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Journal article

Tradeoffs in pushing the spatial resolution of fMRI for the 7T Human Connectome Project

Abstract:
Whole-brain functional magnetic resonance imaging (fMRI), in conjunction with multiband acceleration, has played an important role in mapping the functional connectivity throughout the entire brain with both high temporal and spatial resolution. Ultrahigh magnetic field strengths (7T and above) allow functional imaging with even higher functional contrast-to-noise ratios for improved spatial resolution and specificity compared to traditional field strengths (1.5T and 3T). High-resolution 7T fMRI, however, has primarily been constrained to smaller brain regions given the amount of time it takes to acquire the number of slices necessary for high resolution whole brain imaging. Here we evaluate a range of whole-brain high-resolution resting state fMRI protocols (0.9, 1.25, 1.5, 1.6 and 2mm isotropic voxels) at 7T, obtained with both in-plane and slice acceleration parallel imaging techniques to maintain the temporal resolution and brain coverage typically acquired at 3T. Using the processing pipeline developed by the Human Connectome Project, we demonstrate that high resolution images acquired at 7T provide increased functional contrast to noise ratios with significantly less partial volume effects and more distinct spatial features, potentially allowing for robust individual subject parcellations and descriptions of fine-scaled patterns, such as visuotopic organization.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.neuroimage.2016.11.049

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Institution:
University of Oxford
Division:
MSD
Department:
Clinical Neurosciences
Role:
Author


Publisher:
Elsevier
Journal:
NeuroImage More from this journal
Volume:
154
Pages:
23-32
Publication date:
2016-11-25
Acceptance date:
2016-11-19
DOI:
EISSN:
1095-9572
ISSN:
1053-8119


Language:
English
Keywords:
Pubs id:
pubs:664169
UUID:
uuid:30df7243-84fe-4f79-8702-9df185dc3c58
Local pid:
pubs:664169
Source identifiers:
664169
Deposit date:
2017-03-01
ARK identifier:

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