Pub Date : 2026-01-09DOI: 10.1038/s44220-025-00584-3
Behavioral health and mental health are distinct but overlapping concepts. Behavioral health is a systems-oriented framework to address complex mental health conditions through integrated, continuous care. Although it holds promise for improving access and outcomes, its potential remains constrained by fragmented delivery systems and social inequities.
{"title":"Bolstering behavioral health","authors":"","doi":"10.1038/s44220-025-00584-3","DOIUrl":"10.1038/s44220-025-00584-3","url":null,"abstract":"Behavioral health and mental health are distinct but overlapping concepts. Behavioral health is a systems-oriented framework to address complex mental health conditions through integrated, continuous care. Although it holds promise for improving access and outcomes, its potential remains constrained by fragmented delivery systems and social inequities.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"1-2"},"PeriodicalIF":8.7,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s44220-025-00584-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931240","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 : 2026-01-09DOI: 10.1038/s44220-025-00562-9
Ella Arensman, Anvar Sadath, Aileen Callanan, Almas Khan, Mallorie Leduc, Grace Cully, Niall McTernan, Katharina Schnitzspahn, Kahar Abdulla, Simge Celik, Pia Hauck, Carolina Pina, Giancarlo Giupponi, Michela Roberti, Andreas Conca, Vargiu Nuhara, Marco Lazzeri, Serena Trentin, Manuela Tosti, Aurora Belfanti, Camilla Ferrara, Victor Perez Sola, Saiko Allende, Azucena Justicia Diaz, András Székely, Diana Ruzsa, Éva Zsák, András Székely Jr, Piotr Toczyski, Chantal Van Audenhove, Evelien Coppens, Giota Fexi, Panagiota Deredini, Nikoletta Konsta, Thanasis Arabatzis, Beky Pasho, Eleni Tsagaraki, Tsvety Naydenova, Albena Drobachka, Peeter Värnik, Agnes Sirg, Merike Sisask, Lenne Lillepuu, Rainer Mere, Ulrich Hegerl
The Global Burden of Disease studies have consistently highlighted the persisting burden of mental disorders worldwide. Public health emergencies such as the COVID-19 pandemic, war and conflict, and climate change have exacerbated many determinants of poor mental health, resulting in an increased prevalence of anxiety and depression worldwide. Despite substantial advancements in intervention and prevention programs, treatment gaps in depression and suicidal behavior persist. Addressing these gaps requires a multi-level approach involving both community and health services. This Perspective addresses the urgent need to strengthen mental health systems globally. The primary purpose of this Perspective is to discuss the four-level community-based approaches of the European Alliance Against Depression program, including evidence in support of its four-level intervention as a sustainable model for community-based mental health care that can be effectively adapted to various contexts, including current and future public health emergencies. In this Perspective, the authors provide an overview of the four-level community-based intervention by the European Alliance Against Depression and highlight the need for improved public mental health care for depression and suicide risk.
