( 📝:manuscript, paper or short paper, 📢:abstract)

• 📝Local and long-distance organization of prefrontal cortex circuits in the marmoset brain
  • A. Watakabe, H. Skibbe, K. Nakae, H. Abe, N. Ichinohe, M. F. Rachmadi, J. Wang, M. Takaji, H. Mizukami, A. Woodward, R. Gong, J. Hata, D. C. Van Essen, H. Okano, S. Ishii, T. Yamamori
  • In Neuron, 2023

• 📝Improving Segmentation of Objects with Varying Sizes in Biomedical Images using Instance-wise and Center-of-Instance Segmentation Loss Function
  • M.F. Rachmadi, C. Poon, H. Skibbe
  • MIDL, 2023 (to appear)

• 📝Tomographic tract tracing and data driven approaches to unravel complex 3D fiber anatomy of DBS relevant prefrontal projections to the diencephalic-mesencephalic junction in the marmoset
  • V. A. Coenen, A. Watakabe, H. Skibbe, T. Yamamori, M. D. Döbrössy, B. E.A. Sajonz, P. C. Reinacher, M. Reisert
  • In Brain Stimulation, 2023

• 📝An automated pipeline to create an atlas of in situ hybridization gene expression data in the adult marmoset brain
  • C. Poon, M.F. Rachmadi, M. Byra, M. Schlachter, B. Xu, T. Shimogori, H. Skibbe
  • In IEEE International Symposium on Biomedical Imaging, to appear.

• 📝A dataset of rodent cerebrovasculature from in vivo multiphoton fluorescence microscopy imaging
  • C. Poon, Teikari P., M.F. Rachmadi, H. Skibbe, K. Hynynen
  • In Scientific Data, 2023

• 📝Improving the prediction of white matter hyperintensities evolution in brain MRI of patients with small vessel disease using stroke lesions information
  • M.F. Rachmadi, M.d.C. Valdés-Hernández, S. Makin, J. Wardlaw, H. Skibbe
  • In bioRxiv, Cold Spring Harbor Laboratory, 2022.

• 📝Where Position Matters - Deep learning driven normalization and co-registration of computed tomography in the postoperative analysis of Deep Brain Stimulation
  • M. Reisert, B. Sajonz, P. Reinacher, M. Russe, E. Kellner, H. Skibbe, V. A. Coenen
  • In Neuromodulation: Technology at the Neural Interface, to appear

• 📝Multi-modal brain magnetic resonance imaging database covering marmosets with a wide age range
  • J. Hata, K. Nakae, H. Tsukada, A. Woodward, Y. Haga, M. Iida, A. Uematsu, F. Seki, N. Ichinohe, R. Gong, T. Kaneko, D. Yoshimaru, A. Watakabe, H. Abe, T. Tani, H. Skibbe, M. Maeda, F. Papazian, K. Hagiya, N. Kishi, T. Shimogori, T. Yamamori, H. James Okano, H. Okano
  • In bioRxiv, Cold Spring Harbor Laboratory, 2022.

• 📝The Brain/MINDS Marmoset Connectivity Atlas: Exploring bidirectional tracing and tractography in the same stereotaxic space
  • H. Skibbe, M.F. Rachmadi, K. Nakae, C. E. Gutierrez, J. Hata, H. Tsukada, C. Poon, K. Doya, P. Majka, M. G. P. Rosa, H. Okano, T. Yamamori, S. Ishii, M. Reisert, A. Watakabe.
  • In bioRxiv, Cold Spring Harbor Laboratory, 2022.

• 📝A spiking neural network builder for systematic data-to-model workflow
  • C. E. Gutierres, H. Skibbe, H. Musset, K. Doya.
  • Frontiers in Neuroinformatics (to appear), 2022.

• 📝Distinctiveness and continuity in transcriptome and connectivity in the anterior-posterior axis of the paraventricular nucleus of thalamus
  • Shima, Y., Skibbe, H, Sasagawa, Y., Fujimori, N., Nikaido, I., Hattori, N. and Kato, T.
  • In bioRxiv, Cold Spring Harbor Laboratory, 2022.

• 📢 Semi-supervised semantic segmentation of in situ hybridization gene expression in the marmoset brain
  • C. Poon, M.F. Rachmadi, M. Byra, T. Shimogori, and H. Skibbe
  • Society for Neuroscience, 2022. (California, USA)

• 📢 Semi-supervised contrastive learning for semantic segmentation of in situ hybridization gene expression in the marmoset brain
  • C. Poon, M.F. Rachmadi, M. Byra, T. Shimogori, and H. Skibbe
  • International Symposium on Artificial Intelligence and Brain Science, 2022. (Okinawa, Japan)

• 📢 Development of a Data-driven Prediction Model for the Evolution of White Matter Hyperintensities using Deep Learning: Progress and Challenges
  • M.F. Rachmadi, M.d.C. Valdés-Hernández, S. Makin, J.M. Wardlaw, T. Komura, and H. Skibbe
  • Neuroscience, 2022. (Okinawa, Japan)

