Human Connectome Project Informatics: quality control, database services, and data visualization
By Daniel S. Marcus, M. Harms, Abraham Z. Snyder, Mark Jenkinson, J. A. Wilson, Matthew F. Glasser, D. Barch, K. A. Archie, Gregory C. Burgess, Mohana Ramaratnam and 14 more. NeuroImage, 2013
454 citations recorded by the source corpus.
The Human Connectome Project (HCP) has developed protocols, standard operating and quality control procedures, and a suite of informatics tools to enable high throughput data collection, data sharing, automated data processing and analysis, and data mining and visualization. Quality control procedures include methods to maintain data collection consistency over time, to measure head motion, and to establish quantitative modality-specific overall quality assessments. Database services developed as customizations of the XNAT imaging informatics platform support both internal daily operations and open access data sharing. The Connectome Workbench visualization environment enables user interaction with HCP data and is increasingly integrated with the HCP's database services. Here we describe the current state of these procedures and tools and their application in the ongoing HCP study.
Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/2034ae0a15b1e969cbc9df7281ab15160c36daf3
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Stephen M. Smith
- Matthew F. Glasser
- Timothy Edward John Behrens
- Russell A. Poldrack
- David C. Van Essen
- Jesper L. R. Andersson
- Olaf Sporns
- Kamil Ugurbil
In Radiology, Nuclear Medicine and Imaging
- Stephen M. Smith
- Matthew F. Glasser
- Jesper L. R. Andersson
- Saâd Jbabdi
- David C. Van Essen
- Essa Yacoub
- Kamil Ugurbil
- Timothy Edward John Behrens
Citations in the corpus
- The Human Connectome Project’s Neuroimaging Approach (2016, methodology): “Stereotaxic coordinates have high spatial uncertainty (relatively small changes in the thresh-olded statistical maps used to produce them can have large effects on the resulting coordinate locations; Supplementary Note 12 and Supplementary Fig.”
- Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank (2017, background): “Even the HCP, while having a relatively large number of subjects (1200), relies primarily upon visual inspection for their T1 QC process (Marcus et al., 2013).”
- Recent progress and outstanding issues in motion correction in resting state fMRI (2014, unknown): “To provide a data-driven perspective on these numbers, in ~200-dimensional ICA analyses of Human Connectome Project data, “neural” components account for 4% of variance, “nuisance” components for 16%, motion regressors for 14%, and the rest is labeled unstructured noise (Marcus et al., 2013).”
- The WU-Minn Human Connectome Project: An Overview (2013, methodology): “Workbench is especially well suited for handling grayordinate representations (surface vertices and gray-matter voxels) in the CIFTI format (see Glasser et al., 2013b; Marcus et al., 2013).”
- Making big data open: data sharing in neuroimaging (2014, background): “XNAT has gained particular interest given its open-source availability and that it serves as the basis for the HCP’s ConnectomeDB 63 .”