Journal article
A multi-site, multi-disorder resting-state magnetic resonance image database
- Abstract:
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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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 4.6MB, Terms of use)
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- Publisher copy:
- 10.1038/s41597-021-01004-8
Authors
- Publisher:
- Springer Nature
- Journal:
- Scientific Data More from this journal
- Volume:
- 8
- Issue:
- 1
- Article number:
- 227
- Publication date:
- 2021-08-30
- Acceptance date:
- 2021-07-26
- DOI:
- EISSN:
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2052-4463
- Pmid:
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34462444
- Language:
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English
- Keywords:
- Pubs id:
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1193703
- Local pid:
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pubs:1193703
- Deposit date:
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2023-02-28
Terms of use
- Copyright holder:
- Tanaka et al.
- Copyright date:
- 2021
- Rights statement:
- © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Licence:
- CC Attribution (CC BY)
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