NPP-digital psychiatry and neuroscience最新文献

{"title":"A primer on heart rate variability: use in neuroscience and psychiatry.","authors":"Courtney C Louis, Antonia V Seligowski","doi":"10.1038/s44277-026-00072-1","DOIUrl":"10.1038/s44277-026-00072-1","url":null,"abstract":"","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13672783/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198710","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":"Alignment of natural language processing-derived RDoC scores with depression, anhedonia, and anxiety in psychiatric electronic health records.","authors":"Manuel Kuhn, Seoin Ahn, Vincent Holstein, Annah Rossvall, Savannah Layfield, Lucie A Duffy, Samantha Wong, Robyn Ellis, Eliot Gelwan, Fernando Rodriguez-Villa, Agustin Yip, Ipsit Vahia, Diego A Pizzagalli, Kerry J Ressler, Jonathan Vogelgsang","doi":"10.1038/s44277-026-00070-3","DOIUrl":"https://doi.org/10.1038/s44277-026-00070-3","url":null,"abstract":"<p><p>Traditional categorical diagnoses often fail to capture the dimensional nature of psychiatric disorders such as depression and anxiety. Natural Language Processing (NLP) offers a promising approach to extract dimensional constructs relevant to psychopathology from clinical documentation. In this cross-sectional study, we analyzed clinical notes from 358 psychiatric patients treated at McLean Hospital and enrolled in the \"Psychometrics at McLean\" (PAM) study to examine the alignment between NLP-derived Research Domain Criteria (RDoC) constructs and psychometric assessments of depression, anhedonia, and anxiety. RDoC scores for Positive Valence Systems (PVS) and Negative Valence Systems (NVS) were extracted using NLP methods previously validated at the group level and compared with standardized clinical assessments: Positive Valence Systems Scale (PVSS), Quick Inventory of Depressive Symptomatology (QIDS), Snaith-Hamilton Pleasure Scale (SHAPS), and Generalized Anxiety Disorder-7 (GAD-7). Significant associations were observed between depression (QIDS) and both PVS (β = -0.42, p = 0.008) and NVS (β = 0.45, p = 0.015); anhedonia (SHAPS) and both PVS (β = -0.39, p = 0.005) and NVS (β = 0.36, p = 0.030); and anxiety (GAD-7) and NVS (β = 0.29, p = 0.049), but not anxiety and PVS. These results are consistent with theoretical accounts in which both PVS and NVS are implicated in depression, whereas anxiety primarily involves NVS. These findings demonstrate that NLP-derived RDoC scores align with established clinical measures, providing evidence of convergent validity with symptom-based measures and of discriminant validity, most notably the selective association of anxiety with NVS. NLP-derived RDoC scores may offer a scalable approach for characterizing dimensional psychopathology; however, longitudinal and predictive validation will be required before applications such as treatment-response monitoring can be supported.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13627714/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149122549","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":"Toward actionable biomarkers in psychiatry: a collaborative roadmap for precision clinical trials. An ACNP position paper.","authors":"Sahib S Khalsa, Deanna Barch, Linda S Brady, Teresa Buracchio, William A Carlezon, Christopher Chatham, Wayne C Drevets, C Neill Epperson, Amit Etkin, Bernard Fischer, Danielle L Graham, Steven C Hoffmann, Bashkim Kadriu, Martien J H Kas, Hartmuth Kolb, Marion Leboyer, Sarah H Lisanby, Lauren Liss, Husseini K Manji, Valentina Mantua, Hugh M Marston, William Potter, Frank Yocca, Kerry J Ressler","doi":"10.1038/s44277-026-00069-w","DOIUrl":"10.1038/s44277-026-00069-w","url":null,"abstract":"<p><p>Psychiatric drug development remains constrained by diagnostic heterogeneity, insufficient and inconsistent drug response, high placebo response rates, and the absence of validated biomarkers that can reliably guide patient stratification, trial design, and regulatory decision-making. Despite substantial advances in neuroscience, few biomarkers in psychiatry have progressed beyond exploratory use, limiting the translation of mechanistic insights into effective therapeutics. The process of implementing biomarkers in psychiatry requires new levels of organization and coordination across many stakeholders, each with their own priorities. To date, however, no comprehensive roadmap exists to align scientific, regulatory, clinical, and commercial stakeholders around the development and implementation of predictive biomarkers in psychiatry. This position paper, developed by the American College of Neuropsychopharmacology (ACNP) Precompetitive Stakeholder Task Force and informed by work from the European