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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要脑成像数据结构 (BIDS) 是 BRAIN 的一项倡议 (R24 MH114705) 社区驱动的标准旨在最大化神经影像数据共享并促进分析工具我们建议将该标准扩展到包括实验产生的衍生物。与描述功能和结构的磁共振成像数据相关该提案的重点是推进 BIDS 来描述大脑的宏观连接。整个实验工作流程——来自经过最少处理的解剖、功能和扩散 MRI 数据通过连接矩阵和牵引测量功能——支持 BRAIN 计划对人类和非人类大脑大规模连接的研究最初仅限于 MRI。大脑的数据，但该标准已经建立了坚实的基础设施来指导社区，并且已经自首次推出以来，已扩展到涵盖一系列其他模式（PET、EEG、MEG、ECoG）。公告称，BIDS 已发展成为一个具有共同治理和迄今为止，已有 131 名教师、学生、和博士后为标准的制定做出了贡献，描述 BIDS 的文章已被被引用277次。目前在开发 BIDS 来有效支持科学成果进程方面存在差距这是因为该标准尚未描述可以提取的大脑特征。来自 MRI 数据，通常用于执行统计测试和完成科学研究。这些特征包括连接图、结构和功能连接、主要白质束、扩散信号模型以及白质束图和束测量共享。除了原始数据和经过最低限度处理的数据之外，数据和特征对于加速科学发展至关重要这是因为大量的努力、软件和硬件仪器以及专有技术。需要将原始数据变为可用状态。之前的一个项目（R24 MH114705）奠定了这一点。 BIDS 衍生品标准的基础，最终形成现有的通用衍生品然而，当前的 BIDS 衍生品标准并不涵盖高级数据衍生品。描述大脑连接实验。当前的提议是超越 BIDS 标准。预处理数据来描述实验生成的数据产品和模型拟合后的结果该项目将提供一个社区开发的描述大脑连接的标准。该标准将附带软件来验证数据集。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Denoising diffusion weighted imaging data using convolutional neural networks.

使用卷积神经网络对扩散加权成像数据进行去噪。

DOI：
发表时间：
2022
期刊：
影响因子：
3.7
作者：
Cheng, Hu;Vinci;Wang, Jian;Caron, Bradley;Wen, Qiuting;Newman, Sharlene;Pestilli, Franco
通讯作者：
Pestilli, Franco

GPU-accelerated connectome discovery at scale.

GPU 加速的大规模连接组发现。

DOI：
发表时间：
2022-05
期刊：
影响因子：
0
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。

DOI：
发表时间：
2023-11-08
期刊：
影响因子：
0
作者：
Wogu, Eberechi;Filima, Patrick;Caron, Bradley;Levitas, Daniel;Herholz, Peer;Leal, Catherine;Mehboob, Mohammed F;Hayashi, Soichi;Akintoye, Simisola;Ogoh, George;Godwin, Tawe;Eke, Damian;Pestilli, Franco
通讯作者：
Pestilli, Franco

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：
发表时间：
2023-03-13
期刊：
影响因子：
0
作者：
Renton, Angela I;Dao, Thuy T;Johnstone, Tom;Civier, Oren;Sullivan, Ryan P;White, David J;Lyons, Paris;Slade, Benjamin M;Abbott, David F;Amos, Toluwani J;Bollmann, Saskia;Botting, Andy;Campbell, Megan E J;Chang, Jeryn;Close, Thomas G;Eckstei
通讯作者：
Eckstei

Associative white matter tracts selectively predict sensorimotor learning.

联想白质束选择性地预测感觉运动学习。