CRCNS: Community-supported open-source software for computational neuroanatomy

CRCNS：社区支持的计算神经解剖学开源软件

• 批准号: 9751295
• 负责人: Eleftherios Garyfallidis
• 金额: $ 17.5万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2021-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9751295
• 关键词: Address; Algorithmic Analysis; Algorithms; Area; Back; Beds; Behavior; Benchmarking; Brain; Brain Diseases; Clinical; Clinical Research; Cloud Computing; Cognition; Collection; Communities; Complex; Computational Technique; Computer software; Computing Methodologies; Data; Data Science; Data Set; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Documentation; Education; Face; Generations; Human; Image; Infrastructure; International; Knowledge; Libraries; Mathematics; Measures; Medicine; Methods; Modeling; Neuroanatomy; Neurons; Neurosciences; Perception; Physics; Preparation; Process; Property; Psychology; Pythons; Research; Research Personnel; Role; Scientist; Software Tools; Statistical Data Interpretation; Techniques; Technology; Testing; Translating; Travel; Work; base; biophysical techniques; clinical application; clinical decision-making; clinically relevant; cohesion; community building; computational neuroscience; computer science; computing resources; image processing; meetings; method development; neuroimaging; novel; open source; relating to nervous system; research and development; skills; software development; tool; tractography; white matter

Different parts of the brain share and transmit Information through long-range connections that connect nerve cells in each part of the brain with nerve cells in other brain areas. These connections form bundles of nerve cells, and these bundles comprise a complex and expansive network within the human brain. Understanding these brain networks and their relation to the way that the brain Implements perception and cognition, and understanding how these networks break down In various brain disorders are major challenges In contemporary neuroscience. Diffusion Magnetic Resonance Imaging (dMRI) is the only currently available method to measure these connections In the living human brain non-invasively. The DIPY (Diffusion imaging in Python) project Is an International community-based effort to implement open-source software for analysis and Interpretation of dMRI data. The research and development work proposed here aims to expand the development work in DIPY, and to help share this work more widely with the community of researchers that use dMRI In their research. The project proposes to Implement and share new algorithms for analysis of dMRI data, and to share several important new datasets that will serve as benchmarks for ongoing methods development; it will support the development of software tools for cloud-computing with these data and it will facilitate the participation of a global community of scientists in the development of the software. Furthermore, it will support its wide dissemination to relevant research and enable computation on large datasets.

大脑的不同部分通过远程连接共享和传输信息 将大脑每个部分的神经细胞与其他脑区的神经细胞连接起来。这些 连接形成神经细胞束，这些神经细胞束组成一个复杂的和可扩展的 人类大脑中的网络。 了解这些大脑网络及其与大脑实现方式的关系 感知和认知，以及了解这些网络是如何在不同的大脑中分解的 障碍是当代神经科学面临的主要挑战。扩散磁共振成像 (DMRI)是目前唯一可用来测量活人体内这些联系的方法 非侵入性的大脑。DIPY(Python中的扩散成像)项目是一个国际性的 以社区为基础的努力实施 用于分析和解释dMRI数据的开源软件。研究与开发 提出的工作旨在扩大DIPY的开发工作，并帮助更多地分享这项工作 广泛地与在他们的研究中使用dMRI的研究人员社区。该项目建议 实现和共享用于dMRI数据分析的新算法，并共享几个重要的新技术 将作为持续方法开发的基准的数据集；它将支持 利用这些数据开发用于云计算的软件工具，这将促进 全球科学家社区参与软件的开发。 此外，它将支持将其广泛传播给相关研究，并使 在大数据集上的计算。