College of Neuropsychopharmacology (ECNP) on the Precision Psychiatry Roadmap, brings together key stakeholders to begin the process of engaging in coordinated efforts. By integrating scientific, regulatory, and commercial perspectives, we clarify biomarker definitions and contexts of use, review current FDA regulatory pathways and gaps, and synthesize lessons from real-world examples. We highlight emerging biomarker modalities including fluid-based, digital, electrophysiological, neuroimaging, and multimodal approaches, and propose strategies to de-risk their adoption through standardized platforms, early regulatory engagement, and scalable trial designs. Emphasis is placed on precompetitive collaboration, large-scale data harmonization, and stakeholder alignment to accelerate reproducibility, regulatory interpretability, clinical utility, and improved use of experimental systems. This roadmap identifies actionable strategies to advance biomarker-enabled precision psychiatry and nurture cooperation, which will accelerate therapeutic innovation and improve clinical outcomes.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13588784/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149003329","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":"Responsible and innovative AI for mental health care: five priority themes.","authors":"Martin P Paulus, John Torous, Roy H Perlis, Karthik V Sarma, Olusola Ajilore, Sahib S Khalsa, Carolyn I Rodriguez","doi":"10.1038/s44277-026-00068-x","DOIUrl":"10.1038/s44277-026-00068-x","url":null,"abstract":"<p><p>Artificial intelligence (AI) has entered psychiatry at scale, yet its clinical impact remains constrained by a sizable gap between technical validation and real-world implementation. The central barriers are no longer computational, but infrastructural: unreliable measurement systems, incomplete governance frameworks, and insufficient standards for clinical evidence and integration. This paper synthesizes insights from a 2026 American College of Neuropsychopharmacology (ACNP) study group examining how to responsibly translate AI into clinical mental health care. Building on this perspective, we outline five priorities required for clinical impact. First, robust measurement and phenotyping infrastructure, such as reliable psychometrics, digital phenotyping, and standardized data pipelines, is essential for clinically meaningful AI. Second, the most immediate and scalable impact of AI lies in clinician-facing augmentation tools that reduce workflow burden, such as ambient documentation systems and structured decision-support pipelines, with important research to conduct here. Third, patient-facing AI interventions show promise but require rigorous safety evaluation, particularly for implicit suicide risk and heterogeneous treatment effects. Fourth, governance and equity frameworks must extend beyond privacy to address bias, digital literacy, and research integrity. Fifth, future progress requires moving beyond predictive models toward causal, mechanistic approaches to precision psychiatry that better inform treatment decisions and clinical action. Together, these priorities define a translational agenda for 2026 and beyond: AI in mental health will succeed not through model performance alone, but through disciplined integration into clinical workflows, measurement systems, and governance structures that ensure safety, equity, and real-world effectiveness.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13493768/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148804711","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":"Bouncing back from stress: objective markers of expressive flexibility and resilience in emergency healthcare workers using computer vision.","authors":"Charlotte E Hilberdink, Yiwen Zhao, Scott McKernan, Sapir Gershov, Victoria Mueller, Stephen P Wall, Katharina Schultebraucks","doi":"10.1038/s44277-026-00067-y","DOIUrl":"10.1038/s44277-026-00067-y","url":null,"abstract":"<p><p>Healthcare workers (HCWs) in emergency departments face significant mental health risk due to chronic stressors and repeated trauma, yet symptom underreporting and bias in self-reports hinder accurate assessments. Expressive flexibility, the ability to dynamically modulate and recover from stressor-related changes in emotional arousal as reflected in observable behavior, has been linked to resilience. This NIH-funded study (R01HL156134) utilized digital phenotyping and computer vision to analyze dynamic facial expressivity during video-recorded interviews about work-related stressful situations with 240 HCWs (278 assessments). Participants additionally completed validated questionnaires to assess burnout, PTSD, depression, anxiety, and resilience. Latent profile analysis revealed two clinical phenotypes: At-risk (57.6%) and Resilient/Adaptive (42.4%). Machine learning models demonstrated high classification performance (accuracy = 0.83 ± 0.06, F1-score = 0.87 ± 0.05). Our findings indicate that digital biomarkers of temporal facial dynamics may serve as objective behavioral proxies of expressive flexibility, potentially capturing dynamics consistent with underlying stress-regulatory processes. These findings highlight their potential to improve identification of resilience-related phenotypes and support well-being and mental health in HCWs.