This project will establish a Connectome Coordination Facility (CCF) by capitalizing on recent successes of the Human Connectome Project (HCP), which has 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 will build on these accomplishments and serve the human neuroimaging community in three ways.
One aim i s to provide consultation and support services to the research community for the primary purpose of harmonizing image acquisition protocols with those of the HCP. The effort will establish a help desk whose support functions will 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 i s to provide services that maximize comparability of data acquired by CCF contributors. These services will include pre-data acquisition guidance to contributors to ensure that each project's behavioral data are obtained using HCP-compatible methods. This will entail coordination with data contributors to develop mechanisms to streamline transfers of de-identified data from the study sites to the CCF database. The data from each study will include the unprocessed images, minimally preprocessed data generated by each project's internal pipelines, and all data associated with the project's behavioral battery. Manual and automated quality control procedures will be implemented based on existing HCP methods to generate quality metrics that will be published with the data. A standardized set of pipelines will be run in order to produce minimally preprocessed data that is fully harmonized with the other data sets in the CCF database.
A third aim i s to maintain the existing ConnectomeDB data repository infrastructure for Human Connectome Data and expand it to include Connectome data from other research laboratories that are funded as U01 projects under the Connectomes Related to Human Diseases RFA. The platform will be developed and operated following policies and procedures vetted by the HCP to ensure the privacy and security of the data hosted by the CCF. Together, these three aims will establish the CCF as a central hub for connectomics data aggregation and sharing. The CCF's suite of harmonization services from data acquisition through data sharing will ensure an unprecedented level of compatibility across data sets. The resulting database will enable the scientific community to conduct novel analyses to better understand brain function in health and disease.

Public Health Relevance

The mission of the Connectome Coordination Facility (CCF) is to make connectomics data sets widely available to research community, thus providing researchers with resources to discover fundamental workings of the brain in health and disease and, in time, to develop improved diagnostics and therapies.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Resource-Related Research Projects (R24)
Project #
5R24MH108315-02
Application #
9094666
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Farber, Gregory K
Project Start
2015-07-01
Project End
2020-03-31
Budget Start
2016-04-01
Budget End
2017-03-31
Support Year
2
Fiscal Year
2016
Total Cost
Indirect Cost
Name
Washington University
Department
Neurosciences
Type
Schools of Medicine
DUNS #
068552207
City
Saint Louis
State
MO
Country
United States
Zip Code
63130
Coalson, Timothy S; Van Essen, David C; Glasser, Matthew F (2018) The impact of traditional neuroimaging methods on the spatial localization of cortical areas. Proc Natl Acad Sci U S A 115:E6356-E6365
Glasser, Matthew F; Smith, Stephen M; Marcus, Daniel S et al. (2016) The Human Connectome Project's neuroimaging approach. Nat Neurosci 19:1175-87