Connectome Coordination Facility II

连接组协调设施 II

• 批准号: 10338155
• 负责人: Daniel Scott Marcus
• 金额: $ 74.55万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10338155
• 关键词: Adult; Aging; Algorithms; Architecture; Behavioral; Brain; Child; Communities; Computer software; Consultations; Data; Data Aggregation; Data Security; Data Set; Databases; Diagnostic; Diffusion Magnetic Resonance Imaging; Disease; Docking; Ensure; Functional Magnetic Resonance Imaging; Funding; Health; Human; Image; Informatics; Infrastructure; Laboratory Research; Longevity; Manuals; Methods; Mission; Modality; Monoclonal Antibody R24; Morphologic artifacts; National Institute of Mental Health; Neurosciences; Policies; Population; Privacy; Procedures; Protocols documentation; Publishing; Quality Control; Research; Research Personnel; Resources; Role; Running; Services; Site; Standardization; Structure; Technology; Time; Update; base; connectome; connectome data; data acquisition; data archive; data de-identification; data exchange; data harmonization; data management; data repository; data sharing; human disease; image reconstruction; improved; informatics infrastructure; material transfer agreement; multimodal neuroimaging; neuroimaging; novel; operation; peer; programs; repository infrastructure; response; success

Project Summary This project will continue operation of the Connectome Coordination Facility (CCF) by capitalizing on the successes of the Human Connectome Project (HCP), which acquired, analyzed, and shared multimodal neuroimaging data and behavioral data on a large population of healthy adults. Major advances by the HCP include (i) the establishment of data acquisition protocols that yield high quality data across multiple modalities; (ii) the implementation of preprocessing pipelines that take full advantage of the high quality imaging data; and (iii) the establishment of a robust informatics infrastructure that has allowed widespread sharing of the HCP data within the neuroimaging and neuroscience communities. The CCF builds on these accomplishments and serves the human neuroimaging community in three ways. One aim is to provide consultation and support services to the research community for the primary purpose of harmonizing image acquisition protocols with those of the HCP. To this end, we have established a help desk whose support functions include transfer of data acquisition sequences and image reconstruction algorithms; providing updates and improvements for these sequences and algorithms; harmonization of imaging protocols and image reconstruction support for different software platforms and versions; and consultation for potential problems (e.g. image artifacts). A second aim is to provide services that maximize comparability of data acquired by CCF contributors. These services include pre-data acquisition guidance to contributors to ensure that each project’s behavioral data are obtained using HCP-compatible methods. This entails coordination with data contributors to develop and maitain mechanisms to streamline transfers of de-identified data from the study sites to the CCF database. The data from each study include the acquired images and all data associated with the project’s behavioral battery. Manual and automated quality control procedures based on existing HCP methods are executed to generate quality metrics that are published with the data. A standardized set of pipelines are then run in order to produce minimally preprocessed data that is fully harmonized with the other data sets in the CCF database. A third aim is to maintain the existing data repository infrastructure for Human Connectome Data and expand it to include connectome data from other research laboratories that are funded under the Connectomes Related to Human Diseases program. Together, these three aims enable the CCF to serve a central hub for connectomics data aggregation and harmonization. The CCF’s suite of harmonization services from data acquisition through data sharing ensure an unprecedented level of compatibility across data sets. The resulting database enables the scientific community to conduct novel analyses to better understand brain function in health and disease.

项目总结