NPP-digital psychiatry and neuroscience最新文献

{"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}

{"title":"A primer on circadian rhythms for psychiatry.","authors":"Adriane M Soehner, Colleen A McClung","doi":"10.1038/s44277-026-00060-5","DOIUrl":"10.1038/s44277-026-00060-5","url":null,"abstract":"","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13153416/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147848856","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":"Adherence to ecological momentary assessment studies in children and adolescents with psychopathology: A systematic review with meta-analysis.","authors":"Lauren M Henry, Urmi Pandya, Eleanor Malone, Caroline Miller, Savana Agyemang, Elizabeth Tandilashvili, Kolin Lewis, Clara E Haeffner, Kyunghun Lee, Alicia A Livinski, Miryam Kiderman, Reut Naim, Olivia Metcalf, Silvia Lopez-Guzman, Melissa A Brotman","doi":"10.1038/s44277-026-00058-z","DOIUrl":"10.1038/s44277-026-00058-z","url":null,"abstract":"<p><p>Ecological momentary assessment (EMA) is a tool facilitating the repeated collection of real-time data in naturalistic settings that can be applied to clinical research. Despite established methodological strengths, EMA protocols may be burdensome for participants, potentially leading to problems with adherence. Existing meta-analytic evidence is insufficient to inform the decision making of clinical researchers in using EMA in their work. In the current registered report, we address this need by conducting a systematic review with meta-analysis (1) examining adherence (enrollment, dropout, and compliance) in children and adolescents with psychological disorders and symptoms and (2) identifying aspects of study design that maximize adherence. In January 2023, we searched five databases for empirical EMA studies of youth (8-17 years) with psychological symptoms/diagnoses with data on adherence. We extracted data, assessed study quality, and conducted meta-analyses using random-effects models. We included 130 studies with 14,400 participants. On average, surveys contained 15 items, took over 3 min to complete, and incorporated surveys with multiple response scale types delivered using smartphones/cell phones provided to participants. Meta-analyses revealed that study enrollment was moderate to high (82% in youth with psychopathology, 67% in community youth) and dropout prior to starting EMA protocols was low (<1-2%). Dropout during EMA protocols was 12% for youth with psychopathology and 8% for community youth, underscoring the importance of maintaining participant engagement throughout EMA studies. Compliance approximated the 80% field benchmark for healthy youth but was lower for youth from the community and with psychopathology. Participant characteristics (e.g., younger age) and design factors (e.g., incentive-based compensation) were associated with greater compliance in certain groups. Despite limitations (missing data, methodological constraints), findings have important implications for researchers developing and conducting pediatric EMA protocols. The stage 1 protocol for this Registered Report was accepted in principle on April 19, 2024. The protocol, as accepted by the journal, can be found at: https://doi.org/10.6084/m9.figshare.25732482.v1 . This research was supported by the Intramural Research Program (IRP) of the NIMH and the NIH Library's and Office of Research Service's support of the NIH IRP.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13121683/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147794666","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":"Launching an independent research laboratory in a digital era: practical lessons for early career investigators.","authors":"Christine A Rabinak","doi":"10.1038/s44277-026-00061-4","DOIUrl":"10.1038/s44277-026-00061-4","url":null,"abstract":"<p><p>The transition from trainee to principal investigator represents a major inflection point in an academic research career. While doctoral and postdoctoral training programs provide extensive preparation in experimental design, data analysis, and scientific communication, far less attention is devoted to the practical responsibilities associated with launching and managing an independent research laboratory. Early career investigators must rapidly develop skills in leadership, personnel management, infrastructure planning, and strategic funding development while simultaneously establishing their scientific identity. These challenges have intensified in the modern digital era, as research programs increasingly rely on sophisticated data management systems, collaborative technologies, and interdisciplinary networks. In this Perspective, I discuss several practical considerations that shape the early development of independent research laboratories, including research program design, startup planning, team leadership, collaborative partnerships, and strategic funding trajectories. By highlighting common challenges and lessons learned during the transition to independence, this article aims to help demystify the process of launching a research laboratory and to support early career investigators as they build sustainable scientific programs.