Bio

Ellie Beam is a psychiatry resident pursuing research at the intersection of neuroscience, computer science, and language. She completed MD/PhD training at Stanford Medical School with funding from the MSTP and the NRSA fellowship. Her doctoral thesis synthesized the neuroimaging literature into a framework for knowledge of human brain function, published in Nature Neuroscience and forming the basis for a US patent. Her work has been recognized by the Leah J. Dickstein Medical Student Award and Angier B. Duke Memorial Scholarship.

Clinical Focus

  • Residency

Honors & Awards

  • Angier B. Duke Full Tuition Merit Scholarship, Duke University (2009 - 2013)
  • Graduation with Distinction in English & Neuroscience, Duke University (2013)
  • Summa Cum Laude, Duke University (2013)
  • Leah J. Dickstein Medical Student Award, Association of Women Psychiatrists (2017)
  • Ruth L. Kirschstein National Research Service Award (F30), National Institute of Mental Health (2020 - 2022)
  • Trailblazing Trainee Award, Stanford Department of Psychiatry and Behavioral Sciences (2024 - 2025)

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

All Publications

  • 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