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References
  1. Feilong, M., & Zhang, Y. (2025). Advancing neural decoding with deep learning: Computational neuroscience. Nature Computational Science, 1–2. https://doi.org/10.1038/s43588-025-00837-2
  2. Zhang, Y., Gobbini, M. I., Haxby, J. V., & Feilong, M. (2025). Boosting Hyperalignment Performance with Age-specific Templates. bioRxiv, 2025–02. https://doi.org/10.1101/2025.02.19.639148
  3. Jung, H., Han, X., Feilong, M., Han, J., Shokeen, D., Uden, C. V., Hanson, I., Connolly, A. C., Haxby, J. V., & Nastase, S. A. (2025). Action features dominate cortical representation during natural vision. bioRxiv, 2025–01. https://doi.org/10.1101/2025.01.30.635800
  4. Feilong, M., Jiahui, G., Gobbini, M. I., & Haxby, J. V. (2024). A cortical surface template for human neuroscience. Nature Methods, 21(9), 1736–1742. https://doi.org/10.1038/s41592-024-02346-y
  5. Busch, E. L., Rapuano, K. M., Anderson, K. M., Rosenberg, M. D., Watts, R., Casey, B., 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). https://doi.org/10.1523/JNEUROSCI.0735-23.2023
  6. Jiahui, G., Feilong, M., Visconti di Oleggio Castello, M., Nastase, S. A., Haxby, J. V., & Gobbini, M. I. (2023). Modeling naturalistic face processing in humans with deep convolutional neural networks. Proceedings of the National Academy of Sciences, 120(43), e2304085120. https://doi.org/10.1073/pnas.2304085120
  7. Feilong, M., Nastase, S. A., Jiahui, G., Halchenko, Y. O., Gobbini, M. I., & Haxby, J. V. (2023). The individualized neural tuning model: Precise and generalizable cartography of functional architecture in individual brains. Imaging Neuroscience, 1, 1–34. https://doi.org/10.1162/imag_a_00032
  8. Lee, C., Han, J., Feilong, M., Jiahui, G., Haxby, J., & Baldassano, C. (2023). Hyper-HMM: aligning human brains and semantic features in a common latent event space. Advances in Neural Information Processing Systems, 36, 27005–27019. https://doi.org/10.7554/eLife.86037
  9. Feilong, M., Guntupalli, J. S., & Haxby, J. V. (2021). The neural basis of intelligence in fine-grained cortical topographies. Elife, 10, e64058. https://doi.org/10.7554/eLife.64058
  10. Busch, E. L., Slipski, L., Feilong, M., Guntupalli, J. S., di Oleggio Castello, M. V., 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. https://doi.org/10.1016/j.neuroimage.2021.117975
  11. Sippel, L. M., Holtzheimer, P. E., Huckins, J. F., Collier, E., Feilong, M., Wheatley, T., & Meyer, M. L. (2021). Neurocognitive mechanisms of poor social connection in posttraumatic stress disorder: Evidence for abnormalities in social working memory. Depression and Anxiety, 38(6), 615–625. https://doi.org/10.1002/da.23139
  12. Haxby, J. V., Guntupalli, J. S., Nastase, S. A., & Feilong, M. (2020). Hyperalignment: Modeling shared information encoded in idiosyncratic cortical topographies. Elife, 9, e56601. https://doi.org/10.7554/eLife.56601
  13. Jiahui, G., Feilong, M., di Oleggio Castello, M. V., Guntupalli, J. S., Chauhan, V., Haxby, J. V., & Gobbini, M. I. (2020). Predicting individual face-selective topography using naturalistic stimuli. NeuroImage, 216, 116458. https://doi.org/10.1016/j.neuroimage.2019.116458
  14. Guntupalli, J. S., Feilong, M., & Haxby, J. V. (2018). A computational model of shared fine-scale structure in the human connectome. PLoS Computational Biology, 14(4), e1006120. https://doi.org/10.1371/journal.pcbi.1006120
  15. Feilong, M., Nastase, S. A., Guntupalli, J. S., & Haxby, J. V. (2018). Reliable individual differences in fine-grained cortical functional architecture. NeuroImage, 183, 375–386. https://doi.org/10.1016/j.neuroimage.2018.08.029