{"title":"The European Alliance Against Depression approach: an evidence-based program to reduce depression and suicidal behavior","authors":"Ella Arensman, Anvar Sadath, Aileen Callanan, Almas Khan, Mallorie Leduc, Grace Cully, Niall McTernan, Katharina Schnitzspahn, Kahar Abdulla, Simge Celik, Pia Hauck, Carolina Pina, Giancarlo Giupponi, Michela Roberti, Andreas Conca, Vargiu Nuhara, Marco Lazzeri, Serena Trentin, Manuela Tosti, Aurora Belfanti, Camilla Ferrara, Victor Perez Sola, Saiko Allende, Azucena Justicia Diaz, András Székely, Diana Ruzsa, Éva Zsák, András Székely Jr, Piotr Toczyski, Chantal Van Audenhove, Evelien Coppens, Giota Fexi, Panagiota Deredini, Nikoletta Konsta, Thanasis Arabatzis, Beky Pasho, Eleni Tsagaraki, Tsvety Naydenova, Albena Drobachka, Peeter Värnik, Agnes Sirg, Merike Sisask, Lenne Lillepuu, Rainer Mere, Ulrich Hegerl","doi":"10.1038/s44220-025-00562-9","DOIUrl":"10.1038/s44220-025-00562-9","url":null,"abstract":"The Global Burden of Disease studies have consistently highlighted the persisting burden of mental disorders worldwide. Public health emergencies such as the COVID-19 pandemic, war and conflict, and climate change have exacerbated many determinants of poor mental health, resulting in an increased prevalence of anxiety and depression worldwide. Despite substantial advancements in intervention and prevention programs, treatment gaps in depression and suicidal behavior persist. Addressing these gaps requires a multi-level approach involving both community and health services. This Perspective addresses the urgent need to strengthen mental health systems globally. The primary purpose of this Perspective is to discuss the four-level community-based approaches of the European Alliance Against Depression program, including evidence in support of its four-level intervention as a sustainable model for community-based mental health care that can be effectively adapted to various contexts, including current and future public health emergencies. In this Perspective, the authors provide an overview of the four-level community-based intervention by the European Alliance Against Depression and highlight the need for improved public mental health care for depression and suicide risk.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"42-51"},"PeriodicalIF":8.7,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931256","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 : 2026-01-07DOI: 10.1038/s44220-025-00568-3
Robert Y. Chen, Tiffany A. Greenwood, David L. Braff, Laura C. Lazzeroni, Neal R. Swerdlow, Monica E. Calkins, Robert Freedman, Michael F. Green, Ruben C. Gur, Raquel E. Gur, Keith H. Nuechterlein, Allen D. Radant, Jeremy M. Silverman, William S. Stone, Catherine A. Sugar, Ming T. Tsuang, Bruce I. Turetsky, Gregory A. Light, Debby W. Tsuang
The development of neurocognitive biomarkers for schizophrenia (SCZ) has relied on lengthy test batteries that are infeasible to deploy in clinical settings. Using machine learning, we sought to identify a subset of neurocognitive domains that could distinguish between patients with SCZ and healthy comparison subjects (HCS). Leveraging data from 559 patients with SCZ or schizoaffective disorder and 745 HCS who completed 15 neurocognitive assessments spanning a diverse range of neurocognitive domains, we developed a machine learning model that could accurately separate SCZ from HCS (area under the receiver operating characteristic curve of 0.899), and was replicated in an independent cohort. Recursive feature elimination revealed that just two neurocognitive domains—verbal learning and emotion identification—were sufficient to achieve the same classification accuracy. These findings support a ‘less-is-more’ approach to efficient neurocognitive profiling across the schizophreniform spectrum and highlight what may be the most impaired neurocognitive domains in this debilitating disorder. This study identifies key neurocognitive domains that distinguish patients with schizophrenia from healthy individuals using machine learning. Analyzing data from 1,304 participants, it demonstrates that verbal learning and emotion identification effectively classify conditions, promoting efficient neurocognitive profiling strategies.
