研究概要

主要論文

• 1. Itahashi, T., Yamashita, A., Takahara, Y., Yahata, N., Aoki, Y. Y., Fujino, J., …, Yamashita O. & Hashimoto, R. I.:
  "Generalizable and transportable resting-state neural signatures characterized by functional networks, neurotransmitters, and clinical symptoms in autism."
  Molecular Psychiatry, 1-13. (2024)
• 2. Tanaka, S. C., Kasai, K., Okamoto, Y., Koike, S., Hayashi, T., Yamashita, A., Yamashita O. … & Hanakawa, T.:
  "The status of MRI databases across the world focused on psychiatric and neurological disorders."
  Psychiatry and Clinical Neurosciences, 78(10):563-57. (2024)
• 3. Fukuma, R., Majima, K., Kawahara, Y., Yamashita, O., Shiraishi, Y., Kishima, H., & Yanagisawa, T.:
  "Fast, accurate, and interpretable decoding of electrocorticographic signals using dynamic mode decomposition."
  Communications Biology, 7(1), 595. (2024)
• 4. Endo, H., Ikeda, S., Harada, K., Yamagata, H., Matsubara, T., Matsuo, K., … & Yamashita, O.:
  "Manifold alteration between major depressive disorder and healthy control subjects　using dynamic mode decomposition in resting-state fMRI data."
  Frontiers in Psychiatry, 15, 1288808. (2024)
• 5. Ajioka, T, Nakai, N, Yamashita, O, Takumi, T.:
  "End-to-end deep learning approach to mouse behavior classification from cortex-wide calcium imaging."
  PLoS Comput Biol 20(3): e1011074. (2024)
• 6. Takeda, Y., Gomi, T., Umebayashi, R., Tomita, S., Suzuki, K., Hiroe, N., … & Yamashita, O.:
  "Sensor array design of optically pumped magnetometers for accurately estimating source currents."
  NeuroImage, 277, 120257. (2023)
• 7. Bai, W., Yamashita, O., & Yoshimoto, J.:
  "Learning task-agnostic and interpretable subsequence-based representation of time series and its applications in fMRI analysis."
  Neural Networks, 163, 327-340. (2023)
• 8. Nakai, N., Sato, M., Yamashita, O., Sekine, Y., Fu, X., Nakai, J., … & Takumi, T.:
  "Virtual reality-based real-time imaging reveals abnormal cortical dynamics during behavioral transitions in a mouse model of autism."
  Cell Reports. (2023)
• 9. Hyvärinen, A., Khemakhem, I., Morioka, H.:
  "Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learning."
  Patterns, Vol. 4, Issue 10, 100844, (2023)
• 10. Morioka, H., Hyvärinen, A.:
  "Causal Representation Learning Made Identifiable by Grouping of Observational Variables."
  The 41st International Conference on Machine Learning (ICML2024)，(2024)

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