Author

Yuki Hashimoto

National Center of Neurology and Psychiatry

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Neuroscience· 3

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:differential expression· 2 Organism:human· 2 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Research Focus:dimensionality reduction· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Manabu HondaiD· 2 shared Yuichi YamashitaiD· 2 shared Akitoyo HishimotoiD· 1 shared Dennis W. DicksoniD· 1 shared Genichi SugiharaiD· 1 shared Hidehiko TakahashiiD· 1 shared Hiroaki SekiyaiD· 1 shared Hiroyuki YamaguchiiD· 1 shared

Activity by Year

2021-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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2 shared papersNeuroscience

Manabu HondaORCID

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3 papers

Recent·Most cited

Widespread Distribution of α-Synuclein Oligomers in LRRK2-related Parkinson's Disease

Hiroaki Sekiya, Lukas Franke, Yuki Hashimoto, Mariko Takata, Katsuya Nishida, Naonobu Futamura, Kazuko Hasegawa, Hisatomo Kowa, Owen A. Ross, Pamela J. McLean, Tatsushi Toda, Zbigniew K. Wszołek, Dennis W. Dickson

This study examines the distribution of α-synuclein oligomers in LRRK2-related Parkinson's disease, finding a negative correlation between oligomers and Lewy-related pathology.

Acta Neuropathologica · May 2, 2025Peer-reviewedNeuroscience

Deep Feature Extraction for Resting-State Functional MRI by Self-Supervised Learning and Application to Schizophrenia Diagnosis

Yuki Hashimoto, Yousuke Ogata, Manabu Honda, Yuichi Yamashita

This paper introduces a self-supervised deep learning method for resting-state fMRI analysis, showing improved schizophrenia diagnosis compared to conventional methods.

Frontiers in Neuroscience · Aug 27, 2021Peer-reviewedNeuroscience

Three-dimensional convolutional autoencoder extracts features of structural brain images with a diagnostic label-free approach: Application to schizophrenia datasets

Hiroyuki Yamaguchi, Yuki Hashimoto, Genichi Sugihara, Jun Miyata, Toshiya Murai, Hidehiko Takahashi, Manabu Honda, Akitoyo Hishimoto, Yuichi Yamashita

This paper applies a 3D convolutional autoencoder to structural brain MRI data from schizophrenia and healthy subjects to extract disease-related features without diagnostic labels.

Frontiers in Neuroscience · Jul 7, 2021Peer-reviewedNeuroscience