{"title":"Machine learning enables efficient neurocognitive profiling in patients with schizophrenia","authors":"Robert Y. Chen, Tiffany A. Greenwood, David L. Braff, Laura C. Lazzeroni, Neal R. Swerdlow, Monica E. Calkins, Robert Freedman, Michael F. Green, Ruben C. Gur, Raquel E. Gur, Keith H. Nuechterlein, Allen D. Radant, Jeremy M. Silverman, William S. Stone, Catherine A. Sugar, Ming T. Tsuang, Bruce I. Turetsky, Gregory A. Light, Debby W. Tsuang","doi":"10.1038/s44220-025-00568-3","DOIUrl":"10.1038/s44220-025-00568-3","url":null,"abstract":"The development of neurocognitive biomarkers for schizophrenia (SCZ) has relied on lengthy test batteries that are infeasible to deploy in clinical settings. Using machine learning, we sought to identify a subset of neurocognitive domains that could distinguish between patients with SCZ and healthy comparison subjects (HCS). Leveraging data from 559 patients with SCZ or schizoaffective disorder and 745 HCS who completed 15 neurocognitive assessments spanning a diverse range of neurocognitive domains, we developed a machine learning model that could accurately separate SCZ from HCS (area under the receiver operating characteristic curve of 0.899), and was replicated in an independent cohort. Recursive feature elimination revealed that just two neurocognitive domains—verbal learning and emotion identification—were sufficient to achieve the same classification accuracy. These findings support a ‘less-is-more’ approach to efficient neurocognitive profiling across the schizophreniform spectrum and highlight what may be the most impaired neurocognitive domains in this debilitating disorder. This study identifies key neurocognitive domains that distinguish patients with schizophrenia from healthy individuals using machine learning. Analyzing data from 1,304 participants, it demonstrates that verbal learning and emotion identification effectively classify conditions, promoting efficient neurocognitive profiling strategies.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"146-156"},"PeriodicalIF":8.7,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931257","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 : 2026-01-07DOI: 10.1038/s44220-025-00554-9
Stephen Murtough, Daisy Mills, Noushin Saadullah Khani, Marius Cotic, Lauren Varney, Alvin Richards-Belle, Rosemary Abidoph, Nicholas Bass, Dharmisha Chauhan, Sarah Curran, Yogita Dawda, Jana de Villiers, Frances Elmslie, Robert J. Howard, Sophie E. Legge, Alexander Martin, Andrew McQuillin, Daniele Panconesi, Antonio F. Pardiñas, Suzanne Reeves, Maria Richards-Brown, Jane Sarginson, Anna Skowronska, Oriella Stellakis, James TR Walters, Jessica Woodley, Beverley Chipp, Shreyans Gandhi, Sara Stuart-Smith, Dyfrig A. Hughes, Munir Pirmohamed, Huajie Jin, Olubanké Dzahini, Elvira Bramon
Clozapine is the most effective therapy for treatment-resistant schizophrenia, although it can cause neutropenia. In many countries, neutrophil count monitoring is mandatory for people taking clozapine, who must remain above a minimum threshold to start and continue treatment. Some people have low neutrophil counts without increased infection risk, caused by a homozygous variant in ACKR1 and termed ACKR1/DARC-associated neutropenia (ADAN). When ADAN is confirmed, reduced neutrophil count thresholds are applied to allow people to start and continue clozapine. However, ADAN diagnoses are often missed, resulting in reduced access to clozapine and unnecessary discontinuation. We review the evidence for ACKR1 genetic testing to rapidly identify ADAN in people taking clozapine. With multidisciplinary input, we recommend internationally relevant test eligibility criteria, comprising pre-emptive and reactive testing strategies, and we conduct a health economic analysis, estimating total cost savings between £42,732 and £727,990 for the UK healthcare system during the first year of testing. Finally, we propose how to integrate these criteria into clinical practice to enable equitable access to clozapine. This Perspective considers the addition of ACKR1 genetic testing for identifying ACKR1/DARC-associated neutropenia in patients receiving clozapine, recommending eligibility criteria and testing strategies while estimating substantial cost savings for the UK healthcare system and enhancing equitable treatment access.
