The Human Connectome Project: A data acquisition perspective

By David C. Van Essen, Kamil Ugurbil, E. Auerbach, Deanna M. Barch, Timothy Edward John Behrens, R. Bucholz, A. Chang, Liyong Chen, Maurizio Corbetta, Sandra W. Curtiss and 17 more. NeuroImage, 2012

2,659 citations recorded by the source corpus.

The Human Connectome Project (HCP) is an ambitious 5-year effort to characterize brain connectivity and function and their variability in healthy adults. This review summarizes the data acquisition plans being implemented by a consortium of HCP investigators who will study a population of 1200 subjects (twins and their non-twin siblings) using multiple imaging modalities along with extensive behavioral and genetic data. The imaging modalities will include diffusion imaging (dMRI), resting-state fMRI (R-fMRI), task-evoked fMRI (T-fMRI), T1- and T2-weighted MRI for structural and myelin mapping, plus combined magnetoencephalography and electroencephalography (MEG/EEG). Given the importance of obtaining the best possible data quality, we discuss the efforts underway during the first two years of the grant (Phase I) to refine and optimize many aspects of HCP data acquisition, including a new 7T scanner, a customized 3T scanner, and improved MR pulse sequences.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/3919ed02bf16acd07206c40fcbb2d85ce37e4639

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Citations in the corpus

Mathematical framework for large-scale brain network modeling in The Virtual Brain (2015, neutral): “For an overview of the compromises between preserving network properties and respecting recent estimates of the total number of cortical areas in the human (about 150 to 200 areas per hemisphere) (Essen et al., 2012a), see Zalesky et al. (2010a).”
Unsupervised anomaly localization using VAE and beta-VAE (2020, methodology): “Training dataset: To learn the normal brain MRI image distribution, we trained the VAE and β -VAE on 3T T2 MRI images of Human Connectome Project (HCP) dataset [21], which are from 1113 healthy young adult (age 22-35 ) participants.”
Clinical applications of the functional connectome (2013, neutral): “…Institute-Rockland Sample (Nooner et al., 2012) and Openfmri.org https://openfmri.org/ are tantalizing the community with the prospect of more carefully coordinated, large-scale datasets, such as the Human Connectome Project (Van Essen et al., 2012), upon which discovery can truly be carried out.”
The Minimal Preprocessing Pipelines for the Human Connectome Project (2013, methodology): “Additionally, FreeSurfer's current folding-based registration algorithm introduces more areal distortion of the cortical surfaces than is desired relative to its ability to align cortical areas in these variable regions (D.C. Van Essen et al., 2012).”
MEG and EEG data analysis with MNE-Python (2013, background): “This is particularly relevant in an era of large-scale data analysis with possibly more than a thousand subjects, cf. , the Human Brain Project or the Human Connectome Project (Van Essen et al., 2012).”
Evaluation of slice accelerations using multiband echo planar imaging at 3 Tesla (2013, positive): “All in vivo experiments were performed on the 3 Tesla WU-Minn Human Connectome Project (Van Essen et al., 2012) Skyra scanner (Siemens, Erlangen, Germany) equipped with SC72 gradients operating at 100 mT/m maximum absolute gradient amplitude (maximum readout gradient amplitude, readout Gmax,…”
Investigating White Matter Development in Infancy and Early Childhood using Myelin Water Faction and Relaxation Time Mapping (2012, background): “This result has implications on studies that have used relaxometry measures (or T1 and T2-weighted signals) as surrogate markers of myelin content (Bartzokis et al., 2010; Glasser and Van Essen, 2011; Van Essen et al., 2012).”
MNE software for processing MEG and EEG data (2013, background): “This isparticularly relevant in an era of large-scale data analysis where massive cohorts of subjects may number more than a thousand, cf., the Human Brain Project or the Human Connectome Project (Van Essen et al., 2012).”
The WU-Minn Human Connectome Project: An Overview (2013, methodology): “Including additional (non-twin) siblings provides a further increase in statistical power for analyzing heritability, distinguishing between genetic and environmental influences (Posthuma and Boomsma, 2000; Van Essen et al., 2012a) and relating genotype to phenotype.”
NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data (2014, neutral): “…through integrated models for brain data analysis are grand challenges for the areas of machine learning and information science in general as already pointed in some recent publications (Gerstner, Sprekeler, & Deco, 2012; Koch & Reid, 2012; Poline & Poldrack, 2012; Van Essen et al., 2012).”
Advances in diffusion MRI acquisition and processing in the Human Connectome Project (2013, methodology): “Another important aspect of HCP software development is an interactive visualisation framework (Connectome Workbench) that supports on-the-fly data exploration (Marcus et al., 2013; Van Essen et al., 2013) in a point-and-click fashion.”