Big Data: Human Connectome Project
Bruce R. Rosen1

1Radiology, Massachusetts General Hospital, Charlestown, MA, United States

Synopsis

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Mapping the human brain structures and functions and elucidating their relationships with cognitive behaviors is one of the most challenging yet intriguing missions. An overarching goal of the Human Connectome Project (HCP) is to tackle this amazingly complex problem to explore the unique neural underpinnings of human cognitions and to reveal what makes individuals different from each other. An immediate goal of the HCP is a database of over a thousand healthy subjects1, including high-quality multi-modality in vivo neuroimaging data, as well as the comprehensive demographic, genetic, and behavioral information. The database also included the state of art diffusion MRI (dMRI) data generated on the Connectome Scanner equipped with the 300mT/m gradient system2, and will be expanded to include neuroimaging and behavioral data of various age cohorts across the lifespan. Parallel to the acquisition and sharing of the “big data”, the technical aspects of the project have focused on developing a series of normative procedures of data generation, preprocessing and analyses3-8.

Acknowledgements

The work is supported by funding from the National Institutes of Health Blueprint Initiative for Neuroscience Research Grant U01MH093765.

We acknowledge Siemens Healthcare for constructing the 300mT/m gradient system as well as the 3T platform.

References

1. Hodge, M.R., et al. ConnectomeDB—Sharing human brain connectivity data. NeuroImage.

2. Fan, Q., et al. MGH-USC Human Connectome Project datasets with ultra-high b-value diffusion MRI. Neuroimage 124, 1108-1114 (2016).

3. McNab, J.A., et al. The Human Connectome Project and beyond: initial applications of 300 mT/m gradients. Neuroimage 80, 234-245 (2013).

4. Setsompop, K., et al. Improving diffusion MRI using simultaneous multi-slice echo planar imaging. Neuroimage 63, 569-580 (2012).

5. Marcus, D.S., et al. Human Connectome Project informatics: quality control, database services, and data visualization. Neuroimage 80, 202-219 (2013).

6. Glasser, M.F., et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105-124 (2013).

7. Ugurbil, K., et al. Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project. Neuroimage 80, 80-104 (2013).

8. Sotiropoulos, S.N., et al. Advances in diffusion MRI acquisition and processing in the Human Connectome Project. Neuroimage 80, 125-143 (2013).



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)