{"title":"ACKR1 genetic testing should be offered before starting clozapine treatment","authors":"Stephen Murtough, Daisy Mills, Noushin Saadullah Khani, Marius Cotic, Lauren Varney, Alvin Richards-Belle, Rosemary Abidoph, Nicholas Bass, Dharmisha Chauhan, Sarah Curran, Yogita Dawda, Jana de Villiers, Frances Elmslie, Robert J. Howard, Sophie E. Legge, Alexander Martin, Andrew McQuillin, Daniele Panconesi, Antonio F. Pardiñas, Suzanne Reeves, Maria Richards-Brown, Jane Sarginson, Anna Skowronska, Oriella Stellakis, James TR Walters, Jessica Woodley, Beverley Chipp, Shreyans Gandhi, Sara Stuart-Smith, Dyfrig A. Hughes, Munir Pirmohamed, Huajie Jin, Olubanké Dzahini, Elvira Bramon","doi":"10.1038/s44220-025-00554-9","DOIUrl":"10.1038/s44220-025-00554-9","url":null,"abstract":"Clozapine is the most effective therapy for treatment-resistant schizophrenia, although it can cause neutropenia. In many countries, neutrophil count monitoring is mandatory for people taking clozapine, who must remain above a minimum threshold to start and continue treatment. Some people have low neutrophil counts without increased infection risk, caused by a homozygous variant in ACKR1 and termed ACKR1/DARC-associated neutropenia (ADAN). When ADAN is confirmed, reduced neutrophil count thresholds are applied to allow people to start and continue clozapine. However, ADAN diagnoses are often missed, resulting in reduced access to clozapine and unnecessary discontinuation. We review the evidence for ACKR1 genetic testing to rapidly identify ADAN in people taking clozapine. With multidisciplinary input, we recommend internationally relevant test eligibility criteria, comprising pre-emptive and reactive testing strategies, and we conduct a health economic analysis, estimating total cost savings between £42,732 and £727,990 for the UK healthcare system during the first year of testing. Finally, we propose how to integrate these criteria into clinical practice to enable equitable access to clozapine. This Perspective considers the addition of ACKR1 genetic testing for identifying ACKR1/DARC-associated neutropenia in patients receiving clozapine, recommending eligibility criteria and testing strategies while estimating substantial cost savings for the UK healthcare system and enhancing equitable treatment access.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"30-41"},"PeriodicalIF":8.7,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931254","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 : 2026-01-06DOI: 10.1038/s44220-025-00569-2
Maximilian Oscar Steininger, Jonas Paul Nitschke, Mathew Philip White, Claus Lamm
Pain is a global health issue with substantial individual, societal and economic impacts. Given the risks of pharmacological treatments, complementary approaches to pain management are essential. Nature exposure has emerged as a promising nonpharmacological strategy, but evidence of its effectiveness is inconclusive. Here in this systematic review and meta-analysis we examined 62 studies (96 effects) across 21 countries, including 4,439 participants, to assess the impact of nature exposure on self-reported pain. The results indicate a significant small-to-moderate reduction in pain associated with nature exposure (standardized mean difference of 0.53), but studies exhibited moderate-to-high risk of bias and substantial heterogeneity. Studies evaluating nature against matched comparators reported effects roughly half the size of those using nonmatched controls and multisensory stimuli tended to show stronger effects. These findings support nature as a promising complementary pain management strategy. However, high heterogeneity and risk of bias warrant caution and highlight the need for more rigorous research. The authors conducted a systematic review and meta-analysis of 62 studies, including more than 4,400 participants across 21 countries, to investigate the effects of nature exposure on self-reported pain.
{"title":"Nature exposure reduces self-reported pain: a systematic review and meta-analysis","authors":"Maximilian Oscar Steininger, Jonas Paul Nitschke, Mathew Philip White, Claus Lamm","doi":"10.1038/s44220-025-00569-2","DOIUrl":"10.1038/s44220-025-00569-2","url":null,"abstract":"Pain is a global health issue with substantial individual, societal and economic impacts. Given the risks of pharmacological treatments, complementary approaches to pain management are essential. Nature exposure has emerged as a promising nonpharmacological strategy, but evidence of its effectiveness is inconclusive. Here in this systematic review and meta-analysis we examined 62 studies (96 effects) across 21 countries, including 4,439 participants, to assess the impact of nature exposure on self-reported pain. The results indicate a significant small-to-moderate reduction in pain associated with nature exposure (standardized mean difference of 0.53), but studies exhibited moderate-to-high risk of bias and substantial heterogeneity. Studies evaluating nature against matched comparators reported effects roughly half the size of those using nonmatched controls and multisensory stimuli tended to show stronger effects. These findings support nature as a promising complementary pain management strategy. However, high heterogeneity and risk of bias warrant caution and highlight the need for more rigorous research. The authors conducted a systematic review and meta-analysis of 62 studies, including more than 4,400 participants across 21 countries, to investigate the effects of nature exposure on self-reported pain.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"165-180"},"PeriodicalIF":8.7,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s44220-025-00569-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931210","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 : 2026-01-05DOI: 10.1038/s44220-025-00557-6
Brianna L. Gonzalez, Patrick Amoateng, Nana Kwadwo Obiri, Turhan Canli
Collaborations between neuroscientists and traditional medical practitioners can strengthen the scientific foundations of traditional medicine and enrich neuroscience with culturally grounded insights. Such partnerships, built on mutual learning, can promote more equitable and context-sensitive mental health research.