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13369946/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148451336","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":"Building the foundations for global data banking in digital phenotyping for mental health.","authors":"Bridianne O'Dea, Samra Naz, Larisa T McLoughlin, Alexis E Whitton","doi":"10.1038/s44277-026-00066-z","DOIUrl":"10.1038/s44277-026-00066-z","url":null,"abstract":"<p><p>Mental illness is a leading cause of global disability, underscoring the urgent need for scalable, data-driven approaches to early identification and intervention. Passive sensing technologies in mobile and wearable devices enable continuous and unobtrusive measurement of behavioural and physiological signals that may be relevant to mental health. When translated into interpretable digital markers through digital phenotyping, these data hold significant promise for advancing our understanding of the onset, course, and treatment of mental disorders. However, achieving real-world clinical utility requires large-scale, harmonised, and ethically governed databanks that enable replication and generalisability for diverse populations. Informed by recent international initiatives, the academic literature, and our own expertise in digital phenotyping, this Perspective outlines four key priorities for advancing digital phenotyping databanks in depression and anxiety. First, ensuring data quality through standardisation and harmonisation is essential to comparability across studies and to prevent fragmentation. Second, ethical data stewardship demands hybrid consent models that combine the scalability of broad consent with the flexibility of dynamic consent, ensuring meaningful participant control as analytics evolve. Third, robust, privacy-preserving information governance co-created with people with Lived Experience is vital to maintain trust and prevent misuse, with federated learning and open-source pipelines offering promising technical pathways. Finally, the field must promote data reuse by reforming incentive structures, recognising databank-based scholarship, and investing in sustainable infrastructures that reward secondary analyses. Collectively, these priorities offer a pragmatic framework for building equitable, transparent, and scientifically robust digital mental health databanks. Implementing these recommendations will require sustained international collaboration among researchers, funders, institutions, and people with Lived Experience. By aligning scientific rigour with ethical responsibility, digital phenotyping databanks can become transformative tools for advancing the global understanding and treatment of mental illness.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13365544/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148440654","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":"Characterizing the spiral: potential mechanisms in AI-associated delusions.","authors":"Marc Augustin, Thomas A Pollak, Hamilton Morrin","doi":"10.1038/s44277-026-00065-0","DOIUrl":"10.1038/s44277-026-00065-0","url":null,"abstract":"<p><p>AI-associated delusions represent an emerging phenomenon requiring mechanistic understanding. This review, after summarizing key contributions on AI-associated delusions, proposes the \"amplification spiral\" framework, wherein three empirically observed AI characteristics-linguistic alignment (mirroring user language), hyperpersonalized generation (creating personalized content), and sycophancy (validating without reality-testing)-may converge. Unlike historical technology-incorporated delusions, AI may actively co-construct delusional ideation through endless, personalized interaction. This framework aims to guide systematic inquiry into how human cognitive vulnerabilities interact with AI design features in psychopathology development. Both the framework and its underlying hypotheses require prospective validation through case reports and empirical study.