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Pushing the limits of ultra-high resolution human brain imaging with SMS-EPI demonstrated for columnar level fMRI.

ABSTRACT: Encoding higher spatial resolution in simultaneous multi-slice (SMS) EPI is highly dependent on gradient performance, high density receiver coil arrays and pulse sequence optimization. We simulate gradient amplitude and slew rate determination of EPI imaging performance in terms of minimum TE, echo spacing (ES) and spatial resolution. We discuss the effects of image zooming in pulse sequences that have been used for sub-millimeter resolutions and the trade-offs in using partial Fourier and parallel imaging to reduce TE, PSF and ES. Using optimizations for SMS EPI pulse sequences with available gradient and receiver hardware, experimental results in ultra-high resolution (UHR) (0.45-0.5mm isotropic) SMS-EPI fMRI and mapping ocular dominance columns (ODC) in human brain at 0.5 mm isotropic resolution are demonstrated. We discuss promising future directions of UHR fMRI.

SUBMITTER: Feinberg DA

PROVIDER: S-EPMC5961953 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Pushing the limits of ultra-high resolution human brain imaging with SMS-EPI demonstrated for columnar level fMRI.

Feinberg David A DA Vu An T AT Beckett Alexander A

NeuroImage 20170214

Encoding higher spatial resolution in simultaneous multi-slice (SMS) EPI is highly dependent on gradient performance, high density receiver coil arrays and pulse sequence optimization. We simulate gradient amplitude and slew rate determination of EPI imaging performance in terms of minimum TE, echo spacing (ES) and spatial resolution. We discuss the effects of image zooming in pulse sequences that have been used for sub-millimeter resolutions and the trade-offs in using partial Fourier and paral ...[more]

PMID: 28213116

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Project description:Functional magnetic resonance imaging (fMRI) is the technique of choice for detecting large-scale functional brain networks and to investigate their dynamics. Because fMRI measures brain activity indirectly, electroencephalography (EEG) has been recently considered a feasible tool for detecting such networks, particularly the resting-state networks (RSNs). However, a truly unbiased validation of such claims is still missing, which can only be accomplished by using simultaneously acquired EEG and fMRI data, due to the spontaneous nature of the activity underlying the RSNs. Additionally, EEG is still poorly explored for the purpose of mapping task-specific networks, and no studies so far have been focused on investigating networks' dynamic functional connectivity (dFC) with EEG. Here, we started by validating RSNs derived from the continuous reconstruction of EEG sources by directly comparing them with those derived from simultaneous fMRI data of 10 healthy participants, and obtaining an average overlap (quantified by the Dice coefficient) of 0.4. We also showed the ability of EEG to map the facial expressions processing network (FEPN), highlighting regions near the posterior superior temporal sulcus, where the FEPN is anchored. Then, we measured the dFC using EEG for the first time in this context, estimated dFC brain states using dictionary learning, and compared such states with those obtained from the fMRI. We found a statistically significant match between fMRI and EEG dFC states, and determined the existence of two matched dFC states which contribution over time was associated with the brain activity at the FEPN, showing that the dynamics of FEPN can be captured by both fMRI and EEG. Our results push the limits of EEG toward being used as a brain imaging tool, while supporting the growing literature on EEG correlates of (dynamic) functional connectivity measured with fMRI, and providing novel insights into the coupling mechanisms underlying the two imaging techniques.

Dataset Information

Pushing the limits of ultra-high resolution human brain imaging with SMS-EPI demonstrated for columnar level fMRI.

Publications

Pushing the limits of ultra-high resolution human brain imaging with SMS-EPI demonstrated for columnar level fMRI.

Similar Datasets

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