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13106742/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147794706","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":"Advancing neurotech justice in youth digital mental health: insights from an interdisciplinary and cross-generational workshop.","authors":"Craig W McFarland, Donnella S Comeau, Sepideh Abdi, Mahsa Alborzi Avanaki, Leo Anthony Celi, Francis X Shen, Benjamin C Silverman","doi":"10.1038/s44277-025-00052-x","DOIUrl":"10.1038/s44277-025-00052-x","url":null,"abstract":"<p><p>Researchers and clinicians are increasingly looking to leverage artificial intelligence (AI) and digital tools to improve psychiatric care. Of particular promise is addressing the youth mental health crisis. Yet, the introduction of AI-enabled digital technologies for psychiatric treatment of young adults raises a host of ethical, legal, and societal issues (ELSI). To provide guidance in addressing these issues, we convened a two-day meeting at the Radcliffe Institute for Advanced Study at Harvard University: Advancing Neurotech Justice in Mental Health: Insights from an Interdisciplinary and Cross-Generational Workshop. The meeting brought together a diverse cohort of 17 experts and 5 students from various fields and different countries. In partnership with the MIT Critical Data team, the workshop engaged participants in an interactive Prompt-a-Thon to explore first-hand the potential benefits, biases, and harms related to the use of Large Language Model chatbots in mental health care. This Perspective reports on five principles of digital psychiatry deployment that the workshop participants determined to be the most essential: ensuring accuracy, remaining human-centric, promoting just access, protecting privacy, and providing transparency. We place these five principles within a \"Neurotech Justice\" framework and discuss how guardrails can be built to promote neurotech justice in digital psychiatry.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13061931/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147641157","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":"Setting digital psychiatry in motion: towards dynamic digital markers for digital phenotyping.","authors":"Axel Constant, C Emre Koksal, Lena Palaniyappan","doi":"10.1038/s44277-026-00059-y","DOIUrl":"10.1038/s44277-026-00059-y","url":null,"abstract":"<p><p>Digital phenotyping uses data from smartphones and wearables to extract behavioural and biosocial markers of psychopathology in situ. Traditional entropy-based measures capture static system properties that neglect temporal dependencies critical to psychiatric phenomena. We propose a \"dynamic\" approach to the modelling of digital data capturing the time-varying aspects of processes of mental disorders. We defend that the resulting dynamic digital markers better capture variability in regulatory mechanisms of psychopathology.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12982652/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147446685","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":"In their own words: case studies of adolescent smartphone language preceding suicide-related hospitalizations.","authors":"Isaac N Treves, Paul A Bloom, Samantha Salem, Katherine Durham, Valerio Zaccaria, Jamaal Spence, Peter S Dayan, Lauren S Chernick, Ashley Blanchard, Jaclyn S Kirshenbaum, Esha Trivedi, David A Brent, Nicholas B Allen, Jamie Zelazny, Karla Joyce, Giovanna Porta, David Pagliaccio, Randy P Auerbach","doi":"10.1038/s44277-026-00057-0","DOIUrl":"10.1038/s44277-026-00057-0","url":null,"abstract":"<p><p>Rising adolescent suicide rates underscore an urgent need for better detection of short-term risk in the days and hours leading up to attempts. Passive smartphone sensing of language offers a promising approach, yet its performance during vulnerable periods remains unclear. This case study examined five adolescents (3 male, 2 female) who were hospitalized for suicidal crises while enrolled in a smartphone sensing study. Participants contributed outgoing text entries over six months (M = 21,000/person), which were analyzed using natural language processing (NLP) to assess suicide-related content, sentiment, and topics (e.g., school, treatment). In addition, clinicians conducted qualitative reviews of the text entries to identify potential risk events. Results showed that 4 of 5 adolescents exhibited increased suicide-related language and negative sentiment during the 10 days prior to psychiatric hospitalization. Especially elevated