Publications

Busch, E.L.*, Conley, M.I.*, & Baskin-Somers, A. (2024). Manifold learning uncovers nonlinear interactions between the adolescent brain and environment that predict emotional and behavioral problems. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. DOI: 10.1016/j.bpsc.2024.07.001 PDF

Roskies, A., Busch, E.L., & Walton, A. (2024). Agency as a framework for thinking about neuropsychiatric disease: A prelude to asking causal questions. Causal Concepts in Psychopathology: Multidisciplinary Perspectives, Cambridge University Press.

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, predictability, and heritability in fine- and coarse-scale functional connectomes during development. Journal of Neuroscience, 44(6). DOI: 10.1523/JNEUROSCI.0735-23.2023 PDF

Skalaban, L.J., Chan, I., Lin, Q., Rapuano, K.M., Conley, M.I., Busch, E.L., Watts, R., Murty, V., & Casey, B.J. (2024). Representational dissimilarity of faces and places during a working memory task is associated with subsequent recognition memory during development. Journal of Cognitive Neuroscience, 36(3), 415-434. DOI: 10.1162/jocn_a_02094

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(3), 240-253. DOI: 10.1038/s43588-023-00419-0 PDF

Busch, E.L. & Krishnaswamy, S. (2023). Revealing trajectories of the mind via non-linear manifolds of brain activity. Nature Computational Science, 3(3), 192-193. Invited research briefing. DOI: 10.1038/s43588-023-00423-4 PDF

Busch, E.L.*, Slipski, L.*, Feilong, M., Guntupalli, J., Visconti di Oleggio Castello, M., Huckins, J., Nastase, S., Gobbini, M.I., Wager, T., & Haxby, J. (2021). Hybrid hyperalignment: A single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity. NeuroImage, 233. DOI: 10.1016/j.neuroimage.2021.117975 PDF

Busch, E.L., Fincke, E.C., Lajoie, G., Krishnaswamy, S., & Turk-Browne, N.B. Accelerated learning of a noninvasive human brain-computer interface via manifold geometry. Article under review. Preprint DOI: 10.1101/2025.03.29.646109 PDF

Busch, E.L., Turk-Browne, N.B., & Baskin-Sommers, A.R. Revamping neuroimaging analysis to reveal biomarkers of adolescent mental health. Invited Perspective under review. PDF

Busch, E.L. & Turk-Browne, N.B. Intrinsic dimensionality of brain activity manifolds across diverse tasks and development. In preparation.

Busch, E.L. & Turk-Browne, N.B. (2025). Intrinsic dimensionality of brain activity manifolds across tasks and development. Proceedings of the 8th Annual Conference on Cognitive Computational Neuroscience. Accepted. PDF

Afrasiyabi, A., Bhaskar, D., Busch, E.L., Caplette, L., Singh, R., Lajoie, G., Turk-Browne, N.B., & Krishnaswamy, S. (2025). Latent representation learning for multimodal brain activity translation. IEEE International Conference on Acoustics, Speech, and Signal Processing [ICASSP2025]. DOI: 10.1109/ICASSP49660.2025.10887834 PDF

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. Proceedings of the 7th Annual Conference on Cognitive Computational Neuroscience. PDF

Afrasiyabi, A., Busch, E.L., Singh, R., Bhaskar, D., Capette, L., Turk-Browne, N.B., Krishnaswamy, S. (2024). Looking through the mind's eye via multimodal encoder-decoder networks. Machine as Medium: Proceedings of the Center for Collaborative Arts and Media, Fall 2024 Volume. DOI: 10.48550/arXiv.2410.00047 PDF

Busch, E.L., Yates, T.S., & Turk-Browne, N.B. (2023). Tasks collapse the intrinsic dimensionality of activity in non-selective cortex. Proceedings of the 6th Annual Conference on Cognitive Computational Neuroscience. PDF

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. Proceedings of the 32nd IEEE Machine Learning for Signal Processing. DOI: 10.1109/MLSP55214.2022.9943383 PDF