A multi-site, multi-disorder resting-state magnetic resonance image database

Saori C. Tanaka; Ayumu Yamashita; Noriaki Yahata; Takashi Itahashi; Giuseppe Lisi; Takashi Yamada; Naho Ichikawa; Masahiro Takamura; Yujiro Yoshihara; Akira Kunimatsu; Naohiro Okada; Ryuichiro Hashimoto; Go Okada; Yuki Sakai; Jun Morimoto; Jin Narumoto; Yasuhiro Shimada; Hiroaki Mano; Wako Yoshida; Ben Seymour; Takeshi Shimizu; Koichi Hosomi; Youichi Saitoh; Kiyoto Kasai; Nobumasa Kato; Hidehiko Takahashi; Yasumasa Okamoto; Okito Yamashita; Mitsuo Kawato; Hiroshi Imamizu

doi:10.1038/s41597-021-01004-8

A multi-site, multi-disorder resting-state magnetic resonance image database

Saori C. Tanaka, Ayumu Yamashita, Noriaki Yahata, Takashi Itahashi, Giuseppe Lisi, Takashi Yamada, Naho Ichikawa, Masahiro Takamura, Yujiro Yoshihara, Akira Kunimatsu, Naohiro Okada, Ryuichiro Hashimoto, Go Okada, Yuki Sakai, Jun Morimoto, Jin Narumoto, Yasuhiro Shimada, Hiroaki Mano, Wako Yoshida, Ben SeymourTakeshi Shimizu, Koichi Hosomi, Youichi Saitoh, Kiyoto Kasai, Nobumasa Kato, Hidehiko Takahashi, Yasumasa Okamoto, Okito Yamashita, Mitsuo Kawato, Hiroshi Imamizu

Research output: Contribution to journal › Article › peer-review

61 Citations (Scopus)

Abstract

Machine learning classifiers for psychiatric disorders using resting-state functional magnetic resonance imaging (rs-fMRI) have recently attracted attention as a method for directly examining relationships between neural circuits and psychiatric disorders. To develop accurate and generalizable classifiers, we compiled a large-scale, multi-site, multi-disorder neuroimaging database. The database comprises resting-state fMRI and structural images of the brain from 993 patients and 1,421 healthy individuals, as well as demographic information such as age, sex, and clinical rating scales. To harmonize the multi-site data, nine healthy participants (“traveling subjects”) visited the sites from which the above datasets were obtained and underwent neuroimaging with 12 scanners. All participants consented to having their data shared and analyzed at multiple medical and research institutions participating in the project, and 706 patients and 1,122 healthy individuals consented to having their data disclosed. Finally, we have published four datasets: 1) the SRPBS Multi-disorder Connectivity Dataset 2), the SRPBS Multi-disorder MRI Dataset (restricted), 3) the SRPBS Multi-disorder MRI Dataset (unrestricted), and 4) the SRPBS Traveling Subject MRI Dataset.

Original language	English
Article number	227
Journal	Scientific data
Volume	8
Issue number	1
DOIs	https://doi.org/10.1038/s41597-021-01004-8
Publication status	Published - 12-2021
Externally published	Yes

All Science Journal Classification (ASJC) codes

Statistics and Probability
Information Systems
Education
Computer Science Applications
Statistics, Probability and Uncertainty
Library and Information Sciences

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10.1038/s41597-021-01004-8

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Tanaka, S. C., Yamashita, A., Yahata, N., Itahashi, T., Lisi, G., Yamada, T., Ichikawa, N., Takamura, M., Yoshihara, Y., Kunimatsu, A., Okada, N., Hashimoto, R., Okada, G., Sakai, Y., Morimoto, J., Narumoto, J., Shimada, Y., Mano, H., Yoshida, W., ... Imamizu, H. (2021). A multi-site, multi-disorder resting-state magnetic resonance image database. Scientific data, 8(1), Article 227. https://doi.org/10.1038/s41597-021-01004-8

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title = "A multi-site, multi-disorder resting-state magnetic resonance image database",

abstract = "Machine learning classifiers for psychiatric disorders using resting-state functional magnetic resonance imaging (rs-fMRI) have recently attracted attention as a method for directly examining relationships between neural circuits and psychiatric disorders. To develop accurate and generalizable classifiers, we compiled a large-scale, multi-site, multi-disorder neuroimaging database. The database comprises resting-state fMRI and structural images of the brain from 993 patients and 1,421 healthy individuals, as well as demographic information such as age, sex, and clinical rating scales. To harmonize the multi-site data, nine healthy participants (“traveling subjects”) visited the sites from which the above datasets were obtained and underwent neuroimaging with 12 scanners. All participants consented to having their data shared and analyzed at multiple medical and research institutions participating in the project, and 706 patients and 1,122 healthy individuals consented to having their data disclosed. Finally, we have published four datasets: 1) the SRPBS Multi-disorder Connectivity Dataset 2), the SRPBS Multi-disorder MRI Dataset (restricted), 3) the SRPBS Multi-disorder MRI Dataset (unrestricted), and 4) the SRPBS Traveling Subject MRI Dataset.",

