Bio

Dr. Ellie Beam is a psychiatrist at Stanford Health Care. She is a clinical scholar and a postdoctoral scholar in the Department of Psychiatry and Behavioral Sciences, Division of General Psychiatry and Psychology at Stanford University School of Medicine.

Dr. Beam specializes in psychoanalytic psychotherapy and mental health research. She provides one-on-one psychotherapy and advanced treatments for mood disorders, such as major depressive disorder, post-traumatic stress disorder (PTSD), and anxiety disorders. She has expertise in treating depression that has not responded to standard therapies, specializing in ketamine-assisted psychotherapy and precision psychiatry. Using evidence-based interventions, Dr. Beam takes a highly personalized approach to care that considers each patient’s symptoms, preferences, and goals.

As a physician-scientist, Dr. Beam studies patterns of brain activity and connections between brain regions. She uses methods such as brain imaging, computational linguistics, and machine learning to help explain differences in mental health conditions, including depression and PTSD. Her work also aims to predict treatment responses and improve personalized approaches to psychiatric care.

Dr. Beam has published her research in leading peer-reviewed journals, including Nature Neuroscience and Journal of Cognitive Neuroscience. As an invited speaker, she has presented at meetings of the Cognitive Neuroscience Society and the American Psychoanalytic Association (APsA). She has also authored community articles and book chapters on a range of topics in psychiatry and cognitive neuroscience.

Dr. Beam is a member of the APsA and the American Psychiatric Association (APA).

Clinical Focus

  • Psychiatry

Academic Appointments

  • Clinical Scholar, Psychiatry and Behavioral Sciences

Honors & Awards

  • Angier B. Duke Full Tuition Merit Scholarship, Duke University (2009 - 2013)
  • Summa Cum Laude, Duke University (2013)
  • Leah J. Dickstein Medical Student Award, Association of Women Psychiatrists (2017)
  • F30 Ruth L. Kirschstein National Research Service Award, National Institute of Mental Health (2020 - 2022)
  • Trailblazing Trainee Award, Stanford Department of Psychiatry and Behavioral Sciences (2024 - 2025)
  • Fellowship, American Psychoanalytic Association (2025 - 2026)
  • T32 Biobehavioral Research Fellowship, National Institutes of Health (2025 - 2027)
  • Co-Chief Resident, Neuroscience Research Track, Psychiatry Residency, Stanford University School of Medicine
  • Inaugural Research Training Program Certificate, APsA

Boards, Advisory Committees, Professional Organizations

  • Member, APsA (2024 - Present)
  • Member, APA (2018 - Present)

Professional Education

  • Doctor of Medicine, Stanford University, MED-MD (2022)
  • Bachelor of Science, Duke University, Neuroscience, English (2013)
  • Doctor of Philosophy, Stanford University, NEURS-PHD (2021)
  • Residency: San Francisco Center for Psychoanalysis (2026) CA
  • Residency: Stanford University Psychiatry Residency (2026) CA
  • Medical Education: Stanford University Medical School (2022) CA

Stanford Advisors

Patents

  • Amit Etkin, Elizabeth Beam. "United States Patent 16/888,530 Machine learning based generation of ontology for structural and functional mapping", Leland Stanford Junior University, Dec 24, 0020

Contact

  • Clinical (Primary)  Stanford Psychiatry Clinic 401 Quarry Rd MC 5722 Stanford, CA 94305 (650) 724-9900 (fax)

Additional Clinical Info

Additional Info

  • Other ID(s):
    S0185862
    EBeam

Research Interests

  • Artificial intelligence (AI)
  • Data Science
  • Mental Health
  • Social Psychology

Current Research and Scholarly Interests

In current research projects, my focus is applying large language models to derive latent speech signatures of depression circuit biotypes. This work aims to identify speech signatures which can deep understanding of brain circuit function and help predict response to interventional treatments, including emerging psychedelic therapies.

