Author

Tomoki Tokuda

Advanced Telecommunications Research Institute InternationalORCID 0000-0001-6284-2113Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Neuroscience· 2

Signature Assays, Organisms & Methods

Click to filter · or open tag

Assay:imaging· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:clustering· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Ayumu YamashitaiD· 2 shared Junichiro YoshimotoiD· 2 shared Koichi Ogawa· 2 shared Mitsuo KawatoiD· 2 shared Okito YamashitaiD· 2 shared Toshinori Yoshioka· 2 shared Yasumasa OkamotoiD· 2 shared Yuji TakaharaiD· 2 shared

Activity by Year

2024-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersNeuroscience

Ayumu YamashitaORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

Prediction of antidepressant treatment response with thalamo-somatomotor functional connectivity revealed by generalizable stratification of depressed patients

Yuto Kashiwagi, Tomoki Tokuda, Yuji Takahara, Yuki Sakai, Junichiro Yoshimoto, Ayumu Yamashita, Toshinori Yoshioka, Koichi Ogawa, Go Okada, Yasumasa Okamoto, Mitsuo Kawato, Okito Yamashita

This paper identifies a thalamo-somatomotor functional connectivity biomarker that stratifies MDD patients into subtypes with distinct antidepressant treatment responses, demonstrating generalizability across independent datasets.

bioRxiv · May 11, 2024 · PreprintNeuroscience

Comprehensive evaluation of pipelines for diagnostic biomarkers of major depressive disorder using multi-site resting-state fMRI datasets

Yuji Takahara, Yuto Kashiwagi, Tomoki Tokuda, Junichiro Yoshimoto, Yuki Sakai, Ayumu Yamashita, Toshinori Yoshioka, Hidehiko Takahashi, Hiroto Mizuta, Kiyoto Kasai, Akira Kunimitsu, Naohiro Okada, Eri Itai, Hotaka Shinzato, Satoshi Yokoyama, , Okito Yamashita

The paper evaluates rs-fMRI analysis pipelines for MDD biomarkers using multi-site datasets, finding optimal combinations of parcellation, connectivity estimation, harmonization, and machine learning methods.

bioRxiv · Mar 17, 2024 · PreprintNeuroscience