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13272801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148267621","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":"A primer on corollary discharges: neural signals that distinguish 'Self' from 'World'.","authors":"Thomas J Whitford, Judith M Ford","doi":"10.1038/s44277-026-00063-2","DOIUrl":"10.1038/s44277-026-00063-2","url":null,"abstract":"","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13261007/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148230435","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":"Does ChatGPT need a psychiatrist? Similarities between human psychopathology and errors in large language models.","authors":"Janna N de Boer, Silvia Ciampelli, Araya K Hailemariam, Sanne Koops, Iris E C Sommer","doi":"10.1038/s44277-026-00064-1","DOIUrl":"10.1038/s44277-026-00064-1","url":null,"abstract":"<p><p>Two striking phenomena of the human mind encountered in mental healthcare are hallucinations and confabulations; perceiving things that are not there, or filling memory gaps with invented stories. Interestingly, contemporary artificial intelligence systems, such as large language models (LLMs) and automatic speech recognition tools, show remarkably similar errors. They are known to \"hallucinate\" words, or \"confabulate\" facts when information is missing, producing output that feels coherent but is false. In this article, we explore these parallels between psychiatric symptoms in humans and mistakes in model output. By comparing how and why these errors arise, we aim to illuminate shared computational principles underlying predictive systems. These comparisons highlight both the risks of relying on imperfect AI systems and the opportunity to use them as computational mirrors to better understand the human mind and the other way around: knowledge from psychiatric symptoms may help to improve AI systems to reduce error rates.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13253837/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148221524","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":"Personalized machine learning guided intervention for optimizing lifestyle behaviors in depression: a pilot study.","authors":"Jason Nan, Suzanna Purpura, Satish Jaiswal, Houtan Afshar, Vojislav Maric, James K Manchanda, Charles T Taylor, Dhakshin Ramanathan, Jyoti Mishra","doi":"10.1038/s44277-026-00062-3","DOIUrl":"10.1038/s44277-026-00062-3","url":null,"abstract":"<p><p>Personalized data-driven interventions for depression are much needed. Here, we leveraged N-of-1 machine learning (ML) to optimally target behavioral lifestyle interventions for depression. 50 individuals with mild-to-moderate depression enrolled in the single-arm, open-label Personalized Mood Augmentation (PerMA) pilot clinical trial (NCT05662254). Participants completed a two-week digital monitoring phase using smartphone-based ecological momentary assessments (EMAs, 4×/day) plus smartwatch tracking of mood and lifestyle factors (sleep/exercise/diet/social connection). Personalized ML models were generated from these data to identify lifestyle factors most predictive of individual mood, and results were translated to individualized mood augmentation plans (iMAPs) implemented by participants for six weeks with once-a-week health coach guidance. Intervention completers (n = 40) showed significant reduction in depression symptoms (primary outcome self-rated PHQ9 -3.5 ± 3.8, Cohen's d = -0.89, CI [-1.25 -0.53], p < 0.001; clinician-rated HDRS -7.2 ± 6.8, d = -1.03, CI [-1.41 -0.65], p < 1E-6) with benefits sustained up to 12-week follow-up. Co-morbid anxiety was also significantly reduced (GAD7: d = -0.85, CI [-1.2, -0.49], p < 0.001) and quality of life improved (d = 0.68, CI [0.33, 1.02], p < 0.001). Additionally, objective cognitive measures impacted in depression including selective attention (d = 0.51, CI [0.18, 0.84], p < 0.001), interference processing (d = 0.53, CI [0.2, 0.85], p < 0.01) and working memory (d = 0.66, CI [0.31, 0.99], p < 0.001) showed significant enhancement. EMA tracking confirmed that improvement in depressed mood was specifically predicted by improvement in individually targeted lifestyles (β = 0.4 ± 0.09, p < 0.0005). Finally, decision algorithms and a large-language-model (LLM) could match human coach-led iMAP assignment with up to 95% accuracy. The PerMA trial presents a personalized lifestyle intervention approach for depression and merits scale-up and RCT testing to establish clinical efficacy. PERMA was registered with ClinicalTrials.gov under registry number NCT05662254.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13184138/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147978434","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}