{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T11:36:31Z","timestamp":1785670591738,"version":"3.56.0"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T00:00:00Z","timestamp":1698624000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T00:00:00Z","timestamp":1698624000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Janssen Research & Development, LLC based on the license agreement"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Major depressive disorder (MDD) is a chronic illness wherein relapses contribute to significant patient morbidity and mortality. Near-term prediction of relapses in MDD patients has the potential to improve outcomes by helping implement a \u2018predict and preempt\u2019 paradigm in clinical care. In this study, we developed a novel personalized (N-of-1) encoder-decoder anomaly detection-based framework of combining anomalies in multivariate actigraphy features (passive) as triggers to utilize an active concurrent self-reported symptomatology questionnaire (core\u00a0symptoms of depression and anxiety) to predict near-term relapse in MDD. The framework was evaluated on two independent longitudinal observational trials, characterized by regular\u00a0bimonthly (every other month) in-person clinical assessments, weekly self-reported symptom assessments, and continuous activity monitoring data with two different wearable sensors for\u2009\u2265\u20091\u00a0year or until the first relapse episode. This combined passive-active relapse prediction framework achieved a balanced accuracy of\u2009\u2265\u200971%, false alarm rate of\u2009\u2264\u20092.3 alarm\/patient\/year with a median relapse detection time of 2\u20133\u00a0weeks in advance of clinical onset in both studies. The study results suggest that the proposed personalized N-of-1 prediction framework is generalizable and can help predict a majority of MDD relapses in an actionable time frame with relatively low patient and provider burden.<\/jats:p>","DOI":"10.1038\/s41598-023-44592-8","type":"journal-article","created":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T11:02:28Z","timestamp":1698663748000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Personalized relapse prediction in patients with major depressive disorder using digital biomarkers"],"prefix":"10.1038","volume":"13","author":[{"given":"Srinivasan","family":"Vairavan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Homa","family":"Rashidisabet","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingqin S.","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seth","family":"Ness","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Randall L.","family":"Morrison","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claudio N.","family":"Soares","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rudolf","family":"Uher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benicio N.","family":"Frey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raymond W.","family":"Lam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sidney H.","family":"Kennedy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Madhukar","family":"Trivedi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wayne C.","family":"Drevets","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vaibhav A.","family":"Narayan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,30]]},"reference":[{"key":"44592_CR1","unstructured":"World Health Organization. Depression and other common mental disorders: global health estimates. (2017). Available at: https:\/\/apps.who.int\/iris\/bitstream\/handle\/10665\/254610\/WHO-MSD-MER-2017.2-eng.pdf."},{"key":"44592_CR2","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1176\/appi.ajp.2016.15111462","volume":"173","author":"LL Judd","year":"2016","unstructured":"Judd, L. L., Schettler, P. J. & Rush, A. J. A brief clinical tool to estimate individual patients\u2019 risk of depressive relapse following remission: Proof of concept. Am. J. Psychiatry. 173, 1140\u20131146 (2016).","journal-title":"Am. J. Psychiatry."