Ellie Beam graduated summa cum laude from Duke University in 2013 with a BS in Neuroscience and a BA in English, earning distinction for theses in both majors. Her research with Professor Scott Huettel applied network text analyses to map the semantic structure of cognitive neuroscience. Following graduation, Ellie worked for two years in the lab of Professor Randy Buckner at Harvard University, coordinating large-scale studies of affective illness and leading an independent project that related disruption in frontoparietal network connectivity to executive control impairment in young adults with subthreshold depression. She matriculated at the Stanford School of Medicine in 2015 and is pursuing a PhD in the Neurosciences through the Medical Scientist Training Program. Her research in the lab of Amit Etkin has employed machine learning techniques to identify neurophysiological subtypes of post-traumatic stress disorder. She is currently developing data-driven approaches to validating and engineering ontologies of human brain function.
Amit Etkin, Doctoral Dissertation Advisor (AC)
Registration-free analysis of diffusion MRI tractography data across subjects through the human lifespan.
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 Palgrave Macmillan. 2017: 125–150
Mapping the Semantic Structure of Cognitive Neuroscience
JOURNAL OF COGNITIVE NEUROSCIENCE
2014; 26 (9): 1949-1965
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