• 📢 Development of a Data-driven Prediction Model for the Evolution of White Matter Hyperintensities using Deep Learning: Progress and Challenges
  • M.F. Rachmadi, M.d.C. Valdés-Hernández, S. Makin, J.M. Wardlaw, T. Komura, and H. Skibbe
  • I nternational Symposium on Artificial Intelligence and Brain Science, 2022. (Okinawa, Japan)

• 📢 Semi-supervised contrastive learning for semantic segmentation of in situ hybridization gene expression in the marmoset brain
  • C. Poon, M.F. Rachmadi, M. Byra, T. Shimogori, and H. Skibbe
  • Neuroscience, 2022. (Okinawa, Japan)

• 📝Connectional architecture of the prefrontal cortex in the marmoset brain
  • Watakabe, A., Skibbe, H. Nakae, K., Abe, H., Ichinohe, N., Wang, J., Takaji, M., Mizukami, H., Woodward, A., Gong, R., Hata, J., Okano, H., Ishii, S. and Yamamori, T.
  • In bioRxiv, Cold Spring Harbor Laboratory, 2021.

• 📝Probabilistic Deep Learning with Adversarial Training and Volume Interval Estimation-Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities Evolution
  • Rachmadi, M. F., Valdés-Hernández, M. del C, Maulana, R., Wardlaw, J., Makin, S. and Skibbe, H.
  • In International Workshop on PRedictive Intelligence In MEdicine, 2021.

• 📝Semi-supervised Image-to-Image translation for robust image registration
  • Skibbe, H., Watakabe, A., Rachmadi, F., Gutierrez, C.E, Nakae,K. and Yamamori, T.
  • In Proc. of the Medical Imaging with Deep Learning 2021, 2021.

• 📝Cellular-resolution gene expression profiling in the neonatal marmoset brain reveals dynamic species-and region-specific differences
  • Kita, Y., Nishibe, H., Wang, Y., Hashikawa, T., Kikuchi, S. S., Mami, U,. Yoshida, A. C., Yoshida, C., Kawase, T., Ishii, S., Skibbe, H. and Shimogori, T.
  • In Proceedings of the National Academy of Sciences, National Acad Sciences, volume 118, 2021.

• 📝Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference
  • Gutierrez, Carlos Enrique, Skibbe, Henrik, Nakae, Ken, Tsukada, Hiromichi, Lienard, Jean, Watakabe, Akiya, Hata, Junichi, Reisert, Marco, Woodward, Alexander, Yamaguchi, Yoko, Yamamori, Tetsuo, Okano, Hideyuki, Ishii,Shin and Doya, Kenji
  • In Scientific reports, Nature Publishing Group, volume 10, 2020.

• 📢 Mapping connectivity of the marmoset prefrontal cortex
  • A. Watakabe, H. Skibbe, K. Nakae, J. Wang, M. Takaji, H. Mizukami, A. Woodward, R. Gong, Y. Yamaguchi, J. Hata, H. Okano, S. Ishii and T. Yamamori
  • Neuroscience, 2019. (Niigata, Japan)

• 📢 Fully automated data processing for mapping connectivity of the marmoset prefrontal cortex
  • H. Skibbe, A. Watakabe, K. Nakae, C. E. Gutierrez, A. Woodward, H. Tsukada, R. Gong, J. Hata, K. Doya, H. Okano, T. Yamamori and S. Ishii
  • Neuroscience, 2019. (Niigata, Japan)
  • Abstract

• 📢 A fully automated, AI-driven pipeline for the determination of the marmoset brain connectivity based on tracer data obtained with the TissueCyte microscope
  • H Skibbe, A. Watakabe, K. Nakae, C. E. Gutierrez, A. Woodward, H. Tsukada, R. Gong, J. Hata, H. Okano, T. Yamamori and S. Ishii
  • International Symposium of Brain/MINDS ISBM, 2019. (Tokyo, Japan)

• 📢 Macro-scale connectome by diffusion MRI of exvivo marmoset brain with a pipeline of global fiber reconstruction
  • K. Nakae, J. Hata, H. Skibbe, A. Woodward, C. E. Gutierrez, H. Tsukada, G. Rui, H. Okano and S. Ishii
  • International Symposium of Brain/MINDS ISBM, 2019. (Tokyo, Japan)

• 📢 Multi-objective Parameter Optimization of DWI-based Global Fiber Tracking with Neuronal Tracer Signal as a Reference
  • C. Enrique Gutierrez, H. Skibbe, K. Nakae, J. Liénard, A. Woodward, A. Watakabe, H. Tsukada, J. Hata, H. Okano, T. Yamamori, Y. Yamaguchi, S. Ishii and K. Doya
  • International Symposium of Brain/MINDS ISBM, 2019. (Tokyo, Japan)