Nina de Lacy

Nina de Lacy, MBA, MD

Languages spoken: English

Academic Information

Departments Primary - Psychiatry

Divisions: Child & Adolescent Psychiatry

Board Certification

  • American Board of Psychiatry & Neurology (Psychiatry)

Nina de Lacy, MD, MBA, Assistant Professor, conducts research at the interface of computational science, artificial intelligence and mental health. Her laboratory develops novel computational approaches with the goal of advancing our understanding of risk trajectories, outcomes and mechanisms in mental health and substance use disorders, enabling more impactful and efficient prevention and intervention. She is particularly known for innovation in sequence data such as digital phenotyping and electronic health records and her neuroscientific work developing innovative mathematical models of brain function and its relationship with psychiatric illness. Dr. de Lacy uses artificial intelligence and machine learning approaches well-suited to problems such as individual prediction, precision psychiatry and the identification of psychiatric biotypes and biomarkers. Dr. de Lacy earned her undergraduate degree at the University of Oxford, her medical degree at UCSF and completed her Adult Psychiatry Residency (Neuroscience Track) and her Fellowship in Child and Adolescent Psychiatry at the University of Washington.

Education History

Undergraduate Oxford University
 BA, MA
Diploma Oxford University
 Postgraduate Diploma
Graduate Training Northwestern University
 MBA
Professional Medical University of California, San Francisco School of Medicine
 MD
Residency University of Washington
 Resident
Fellowship University of Washington
 Fellow

Selected Publications

Journal Article

  1. Herzig L, de Lacy N, Capone G, Radesky J (2018). Intellectual Disability and Psychotropic Medications. J Dev Behav Pediatr, 39(7), 591-593.
  2. Bachmann-Gagescu R, Dempsey JC, Phelps IG, O'Roak BJ, Knutzen DM, Rue TC, Ishak GE, Isabella CR, Gorden N, Adkins J, Boyle EA, de Lacy N, O'Day D, Alswaid A, Ramadevi A R, Lingappa L, Lourenço C, Martorell L, Garcia-Cazorla À, Ozyürek H, Halilo'lu G, Tuysuz B, Topçu M, University of Washington Center for Mendelian Genomics., Chance P, Parisi MA, Glass IA, Shendure J, Doherty D (2015). Joubert syndrome: a model for untangling recessive disorders with extreme genetic heterogeneity. J Med Genet, 52(8), 514-22.
  3. Mac Donald CL, Barber J, Wright J, Coppel D, De Lacy N, Ottinger S, Peck S, Panks C, Sun S, Zalewski K, Temkin N (2019). Longitudinal Clinical and Neuroimaging Evaluation of Symptomatic Concussion in 10- to 14-year-old Youth Athletes. J Neurotrauma, 36(2), 264-274.
  4. Calhoun VD, de Lacy N (2017). Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis. Neuroimaging Clin N Am, 27(4), 561-579.
  5. de Lacy N, McCauley E, Kutz JN, Calhoun VD (2019). Sex-related differences in intrinsic brain dynamism and their neurocognitive correlates. Neuroimage, 202, 116116.
  6. Fu Z, Tu Y, Di X, Du Y, Sui J, Biswal BB, Zhang Z, de Lacy N, Calhoun VD (2019). Transient increased thalamic-sensory connectivity and decreased whole-brain dynamism in autism. Neuroimage, 190, 191-204.
  7. Mac Donald CL, Barber J, Wright J, Coppel D, De Lacy N, Ottinger S, Peck S, Panks C, Zalewski K, Sun S, Temkin N (2018). Quantitative Volumetric Imaging and Clinical Outcome Characterization of Symptomatic Concussion in 10- to 14-Year-Old Adolescent Athletes. J Head Trauma Rehabil, 33(6), E1-E10.
  8. de Lacy N, Kodish I, Rachakonda S, Calhoun VD (2018). Novel in silico multivariate mapping of intrinsic and anticorrelated connectivity to neurocognitive functional maps supports the maturational hypothesis of ADHD. Hum Brain Mapp, 39(8), 3449-3467.
  9. King BH, de Lacy N, Siegel M (2014). Psychiatric assessment of severe presentations in autism spectrum disorders and intellectual disability. Child Adolesc Psychiatr Clin N Am, 23(1), 1-14.
  10. de Lacy N, King BH (2013). Revisiting the relationship between autism and schizophrenia: toward an integrated neurobiology. Annu Rev Clin Psychol, 9, 555-87.
  11. de Lacy N, McCauley E, Kutz JN, Calhoun VD (2019). Multilevel Mapping of Sexual Dimorphism in Intrinsic Functional Brain Networks. Front Neurosci, 13, 332.
  12. de Lacy N, Kutz JN, Calhoun VD (2020). Sex-related differences in brain dynamism at rest as neural correlates of positive and negative valence system constructs. Cogn Neurosci, 12, 1-24.
  13. de Lacy N (2021). Sex/gender differences in the neural substrate of long-term memory. Cogn Neurosci, 12, 1-2.
  14. de Lacy N, Doherty D, King BH, Rachakonda S, Calhoun VD (2017). Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum. Neuroimage Clin, 15, 513-524.
  15. de Lacy N, Calhoun VD (2018). Dynamic connectivity and the effects of maturation in youth with attention deficit hyperactivity disorder. Netw Neurosci, 3(1), 195-216.
  16. de Lacy N, Ramshaw MJ, Kutz J (2022). Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning. Frontiers in artificial intelligence, 5, 832530.
  17. de Lacy N, Ramshaw MJ, McCauley E, Kerr KF, Kaufman J, Nathan Kutz (2023). Predicting individual cases of major adolescent psychiatric conditions with artificial intelligence. Translational psychiatry, 13(1), 314.
  18. de Lacy N, Ramshaw M (2023). Selectively predicting the onset of ADHD, oppositional defiant disorder, and conduct disorder in early adolescence with high accuracy. Frontiers in psychiatry, 14, 1280326.
  19. de Lacy N, Ramshaw M, Lam W (2025). RiskPath: Explainable deep learning for multistep biomedical prediction in longitudinal data. Patterns (New York, N.Y.), 6(8), 101240.
  20. de Lacy N, Ramshaw M, Lam W (2025). Predicting the onset of internalizing disorders in early adolescence using deep learning optimized with AI. Frontiers in psychiatry, 16, 1487894.

Letter

  1. Kaufman J, Kobak K, Birmaher B, de Lacy N (2021). KSADS-COMP Perspectives on Child Psychiatric Diagnostic Assessment and Treatment Planning. [Letter to the editor]. J Am Acad Child Adolesc Psychiatry, 60(5), 540-542.