{"title":"How neural rhythms can guide word recognition","authors":"Sophie Slaats","doi":"10.1038/s43588-025-00888-5","DOIUrl":"10.1038/s43588-025-00888-5","url":null,"abstract":"The recent computational model ‘BRyBI’ proposes that gamma, theta, and delta neural oscillations can guide the process of word recognition by providing temporal windows for the integration of bottom-up input with top-down information.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 10","pages":"848-849"},"PeriodicalIF":18.3,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256869","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}
Renwen Zhang, Han Meng, Marion Neubronner, Yi-Chieh Lee
{"title":"Computational and ethical considerations for using large language models in psychotherapy","authors":"Renwen Zhang, Han Meng, Marion Neubronner, Yi-Chieh Lee","doi":"10.1038/s43588-025-00874-x","DOIUrl":"10.1038/s43588-025-00874-x","url":null,"abstract":"Large language models (LLMs) hold great potential for augmenting psychotherapy by enhancing accessibility, personalization and engagement. However, a systematic understanding of the roles that LLMs can play in psychotherapy remains underexplored. In this Perspective, we propose a taxonomy of LLM roles in psychotherapy that delineates six specific roles of LLMs across two key dimensions: artificial intelligence autonomy and emotional engagement. We discuss key computational and ethical challenges, such as emotion recognition, memory retention, privacy and emotional dependency, and offer recommendations to address these challenges. Large language models (LLMs) offer promising ways to enhance psychotherapy through greater accessibility, personalization and engagement. This Perspective introduces a typology that categorizes the roles of LLMs in psychotherapy along two critical dimensions: autonomy and emotional engagement.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 10","pages":"854-862"},"PeriodicalIF":18.3,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256872","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":"Developing mental health AI tools that improve care across different groups and contexts","authors":"Nicole Martinez-Martin","doi":"10.1038/s43588-025-00882-x","DOIUrl":"10.1038/s43588-025-00882-x","url":null,"abstract":"In order to realize the potential of mental health AI applications to deliver improved care, a multipronged approach is needed, including representative AI datasets, research practices that reflect and anticipate potential sources of bias, stakeholder engagement, and equitable design practices.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 10","pages":"839-840"},"PeriodicalIF":18.3,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256873","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}
Gaole Dai, Rongyu Zhang, Qingpo Wuwu, Cheng-Ching Tseng, Yu Zhou, Shaokang Wang, Siyuan Qian, Ming Lu, Ali Ata Tuz, Matthias Gunzer, Tiejun Huang, Jianxu Chen, Shanghang Zhang
{"title":"Implicit neural image field for biological microscopy image compression.","authors":"Gaole Dai, Rongyu Zhang, Qingpo Wuwu, Cheng-Ching Tseng, Yu Zhou, Shaokang Wang, Siyuan Qian, Ming Lu, Ali Ata Tuz, Matthias Gunzer, Tiejun Huang, Jianxu Chen, Shanghang Zhang","doi":"10.1038/s43588-025-00889-4","DOIUrl":"https://doi.org/10.1038/s43588-025-00889-4","url":null,"abstract":"<p><p>The rapid pace of innovation in biological microscopy has produced increasingly large images, putting pressure on data storage and impeding efficient data sharing, management and visualization. This trend necessitates new, efficient compression solutions, as traditional coder-decoder methods often struggle with the diversity of bioimages, leading to suboptimal results. Here we show an adaptive compression workflow based on implicit neural representation that addresses these challenges. Our approach enables application-specific compression, supports images of varying dimensionality and allows arbitrary pixel-wise decompression. On a wide range of real-world microscopy images, we demonstrate that our workflow achieves high, controllable compression ratios while preserving the critical details necessary for downstream scientific analysis.</p>","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":18.3,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145276874","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":"Trials for computational psychiatry","authors":"Quentin J. M. Huys, Michael Browning","doi":"10.1038/s43588-025-00879-6","DOIUrl":"10.1038/s43588-025-00879-6","url":null,"abstract":"Computational psychiatry is increasingly delivering causal evidence by focusing on interventions research and clinical trials. Causal evidence could improve patient outcomes through improved precision, repurposing, novel interventions, scaling of psychotherapy and better translation to the clinic.