Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project

By Kamil Ugurbil, Junqian Xu, E. Auerbach, Steen Moeller, An T. Vu, Julio Martin Duarte-Carvajalino, C. Lenglet, Xiaoping Wu, S. Schmitter, P. Moortele and 17 more. NeuroImage, 2013

848 citations recorded by the source corpus.

The human connectome project (HCP) relies primarily on three complementary magnetic resonance (MR) methods. These are: 1) resting state functional MR imaging (rfMRI) which uses correlations in the temporal fluctuations in an fMRI time series to deduce ‘functional connectivity’; 2) diffusion imaging (dMRI), which provides the input for tractography algorithms used for the reconstruction of the complex axonal fiber architecture; and 3) task based fMRI (tfMRI), which is employed to identify functional parcellation in the human brain in order to assist analyses of data obtained with the first two methods. We describe technical improvements and optimization of these methods as well as instrumental choices that impact speed of acquisition of fMRI and dMRI images at 3 Tesla, leading to whole brain coverage with 2 mm isotropic resolution in 0.7 second for fMRI, and 1.25 mm isotropic resolution dMRI data for tractography analysis with three-fold reduction in total data acquisition time. Ongoing technical developments and optimization for acquisition of similar data at 7 Tesla magnetic field are also presented, targeting higher resolution, specificity of functional imaging signals, mitigation of the inhomogeneous radio frequency (RF) fields and power deposition. Results demonstrate that overall, these approaches represent a significant advance in MR imaging of the human brain to investigate brain function and structure.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/543f1c7ba8c7aef3636f5f6a680b2e586908112d

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Citations in the corpus

Interpretable LSTMs For Whole-Brain Neuroimaging Analyses (2018, methodology): “The Human Connectome Project preprocessing pipeline for functional MRI data (“fMRIVolume”) [32] includes the following steps: gradient unwarping, motion correction, fieldmap-based EPI distortion correction, brain-boundary based registration of EPI to structural T1weighted scan, non-linear registrati”
Intrinsic and task-evoked network architectures of the human brain. (2014, positive): “…studies showing correlations between individual differences in resting-state FC and task performance (Cole et al., 2011, 2012; Kelly et al., 2008; van den Heuvel et al., 2009) and with studies that identified correlations between resting-state FC and task coactivation patterns (based on…”
Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues (2019, background): “Due to these favorable properties, GRAPPA has found utility in multiple large-scale projects, such as the Human Connectome Project [34].”
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).”