I am a PhD candidate in the Department of Psychology at Yale University (Neuroscience track) supported by a NSF GRFP. I primarily work with Nick Turk-Browne and collaborate with the Krishnaswamy Lab in the Department of Computer Science and Department of Genetics at Yale. My work lies at the intersection of human and machine learning. I am interested in the computational principles of human brain activity that enable and constrain learning new skills, and manipulating those with neurofeedback to facillitate more efficient learning. I also develop and apply novel analysis techniques to explore individual differences in neuroimaging phenotypes and their relation to perception, behavior, and mental health. I completed my undergraduate degree in Cognitive Science, Computer Science, and Spanish at Dartmouth College in winter of 2020, where I worked with Jim Haxby and Caroline Robertson.

Research

Publications, Conference Proceedings, and Preprints

Busch, E.L.*, Conley, M.I.*, & Baskin-Sommers, A. (2024) Manifold learning uncovers nonlinear interactions between the adolescent brain and environment that predict psychopathology. In Press, Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. Early Access

Busch, E.L., Fincke, E.C., Lajoie, G., Krishnaswamy, S., & Turk-Browne, N.B. (2024). Learning along the manifold of human brain activity via real-time neurofeedback. (Accepted) Proceedings of the 8th Annual Conference on Cognitive Computational Neuroscience, Paper.

Busch, E.L., Rapuano, K.M., Anderson, K.M., Rosenberg, M.D., Watts, R., Casey, BJ, Haxby, J.V., & Feilong, M. (2024). Dissociation of reliability, heritability, and predictivity in coarse- and fine-scale functional connectomes during development. Journal of Neuroscience, 44 (6). Paper | Code

Busch, E.L., Yates, T.S., Turk-Browne, N.B. (2023). Tasks constrain the intrinsic dimensionality of activity in non-selective cortex. Proceedings of the 7th Annual Conference on Cognitive Computational Neuroscience, Paper.

Busch, E.L., Huang, J., Benz, A., Wallenstein, T., Lajoie, G., Wolf, G., Krishnaswamy, S.*, & Turk-Browne, N.B.* (2023). Multi-view manifold learning of human brain-state trajectories. Nature Computational Science, 3, 240-253. Paper | Research Briefing | Analysis Code | T-PHATE Software.

Huang, J.*, Busch, E.L.*, Wallenstein, T.*, Gerasimiuk, M., Benz, A., Lajoie, G., Wolf, G., Turk-Browne, N.B., Krishnaswamy, S. (2022). Learning shared neural manifolds from multi-subject FMRI data. 32nd IEEE Machine Learning for Signal Processing [MLSP 2022] arXiv

Busch, E.L.*, Slipski, L.*, Feilong, M., Guntupalli, J.S., Visconti di Oleggio Castello, M., Huckins, J.F., Nastase, S.A., Gobbini, M.I.,Wager, T.D., Haxby, J.V. (2021). Hybrid hyperalignment: A single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity. NeuroImage, 233, 117975. Paper | Code

Select posters & presentations (2022-Present)

Posters

Busch, E.L., Conley, M.I., Baskin-Sommers, A., (2024). Using manifold learning to uncover the embedded brain and implications for mental health in youth. Accepted at the Organization for Human Brain Mapping Annual Meeting. Seoul, South Korea.

Busch, E.L., Fincke, E.C., Lajoie, G., Krishnaswamy, S., Turk-Browne, N.B., (2024). Learning on the manifold of human brain activity through real-time neurofeedback. Accepted at the Organization for Human Brain Mapping Annual Meeting. Seoul, South Korea.

Busch, E.L., Yates, T.S., Turk-Browne, N.B., (2023). Tasks constrain the intrinsic dimensionality of activity in non-selective cortex. 7th Annual Conference on Cognitive Computational Neuroscience. Oxford, United Kingdom.

Busch, E.L., Bhaskar, D., Letrou, A., Zhang, X., Noah, J.A., Lajoie, G., Hirsch, J., Turk-Browne, N.B., Krishnaswamy, S. (2022). An encoder-decoder framework for cross-modal translation of brain imaging data. Montreal AI-Neuroscience Meeting. Montreal, Quebec, Canada.

Busch, E.L., Letrou, A., Huang, J., Lajoie, G., Wolf, G., Krishnaswamy, S., & Turk-Browne, N.B. (2022). A neural manifold learning framework for real-time fMRI neurofeedback. Real-time Functional Imaging and Neurofeedback Meeting. New Haven, Connecticut.

Busch, E.L., Rapuano, K.M., Anderson, K.M., Rosenberg, M.D., Watts, R., Casey, BJ, Haxby, J.V., & Feilong, M. (2022). Heritable template underlies reliable idiosyncrasies in the developing fine-scale connectome. Organization for Human Brain Mapping Annual Meeting. Glasgow, Scotland.

Letrou, A., Busch, E.L., & Turk-Browne, N.B., (2022). Relating neural dynamics and emotion dynamics with nonlinear manifold learning. Social and Affective Neurosience Society Annual Meeting.Virtual.

Select talks

Learning on the manifold of human brain activity via real-time neurofeedback. (November 2024) Oral Presentation. Real-time Functional Imaging and Neurofeedback Meeting. Heidelberg, Germany.

Learning on the manifold of human brain activity via real-time neurofeedback. (August 2024) Contributed Talk. Cognitive Computational Neuroscience 2024, Cambridge, MA.

Dissociable scales reflect reliable, heritable, and behaviorally-relevant individual differences in the developing connectome. (March 2024) ABCD Innovations and Insights Meeting. NIH Campus.

Learning on the manifold of human brain activity via real-time neurofeedback. (November 2023) Nanosymposium on Neural Decoding and Neuroprosthetics. Society for Neuroscience Annual Meeting, Washington D.C.

Multi-view manifold learning of human brain-state trajectories. (April 2023) Shine Lab Meeting, University of Sydney.

An encoder-decoder framework for cross-modal translation of brain imaging data. (December 2022) MAIN 2022 Conference Lightning Talk.

Enhancing human learning along the neural manifold. (September 2022) Yale FAS Brain Imaging Center Users Meeting.

The LEGO theory of the developing functional connectome. (September 2022) ABCD Imaging Analytics Working Group.

Modern fMRI analysis techniques. Guest Lecture, Yale NSCI 270 November 2021.

Hyperalignment: Foundations, flavors, and functions. (April 2021) FINN Lab Meeting, Dartmouth College.

For more details, see CV.