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

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 class...

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Published inScientific data Vol. 8; no. 1; pp. 227 - 15
Main Authors Tanaka, Saori C., Yamashita, Ayumu, Yahata, Noriaki, Itahashi, Takashi, Lisi, Giuseppe, Yamada, Takashi, Ichikawa, Naho, Takamura, Masahiro, Yoshihara, Yujiro, Kunimatsu, Akira, Okada, Naohiro, Hashimoto, Ryuichiro, Okada, Go, Sakai, Yuki, Morimoto, Jun, Narumoto, Jin, Shimada, Yasuhiro, Mano, Hiroaki, Yoshida, Wako, Seymour, Ben, Shimizu, Takeshi, Hosomi, Koichi, Saitoh, Youichi, Kasai, Kiyoto, Kato, Nobumasa, Takahashi, Hidehiko, Okamoto, Yasumasa, Yamashita, Okito, Kawato, Mitsuo, Imamizu, Hiroshi
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 30.08.2021
Nature Publishing Group
Nature Portfolio
Subjects
631/378/3920
692/53/2421
692/699/375
692/699/476
Adult
Brain - diagnostic imaging
Brain mapping
Data Descriptor
Databases, Factual
Datasets
Female
Functional magnetic resonance imaging
Humanities and Social Sciences
Humans
Learning algorithms
Machine Learning
Magnetic Resonance Imaging
Male
Medical imaging
Mental disorders
Mental Disorders - diagnostic imaging
Middle Aged
multidisciplinary
Neural networks
Neuroimaging
Patients
Science
Science (multidisciplinary)
Young Adult
Online AccessGet full text

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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. Measurement(s) mental or behavioural disorder • brain measurement • Demographic Data Technology Type(s) functional magnetic resonance imaging • magnetic resonance imaging • Resting State Functional Connectivity Magnetic Resonance Imaging Factor Type(s) age • sex • site • disorder Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14716329
AbstractList 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.
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. Measurement(s) mental or behavioural disorder • brain measurement • Demographic Data Technology Type(s) functional magnetic resonance imaging • magnetic resonance imaging • Resting State Functional Connectivity Magnetic Resonance Imaging Factor Type(s) age • sex • site • disorder Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14716329
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. Measurement(s) mental or behavioural disorder • brain measurement • Demographic Data Technology Type(s) functional magnetic resonance imaging • magnetic resonance imaging • Resting State Functional Connectivity Magnetic Resonance Imaging Factor Type(s) age • sex • site • disorder Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14716329
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.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.
Measurement(s) mental or behavioural disorder • brain measurement • Demographic Data Technology Type(s) functional magnetic resonance imaging • magnetic resonance imaging • Resting State Functional Connectivity Magnetic Resonance Imaging Factor Type(s) age • sex • site • disorder Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14716329
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.Measurement(s)mental or behavioural disorder • brain measurement • Demographic DataTechnology Type(s)functional magnetic resonance imaging • magnetic resonance imaging • Resting State Functional Connectivity Magnetic Resonance ImagingFactor Type(s)age • sex • site • disorderSample Characteristic - OrganismHomo sapiensMachine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14716329
ArticleNumber 227
Author Ichikawa, Naho
Yamada, Takashi
Shimizu, Takeshi
Narumoto, Jin
Lisi, Giuseppe
Okada, Go
Yoshida, Wako
Tanaka, Saori C.
Takahashi, Hidehiko
Saitoh, Youichi
Yamashita, Ayumu
Kasai, Kiyoto
Okamoto, Yasumasa
Shimada, Yasuhiro
Yahata, Noriaki
Sakai, Yuki
Kunimatsu, Akira
Kawato, Mitsuo
Hosomi, Koichi
Itahashi, Takashi
Okada, Naohiro
Hashimoto, Ryuichiro
Yoshihara, Yujiro
Kato, Nobumasa
Takamura, Masahiro
Morimoto, Jun
Seymour, Ben
Imamizu, Hiroshi
Mano, Hiroaki
Yamashita, Okito
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/34462444$$D View this record in MEDLINE/PubMed
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Snippet Machine learning classifiers for psychiatric disorders using resting-state functional magnetic resonance imaging (rs-fMRI) have recently attracted attention as...
Measurement(s) mental or behavioural disorder • brain measurement • Demographic Data Technology Type(s) functional magnetic resonance imaging • magnetic...
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Title A multi-site, multi-disorder resting-state magnetic resonance image database
URI https://link.springer.com/article/10.1038/s41597-021-01004-8
https://www.ncbi.nlm.nih.gov/pubmed/34462444
https://www.proquest.com/docview/2566145510
https://www.proquest.com/docview/2567988679
https://pubmed.ncbi.nlm.nih.gov/PMC8405782
https://doaj.org/article/44b7a586914e4be29627ff2e5d84e4cb
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