Pub Date : 2025-01-10DOI: 10.1038/s44220-024-00383-2
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.
{"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}
Pub Date : 2025-01-10DOI: 10.1038/s44220-024-00354-7
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
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.
{"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}
Pub Date : 2025-01-10DOI: 10.1038/s44220-024-00373-4
S. Parker Singleton, Brooke L. Sevchik, Simon N. Vandekar, Eric C. Strain, Sandeep M. Nayak, Robert H. Dworkin, J. Cobb Scott, Theodore D. Satterthwaite
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.
{"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}
Pub Date : 2025-01-08DOI: 10.1038/s44220-024-00359-2
Aaron Baird, Yusen Xia
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.
{"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}
Pub Date : 2025-01-07DOI: 10.1038/s44220-024-00365-4
Wivine Blekic, Fabien D’Hondt, Arieh Y. Shalev, Katharina Schultebraucks
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.
近年来，机器学习（ML）技术在创伤后应激障碍（PTSD）预测研究中的应用越来越多。然而，对临床相关性和ML发现的普遍性的担忧阻碍了临床医生和研究人员的实施。在这篇系统综述中，我们检查了(1)使用ML方法确定的创伤前、创伤周围和创伤后风险因素与对该障碍的理论认识相吻合的程度；(2)是否通过ML技术获得了新的见解；(3) ML研究结果与以往的研究相结合，是否能够建立一个包括预测因子类别及其理论相关性的创伤后应激障碍风险综合模型。我们回顾了PubMed、Web of Science和Scopus中关于PTSD危险因素的ML研究。如果研究明确了何时收集预测因子和PTSD症状与创伤性事件的时间关系，则纳入研究。共纳入30项研究，12908名参与者（平均年龄36.5岁）。在从所有研究中提取出15个最重要的预测因子后，我们将它们分为创伤前、创伤中和创伤后暴露预测因子，并检查它们与已建立的创伤后应激障碍理论模型的关联。许多研究显示存在偏倚风险，使用预测模型偏倚风险评估工具（PROBAST）进行评估。然而，我们发现不同研究中确定的预测因素存在重叠，数据驱动的结果和理论驱动的研究之间存在一致性，以及通过ML确定的未被探索的预测因素。我们提出了一个整合数据驱动和理论驱动结果的PTSD风险综合模型，并讨论了未来的发展方向。我们强调关于如何应用和报告ML方法用于心理健康的标准的重要性。本系统综述综合了来自30项研究的证据，这些研究使用机器学习方法来识别创伤后应激障碍风险的预测因素。作者发现了研究中未充分开发的预测因子和预测因子的重叠，并发现了数据驱动结果和基于理论的模型之间的一致性。
{"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}
Pub Date : 2025-01-07DOI: 10.1038/s44220-024-00352-9
Raluca Petrican, Sidhant Chopra, Ashlea Segal, Nick Fallon, Alex Fornito
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.
{"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}
Pub Date : 2025-01-06DOI: 10.1038/s44220-024-00367-2
Christopher Lucasius, Mai Ali, Tanmay Patel, Deepa Kundur, Peter Szatmari, John Strauss, Marco Battaglia
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.
{"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}
Pub Date : 2025-01-03DOI: 10.1038/s44220-024-00362-7
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.
{"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}
Pub Date : 2025-01-03DOI: 10.1038/s44220-024-00356-5
Maya G. T. Ogonah, Stella Botchway, Rongqin Yu, Peter W. Schofield, Seena Fazel
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.
{"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}
Pub Date : 2025-01-02DOI: 10.1038/s44220-024-00361-8
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
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.
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