},{"key":"44592_CR3","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1016\/S0006-3223(02)01866-8","volume":"54","author":"AJ Rush","year":"2003","unstructured":"Rush, A. J. et al. The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): A psychometric evaluation in patients with chronic major depression. Biol. Psychiatry. 54, 573\u2013583 (2003).","journal-title":"Biol. Psychiatry."},{"key":"44592_CR4","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1111\/acps.12720","volume":"135","author":"N De La Garza","year":"2017","unstructured":"De La Garza, N., John Rush, A., Grannemann, B. D. & Trivedi, M. H. Toward a very brief self-report to assess the core symptoms of depression (VQIDS-SR5). Acta Psychiatr. Scand. 135, 548\u2013553 (2017).","journal-title":"Acta Psychiatr. Scand."},{"key":"44592_CR5","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.coisb.2020.07.003","volume":"20","author":"H Rashidisabet","year":"2020","unstructured":"Rashidisabet, H. et al. A systems biology approach to the digital behaviorome. Current Opin. Syst. Biol. 20, 8\u201316 (2020).","journal-title":"Current Opin. Syst. Biol."},{"key":"44592_CR6","doi-asserted-by":"publisher","first-page":"1254","DOI":"10.4088\/JCP.v66n1008","volume":"66","author":"N Tsuno","year":"2005","unstructured":"Tsuno, N., Besset, A. & Ritchie, K. Sleep and depression. J. Clin. Psychiatry. 66, 1254\u20131269 (2005).","journal-title":"J. Clin. Psychiatry."},{"key":"44592_CR7","doi-asserted-by":"publisher","first-page":"zsz254","DOI":"10.1093\/sleep\/zsz254","volume":"43","author":"CM Depner","year":"2020","unstructured":"Depner, C. M. et al. Wearable technologies for developing sleep and circadian biomarkers: A summary of workshop discussions. Sleep 43, zsz254 (2020).","journal-title":"Sleep"},{"key":"44592_CR8","doi-asserted-by":"publisher","first-page":"1569","DOI":"10.5665\/sleep.1400","volume":"34","author":"AS Lim","year":"2011","unstructured":"Lim, A. S. et al. Quantification of the fragmentation of rest-activity patterns in elderly individuals using a state transition analysis. Sleep. 34, 1569\u20131581 (2011).","journal-title":"Sleep."},{"key":"44592_CR9","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.jad.2019.04.087","volume":"253","author":"Y Tazawa","year":"2019","unstructured":"Tazawa, Y. et al. Actigraphy for evaluation of mood disorders: A systematic review and meta-analysis. J. Affect. Disord. 253, 257\u2013269 (2019).","journal-title":"J. Affect. Disord."},{"key":"44592_CR10","doi-asserted-by":"publisher","first-page":"e24604","DOI":"10.2196\/24604","volume":"9","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y. et al. Relationship between major depression symptom severity and sleep collected using a wristband wearable device: Multicenter longitudinal observational study. JMIR Mhealth Uhealth. 9, e24604 (2021).","journal-title":"JMIR Mhealth Uhealth."},{"key":"44592_CR11","doi-asserted-by":"publisher","first-page":"eaax1977","DOI":"10.1126\/scitranslmed.aax1977","volume":"11","author":"P Li","year":"2019","unstructured":"Li, P. et al. More random motor activity fluctuations predict incident frailty, disability, and mortality. Sci. Transl. Med. 11, eaax1977 (2019).","journal-title":"Sci. Transl. Med."},{"key":"44592_CR12","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/j.physa.2004.01.042","volume":"337","author":"K Hu","year":"2004","unstructured":"Hu, K. et al. Non-random fluctuations and multi-scale dynamics regulation of human activity. Phys. A Stat. Mech. Appl. 337, 307\u2013318 (2004).","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"44592_CR13","doi-asserted-by":"publisher","first-page":"6026","DOI":"10.1103\/PhysRevLett.86.6026","volume":"86","author":"LA Nunes Amaral","year":"2001","unstructured":"Nunes Amaral, L. A. et al. Behavioral-independent features of complex heartbeat dynamics. Phys. Rev. Lett. 86, 6026\u20136029 (2001).","journal-title":"Phys. Rev. Lett."