suicide language was found within 5 days of hospitalization, while negative sentiment peaked between 5-10 days prior to hospitalization. These signals, however, also occurred outside of acute risk periods, highlighting the challenge of separating suicide risk from distress more generally. Clinical annotations revealed that suicidal thoughts and behaviors often co-occurred with NLP signals of suicide-related language, and topic models identified clinically relevant language related to substance use and psychiatric treatment. Clinical annotations of interpersonal conflict and school stressors were not identified by topic models. Discrepancies largely originated from the inability of NLP methods to infer context (e.g., text conversation history). Although smartphone language data showed low missingness and some sensitivity to acute crises, enhancing contextual analysis is essential for personalized risk detection.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12953750/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147346201","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 sleep neuroscience for psychiatry.","authors":"Jared M Saletin, Giulia R Righi","doi":"10.1038/s44277-026-00055-2","DOIUrl":"10.1038/s44277-026-00055-2","url":null,"abstract":"","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"4 1","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12913715/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146215477","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":"Correction: Mindbench.ai: an actionable platform to evaluate the profile and performance of large language models in a mental healthcare context.","authors":"Bridget Dwyer, Matthew Flathers, Akane Sano, Allison Dempsey, Andrea Cipriani, Asim H Gazi, Bryce Hill, Carla Gorban, Carolyn I Rodriguez, Charles Stromeyer, Darlene King, Eden Rozenblit, Gillian Strudwick, Jake Linardon, Jiaee Cheong, Joseph Firth, Julian Herpertz, Julian Schwarz, Khai Truong, Margaret Emerson, Martin P Paulus, Michelle Patriquin, Yining Hua, Soumya Choudhary, Steven Siddals, Laura Ospina Pinillos, Jason Bantjes, Stephen M Schueller, Xuhai Xu, Ken Duckworth, Daniel H Gillison, Michael Wood, John Torous","doi":"10.1038/s44277-025-00054-9","DOIUrl":"10.1038/s44277-025-00054-9","url":null,"abstract":"","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"4 1","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12865003/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146109555","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 probabilistic deep learning approach for choroid plexus segmentation in autism spectrum disorder.","authors":"Filippo Bargagna, Thomas M Morin, Ya-Chin Chen, Ylind Lila, Chieh-En J Tseng, Maria F Santarelli, Nicola Vanello, Christopher J McDougle, Jacob M Hooker, Nicole R Zürcher","doi":"10.1038/s44277-026-00056-1","DOIUrl":"10.1038/s44277-026-00056-1","url":null,"abstract":"<p><p>The choroid plexus serves as the primary barrier between the brain's blood and cerebrospinal fluid and mediates neuroimmune function. A subset of individuals with autism spectrum disorder (ASD) may exhibit morphological alterations of the choroid plexus. However, to power larger population analyses, an automated tool capable of accurately segmenting the choroid plexus based on magnetic resonance imaging (MRI) is needed. Automated Segmentation of CHOroid PLEXus (ASCHOPLEX) is a deep learning tool that enables finetuning using new, patient-specific, training data, allowing its usage across cohorts for which the model was not originally trained. We evaluated ASCHOPLEX's generalizability to individuals with ASD by performing finetuning on a local dataset of ASD and control (CON) participants. To assess generalizability, we implemented a probabilistic version of the algorithm, which allowed us to quantify the uncertainty in choroid plexus segmentation and evaluate the model's confidence. ASCHOPLEX generalized well to our local dataset, in which all participants were adults. To further assess its performance, we tested the algorithm on the Autism Brain Imaging Data Exchange (ABIDE) dataset, which includes data from children and adults. While ASCHOPLEX performed well in adults, its accuracy declined in children, suggesting limited generalizability to different age groups without additional finetuning. Our findings show that the incorporation of a probabilistic approach during finetuning can strengthen the use of this deep learning tool by providing confidence metrics which allow assessing model reliability. Overall, our findings demonstrate that ASCHOPLEX can generate accurate choroid plexus segmentations in previously unseen data.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"4 1","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12858829/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095302","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}