| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2026-01-30 |
| タイトル |
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タイトル |
Optical Neuroimage Studio (OptiNiSt): Intuitive, scalable, extendable framework for optical neuroimage data analysis |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Yamane, Yukako
Li, Yuzhe
Matsumoto, Keita
Kanai, Ryota
Desforges, Miles
Gutierrez, Carlos Enrique
Doya, Kenji
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
Advancements in calcium indicators and optical techniques have made optical neural recording common in neuroscience. As data volumes grow, streamlining the analysis pipelines for image preprocessing, signal extraction, and subsequent neural activity analyses becomes essential. Challenges in analysis includes 1) ensuring data quality of original and processed data at each step, 2) selecting optimal algorithms and their parameters from numerous options, each with its own pros and cons, by implementing or installing them manually, 3) systematically recording each analysis step for reproducibility, and 4) adopting standard data formats for data sharing and meta-analyses. To address these challenges, we developed Optical Neuroimage Studio (OptiNiSt), a scalable, extendable, and reproducible framework for creating calcium data analysis pipelines. OptiNiSt includes the following features. 1) Researchers can easily create analysis pipelines by selecting multiple processing modules, tuning their parameters, and visualizing the results at each step through a graphic user interface in a web browser. 2) In addition to pre-installed tools, new analysis algorithms can be easily added. 3) Once a processing pipeline is designed, the entire workflow with its modules and parameters are stored in a YAML file, which makes the pipeline reproducible and deployable on high-performance computing clusters. 4) OptiNiSt can read image data in a variety of file formats and store the analysis results in NWB (Neurodata Without Borders), a standard data format for data sharing. We expect that this framework will be helpful in standardizing optical neural data analysis protocols. |
| 書誌情報 |
en : Plos Computational Biology
巻 21,
号 5,
p. 1-15,
ページ数 15,
発行日 2025-05-19
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| 出版者 |
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出版者 |
Public Library of Science |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
1553-7358 |
| 出版者版DOI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1371/journal.pcbi.1013087 |
| 出版者版URI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
URI |
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関連識別子 |
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013087 |
| 権利 |
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権利情報Resource |
https://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
Copyright: © 2025 Yamane et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, |
| 著者版フラグ |
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出版タイプ |
NA |
| 助成情報 |
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助成機関名 |
Japan Agency for Medical Research and Development（AMED） |
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研究課題番号 |
JP19dm0207001 |
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研究課題名 |
革新的技術による脳機能ネットワークの全容解明(中核拠点) |
| 助成情報 |
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助成機関名 |
Japan Agency for Medical Research and Development（AMED） |
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研究課題番号 |
JP23wm0625001 |
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研究課題名 |
脳データ統合プラットフォームの開発と活用による脳機能と疾患病態の解明 |
