A community-driven development of the brain imaging data standard (BIDS) to describe macroscopic brain connections

由社区驱动的大脑成像数据标准（BIDS）的开发，以描述宏观的大脑连接

• 批准号: 10460628
• 负责人: Franco Pestilli
• 金额: $ 32.86万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-06 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10460628
• 关键词: Adopted; Adoption; Anatomy; Archives; Area; BRAIN initiative; Brain; Brain imaging; Cloud Computing; Communities; Computer software; Data; Data Collection; Data Set; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Directories; Disease; Electroencephalography; Ensure; Faculty; Feedback; Foundations; Functional Magnetic Resonance Imaging; Funding; Generations; Goals; Health; Human; Individual; Infrastructure; Letters; Libraries; Magnetic Resonance Imaging; Maps; Measures; Metadata; Methods; Modality; Modeling; Monoclonal Antibody R24; Neurosciences; Persons; Positron-Emission Tomography; Postdoctoral Fellow; Process; Published Comment; Publishing; Recommendation; Reproducibility; Research Personnel; Scientific Advances and Accomplishments; Services; Signal Transduction; Solid; Standardization; Structure; Students; Testing; Time; Update; Validation; Work; base; connectome; data fusion; data sharing; data standards; data structure; design; diffusion weighted; experimental study; file format; instrumentation; interest; meetings; neuroimaging; prototype; social media; software development; tool; tool development; usability; virtual; white matter

Project Summary/Abstract The Brain Imaging Data Structure (BIDS) is a BRAIN initiative (R24 MH114705) community-driven standard meant to maximize neuroimaging data sharing, and facilitate analysis tool development. We propose to extend the standard to encompass derivatives resulting from experiments related to both functional as well as structural magnetic resonance imaging data that describe macroscopic brain connectivity estimates. The focus of this proposal is to advance BIDS to describe the entire experimental workflow—from minimally processed anatomical, functional and diffusion MRI data through connectivity matrices and tractometry features—in service of supporting BRAIN initiative studies of large-scale connectivity of human and nonhuman brains. BIDS was initially scoped to MRI data of the brain, but the standard has set up a solid infrastructure to steer the community and has been extended to cover a range of other modalities (PET, EEG, MEG, ECoG). Since its first announcement, BIDS has evolved to become an organized community with shared governance and a strong impact well beyond the U.S. BRAIN initiative. To date, 131 individuals among faculty, students, and postdocs contributed to the development of the standard and the article describing BIDS has been cited 277 times. Current gaps exist in developing BIDS to effectively support the process of scientific results generation. This is because the standard does not yet describe brain features that can be extracted from MRI data and that are routinely used to perform statistical tests and complete scientific studies. These features comprise connectivity maps, structural and functional connections, major white matter tracts, diffusion signal models as well as white matter tractograms and tractometry. Sharing processed data and features in addition to raw and minimally-processed data is critical to accelerating scientific discovery. This is because substantial effort, software, and hardware instrumentation, and know-how are required to bring raw data to a usable state. One previous project (R24 MH114705) laid the foundations for the BIDS derivatives standard, ultimately leading to the existing Common Derivatives standard. However, the current BIDS derivative standard does not cover advanced data derivatives that describe brain connectivity experiments. The current proposal is to advance the BIDS standard beyond preprocessed data to describe data products generated from experiments and models fit after preprocessing. The project will deliver a community-developed standard describing brain connectivity experiments. The standard will be accompanied by software to validate the datasets.

项目总结/文摘

项目成果

Neurodesk: An accessible, flexible, and portable data analysis environment for reproducible neuroimaging.

Neurodesk：一个可访问、灵活且便携式的数据分析环境，用于可重复的神经成像。

• DOI: 10.21203/rs.3.rs-2649734/v1
• 发表时间: 2023
• 期刊: Research square
• 作者: Renton,AngelaI;Dao,ThuyT;Johnstone,Tom;Civier,Oren;Sullivan,RyanP;White,DavidJ;Lyons,Paris;Slade,BenjaminM;Abbott,DavidF;Amos,ToluwaniJ;Bollmann,Saskia;Botting,Andy;Campbell,MeganEJ;Chang,Jeryn;Close,ThomasG;Eckstei
• 通讯作者: Eckstei

GPU-accelerated connectome discovery at scale.

• DOI: 10.1038/s43588-022-00250-z
• 发表时间: 2022-05
• 期刊: NATURE COMPUTATIONAL SCIENCE
• 作者: Sreenivasan, Varsha;Kumar, Sawan;Pestilli, Franco;Talukdar, Partha;Sridharan, Devarajan
• 通讯作者: Sridharan, Devarajan

A labeled Clinical-MRI dataset of Nigerian brains.

尼日利亚大脑的标记临床 MRI 数据集。

• 发表时间: 2023
• 期刊: ArXiv
• 作者: Wogu,Eberechi;Filima,Patrick;Caron,Bradley;Levitas,Daniel;Herholz,Peer;Leal,Catherine;Mehboob,MohammedF;Hayashi,Soichi;Akintoye,Simisola;Ogoh,George;Godwin,Tawe;Eke,Damian;Pestilli,Franco
• 通讯作者: Pestilli,Franco