author = "Tanaka, {Saori C.} and Ayumu Yamashita and Noriaki Yahata and Takashi Itahashi and Giuseppe Lisi and Takashi Yamada and Naho Ichikawa and Masahiro Takamura and Yujiro Yoshihara and Akira Kunimatsu and Naohiro Okada and Ryuichiro Hashimoto and Go Okada and Yuki Sakai and Jun Morimoto and Jin Narumoto and Yasuhiro Shimada and Hiroaki Mano and Wako Yoshida and Ben Seymour and Takeshi Shimizu and Koichi Hosomi and Youichi Saitoh and Kiyoto Kasai and Nobumasa Kato and Hidehiko Takahashi and Yasumasa Okamoto and Okito Yamashita and Mitsuo Kawato and Hiroshi Imamizu",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s).",

year = "2021",

month = dec,

doi = "10.1038/s41597-021-01004-8",

language = "English",

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Tanaka, SC, Yamashita, A, Yahata, N, Itahashi, T, Lisi, G, Yamada, T, Ichikawa, N, Takamura, M, Yoshihara, Y, Kunimatsu, A, Okada, N, Hashimoto, R, Okada, G, Sakai, Y, Morimoto, J, Narumoto, J, Shimada, Y, Mano, H, Yoshida, W, Seymour, B, Shimizu, T, Hosomi, K, Saitoh, Y, Kasai, K, Kato, N, Takahashi, H, Okamoto, Y, Yamashita, O, Kawato, M & Imamizu, H 2021, 'A multi-site, multi-disorder resting-state magnetic resonance image database', Scientific data, vol. 8, no. 1, 227. https://doi.org/10.1038/s41597-021-01004-8

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T1 - A multi-site, multi-disorder resting-state magnetic resonance image database

AU - Tanaka, Saori C.

AU - Yamashita, Ayumu

AU - Yahata, Noriaki

AU - Itahashi, Takashi

AU - Lisi, Giuseppe

AU - Yamada, Takashi

AU - Ichikawa, Naho

AU - Takamura, Masahiro

AU - Yoshihara, Yujiro

AU - Kunimatsu, Akira

AU - Okada, Naohiro

AU - Hashimoto, Ryuichiro

AU - Okada, Go

AU - Sakai, Yuki

AU - Morimoto, Jun

AU - Narumoto, Jin

AU - Shimada, Yasuhiro

AU - Mano, Hiroaki

AU - Yoshida, Wako

AU - Seymour, Ben

AU - Shimizu, Takeshi

AU - Hosomi, Koichi

AU - Saitoh, Youichi

AU - Kasai, Kiyoto

AU - Kato, Nobumasa

AU - Takahashi, Hidehiko

AU - Okamoto, Yasumasa

AU - Yamashita, Okito

AU - Kawato, Mitsuo

AU - Imamizu, Hiroshi

PY - 2021/12

Y1 - 2021/12

N2 - Machine learning classifiers for psychiatric disorders using resting-state functional magnetic resonance imaging (rs-fMRI) have recently attracted attention as a method for directly examining relationships between neural circuits and psychiatric disorders. To develop accurate and generalizable classifiers, we compiled a large-scale, multi-site, multi-disorder neuroimaging database. The database comprises resting-state fMRI and structural images of the brain from 993 patients and 1,421 healthy individuals, as well as demographic information such as age, sex, and clinical rating scales. To harmonize the multi-site data, nine healthy participants (“traveling subjects”) visited the sites from which the above datasets were obtained and underwent neuroimaging with 12 scanners. All participants consented to having their data shared and analyzed at multiple medical and research institutions participating in the project, and 706 patients and 1,122 healthy individuals consented to having their data disclosed. Finally, we have published four datasets: 1) the SRPBS Multi-disorder Connectivity Dataset 2), the SRPBS Multi-disorder MRI Dataset (restricted), 3) the SRPBS Multi-disorder MRI Dataset (unrestricted), and 4) the SRPBS Traveling Subject MRI Dataset.

AB - Machine learning classifiers for psychiatric disorders using resting-state functional magnetic resonance imaging (rs-fMRI) have recently attracted attention as a method for directly examining relationships between neural circuits and psychiatric disorders. To develop accurate and generalizable classifiers, we compiled a large-scale, multi-site, multi-disorder neuroimaging database. The database comprises resting-state fMRI and structural images of the brain from 993 patients and 1,421 healthy individuals, as well as demographic information such as age, sex, and clinical rating scales. To harmonize the multi-site data, nine healthy participants (“traveling subjects”) visited the sites from which the above datasets were obtained and underwent neuroimaging with 12 scanners. All participants consented to having their data shared and analyzed at multiple medical and research institutions participating in the project, and 706 patients and 1,122 healthy individuals consented to having their data disclosed. Finally, we have published four datasets: 1) the SRPBS Multi-disorder Connectivity Dataset 2), the SRPBS Multi-disorder MRI Dataset (restricted), 3) the SRPBS Multi-disorder MRI Dataset (unrestricted), and 4) the SRPBS Traveling Subject MRI Dataset.

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VL - 8

JO - Scientific data

JF - Scientific data

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ER -

A multi-site, multi-disorder resting-state magnetic resonance image database

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