},{"key":"44592_CR14","doi-asserted-by":"publisher","first-page":"2229","DOI":"10.1038\/srep02229","volume":"3","author":"K Hu","year":"2013","unstructured":"Hu, K., Harper, D. G., Shea, S. A., Stopa, E. G. & Scheer, F. A. Noninvasive fractal biomarker of clock neurotransmitter disturbance in humans with dementia. Sci. Rep. 3, 2229 (2013).","journal-title":"Sci. Rep."},{"key":"44592_CR15","doi-asserted-by":"publisher","first-page":"2490","DOI":"10.1073\/pnas.0806087106","volume":"106","author":"K Hu","year":"2009","unstructured":"Hu, K., Van Someren, E. J., Shea, S. A. & Scheer, F. A. Reduction of scale invariance of activity fluctuations with aging and Alzheimer\u2019s disease: Involvement of the circadian pacemaker. Proc. Natl. Acad. Sci .U.S.A. 106, 2490\u20132494 (2009).","journal-title":"Proc. Natl. Acad. Sci .U.S.A."},{"key":"44592_CR16","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1038\/20924","volume":"399","author":"PC Ivanov","year":"1999","unstructured":"Ivanov, P. C. et al. Multifractality in human heartbeat dynamics. Nature. 399, 461\u2013465 (1999).","journal-title":"Nature."},{"key":"44592_CR17","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/S0378-4371(02)01383-3","volume":"316","author":"JW Kantelhardt","year":"2002","unstructured":"Kantelhardt, J. W. et al. Multifractal detrended fluctuation analysis of nonstationary time series. Phys. A Stat. Mech. Appl. 316, 87\u2013114 (2002).","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"44592_CR18","first-page":"15761","volume":"1","author":"RA Heath","year":"2016","unstructured":"Heath, R. A. & Murray, G. Multifractal dynamics of activity data in bipolar disorder: Towards automated early warning of manic relapse. Fract. Geom. Nonlinear Anal. Med. Biol. 1, 15761 (2016).","journal-title":"Fract. Geom. Nonlinear Anal. Med. Biol."},{"key":"44592_CR19","doi-asserted-by":"publisher","first-page":"e16291","DOI":"10.1371\/journal.pone.0016291","volume":"6","author":"ER Hauge","year":"2011","unstructured":"Hauge, E. R., Berle, J. O., Oedegaard, K. J., Holsten, F. & Fasmer, O. B. Nonlinear analysis of motor activity shows differences between schizophrenia and depression: A study using Fourier analysis and sample entropy. PLoS ONE 6, e16291 (2011).","journal-title":"PLoS ONE"},{"key":"44592_CR20","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1038\/s41398-021-01445-0","volume":"11","author":"RV Shah","year":"2021","unstructured":"Shah, R. V. et al. Personalized machine learning of depressed mood using wearables. Transl. Psychiatry. 11, 338 (2021).","journal-title":"Transl. Psychiatry."},{"issue":"12","key":"44592_CR21","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1002\/da.23298","volume":"39","author":"S Difrancesco","year":"2022","unstructured":"Difrancesco, S. et al. Within-day bidirectional associations between physical activity and affect: A real-time ambulatory study in persons with and without depressive and anxiety disorders. Depress Anxiety 39(12), 922\u2013931 (2022).","journal-title":"Depress Anxiety"},{"key":"44592_CR22","unstructured":"Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., & Shroff, G. LSTM-based encoder-decoder for multi-sensor anomaly detection. http:\/\/arxiv.org\/abs\/1607.00148. (2016)"},{"key":"44592_CR23","unstructured":"Malhotra, P., Vig, L., Shroff, G., & Agarwal P. Long Short Term Memory Networks for Anomaly Detection in Time Series. In Proceedings European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) Proceedings vol. 89, 89\u201394 (2015)."},{"key":"44592_CR24","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.neurobiolaging.2019.08.023","volume":"83","author":"P Li","year":"2019","unstructured":"Li, P. et al. Interaction between the progression of Alzheimer\u2019s disease and fractal degradation. Neurobiol. Aging. 83, 21\u201330 (2019).","journal-title":"Neurobiol. Aging."},{"issue":"Suppl 1","key":"44592_CR25","doi-asserted-by":"publisher","first-page":"2466","DOI":"10.1073\/pnas.012579499","volume":"99","author":"AL Goldberger","year":"2002","unstructured":"Goldberger, A. L. et al. Fractal dynamics in physiology: Alterations with disease and aging. Proc. Natl. Acad. Sci. U.S.A. 99(Suppl 1), 2466\u20132472 (2002).","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"issue":"82","key":"44592_CR26","first-page":"262","volume":"1997","author":"JM Hausdorff","year":"1985","unstructured":"Hausdorff, J. M. et al. Altered fractal dynamics of gait: Reduced stride-interval correlations with aging and Huntington\u2019s disease. J. Appl. Physiol. 