David Benrimoh, MD, MSc




6875 Boulevard LaSalle
Montréal, QC
H4H 1R3

 Office:Burland Pavilion

Researcher, Douglas Research Centre
Psychiatrist, Douglas Mental Health University Institute
Assistant Professor, Department of Psychiatry, McGill University

Lab name: McGill Lab for Computational Psychiatry and Translation (McPsyt)

Theme-Based Group: Youth Mental Health and Early Intervention
Division: Clinical Research


Dr. David Benrimoh leads the McGill Center for Computational Psychiatry and Translation (McPsyt) which is aimed at using Computational Psychiatry as a unifying framework to tie together neurobiological, cognitive, clinical, social, and societal levels of explanation of psychiatric illness to develop improved mechanistic understanding of these illnesses, as well as predictive models. Using this improved understanding, the center will develop and test novel treatments (including neuromodulation) aimed at reducing the severity, incidence, and burden of psychiatric disease. We will also use digital mental health tools as mechanisms for the collection of data and delivery of interventions. Our current focus is in understanding early psychosis.

David Benrimoh is a neuropsychiatrist who completed his psychiatry residency at McGill. He received an MSc. in Neuroscience from UCL, working with Karl Friston on computational models of auditory hallucinations. He received a second MSc. in Psychiatry from McGill, working with Simon Ducharme and Bratislav Misic on transdiagnostic imaging in psychosis. He is the founder and chief science officer at Aifred Health, a digital mental health company using artificial intelligence to create decision support tools for mental health clinicians; Aifred won the $1M USD second place prize in the global IBM-sponsored AI XPRIZE. He is the author of over 40 peer-reviewed publications. He recently completed his fellowship in Neuropsychiatry at Stanford University, where he conducted research on the mechanistic underpinnings of rapid acting rTMS. Returning to McGill, his research will focus on computational psychiatry approaches to understanding and predicting psychosis onset and using this knowledge to develop novel treatment paradigms. Clinically, he will be working in psychotic disorders, with a focus on early psychosis, and he also hopes to set up a neuropsychiatric consultation clinic for functional neurological disorder and other neuropsychiatric conditions. 

  • Young Investigator Award, American Neuropsychiatric Association, 2023
  • Team Lead for Aifred Health, finalist (2nd place, $1M USD) in the IBM Watson AI XPRIZE Competition (2021)
  • Committee of Psychiatric Educations Award for Best Psychiatry Resident Paper (2019)
  • McGill Psychiatry Serge Bikadoroff Prize for best paper in CL Psychiatry (2021)
  • CSCI/CIHR Resident Research Award (McGill-wide competition, 2020)
  • McGill Psychiatry Chairman’s Prize for best paper (2019)
  • McGill Psychiatry Academic Excellence Prize (2019)
  • McGill Psychiatry Serge Bikadoroff Prize for best paper in CL Psychiatry (2019)
  • Richard and Edith Strauss Fellowship in Medicine Grant for Schizophrenia Modelling Research (2017-2018)

Benrimoh, D., Fisher, V., Mourgues, C., Sheldon, A. D., Smith, R., & Powers, A. R. (2023). Barriers and solutions to the adoption of translational tools for computational psychiatry. Molecular Psychiatry, 1–8. https://doi.org/10.1038/s41380-023-02114-y

Benrimoh, D. A., & Friston, K. J. (2020). All grown up: Computational theories of psychosis, complexity, and progress. Journal of Abnormal Psychology, 129, 624–628.https://doi.org/10.1037/abn0000543

Benrimoh, D., Parr, T., Adams, R. A., & Friston, K. (2019). Hallucinations both in and out of context: An active inference account. PLOS ONE, 14(8), e0212379.https://doi.org/10.1371/journal.pone.0212379

Benrimoh, D., Parr, T., Vincent, P., Adams, R. A., & Friston, K. (2018). Active Inference and Auditory Hallucinations. Computational Psychiatry (Cambridge, Mass.), 2, 183–204.https://doi.org/10.1162/cpsy_a_00022

