N</i> = 35,901), we found that network temperature decreases across adolescence, with the steepest decline during early adolescence, particularly in males. This suggests that depression symptom networks stabilize throughout development via increased symptom alignment, potentially explaining why adolescence is a crucial period for depression onset. These findings highlight early adolescence as a key intervention window and underscore the importance of sex-specific and personalized interventions.</p>","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 5","pages":"548-557"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12066352/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144044975","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}