1997(82), 262\u2013269 (1985).","journal-title":"J. Appl. Physiol."},{"key":"44592_CR27","doi-asserted-by":"publisher","first-page":"189","DOI":"10.3389\/fnhum.2015.00189","volume":"9","author":"V Matic","year":"2015","unstructured":"Matic, V. et al. Objective differentiation of neonatal EEG background grades using detrended fluctuation analysis. Front. Hum. Neurosci. 9, 189 (2015).","journal-title":"Front. Hum. Neurosci."},{"key":"44592_CR28","doi-asserted-by":"publisher","first-page":"15736","DOI":"10.1038\/s41598-019-52091-y","volume":"9","author":"K Torre","year":"2019","unstructured":"Torre, K., Vergotte, G., Viel, E., Perrey, S. & Dupeyron, A. Fractal properties in sensorimotor variability unveil internal adaptations of the organism before symptomatic functional decline. Sci. Rep. 9, 15736 (2019).","journal-title":"Sci. Rep."},{"key":"44592_CR29","doi-asserted-by":"publisher","first-page":"141","DOI":"10.3389\/fphys.2012.00141","volume":"3","author":"EA Ihlen","year":"2012","unstructured":"Ihlen, E. A. Introduction to multifractal detrended fluctuation analysis in matlab. Front. Physiol. 3, 141 (2012).","journal-title":"Front. Physiol."},{"key":"44592_CR30","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.physa.2006.10.077","volume":"376","author":"R Govindan","year":"2007","unstructured":"Govindan, R., Wilson, J., Eswaran, H., Lowery, C. & Prei\u00dfl, H. Revisiting sample entropy analysis. Phys. A Stat. Mech. Appl. 376, 158\u2013164 (2007).","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"44592_CR31","doi-asserted-by":"publisher","first-page":"e19962","DOI":"10.2196\/19962","volume":"8","author":"DA Adler","year":"2020","unstructured":"Adler, D. A. et al. Predicting early warning signs of psychotic relapse from passive sensing data: An approach using encoder-decoder neural networks. JMIR Mhealth Uhealth. 8, e19962 (2020).","journal-title":"JMIR Mhealth Uhealth."},{"key":"44592_CR32","doi-asserted-by":"crossref","unstructured":"Hundman, K., Constantinou, V., Laporte, C., Colwell, I., & Soderstrom, T. Detecting spacecraft anomalies using LSTMS and nonparametric dynamic thresholding. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 387\u201395.","DOI":"10.1145\/3219819.3219845"},{"key":"44592_CR33","doi-asserted-by":"crossref","unstructured":"Lin, E., & Ho, P. 50 Studies Every Psychiatrist Should Know (eds Bhalla, I.P., Tampi, R.R., Srihari, V.H.) Ch. 27 Sequenced Treatment Alternatives to Relieve Depression. (OXFORD University Press, 2018).","DOI":"10.1093\/med\/9780190625085.003.0027"},{"key":"44592_CR34","volume-title":"Repeatable Battery for the Assessment of Neuropsychological Status (RBANS)","author":"C Randolph","year":"1998","unstructured":"Randolph, C. Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) (Psychological Corporation San Antonio, 1998)."},{"key":"44592_CR35","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1016\/j.euroneuro.2006.04.008","volume":"16","author":"TJ Carmody","year":"2006","unstructured":"Carmody, T. J. et al. The Montgomery Asberg and the Hamilton ratings of depression: A comparison of measures. Eur. Neuropsychopharmacol. 16, 601\u2013611 (2006).","journal-title":"Eur. Neuropsychopharmacol."},{"key":"44592_CR36","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.1001\/archinte.166.10.1092","volume":"166","author":"RL Spitzer","year":"2006","unstructured":"Spitzer, R. L., Kroenke, K., Williams, J. B. & Lowe, B. A brief measure for assessing generalized anxiety disorder: The GAD-7. Arch. Intern. Med. 166, 1092\u20131097 (2006).","journal-title":"Arch. Intern. Med."},{"key":"44592_CR37","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1192\/bjp.134.4.382","volume":"134","author":"SA Montgomery","year":"1979","unstructured":"Montgomery, S. A. & Asberg, M. A new depression scale designed to be sensitive to change. Br. J. Psychiatry. 