{"title":"Building evidence-based knowledge in traditional medicine provides an opportunity for neuroscientists and traditional medical practitioners","authors":"Brianna L. Gonzalez, Patrick Amoateng, Nana Kwadwo Obiri, Turhan Canli","doi":"10.1038/s44220-025-00557-6","DOIUrl":"10.1038/s44220-025-00557-6","url":null,"abstract":"Collaborations between neuroscientists and traditional medical practitioners can strengthen the scientific foundations of traditional medicine and enrich neuroscience with culturally grounded insights. Such partnerships, built on mutual learning, can promote more equitable and context-sensitive mental health research.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"3-5"},"PeriodicalIF":8.7,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931247","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 : 2026-01-05DOI: 10.1038/s44220-025-00565-6
Briana S. Last, Gabriela Kattan Khazanov
Spurred by billions of dollars in public and private investments, artificial intelligence (AI) technologies are being rapidly developed and deployed to automate, supplement and even replace the role of skilled behavioral health providers. Most discussions of AI in behavioral healthcare have focused on the safety and efficacy of these technologies and have largely neglected more fundamental questions about who decides whether and how AI should be used in behavioral healthcare. We argue that, despite substantial public investments in AI and the significant impacts these technologies will have on the lives of behavioral health service users, the public and providers, the private sector—not these key stakeholders—has played an outsized role in shaping the future of AI in behavioral healthcare. We offer recommendations to democratize the development and deployment of AI technologies in behavioral healthcare by prioritizing the needs and interests of behavioral health service users, the public and providers. In this Perspective, Last and Khazanov call for democratizing AI in behavioral healthcare, urging that service users, providers and the public—not private interests—shape its development and deployment.
{"title":"Empowering service users, the public, and providers to determine the future of artificial intelligence in behavioral healthcare","authors":"Briana S. Last, Gabriela Kattan Khazanov","doi":"10.1038/s44220-025-00565-6","DOIUrl":"10.1038/s44220-025-00565-6","url":null,"abstract":"Spurred by billions of dollars in public and private investments, artificial intelligence (AI) technologies are being rapidly developed and deployed to automate, supplement and even replace the role of skilled behavioral health providers. Most discussions of AI in behavioral healthcare have focused on the safety and efficacy of these technologies and have largely neglected more fundamental questions about who decides whether and how AI should be used in behavioral healthcare. We argue that, despite substantial public investments in AI and the significant impacts these technologies will have on the lives of behavioral health service users, the public and providers, the private sector—not these key stakeholders—has played an outsized role in shaping the future of AI in behavioral healthcare. We offer recommendations to democratize the development and deployment of AI technologies in behavioral healthcare by prioritizing the needs and interests of behavioral health service users, the public and providers. In this Perspective, Last and Khazanov call for democratizing AI in behavioral healthcare, urging that service users, providers and the public—not private interests—shape its development and deployment.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"52-59"},"PeriodicalIF":8.7,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931252","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 : 2026-01-05DOI: 10.1038/s44220-025-00561-w
Adam Benzekri, Marco Thimm-Kaiser, Francis Kwadwo Amankwah, Vincent Guilamo-Ramos
A behavioral healthcare workforce — concordant in race, ethnicity, lived experience, language, and geography with the populations it serves — is urgently needed to end the US behavioral health crisis.
{"title":"The need for a representative workforce to address the US behavioral health crisis","authors":"Adam Benzekri, Marco Thimm-Kaiser, Francis Kwadwo Amankwah, Vincent Guilamo-Ramos","doi":"10.1038/s44220-025-00561-w","DOIUrl":"10.1038/s44220-025-00561-w","url":null,"abstract":"A behavioral healthcare workforce — concordant in race, ethnicity, lived experience, language, and geography with the populations it serves — is urgently needed to end the US behavioral health crisis.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"6-8"},"PeriodicalIF":8.7,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931213","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 : 2026-01-05DOI: 10.1038/s44220-025-00564-7
Stephen V. Faraone, Jeffrey H. Newcorn
Stimulant medications are the first-line treatment for ADHD, with non-stimulants often used if stimulants are ineffective. Here, by reinterpreting randomized controlled trials, addressing heterogeneity of treatment effects, and considering societal impact, we argue for equal consideration of stimulant and non-stimulants as first-line treatment options.
