Resting-state fMRI in the Human Connectome Project

By Stephen M. Smith, Christian F. Beckmann, Jesper L. R. Andersson, E. Auerbach, Janine D. Bijsterbosch, Gwenaëlle Douaud, Eugene P. Duff, David A. Feinberg, Ludovica Griffanti, M. Harms and 15 more. NeuroImage, 2013

1,605 citations recorded by the source corpus.

Resting-state functional magnetic resonance imaging (rfMRI) allows one to study functional connectivity in the brain by acquiring fMRI data while subjects lie inactive in the MRI scanner, and taking advantage of the fact that functionally related brain regions spontaneously co-activate. rfMRI is one of the two primary data modalities being acquired for the Human Connectome Project (the other being diffusion MRI). A key objective is to generate a detailed in vivo mapping of functional connectivity in a large cohort of healthy adults (over 1,000 subjects), and to make these datasets freely available for use by the neuroimaging community. In each subject we acquire a total of one hour of whole-brain rfMRI data at 3 Tesla, with a spatial resolution of 2×2×2mm and a temporal resolution of 0.7s, capitalizing on recent developments in slice-accelerated echo-planar imaging. We will also scan a subset of the cohort at higher field strength and resolution. In this paper we outline the work behind, and rationale for, decisions taken regarding the rfMRI data acquisition protocol and pre-processing pipelines, and present some initial results showing data quality and example functional connectivity analyses.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/5a983551a752b786892c042ae415be4b3d585580

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Citations in the corpus

Hand classification of fMRI ICA noise components (2017, neutral): “…70%), as shown in previous works: – standard sequences at 3T: around 70% in (Rummel et al., 2013) and 88% in (Griffanti et al., 2014); – multiband sequences at 3T: 90% in the HCP (Smith et al., 2013) and 88% in (Griffanti et al., 2014); – standard sequence at 1.5T, 82% in (Griffanti et al., 2015).”
Automatic Denoising of Functional MRI Data: Combining Independent Component Analysis and Hierarchical Fusion of Classifiers (2014, neutral): “The3 TrfMRIdatabeingacquiredintheHumanConnectomeProject has fairly high spatial and temporal resolution (2 × 2 × 2 mm, 0.73 s), utilising a multiband acceleration of ×8 (Ugurbil et al., 2013), and is acquired in relatively long runs (15 min) (Smith et al., 2013; Van Essen et al., 2013).”
Evaluation of slice accelerations using multiband echo planar imaging at 3 Tesla (2013, neutral): “, 1986) and parallel imaging (Griswold et al., 2002; Pruessmann et al., 1999; Sodickson and Manning, 1997) methods, resulting in reductions in the amount of k-space data collected and, hence, the total echo train length”
The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture (2016, neutral): “The details of this pipeline have been described previously (Jenkinson et al. 2002, 2012; Glasser et al. 2013; Smith et al. 2013) and are only summarized in the supplement for completeness.”
A multi-modal parcellation of human cerebral cortex (2016, neutral): “(6) Early runs of a supervised machine learning algorithm (see the”
Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality and Emotion (2017, methodology): “Details of the data collection can be found elsewhere (Van Essen et al., 2012b; Smith et al., 2013).”
Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI (2017, methodology): “12 matrix computation can be found elsewhere (HCP S900 manual; Van Essen et al. 2012b; Glasser et al. 2013; Smith et al. 2013).”
Data Quality Influences Observed Links Between Functional Connectivity and Behavior (2017, methodology): “The downloaded data had been minimally preprocessed as described in Smith et al. (2013) and Glasser et al. (2013).”
MSM: a new flexible framework for Multimodal Surface Matching☆ (2014, positive): “ICA was run on the group-wise concatenated time series data following dimensionality reduction using a specially adapted principle component analysis known as MIGP (Melodic’s Incremental Group-PCA, Smith et al. (2013a)).”