کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6028677 | 1580924 | 2013 | 25 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project
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موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
علوم اعصاب شناختی
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چکیده انگلیسی
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 3Â T, leading to whole brain coverage with 2Â mm isotropic resolution in 0.7Â s for fMRI, and 1.25Â mm isotropic resolution dMRI data for tractography analysis with three-fold reduction in total dMRI data acquisition time. Ongoing technical developments and optimization for acquisition of similar data at 7Â T magnetic field are also presented, targeting higher spatial resolution, enhanced specificity of functional imaging signals, mitigation of the inhomogeneous radio frequency (RF) fields, and reduced 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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage - Volume 80, 15 October 2013, Pages 80-104
Journal: NeuroImage - Volume 80, 15 October 2013, Pages 80-104
نویسندگان
Kamil UÄurbil, Junqian Xu, Edward J. Auerbach, Steen Moeller, An T. Vu, Julio M. Duarte-Carvajalino, Christophe Lenglet, Xiaoping Wu, Sebastian Schmitter, Pierre Francois Van de Moortele, John Strupp, Guillermo Sapiro, Federico De Martino, Dingxin Wang,