{"title":"Rethinking the role of non-stimulants in ADHD treatment","authors":"Stephen V. Faraone, Jeffrey H. Newcorn","doi":"10.1038/s44220-025-00564-7","DOIUrl":"10.1038/s44220-025-00564-7","url":null,"abstract":"Stimulant medications are the first-line treatment for ADHD, with non-stimulants often used if stimulants are ineffective. Here, by reinterpreting randomized controlled trials, addressing heterogeneity of treatment effects, and considering societal impact, we argue for equal consideration of stimulant and non-stimulants as first-line treatment options.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"9-12"},"PeriodicalIF":8.7,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931250","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 : 2026-01-05DOI: 10.1038/s44220-025-00555-8
Yunfei Luo, Iman Deznabi, Bhanu Teja Gullapalli, Mark Tuomenoksa, Madalina Brostean Fiterau, Eric L. Garland, Tauhidur Rahman
Fluctuations in pain, stress and craving are thought to contribute to opioid misuse. Developing accurate prediction models is vital for intervention and prevention efforts. In this work, we leverage physiological data and semantic analysis of electronic health records to tackle the challenge of detecting opioid misuse. Utilizing personalized hierarchical deep-learning models, we analyze trajectories of predicted pain, stress and craving states with 10,140 hours of heart-rate data collected by wearables from patients on long-term opioid therapy. From these trajectories, we extract entropy features from nonlinear dynamical analysis and develop a novel relevance-based temporal fusion model of opioid misuse risk. We incorporate clinical data into a large language model to enhance opioid misuse risk detection. We then fuse these modalities to achieve an accurate opioid misuse risk assessment with area under the precision-recall curve of 0.94 ± 0.05. This study marks a substantial advancement in personalized prediction of addictive behavior by elucidating the entropic nature of underlying affective state dynamics. This study addresses opioid misuse prediction by integrating physiological data and electronic health records. Utilizing personalized deep-learning models, it achieves a high accuracy in risk assessment through entropy feature extraction and relevance-based temporal fusion, demonstrating effective intervention potential.
{"title":"Personalized entropy-informed deep learning for identifying opioid misuse","authors":"Yunfei Luo, Iman Deznabi, Bhanu Teja Gullapalli, Mark Tuomenoksa, Madalina Brostean Fiterau, Eric L. Garland, Tauhidur Rahman","doi":"10.1038/s44220-025-00555-8","DOIUrl":"10.1038/s44220-025-00555-8","url":null,"abstract":"Fluctuations in pain, stress and craving are thought to contribute to opioid misuse. Developing accurate prediction models is vital for intervention and prevention efforts. In this work, we leverage physiological data and semantic analysis of electronic health records to tackle the challenge of detecting opioid misuse. Utilizing personalized hierarchical deep-learning models, we analyze trajectories of predicted pain, stress and craving states with 10,140 hours of heart-rate data collected by wearables from patients on long-term opioid therapy. From these trajectories, we extract entropy features from nonlinear dynamical analysis and develop a novel relevance-based temporal fusion model of opioid misuse risk. We incorporate clinical data into a large language model to enhance opioid misuse risk detection. We then fuse these modalities to achieve an accurate opioid misuse risk assessment with area under the precision-recall curve of 0.94 ± 0.05. This study marks a substantial advancement in personalized prediction of addictive behavior by elucidating the entropic nature of underlying affective state dynamics. This study addresses opioid misuse prediction by integrating physiological data and electronic health records. Utilizing personalized deep-learning models, it achieves a high accuracy in risk assessment through entropy feature extraction and relevance-based temporal fusion, demonstrating effective intervention potential.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"112-124"},"PeriodicalIF":8.7,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931211","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}
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