Benrimoh, D., Fratila, R., Israel, S., Perlman, K., Mirchi, N., Desai, S., Rosenfeld, A., Knappe, S., Behrmann, J., Rollins, C., You, R. P., & Aifred Health Team, T. (2018). Aifred Health, a Deep Learning Powered Clinical Decision Support System for Mental Health. In S. Escalera & M. Weimer (Eds.), The NIPS ’17 Competition: Building Intelligent Systems (pp. 251–287). Springer International Publishing. https://doi.org/10.1007/978-3-319-94042-7_13

Mehltretter, J., Fratila, R., Benrimoh, D. A. , Kapelner, A., Perlman, K., Snook, E., Israel, S., Armstrong, C., Miresco, M., & Turecki, G. (2020). Differential Treatment Benefit Prediction for Treatment Selection in Depression: A Deep Learning Analysis of STAR*D and CO-MED Data. Computational Psychiatry, 4(0), Article 0. https://doi.org/10.1162/cpsy_a_00029

Mehltretter, J., Rollins, C., Benrimoh, D., Fratila, R., Perlman, K., Israel, S., Miresco, M., Wakid, M., & Turecki, G. (2020). Analysis of Features Selected by a Deep Learning Model for Differential Treatment Selection in Depression. Frontiers in Artificial Intelligence, 2.https://www.frontiersin.org/articles/10.3389/frai.2019.00031

Kleinerman, A., Rosenfeld, A., Benrimoh, D., Fratila, R., Armstrong, C., Mehltretter, J., Shneider, E., Yaniv-Rosenfeld, A., Karp, J., Reynolds, C. F., Turecki, G., & Kapelner, A. (2021). Treatment selection using prototyping in latent-space with application to depression treatment. PLOS ONE, 16(11), e0258400. https://doi.org/10.1371/journal.pone.0258400

Benrimoh, D., Tanguay-Sela, M., Perlman, K., Israel, S., Mehltretter, J., Armstrong, C., Fratila, R., Parikh, S. V., Karp, J. F., Heller, K., Vahia, I. V., Blumberger, D. M., Karama, S., Vigod, S. N., Myhr, G., Martins, R., Rollins, C., Popescu, C., Lundrigan, E., … Margolese, H. C. (2021). Using a simulation centre to evaluate preliminary acceptability and impact of an artificial intelligence-powered clinical decision support system for depression treatment on the physician–patient interaction. BJPsych Open, 7(1), e22.https://doi.org/10.1192/bjo.2020.127

Benrimoh, D., Israel, S., Perlman, K., Fratila, R., & Krause, M. (2018). Meticulous Transparency—An Evaluation Process for an Agile AI Regulatory Scheme. In M. Mouhoub, S. Sadaoui, O. Ait Mohamed, & M. Ali (Eds.), Recent Trends and Future Technology in Applied Intelligence (pp. 869–880). Springer International Publishing. https://doi.org/10.1007/978-3-319-92058-0_83

Popescu, C., Golden, G., Benrimoh, D. , Tanguay-Sela, M., Slowey, D., Lundrigan, E., Williams, J., Desormeau, B., Kardani, D., Perez, T., Rollins, C., Israel, S., Perlman, K., Armstrong, C., Baxter, J., Whitmore, K., Fradette, M.-J., Felcarek-Hope, K., Soufi, G., … Turecki, G. (2021). Evaluating the Clinical Feasibility of an Artificial Intelligence–Powered, Web-Based Clinical Decision Support System for the Treatment of Depression in Adults: Longitudinal Feasibility Study. JMIR Formative Research, 5(10), e31862.https://doi.org/10.2196/31862

Pfotenhauer, S. M., Frahm, N., Winickoff, D., Benrimoh, D., Illes, J., & Marchant, G. (2021). Mobilizing the private sector for responsible innovation in neurotechnology. Nature Biotechnology, 39(6), Article 6. https://doi.org/10.1038/s41587-021-00947-y

Perlman, K., Benrimoh, D., Israel, S., Rollins, C., Brown, E., Tunteng, J.-F., You, R., You, E., Tanguay-Sela, M., Snook, E., Miresco, M., & Berlim, M. T. (2019). A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder. Journal of Affective Disorders, 243, 503–515. https://doi.org/10.1016/j.jad.2018.09.067