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 10","pages":"841-843"},"PeriodicalIF":18.3,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256848","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":"Rethinking mental illness through a computational lens","authors":"","doi":"10.1038/s43588-025-00894-7","DOIUrl":"10.1038/s43588-025-00894-7","url":null,"abstract":"Nature Computational Science presents a Focus that explores the field of computational psychiatry and its key challenges, from privacy concerns to the ethical use of artificial intelligence, offering new insights into the future of mental health care.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 10","pages":"837-838"},"PeriodicalIF":18.3,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s43588-025-00894-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256868","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}
Teddy J. Akiki, Leanne M. Williams, Thomas Wolfers, Yanwu Yang, Daniel Stahl, Claire M. Gillan
{"title":"Transforming psychiatry with computational and brain-based methods","authors":"Teddy J. Akiki, Leanne M. Williams, Thomas Wolfers, Yanwu Yang, Daniel Stahl, Claire M. Gillan","doi":"10.1038/s43588-025-00884-9","DOIUrl":"10.1038/s43588-025-00884-9","url":null,"abstract":"Integrating computational methods with brain-based data presents a path to precision psychiatry by capturing individual neurobiological variation, improving diagnosis, prognosis, and personalized care. This Viewpoint highlights advances in normative and foundation models, the importance of clinically grounded principles, and the role of robust measurement and interpretability in progressing mental health care.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 10","pages":"844-847"},"PeriodicalIF":18.3,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256870","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":"Towards privacy-aware mental health AI models","authors":"Aishik Mandal, Tanmoy Chakraborty, Iryna Gurevych","doi":"10.1038/s43588-025-00875-w","DOIUrl":"10.1038/s43588-025-00875-w","url":null,"abstract":"Mental health disorders create profound personal and societal burdens, yet conventional diagnostics are resource-intensive and limit accessibility. Recent advances in artificial intelligence, particularly natural language processing and multimodal methods, offer promise for detecting and addressing mental disorders. However, these innovations also introduce privacy concerns. Here we examine these challenges and propose solutions, including anonymization, synthetic data and privacy-preserving training, while outlining frameworks for privacy–utility trade-offs, aiming to advance reliable, privacy-aware artificial-intelligence tools that support clinical decision-making and improve mental health outcomes. In this Perspective, the authors examine privacy risks in mental health AI, and explore solutions and evaluation frameworks to balance privacy–utility trade-offs. They suggest a pipeline for developing privacy-aware mental health AI systems.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 10","pages":"863-874"},"PeriodicalIF":18.3,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256871","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":"Pioneering real-time genomic analysis by in-memory computing","authors":"Kaichen Zhu, Mario Lanza","doi":"10.1038/s43588-025-00883-w","DOIUrl":"10.1038/s43588-025-00883-w","url":null,"abstract":"Rapid identification of pathogenic viruses remains a critical challenge. A recent study advances this frontier by demonstrating a fully integrated memristor-based hardware system that accelerates genomic analysis by a factor of 51, while reducing energy consumption to just 0.2% of that required by conventional computational methods.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 10","pages":"850-851"},"PeriodicalIF":18.3,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245876","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}
Yu Zheng, Fengli Xu, Yuming Lin, Paolo Santi, Carlo Ratti, Qi R Wang, Yong Li
{"title":"Publisher Correction: Urban planning in the era of large language models.","authors":"Yu Zheng, Fengli Xu, Yuming Lin, Paolo Santi, Carlo Ratti, Qi R Wang, Yong Li","doi":"10.1038/s43588-025-00896-5","DOIUrl":"https://doi.org/10.1038/s43588-025-00896-5","url":null,"abstract":"","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":18.3,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240615","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}