Abstract

Typical simultaneous blood oxygenation-level dependent (BOLD) and arterial spin labeling (ASL) sequences acquire two echoes, one perfusion-sensitive and one BOLD-sensitive. However, for ASL, spatial resolution and brain coverage are limited due to the T1 decay of the labeled blood. This study applies a sequence combining a multiband acquisition with four echoes for simultaneous BOLD and pseudo-continuous ASL (pCASL) echo planar imaging (MBME ASL/BOLD) for block-design task-fMRI. A multiband acceleration of four was employed to increase brain coverage and reduce slice-timing effects on the ASL signal. Multi-echo independent component analysis (MEICA) was implemented to automatically denoise the BOLD signal by regressing non-BOLD components. This technique led to increased temporal signal-to-noise ratio (tSNR) and BOLD sensitivity. The MEICA technique was also modified to denoise the ASL signal by regressing artifact and BOLD signals from the first echo time-series. The MBME ASL/BOLD sequence was applied to a finger-tapping task functional MRI (fMRI) experiment. Signal characteristics and activation were evaluated using single echo BOLD, combined ME BOLD, combined ME BOLD after MEICA denoising, perfusion-weighted (PW), and perfusion-weighted after MEICA denoising time-series. The PW data was extracted using both surround subtraction and high-pass filtering followed by demodulation. In addition, the CBF/BOLD response ratio and CBF/BOLD coupling were analyzed. Results showed that the MEICA denoising procedure significantly improved the BOLD signal, leading to increased BOLD sensitivity, tSNR, and activation statistics compared to conventional single echo BOLD data. At the same time, the denoised PW data showed increased tSNR and activation statistics compared to the non-denoised PW data. CBF/BOLD coupling was also increased using the denoised ASL and BOLD data. Our preliminary data suggest that the MBME ASL/BOLD sequence can be employed to collect whole-brain task-fMRI with improved data quality for both BOLD and PW time series, thus improving the results of block-design task fMRI.

Highlights

  • Functional MRI is a powerful, noninvasive tool to measure brain function

  • While the blood oxygenation-level dependent (BOLD) Functional MRI (fMRI) signal is sensitive to magnetic susceptibility fluctuations caused by changes in blood oxygenation, it is related to changes in cerebral blood flow (CBF), cerebral blood volume (CBV), and the cerebral metabolic rate of oxygenation (CMRO2)

  • Whole-brain temporal SNR (tSNR) significantly increased for the multi-echo combined (MEC) data compared to the E2 data (p

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Summary

Introduction

Functional MRI (fMRI) is a powerful, noninvasive tool to measure brain function. Two major contrasts used for fMRI are blood oxygenation-level dependent (BOLD) and arterial spin. While the BOLD fMRI signal is sensitive to magnetic susceptibility fluctuations caused by changes in blood oxygenation, it is related to changes in cerebral blood flow (CBF), cerebral blood volume (CBV), and the cerebral metabolic rate of oxygenation (CMRO2). ASL fMRI measures blood flow changes directly by magnetically tagging blood flowing into the brain. CBF and BOLD are commonly used to study the hemodynamic response to neuronal activity and extract information regarding the role of the microvasculature in the brain [1,2,3,4,5]

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