All Publications

  • A decade of drug discovery for schizophrenia: TAAR1 and muscarinic agonists Awakenings: Stories of Recovery and Emergence from Schizophrenia Beam, E. H., Ballon, J. edited by Yeiser, B., Nasrallah, H. Amazon. 2024: 217-220
  • Neurocysticercosis Tu, J., Tran, D., Beam, E. RSNA Case Collection. 2022
  • A data-driven framework for mapping domains of human neurobiology Nature Neuroscience Beam, E., Potts, C., Poldrack, R. A., Etkin, A. 2021

    View details for DOI 10.1038/s41593-021-00948-9

  • Registration-free analysis of diffusion MRI tractography data across subjects through the human lifespan. NeuroImage Siless, V., Davidow, J. Y., Nielsen, J., Fan, Q., Hedden, T., Hollinshead, M., Beam, E., Vidal Bustamante, C. M., Garrad, M. C., Santillana, R., Smith, E. E., Hamadeh, A., Snyder, J., Drews, M. K., Van Dijk, K. R., Sheridan, M., Somerville, L. H., Yendiki, A. 2020: 116703

    Abstract

    Diffusion MRI tractography produces massive sets of streamlines that need to be clustered into anatomically meaningful white-matter bundles. Conventional clustering techniques group streamlines based on their proximity in Euclidean space. We have developed AnatomiCuts, an unsupervised method for clustering tractography streamlines based on their neighboring anatomical structures, rather than their coordinates in Euclidean space. In this work, we show that the anatomical similarity metric used in AnatomiCuts can be extended to find corresponding clusters across subjects and across hemispheres, without inter-subject or inter-hemispheric registration. Our proposed approach enables group-wise tract cluster analysis, as well as studies of hemispheric asymmetry. We evaluate our approach on data from the pilot MGH-Harvard-USC Lifespan Human Connectome project, showing improved correspondence in tract clusters across 184 subjects aged 8-90. Our method shows up to 38% improvement in the overlap of corresponding clusters when comparing subjects with large age differences. The techniques presented here do not require registration to a template and can thus be applied to populations with large inter-subject variability, e.g., due to brain development, aging, or neurological disorders.

    View details for DOI 10.1016/j.neuroimage.2020.116703

    View details for PubMedID 32151759

  • Mapping rhetorical topologies in cognitive neuroscience TOPOLOGIES AS TECHNIQUES FOR A POST-CRITICAL RHETORIC Jack, J. L., Appelbaum, G., Beam, E. H., Moody, J., Huettel, S. A. Palgrave Macmillan. 2017: 125-150
  • Mapping the Semantic Structure of Cognitive Neuroscience JOURNAL OF COGNITIVE NEUROSCIENCE Beam, E., Appelbaum, L. G., Jack, J., Moody, J., Huettel, S. A. 2014; 26 (9): 1949-1965

    Abstract

    Cognitive neuroscience, as a discipline, links the biological systems studied by neuroscience to the processing constructs studied by psychology. By mapping these relations throughout the literature of cognitive neuroscience, we visualize the semantic structure of the discipline and point to directions for future research that will advance its integrative goal. For this purpose, network text analyses were applied to an exhaustive corpus of abstracts collected from five major journals over a 30-month period, including every study that used fMRI to investigate psychological processes. From this, we generate network maps that illustrate the relationships among psychological and anatomical terms, along with centrality statistics that guide inferences about network structure. Three terms--prefrontal cortex, amygdala, and anterior cingulate cortex--dominate the network structure with their high frequency in the literature and the density of their connections with other neuroanatomical terms. From network statistics, we identify terms that are understudied compared with their importance in the network (e.g., insula and thalamus), are underspecified in the language of the discipline (e.g., terms associated with executive function), or are imperfectly integrated with other concepts (e.g., subdisciplines like decision neuroscience that are disconnected from the main network). Taking these results as the basis for prescriptive recommendations, we conclude that semantic analyses provide useful guidance for cognitive neuroscience as a discipline, both by illustrating systematic biases in the conduct and presentation of research and by identifying directions that may be most productive for future research.

    View details for DOI 10.1162/jocn_a_00604

    View details for Web of Science ID 000340545300006

    View details for PubMedID 24666126