134, 382\u2013389 (1979).","journal-title":"Br. J. Psychiatry."},{"key":"44592_CR38","first-page":"28","volume":"4","author":"J Busner","year":"2007","unstructured":"Busner, J. & Targum, S. D. The clinical global impressions scale: Applying a research tool in clinical practice. Psychiatry (Edgmont). 4, 28\u201337 (2007).","journal-title":"Psychiatry (Edgmont)."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-44592-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-44592-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-44592-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T01:22:27Z","timestamp":1730424147000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-44592-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,30]]},"references-count":38,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["44592"],"URL":"https:\/\/doi.org\/10.1038\/s41598-023-44592-8","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,30]]},"assertion":[{"value":"3 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 October 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing non-financial interests but the following competing financial interests. SV, SN, WCD, and QSL are employees of Janssen Research & Development, LLC, of Johnson & Johnson, and may hold equity in Johnson & Johnson. RM and VAN were employees of Janssen Research & Development, LLC when the study was conducted; RM is now retired and a consultant to Janssen Research & Development, LLC. VAN is currently affiliated with Davos Alzheimer\u2019s Collaborative. MT is or has been an advisor\/consultant and received fee from (lifetime disclosure): Abbott Laboratories, Inc., Abdi Ibrahim, Akzo (Organon Pharmaceuticals Inc.), Alkermes, AstraZeneca, Axon Advisors, Bristol-Myers Squibb Company, Cephalon, Inc., Cerecor, CME Institute of Physicians, Concert Pharmaceuticals, Inc., Eli Lilly & Company, Evotec, Fabre Kramer Pharmaceuticals, Inc., Forest Pharmaceuticals, GlaxoSmithKline, Janssen Global Services, LLC, Janssen Pharmaceutica Products, LP, Johnson & Johnson PRD, Libby, Lundbeck, Meade Johnson, MedAvante, Medtronic, Merck, Mitsubishi Tanabe Pharma Development America, Inc., Naurex, Neuronetics, Otsuka Pharmaceuticals, Pamlab, Parke-Davis Pharmaceuticals, Inc., Pfizer Inc., PgxHealth, Phoenix Marketing Solutions, Rexahn Pharmaceuticals, Ridge Diagnostics, Roche Products Ltd., Sepracor, SHIRE Development, Sierra, SK Life and Science, Sunovion, Takeda, Tal Medical\/Puretech Venture, Targacept, Transcept, VantagePoint, Vivus, and Wyeth-Ayerst Laboratories. In addition, he has received grants\/research support from: Agency for Healthcare Research and Quality (AHRQ), Cyberonics, Inc., National Alliance for Research in Schizophrenia and Depression, National Institute of Mental Health and National Institute on Drug Abuse. RWL has received honoraria for ad hoc speaking or advising\/consulting, or received research funds, from: Asia-Pacific Economic Cooperation, BC Leading Edge Foundation, Canadian Institutes of Health Research, Canadian Network for Mood and Anxiety Treatments, Healthy Minds Canada, Janssen, Lundbeck, Lundbeck Institute, Medscape, Michael Smith Foundation for Health Research, MITACS, Myriad Neuroscience, Ontario Brain Institute, Otsuka, Pfizer, Sanofi, Unity Health, Vancouver Coastal Health Research Institute, and VGH-UBCH Foundation. CNS has received honoraria for ad hoc speaking or advising\/consulting, or received research funds, from Canadian Institutes of Health Research, Lundbeck, Ontario Brain Institute, Otsuka, Pfizer, Bayer and Eisai. SHK has received research funding or honoraria from the following sources: Abbott, Alkermes, Allergan Abbvie, Brain Canada, Canadian Institutes for Health Research, Janssen, Lundbeck, Lundbeck Institute, Ontario Brain Institute, Ontario Research Fund, Otsuka, Pfizer, Servier, Sunovion and Xian-Janssen and holds stock in Field Trip Health. HR, BNF, and RU report no competing financial or non-financial interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"18596"}}